International Journal of Computer Information Systems and Industrial Management Applications
Beyond Quotas: Organisational Support, Leadership Commitment, and Employee Development for Sustainable Workforce Localisation in Oman’s Logistics and Shipping Industry
Workforce localisation is a strategic priority for Gulf Cooperation Council economies seeking to reduce reliance on expatriate labour and strengthen national human capital. This study examines how organisational support, leadership commitment, and employee development contribute to sustainable workforce localisation in Oman’s logistics and shipping industry. Guided by an interpretivist philosophy, a qualitative multiple-case study design was adopted, drawing on semi-structured interviews with twelve senior professionals from logistics organisations, government agencies, and higher education institutions. Data were analysed using reflexive thematic analysis supported by NVivo. The findings show that sustainable Omanization depends on leadership commitment embedded in performance management, organisational support that promotes employee adjustment and engagement, and continuous development that transforms recruitment into long-term capability. Skills shortages, competency mismatches, and retention pressures further demonstrate the limitations of quota-based compliance. Integrating the Resource-Based View, Social Exchange Theory, Institutional Theory, and Human Capital Theory, the study proposes a capability-based framework that positions workforce localisation as an organisational capability rather than a compliance outcome. The findings highlight the importance of leadership accountability, mentoring, career development, and integrated human resource systems for attracting, developing, and retaining Omani talent.
Copyright (c) 2026 Abdulaziz Nasser Hilal Al Rahbi, Abdul Khabir Rahmat, Abebe Ejigu Alemu
An Integrated Multimodal Deep Learning and Reinforcement Framework for Intelligent Travel Demand Forecasting
Precise short-term travel demand forecasting and personalized trip advice are two of the most challenging problems in intelligent transportation systems in part due to the fact that traveler behavior is influenced concurrently by spatial configuration, time dynamics, socio-demographic environment, and cost sensitivity. The majority of the current models cover these dimensions separately. The current paper suggests an end-to-end, monolithic framework that pairs the learning of multimodal representations with a spatio-temporal graph neural network (ST-GNN), a Transformer-based forecasting module, a causal inference layer, and a Q-learning recommender. Heterogeneous travel history, such as demographics, trip purpose, mode of transport, cost of accommodation, and temporal attitudes are co-embedded and propagated through graph convolutions and multi-head self-attention to generate multi-horizon destination-level demand prediction. An actionable do-calculus layer measures the impact of traveler characteristics on decisions and the policy implications guide a reinforcement learning agent that modulates transport and accommodation suggestions to traveler groups. The ST-GNN + Transformer forecaster achieves a 25% lower RMSE than an ARIMA baseline on a publicly available dataset of approximately 8,700 traveler records and, in comparison to the LSTM and DCRNN alternatives, converges within just 300 training episodes. The framework provides a replicable base to adaptable, understandable model of travel behavior in smart-city and tourism analytics contexts.
Copyright (c) 2026 Santosh Kumar Sharma, Satish Chander, Piyush Gupta
Hybrid Deep Learning and Machine Learning Framework for Intelligent Travel Demand Forecasting
Almost all operations of a smart transportation or tourism platform are based on reliable travel demand forecasts, including fleet allocation and dynamic pricing. The information that is now at hand to do the task, trip records, accommodation logs, demographic surveys, are large and heterogeneous, a combination of spatial structure and long-range temporal dependence with sequential behavior and tabular properties that cannot be handled by any single model family. In this paper, an intelligent framework is developed as a hybrid one, where four complementary learners are used to operate in parallel: a spatio-temporal graph neural network (ST-GNN) to represent dependencies between travel zones, a Transformer to represent long-horizon temporal patterns, an LSTM to represent sequential mobility dynamics, and an XGBoost to represent structured demographic and cost characteristics. A stacked meta-learner amalgamates the four branch predictions, and a Q-learning agent transforms predictions into adaptive transport and accommodation suggestions with a reward based on satisfaction-minus-cost. The fused model, tested on the New York City TLC taxi-trip corpus with demographic information, achieves lower RMSE than either ARIMA (0.214) or the best single learner (0.169) and higher R² (0.91), and the policy recommendation policy converges in about 300 episodes. In addition to accuracy, the framework provides a formal fusion architecture, a full algorithmic specification, and a statistical validation protocol, and provides a scalable and interpretable platform to intelligent mobility analytics.
FLOPBONSNet: ROI-Preserved Deep Watermarking for Secure Medical Image Authentication
In the context of medical image storage, cloud-based exchange of digital images, and telemedicine transmission, the medical image watermarking technique is a crucial tool for verifying ownership, integrity, and authenticity. Most medical watermarking schemes currently in existence have its drawbacks, such as fixed-strength embedding, weak protection for important diagnostic regions, weak geometric robustness, and poor implementation reproducibility. In this paper, FLOPBONS-Net is proposed as an ROI-preserved hybrid watermarking framework, which is based on fuzzy logic, flower pollination optimization, DWT-DCT-SVD transform embedding, BCH/CRC payload protection, and attack-aware convolutional neural network decoder. The proposed method classifies ROI and NROI domain and calculates texture, edge, contrast and ROI-risk maps, and finally applies a fuzzy inference system to assign clinically safe embedding strength. A flower pollination optimizer is used to find optimal candidate blocks, embedding gains and transform coefficients with a multi-objective cost function, that optimally minimizes global distortion, ROI distortion, extraction error and maximizes payload and robustness. The watermark payload is scrambled and protected using BCH error correction and CRC verification. Noise, compression, cropping, rotation, blur, filtering, resizing, gamma correction and histogram equalization are used to train an attack-aware CNN decoder. Under major attacks, PSNR more than 50 dB, SSIM more than 0.99, and normalized correlation more than 0.91 are achieved in the evaluation of public chest radiography datasets. The manuscript contains links to datasets of Manusia, implementation architecture, formal equations, numbered algorithms, real-image figure slots with image identification command, visual attack panels, ablation analysis, statistical validation, runtime/memory analysis, scalability, and security metrics. Therefore, the proposed framework will be appropriate for secure radiology image exchange for both authentication and diagnostic quality.
Copyright (c) 2026 Barkha Sahu, Neeraj Kumar Rathore, Abhishek Bansal
Fiber Bragg Grating Sensors for Temperature and Strain Monitoring: Simulation-Based Analysis of Quasi-Distributed Configurations
Fiber Bragg Grating (FBG) sensor is one of the most mature wavelength encoded precision measurement transducers, which has the advantage of immunity to electromagnetic disturbance, millimetre-level sensing scale and the ability to mount many independent sensing nodes on a single fibre. In this paper, a simulation study of the FBG sensors and their quasi-distributed configuration has been performed in OptiSystem, in which the sensors are used for both temperature and axial strain monitoring. The first FBG is a uniform FBG with a grating length of 10 mm, and its Bragg wavelength, total output power, noise power and signal to noise ratio (SNR) are measured as the temperature and axial strain are sequentially applied in the range of 30 °C to 210 °C and the range of 1 με to 9 με, respectively. This model is then replicated in a four-element quasi-distributed network consisting of four FBGs positioned at 1545, 1550, 1555 and 1560 nm, and multiplexed and interrogated serially with wavelength division multiplexing (WDM) across three separate sensing locations, with a co-located temperature–strain grating pair included at the third sensing location to enable cross-sensitivity discrimination. The point sensor has a linear thermal response of ~14.2 pm/°C and a linear strain response of ~1.2 pm/με, which is in good agreement with the thermo-optic and elasto-optic models. The quasi-distributed network generates four completely separated spectral channels (no measurable inter-channel overlap) at a channel spacing of 5 nm, and ensures wavelength traceability at each node. The four gratings are represented on a three-span girder bridge to show how the simulated channel plan is translated into an instrumentation layout for structural health monitoring, and how the extracted sensitivities are compared with experimental and numerical results reported.
Copyright (c) 2026 Shivprakash Palhewar, Anubhuti Khare
A Bibliometric Analysis of Green Supply Chain Management Practices : Trends, Challenges, and Future Research
This study presents a bibliometric analysis of Green Supply Chain Management, focusing on key trends, emerging themes and challenges. Green Supply Chain Management (GSCM) in universities transcends conventional procurement methods to include sustainable campus operations, environmentally responsible purchasing policies, waste minimisation, and green logistics. This systematic review analyses recent studies on GSCM trends and challenges from 2002 to 2025. Articles were methodically obtained from Scopus, focusing on peer-reviewed research that examines the incorporation of environmental sustainability concepts in campus supply chains. The finding indicate that dominants contributors to sustainability research include the China, India and United States with leading and contribute to publication. The study identifies core research themes, including integration of technology, environmental and industry in Green Supply Chain Management. Research indicates an increasing inclination towards sustainable procurement strategies, zero-waste programs, circular economy frameworks, and the integration of digital technologies to improve environmental performance. However, considerable obstacles persist, such as insufficient stakeholder involvement, poor sustainable supply networks, and budgetary limitations. This analysis closes by suggesting research avenues that promote comprehensive sustainability frameworks, inter-campus collaborations, and the use of intelligent technology to enhance GSCM practices in higher education institutions. Furthermore, subsequent research ought to examine data from Web of Science, Google Scholar, dimensions and others. Combining different datasets could lead to interesting and important results.
Copyright (c) 2026 Norhafizah Mohamad Dasuki, Fauzan, Nurhayati Kamarudin, Fairuz Izzuddin Romli, Aslina Siman, Muhammad Muizzuddin Zulkafli
An AI-Assisted Adaptive Learning Continuity Model for Future-Ready Higher Education in Unstable Digital Environments
Universities operating in unstable digital environments face persistent challenges in maintaining educational continuity. The efficiency of online and blended learning is negatively impacted by various issues, including inadequate internet connectivity, disruptions in electrical supply, restricted access to electronic devices, and disparities in digital preparation. The majority of the time, learning management systems like Moodle offer predefined instructional paths with limited adaptive support, although they support material distribution, evaluation, and communication. A model for adaptive learning continuity that is helped by artificial intelligence is proposed in this research for use by higher education institutions that are constrained by technological and infrastructural limitations. Learner profiles, digital accessibility monitoring, suggestions assisted by artificial intelligence, adaptive material delivery, adaptive assessment, remedial learning pathways, and instructor intervention are all incorporated into the concept. It is designed as an adaptive layer that can be connected to an existing LMS. The paper presents the model architecture, main components, operational workflow, and a pilot implementation plan. The proposed model provides a practical foundation for supporting personalized learning, low-bandwidth alternatives, early risk detection, and targeted remedial support in constrained educational environments.
An intelligent system for early cancer diagnosis using machine learning methods
Oncological diseases are one of the socially significant problems. The detection rate of cancer in the early stages is currently low. In this regard, the development of early cancer diagnosis systems is an important task. The use of artificial intelligence methods makes it possible to solve forecasting problems at a high level. In this study, an early cancer diagnosis system was built using the example of lung cancer. A questionnaire for the diagnosis of lung cancer was created according to the protocol for the diagnosis of the disease. The results of the questionnaire were processed using mesh learning methods. 8 methods were used. Based on the results of the interpretation of the models, it can be concluded that machine learning methods can predict the risk of cancer.
Copyright (c) 2026 Karymsakova I.B., Kozhakhmetova D.O., Shyrynkhanova D.Zh., Bekenova D.B.
A Comprehensive Review of The Circular Economy Model for Solid Waste Management in Malaysia
The Circular Economy concept has been accepted worldwide, including in Malaysia. This concept is a shift from a linear economy, which is a take-and-dispose system, to a system that focuses on waste prevention, resource recovery, recycling, valorisation of organic waste, energy recovery, and active stakeholder engagement. The Malaysian Government has introduced the Blueprint for Solid Waste Circular Economy 2025-2035, the National Cleanliness Policy 2030, source-separation programmes, and plastic sustainability initiatives to support the transition to the Circular Economy. However, the implementation of these practices remains fragmented at the institutional and operational levels. Hence, this paper aims to create awareness among all concerned stakeholders by proposing an integrated Circular Economy Model for Solid Waste Management in Malaysia. The suggested model, drawing on the literature on global and Malaysian initiatives, comprises source separation, food waste prevention, material recovery, plastic circularity, digital waste monitoring, stakeholder governance, market creation, waste-to-energy, and performance measurement. The paper contributes a comprehensive framework that merges Circular Economy principles with energy efficiency. It aims to guide policymakers, local governments, industry stakeholders, and academics in promoting solid waste sustainability in Malaysia.
Copyright (c) 2026 Nurul Farhaini Razali, Yusri Arshad, Fam Soo Fen, Norhafizah Mohamad Dasuki, Syakirah Ahmad, Azman Mohamad
ARTIFICIAL INTELLIGENCE-DRIVEN FINTECH INNOVATIONS AND CIRCULAR ECONOMY TRANSITION: BRIDGING INNOVATION, INCLUSION, AND SUSTAINABLE IMPACT FOR PEOPLE AND PLANET
As the need for sustainable development has grown, developing new and innovative technologies that help the move from a linear to a circular economy has been enhanced. The application of Artificial Intelligence (AI) and Financial Technology (FinTech) has become a game-changer with great potential to optimize resources, boost sustainable finance, and to drive environmentally responsible consumer behaviours. This research explores the impact of Artificial Intelligence (AI) powered FinTech Innovations on Circular Economy Transition (CET) and assesses Sustainable Consumption Behaviour (SCB) as a mediator. This study is quantitative research that uses primary data obtained from a structured questionnaire by using 384 respondents. The statistical program used was SPSS and AMOS software for statistical analyses. To verify the proposed relationships, Pearson correlation analysis, multiple regression analysis, and mediation analysis via Structural Equation Modeling (SEM) were used. The results showed that the quantity of FinTech Innovation using AI and Circular Economy Transition has a strong positive correlation (r = 0.712, p < 0.001). The results of the regression analysis suggest that three factors are significant determinants for Circular Economy Transition: AI Capability (with a β-value of 0.392), Sustainable Financial Inclusion (with a β-value of 0.311), and Digital Trust & Transparency (with a β-value of 0.259); these factors account for 61.7% of the variation (R2 = 0.617) in the Circular Economy Transition. Out of the listed predictors, AI Capability was found to be the best predictor when it comes to the adoption of the Circular Economy approach. The results of the mediation analysis also support this and reveal that AI-driven FinTech Innovations have both direct and indirect impacts on Circular Economy Transition via Sustainable Consumption Behaviour, which serves as a mediated pathway. The model achieved good fitness statistics, supporting the theoretical model. The study makes a significant contribution to the existing literature by synthesizing AI, FinTech, and Circular Economy perspectives in a common framework. The insights have policy and implications for actors such as policymakers, financial institutions, technology developers, and business organizations who aim to harness digital innovations to drive sustainability, financial inclusion, and circular economic growth. The study's bottom line is that AI-powered FinTech innovations could act as a strategic enabler for developing a sustainable future for the planet and its people.
AI-Enhanced Anomaly Detection with ARIMA Rainfall Forecasting
Finding anomalous patterns in large datasets that may indicate security risks or system malfunctions is a crucial task in cloud computing. Recent advances in machine learning, combined with the rapid growth of cloud computing, have enabled several cloud-based, data-driven approaches for autonomous anomaly detection. However, conventional anomaly detection methods are often resource- and time-intensive, particularly when processing massive volumes of data. The method proposed in this study addresses these challenges by providing a scalable and efficient mechanism for real-time identification of irregularities. Furthermore, the approach supports timely detection and resolution of system malfunctions, ensuring the continuous availability and effectiveness of cloud-based systems. The investigation utilizes a 50 GB NCEP (National Centers for Environmental Prediction) time-series dataset covering the period from 2010 to 2024 with a daily temporal resolution. The selected geographic region spans 77° longitude by 12° latitude, focusing on the Bangalore region. Since the Z-score is a key statistical measure for anomaly identification, it is computed to detect deviations from normal behavior. Monthly anomaly detection is performed for precipitation values from 2010 to 2024 to analyze temporal variations. In addition, a comparative analysis is carried out between ARIMA, SARIMA, and existing machine learning models to evaluate performance, accuracy, scalability, and robustness.
Copyright (c) 2026 Radhika T V, K C Gouda, Krupashankari S Sandyal, Chandrakala B M, S Mercy
AN ENHANCED SYSTEM FOR DETECTION OF DENIAL OF SERVICE ATTACKS IN DISTRIBUTED SYSTEMS USING DEEP LEARNING
The proliferation of distributed systems has fundamentally transformed how organizations manage their computational infrastructure, yet this advancement has simultaneously exposed critical vulnerabilities to Denial of Service (DoS) attacks. Traditional detection mechanisms struggle to identify sophisticated attack patterns in real-time, particularly within cloud-based and edge computing environments. This research introduces an enhanced detection framework leveraging deep learning architectures, specifically combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to analyze network traffic patterns. Through experimental validation on a dataset comprising 2.3 million network packets collected from enterprise distributed systems, our proposed model achieved a detection accuracy of 98.7%, significantly outperforming conventional machine learning approaches. The system demonstrates remarkable capability in identifying zero-day attack variants while maintaining minimal false positive rates below 1.2%. Implementation across three distinct cloud environments revealed average detection latency of 47 milliseconds, making it viable for real-time deployment. This research contributes to cybersecurity literature by establishing a scalable, adaptive framework that addresses the evolving threat landscape facing distributed computing infrastructure, offering practical implications for system administrators and security professionals managing large-scale networked environments.
Copyright (c) 2026 S. Muthukumar, A.K. Ashfauk Ahamed
Understanding Housing Challenges and Needs in Malaysia: Implications for Inclusive Housing Policy
Housing should be accessible to all, regardless of their social background. Nevertheless, it was argued that the current housing initiatives did not reflect their housing needs and that more housing awareness was needed among the people. Hence, this study investigates the housing awareness and needs of underprivileged groups living in urban areas. A focus group discussion with ten (10) informants from low-cost public housing in Kampung Kerinchi, Kuala Lumpur, Malaysia, was conducted in a separate session to validate the housing issues and needs facing them using the inductive approach of Behavioural Insights (PRIME Framework) as proposed by the Malaysian Productivity Corporation (2021). The data is also collected via focus group discussions (FGDs) with five (5) experts from the academic area, property, local authority, Ministry, and non-governmental organisations to understand the provision and housing issues in Malaysia. The study's findings reveal that people aspire to own a house, but the current housing practice does not support them. Even though housing is the basic need of all citizens, and everyone should have equal access to adequate and suitable housing as outlined in the human rights principles, the current housing policy in Malaysia has yet to fulfil this role. Findings from the need-based analysis and valuable input from stakeholders should be consolidated to ensure effective interventions are implemented at the earliest possible.
Copyright (c) 2026 Mahazril ‘Aini Yaacob, Nor Syamaliah Ngah, Saidatul Akma Hamik, Farihah Hassan
A Hybrid MFCC–WavLM Feature Fusion for Audio Deepfake Detection
Recent advances in generative artificial intelligence have enabled highly realistic speech synthesis using text-to-speech (TTS), voice conversion (VC), and neural voice cloning techniques, posing significant security threats to Automatic Speaker Verification (ASV) systems. Conventional handcrafted features such as Mel-Frequency Cepstral Coefficients (MFCCs) effectively capture spectral characteristics but may fail to detect sophisticated artifacts introduced by modern speech synthesis models. In contrast, self-supervised models such as WavLM provide rich contextual representations but may not fully capture complementary lowlevel acoustic information. This paper proposes a hybrid audio deepfake detection framework that combines an 80-dimensional MFCC feature vector with a 768-dimensional WavLM embedding to form an 848-dimensional hybrid representation. The extracted features are standardized using StandardScaler, balanced using the Synthetic Minority Over-sampling Technique (SMOTE), and classified using a Deep Neural Network (DNN). Experiments on the ASVspoof2019 Logical Access (LA) dataset show that the proposed framework achieves 94.8% classification accuracy and a weighted F1-score of 0.95 on a 5,000-sample evaluation subset, demonstrating the effectiveness of the hybrid representation for spoofed speech detection.
Copyright (c) 2026 K. Sunil Kumar, Madduluri Suneetha, K. R. Anudeep Laxmi Kanth, M. Manoj Kumar, T. Kishore Kumar
Determinants of Consumer Buying Behaviour in the Edible Oil Sector: A Systematic Review with Reference to Gujarat
The issue of consumer buying behaviour in the edible oil industry is gaining more momentum with the rising concern of health, nutrition, sustainability and quality of products. This paper is a systematic literature review of research studies conducted on the determinants that have a significant role in determining consumer purchasing behavior of edible oils. The review was done based on PRISMA framework so that a transparent and systematic process of selection of studies is achieved. Therefore, 554 records were first identified in major academic databases and manual searches and then duplicate elimination and screening processes were performed. After the eligibility evaluation, 88 studies were chosen to be qualitatively synthesized. The analysis shows that the buying behaviour of consumers in the edible oil market relies on several determinants, such as price sensitivity, health consciousness, brand trust, product quality, food labeling and sustainability awareness. The practices of nutritional information and labeling were identified to have a huge impact on consumer knowledge and product judgments. In addition, the problem of environment and the ethical aspect of palm oil production and sustainable sourcing becomes more and more influential on consumer attitude and preference. Other product attributes such as origin, certification and organic attributes also identify with consumer quality and authenticity perceptions. Overall, the review proposes the ways in which consumer decision-making within the edible oil sector is multidimensional and the role of transparency, a health-oriented positioning of the food, and sustainability among the food producers and marketers.
Copyright (c) 2026 Vishwas Kiranbhai Kariya, Pragnesh D. Navlakha
Scalable Non-Contact Stress Detection Using Hybrid Multimodal Intelligence
Scalable digital health and human-computer interaction systems require non-contact stress detection methods, where wearable sensors and self-reports are impractical. This paper introduces a new reliability-aware hybrid multimodal stress detection scheme for stress detection based on speech characteristics and multimodal cues such as facial cues, eye/pupil features, keyboard dynamics, and handwriting behavior. The model includes encoders specialized for each modality, temporal learning and adaptive reliability-aware fusion to mitigate noisy or missing modalities. TExperiments conducted in a multimodal setting derived from ForDigitStress achieved 94.30% accuracy, 94.00% precision, 93.20% recall, 93.60% F1-score, 0.967 AUROC, 0.036 calibration error and 38.40 ms inference latency per window. Results show strong, interpretable and scalable stress detection in real-world digital environments.
Copyright (c) 2026 Anees Fatima, Mohd Nazeer
REGULATORY COMPLIANCE AS A COMPETITIVE ADVANTAGE IN AUTOMOTIVE AFTER-SALES SERVICE: An Integrative Framework Based on Service Quality, Consumer Rights and Customer Satisfaction in Colombia
This article examines Colombian regulatory compliance in consumer rights, with particular emphasis on Law 1480 of 2011 (Consumer Protection Statute), as a strategic asset for automotive after-sales service management. Drawing on a systematic documentary review that integrates legislative, doctrinal, and jurisprudential sources, the study proposes a conceptual analytical framework linking two variables: (1) regulatory compliance in after-sales service, operationalized through the dimensions of the AutoSERVQUAL model adapted to the consumer rights context, and (2) customer satisfaction, measured through the dimensions of repurchase, retention, and recommendation (3R). The theoretical analysis suggests that regulatory compliance transcends its legal function to become a strategic element of service quality, capable of strengthening consumer trust and fostering long-term relationships, particularly in markets where authorized and unauthorized workshops coexist, such as Colombia. The study concludes that integrating regulatory compliance into after-sales service management not only helps mitigate legal risk but also strengthens customer satisfaction and adds strategic value to the organization.
Copyright (c) 2026 María Victoria Tapia Ponce, Yineima Rodelo Arrieta
Comparative Analysis of Sarima and Holt-Winters Models for Rainfall Prediction in Cajamarca, Peru
The research compares two models for rainfall prediction in the Cajamarca Valley, Peru, for the period 2021-2024 using the SARIMA econometric models and the Holt-Winters Additive Exponential Smoothing. The analysis of monthly rainfall in the Cajamarca Valley reveals considerable variability in the data. The rainiest year was 2012 with a total of 823.9 mm, while 2020 was the least rainy recording only 340.2 mm. Rainfall is unpredictable for agricultural activities, as well as for human consumption. In the Cajamarca valley there are significant fluctuations over the years, with months of less rainfall generally between May and September. When performing the comparative analysis of the statistics corresponding to both models in order to evaluate their predictive capacity, it has been observed that the Holt-Winters Additive Exponential Smoothing Model stands out significantly compared to the SARIMA Model. In this sense, the Root Mean Squared of Errors (RMSE) shows values of 28,697 versus 41,546, and the Mean Absolute Error (MAE) shows values of 20,044 as opposed to 29,260, both results being lower in the SARIMA model. It is worth noting that lower values in these metrics indicate greater model precision. In addition, the R² (Coefficient of Determination) evaluates the effectiveness of the fit of a model, where a higher value indicates a better quality of fit. In this situation, a value of 0.657 is observed, which exceeds the 0.287 corresponding to the SARIMA model. This high R² value suggests that the Holt-Winters Additive Exponential Smoothing model provides superior tuning and more accurate predictions of rainfall in the Cajamarca Valley. The methodology to predict rainfall in the Cajamarca Valley can be applicable to any city in the country and other places with similar characteristics.
Copyright (c) 2026 José Romero-Rojas, Segundo Sánchez-Tello, Mariela Núñez-Figueroa, Sandra Rodriguez-Avila, Edwin Valencia-Castillo
Underlying Structure in the Clustering of Peruvian Departments According to Demographic Indicators, 2022
Territorial gaps in demographic indicators hinder the effective targeting of public policies in Peru. This study aimed to segment the 24 departments and the Constitutional Province of Callao (2022) to identify differentiated demographic profiles that support territorial planning. Following a quantitative, non-experimental, cross-sectional design, six standardized indicators were used: crude birth rate, total fertility rate, crude death rate, life expectancy at birth, infant mortality rate, and crude nuptiality rate. k-means (Lloyd, MacQueen, and Hartigan–Wong variants) and k-medoids (PAM) were applied, and cluster quality was assessed through average silhouette width, the elbow method, the gap statistic, and inter-algorithm stability (Jaccard index). Principal component analysis (PCA) supported interpretation (PC1 = 59.5 %; PC2 = 24.2 %; 83.8 % cumulative variance), and the resulting structure was contrasted with a Ward hierarchical dendrogram. The results converged on k = 2 as the main solution, given its higher silhouette (≈ 0.33), the absence of negative widths, and high replicability across algorithms; an advanced cluster (low birth and fertility rates, high life expectancy, very low infant mortality) and a lagging cluster with the opposite pattern emerged. The slightly higher crude mortality in the advanced cluster is consistent with population aging. As an operational disaggregation, k = 4 distinguished a metropolitan-southern pole, an Andean-Amazonian bloc facing greater challenges, and two intermediate transitional groups. ANOVA confirmed significant between-group differences in most indicators. The contribution is a reproducible, visualizable typology that strengthens territorial planning and the prioritization of resources for maternal-child health and basic services.
Copyright (c) 2026 Leticia de los Ángeles Corella Prieto, Raúl Alfredo Sánchez Ancajima, Gaspar Chávez Dioses, Kelvin Howard Pizarro Romero, José Luis González Márquez
Influence of the phenological stages of Heliconia psittacorum on the removal of hydrocarbons from contaminated soils
The phytoremediation potential of Heliconia psittacorum was evaluated for removing total petroleum hydrocarbons (TPH) and heavy metals (Cd, Pb, Ni) from highly contaminated soils in the Ecuadorian Amazon. An experiment was set up with 225 plants distributed across three phenological stages: rhizome propagation, growth, and pre-flowering. Survival, plant height, and contaminant levels were measured before and after application. The soil had an initial TPH level of 10,457.69 mg/kg, exceeding the regulatory limit for agricultural use by more than 2,000%. The results indicated that the growth stage achieved the highest TPH removal efficiency (56.43%), followed by pre-flowering (44.30%), while rhizome propagation achieved only 6.01%. No significant removal of Cd or Pb was observed, although Ni showed a reduction only in the growth phase (12.07%). The highest survival and growth rates were observed in pre-flowering, reflecting greater tolerance to contamination stress. It was concluded that H. psittacorum has high phytoremediation potential in advanced phenological phases, making it a viable biotechnological alternative for the ecological recovery of soils affected by oil activity in tropical regions. These findings support the implementation of strategies based on plant phenology to optimize environmental remediation.
Copyright (c) 2026 Jennifer Alexandra Orejuela-Romero, Ligia Elizabeth Paredes Peñafiel, William Estuardo Carrillo Barahona, Orley Alejandro Acosta Ordoñez
Analysing Commuters’ Transit Mode Chosen Pattern in Urban Cities to Build Sustainable Intelligent Transit Mobility
Brisk urbanization and growing transportation demand have deepened the essential for sustainable and intelligent transit systems. Understanding commuter travel behaviour is important for building active transportation plans and mobility solutions. This research examines the commuter mode-choice behaviour using advanced approach based on the Random Forest algorithm. Here, travel dataset containing of 544 observations and 44 descriptive variables was used to analyse the aspects dominating the chosen of among private motorized transport, public motorized transport, and non-motorized transport modes. The Random Forest algorithm have attained an overall prediction accuracy of 89.02%, demonstrating superior capability in identifying complex nonlinear relationships among socio-economic, demographic, and travel-related variables. Feature importance analysis expressed that specified mode priority, automobile requirement, travel cost, driving license ownership, and travel distance meaningfully influence the commuter decisions. The findings give valuable insights for urban organisers and policymakers in scheming sustainable mobility plans and intelligent transportation systems possible of plummeting the congestion, emissions, and dependence on private vehicles.
Copyright (c) 2026 J Sekaran, M Rameshkumar
From Boolean Functions to Secure S-Boxes: Construction, Challenges, and a Chaos-Based Dynamic Solution
Substitution boxes, commonly called S-boxes, are the primary source of nonlinearity in modern symmetric block ciphers, and their strength rests almost entirely on the cryptographic quality of the Boolean functions from which they are built. This paper traces that dependency from first principles. It reviews the algebraic foundations of cryptographic Boolean functions, including nonlinearity, algebraic degree, balancedness, bentness, and the avalanche and correlation immunity criteria that a coordinate function of a strong S-box must satisfy. It then surveys the main families of S-box construction, algebraic, heuristic, chaos-based, and hybrid, drawing on a paper bank of 43 verified sources retrieved through the Semantic Scholar API, and explains why static, fixed S-boxes remain structurally exposed to differential, linear, and algebraic cryptanalysis. Building on this review, the paper implements and evaluates a dynamic, key-dependent S-box generation framework that couples a compound tent-logistic chaotic map with an algebraic base layer, and reports the framework's actual measured performance, nonlinearity, strict avalanche criterion, bit independence criterion, differential uniformity, and linear probability, against the AES S-box baseline over twelve independently generated instances. The results are reported honestly, including a gap between the proposed construction's nonlinearity and the AES baseline that mirrors a limitation already documented in the literature, and the paper closes with a discussion of what a genuinely competitive dynamic S-box design would need to add to close that gap.
Copyright (c) 2026 Dhanashree Hadsul, Zahir Aalam
SOCIAL MEDIA AS DIGITAL MARKETING TOOL AND ITS IMPACT ON BUYING INTENTION
Given the increasing popularity of social media and its unstoppable growth, it seems likely that social media will largely supplant other aspects of marketing. Social media endeavors to establish a connection between organizations and consumers by promoting continuous contact, cultivating a feeling of confidence, and promptly involving the relevant audience. This study aims to gain a deeper understanding of the impact of social media on businesses. The dissemination of knowledge and information through online platforms among diverse communities is a prominent advantage of utilizing social media. This digital platform for sharing information also facilitates the enhancement of interpersonal communication abilities, especially for individuals who are enrolled in educational institutions. The advent of online tools and technology has not only significantly enhanced communication, but it has also fundamentally transformed the nature of communication itself, as well as our approach to discussing and conceptualizing it. Social media possess the ability to radically transform the character of our social life, both on an individual and collective level. In the last decade, social media has experienced a significant and profound metamorphosis. Businesses are utilizing social media as a means of promoting their products or services. Social media is the most efficient and cost-effective means of communication for any sector, particularly in businesses that undergo frequent changes. A convenience sample consisting of 140 respondents was obtained and the data was then evaluated. The study's findings unequivocally demonstrate that social media marketing significantly influences customers' purchasing behavior.
Copyright (c) 2026 Premjeet Kumar, Yaaseen Masvood
Enhancing Procurement Efficiency through Vendor Relationship Management (VRM) in the FMCG Sector: An Illustrative Research Framework Integrating Natural Language Processing (NLP) and Sentiment Analysis
The Fast-Moving Consumer Goods (FMCG) industry operates in a highly competitive environment characterized by short product life cycles, volatile demand, and complex supplier ecosystems. Traditional Vendor Relationship Management (VRM) approaches primarily rely on structured performance metrics such as cost, quality, and delivery performance, often overlooking valuable unstructured information embedded in emails, supplier feedback, meeting transcripts, complaint records, and social media discussions. This conceptual paper proposes an integrated research framework that combines Vendor Relationship Management with Natural Language Processing (NLP) and sentiment analysis to enhance procurement efficiency. The framework suggests that automated analysis of textual interactions can generate actionable insights into supplier trust, collaboration quality, risk signals, and relationship health, thereby improving procurement outcomes. The proposed model contributes to digital procurement literature by positioning AI-enabled text analytics as a strategic capability for procurement decision-making in the FMCG sector.
Copyright (c) 2026 Gunjan Bhayana, Vanik Gosai
Explainable Hybrid Framework for Zero Day Web Attack Detection Using Anomaly Detection and Classification ModelsThe rise in the usage of web applications has elevated the risk of cyber attacks, specifically zero day web attacks. Zero day web attacks are t
The rise in the usage of web applications has elevated the risk of cyber attacks, specifically zero day web attacks. Zero day web attacks are the unseen and unusual attacks which traditional methods fail to detect. This essay's primary goal is to build a system that detects zero day web attacks efficiently. The proposed system utilises CICIDS2017 dataset and applies SMOTE method for creating artificial samples to balance the class. For anomaly detection, the system utilizes unsupervised techniques like autoencoder and isolation forest. The study aims to implement supervised algorithms as well, such as Logistic regression, linear support vector machines, and random forests and XGBoost respectively. Among all these models, XGBoost outperforms with 0.99 accuracy, 0.98 precision, 1.00 recall and 0.99 F1-score. The system classifies the traffic as normal or attack enabling efficient detection of cyber attack. Explainable AI (XAI) techniques like SHaply Addictive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) are applied To enhance the interpretability. It is observed that SHAP is the higher suitable technique for explaining the identification of attacks. The paper mainly focuses on improving the detection capability of the setup and maintaining transparency in decision making. This study marks its contribution in providing important perspectives in the area of cybersecurity for efficient detection of zero day web attacks.
Copyright (c) 2026 Shreya A Patil, Anita Harsoor
A Lightweight Forward-Unlinkable Authenticated Key Agreement Protocol for Secure UAV Communication in Internet of Drones Environments
Secure authentication, confidentiality, and communication privacy are critical requirements in mission-critical Internet of Drones (IoD) applications such as disaster response, defense, border monitoring, and surveillance, where Unmanned Aerial Vehicles (UAVs) are being increasingly used. Many lightweight authentication and key agreement protocols initially developed for IoT and wireless sensor networks, however, are not sufficiently robust to provide an appropriate level of security for IoD applications, especially considering physical drone capture, long-term credential compromise, and post-compromise transcript correlation challenges. This paper proposes a lightweight forward-unlinkable authenticated key agreement protocol for secure UAV communication in adversarial IoD environments. The protocol is based on an authenticated ephemeral X25519 Diffie–Hellman exchange, key derivation from HKDF, transcript authentication using HMAC and explicit key confirmation. The session keys are based on newly created ephemeral secrets and don't necessarily stem from long-term stored credentials, so if a drone's memory is compromised, the session keys of previous sessions are not exposed. Pseudonym rotation and context-bound key derivation are incorporated to strengthen session separation and reduce the possibility of linking previous or future protocol transcripts after drone capture. The proposed protocol is analyzed under an extended Dolev–Yao adversary model with drone-capture capability and is supported through ProVerif-style symbolic validation. The analysis indicates that the protocol provides mutual authentication, session-key secrecy, replay resistance, man-in-the-middle protection, forward secrecy, and forward unlinkability. Operation-level cost analysis and published embedded-platform benchmarks show that the cryptographic overhead remains lightweight, with per-session computation requiring only a few milliseconds and communication overhead remaining within a few hundred bytes. The results demonstrate that lightweight authenticated key agreement can provide practical cyber-security and privacy protection for resource-constrained UAV communication without relying on heavy infrastructures such as blockchain, certificate-based PKI, or zero-knowledge proof systems.
Copyright (c) 2026 Kamal A. ElDahshan, Hani Saad Hassan Issa, Hala Mohamed Abbas
A SEMANTICALLY GROUNDED LLM ARCHITECTURE FOR INTENT-AWARE AND USER-ALIGNED HUMAN–COMPUTER INTERACTIONS
Large Language Models (LLM) have transformed the Human-Computer Interaction (HCI) as it offers open and adaptable communication via the context. However, the existing systems built on LLM also lack limits on semantic stability and intent understanding as well as optimising behaviour guided by the user. In this paper, the researcher proposes an integrated architecture, whereby, the contextual encoding is based on LLM, contrastive semantic reasoning network, explicit intent modeling layer, and a reinforcement learning (RL) framework along with a user experience (UX). The system employs a multi-step training, and it entails supervised multi-task learning to acquire representation grounding, joint optimization to acquire semantic intent alignment, and RL-based fine-tuning to acquire greater task success, satisfaction and trust calibration. The large-scale experiments suggest that the proposed architecture attains high semantic consistency, intent discrimination, conversational coherence, and UX levels, in comparison to powerful baselines of LLM. The need of each of the modules is proven by the ablation studies and the analysis of errors underlines the existing difficulties of dealing with ambiguous and multi-intent utterances. This study reflects a comprehensive, semantically based, and user-complementary approach to the methodology of the development of intelligent conversational systems and next-generation interactive AI interfaces.
Copyright (c) 2026 Barkha Shakeelahmad Kasab, Thota Siva Ratna Sai
Fuora Social: An AI-Driven Microservices Framework for Automated Social Media Content Generation and Influencer Workflow Optimization
Increasing demands for content creators and influencers have resulted with the advent of social media platforms. They have to deliver up-tempo engaging content on multiple platforms consistently. Existing social media monitoring tools provide partial answers: they concentrate on specific areas such as scheduling or simple analytics. These solutions have no learning capabilities and no AI-powered content generation. This paper presents Fuora Social, a microservices based framework. It leverages transformer-based generative AI, intelligent scheduling, real-time analytics, human validation to enable end-to-end influencer workflow solutions. The proposed system has a multi-layer architecture as follows: user interface, authentication, application logic, AI services, data storage, and external API services. A domain-specific transformer model generates content such as captions, hashtags, and video scripts for a number of platforms. It is accomplished via context encoding, structured prompt conditioning, and refinement. In turn, recommendation systems are continually adapted to improve the quality of content recommendations according to the engagement metrics received post publication. A pilot study following 25 influencer accounts on Instagram, Facebook and YouTube over a period of 30 days demonstrated time savings of 73% in creating content, decreasing from 22 minutes to 6 minutes. Posting consistency also improved by 26%, from 63 to 89%. Engagement growth spiked by 240%, jumping from 5% to 17%. User satisfaction scores went up from 3.8 to 4.4 on a 5-point scale. The microservices architecture also enables independent scaling and management of each AI module to prevent system failure and solve the bottleneck problem that existing traditional social media management platforms are facing in terms of scalability.
Copyright (c) 2026 Khushbu Yogesh Konde, Madhav Mohan Vagmare, Vagmare Deepmala, Jayshree Pargee, Kavita Deshmukh, Chamandeep Vimal
Preserving Traditions of Particularly Vulnerable Tribal Groups (PVTGs) through Artificial Intelligence; A Scoiological Study of Documentation of Lodha Tribal Heritage of Odisha
Lodha is one of the Particularly Vulnerable Tribal Groups (PVTG) found in the Mayurbhanj district of Odisha whose culture is rich in forest-based oral traditions and ethnobotany, including various crafts. But due to factors like modernization, poverty, and historical experiences of oppression during the colonial era, the existence of the group is now threatened. With reference to examples from other parts of the world such as ethical AI frameworks by UNESCO and NLP and GANs for preserving dying languages and rituals, this paper examines the role of artificial intelligence in the Lodha legacy. Some of the applications include motion capture animation for performing arts, archiving material artifacts in three-dimensional VR, AI-powered transcription, and GamifiED mobile application for preserving dialects, along with predictive modeling for socio-cultural erosion. This research encourages collaboration with the local community and institutes, including the co-design of a "Digital Heritage Repository of Lodhas" and partnership between IIT Bhubaneswar and SCSTRTI, Odisha, while emphasizing the need for ethics, bridging the digital divide, data ownership, and mitigating any potential bias in the data analysis process. The research advocates for the inclusion of legislative measures to encourage sustainable tourism and AI literacy. Ultimately, it is demonstrated that artificial intelligence becomes a win-win solution to integrate traditional knowledge with sustainable development to preserve the natural and cultural heritage of the Lodhas for posterity.
Copyright (c) 2026 Karisma Sahoo, Sipra Sagarika
Governed Serverless Automation Ecosystem for AI-Driven Enterprise Integration and Sustainable Cloud Operations
Governance, serverless, and automation facilitate enterprise integration that is adaptive, cost-effective, low-code, and capable of creating dynamic, context-aware, and value-adding workloads. This creates a need for policy-based management that simplifies implementation in multicloud scenarios. Serverless automation, enabled by event-driven workloads and dynamic resource provisioning for short-lived tasks, can operate in multicloud environments without being controlled by any provider. Introducing artificial intelligence facilitates improvements in scheduling and resource optimization. However, these benefits are not well understood, nor are these ecosystems properly managed. What governance is required for such serverless automation in an AI-enable enterprise integration setting? What does governance in serverless environments achieve? These questions are addressed through a structured research-product approach. The concept of serverless automation is first established, then applied to governance and a serverless context. A critical perspective on governance emerges through examination of central concepts and their interplay within a formal control-compliance-risk framework. From a practical angle, governance focuses on preserving trust in business operation and service delivery. This leads to the consideration of a Cloud Automation Control Plane that governs the core events and processes of Cloud Automation on behalf of initiation parties. The enterprise integration layer can be made serverless to minimize code development, improve resilience, and enhance security with AI assistance. The AI inclusion can be exploited for intelligent workload and resource management without compromising the governing principles. These avenues in combination demonstrate that a clear governance model can enable sustainable Cloud AI Automation for the enterprises and the ecosystem.
A Hybrid Random Forest–XGBoost Framework for Power Transformer Health Index Prediction
In this study, a health index prediction framework for the power transformer is developed based on machine learning (ML) for evaluating the reliability and operational state of it. The feature engineering and Gaussian noise-based data augmentation are performed on the dataset to select the appropriate features and generate a large amount of data for ML algorithms. Several ML algorithms, including Linear Regression (LR), Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Lightweight Gradient Boosting Method (LightGBM), are employed. Among these algorithms, the two most superior are chosen based on their individual performance. The Optuna optimizer and residual learning strategy are applied to improve the performance of the proposed method and combine the predictions of superior ML algorithms for the final prediction. The simulation evaluation is done by collecting the dataset from the previous studies. The proposed method achieves an RMSE value of 2.49, an MAE value of 1.45, a MedAE value of 0.72, and an R² value of 0.98. Finally, the feature interpretation is done using the SHAP analysis, which shows that Dibenzyl Disulfide (DBDS), Interfacial V, Methane, and DBDS-Water exhibit the largest impact on transformer life expectancy.
Copyright (c) 2026 Pushpinder Kumar, Ved Parkash, Naveen Kumar Sharma
AI-Based Hybrid Control for Renewable Energy Systems: A Review
Efficient control strategies are needed for renewable energy systems due to the nonlinear nature of their dynamics, their intermittent characteristics, parameter uncertainties, as well as grid connection limitations and the need for real-time implementation. Recently, hybrid control structures where traditional real-time control principles combine with artificial intelligence adaptation, tuning or decision support are being used more often to address these issues. This paper presents a systematic review of the recent literature concerning the use of hybrid controls in renewable energy systems. In particular, it will focus on solar energy, wind energy, microgrids, as well as hybrid renewable configurations, providing both a theoretical framework and practical examples. The methodology applied to review scientific articles of both AI-tuned PID controllers, which will include literature about the following groups of controllers: artificial intelligent neural networks, fuzzy and neuro-fuzzy approaches, adaptive PID and predictive-control schemes that highlight their workflow, performance enhancement, robustness considerations, computational complexity, practicality of application/controller family and usage modes, as well as other issues. The analysis of scientific works presented so far shows three main tendencies in the research: researchers mostly rely on computer simulation results to present their findings; comparisons of controller types fail to provide methodological consistency; and their failure to address practical problems of complexity, tuning, and embedded implementation. In conclusion, this systematic review will indicate what methodological and practical aspects are missing in the literature and provide some suggestions on how to improve future research on intelligent hybrid control schemes to achieve more manageable solutions.
Copyright (c) 2026 Ahmed Sameer Abdulmohsin
Excitation-Based Damping Controller Synthesis and Benchmark Verification for Mitigation of Low-Frequency Electromechanical Oscillations in DFIG-Penetrated Multi-Machine Grids
Large-scale absorption of converter-interfaced renewable generation is reshaping the oscillatory signature of interconnected grids and is making the suppression of low-frequency electromechanical modes markedly harder across wide operating envelopes. Stabilizers of the conventional class—the classical Δω-based PSS and the IEEE MBPSS4B structure—were conceived for networks dominated by synchronous plant, and their damping authority can deteriorate once converter-coupled sources dilute effective inertia and reshape modal participation. The present study reports a robust power system stabilizer (PSS) together with a benchmark-driven verification protocol aimed at damping electromechanical oscillations in grids hosting doubly-fed induction generators (DFIGs). Controller synthesis is carried out on the Heffron–Phillips single-machine infinite-bus (SMIB) representation, after which the design is exercised on the Kundur four-machine two-area benchmark. So that the assessment is not skewed by the renewable interface itself, a DFIG plant is embedded in Area 2 at shares of 4%, 6%, 8% and 10%, and its converter-side quantities—PWM modulation index, LC filter elements and inverter PI gains—are first optimized by Particle Swarm Optimization (PSO) under an Integral of Time-weighted Absolute Error (ITAE) criterion, which curtails converter-induced oscillatory coupling and preserves dynamic feasibility of the hybrid plant. Time-domain runs in MATLAB/Simulink R2024b indicate pronounced transient gains: for the SMIB case the settling time falls to roughly 3.184 s against 6.016 s for MBPSS4B and 11.76 s for the Δω-PSS. In the multi-machine study the stabilizer tightens rotor-angle coherency and curtails inter-area swings at every penetration level examined, while a companion small-signal frequency-domain study confirms improved phase compensation and tempered resonance peaks within 0.1–10 Hz. Damping capability is therefore retained consistently as renewable share grows, strengthening the dynamic security of low-inertia networks.
Copyright (c) 2026 Dinesh Dilip Majumdar, Umesh E. Hiwase
A Survey of AES-256 and SHA-256 Hardware Architectures on FPGA
In the present day, data confidentiality and integrity are among the major concerns in the embedded and communication systems and hardware-based cryptographic systems are playing a significant role in this regard. Moreover, the flexible features of FPGA implementations of cryptographic algorithms, such as greater performance, higher parallelism, and greater protection for sensitive data, are better than software-based approaches. This survey discusses the latest FPGA-based architectures of the AES-256 and the SHA-256, along with the design methodologies, optimization techniques and integration strategies. An analysis of existing studies is conducted to understand the pros and cons of various architectures, resource usage, throughput and implementation methods. The survey reveals that most current designs focus on optimizing only one aspect of encryption and hashing separately, whereas integrated designs may require extra hardware because of the duplicated control and interface logic. From these observations, it can be deduced that an efficient resource usage, scalability and secure hardware implementation is possible by integrating the AES-256 and SHA-256 components into a modular FPGA architecture.
Copyright (c) 2026 Vasudeva G., Bharathi Gururaj, Mruthika N., Raghavendra Chinmai S., Sowjanya M. Sharma, Yashwanth S.
Mitigating Corruption For Small Businesses In South Sudan: a Delphi Study
The purpose of this qualitative Delphi study was to gather a panel of small business entrepreneur experts to discuss the types of corruption they experienced, and to share the best strategies they developed and implemented for mitigating these challenges in their businesses located in South Sudan. Twelve small business owners volunteered to share their experiences on how their businesses were affected by corruption, and the strategies they implemented to transform their businesses. The entrepreneurs’ expert advice underwent refinement through three rounds of questions before reaching consensus. Four themes emerged in support of the research questions. The findings revealed that successful entrepreneurs need to develop a solid business plan to include core ethical values, promoting good governance, with the mission to improve performance and increase revenue with honesty, fairness, respect, and accountability.
Copyright (c) 2026 Joseph Z. Yeljak, Julie M. Ballaro, Nancy J. Arduengo
ClustChain: A Blockchain-Based Framework for Efficient Genomic Data Storage Using Attribute-Based Clustering
Genomic data has become an essential component of modern healthcare, driving innovations leading to more precise personalized care. While the exponential growth of genomic data opens significant opportunities for personalized healthcare, it has introduced several challenges related to genomic data. Among these, data storage and data retrieval remain the major concerns due to the large size of genomic data, while issues related to data integrity, data privacy and data security complicate the situation further. To address these challenges, blockchain technology has been explored as a viable solution to provide tamperproof, secure, and trustworthy data management solution. This paper proposes an on-chain storage model for genomic data management on blockchain platform. The proposed solution aims to reduce storage requirement through an attribute-based clustering mechanism in combination with a data compression method applied to genomic data. At the same time, it focuses on reducing stream data retrieval time through a query-aware data organisation in a data stream. Multichain platform and python 3 have been used to perform the experiment. The results indicate an average reduction in storage requirement of 18% for 10k reads, 20% for 100k reads, and 22% for 1000k reads in a BAM file. However, the reduction can be even more depending upon the cluster to which the BAM reads are mapped. Additionally, there is an improvement in stream load time due to reduced storage requirements on data streams and supports both simple and complex queries. It offers a memory-efficient data management solution for genomic data.
Copyright (c) 2026 Anju Arya, Amita Malik
AI-Enabled Pedagogical Framework for Integrating Indian Knowledge Systems in Teacher Education
Indian Knowledge Systems (IKS) represent a diverse body of knowledge developed and transmitted through India's intellectual, scientific, philosophical, cultural, linguistic, artistic, and educational traditions. The National Education Policy (NEP) 2020 emphasizes the importance of developing a strong understanding of Indian knowledge, values, traditions, languages, arts, and cultural heritage within contemporary education. However, the integration of IKS into teacher education remains challenging because of fragmented digital resources, heterogeneous knowledge formats, limited personalization, inadequate pedagogical mapping, and the absence of intelligent mechanisms for recommending context-specific IKS learning resources. This paper proposes an AI-Enabled Pedagogical Framework for integrating Indian Knowledge Systems into teacher education. The proposed framework combines Natural Language Processing (NLP), semantic analysis, knowledge representation, recommendation techniques, adaptive learning, intelligent assessment, and learning analytics to support teacher educators and student teachers. The framework consists of five major layers: Data Collection, AI Processing, Pedagogical, Application, and Analytics layers. The Data Collection Layer manages IKS resources obtained from digital repositories, books, manuscripts, educational resources, multimedia content, curriculum documents, and expert-curated materials. The AI Processing Layer performs preprocessing, semantic classification, entity extraction, topic identification, content recommendation, and knowledge graph construction. The Pedagogical Layer maps IKS resources with teacher education learning outcomes and generates adaptive learning pathways. A prototype implementation is proposed through an integrated dashboard containing an IKS content library, AI tutor, adaptive learning module, assessment engine, recommendation system, and learning analytics dashboard. The system can recommend resources related to areas such as Indian mathematics, Ayurveda, Yoga, philosophy, astronomy, agriculture, architecture, linguistics, arts, and traditional pedagogical practices. A controlled evaluation involving teacher education learners can be used to measure recommendation accuracy, learning improvement, user satisfaction, assessment performance, and system usability. The proposed framework provides a technology-supported mechanism for contextualizing Indian Knowledge Systems within teacher education while maintaining human oversight over educational interpretation and knowledge validation. The framework can support personalized learning, curriculum integration, intelligent assessment, and data-driven pedagogical decision-making. Thestudy demonstrates how artificial intelligence can act as an enabling technology for strengthening IKS-based teacher education rather than replacing teacher educators or domain experts.
Copyright (c) 2026 Shrawan Kumar Sharma, Nirupama Dubey, Raj Sinha, Rahul Kumar, Ashish Sen, Prathviraj Singh Rathore
Optimizing Human Resource Management in Education through Artificial Intelligence: A Quantitative Study
This article examines the role of artificial intelligence (AI) in enhancing human resource management (HRM) within the education industry. Building upon recent growth, it examines how AI-enabled tools automate repetitive HR tasks, support data-driven decision-making, and augment strategic functions such as recruitment, workforce planning, and staff development. A quantitative research design was adopted, with a purposive sample of 50 HR professionals from educational institutions. Data was collected through a structured questionnaire using a five-point Likert scale and analyzed with descriptive statistics. Findings indicate high levels of awareness of AI among HR professionals and strong agreement that AI improves recruitment accuracy, workforce planning and personalized training. Respondents also acknowledged AI’s value in enhancing employee engagement and supporting data-driven HR decision-making, although concerns about data privacy and reduced human interaction remain. The study contributes to ongoing debates on digital transformation in HRM by providing empirical evidence from the education sector and suggests directions for both practice and future research.‬
Copyright (c) 2026 Yasmin Mirzani, Anshuman Shastri
An Intelligent Morphology-Driven Framework for Leaf Venation Analysis and Plant Classification Using Advanced Digital Image Processing and Machine Learning
Automated plant identification based on leaf morphology has gained significant attention in recent years due to its wide range of applications in precision agriculture, biodiversity conservation, environmental monitoring, and botanical informatics. Advances in digital image processing and machine learning have enabled the development of intelligent systems capable of identifying plant species from leaf characteristics with minimal human intervention. Despite these advancements, achieving reliable and accurate classification remains challenging because leaf images are often affected by variations in illumination, complex backgrounds, image noise, differences in orientation and scale, as well as natural leaf deformation. These factors can obscure important morphological features, reduce the effectiveness of feature extraction, and ultimately decrease the accuracy and robustness of automated plant classification systems. Consequently, there is a growing need for intelligent frameworks that can effectively handle these challenges while preserving critical leaf morphology and venation information for reliable plant identification. This study proposes an Intelligent Morphology-Driven Framework that integrates advanced digital image processing and machine learning for robust leaf venation analysis and plant classification. The proposed framework integrates multiple digital image processing and machine learning techniques to enable accurate and automated leaf venation analysis and plant classification. Initially, leaf images undergo preprocessing using grayscale conversion, histogram equalization, Contrast Limited Adaptive Histogram Equalization (CLAHE), Gaussian filtering, Laplacian sharpening, Gabor filtering, and homomorphic filtering to improve image quality and enhance venation and structural details. The enhanced images are then processed through threshold-based segmentation followed by morphological operations, including erosion, dilation, opening, closing, convex hull generation, and skeletonization, to accurately isolate leaf regions while preserving their geometric structure. To characterize leaf morphology, the framework extracts a comprehensive set of features, including geometric descriptors such as area, perimeter, circularity, aspect ratio, solidity, eccentricity, and vein density, together with Hu invariant moments that provide rotation-, translation-, and scale-invariant shape representation. In addition, the framework investigates the influence of image compression by comparing lossless PNG and lossy JPEG formats to evaluate their impact on preserving morphological features and venation details. The extracted feature vectors are subsequently classified using a Random Forest classifier to categorize leaf venation patterns into parallel, reticulate-pinnate, and reticulate-palmate classes.Experimental evaluation demonstrates that the proposed framework achieves an overall classification accuracy of 93.2%, while effectively preserving important morphological characteristics and maintaining computational efficiency. The combination of adaptive image enhancement, morphology-preserving segmentation, comprehensive feature extraction, and robust machine learning classification makes the proposed approach reliable, interpretable, and scalable. Consequently, the framework has significant potential for applications in digital herbarium systems, automated plant identification, biodiversity monitoring, botanical informatics, and precision agriculture.
Copyright (c) 2026 Manjula K, Usha N, Varun K S, Nimisha C B, Ayesha Khannum
DOWNSTREAM DEVELOPMENT OF RURAL-OWNED ENTERPRISES (BUMDes) THROUGH DIGITAL FINANCIAL TECHNOLOGY TO ENHANCE RURAL ECONOMIC PERFORMANCE
This research aims to analyze the downstream development of Rural-Owned Enterprises (BUMDes) through the utilization of digital financial technology to improve rural economic performance in Pangkep Regency. BUMDes are rural economic institutions that play a strategic role in managing local potential, strengthening rural economic self-reliance, and improving community welfare. However, the management of BUMDes still faces various challenges, such as limited managerial capacity, low transparency in financial management, and limited access to modern financial systems. Therefore, the application of digital financial technology is viewed as a strategic innovation to improve business management efficiency, expand market access, and strengthen the governance of rural economic institutions. This research uses a mixed methods approach with a sequential explanatory design, which combines quantitative and qualitative methods in sequence. The first phase involved a survey of BUMDes managers, rural officials, and local business operators in Pangkajene and Kepulauan Regency, South Sulawesi, Indonesia, using a structured questionnaire to measure variables related to the adoption of digital financial technology, business management capacity, business innovation, and rural economic performance. Quantitative data were analyzed using descriptive statistics and regression analysis to determine the relationships among variables. The second stage involved in-depth interviews and focus group discussions (FGDs) with BUMDes managers and rural stakeholders to deepen understanding of the enabling factors and constraints in the implementation of digital financial technology. The research findings indicate that the adoption of digital financial technology and the downstream development of BUMDes businesses have a significant and positive impact on rural economic performance, particularly in enhancing financial transparency, operational efficiency, and expanding the marketing network for rural products managed by BUMDes. Qualitative findings also reveal that the success of BUMDes digitalization is heavily influenced by human resource capacity, support from rural government policies, and the availability of digital infrastructure. Thus, the development of BUMDes downstreaming based on digital financial technology can serve as an innovative strategy to strengthen rural economic performance and promote sustainable rural economic development.
Copyright (c) 2026 Abdul Rahman, Askari Hasanuddin, Sardi, Desi Arisandi
Financial Literacy, Digital Financial Access, and SME Distribution Performance in the UAE: A Mediation Analysis
Purpose: This study aims to examine the relationship between FL and SME distribution performance, and to investigate the mediating role of access to digital finance in strengthening distribution-related business efficiency within the context of the United Arab Emirates. The research seeks to provide empirical evidence on how financial knowledge and digital financial accessibility jointly influence distribution-channel performance, operational efficiency, and SME sustainability. Design/methodology/approach: The study adopts a quantitative research design, using a structured questionnaire distributed to SME owners and managers. A total of 170 valid responses were collected and analysed using statistical software. Data screening procedures were conducted, including missing data analysis, outlier detection, multicollinearity checks, and normality testing. Regression and mediation analyses were employed to test the proposed hypotheses and examine both direct and indirect relationships among the variables. Findings: The findings reveal that FL did not have a significant direct effect on SME distribution performance; however, it exerted a significant indirect effect through access to digital finance. Additionally, access to digital finance fully mediates the relationship between FL and SME distribution performance. SMEs with better access to digital financial services demonstrated improved customer reach, payment efficiency, and distribution-channel effectiveness. Research limitations/implications: The study is limited to SMEs operating within the United Arab Emirates and is based on cross-sectional data, which may limit the generalisability of the findings. Future research is recommended to apply longitudinal designs and expand the study to different geographical contexts to validate and extend the results. The study should be interpreted in light of several limitations. First, the sample was predominantly composed of female respondents (88.2%), employees (62.4%), service-sector SMEs (80.0%), and relatively young firms operating for 3–5 years (78.2%). Consequently, the findings may not be fully representative of the wider SME population in the UAE. Second, the use of convenience sampling limits the generalisability of the findings. Future studies should employ probability sampling techniques and include SMEs from a wider range of industries, ownership structures, and organisational maturity levels. The study relied on self-reported data collected from a single source using a cross-sectional survey, which may increase the risk of common method bias. Although procedural measures such as anonymity and voluntary participation were implemented to minimise this risk, future studies are encouraged to employ formal statistical assessments (e.g., Harman’s single-factor test or marker-variable techniques) to further evaluate the potential influence of common method bias. Practical implications: The results highlight the importance of promoting FL programmes and expanding access digital financial infrastructure to support SME distribution activities. Policymakers and financial institutions should focus on strengthening digital payment systems, online transaction capabilities, and fintech accessibility to improve SME distribution efficiency and competitiveness. Social implications: Enhancing FL and digital financial inclusion can contribute to broader economic development by empowering SMEs, increasing employment opportunities, and fostering innovation within the economy. Originality/value: This study contributes to the distribution science literature by integrating FL and digital financial access within a distribution-performance framework. It provides empirical evidence from the UAE context on how digital finance supports SME distribution efficiency and operational competitiveness. Conclusion: The study concludes that financial knowledge and digital financial inclusion are essential drivers of SME growth and sustainability. Strengthening access to digital financial services can significantly improve operational efficiency and strategic decision-making among SMEs in the UAE.
Copyright (c) 2026 Maryam Khameis Saeed Alfalahi Alyammahi, Mohamad Rohieszan Ramdan, Novika Candra Astuti, Hariyaty Ab Wahid
Document process management for efficiency in the handling of additional budget demands in local governments of Peru, 2025
The aim of this study was to propose and validate a document process management model to optimize the efficiency of handling additional budget demands in Peruvian local governments during 2025, addressing delays caused by poor traceability, lack of document standardization, limited interdepartmental coordination, and rework. A quantitative, applied approach was used, with a pre-experimental longitudinal design and explanatory scope. The sample comprised 31 municipalities, which were administered a Document Registration Form (FRD) and a Document Process Management Questionnaire (CGPD) with a Likert scale, both before (AS-IS) and after (TO-BE) implementation. According to the Shapiro-Wilk, Student's t and Wilcoxon signed-rank tests for related samples, cycle time was reduced from 52.77 to 34.29 days (t = 4.528; p < 0.001), and rework decreased in all files (Z = −4.998; p < 0.001). Deadline compliance (SLA) rose from 41.9% to 100%, end-to-end (E2E) procedures from 48.4% to 100%, and the questionnaire score increased from 4.15 to 4.90 out of five (t = −14.968; p < 0.001). It is concluded that the model significantly increases efficiency in meeting additional demands, confirming the general and the four specific hypotheses; nonetheless, contrasting it with a control group and a larger sample is recommended.
Copyright (c) 2026 Ulices de la Cruz Vilchez, Jose Pablo Rivera Rodriguez
DIALOGUE BETWEEN CULTURE, TECHNOLOGY AND INTERCULTURAL EDUCATION: A CO-CONSTRUCTION FROM CRITICAL HERMENEUTICS
This theoretical-reflective article, derived from the doctoral dissertation Intercultural Education Based on Curriculum Management for Strengthening the Quality of Higher Education, is grounded in a critical-hermeneutic documentary review. The study adopts a qualitative perspective supported by micro-ethnographies and an introspective-experiential approach, assuming the co-construction of the corpus through information analysis, textualization, theoretical triangulation, and analytical systematization. It examines the relationship between culture, intercultural education, curriculum management, and technology, understanding the latter not as a neutral resource but as a sociocultural mediation capable of expanding or restricting recognition, participation, and curricular justice. The findings suggest that intercultural education goes beyond discourses of inclusion and coexistence to become a transformative practice in the face of historically rooted cultural exclusion. They also show that the critical, ethical, accessible, and pedagogically relevant integration of digital technologies can foster dialogue among knowledge systems, the visibility of identities, collaborative knowledge production, and the democratization of learning. However, technology may also deepen inequalities when gaps in connectivity, accessibility, digital competencies, and cultural representation persist. The article concludes that recognizing diversity, developing inclusive curricular practices, and critically managing technology contribute to strengthening educational quality.
Copyright (c) 2026 Greys Patricia Núñez Rios, Leonardo Antonio Diaz Pertuz, Fredy Julián Gómez Pedroza, Melissa Alejandra Negrete Cervantes, Edgardo Sánchez Montero, Enrique José Orozco Daza, Madeleyne Mercedes Daza Cuello
AgentCodeReview: Implementation and Comprehensive Benchmark Evaluation of a Multi-Agent Framework for Explainable Code Review and Automated Bug Repair
Large Language Models (LLMs) have revolutionized software development, from analyzing code and generating suggestions to detecting bugs and errors, and even creating entire programs. Despite these advances, existing AI-driven code review solutions still provide a one-size-fits-all approach to code review with overall feedback and suggestions, often of a non-specific nature. This restriction promotes modular architectures which would be able to provide specific and direct code quality reports. This paper presents the AgentCodeReview system, a multi-agent system that is able to conduct explainable code review and automated bug repair by leveraging software engineering agents with different code review tasks. There would be five independent entities, each one to be able to review code, analyze security, evaluate performance, document it and be able to automatically fix bugs. They run parallelly under the guidance of a centralized orchestration layer that collects the results from the analytical agents, calculates software quality scores and creates comprehensive HTML and PDF reports. Moreover, a Streamlit-based web interface was created that allows the interactive visualization of the results of the analysis and interactive entry of the input values. A set of twenty python programs was created to test the framework for effectiveness, consisting of a variety of runtime errors, security flaws, performance issues, documentation issues and a mixture of these types of errors. Two metrics, namely execution time and qualitative assessment were used to compare the proposed multi-agent framework with a single-agent framework as baseline. Experimental results demonstrated the benchmark execution success rate was 95%, while the multi-agent architecture provided more structured, explainable and domain specific feedback than the single agent. The extra computational cost of the coordinated analyses was acceptable for software quality assessment tasks because of the resulting interpretability and modularity. Through implementation and experiments, the results demonstrate AgentCodeReview's utility and extensibility to the field of explainable AI in software quality assurance. The proposed architecture can be expanded to other programming languages, integrated into the industrial development flow, and enhanced with the advanced LLMs for scalable intelligent code review.
Copyright (c) 2026 Bharath Kumar N, T L Manasa
Effectiveness of Tax Collection, Tax Equity, and Business Competitiveness: A Comparative Analysis of Tax Systems in Latin America
Latin American tax systems are simultaneously asked to do three things that often pull in opposite directions: raise adequate and stable revenue, redistribute income in the most unequal region in the world, and remain attractive to firms and investors. This paper develops an integrated, indicator-based framework to compare how fourteen Latin American countries perform along these three dimensions—collection effectiveness, tax equity, and business competitiveness—using harmonised data compiled from OECD/ECLAC/CIAT/IDB Revenue Statistics, ECLAC fiscal-panorama estimates, and firm-level tax-administration indicators for a common 2023–2024 benchmark. We construct three composite indices through min–max normalisation, examine their pairwise associations, and derive a country typology using Ward hierarchical clustering. Three findings stand out. First, collection effectiveness and tax equity are strong complements (r = 0.78): countries that mobilise more revenue also redistribute more, because both depend on the same under-developed direct-tax capacity. Second, business competitiveness—proxied by compliance time and the total tax-and-contribution rate on firms—is mildly and negatively associated with the other two pillars (r = −0.36 and −0.22), indicating a real but moderate policy tension rather than an insurmountable trade-off. Third, the region splits into four archetypes—low-mobilisation/business-light systems, a high-burden low-yield outlier, high-mobilisation/high-burden systems, and a balanced-competitive group led by Uruguay, Chile and Costa Rica—each facing a distinct reform frontier. The results imply that the region's central fiscal problem is not an equity–efficiency trade-off per se but a shared deficit in progressive direct taxation, high informality, and administrative complexity that erodes all three objectives at once. We discuss reform priorities, including Brazil's 2023 consumption-tax overhaul, and the conditions under which broadening the personal-income-tax base can advance effectiveness and equity without undermining competitiveness.
Copyright (c) 2026 Charli Manuel Bustamante Montaño, Johana Guadalupe García León, Paola Estefania Albán-Trujillo, Emiliano López Obrador
Comparative analysis and mathematical modeling of energy performance in urban electrical distribution systems with smart grid technologies
The sustained growth of urban electricity demand and the high technical losses of conventional distribution systems pose significant challenges for energy efficiency and supply quality. This paper presents a comparative analysis and a mathematical modeling framework to evaluate the energy performance of an urban distribution system under the progressive integration of smart grid technologies: distributed generation (DG), reactive power compensation through power-electronic devices and capacitor banks, and demand response supported by advanced metering infrastructure (AMI). A radial power flow model solved with the backward/forward sweep method is formulated, complemented by the linearized DistFlow model and a set of quantitative performance indices (active and reactive losses, voltage profile, energy efficiency and voltage deviation). The framework is validated on the IEEE 33-bus benchmark and assessed over four operating scenarios. Results show that the full smart grid configuration reduces active losses from 202.7 kW to 11.6 kW (a 94.3 % reduction), raises the minimum voltage from 0.913 to 0.995 p.u., and increases system energy efficiency from 94.8 % to 99.7 %. Sensitivity analysis reveals an optimal DG penetration level of about 55 %, beyond which losses rise again. The findings provide quantitative evidence and a reproducible methodology for planning the modernization of urban distribution networks.
Copyright (c) 2026 Cristian Paúl Topa Chuqitarco, Edwin David Chavez Oña
Label-Free Electrochemical Biosensors for Triple-Negative Breast Cancer Biomarkers: Emerging Recognition Strategies, Nanomaterials, and Clinical Translation
Triple-negative breast cancer (TNBC) is when the patient does not have the three most targeted molecules (estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 or HER2) and the patient resorts to chemotherapy (cytotoxic), and poor prognosis due to the fact that the three most targeted molecules are the ones that are monitored. There has been growing interest in the measurement of alternative circulating markers e.g. epidermal growth factor receptor (EGFR), soluble programmed death-ligand 1 (sPD-L1), exosomal PD-L1, CD44, mucin-1, tumour-derived microRNAs (miR-10b, miR-21, miR-652), and circulating tumour cells (CTCs) that do not require repeated tissue biopsies. Label-free electrochemical biosensors that can detect a binding event as a change in charge-transfer resistance, capacitance or redox current instead of a secondary enzymatic or fluorescent reporter provide a means of fast, inexpensive, and perhaps point-of-care measurement of these markers. In this review, the recognition strategies that have been used to impart selectivity to these platforms (antibodies, aptamers, molecularly imprinted polymers, peptides and nucleic-acid probes) and the nanomaterial transducers (graphene and its derivatives, 2D MXenes, metal and bimetallic nanoparticles, metal/covalent-organic frameworks) that have been used to amplify the signal generated by these platforms are reviewed. The platforms used to detect HER2, EGFR, PD-L1, CD44 and TNBC-specific miRs that were representative in the last 5 years are compared and the special lack of TNBC-specific multi-marker, clinically validated devices is highlighted. The review ends with a discussion of issues that present practical problems to address, namely sample-matrix fouling, recognition-layer stability, reproducibility/regulatory validation between the lab-scale sensors and the deployable point-of-care biosensors and diagnostics, and prioritizes the multiplexed clinically translatable TNBC biosensing.
Copyright (c) 2026 T L Manasa, Rajesh Kumar Upadhyay, Divyashree H B
Exploring the Role of Artificial Intelligence in Entrepreneurial Education
The growing use of artificial intelligence in higher education is reshaping entrepreneurship education, a field which was traditionally built on experience-based learning and mentorship. This study investigates how AI technologies such as personalised learning systems, generative AI, natural language processing (NLP), AI-powered incubators, and hybrid teaching models, are able to influence the development of skill for entrepreneurs, help adapt academic curriculums to the ever-changing growth of technologies, and help support practices within their facilities. A qualitative methodology was adopted using a structured review of literature from references such as Scopus, Web of Science (WoS), the Australian Business Deans Council (ABDC) Journal Quality List, and the IEEE Xplore Digital Library. The findings of the research were supported by evidence from educational institutions from references such as McKinsey & Company, Deloitte, Harvard Business Review, Statista, and Gallup. The study draws on constructivist learning theory, self-determination theory, and innovation ecosystem frameworks. Furthermore, the analysis is guided by three main research questions, how AI based personalised learning affects entrepreneurial competencies, how generative AI and NLP tools support innovation and Sustainable Development Goal (SDG) related outcomes, and how AI supported teaching models are changing entrepreneurship education in universities. A brief analysis of themes and case study evidence were used to examine these areas. Therefore, these findings suggest that AI strengthens both technical and interpersonal entrepreneurial skills which supports sustainable innovation and enhances entrepreneurial activities within universities. Based on these findings, the research proposes the AI Integrated Entrepreneurship Learning (AIEL) framework which provides practical guidance for educators, policymakers, and curriculum designers who plan to integrate AI for their academic curricula and help support future entrepreneurial development.
Estimation of Battery charging for Electric vehicle using soft computing techniques
The objective of this work is to estimate the SOC in Lithium-Ion batteries used in electric vehicles (EVs). The generalized linear model (GLM) is applied, the GLM method with Poisson regression and a linear interpolation in estimating the SOC. The coefficient of the models is obtained from three machine learning techniques. The new SOC estimation strategies are tested applied to a Lithium battery model computationally implemented in four driving profiles and comparison of the results obtained between the proposed method and other existing methods in the literature are performed.
Copyright (c) 2026 S.Arulkumar, K.E.Lakshmiprabha, P.Rajivgandhi, M.Saravanan
Enhancing Data Privacy in Linear Regression Through Homomorphic Encryption
Big data analysis is required to predict future trends using machine learning. A lot of processing and computation is required to make this analysis. Conventional encryption methods may help to achieve privacy during the resting stage of data over the cloud. The computation of data needs to be in its original form or needs to be decrypted before any computation, which leaves the data again at risk and makes it more vulnerable. The latest homomorphic encryption algorithms provide a way to perform the computation over data in its encrypted form and help to analyze big data without compromising the privacy and security of data, even when data is in use. In this paper, we implemented the linear regression model over plain data and encrypted data to measure and analyze the fitness, MSE, and RMSE scores of models and the space complexity of plain data and encrypted data. The results show that the fitness score of the linear regression model for plain data and encrypted data is almost the same and differs by only 0.00003551, whereas the RMSE score is 386.9949011 and 364.03753641, respectively. It is observed that the overall performance of the linear regression model improved for encrypted data, and the error is also reduced while maintaining data privacy
Copyright (c) 2026 Krishnakumar D, Sonam Mittal, K.R. Ramkumar
ECAR–QIAVO-Based Energy-Efficient Routing for Intelligent Pollution Monitoring of High-Voltage Transmission Lines
High voltage transmission lines are exposed to a wide range of environments that could lower system reliability due to dust, industrial emissions, humidity, salt deposits, temperature range, and electromagnetic interference. This research presents an ECAR–QIAVO-based energy-efficient wireless sensor network for smart pollution monitoring system. The selection of suitable cluster heads is based on residual energy, communication distance, and link quality in the clustering mechanism provided by ECAR, while the adaptive multi-hop routing mechanism provided by QIAVO is based on the energy level, transmission delay, hop distance, traffic load and link reliability. The deep learning is embedded to interpret the pollution data, to understand the environmental anomalies and to modify the priorities of pollution sensing and transmission. The framework is assessed by the metrics of packet delivery ratio, energy consumption, network lifetime, delay and control overhead. The results reported show that the proposed system improves the packet delivery rate to nearly 94%, decrease the energy consumption up to 38% and achieve the extension of network lifetime up to 40-45%. The framework is thus a reliable and scalable tool for continuous monitoring and proactive maintenance of high-voltage transmission power systems.
Copyright (c) 2026 Deepak Sharma, Navneet Kaur Sandhu
Automatic Gujarati Text Summarization Using Natural Language Processing: A Gujarati-Specific Abstractive Framework
With the ever-increasing digital information, there is a high demand for automatic text summarization systems to produce meaningful summaries from longer documents. Despite significant efforts in automatic text summarization for high resource languages like English, research on automatic text summarization in Gujarati is limited because of the lack of linguistic resources, a lack of annotated datasets, and the difficult grammar of the Gujarati language. Previous multilingual transformer models like mBART, IndicBART and mT5 have shown promising results; however, they tend to produce grammatically incorrect, repetitive and incongruent summaries for Gujarati documents. In this study, we present a Gujarati specific abstractive text summarization system that leverages an improved multilingual mT5 model with a linguistic preprocessing step on the input text, morphological normalization, named entity preservation and coverage-aware decoding step. The proposed system takes as input any paragraph or article of Gujarati text or any document of large text, and produces one or two sentence summaries which are semantically equivalent to the original document and contain the information. The proposed framework would achieve better quality of summarization, grammatical correctness, semantic consistency, computational efficiency, and solve the issues with low-resource languages from India. To prove the superiority of the proposed model over the existing multilingual summarization models, automatic evaluation metrics such as ROUGE, BLEU, BERTScore will be employed in addition to human evaluation.
Copyright (c) 2026 Ankit Dhansukhbhai Prajapati, Rakesh Kumar Bhujade
FINANCIAL LITERACY IN MSMEs/SMEs: A SYSTEMATIC LITERATURE REVIEW AND BIBLIOMETRIC ANALYSIS Evidence Synthesis: 2010–2026
Financial literacy is increasingly recognised as a core capability shaping the sustainability, performance, and financing prospects of micro, small, and medium enterprises (MSMEs/SMEs), particularly in developing and emerging economies. Although empirical research on this relationship has expanded rapidly, the evidence remains dispersed across methods, regions, and thematic streams. This study provides an updated synthesis by combining a systematic literature review with bibliometric analysis of 346 peer-reviewed and scholarly publications drawn from Scopus, Web of Science, EBSCO, Google Scholar, and ProQuest for the period January 2010 to July 2026. Using PRISMA 2020 screening procedures and VOSviewer-supported co-citation and keyword co-occurrence mapping, the paper identifies six major intellectual clusters and seven emerging research gaps, traces the intellectual structure of the field, and highlights influential authors, journals, and country-level concentrations. The most recent literature from 2025–2026 points to three important emerging areas: the interaction between artificial intelligence and financial literacy, where AI advisory systems may both support and intensify the value of human financial judgement; climate and ESG finance literacy, which is becoming essential for MSMEs seeking sustainability-linked finance; and the maturation of digital financial literacy research in the context of embedded fintech and decentralised finance. Overall, evidence indicates that financial knowledge, financial attitudes, and financial behaviour together explain between 31% and 58% of the variation in SME performance indicators. The paper proposes an updated conceptual framework incorporating these new dimensions and identifies seven research gaps that frame a forward-looking research agenda up to 2030.
Copyright (c) 2026 Niranjan Behara, Santanu Ray Chaudhuri
Model Development and Validation for Repetition Severity Assessment in Stuttered Speech Using Clinical Speech Datasets
Stuttering disrupts the forward flow of speech through involuntary repetitions, prolongations, and blocks, with repetitions being the most common and clinically telling of the three. Quantifying how severe those repetitions are matters for diagnosis, therapy planning, and tracking whether treatment is actually working. The catch is that severity has long been judged by ear, by trained speech-language pathologists counting disfluent events and assigning ratings on standardised scales. That process is slow, variable between clinicians, and constrained by who is available. This paper builds and tests a computational model that grades repetition severity from clinical speech recordings. The model draws on a multimodal feature set combining acoustic, prosodic, temporal, and spectral descriptors, evaluated on 480 audio samples from 60 adult speakers with persistent developmental stuttering. Two certified clinicians labelled each sample as mild, moderate, or severe, with strong inter-rater agreement. Recursive feature elimination trimmed the feature set to a compact, discriminative subset, and five classifiers were trained under stratified ten-fold cross-validation repeated five times. A Gradient Boosted Trees model reached 91.46 percent mean accuracy, a macro F1-score of 0.90, and a Cohen kappa of 0.86, ahead of Random Forest, a Support Vector Machine, a Multilayer Perceptron, and Logistic Regression. Per-class scores were strong for mild and severe cases and weaker for moderate, where acoustic characteristics overlap with both neighbouring classes. Friedman and Nemenyi tests confirmed the top model's lead was significant at the 0.05 level. The pipeline is reproducible and the results support its use as a clinical decision-support tool.
Copyright (c) 2026 Pooja J N, H Y Vani, Rakshith P, M A Anusuya, Swetha P M
Cloud Security Challenges and Risk Management in Modern Cloud Computing Environment
Cloud Computing has completely transformed the way businesses operate today with its scalability, flexibility, and cost efficiency. Cloud platforms have become a key component for organizations in various sectors to store data, deploy software, manage infrastructure, and implement digital transformation efforts. But the adoption of cloud computing has made it extremely challenging to deal with several security concerns like data breach, cyberattacks, unauthorized access, insider threats, and compliance concerns. The research paper employs secondary data collection and thematic analysis to critically discuss challenges of cloud security and risk management approaches in contemporary cloud computing environment. A thorough literature review of the scholarly papers from 2015 to 2023 is conducted to assess cybersecurity threats, cloud vulnerabilities, identity management systems, encryption technologies, compliance frameworks, and artificial intelligence-based threat detection systems. Peer reviewed journal articles, conference papers and industry reports were analysed using the method of themed analysis, which identified themes that appeared repeatedly throughout. The results show that cloud environments are still very susceptible to advanced cyberattacks, ranging from weak authentication and insecure APIs to misconfigured cloud services and inadequate governance. The study also points out the effectiveness of modern technologies like multi-factor authentication, zero-trust security models, artificial intelligence, and encryption enhance cloud security performance. But there are still issues in the way of the organizations' operations regarding regulatory compliance, data governance and shared security responsibilities. The study demonstrates that the key factors to achieving effective cloud security are continuous monitoring, robust governance policies, employee awareness training, and high-level cybersecurity frameworks. The study adds to the body of knowledge about the security of cloud computing, and offers practical suggestions for any organization working in the cloud.
Copyright (c) 2026 Veeramani Sampathkumar, Dinesh Kumar Ramaraj, Rajesh Kotha, Anand Edward
Design and Implementation of a Lightweight Adaptive Machine Learning Framework for Real-Time DDoS Mitigation in Resource-Constrained IoT Devices
The rapid expansion of the Internet of Things (IoT) has raised additional concerns about security, and there was a major risk of Distributed Denial-of-Service (DDoS) attacks because the IoT devices have limited computation, memory, and energy capabilities. Traditional intrusion detection methods, which are at times contrived to support a high capacity, are incompetent at these limitations, delaying detections, having too many false alarms, and also compromising the system performance. This study offers a resource-efficient, adaptive machine learning system that was suitable to be used in the operation of DDoS attacks in resource-confined IoT settings. The technique combines the hybrid feature selection algorithms based on mutual information gain and recursive feature elimination to construct a more compact and high-utility feature set together with the optimization of the lightweight classifiers, including stochastic gradient descent and shallow decision trees. The concept drift was solved by an online incremental learning mechanism that guarantees long-term trend detection over time against changing patterns of attacks. The evaluation of the benchmark datasets (CICDDoS2019, BoT-IoT, TON_IoT) using experimental evaluation on a heterogeneous testbed IoT and assessing both security metrics and resource efficiency was researched. The model suggested had a precision of 0.973, a recall of 0.959, an F1-score of 0.966, and an average decrease of malicious traffic by 93 percent at the expense of legitimacy throughput. Latency was decreased to 2.6 seconds when detecting high-intensity attacks, and the CPU and memory usage continued to be less than 35 percent and 70 percent of the device capacity, respectively. A better result in terms of accuracy, response time, false positive rates, and not using resource budgets was witnessed when compared to baseline models through comparative analysis. The results verify the framework's ability to provide low latency and correct DDoS mitigation directly on the IoT devices, which can be considered a feasible solution to achieve resilience improvement of critical IoT deployments in health care, industrial automation, and smart cities.
Copyright (c) 2026 Selvi. T, Jayaganesh. J
A SYSTEMATIC REVIEW OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN STRUCTURAL HEALTH MONITORING OF CIVIL INFRASTRUCTURE
Structural Health Monitoring (SHM) has evolved from traditional periodic inspection methods to intelligent, data-driven systems powered by artificial intelligence (AI). This systematic review synthesizes recent advances in AI applications for SHM across civil infrastructure including bridges, buildings, tunnels, and dams. A comprehensive literature search covering 2020-2025 across Web of Science, Scopus, and IEEE Xplore identified 31 high-quality studies for qualitative analysis. The review examines AI methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and physics-informed neural networks (PINNs) applied to damage detection, localization, classification, and prognostic tasks. Sensor technologies—including accelerometers, fiber optics, piezoelectric transducers, and vision-based systems—are integrated with IoT platforms and digital twins for real-time monitoring. Key findings indicate that AI-driven SHM achieves damage detection accuracies exceeding 95%, with significant reductions in inspection time and maintenance costs. However, critical challenges persist: data scarcity in operational environments, model generalization across diverse infrastructure types, uncertainty quantification, cybersecurity vulnerabilities, and limited model interpretability for safety-critical decisions. Physics-informed and hybrid AI-physics models emerge as promising solutions to enhance robustness and transferability. This review identifies interdisciplinary opportunities including federated learning for decentralized monitoring, explainable AI for stakeholder trust, and autonomous inspection systems. Future research must prioritize standardized benchmark datasets, field-validated deployment studies, and sustainable practices for infrastructure resilience in the context of aging, climate-stressed, and increasingly complex built environments.
Copyright (c) 2026 M. Adil Khan, Muhammad Shoaib Ashraf, Muhammad Jahanzeb, Engr. Baitullah Khan Kibzai, Abdul Wahab, Khalid Rehman, Ezaz Jehangir
Unified Iris Segmentation and Recognition with Hybrid Mask2Former-Swin Transformer Architecture for Biometric Authentication
Although iris recognition is widely recognized as a highly dependable biometric modality, conventional systems usually segregate segmentation and classification into distinct steps, which results in error propagation and subpar overall performance. In this paper, we present an end-to-end Transformer-based architecture that simultaneously learns identity categorization and accurate iris segmentation within a single computational graph. Initially, a Mask2Former decoder based on a Swin-Tiny backbone creates high-fidelity binary iris masks by optimizing a combined Dice–Focal loss to improve boundary delineation and combining multi-scale data via cross-attention. After resizing the masked iris picture to 224 by 224 pixels, it is fed into a Swin-Base transformer, which extracts robust radial and circular texture representations via hierarchical shifted-window self-attention and patch-merging layers.These embeddings are mapped to C identity classes under a cross-entropy loss by a lightweight classification head that consists of two linear layers and a softmax activation. Our combined loss function guarantees mutual feedback between the two tasks by giving the segmentation and classification losses similar weights, which speeds up convergence and improves accuracy. The suggested method outperforms traditional two-stage pipelines, achieving average accuracy, recall, and F1-score above 98.5% and producing more compact, easily deployable models, according to extensive experiments conducted on three benchmark datasets (CASIA-Iris-Thousand, CASIA-Iris-Lamp, and CASIA-IntervalV4). The efficiency of integrated Transformer topologies for reliable, high-precision iris authentication is demonstrated in this work.
Copyright (c) 2026 Mustafah Sbahe Sahib, Amir Lakizadeh
AI Powered Intrusion Detection Systems: Revolutionizing Cybersecurity Through Deep Learning and Anomaly Detection
The rapid expansion of interconnected digital infrastructures, cloud computing environments, Internet of Things ecosystems, and software-defined networks has significantly increased the complexity and sophistication of contemporary cyber threats, thereby challenging the effectiveness of conventional security mechanisms designed primarily for signature-based attack identification. Traditional intrusion detection systems often exhibit limitations in recognizing previously unseen attack patterns, adapting to rapidly evolving threat landscapes, and processing the enormous volume of network traffic generated by modern communication systems. Consequently, the integration of artificial intelligence techniques into cybersecurity frameworks has emerged as a promising approach for enhancing threat detection capabilities, improving response times, and reducing the operational burden imposed on security analysts. This study examines the application of artificial intelligence-powered intrusion detection systems that leverage deep learning architectures and anomaly detection methodologies to identify malicious activities within dynamic network environments. The proposed framework utilizes large-scale cybersecurity datasets comprising network flow characteristics, packet metadata, system logs, user behavior profiles, and communication patterns to develop intelligent detection models capable of distinguishing legitimate activities from suspicious or adversarial behaviors. Various deep learning algorithms, including convolutional neural networks, recurrent neural networks, long short-term memory networks, autoencoders, and hybrid architectures, are explored to capture complex temporal and spatial dependencies associated with cyberattacks. In parallel, anomaly detection mechanisms based on unsupervised and semi-supervised learning techniques are employed to identify deviations from established behavioral baselines, thereby enabling the recognition of zero-day exploits, advanced persistent threats, insider attacks, distributed denial-of-service campaigns, and stealthy intrusion attempts that may evade traditional security controls. Feature engineering, dimensionality reduction techniques, and model optimization procedures are incorporated to improve computational efficiency and enhance predictive accuracy while minimizing false alarm rates. Experimental evaluations indicate that deep learning-enabled intrusion detection systems achieve superior performance in terms of detection precision, adaptability, and scalability when compared with conventional rule-based approaches, particularly in environments characterized by high traffic volumes and continuously evolving attack vectors. Furthermore, real-time analytical capabilities supported by artificial intelligence facilitate proactive threat intelligence generation, automated incident prioritization, and adaptive cybersecurity defense strategies. Despite these advantages, challenges related to data imbalance, adversarial manipulation, model interpretability, privacy concerns, and computational resource requirements remain significant considerations for practical deployment. The study concludes that the convergence of deep learning methodologies and anomaly detection techniques provides a robust foundation for next-generation intrusion detection systems capable of strengthening cyber resilience, reducing organizational exposure to emerging threats, and supporting the development of intelligent, autonomous, and context-aware security infrastructures suited to the demands of increasingly digitized societies.
Copyright (c) 2026 Mohd. Asif I. Gandhi, Dinesh Baban Kute, U. Hemavathi
DIGITAL POLITICAL ENGAGEMENT AMONG FIRST-TIME AND EXPERIENCED VOTERS: EVIDENCE FROM TAMIL NADU
The increasing use of social media has transformed political communication by providing voters with immediate access to political information, campaign content, and public discourse. This study investigates the patterns of digital political engagement among first-time and experienced voters in Tamil Nadu, with particular emphasis on social media usage, political news consumption, and the forms of digital political content that most effectively influence voters. A quantitative descriptive research design was employed using convenience sampling, and primary data were collected through an online questionnaire from 300 respondents aged 18–35 years across selected districts of Tamil Nadu. The collected data were analysed using descriptive statistical techniques, including frequencies and percentages. The findings reveal that 98% of respondents use social media, with Instagram emerging as the most preferred platform (74%), followed by YouTube (18%). Approximately 64% of respondents reported following political news through social media, while 58% indicated that political memes and trolls were the most influential forms of digital political content, followed by short videos (22%). Experienced voters demonstrated higher levels of social media use and political engagement than first-time voters across most indicators. The study concludes that digital platforms have become important channels for political communication and participation among voters in Tamil Nadu. The findings underscore the growing influence of social media in shaping political awareness and electoral engagement and highlight the importance of strengthening digital literacy initiatives to promote informed political participation and reduce the impact of misinformation in the digital political environment.
Copyright (c) 2026 Vignesh R, X Mary Genila
The rapid transformation of global business environments driven by digitalization, technological advancement, changing consumer expectations, and competitive market dynamics has significantly altered traditional marketing practices and strategic business operations. Organizations operating in highly dynamic economic ecosystems are increasingly recognizing that conventional marketing frameworks alone are insufficient to sustain long-term growth, customer engagement, and market relevance. In this context, innovation-driven marketing models have emerged as a critical strategic approach that integrates creativity, data intelligence, technological innovation, customer-centric design, and adaptive business strategies to enhance organizational competitiveness and sustainable value creation. This research examines the growing significance of innovation-driven marketing models and their influence on consumer behavior, brand positioning, digital engagement, operational efficiency, and business sustainability across modern industries. The study explores how emerging technologies such as artificial intelligence, machine learning, big data analytics, blockchain, cloud computing, augmented reality, and social media ecosystems are transforming traditional marketing processes into highly personalized, predictive, and experience-oriented systems capable of responding to rapidly evolving market demands. The research further investigates how innovation-oriented marketing strategies support product differentiation, dynamic pricing, omnichannel communication, customer relationship management, and real-time market responsiveness in both online and offline commercial environments. Particular emphasis is placed on the role of innovation in enhancing customer engagement through interactive digital platforms, data-driven personalization, automated communication systems, influencer-based branding strategies, and experiential marketing campaigns. The study also evaluates how organizations leverage innovative business models to improve customer retention, market expansion, and strategic decision-making while simultaneously addressing challenges related to market uncertainty, consumer trust, technological adaptation, and ethical data utilization. A comparative assessment of traditional marketing approaches and innovation-driven marketing frameworks demonstrates that organizations adopting innovation-centric strategies experience stronger consumer loyalty, improved operational agility, enhanced brand visibility, and higher adaptability to changing economic conditions. Additionally, the research highlights the growing importance of sustainability-oriented marketing innovation, where businesses integrate environmental responsibility, social value creation, and ethical consumer engagement into their branding and communication practices. The findings indicate that innovation-driven marketing models not only contribute to commercial profitability but also strengthen organizational resilience and long-term strategic sustainability in highly competitive global markets. The study concludes that future business success increasingly depends on the ability of organizations to continuously innovate their marketing structures, technological capabilities, and customer engagement mechanisms in alignment with digital transformation and evolving consumer expectations. Therefore, innovation-driven marketing represents a transformative strategic paradigm capable of reshaping modern business ecosystems through intelligent, adaptive, and customer-focused value creation models.
Copyright (c) 2026 R. Priyadharsini, Ravikanth Reddy Vadamala, R. Raajalakshmi, K. Raghav Prasad, Abdulkarim Jabr Alsulami
Nanocomposites for Sustainable Environmental Remediation: Innovations in Water and Air Purification
The increasing deterioration of environmental quality caused by rapid industrialization, urban expansion, intensive agricultural practices, and unsustainable resource utilization has intensified the need for advanced materials capable of addressing complex pollution challenges in both aquatic and atmospheric environments. Conventional remediation technologies frequently encounter limitations such as low pollutant selectivity, incomplete contaminant removal, high operational costs, secondary waste generation, and limited reusability, thereby restricting their long-term effectiveness in achieving sustainable environmental management. In this context, nanocomposite materials have emerged as promising multifunctional platforms owing to their unique physicochemical properties, including high surface area, tunable porosity, enhanced catalytic activity, superior adsorption capacity, and improved mechanical and chemical stability. This study investigates recent innovations in nanocomposite-based technologies for sustainable environmental remediation, with particular emphasis on water and air purification applications. The proposed approach examines the integration of metal and metal oxide nanoparticles, carbon-based nanomaterials, polymeric matrices, bio-derived materials, and hybrid nanostructures to develop highly efficient remediation systems capable of removing a broad spectrum of contaminants, including heavy metals, organic dyes, pharmaceutical residues, volatile organic compounds, particulate matter, pathogenic microorganisms, and hazardous gaseous pollutants. The study further evaluates the synergistic interactions between nanocomposite constituents that enhance adsorption efficiency, photocatalytic degradation, antimicrobial performance, and pollutant mineralization under varying environmental conditions. Consideration is also given to material synthesis strategies that promote sustainability through reduced chemical consumption, energy-efficient fabrication processes, and the incorporation of renewable or biodegradable components. Performance assessment focuses on key parameters such as contaminant removal efficiency, adsorption kinetics, catalytic stability, regeneration capability, durability, selectivity, operational lifespan, and environmental compatibility. The findings indicate that nanocomposite materials significantly outperform many conventional remediation techniques by providing faster reaction rates, higher contaminant removal capacities, improved structural stability, and greater adaptability to diverse environmental conditions. Furthermore, the incorporation of recyclable and reusable nanocomposite systems contributes to minimizing operational costs and reducing secondary environmental impacts associated with remediation processes. Despite challenges related to large-scale production, long-term environmental safety, material recovery, and regulatory standardization, continuous advances in nanomaterial engineering and green synthesis techniques are expanding the practical applicability of nanocomposites in environmental protection. The study concludes that innovative nanocomposite technologies offer a robust and sustainable pathway for addressing contemporary pollution challenges while supporting cleaner water resources, improved air quality, and environmentally responsible remediation practices. By combining advanced material design with sustainable engineering principles, nanocomposites have the potential to play a transformative role in future environmental management strategies and the development of resilient pollution control systems.
Copyright (c) 2026 Mamata Mohanty, Maaz Allah Khan, Anand Thangavel
TWO PATHS TO DIGITAL RIGHTS: A COMPARATIVE CONSTITUTIONAL ANALYSIS OF DIGITAL CONSTITUTIONALISM IN INDIA AND THE EUROPEAN UNION
Digital technologies have upended some of the core tenets of constitutional theory, namely the state-centric approach. This article examines the comparative constitutional study of two different jurisdictions on digital constitutionalism: India and European Union, focusing on the extension of fundamental rights to digital space. It follows the EU's rights-first approach (Article 8 ECHR and the GDPR) as opposed to the judicially emergent approach of India, as enshrined through Justice K.S. Puttaswamy in Article 14, 19 and 21. The analysis is based on those of Shreya Singhal, Anuradha Bhasin, Google Spain and the Schrems cases, and seeks to consider issues of privacy, speech, intermediary regulation and cross-border data governance. The results reveal both constitutional doctrine and a lack of institutional independence and algorithmic accountability mechanisms in the Indian model, and an original Indian model that integrates the existing principle into a coherent regulatory design is proposed.
Copyright (c) 2026 Rakesh Kumar Singh, Anand Kumar Vishwakarma, Varalika Nigam
The Role of Generative Artificial Intelligence in Modern Business Decision-Making: Applications, Opportunities, and Future Challenges
Generative artificial intelligence (GenAI) has moved rapidly from an experimental technology to a tool actively reshaping how managers gather information, evaluate options, and reach decisions across strategic, operational, and customer-facing business functions. This paper reviews the theoretical and empirical literature on GenAI's role in business decision-making, situating recent large language model (LLM)-based applications within the older organizational decision-making and bounded-rationality literature that predates generative AI by decades. The review synthesizes controlled productivity experiments, large-scale economic-potential estimates, organizational decision-structure theory, and the emerging literature on human-AI complementarity and its limits, including evidence that AI assistance can degrade performance when applied outside a model's effective capability frontier. Particular attention is given to the distinction between GenAI's demonstrated value in well-structured, information-synthesis-heavy decision tasks and its more contested role in tasks requiring novel judgment or accountability. Comparative tables summarize reported productivity effects, business function applications, and organizational barriers to adoption across the reviewed literature. The paper concludes that GenAI's current business value is concentrated in augmenting, rather than automating, decision-making, with the strongest evidence for productivity gains among relatively lower-skilled or lower-performing decision-makers, and identifies the mapping of AI capability boundaries within specific decision domains as the central future research prospect. Generative artificial intelligence (GenAI) has moved rapidly from an experimental technology to a tool actively reshaping how managers gather information, evaluate options, and reach decisions across strategic, operational, and customer-facing business functions. This paper reviews the theoretical and empirical literature on GenAI's role in business decision-making, situating recent large language model (LLM)-based applications within the older organizational decision-making and bounded-rationality literature that predates generative AI by decades. The review synthesizes controlled productivity experiments, large-scale economic-potential estimates, organizational decision-structure theory, and the emerging literature on human-AI complementarity and its limits, including evidence that AI assistance can degrade performance when applied outside a model's effective capability frontier. Particular attention is given to the distinction between GenAI's demonstrated value in well-structured, information-synthesis-heavy decision tasks and its more contested role in tasks requiring novel judgment or accountability. Comparative tables summarize reported productivity effects, business function applications, and organizational barriers to adoption across the reviewed literature. The paper concludes that GenAI's current business value is concentrated in augmenting, rather than automating, decision-making, with the strongest evidence for productivity gains among relatively lower-skilled or lower-performing decision-makers, and identifies the mapping of AI capability boundaries within specific decision domains as the central future research prospect.
Copyright (c) 2026 Rajidi Rammohan Reddy, Vinodray Thumar, Amar Jyoti Borah, T. Vijayakumar, Shabina, Tara Sasanka, C
Unified Multimodal Misinformation Detection Model Based on EfficientNet-B0, BERT and Stack Ensemble
Multimodal misinformation has been identified as a key challenge in social media platforms, where the misleading information is presented using both textual and visual modalities. The current state-of-the-art methods for detecting misinformation are primarily unimodal or based on monolithic deep learning models that lack the ability to generalize across different patterns of misinformation. This paper presents a Hybrid Vision-Language Stacked Ensemble Model that combines deep semantic features from EfficientNet-B0 and BERT with traditional ensemble learning techniques to handle multimodal misinformation detection. In particular, high-level image features were extracted using EfficientNet-B0 and text embeddings using a pre-trained BERT model. The learned multimodal feature representations were then used by a stacked ensemble classifier that leverages the complementary capabilities of Random Forest, Gradient Boosting, and a Logistic Regression meta-learner. Experiments conducted on the MMFakeBench dataset for binary and multiclass misinformation classification tasks have shown that the proposed framework achieves better performance compared to individual base learners and conventional fusion approaches. Further detailed evaluation had also validated the efficacy of modality-wise feature fusion and the robustness of the ensemble learning approach against class imbalance and noisy data. The proposed framework provides a scalable, interpretable, and robust solution for real-world multimodal misinformation detection on social media platforms.
Copyright (c) 2026 Javeriya Naaz I. Syed, Ranjit R. Keole
Machine Learning Enabled IoT Framework for Early Health Risk Prediction Using Wearable Sensor Data
This research presents a novel machine learning Internet of Things (IoT) framework designed for the early prediction of health risks utilizing wearable sensor data. Employed a hybrid methodology combining data collection from diverse wearable devices, preprocessing to ensure high-quality input, and advanced machine learning algorithms for risk assessment. The framework integrates real-time data analytics with predictive modeling, allowing for the continuous monitoring of health metrics such as heart rate variability, physical activity levels, and sleep patterns. Our findings reveal that the implemented model accurately predicts potential health risks, achieving a classification accuracy of over 90% across multiple test cases involving various demographics. The model also demonstrated robustness in handling noise and incomplete data, highlighting its applicability in real-world settings. The significance of this research lies in its potential to transform preventive healthcare by enabling timely interventions, thereby mitigating severe health conditions before they develop. This framework lays the groundwork for further exploration in automated health monitoring systems and paves the way for personalized healthcare strategies through the integration of IoT technologies, ultimately contributing to enhanced health outcomes and improved quality of life for users.
Copyright (c) 2026 Rekha K R, Gurunadham R, G.Vijayakumar, Kasthuri Natarajan, Neeraja B, Parna Dhara
DYNAMIC UNCERTAINTY-AWARE BAYESIAN NEURAL MODEL FOR REAL-TIME WORKFORCE DISPLACEMENT RISK UNDER AI AUTOMATION
The growing adoption of artificial intelligence (AI) in the workplace requires moving workforce displacement prediction in real-time and with uncertainty. This paper presents a probabilistic deep learning system that combines Bayesian Neural Nets (BNN) with Deterministic Deep Neural Networks (DNN), Monte Carlo (MC) Dropout, and a hybrid ensemble architecture to perform task-level displacement prediction. Empirical testing of 18,796 standardized task-level data indicates that all models converge to the same place, with BNN Gaussian NLL converging to -0.94 and MSE converging to approximately 0.056. The Bayesian model produced better results with a MSE = 0.0558, RMSE = 0.2361, MAE = 0.1902, and R 2 = 0.0670, whereas the Hybrid model marginally increased the results (MSE = 0.0556, R 2 = 0.0691). Calibrated probabilistic forecasting was confirmed based on the diagnostic analysis of heteroscedastic residual patterns and positive correlation between predictive uncertainty and absolute error. The error in the segmentation in terms of classes was the lowest in the Medium-risk occupations and the highest in the High-risk groups. The framework will be useful in uncertainty-sensitive and dynamic displacement predictions under AI-based labor transformation.
Copyright (c) 2026 Imran Raza Khan, Manoj Varshney
An Efficient Secure Framework for Hybrid Li-Fi/Wi-Fi Transmission Systems
In the era of the Internet of Things (IoT), data on people, objects, and the environment flows are ever-increasing; light fidelity technology is one of the most creative but experimental futuristic networking techniques. To enhance the performance of complex narrowband Internet of Things (NB-IoT) systems, a recently developed hybrid network architecture that consists of Wi-Fi and Light Fidelity (Li-Fi) is investigated in this article. Feasibility studies show that it can significantly improve the availability, dependability, productivity, and safety of systems , while drastically reducing latency and bandwidth costs. The experimental results show that, compared with existing models, the hybrid model can effectively support multiple large data streams and also facilitate quick computing convergence. Simulation results demonstrate that 1) the performance of this scheme also outperforms traditional schemes in terms of throughput, speed, packet drop ratio, jitter and coverage range. The benefits of the proposed algorithm and other widely used algorithms in practical applications are compared in the conclusion, while some further improvements are also suggested.
Copyright (c) 2026 Charanjit Kaur, Mithilesh Kumar Dubey, Sudha Dubey
A Deep Learning-Based Predictive Framework for Early Heart Disease Risk Assessment Using Multi-Factor Clinical Data
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for approximately 17.9 million deaths annually according to the World Health Organization. Early and accurate risk assessment is critical for timely intervention and improved patient outcomes. However, existing machine learning and deep learning approaches face significant limitations including poor modeling of nonlinear relationships, inadequate feature interaction learning, lack of uncertainty quantification, class imbalance challenges, overfitting tendencies, limited interpretability, insufficient multimodal fusion capabilities, poor cross-dataset generalization, absence of attention-based learning mechanisms, and inadequate clinical explainability. To address these critical gaps, we propose CardioAttentionNet, a novel hybrid deep learning framework that integrates residual convolutional neural networks, transformer encoders with multi-head self-attention, bidirectional long short-term memory networks, and cross-attention modules for comprehensive cardiovascular risk prediction. The framework incorporates intelligent data preprocessing, hybrid feature engineering with attention-based feature selection, adaptive feature fusion with learnable weights, channel attention mechanisms, probability calibration, and explainable AI components including SHAP, LIME, and Integrated Gradients. Evaluated on a large-scale multi-source dataset comprising 75,000 samples from UCI Cleveland, Framingham Heart Study, Kaggle, and Cardiovascular Disease datasets, CardioAttentionNet achieved exceptional performance with 98.12% accuracy, 97.84% precision, 98.37% recall, 97.93% specificity, 98.10% F1-score, 0.983 AUC, 0.962 MCC, and 0.961 Cohen’s Kappa, significantly outperforming 13 baseline methods. Comprehensive ablation studies, statistical validation, and explainability analysis demonstrate the framework’s clinical utility for early heart disease risk stratification and decision support in real-world healthcare settings.
Copyright (c) 2026 Swapnil Hiralal Chaudhari, Anish Kumar Choudhary
Sovereign Cloud and Data Nationalisation Conceptual Frameworks For Accounting Professionals in India
Purpose The enhanced consolidation of cloud accounting models within geographical boundaries of India has established latest standards in financial auditing, reporting, compliance procedures and virtual accessibility. Nonetheless the legal framework in the nation is evolving simultaneously to accentuate audit trails, nationalized storage of data and sovereignity of data. Latest modifications under the companies act 2013; the company’s fourth amendment rules and the new policies issued by RBI for data localization have radically shifted the compliance framework for all the accounting professionals and the service providers in the country. Regardless of the mounting academic discussion on adaptability of cloud accounting around the globe, meagre research has highlighted hoe nationalized legal requirements have modified the framework infrastructure, risks involved and acceptability of accounting professionals in india which will be investigated in this study. This study will further identify the pros and cons for adoption of cloud accounting and will come out with suggestive cloud accounting models for Indian scenario. Design/Methodology/Approach An empirical and analytical research design has been adopted for the study and snowball and convenient sampling has been used for primary data collection..A sample size of 140 has been calculated using G-power. The research is confined to chartered accountants of agra district to whom a well structured questionnaire was sent using google forms.stastical tools used in this study is chi square test. Findings Cloud accounting is a tremendous shift towards triple entry system wherein a transaction is verified by a third party using cryptography and blockchain technology thereby increasing authenticity and trust by piling all entries in a public ledger. As a result of this more businesses are adopting virtual workforce models. Introduction of cloud based models in accounting profession has enhanced the roles of key processing indicators in the business. Cloud technology magnifies employees networking and association thereby increasing efficiency and effectiveness. Chartered accountants who will accept this change will have new opportunities open for them and those who will look at this technology with ostrich approach will be left behind. Originality The findings will be valuable for further research work to be done in this area. The findings will help various researchers, chartered accountants, accounting professionals etc to understand the implementation of cloud accounting in developing countries like India and to understand in depth the implementation and adoption of cloud based accounting in the Indian scenario.
Copyright (c) 2026 Shazpreet Kaur, Anjani Srivastava, Kirti Khanna
THE SHARING ECONOMY AND SUSTAINABLE TOURISM DEVELOPMENT: A CRITICAL REVIEW AND FUTURE RESEARCH AGENDA
The sharing economy has profoundly restructured global tourism value chains over the past decade, yet its relationship with sustainable tourism development remains contested both theoretically and empirically. This study conducts a critical integrative review of influential scholarship on the sharing economy in tourism published between 2010 and 2026, combining citation tracking from landmark works with the purposive selection of studies offering salient contributions to each sustainability pillar, and anchoring the synthesis in a decade of audited platform and industry data (2015–2025). The study synthesises extant knowledge into six thematic streams: (1) conceptualisation and definitional debates; (2) motivations, trust, and participant behaviour; (3) economic impacts and competition with the traditional tourism sector; (4) social impacts on destination communities; (5) environmental impacts and ecological sustainability; and (6) governance and institutional responses. Descriptive evidence shows that the leading peer-to-peer accommodation platform grew from 72.4 million to 533.0 million booked nights and experiences between 2015 and 2025—an average growth rate roughly four times that of international tourist arrivals—while regulation moved through three generations, culminating in structural interventions such as New York City’s Local Law 18 and the European Union’s platform data regulation. The findings reveal four imbalances in the current literature: a platform bias towards accommodation (especially Airbnb), a contextual bias towards North America and Western Europe, a pillar bias towards economic over environmental dimensions, and a design bias towards cross-sectional over longitudinal studies. Drawing on the Theory–Context–Method (TCM) framework, the study proposes a future research agenda comprising nine directions, emphasising the need to test environmental rebound effects, extend research to emerging economies, integrate the Sustainable Development Goals (SDGs), and develop localised theoretical frameworks for transitional institutional contexts.
Copyright (c) 2026 Luong Thi Giang, Nguyen Duc Duong
EXISTENCE AND UNIQUENESS OF SOLUTIONS OF CAPUTO FRACTIONAL DIFFERENTIAL EQUATIONS WITH NONLOCAL BOUNDARY CONDITIONS
In this paper, we investigate the existence and uniqueness of solution of fractional differential equations involving Caputo fractional derivative with nonlocal boundary conditions. The monotone method is constructed using lower and upper solutions. Two monotone sequences are constructed and shown to converge to the minimal and maximal solutions.
Copyright (c) 2026 J.A.Nannaware, Vikas K.Wakode, S.S.Phulsagar, P.D.Kundgar, N Krishnamoorthy
A Hybrid PSO–LSTM Framework For Intelligent Demand-Side Management In Smart Grids With Renewable Energy Integration
The growing penetration of renewable energy resources, electric vehicles (EVs) and distributed energy systems, demand-side management (DSM) in modern smart grids has become increasingly complex. Conventional DSM methods usually show lower predictive capability and ineffective load scheduling in a tight operating environment, which leads to increased operational costs, hindered grid stability. This paper presents a hybrid PSO–RNN framework for intelligent freedom of management for power systems. The proposed framework combines an RNN for precise short-term load forecasting with PSO for effective optimal load scheduling, EV charging coordination, and energy distribution by utilizing data on historical electricity consumption, outdoor weather conditions, renewable energy generation variables, electricity pricing data of local suppliers in Singapore,, average grid load from the previous day (24 hours) as well as #datetimeindex. The experimental evaluation shows that the proposed PSO–RNN model can reach 98.24% forecasting accuracy, reduce RMSE to 0.081 and MAE to 0.061 value, while achieving over 24.76 % of peak-load reduction; saving about 21.35 % of electricity cost; using approximately by up to 91.42 % renewable energy as well, compared with ANN, SVM, DRL and RNN standalone models. These features increase reliability, lower operation costs and assist in sustainable energy recovery. The designed framework can effectively serve as a scalable, adaptive and computationally efficient next-generation intelligent smart grid and real-time electricity demand side management solution.
Copyright (c) 2026 Rajendra B. Sadaphale, Prakash Burade
Multi-Objective Intelligent Demand-Side Management Using Explainable PSO-Optimized LSTM in Renewable Energy-Based Smart Grids
The fast penetration of renewable energy resources, electric vehicles (EVs), and distributed energy systems has made demand-side management (DSM) in modern smart grids very complex. Traditional DSM methods usually consider load forecasting and scheduling as separate tasks, which results in suboptimal energy consumption, higher operational cost, greater carbon emissions, and lower grid stability under dynamic operating conditions. To address the above limitations, an Explainable Hybrid Particle Swarm Optimization-Long Short-Term Memory (PSO-LSTM) framework for intelligent multi-objective demand-side management in renewable energy-integrated smart grids is proposed. The proposed framework integrates the temporal learning capability of LSTM to accurately forecast the short-term electricity demand and the global optimization capability of PSO to optimally schedule appliances, coordinate EV charging, allocate renewable energy, and implement dynamic demand response. Hence, the optimization problem is formulated as a constrained multi-objective mathematical model to minimize the electricity cost, peak-to-average ratio (PAR), carbon emissions, and scheduling delay and to maximize the renewable energy utilization, consumer comfort, and grid reliability. The framework is validated on multivariate smart grid datasets, including historical electricity consumption, weather, photovoltaic generation, electricity prices, battery state-of-charge, EV charging profiles, and grid load data. Experimental results show that the proposed framework PSO–LSTM obtains an accuracy of 98.24% for forecasting and decreases RMSE and MAE to 0.081 and 0.061, respectively. Moreover, the proposed model achieves 24.76% peak-load reduction, 21.35% electricity cost savings, 91.42% renewable energy utilization, and significant reductions in carbon emissions compared with ANN, SVM, standalone LSTM, and Deep Reinforcement Learning (DRL) models. The statistical analysis also validates the robustness and superiority of the proposed framework in multiple performance metrics. The proposed explainable hybrid framework provides a scalable, computationally efficient, and real-time intelligent energy management solution for next-generation sustainable smart grids and smart city applications.
A Comprehensive Review Of Statistical Methods For Enhancing Performance In Precision Agriculture
Even more precision agriculture tools rely on statistical techniques for fruit detection and recognition optimisation. A thorough review of these statistical techniques was built in this paper, and they have been applied to fruit detection systems since 2018 for efficiency, accuracy, and robustness. Thinking about how advanced statistical methods integrate with machine learning algorithms and deep neural networks, we face significant obstacles such as handling imbalanced datasets, improving detection in occluded or dense environments and dealing with variation between fruit size, shape, and species. Statistical methods also enhance data cleaning before use, feature extraction, and model evaluation, leading to faster fruit detection and more accurate results. A review paper is the result of combining ideas from many experts. It should help the development of precision agriculture focused on lower resource usage farther afield and seeing agro-environmental detection systems as equivalent. Now, we discuss solutions to those problems and future trends in statistical methodology. This work aims to set the direction for future precision agriculture research and practice by pointing out how to reduce the computational complexity of detection systems under various agro-environmental conditions.
Copyright (c) 2026 Raviprakash S. Shriwas, J. B Helonde, Prakash G. Burade
Combined Natural Convection and Surface Radiation Heat Transfer from Two Aligned Horizontal Cylinders in a Vented Square Enclosure
Combined natural convection and surface radiation from two horizontally aligned cylinders at unequal surface temperatures in a vented square enclosure are solved numerically. A two-dimensional turbulent finite-volume model couples the k–ω SST closure with surface-to-surface radiation using ANSYS Fluent Software. The left cylinder is held at a constant lower temperature and the right at a higher, variable one, while a constant inlet velocity at the bottom vent keeps the flow natural-convection-dominated. The model reproduces 18 experimental cases with a mean error of and a maximum of . Sweeps cover Rayleigh numbers from , opening ratios from , spacing ratios from , and surface emissivity from . Emissivity is the controlling parameter. Raising it from increases the total Nusselt number by for the left cylinder and for the right, while the convective contribution changes by less than . Convective transfer peaks near an opening ratio of and at the smallest spacing, whereas the total Nusselt number is highest at the tightest opening, where trapped hot air raises the radiative share. Radiation supplies of the total at and – at unit emissivity, so a convection-only model under-predicts by the same margins. The current arrangement is of significant interest for numerous applications in electronic housings, heat exchangers, and renewable energy fields including solar receiver cavities, flat plate solar collectors, and photovoltaic thermal solar systems.
Copyright (c) 2026 Ameen M. Atroshy, Omar Mohammed Ali
Healthcare Monitoring of patient Using 3D Classification and diagnosis
The healthcare monitoring of patients using 3D classification and diagnosis system establishes a robust framework for continuous patient healthcare monitoring through advanced multi-dimensional classification and diagnostic analysis. Designed to streamline clinical workflows, the architecture processes volumetric medical imaging data natively in NIfTI (. nii /. nii.gz) formats, preserving the rich spatial metadata of MRI and CT scan sequences. The system's pipeline facilitates seamless data ingestion, allowing clinical operators to interface with two-dimensional (2D) cross-sectional slices while concurrently rendering fully interactive three-dimensional (3D) anatomical reconstructions. To achieve precise pathological classification without manual intervention, the platform integrates automated image preprocessing and deep learning architectures, utilizing Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and optimized hybrid deep learning topologies. These models extract hierarchical spatial and sequential features from the volumetric datasets to detect and classify tissue anomalies. Once classification is complete, the system automatically compiles a comprehensive diagnostic output containing the identified pathology, statistical confidence scores, precise localization of the affected regions, and tailored treatment recommendations. This 3D visualization and diagnostic module grant medical professionals the ability to inspect complex anatomical structures with high spatial fidelity, directly enhancing clinical decision-making, surgical planning, and long-term patient monitoring across hospital networks, radiology departments, and telemedicine platforms.
Copyright (c) 2026 Bhagyashree, Suvarna Nandyal
Social Media Fake news detection Utilizing a variety of datasets with probabilistic latent semantic analysis & k-means algorithms
Social media has become usefull and most important platform for individuals to access news due to its speed and cost-effectiveness of disseminating information on a particular channel. However, these platforms also make it a breeding ground for the spread of fake news, which can impact society and individuals. With the advent of modern technology that comes with the fourth industrial revolution, promoting openness and increased engagement, social media has evolved to serve multiple purposes. It has become an integral part of our lives, beyond what was originally intended for just a small group of people. Consequently, identifying such fake news has become a crucial task for researchers and remains a major concern. This paper aims to examine the methods of publishing and distributing fake news, and outlines the approach of classifying, organizing, and developing algorithms. Our proposed solution is called the "Fake News Detection using Hybrid-kMeans (FNDHKM)" algorithm that utilizes Natural Language Processing (NLP) and machine learning (ML) techniques to detect fake news on social media platforms, specifically Twitter. The experiments were performed on three different datasets obtained from Twitter and involved dimensionality reduction on the extracted data. The highest accuracy achieved was 85% and 90% for precision and accuracy, respectively. The proposed FNDHKM approach showed significantly high accuracy with low overhead.
Cross-Domain Generalization Neural Architecture Search for Robust Clinical Image Analysis
In recent years, artificial intelligence (AI)-based clinical image analysis has achieved remarkable diagnostic performance; however, when it is practical deployment remains limited by poor generalization across heterogeneous clinical environments. Variations in imaging devices, acquisition protocols, patient demographics, and institutional practices frequently introduce domain shifts that substantially reduce the reliability of deep learning models when evaluated on previously unseen hospitals. This study proposes a Cross-Domain Generalization Neural Architecture Search (CDG-NAS) framework that automatically discovers neural network architectures capable of maintaining high diagnostic accuracy under diverse clinical domain shifts. The proposed framework integrates domain generalization directly into the neural architecture search process by combining multi-domain architecture optimization, Domain Consistency Regularization (DCR), and a Robustness-Aware Architecture Scoring (RAS) strategy. During architecture search, candidate models are trained using multiple source domains while simultaneously being validated on unseen domains to encourage domain-invariant feature learning. Experiments were conducted using four publicly available clinical imaging datasets representing chest radiography, histopathology, brain magnetic resonance imaging, and retinal fundus imaging. The performance was evaluated using Accuracy, Precision, Recall, F1-score, Area Under the ROC Curve (AUC), Expected Calibration Error (ECE), Robustness Score, and paired statistical significance tests for the proposed framework. The proposed CDG-NAS framework achieved an average cross-domain classification accuracy of 94.6%, outperforming conventional architectures including ResNet50 (85.7%), DenseNet121 (87.3%), EfficientNet-B4 (89.4%), Vision Transformer (90.2%), and Differentiable Architecture Search (91.1%). The framework improved the average robustness score by more than 8% under unseen domain conditions while simultaneously reducing inter-domain performance variance. Ablation experiments demonstrated that removing Domain Consistency Regularization reduced accuracy from 94.6% to 91.8%, whereas eliminating the Robustness-Aware Architecture Score decreased accuracy to 92.4%, confirming the contribution of each proposed component. Statistical analysis further verified that the observed improvements were significant (p < 0.01). Unlike existing neural architecture search methods that optimize architectures primarily for source-domain accuracy, the proposed CDG-NAS framework incorporates domain robustness as a first-class optimization objective throughout the search process. The integration of multi-domain validation, Domain Consistency Regularization, and Robustness-Aware Architecture Scoring enables the automatic discovery of architectures that exhibit superior generalization across previously unseen clinical environments. This approach provides a practical and scalable solution for developing trustworthy clinical AI systems suitable for real-world deployment.
Copyright (c) 2026 A. Sindhu Devi, L. Godlin Atlas
Artificial Intelligence Driven E-Learning in Hybrid Higher Education: Applications, Emerging Trends, Challenges, and Future Perspectives
Introduction: Artificial intelligence (AI), when applied in e-learning, transforms in the concept of blended learning. Artificial intelligence integrated into the learning environment has helped the learners to be more engaging and interactive with their mode of learning. The research work aims to analyze the trend of the usage of Artificial Intelligence in the context of an e-learning environment and also intends to incorporate the trends of its usage and apply the same to create a successful learning environment. The utility of conducting the research work is to analyze the use and application of Artificial Intelligence incorporated through the use of data analytics and natural language processing. Method: The study was undertaken as a thorough review with emphasis placed on successful implementation of artificial intelligence in hybrid higher education, and its success can be demonstrated through its effects or implications in curriculum development and performance in general. Results: Based on the research study, the study shows that the pursuit of personalized instruction through heightening the level of processing various administrative issues and assisting teachers in delivering quality training, the use of AI technology shall be vital within the realm of hybrid education. Discussion: This particular research covers challenges and ethics surrounding biases within algorithms, data privacy, and the need for transparency in AI-driven learning. This particular article also manifests the efficacy of AI technology in offering a personalized learning environment, administrative efficacy, and teaching efficacy. Conclusion: The future will involve applying AI technologies in the domain of hybrid education. Hence, it is essential for all stakeholders to ensure that development with regard to AI is in line with the learning process and establishes a fair learning environment for all individuals.
Copyright (c) 2026 VIRENDRA S. GOMASE, SUHAS B. DHANDE, PANKAJ R. NATU
Artificial Intelligence-Driven Decision Support Systems for Strategic Management: A Machine Learning Framework for Organizational Performance
Artificial Intelligence (AI) driven Decision Support Systems (DSS) help transform the field of strategic management through rapid, accurate and data-driven decision-making in increasingly complicated business environments. This article is centered on a comprehensive machine learning approach for AI-enabled strategic decision support involving all aspects of data acquisition, predictive analytics models, intelligent recommendation systems and collaboration between humans and AI. The framework suggested consists in the integration of data coming from the enterprise, customer relationship management systems, financial databases and market intelligence services and results obtained through machine learning technologies. The process of decision-making involves both the utilization of recommendation systems to develop possible strategies based on analytical results and human involvement ensuring that the outcome is ethical, clear and considerate. The research shows that AI-enabled DSS helps to enhance decision-making quality, operational efficiency, competitive edge, innovation capabilities, and organizational agility as a result of continuous learning. In addition, the paper argues that explainable AI, data governance, organizational preparedness, and responsible AI governance are important factors for successful implementation. Despite numerous advantages available, there are some challenges connected with data quality, transparency of algorithms, data confidentiality, safety, and managerial acceptance that should also be taken into account. The conceptual framework proposed emphasizes a focus on effective cooperation of sophisticated AI technologies with rational judgments that assist decision-makers while staying accountable and trustworthy. The results imply that AI-enabled decision support systems can be essential for businesses striving to adopt digitalization, ensure sustainability, and maintain long-term competitiveness in the context of intelligent organizations. Further studies should investigate target industries, Explainable AI (XAI), ethical frameworks of governance, and the integration of new technologies such as Digital Twins, Generative AI, and autonomous decision-making systems.
Explainable Artificial Intelligence for Executive Decision-Making: Enhancing Trust and Transparency in Management Information Systems
AI has increasingly found its place in Management Information Systems (MIS), allowing organizations to process a huge amount of information and analyze it to find trends, predict business results, and enable decision-making. Yet, most AI technologies function as almost "black boxes," generating results without explaining the underlying logic behind them. This may lead to concerns about accountability, fairness, and ownership of decisions, hampering trust in the technology and limiting its acceptance in the company. Explainable Artificial Intelligence (XAI) solves these challenges through the provision of clear and intelligible accounts of the predictions and recommendations made by AI. This study investigates the significance of XAI in improving the executives’ decision-making process with greater transparency, trust, accountability, and the human–AI collaboration. By applying a conceptual and literature research methodology, the paper proposes a new MIS framework using XAI, where quality of data, transparency of model, relevance of the explanation, and human supervision advance the executives’ degree of trust and decision-making quality.The study claims that XAI must be regarded not only as a technical tool but as an organisational capability enabling managers to assess AI suggestions critically and act on them wisely. The research establishes the significance of user-centric explanations, governance systems, ongoing monitoring, and human responsibility. Findings indicate that explainable AI can boost managers’ confidence and enhance the quality, speed, and defensibility of strategic choices if the provided explanations are accurate, meaningful, easy to comprehend, and correlating with the goals of the organisation.
Artificial Intelligence in Smart Waste Management: A Systematic Review of Algorithms, Integrated Architectures, and Future Research Directions
Urbanization, industrial development and changing consumption patterns have also been factors in ongoing growth of municipal solid waste (MSW) generation, which places extreme pressure on urban infrastructure, environmental quality and public health. Yet, traditional waste management approaches that focus predominantly on fixed collection times and manual sorting face challenges in contexts where the volume of production is fluid – resulting in overflowing bins, poor segregation of waste streams, ineffective routing of vehicles and higher emissions. These limitations have been addressed by smart waste management systems which have shifted from IoT monitoring (fill-level sensing) to AI systems that can be predictive, adaptive, and self-optimizing. This paper summarizes the available literature on various Artificial Intelligence (AI) technologies used for smart waste management, with a particular focus on detection/classification, fill level estimation, monitoring and operational decision-making. To be transparent and reproducible, a PRISMA-guided screening process was used: 780 records were identified, 602 records after duplicates, 450 records were screened, 150 were full texts and after final synthesis 128 studies were included. Results indicate research is typically spread out across individual components, and there is a lack of standardization in datasets, benchmarking and widespread implementation of real-world validation, which calls for an integrated architecture. Therefore, this study suggests a YOLOv8-based smart waste framework that integrates real-time waste detection/classification, waste bin fill-level estimation (empty/half-full/full) and monitoring/decision layer, which can be delivered at edge or hybrid (edge–cloud) architecture for low latency operation and scalability. The direction of the proposed concept is towards designing a unified system to increase the efficiency of the operations, decrease manual efforts, and increase sustainable, data-driven waste management in cities.
Copyright (c) 2026 Maheshwari Divate, Ramakant Chandrakar
A PERFORMANCE INVESTIGATION ON BAGASSE ASH –BASED GEOPOLYMER CONCRETE FOR DIFFERENT CURING REGIME
In this rapid growing development of country, there is demand for sustainability. Most important part of development is infrastructure because its increase GDP of the country and help economically and financially. As the demand for infrastructure increases there is increase in demand of cement which is one of the most and necessary material for the construction of infrastructure. Cement is a material which acts as binder and main ingredient in the concrete. The cement is also one of the materials, which is responsible for contribution to pollution because it emits tonnes of CO2 during manufacturing. This construction material is manufactured by using raw material which is quarried. To gain this sustainability, there is need to have a green concrete which will not contain a material that will affect our environment. So there is need to have a material which will be used instead of cement but not affect the environment. This paper investigates about the behaviour of geopolymer concrete for the replacement of the fly ash by sugarcane bagasse ash by 10-30% for different type of curing. Alkaline activators are used, NaOH and Na2SiO3 with 2.5 ratio. Compressive strength test had been done on different percentage replacement of fly ash by bagasse ash at different curing temperature and curing time for oven curing, room temperature and water curing for 7 days. As per the research work finding maximum strength at 15% replacement of Fly ash by Bagasse ash at 100℃ for 18hrs gives maximum strength up to 43.42Mpa.
Copyright (c) 2026 Varsha V. Yewale, Sachin B. Mulay
Generative Artificial Intelligence in Business Decision-Making: Emerging Frameworks, Enterprise Applications, and Future Challenges
Generative artificial intelligence (GenAI) has moved from experimental novelty to a central input in organizational decision-making, with McKinsey's Q1 2026 Global AI Survey finding that 65 percent of organizations now use generative AI in at least one business function, roughly double the adoption rate reported ten months earlier, and 72 percent report at least one AI workload in production. Despite this scale of adoption, the empirical record on business value remains sharply divided. This paper synthesizes recent academic and industry evidence, drawing on 24 sources published primarily between 2023 and 2026, to examine three interlocking questions: what theoretical frameworks currently explain GenAI's role in managerial and strategic decision-making, how enterprises are applying GenAI in practice across functional areas, and what structural challenges limit the translation of GenAI adoption into measurable business value. The paper synthesizes evidence from strategic management research on AI-assisted evaluation of business alternatives, organizational theory on GenAI's emerging roles in decision processes, and empirical field studies on ambiguity handling and sycophantic behavior in AI-generated business advice, alongside a widely cited 2025 MIT study finding that 95 percent of enterprise generative AI pilots fail to deliver measurable profit-and-loss impact. Findings indicate that GenAI functions most reliably as an augmentation tool that aggregates and structures diverse inputs for human judgment, rather than as an autonomous decision-maker, that single-model evaluations of strategic alternatives are frequently inconsistent and biased while aggregated multi-model evaluations approximate expert human judgment, and that the primary barrier to enterprise value is organizational and workflow integration rather than model capability. The paper concludes with a proposed decision-integration framework and implications for executives, AI governance functions, and researchers.
Copyright (c) 2026 M.SANGEETHA, Una Suman Kumar Patro, K.ARPITHA, Piyal Roy, Burri Naresh, P. Mayavel
Machine Learning and Differential Equation-Based Epidemic Model Framework for Predicting Student Burnout and Depression Due to Academic Workload During and Post-Pandemic.
The rapid increase in academic workload during and after the COVID-19 pandemic has significantly affected the mental health of university students, leading to higher incidences of burnout and depression. Traditional statistical methods provide valuable insights into the relationships between academic and psychological variables but are often inadequate for capturing complex nonlinear interactions and forecasting the temporal progression of mental health conditions. This study proposes a novel Machine Learning and Differential Equation-Based Epidemic Model Framework for predicting student burnout and depression resulting from academic workload. Initially, statistical analyses, including descriptive statistics, correlation analysis, and regression techniques, are employed to identify significant academic, behavioral, and psychological factors influencing student mental health. Subsequently, an Artificial Neural Network (ANN) is developed to learn nonlinear relationships between these factors and to accurately predict individual burnout and depression levels. The ANN-predicted outcomes are then integrated into a compartmental epidemic-inspired differential equation model, where the transition parameters are dynamically estimated rather than assumed constant. The proposed mathematical model describes the temporal evolution of healthy, burnout, depressed, and recovered student populations under varying academic conditions. Numerical simulations are performed using synthetic and survey-based datasets to investigate the effects of workload intensity and intervention strategies on student mental health. The results indicate that combining ANN with differential equation modeling provides both high prediction accuracy and mathematical interpretability while enabling long-term forecasting of burnout and depression dynamics. The proposed framework offers an effective decision-support tool for educational institutions to implement early intervention strategies, optimize academic policies, and promote student psychological well-being.
Copyright (c) 2026 Saswati Sahoo, N. Punitha, Devarsu Radha Pyari, J. Pandima Devi, Praveen Kumar, Mithilesh Deo Pandey, G. Jenitha, Sanjay Oli
Asymptotic Performance Analysis of Diversity-Enabled Finite-Block length Systems over the Family of Single and Double Shadowed κ-μ Fading Channels
The analytical complexity inherent in the family of κ−μ/shadowed fading family often restricts its practical applicability in diversity-based systems and complicates the direct interpretation of performance metrics for wireless communication systems. To overcome these challenges, this work proposes a unified and simplified asymptotic framework for the family of κ−μ/shadowed fading models, accommodating both single and double shadowing scenarios with diversity. Initially, we derive the fundamental probability density function expressions for single-input and single-output (SISO) system than extended it with employing selection combining (SC) schemes. Building on this, asymptotic analysis of short-packet communication performance metrics, specifically average block error rate and delay outage rate, is presented. The framework is further extended to quantify diversity and signal-to-noise ratio (SNR) gains. At last, we validated the analytical finding with Monte-Carlo simulations and found these results to be in close-agreement, confirming the accuracy of the provided formulations.
Copyright (c) 2026 Ankit Jain, Imran Ullah Khan, Puspraj Singh Chauhan, Utkarsh Pandey, Vimal Bhatia
A Review of Semantic Analysis of Food Labels for Expiry Date Recognition Using Deep Learning and NLP
Ensuring freshness and safety of packed food products is a growing concern, especially with an increase in consumption of processed goods. Expiry dates on the product label provide an important indicator for safe use. However, discrepancies in the quality of label formats, source styles, languages and prints often monitor manual and prone to errors. This can lead to involuntary consumption of expired foods and can increase food waste. The paper proposes an automated system to detect expiration dates using deep learning techniques. The system can identify the text related to the termination of the product and separate it from other types of dates, such as manufacturing and packaging dates, computer visions and a combination of natural language processing. It is designed to handle real-world challenges, such as various date formats and packaging materials, multilingual materials, low quality images, and several concurrent dates on single labels. The review contributes to improving food security and reducing waste and supports intelligent automation in food inventory management with the help of semantic analysis on the basis of intensive learning of the product label.
Copyright (c) 2026 Roshni Bhave, Vibhakti Bagade, Vijaya Kamble, Amita Meshram, Purva Gogte Vyawahare, Shilpa Dhopte
Adaptive Wavelet Decomposition with Cross-Band Attention for Unsupervised 3D Medical Image Registration
This report introduces a new unsupervised deformable 3D registration network called AWaRe Net developed for the purpose of overcoming the drawbacks of existing techniques with regard to multi scale fusion and losing key information. AWaRe Net replaces fixed Haar wavelets with the ability to create adaptive wavelet filters through a technique called Adaptive Learnable Wavelet Decomposition (ALWD) that allows each filter to have parameters optimized using backpropagation while simultaneously allowing for attention to dynamically adjust weights across sub bands using CrossBand Attention Fusion (CBAF). Additionally, a hybrid lightweight bottleneck architecture that combines ConvNexT and axial attention is used to create long range dependencies between data points in a Linearithmic (i.e, O(N log N)) fashion, whilst an explicit frequency loss provides additional alignment of wavelets and other properties found within traditional 2D methods through conventional spatial similarity. AWaRe Net has been evaluated against two publicly available, currently utilized datasets: IXI (image registration from Atlas to Patient) and OASIS (inter-patient), and demonstrates mean values of 79.1% and 83.9% Dice scores, respectively which significantly outperform comparable algorithms (Wave Morph, Trans Morph, and Voxel Morph) (p < 0.001). This allowed for achieving a lower folding ratio of approx. 0.168% relative to previously published algorithms on the same dataset (OASIS), with an average inference time of 68 ms per image pair, and only 0.82 million parameters. Each proposed component was confirmed through an ablation study. Additionally, generalization tests on synthetic multi organ datasets have demonstrated enhanced transferability of registration results to abdominal, cardiac and lung images. This study demonstrates that the use of adaptive wavelet decomposition with cross band attention substantially increases accuracy within the registration process, the smoothness of deformation across the registered image, as well as the computational efficiency of the entire process as it relates to clinical deployment and longitudinal disease monitoring in real time.
Copyright (c) 2026 Hussein Abdulkhalek Midhat, Asim Majeed Murshid
DEVELOPING A SUSTAINABLE SMART CITY MODEL WITH INTEGRATED SUSTAINABILITY PERFORMANCE (ISP): THE MEDIATING ROLE OF INCLUSIVE JUSTICE AND TELEMEDICINE – A CASE STUDY OF MAKASSAR CITY, INDONESIA
The rapid population growth and increasing global energy demand have prompted the need for more resilient urban infrastructure, with Smart City serving as the primary solution. Objectives: This study aims to develop a Smart City model integrated with Integrated Sustainability Performance (ISP), involving five key variables: economy, environment, governance, livability, and pandemic resilience. As an innovation, inclusive city indicators and telemedicine are incorporated to strengthen the city's sustainability and resilience. Methods: This study applies a sequential explanatory mixed-methods design, starting with a quantitative survey analyzed using PLS-SEM, followed by qualitative interviews and FGDs to explain and enrich the findings. Results: The results of the analysis show that the implementation of Smart City in Makassar positively impacts ISP, with Telemedicine and Inclusive Justice serving as significant mediators. Conclusions: This study contributes to the development of a measurable and applicable Smart City model to enhance urban sustainability and resilience.
Copyright (c) 2026 Darmawansyah, Delly Mustafa, Jimmy Torar, Roy GA Massie, Muhammad Ahsan Samad, Amaliyah Saru
EFFICIENCY FISCAL AND CONTESTATION INTERESTS: ANALYSIS POLITICAL BUDGET IN PROVINCE PAPUA MOUNTAINS AS A NEW AUTONOMOUS REGION
This research aims to analyze the relationship between fiscal efficiency policies and the dynamics of interest contestation in the process political budget in Papua Province Mountains as area new autonomy. In condition capacity Limited fiscal resources and high priority development needs have very negative implications for the acceleration of development. regional progress. This study uses a qualitative approach with a descriptive study design. case through analysis of APBD documents 2023–2026, interview deep with Team Budget Government Area, Budget Agency, and the Papua Mountains People's Assembly. The research results show that a significant decline in fiscal capacity not only has implications for adjustments shopping area with framework expenditure term medium regional, integrated budgeting and performance-based budgeting but more determined by power relations and priority of interests. Contestation of interests, reflected in the allocation adjustments regional spending based on bargaining executive interests And legislative. Occurrence budgetary slack , namely proportion structure Regional Budget more dominated by transfer funds to regions compared to local original income, the realization of which often does not reach the target, as a result of resources which has the potential to be controlled by the central government. The absence of involvement of the Mountain Papuan People's Assembly (MRPP) as an institution cultural. Recommendations study This, the need for the involvement of non-governmental actors from MRPP in stages Determination of priority for temporary budget ceiling (PPAS), especially in the distribution of sources of Autonomy Funds Special. Novelty study This, is efficiency fiscal No only It functions as a technocratic instrument for budget adjustment, but also as a mechanism that directly triggers and shapes the contestation of interests in the budgetary political process between Executive, Legislative and MRPP actors. This differs from previous research which tend separate between fiscal efficiency and budget politics, this study shows that these two aspects interact with each other. in a way dynamic, especially in condition pressure fiscal and limited institutional capacity.
Copyright (c) 2026 Gasper Liauw, David E. Mayor, Michael Wutoy
CAUSES OF CONFLICT BETWEEN STUDENT GROUPS FROM THE PALOPO (IPMIL) AND BONE (KEPMI) REGIONS IN MAKASSAR CITY
The conflict between regional student groups (Organda) between IPMIL Palopo and KEPMI Bone in Makassar City is a social phenomenon that cannot be understood as a single event, but rather as a process formed through a series of repeated and interrelated daily social interactions. This study aims to analyze the causes and escalation processes of the conflict between the two organizations using qualitative methods through a case study approach. Data were collected through in-depth interviews, observations, and documentation of informants from both organizations. The results of the study indicate that the conflict was triggered by four interrelated patterns, namely: (1) small incidents in daily interactions such as bumps, jokes, and misunderstandings that developed into open conflict due to the failure of social interactions; (2) the activation of regional identity and organizational solidarity that transformed individual problems into collective conflicts; (3) the role of seniors, organizational doctrine, and collective memories of past conflicts that inherited accumulated emotions between generations; and (4) structural conditions such as campus political momentum, new student admission activities, slow institutional responses, and a large mass base that strengthened the escalation of the conflict. These findings indicate that conflict develops as a process of social interaction that occurs repeatedly and continuously, not solely as a result of a spontaneous, isolated event.
Copyright (c) 2026 Uslan Nazab, Suparman Abdullah, Arsyad Genda
CONSUMER PROTECTION OF TRADEMARKS IN THE ERA OF DIGITAL TRANSFORMATION
Technology brings various changes to human life. The presence of technology is undeniably a means that facilitates the circulation of various products bearing various marks. As one manifestation of intellectual work, a mark plays an important role in facilitating and enhancing trade in goods or services. For consumers, a mark provides a choice among the goods and services needed according to the priority of their needs. The assignment of a mark also indicates the quality of the goods and services and seeks to prevent imitation. Consumer protection is an inseparable part of healthy business activity. Consumers may be led astray by purchasing goods of the same kind but being deceived as to quality, or drawn toward goods of a different kind that nonetheless bear a similar mark. Consumer protection is therefore an important matter to undertake. Legal certainty ought to be granted to consumers as the party suffering loss. The problem raised in this study concerns consumer protection in relation to trademarks arising in the era of digital transformation. This study is normative legal research. The approach used is the juridical approach, carried out by analyzing the provisions of the Consumer Protection Law, the Trademark Law, and the Electronic Information and Transactions Law relating to consumer protection in respect of trademarks. Indonesia already has regulations concerning consumer protection in relation to trademarks. The presence of several of these statutes constitutes a form of the state's responsibility toward its citizens. The statutes in question include the UUPK, the UU Merek, and the UU ITE. For the problem of consumer loss relating to trademarks, the UUPK and the UU Merek provide means of resolution. Resolution may be pursued through the courts or through other alternative dispute resolution. For consumer protection disputes existing in the digital domain, the UU ITE provides means of dispute resolution.
Copyright (c) 2026 Desyanti Suka Asih K. Tus, Ahmad M. Ramli, Rika Ratna Permata
REVISITING THE TOURISM-LED GROWTH HYPOTHESIS IN THE PHILIPPINES: EVIDENCE FROM AUTOREGRESSIVE DISTRIBUTED LAG MODEL (ARDL) BOUNDS TESTING APPROACH
Tourism has emerged as one of the most dynamic sectors contributing to economic growth in many developing economies. This study revisits the tourism-led growth hypothesis in the Philippines by examining the relationship between tourism expenditure and economic growth using annual time-series data from 1980 to 2022. Employing the Autoregressive Distributed Lag (ARDL) bounds testing approach, the study investigates both the short-run and long-run dynamics between tourism expenditure and Gross Domestic Product (GDP). The results of the unit root tests reveal mixed orders of integration among the variables, justifying the application of the ARDL model. The bounds test confirms the existence of a long-run equilibrium relationship between tourism expenditure and economic growth. The long-run estimation results indicate that tourism expenditure exerts a positive and statistically significant influence on GDP, providing support for the tourism-led growth hypothesis. The error correction model further reveals a significant adjustment mechanism, indicating that deviations from long-run equilibrium are corrected over time. Moreover, the Granger causality analysis identifies a significant unidirectional causal relationship running from economic growth to tourism expenditure, suggesting that economic expansion facilitates tourism sector development. Diagnostic and stability tests confirm the robustness and reliability of the estimated model. Overall, the findings highlight the important role of tourism expenditure in supporting economic activity while demonstrating that sustained economic growth also serves as a foundation for tourism development. The study provides valuable policy insights for strengthening tourism strategies and enhancing the contribution of the tourism sector to sustainable economic growth in the Philippines.
Copyright (c) 2026 JESSON REY F. SABADO, CLARENCE JOY D. ALCARAZ
Nursing Workload and Staffing Adequacy as Healthcare Workforce Management Factors Associated with Missed Nursing Care among Registered Nurses in Chennai
Missed nursing care is an important concern in healthcare because omissions or delays in essential nursing activities may affect patient safety and quality of care. Regarding nursing workload and nurses' ability to perform required care activities, it is known that nursing workload is an important factor that may affect their ability to perform nursing care, but research findings have been inconsistent on the relationship between nursing workload and missed nursing care. This study set out to determine if the staffing level for nurses and the amount of work they have is predictive of missed nursing care by registered nurses. The research design used was quantitative, cross sectional, predictive correlational. Structured online questionnaire with Google forms was used for data collection and snowball sampling technique was used. The National Aeronautics and Space Administration Task Load Index (NASA-TLX), an eight-item Staffing Adequacy Scale and a 22-item MISSCARE Survey were used to assess the nursing workload, staffing adequacy and missed nursing care, respectively. One hundred and one responses were received, and 100 of these were complete responses that were analyzed. Data is analyzed by descriptive statistics, Pearson correlation and multiple linear regression software package IBM SPSS Statistics version 16.0. The results indicated that neither nursing workload nor staffing adequacy were significantly associated with the missed nursing care (β = 0.105, p = 0.275; β = 0.377, p < 0.001). The positive regression coefficient meant that reduced perceived staffing adequacy was related to increase missed nursing care given that higher scores on the Staffing Adequacy Scale represented poorer perceived staffing adequacy. Goodness of fit of the regression model was revealed by R² = 0.143 (p = 0.001), explaining 14.3% of the variance in missed nursing care. The results indicate the need for sufficient staffing in order to provide the necessary nursing care in a timely manner and that staffing conditions may contribute more than just the perception of workload. Improving staffing levels and planning for staffing needs may also help to enhance the quality of nursing care and safety of patients.
Copyright (c) 2026 M. Meghalakshmi, M. Radhikaashree
Encoding Statutory Variance in Property Rating Systems: A Mandate-Strength Classification and Specification Method for Multi-State Catastrophe Mitigation Credits
Property insurance rating engines encode jurisdiction-specific catastrophe mitigation credits as local configuration, with no machine-resolvable link to the statutory provisions that require them. Practitioner discussion of this problem generally treats inter-state variation as a matter of differing credit percentages, to be handled by parameterising a single rule. We show that this framing is incorrect. Examining the operative catastrophe mitigation credit provisions of five U.S. jurisdictions from primary sources, we find that obligations differ not in magnitude but in kind: Florida imposes the credit in mandatory terms on the rate filing itself; New York's mandate is delegated, with the statute binding the regulator and the implementing regulation binding the carrier under trigger conditions absent from the statute; California's obligation binds conditionally, only on insurers whose property rating plans segment on wildfire risk, and is mandatory in content once that gate is passed; Texas authorises a construction-system discount without requiring it in the voluntary market, while its building-code mitigation credits bind only the residual-market association through a manual adopted by reference; and Ohio's property rate filing provisions contain no analogous mandate. Four obligation classes and two further structural axes -instrument layer and bound population -therefore appear across five jurisdictions on a single compliance dimension. From this finding we derive three contributions. First, a mandate-strength classification -mandatory, conditional, permissive, absent -stated as a triple over applicability, content strength, and bound population, so that obligation class becomes a first-class property of a rating rule rather than an implicit assumption. Second, a specification method encoding each obligation as a rule object that carries its enabling and operative instruments separately, together with version, mandate class, trigger, adjustment target set, and ordering constraint, and binds to core platform primitives including SAP BRFplus decision tables. Third, a compliance index whose satisfaction predicate is decidable from carrier-internal state and therefore does not presuppose the correctness oracle it exists to approximate, with its alert threshold derived from carrier loss tolerance rather than asserted. A worked analytical example over a synthetic 20,000-policy multi-state portfolio quantifies the consequence of ignoring mandate class: a uniform parameterised configuration diverges from the statute-faithful encoding on 11,875 policies (59.4%), and the divergence is driven by trigger mismatch rather than factor mismatch by a ratio of 22:1 in value. The statutory analysis is the paper's empirical core and is verifiable against the cited enacting texts; the portfolio is synthetic and the quantitative results are analytical rather than observed.
Copyright (c) 2026 Anushka V. Rodi, Animesh Dhole, Harpreet Singh Siddhu, Sravan Reddy Kathi
Intention to Adopt Digitalized E-Invoicing Systems among Small and Medium Enterprises (SMEs) in China: A PRISMA-Guided Systematic Review
This study used a PRISMA guided Systematic Literature Review to synthesize 28 peer-reviewed studies published between 2015 and 2025. The aim was to examine Chinese SMEs’ intention to adopt digitalized e-invoicing in China and closely related, invoice-proximate digitalization. As the literature on Chinese SME’s e-invoicing strategies was found to be scarce, the scope of the study was expanded. The evidence found clustered within a TOW-UTAUT institutional framework. This precluded that technological benefits (relative advantage, compatibility, trialability) and organizational readiness (top-management support, IT capability, training) increase intention, while complexity, integration burden, and security/privacy risks dampen it. Additionally, individual-level trust and perceived risk shape acceptance of public/platform services, and environmental forces, standards, mandates/clearance regimes, buyer/platform pressure. They often catalyze intention into use. Additionally, it was found that most studies that focus on digitalization or e-invoicing use methodologies like survey/PLS-SEM, qualitative/case work, and quasi-experimental designs. However, recent years show growth in policy-analytics. This links e-invoicing to compliance and reporting outcomes. The review thus proposes an integrative China-specific framework and offers policy and managerial guidance. It pairs phased mandate with investments in service-design, standardization and building SME capabilities. It also underscores leveraging invoice data for finance.
Copyright (c) 2026 Zhengguang Shi, Fariza Hanim Binti Rusly, Idawati Binti Ibrahim
Intelligent Cyber Risk Assessment Framework for Industrial IoT using Ensemble Learning and Explainable AI
Industrial 4.0 is supported by the IIoT, which can enable increased automation, efficiency, and real-time monitoring of industrial control systems. Regardless of these benefits, the rise of connectivity makes the IIoT systems susceptible to some of the greatest cybersecurity threats, which undermine confidentiality, integrity, and availability. This paper presents a ML-based model of cyber risk assessment designed to identify a threat in IIoT proactively. Supervised models, such as Multi-Layer Perceptron, XGBoost, LightGBM, RF, LR, KNN, DT, SVM, FSVM,FXGBoost, and ensemble voting classifiers are fully evaluated. According to the experimental findings, the ensemble voting-based model is more effective as it reaches a higher accuracy of 99.3 percent, with a higher F1-score of 0.993, compared to the isolated learners. Using eAI methodologies, such as LIME and SHAP, to explain the impact of factors on the prediction improves transparency. The trained model is then deployed using the Flask web framework, enabling real-time risk assessments and interactions with users. The system categorizes the IIoT cyber risk levels as Very Low, Low, Medium, High and Very High which are immediately mitigated. The proposed solution provides an effective, interpretable and scalable treatment, and enhances the cybersecurity resilience of the IIoT.
Copyright (c) 2026 Nimmala Bhavana, SK Mahaboob Basha
AI-Enabled Smart Healthcare Systems: Innovative Approaches to Disease Prediction, Accurate Diagnosis, and Effective Public Health Management
Artificial intelligence (AI) has moved from a peripheral research interest to a central component of contemporary healthcare delivery, with deep learning systems now matching or exceeding specialist-level performance on discrete diagnostic tasks spanning dermatology, ophthalmology, radiology, and oncology. This paper reviews the technical foundations and applied evidence base for AI-driven smart healthcare systems across three domains: disease prediction from structured and unstructured clinical data, image-based diagnostic classification, and population-level public health management, including the accelerated adoption of AI tools during the COVID-19 pandemic. The review synthesizes landmark diagnostic-performance studies, including dermatologist-level skin cancer classification, diabetic retinopathy detection, pneumonia detection from chest radiographs, and international breast cancer screening evaluation, alongside the clinical machine learning literature addressing implementation, validation, and algorithmic bias. Comparative tables summarize reported diagnostic performance metrics, data modalities, and clinical domains across the reviewed systems. The paper concludes that AI-driven diagnostic systems have achieved genuine, reproducible performance parity with human specialists on narrow, well-defined tasks, while broader clinical deployment remains constrained by validation, generalizability, and algorithmic-bias challenges that the reviewed literature has only begun to resolve, and identifies prospective real-world validation and equity-focused model auditing as the central future research prospects.
Copyright (c) 2026 Vinit Kumar Ramawat, G.PRABHAKARAN, Pinki Das, A. Sasi Kumar, Ramesh Kumar, Tara Sasanka, C