International Journal of Computer Information Systems and Industrial Management Applications
Energy-Efficient Task Scheduling in Fog-Cloud Environments Using an Improved Single Candidate Optimizer
Fog-cloud computing has emerged as a promising paradigm for supporting latency-sensitive and data-intensive Internet of Things (IoT) applications through the use of fog computing to bring cloud-style functions closer to the end user. Task management in these heterogeneous environments is challenging due to dynamic workloads and resource constraints; thus, there are also difficulties in balancing the trade-offs among energy consumption, execution time, and operational cost. Metaheuristic-based scheduling algorithms have typically exhibited relatively high computational complexity, slow convergence rates, and poor adaptability to real-time conditions. In this study we present an energy efficient task scheduling framework using an improved single-candidate optimizer (ISC-F). The proposed algorithm improves the existing single-candidate optimizer (SCO) by using a two-phase optimization method that combines exploration and exploitation effectively. In addition, ISC-F allows fog nodes to be prioritized for the execution of latency-sensitive tasks, while dynamically offloading tasks to cloud resources as network conditions change. The optimization process considers multiple objectives including minimum energy consumption, makespan, execution time, and total cost. To evaluate the potential of ISC-F against competing scheduling algorithms, we conducted a series of extensive simulations comparing the aforementioned competing scheduling methods (HEFT, IHEFT, IKH-EFT, IWO-CA). The results demonstrate that ISC-F significantly outperforms existing scheduling techniques; therefore, there is strong evidence that the proposed optimization algorithm provides significant benefits to both fog-cloud computing systems and their respective users.
Copyright (c) 2026 Malik Shahzad Ahmed Iqbal, Mohammad Haroon
A Study on Exploring the Cointegration between Green Cryptocurrencies and G7 Sustainable Indices: An Empirical Investigation.
This study investigates the cointegration and dynamic interactions between selected green cryptocurrencies and G7 sustainable indices over the period 2019 to 2023, aiming to provide empirical insights into the integration of emerging digital assets within the sustainable finance ecosystem. Green cryptocurrencies, including Cardano (ADA), Algorand (ALGO), and Stellar (XLM), have gained prominence due to their energy-efficient consensus mechanisms and alignment with environmental, social, and governance (ESG) principles. Simultaneously, G7 sustainable indices such as FTSE4Good UK and MSCI ESG Japan represent established benchmarks reflecting the performance of companies committed to sustainability practices in developed economies. This paper applies rigorous econometric techniques, including stationarity tests, Johansen cointegration, Vector Error Correction Model (VECM), and Granger causality analysis to assess the existence and nature of long-term equilibrium relationships and causal linkages between these asset classes. Results from the Augmented Dickey-Fuller test indicate all variables are integrated of order one, justifying the use of cointegration analysis. Johansen’s test confirms at least two cointegrating vectors, signifying that green cryptocurrencies and sustainable indices move together over the long run despite short-term market fluctuations. VECM results reveal significant error correction terms, particularly for Cardano, indicating a rapid adjustment to equilibrium after deviations. Granger causality tests demonstrate a unidirectional influence running from G7 sustainable indices to green cryptocurrencies, suggesting that traditional sustainable markets continue to guide price dynamics in the emerging green cryptocurrency sector. These findings underline the growing interdependence between digital green assets and established sustainable financial markets, offering valuable implications for investors, portfolio managers, and policymakers seeking to integrate sustainability into their investment strategies. The study contributes to the nascent literature on the convergence of green finance and digital innovation, highlighting the importance of monitoring sustainable indices to anticipate trends in green cryptocurrencies and supporting the development of regulatory frameworks that promote transparency and sustainability in the cryptocurrency space.
Copyright (c) 2026 Shruti Dubey, Babli Dhiman
Scalable Predictive Maintenance Architecture for Oracle Fusion Cloud Using External ML Models
Manufacturing organizations increasingly rely on data-driven and cloud-based technologies to improve asset reliability and reduce unplanned downtime. During an 18-month deployment across multiple manufacturing sites, we implemented a predictive maintenance solution integrated with Oracle Fusion Cloud Maintenance using externally trained machine learning models and third-party industrial IoT platforms. The implementation was driven by the need to continue predictive maintenance capabilities following the removal of Oracle’s native IoT services. In the deployed solution, real-time equipment telemetry was ingested through an external IoT platform, where data preprocessing, feature engineering, and model training were performed. Predictive failure events generated by the machine learning models were transmitted to Oracle Fusion Cloud using event-driven RESTful APIs and evaluated against configurable maintenance business rules to automatically initiate or optimize maintenance work orders. Oracle Fusion Cloud remained the system of record for asset management and maintenance execution throughout the deployment. Measured results from the pilot showed a reduction in unplanned equipment downtime, improved maintenance scheduling accuracy, and increased overall equipment effectiveness. These outcomes indicate that separating predictive analytics from ERP systems, while maintaining tight event-driven integration, provides a scalable and production-ready approach for implementing predictive maintenance in Industry 4.0 environments.
TOWARDS FIGURATIVE-AWARE SENTIMENT ANALYSIS: A COMPARATIVE REVIEW ON DEEP LEARNING AND TRANSFORMER MODELS
Sentiment analysis is a main task in NLP which includes social media analytics, e-commerce, opinion mining, and customer feedback. However, transformer models and deep learning techniques have enhanced contextual understanding; sentiment analysis continues to exhibit endless challenge in figurative language. Sarcasm and metaphor frequently reverse expected sentiment, which causes misclassification in many natural language processing models. Despite this, most studies treat sarcasm and metaphor as separate tasks rather than within a unified framework. This paper presents a comparative review of sentiment analysis techniques, determining the purpose of handling figurative languages across different domains. This analysis highlights the limitations in existing models and enhanced the need for unified frameworks that jointly model sarcasm and metaphor. Index Terms— deep learning, sarcasm detection, metaphor detection, metaphor detection, sentiment analysis, figurative language, natural language processing, transformer models.
Copyright (c) 2026 Nayana R. T., Shivakumar V.
A Hybrid KNN–SVD, CNN, and RoBERTa-Enhanced Framework for Sentiment Analysis of OTT Film Reviews
The exponential increase in Over-the-Top (OTT) broadcasting platforms possesses resulted in a massive inflow of user-generated film reappraisals, requiring a scalable and highly accurate sentiment analysis structures to support a successful recommendation. The present study proposes a hybrid KNN–SVD, CNN, and RoBERTa-enhanced foundation for sentiment classification of OTT film reviews, integrating lightweight machine learning and transformer-based contextual understanding. A benchmark dataset consisting of 50,000 multilingual OTT movie review sources from IMDb, Amazon top Video review, and YouTube remark was pre-processed using NLP strategies including tokenization, lemmatization, tri-gram TF–IDF vectorization, semantic standardization, and RoBERTa-based contextual embeddings for comparative analysis. The Singular Value Decomposition (SVD), which reduces the aspect space from 32,000 to 250 latent components, thereby reducing the computational cost by 68%. The vector was classified using a cosine-similarity-based K-Nearest Neighbor (KNN) model with k=5, optimizing using a Bayesian search. 94.27% accuracy, 93.81% precision, 94.12% recall, and 94.01% F1-score exceeded the baseline machine learning model. Further robustness valuation using Convolutional Neural Network (CNN) produces probabilistic soft-label distribution, efficiently detecting ambiguity in various other context-dependent reviews. For benchmarking transformer performance and second reliability of hybrid models, a fine-tuned RoBERTa model was integrated, together with a review of the confusion matrix revealing reduced misclassification rates (FPR 3.9%, FNR 4.6%). The novelty of the current task lies in the combination of SVD-enabled latent semantic compression, non-parametric KNN similarity study, CNN-driven soft resolution marking, and RoBERTa-based contextual knowledge in the context of an integrated architecture. This fusion achieves high-achieving sentiment analysis while remaining computationally efficient and explainable, thus ideally suited to real-time sentiment monitoring in large OTT systems.
Copyright (c) 2026 Jennifer June Mohanraj, Navin M George, Gunamony Shine Let, Gokul J
A Multi-Task Machine Learning Approach for Joint SMS and URL Safety Classification
The sudden evaluation of SMS communication has rendered mobile consumers more susceptible to spam and phishing threats, particularly across embedded URLs found in both unsolicited and authentic texts. Current methods failing to particularly assess the safety of the embedded URLs, focusing on SMS text as either spam or ham, so allowing hidden threats to remain invisible. This article introduces a dual-task architecture that URL safety classification on a managed SMS dataset, and also concurrently executes SMS spam detection and wherein each message includes at least one URL. We apply preprocessing technique in SMS text utilizing natural language processing approaches and extract SMS features, lexical, structural, and reputational information from cleaned SMS Dataset to train separate yet collaborative models. When SMS is marked as authentic, The SMS level classifier employs text-based features to differentiate between spam and ham, whereas the URL level classifier autonomously assesses the safety of each link. This article is displayed on machine learning approaches like support vector machine, Logistic regression and Naïve Bayes, which algorithm provides the best recall, accuracy, precision, and F1 score for a real-world SMS spam dataset enhanced with URL annotations demonstrate that the both tasks individually and combined for mobile messaging services. cyber security mechanism that supports more comprehensive real-time protection against phishing and spam attacks in SMS mobile services.
Copyright (c) 2026 M. P. Sudha, M. Ramesh Kumar
Generative AI-Enabled Micro-Frontend Framework for Scalable and Intelligent Enterprise Retail Applications
Generative artificial intelligence (AI) and micro-frontend architectures are two of the most disruptive technologies on this planet today, with enormous potential to change how we build large scale enterprise retail applications. We present a novel strategy to integrate generative AI capabilities into a micro-frontend architecture, for building extensible, cognitive modular retail systems. While traditional monolithic frontend architectures are not efficient enough to cater to modern retail environments which are highly dynamic, personalized & data-driven. This paradigm enables an end-to-end framework for frontend component design to decompose into independent deployable micro-units, encodable by generative AI models processed in real-time during model generation through user profiles driven context. We framed our solution around large language models (LLMs), retrieval-augmented generation (RAG) and the AI-driven decision pipelines are designed to integrate natively within their respective micro-frontend modules. In addition, it brings in federated deployment strategies along with event driven communication protocols and DDD based principles to ensure inter module consistency as well as resilience against operational failure. Clear experimental evidence demonstrates system scalability, user engagement and operational efficiency all outperform significantly on the parts of traditional enterprise retail architectures. The solution can be a template for any retailer looking to synthesize generative AI and micro-frontend engineering to deliver creative new intelligent shopping experiences.
Copyright (c) 2026 Bhuvan Chandra Kasarapu
Enhancing Throughput and Energy Efficiency in GSM Networks Through Watts–Strogatz Graph Modeling and Reinforcement Learning
The problem of simultaneous optimization of both throughput and energy consumption in GSM network is considered as a multi-objective problem and studied in this paper. The cell topology is modeled as a “small world” graph based on the stochastic graph model of Watts and Strogatz, and properties of the graph such as degree, centrality, traffic load and QoS indices of each cell are extracted through exploration. With these features along with the channel information of each channel and the power level of the base stations, a deep reinforcement learning agent has the state space. The DRL agent learns the control actions (transmit power, ON/OFF of cells, routing traffic between adjacent cells) so that a reward function defined as normalized throughput, normalized energy consumption and penalty on QoS violation is maximized by using a neural network based Q-value function approximation. Simulation results in the scenario of one macro cell and 12 micro cells with 200 to 2000 users show that the proposed method has a significant advantage over the three reference methods (basic, heuristic and classical Q-learning); so that the average cumulative reward of the DRL algorithm reached 3.41, while for Q-learning and the heuristic method it was 2.28 and 0.7, respectively, and for the basic method it was reported to be negative 3.55. Moreover, the energy efficiency of the network, i.e., ratio of total throughput to total power at the base-station, for the proposed method is kept at a stable value of 2.5, whereas Q-learning has a value of 2.05, and the two non-intelligent methods have values of 1.56 and 1.21, respectively. The obtained results show that the small-world graph modeling and deep reinforcement learning can be an efficient framework for intelligent energy and capacity management in GSM networks.
Copyright (c) 2026 Imam Qasim Mousa, Kheirolah Rahsepar Fard
Digital literacy has become an essential skill in today's technology-driven world. With the rise of Artificial Intelligence (AI), the scope of digital literacy has expanded, necessitating a deeper understanding of not only basic technological skills but also AI's implications on society, ethics, and individual decision-making. This research explores the evolving nature of digital literacy in the AI age, emphasizing the need for understanding AI tools, algorithms, data processing, and their ethical implications. It reviews previous studies on digital literacy and AI, highlights the challenges such as data bias, privacy concerns, and the digital divide, and examines the opportunities AI provides in education, workplaces, and social good. The research further discusses strategies for promoting digital literacy, focusing on curriculum development, government-industry partnerships, and raising awareness about AI ethics and security. The goal is to equip individuals with the knowledge and skills required to navigate an AI-driven world responsibly and effectively.
Copyright (c) 2026 Nirna Sanjana, Preeti Yadav, Masanam Gopi, Shalu Kumari, Nallabolu Krishna Reddy, Nargani Sathibabu, Chebrolu Gangeya Naga Venkata Sathwik
An IoT Driven HTM Based Predictive Model for Early Detection of Sheath Blight Disease in Paddy Crops
Rhizoctonia solani is a highly destructive fungal disease which can cause as much as 50% paddy loss. Timely and timely prevention and early prediction of these crop diseases are crucial to reduce losses and to foster sustainable agriculture. This study presents a new Internet of Things (IoT) based predictive model of sheath blight appearance using Hierarchical Temporal Memory (HTM), which mimics the human neocortex function to predict the probability of sheath blight occurrence in advance of the appearance of visible symptoms. Using IoT sensors, time-series information of the environment, such as temperature, humidity and rainfall in paddy fields were collected continuously. The HTM model was used to learn temporal patterns to predict disease occurrence using these parameters as input. The model was applied and tested with the data collected from the Cuddalore district of India from 2019 to 2023. The proposed HTM-IoT framework resulted in a consistent increase of the prediction accuracy as indicated, reaching the highest accuracy of 94% in 2023. Using correlation analysis, it was noted that there was a strong correlation between the environmental variables and the severity of the disease, such as humidity (0.86), rainfall (0.83) and temperature (0.79). The resulting model can be used to detect sheath blight outbreaks early, helping farmers to take timely actions to control it, minimize the use of pesticides, and move towards precision agriculture to ensure sustainable crop production.
Copyright (c) 2026 P. Nehru P, J. Mercy Geraldine
Cognition in Fashion E-Commerce and Sustainability Design
The rapid expansion of the e-commerce landscape requires companies to understand the mechanism through which the purchase intention of customers could translate into their favourable experiential outcomes. As such, the present study aims to extensively investigate the mediating role of perceived service quality (PSQ) in the relationship between online shopping intention (OSI) and customer experience (CX) in the context of online fashion retailing. A quantitative study was conducted, collecting the data from 467 online fashion shoppers to test the proposed research model using Structural Equation Modelling (SEM). The results revealed that OSI positively influences PSQ and CX, and that PSQ, in turn, significantly enhances CX. Furthermore, PSQ fully mediates the relationship between OSI and CX. The findings of the study theoretically revealed reconceptualizing service quality as an experiential mechanism rather than merely an antecedent of satisfaction. Therefore, the managers and e-retailers should prioritize reliable service delivery, transparent communication, personalization, and seamless return processes to enhance customer experience and foster long-term engagement.
Copyright (c) 2026 Arunima Rudra, Nibir Khawash, Anupam Karmakar, Samrat Ray
WASTE BAOBAB AND TAMARIND LEAVES OF DHOFAR AS ADSORBENTS FOR PROPIONIC ACID REMOVAL FROM ITS AQUEOUS SOLUTION: ADSORPTION KINETIC MODELLING
The adsorption kinetics of propionic acid from its aqueous solution onto raw and activated baobab (RB, AB) and tamarind (RT, AT) biosorbents of Dhofar were systematically investigated to evaluate the influence of biosorbent activation on adsorption performance and mechanism. Experimental data were analyzed using linear forms of five kinetic models, including pseudo-first order (PFO), pseudo-second order (PSO), Elovich (EV), intra-particle diffusion (IPD), and Boyd (BY) models. Model performance was assessed through comprehensive error analytical parameters, including sum of squared errors (SSE), mean squared sum of errors (MSSE), standard error (SE), average absolute relative deviation (AARD), and determination coefficient (R2). The results revealed that adsorption capacity and rate were significantly enhanced after chemical activation, with AB showing the highest overall performance followed by RB, AT, and RT. Kinetic analysis indicated that the pseudo-second-order model best described the adsorption process for all biosorbents, supported by the highest R² values and the lowest error statistics, suggesting that chemisorption is the dominant mechanism controlling propionic acid uptake. The Elovich model further confirmed adsorption on heterogeneous surfaces, while IPD and Boyd models indicated that both film diffusion and intraparticle diffusion contributed to the overall rate, without being sole rate-limiting steps. Error analysis validated the robustness and reliability of the PSO model in predicting adsorption behavior. Overall, the study demonstrates that waste-derived baobab and tamarind biosorbents, particularly in activated form, are effective, low-cost, and sustainable materials for propionic acid removal from aqueous solutions, offering promising applications in wastewater treatment.
Copyright (c) 2026 Sivamani Selvaraju, B. S. Naveen Prasad, Saikat Banerjee, Raja Thiruvengadam
Design and Implementation of a Job Skill Entity Recognition (JSER) for Custom Entity Annotation using Trie Structure
Abstract—Named Entity Recognition in computer science is tricky because it uses terms and needs detailed annotations. Even though Named Entity Recognition has gotten better in uses it is not widely used in academic computer science. This paper presents JSER, a method that uses Natural Language Processing techniques to find and classify both known and unknown entities. Our approach uses Part-of-Speech tagging to pull out entities from unstructured text. We make annotations better and faster with techniques like tuning models adjusting thresholds and adding more data. JSER seems promising for tasks, like finding information analyzing text automatically and managing knowledge.
Copyright (c) 2026 Pareshkumar Prajapati, Sandeep Vasant, Jaideepsinh Raulji
Digital and Technological Sovereignty in the Age of Strategic Competition: Implications for Security and Defense
The transformation of digital and technological sovereignty from abstract concepts to operational imperatives marks a defining feature of contemporary international security. This comprehensive analysis examines how states navigate the intersection of digital governance and technological autonomy in an era characterized by intensifying strategic competition, supply chain vulnerabilities, and the weaponization of interdependence. Drawing on recent developments through 2025, we analyze divergent sovereignty models across major powers and regions—the European Union's normative-regulatory approach, the United States' security-industrial paradigm, China's state-centered control model, Russia's defensive isolation strategy, and emerging hybrid frameworks in the Indo-Pacific and Latin America. The study reveals that while convergence exists in recognizing sovereignty as essential for strategic autonomy, fundamental divergences persist in normative foundations, policy instruments, and security implications. For defense communities, these sovereignty dynamics reshape military planning, alliance structures, and deterrence strategies, suggesting that the future security architecture will be determined by how states reconcile autonomy aspirations with the inescapable realities of technological interdependence.
Copyright (c) 2026 Roger Sanz Gonzalez, Claudio Paya Santos, Juan Carlos Fernández-Rodríguez, Neidy Zenaida Domínguez Pineda, Rosa Vera García, Rafael Canorea-García
CYBERCRIME LEGISLATION AND THE PROTECTION AGAINST ONLINE RACISM AND CYBERBULLYING: A COMPARATIVE LEGAL ANALYSIS
The recent exponential increase in the development of digital platforms has increased online racism and cyberbullying, which tend to be inconsistent because they are all types of digital aggression leading to psychological damage, social segregation, and incitement to violence. This comparative legal study explores cybercrime in the Kingdom of Saudi Arabia (KSA), the United Kingdom (UK), and the European Union (EU) through the Digital Services Act (DSA), with a brief US comparison, on safeguarding against online racism (hate speech based on race/ethnicity) and cyberbullying/harassment. The paper is based on the research of Dr. Fahad A. Moafa, such as his comparative typology of cybercrimes in the UK and KSA, models of factors influencing cyberbullying in university students, comparisons of user behavior as a means of prevention. The analysis compares and contrasts definitions of offenses, platform duties, enforcement, and balance with freedom of expression with the help of doctrinal and comparative approaches. The results put emphasis on the criminal sanctioning of KSA in line with Sharia and national security, as compared to the national responsibility of care of the UK and systemic mitigation of risk by the EU. Hybrid models, which combine behavioral prevention and platform accountability, with Arab contexts, are recommended.
Copyright (c) 2026 Hamad Salem Almarri, Fahad Abdullah Moafa
ASSESSING THE EFFECTIVENESS OF SAUDI CYBERSECURITY REGULATIONS IN COMBATING CYBER SEXUAL HARASSMENT
Cyber sexual harassment, which includes unwanted sexual advances, sharing of explicit content, blackmail, and stalking using digital platforms has increased as the Saudi Arabia experiences a fast digital transformation as outlined by Vision 2030. This paper evaluates how Saudi cybersecurity and associated laws, mainly the Anti-Cyber Crime Law (2007) and the Anti-Harassment Law (2018), respond to these harms considering cultural and enforcement issues and processes. The paper assesses legal provisions, enforcement mechanisms, protection of victims, and gaps in dealing with cyber sexual harassment, specifically, using a doctrinal-comparative and qualitative approach. The most important tools are Articles 3 and 6 of the Anti-Cyber Crime Law (invasion of privacy, defamation, intrusion on the public morals) and the general meaning of the Anti-Harassment Law which includes acts done via modern technology. Results show moderate influence of criminalization and public awareness with the support of National Cybersecurity Authority (NCA) campaigns and warnings of Public Prosecution, but there are still weaknesses in the underreporting by cultural shame, difficulties in the evidence in a digital case, the alignment of procedures with general criminal law, and the absence of specific provisions on cyber sexual harassment. It has been recommended to focus on special training, behavioral prevention models, better reporting systems, and possible improvements in the legislature to better delineate and provide support to the victims.
Impact of Information Technology on Teaching Effectiveness and Student Learning Outcomes in Maritime Education
Information technology has become central to maritime education because modern seafarers and maritime professionals must learn in an environment shaped by digital navigation, simulators, learning management systems, remote communication, cyber awareness and data-driven ship operations. This paper examines how information technology influences teaching effectiveness and student learning outcomes in maritime education. The study is framed around three linked dimensions: digital infrastructure and instructional tools, teacher readiness and pedagogical use, and learner engagement and competency development. A descriptive survey-based research design is presented using an illustrative dataset of 150 respondents from maritime education settings. The results suggest that simulator and virtual-reality practice, instructor digital support and learning-management-system access are perceived as the strongest contributors to effective teaching. Correlation and regression summaries indicate a positive association between IT integration, teaching effectiveness and learning outcomes. The findings support the argument that technology alone does not improve maritime learning; improvement depends on pedagogically aligned use, clear learning outcomes, instructor competence, assessment quality and equitable access. The paper concludes that maritime institutions should adopt blended, simulation-rich and outcome-based digital strategies while maintaining the professional judgement, mentoring and safety culture required by maritime training standards.
Copyright (c) 2026 Parag Mehrotra, Ashutosh Apandkar, G. Veera Senthil Kumar
A Predictive Analytics Framework for Data-Driven Sustainability in Reducing Energy Consumption and Carbon Footprint Across Urban Infrastructure
Rapid urbanization has intensified the energy demand and carbon intensity of the built environment, positioning cities at the center of global decarbonization efforts. Although predictive analytics, machine learning, and ubiquitous sensing now generate unprecedented volumes of urban energy data, their translation into measurable reductions in consumption and emissions remain fragmented, opaque, and weakly connected to decision-making. This paper develops an integrative predictive analytics framework that operationalizes data-driven sustainability across urban infrastructure. Grounded in a structured synthesis of recent scholarship on urban building energy modelling, smart-grid analytics, digital twins, and explainable artificial intelligence, the framework is organized as six interdependent layers: data acquisition and sensing, data integration and governance, predictive modelling, explainability and trust, optimization and decision support, and a continuous feedback loop linking analytical outputs to policy and operational action. The framework treats interpretability not as an optional refinement but as a precondition for institutional uptake, embedded carbon accounting within the analytical pipeline rather than appending it downstream and explicitly aligns analytical objectives with the Sustainable Development Goals, particularly those concerning affordable clean energy, sustainable cities, resilient infrastructure, and climate action. A proposed validation design is specified, comprising candidate data sources, a comparative modelling pipeline, an evaluation protocol, and a staged deployment with outcome feedback, so that the conceptual contribution can be empirically tested without recourse to fabricated results. The paper contributes a coherent theoretical scaffolding that connects technical prediction to governance, clarifies persistent barriers related to data quality, interoperability, privacy, and trust, and articulates a research agenda for evidence-based, transparent, and equitable urban decarbonization.
Copyright (c) 2026 Evha Rozario, Shuchita Shahnaz, Foysal Mahmud, Shuchona Malek Orthi, Gazi Touhidul Alam, Ashraful Islam, Subha Shamarukh
A Machine Learning Framework for Dual-Population Sepsis Prediction Using Real and Synthetic Clinical Datasets
Sepsis is a life-threatening medical condition that requires timely diagnosis to reduce morbidity and mortality. However, the development of accurate machine learning models for sepsis prediction is often constrained by limited access to clinical data due to privacy regulations. This study presents a comprehensive machine learning framework for comparative sepsis prediction using both real and synthetic Adult and Neonatal clinical datasets. The proposed framework integrates data preprocessing, synthetic data generation, exploratory data analysis, feature importance analysis, supervised machine learning, and Deep Cross Network (DCN) learning to evaluate the effectiveness of privacy-preserving synthetic datasets for clinical prediction. Ten supervised machine learning algorithms, including Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Extra Trees, Histogram Gradient Boosting, AdaBoost, XGBoost, Multi-Layer Perceptron, and Voting Classifier, were evaluated using Accuracy, Precision, F1-score, Log Loss, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Experimental results demonstrate that ensemble learning methods consistently outperform conventional classifiers across both datasets. For the Adult dataset, the Decision Tree achieved the highest classification accuracy of 98.45%, while the Voting Classifier obtained the highest AUC-ROC of 0.9795. For the Neonatal dataset, XGBoost and Histogram Gradient Boosting achieved 100% Accuracy, Precision, F1-score, and AUC-ROC on the real dataset. Although predictive performance decreased moderately on synthetic datasets, ensemble models such as Voting Classifier, Random Forest, and XGBoost maintained strong discriminative capability, confirming that synthetic data preserve important statistical relationships required for reliable machine learning model development. Furthermore, DCN learning curves demonstrated stable convergence and effective feature representation for both Adult and Neonatal datasets. The findings indicate that privacy-preserving synthetic datasets provide a reliable alternative for early-stage machine learning research, model benchmarking, and clinical decision-support system development while ensuring patient confidentiality.
Copyright (c) 2026 Smitha N, Ganesha G, Tanuja R, Manjula S H
Knee Disease Detection and Severity Classification Using Deep Learning Models: A Vision Transformer Approach
Knee diseases, particularly Knee Osteoarthritis (KOA), represent one of the most prevalent musculoskeletal disorders worldwide, affecting hundreds of millions of individuals and imposing a substantial burden on healthcare systems. Early and accurate diagnosis is paramount for preventing disease progression and enabling timely therapeutic intervention. Conventional diagnostic approaches relying on radiologist interpretation of plain radiographs are inherently subjective, time-consuming, and susceptible to inter-observer variability, necessitating the development of robust automated systems capable of consistent, reproducible, and clinically meaningful assessments. The automated analysis of knee X-ray images presents multifaceted challenges, including significant class imbalance across Kellgren-Lawrence (KL) grading categories, subtle radiographic distinctions between adjacent severity grades, heterogeneity in imaging acquisition protocols, and the limited availability of large-scale annotated clinical datasets. Furthermore, existing deep learning models exhibit limitations in interpretability, which constrains their translational utility in clinical practice. This research proposes a novel deep learning framework integrating Vision Transformer (ViT) architecture with transfer learning and ensemble strategies for automated KOA detection and severity grading. The methodology encompasses comprehensive image preprocessing, augmentation pipelines, patch-based spatial tokenization, multi-head self-attention mechanisms, and a multi-class classification head calibrated for five-grade KL severity assessment. The system is trained and validated on the publicly available Knee Osteoarthritis Dataset (KOA Dataset) sourced from Kaggle, comprising radiographic images representative of all KL grades.Three core algorithmic strategies are employed—Vision Transformer (ViT) for global feature extraction via self-attention, Adaptive Learning Rate Scheduling for optimization stability, and a Focal Loss mechanism for addressing class imbalance. Mathematical formulations are rigorously derived for each algorithmic component. The proposed model achieves a peak validation accuracy of 95.2%, a macro-averaged F1-score of 95.0%, and AUC values exceeding 0.94 across all five KL grades, significantly outperforming ResNet-50, VGG-16, EfficientNet, and baseline ViT configurations. These results demonstrate the superior capacity of the proposed framework to capture fine-grained spatial features critical for reliable KOA severity stratification. The proposed ViT-based deep learning system offers a clinically viable, scalable, and highly accurate solution for automated knee disease diagnosis. It constitutes a significant contribution toward AI-assisted orthopedic radiology, with potential for direct deployment in clinical decision support systems and telemedicine platforms.
Copyright (c) 2026 Nilesh Goriya, Ajay Upadhyaya
Retrieval of Time of Death Basing on Forensic Entomology through Digital Imaging
Digital investigation is one of innovative enhance in the field of forensic science. Investigating a crime scene is very crucial and challenging for both the police and investigating departments. As a novel solution for manual intervention, out research embellishes various studies and observations during the investigation. To digitize the investigations process, research motivated us biologically to present the simulation of various events present at the crime scene. The approach started with the bodily fluids, blood pooling, surface of the body lay down, oxidization after death ,skin tones observed and finally Sarcophagid flies. As a part of our research we demonstrate different approaches on various models to compare the real world photographic examples.
Copyright (c) 2026 N. Sharmili, Mettu Sumender Roy, Parameshwari M. Goud, Yerramsetti Srinivasarao, M. V. Rajesh, N. Lakshmi Devi, Kumar Devapogu, Subba Rao Polamuri
QPSO-LF-MOBO: A Quantum-Inspired Evolutionary Deep Learning Framework for ALL Detection and Localization in Digital Pathology Images
Acute Lymphoblastic Leukemia (ALL) diagnosis is hindered by high inter-observer variability (up to 23%), long processing times (6–8 hours), and limited clinical reproducibility. This paper proposes QPSO-LF-MOBO, a quantum-inspired evolutionary neural architecture integrating Capsule Networks, Quantum-Inspired Particle Swarm Optimization with Lévy Flight (QPSO-LF), Multi-Objective Bayesian Optimization (MOBO) with Expected Hypervolume Improvement (EHVI), and Swin Transformer-based cross-attention for joint hierarchical classification and precise localization of leukemic blast cells in digital pathology images. The framework simultaneously optimizes accuracy, inference speed, and model complexity through a unified Pareto-optimal pipeline, further enhanced by contrastive learning and Monte Carlo Dropout-based uncertainty quantification for reliable clinical decision support. Evaluated on four benchmark datasets (ALL-IDB1, ALL-IDB2, C-NMC, and LISC), QPSO-LF-MOBO achieves 99.2% accuracy, 98.9% F1-score, 99.3% specificity, and an IoU of 0.972, with a 3.4× FLOPS improvement over 15 state-of-the-art methods while operating at real-time speed (42.3 FPS), making it a robust and deployable solution for clinical ALL diagnosis.
Copyright (c) 2026 Samah Hussein Mohsin, Gangadhara Rao Kancharla
Res-Caps: A Modified Capsule Network for Ultra Sonic Image Classification and Detection of Chronic Cysts
persons with renal stones have significant health issues when they are not treated properly in time. They face a potential pain and many complications in their daily routine. Early detection in time prevents the disease and circumstances and some time even more complicated. These study outcomes the automated detection methodologies for renal stones through analyzing the medical images which undergo various image processing methods. Irregular, small uneven shaped stones can be well determined. Res-Caps, a innovative enhanced capsule network, applied for classifying cysts characteristics in ultrasonic images, demonstrating improved classification accuracy compared to standard capsule networks. The analyses described here provide insight into the functioning of capsule networks and demonstrate their potential advantages over traditional convolution neural networks. The capacity of capsule networks to represent and encode data across vector components. Object embodiment parameters via optical transformations represent a significant advancement over current models in networks. Our implementation methodology gained accuracy of 96.3%, precision 95.8%, recall 96.7%, and an F1 score of 96.2%. The best part is that it can accurately change normal renal anatomy when renal stones are present. These metrics prove that the system could be a useful diagnostic tool in medical imaging.
Copyright (c) 2026 Mahendra Narla, Gottapu Sankar Rao, K. Anand Kumar, V. N. S. Vijay Kumar, M. V. Rajesh, N. Lakshmi Devi, Subba Rao Polamuri
Multilingual Natural Language Prompts and Code Generation: A Study on Large Language Model Cross-Linguistic Performance
Large Language Models (LLMs) have become the most powerful technique for code generation and have profound impact on contemporary software development process. Moreover, while existing GPT models perform well in translating natural language prompts into executable code, their behavior when prompted with input in multiple lan- guages has not been sufficiently studied till now, even though developers around the world speak and write in many different tongues. It is crucial to understand such a behavior in order to achieve fair AI-aided programming. Most prior work centers on English or limited bilingual studies, leaving uncertainty about how language influences code quality and efficiency. In this paper, we examine GPT-4.5’s cross-lingual perfor- mance in Python code generation. Our multilingual bench- mark includes 30 algorithmic tasks across six computer science domains, tested in 30 languages. We measure execution time, efficiency and accuracy using Language Efficiency Score (LES) and Code Efficiency Index (CEI) and supported by clustering and correlation analysis .We have considered 900 samples, efficiency and execution stability showes a strong correlation (r > 0.92), while prompt length has little impact.The Natural Languages Tamil, Ukrainian, and Japanese yield the most efficient code, whereas English, Persian, and Mandarin produce d longer, slower scripts. Our results proved that prompt language(Natural Language) matters in LLM code generation, emphasizing the importance of multilingual-aware prompt engineering for efficiency and robustness in real-world software development process.
Copyright (c) 2026 Madhuri Nakkella, B. Manikyala Rao, Neti Praveen, Ranjith Kumar Chinnam, Dhanshree Mukund Pande, Prabhakararao Kolli, Kumar Devapogu, M. V. Rajesh
FINANCIAL ANTECEDENTS AND RETAIL INVESTORS INTENTION TOWARDS EXCHANGE TRADED FUNDS: AN EMPIRICAL INVESTIGATION FROM INDIA
Exchange-Traded Funds (ETFs) have become an increasingly attractive investment option for retail investors in India; however, retail participation remains comparatively low. This study examines whether advertisement informativeness, involvement, credibility, and truthfulness influence investors' intention towards ETFs. Data were collected from 507 retail investors using a structured questionnaire. Reliability was assessed through Cronbach's alpha, and the proposed relationships were analysed using Pearson correlation, multiple regression, hierarchical regression, and Importance–Performance Map Analysis (IPMA). Based on the results, each of the four main categories of advertisement antecedents has an overall positive effect on the intention to invest. Of these antecedents, informativeness has the greatest influence on investment intention, followed by truthfulness, credibility and involvement. The model accounted for 60.4% of the variance in intention to invest. In addition, results from hierarchical regression analyses indicated that factors related to advertisement had greater explanatory power for investment intentions than demographic factors. IPMA identified informativeness and truthfulness as priority areas for improvement. The findings provide useful insights for asset management companies seeking to design more informative, credible, and transparent ETF advertisements.
Copyright (c) 2026 Priyanka Joe, Ganesraman K.
ANN-Based Early Plant Disease Detection for Precision Crop Management in India
Agriculture is a very important part of our human society. In India, many people think of it as the backbone of the economy because it creates a lot of jobs. It not only protects food, but it also helps the economy stay strong. Food and the economy are very tightly related in some ways; thus, it is really important to keep farming going. But farming is not perfect. Like different industries have different issues same as agricultural field also have the issues related to the crops. One of the main issues in agriculture is keeping crops and plants healthy. Fungi, bacteria, viruses, nematodes, pests, and even severe weather can all make crops plants sick. Finding and treating diseases in agriculture is still a difficult and time-consuming task. With help of image processing techniques farmers can detect plant disease easily. For early detection and management researchers uses different techniques. Here in this research we have used some machine learning techniques like MobileNetV3, DenseNet201, VGG19 and ANN. We got an accuracy of 98% with DenseNet201.
Copyright (c) 2026 Shikha Singh, Rahul Kumar Mishra, Arvind Kumar Shukla
Dragonfruit Stem Health Classification with Deep Learing and Attention Mechanisms
Plant disease detection is important for maintaining the health and quality of crop yields. For the detection of health problems in images of dragon-fruit (Hylocereus) stems, we present a deep learning architecture that is aug-mented by the application of spatial, channel, and domain-specific attention mechanisms. For the purpose of improving accuracy and robustness, the model architecture is combined using features from ResNet50 and EfficientNetB0 back-bones, along with separate attention branches. Training and testing were per-formed using a proprietary dataset of images of dragonfruit stems that are healthy as well as showing various levels of disease. The model initially used frozen fea-ture extractors for training, which were subsequently fine-tuned to improve over-all performance. Experimental results provide high accuracy for classification, with ROC-AUC values above 0.94 for all classes. The proposed method enables precision agriculture operations by providing a robust method for early detection of dragonfruit diseases. Future work will include adapting the model to real-world field images and further optimising it for application in agricultural moni-toring systems.
Copyright (c) 2026 Ashwini Prasad S, Uma S, Sheba Pari N
An Explainable Lightweight Deep Learning Framework for Early Diabetes Risk Prediction Using Clinical Dataset and Hybrid Optimization Techniques
Early diabetes risk identification is vital for preventing progression to disease and managing long-term complications of health care. However, many existing AI risk prediction models suffer from poor generalization due to several issues; class imbalance, irrelevant clinical features, lack of explainability, high computational complexity, and inadequate validation on heterogeneous patient data sets. This research examines these issues utilizing the Early Stage Diabetes Risk Prediction Dataset 2025, which contains demographic, clinical, and symptom attributes that can be used for early diabetes risk screening purposes. The framework includes missing value imputation, z-score normalization, data augmentation using Conditional Tabular Generative Adversarial Network (CTGANs), mutual information and chi-square feature selection, hybrid optimization methods, and Integrated Gradient-based methods for providing explanation of predictions. Classification is performed using a low computational complexity convolutional model, Lightweight Convolutional Integrated Gradients-Griffon Vultures Optimization Algorithm and Revolution Optimization Algorithm (LwCIG-GVROA). Experimental evaluation resulted in 95.20% accuracy, 96.22% precision, 97.12% recall and 99.65% F1 score; indicating that this approach to early diabetes risk prediction has good reliability, computational efficiency, and clinical interpretability.
Copyright (c) 2026 S. Sai Prakash, A. C. Subhajini
Competitive Multi-Class Alzheimer’s Disease Classification Using a Hybrid GAN–DWT–CNN Model on MRI Data
To diagnose AD as it advances to a third pathological stage‚ MRI images of the brain still suffer from accuracy issues․ Subtle structure changes occur even at such an early stage of the disorder that they can barely be detected with existing technology․ In order to conduct multi-classification of Alzheimer disease cases from MRI images‚ this paper proposes an automatic diagnosis system of Alzheimer's based on Generative Adversarial Nets (GANs) and Discrete Wavelet Transform (DWT)․ Significantly as well as increasing the number of classes‚ has it been able to do this․ In addition to the data imbalance problem and the classification quality of deep learning algorithms‚ GAN-based augmentation can tackle changed scale of the features․ The DWT can also improve the discrimination power of multiresolution features extracted from MRI images classified by a deep learning model based on the four-class classification problem: Non-Demented‚ Very Mild Demented‚ Mild Demented‚ and Moderate Demented subjects․ The model achieved a precision of 92 percent for overall classification accuracy‚ and 91% when computing the macro-averaged F1-score across all classes․ What should be borne in mind is that the proposed framework has high recall on early stage patients (where other methods are difficult to apply)‚ which shows off its ability more․ The stability of the convergence of the training process of the model‚ together with the results of the confusion matrices‚ confirms the robustness of the model's use․ The stable process if training of the model together with analysis for the confusion matrices further confirms the stability of the proposed model and its training process․ The results of the experiments show that for diagnosing Alzheimer's disease‚ GAN-based augmentation and DWT feature extraction can provide a very important performance improvement‚ and that‚ overall‚ we can therefore believe that it is feasible to use the proposed framework as a computer-helped diagnosis tool in the clinical care of sufferers from this dread disease․
Copyright (c) 2026 Ali Kareem Obaid, Yaghoub Farjami
Predicting Voting Behavior in Online Social Platforms Using Machine Learning
Modeling voting behavior in online social networks is challenging because of the visibility of prior votes and the ability to view and respond to others' voting choices. This paper uses the Wikipedia Request for Adminship (RFA), in which the task is formulated as a binary classification problem, where voters cast ballots either supporting or opposing a nominated administrator candidate. Features tested include behavioral history, sentiment derived from comments, graph-based characteristics, and temporal herding. Three machine learning algorithms were tested, namely Logistic Regression, Random Forest, and XGBoost. Behavioral features alone provide limited predictive performance. Adding sentiment features did not lead to a noticeable improvement. However, Adding sentiment and graph-based features resulted in only marginal improvements.The top-performing model using the full feature set was XGBoost, achieving an Accuracy of 84.01%, a Macro F1-Score of 77.24%, and a Precision-Recall Area Under Curve (PR-AUC) of 95.82%. These results show that overall, voters make their voting decisions based on previous voting decisions from other voters as well as the characteristics of the specific voter making that decision.
Copyright (c) 2026 Nour Alhuda Alhameedi, Saba M. Hussain
EFFICIENT NETB3-POWERED DEEP LEARNING ARCHITECTURE FOR AUTOMATED SKIN DISEASE IDENTIFICATION IN CLINICAL APPLICATIONS
Skin diseases represent a major global healthcare challenge, affecting millions of individuals and often requiring timely diagnosis to prevent severe complications. Traditional dermatological diagnosis relies heavily on expert clinical assessment, which can be time-consuming, subjective, and inaccessible in resource-constrained regions. Recent advances in artificial intelligence and deep learning have demonstrated significant potential for automated skin disease detection using dermoscopic and clinical images. This study proposes an Advanced EfficientNetB3-Based Deep Transfer Learning Architecture for accurate and robust skin disease classification. The proposed framework leverages the EfficientNetB3 convolutional neural network as a feature extraction backbone, benefiting from its optimized compound scaling strategy that balances network depth, width, and resolution while maintaining computational efficiency. Transfer learning is employed by initializing the model with pre-trained ImageNet weights, enabling effective knowledge transfer and reducing training time. To enhance classification performance, advanced data preprocessing, image augmentation, batch normalization, dropout regularization, and fine-tuning strategies are incorporated. The architecture is evaluated on a benchmark skin disease image dataset containing multiple dermatological categories, including melanoma, eczema, psoriasis, acne, and benign lesions. Experimental results demonstrate superior classification accuracy, precision, recall, F1-score, and area under the ROC curve compared with conventional convolutional neural networks and existing transfer learning models. The proposed model achieves improved generalization capability while reducing computational complexity, making it suitable for real-world clinical deployment and mobile healthcare applications. The findings indicate that EfficientNetB3-based deep transfer learning can serve as an effective decision-support tool for dermatologists, facilitating early detection, accurate diagnosis, and improved patient outcomes in skin disease management.
Copyright (c) 2026 Muthupandian V., Bindushree K., A. Ranjini, Anishmija S. L., Yashaswini S., Madhusudhan M., Sivalingam T., M. Deepa
High-Resolution Short-Range Weather Forecasting Using Data Driven BDCL-Net Forecasting Model
Recent developments in computational techniques have significantly increased interest in probabilistic weather forecasting. Despite these advancements, accurately predicting weather remains a complex task because atmospheric processes are highly nonlinear and involve numerous interacting variables. Even minor variations in the initial atmospheric conditions can result in considerable differences in forecast outcomes, making reliable prediction a challenging problem. Conventional forecasting approaches, including persistence methods, climatology-based models, linear regression, Markov models, and Auto-Regressive Integrated Moving Average (ARIMA), have demonstrated limited effectiveness in capturing the complex temporal relationships present in weather data. Artificial intelligence (AI)-based methods have surfaced as a viable substitute for weather prediction in order to overcome these constraints. These approaches are capable of learning complex nonlinear patterns directly from historical observations while providing increased computational effectiveness and forecasting accuracy. Motivated by these advantages, the present research proposes a deep learning-based forecasting framework trained on real-world meteorological datasets. The proposed Bidirectional Drop Connect-Regularized LSTM Network (BDCL-Net) has been developed using meteorological information gathered from Jaipur, Delhi, and Chandigarh. The effectiveness of the proposed model is assessed through comprehensive experimental evaluation using multiple performance measures, including the Coefficient of Determination (R²), Mean Squared Error (MSE), and Root Mean Square Error (RMSE), to determine its forecasting accuracy and overall predictive capability.
Copyright (c) 2026 Suman Lata, Amit Verma
Blockchain-Based Multi-Chain Framework for Secure Medicine Supply Chain Management
The pharmaceutical supply chain operates as a multi-stakeholder network where maintaining visibility, authenticity, and secure data exchange is essential at every stage. Continuous monitoring is required to ensure quality and regulatory for drug manufacturing, storage and final distribution. Existing supply chain models rely heavily on centralized databases and manual documentation, which often leads to data in-consistencies, delayed audits, and increased exposure to counterfeit products. This study introduces a decentralized pharmaceutical traceability system based on a multi-chain blockchain architecture supported by a Django backend. The proposed framework segregates distributor and customer data across independent blockchain ledgers, enhancing transparency while preserving data privacy. Each ledger is secured using SHA-256 cryptographic hashing combined with proof-of-work consensus to ensure tamper resistance. Cold Chain Event Integrity Framework (CCEIF) is incorporated to monitor logistics conditions. It is integrated with QR-based batch verification with real-time IoT sensor data. This enables continuous tracking of drug movement and environmental parameters such as temperature and humidity, while sensor-triggered alerts allow immediate corrective actions when deviations occur. The system employs a role-based access mechanism with customized dashboards that facilitate secure communication among manufacturers, suppliers, retailers, and regulatory authorities. Django enables efficient user management, secure APIs, and automated compliance reporting. Validation through simulated pharmaceutical logistics scenarios demonstrated robust data immutability, secure multi-stage traceability, and alignment with global standards, including World Health Organization (WHO) guidelines and Health Insurance Portability and Accountability Act (HIPAA). The framework offers a scalable and dependable solution for enhancing pharmaceutical supply chain integrity across networks.
Copyright (c) 2026 Bidyutmala Saha, Arun Kumar Majumdar, Debapriya Roy
From Molecules to Medicines: The Role of Artificial Intelligence in Next-Generation Pharmaceutical Systems
Artificial Intelligence (AI) is transforming the pharmaceutical sector by accelerating drug discovery, improving predictive modelling, and optimising manufacturing processes. Traditional drug development is slow, costly, and associated with high failure rates due to complex biological systems and limited predictive accuracy. AI-based approaches, including machine learning and deep learning, enable rapid analysis of large biomedical datasets, facilitating efficient target identification, drug design, toxicity prediction, and clinical trial optimisation. These technologies also support advanced applications such as virtual screening, generative molecular design, and real-time process control in pharmaceutical manufacturing. Despite these advantages, the integration of AI introduces challenges related to data quality, model transparency, cybersecurity risks, and regulatory compliance. Ensuring robust validation, ethical governance, and secure digital infrastructure is essential for safe deployment in healthcare systems. In addition, AI should function as a decision-support tool that complements rather than replaces human expertise in pharmaceutical research and development. AI represents a paradigm shift in modern drug development by improving efficiency, reducing costs, and enhancing innovation potential. With appropriate safeguards and regulatory oversight, it can significantly strengthen the pharmaceutical pipeline and contribute to more effective and timely delivery of therapeutics.
Copyright (c) 2026 Satish Singh Chandra, Manish Bhardwaj