International Journal of Computer Information Systems and Industrial Management Applications
Halicon Publication is solely responsible for the peer review, publication, and indexing-related work of the International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM, ISSN 2150-7988).
Volatility Analysis and Persistence in an ESG Mutual Fund: E-GARCH Evidence from the Quantum ESG Best in Class Strategy Fund
This paper examines the return behavior, long-run equilibrium relationships and volatility dynamics of the Quantum ESG Best-in-Class Strategy Fund, an Indian equities mutual fund with an Environmental, Social and Governance (ESG) theme, using daily Net Asset Value (NAV) data from the fund’s launch to December 31, 2025. Daily returns are calculated and standard time series diagnostics are used. A unit-root test confirms the stationarity of the returns and the non-stationarity of the level series.Johansen cointegration tests are used to look at long-term connections between: (i) The return on an ESG fund and the general market return (BSE return); (ii) The return on an ESG fund and a competitor peer fund of the same AMC having highest AUM i.e Quantum Value Fund. Finally, conditional volatilities and the performance of ESG funds compared to the BSE ESG index and peer fund are analyzed using EGARCH (calculated in Eviews). The research extends the limited work in India on cointegration and asymmetric volatility modeling of ESG mutual funds, and it provides a useful resource for managers and investors making ESG strategy valuation and investment decisions in emerging nations.
IMPROVED MULTILINGUAL HATE SPEECH DETECTION THROUGH INDEXED COPHENETIC MULTILAYEREDEXTREME MACHINE RELYING ON N-GRAMS AND HIDDEN MARKOV MODEL
This study consider hate speech detection in multiple languages such as English, Tamil, and Tanglish by achieving higher accuracy, precision, recall, and minimal training time utilizing machine learning based Natural Language Processing (NLP) techniques. Proposed Indexed Cophenetic Multilayered Extreme Learning based on N-Grams and Hidden Markov (ICMEL-NGHM) technique introduced for achieving multilingual hate speech detection. Proposed technique and two existing methods are compared for effectiveness in hate speech identification with diverse metrics. Accuracy of proposed technique for detecting hate speech in English, Tamil, and Tanglish classes is 97.89%, 97.32%, and 96.34%,. Precision of proposed technique in detecting hate speech for English, Tamil, and Tanglish classes is 97.34%, 95.34%, and 94.32%. For English, Tamil, and Tanglish groups, Recall in identifying hate speech of proposed ICMEL-NGHM is 99.23%, 96.34%, and 98.23%. Proposed technique detects hate speech in 12.35 ms for English class, 10.55 ms for Tamil class, and 9.45 ms for Tanglish class. Results of proposed technique is better than state-of-the-art approaches.
Copyright (c) 2026 N Zahira Jahan, R Pushpalatha
A Multimodal Biosignal Fusion Framework for Epileptic Seizure Detection Using Machine Learning, Ensemble Learning, and Optimization
Epileptic seizure detection requires reliable analysis of complex physiological changes that may not be adequately represented by a single biosignal. This study developed an optimized multimodal biosignal fusion framework integrating electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), and accelerometer (ACC) signals for automated seizure detection. Data from 120 patients were processed using data quality assurance, preprocessing, feature extraction, and patient-wise partitioning to minimize data leakage. A parameterized quadratic fusion framework was applied to capture complementary and nonlinear cross-modal interactions and evaluated using twelve traditional machine-learning and deep-learning models. Particle Swarm Optimization (PSO) was employed for joint optimization of model and fusion parameters, while SHAP and PCA supported model interpretation. In addition, ensemble-learning strategies were tested for robustness and generalizability. Full quasi-quadratic fusion achieved 98.10% accuracy, 97.40% F1-score, and 98.90% AUC, while full EEG–ECG–EMG–ACC fusion achieved 99.67% accuracy and 99.79% AUC. The smallest generalization gap (ΔAUC = 0.60 percentage points) was also obtained from the fusion-aware ensemble. In summary, the results presented here demonstrate that fundamental techniques of quadratic multimodal fusion and optimization methods in machine learning for explainable ensemble approaches, when applied together, can afford an accurate, interpretable, and well-generalized fail-safe framework for multimodal automated epileptic seizure detection.
Copyright (c) 2026 Abdul-Mumin Khalid, Musah Sulemana, Wahab Abdul Iddrisu
Explainable AI for IoT-Driven Decision Support: Building Trust in Human-AI Collaboration via Deep Learning Interpretability
This study was focused on the development and assessment of an explainable artificial intelligence (XAI) system for an Internet of Things (IoT) based decision support system, not only in terms of prediction accuracy, but also in how it impacts human-AI collaboration.A computational experimental methodology was used in which, IoT sensor data were gathered, cleaned, normalized, treated for missing values, and selected features, and then partitioned into training, validation, and testing datasets.A deep learning model was developed to make predictions that supported decision making; subsequently, model agnostic explanation techniques were embedded, post hoc, to uncover and feed-back the most salient features that lead to each prediction.Predictive performance was assessed using accuracy, precision, recall, F1-score and area under the receiver operating characteristic curve compared to an existing deep learning model without explanatory mechanisms.The consistency and the relevance of generated explanations were then evaluated and 160 participants were then exposed to AI-driven decisions with or without explanations to test the impact on trust, perceived understanding, decision confidence, and willingness to trust the system.The findings showed that the explainable model can predict as well as the conventional model, the explanations produced by the two different interpretability techniques were very similar, and participants who received the explanations of the decisions they made were significantly more confident, understood, trusted and willing to rely on the decision made by the explainable model than those who were not given the explanations.Further, the quality of explanations was also determined to be significantly and positively correlated with user trust.The results indicate that interpretability can be integrated into the deep learning-based decision support for IoT without compromising its predictive power and effectively enhance the integration between humans and AI.
Copyright (c) 2026 Faisal Rahman, Ayesha Anzer, Ameer Hamza Nawaz, Zahoor Ahmed, Hafiz Tasawar Hussain, Shoaib Hayat, Zaid Wali, Nazia Azim
This study was focused on the development and assessment of an explainable artificial intelligence (XAI) system for an Internet of Things (IoT) based decision support system, not only in terms of prediction accuracy, but also in how it impacts human-AI collaboration. A computational experimental methodology was used in which, IoT sensor data were gathered, cleaned, normalized, treated for missing values, and selected features, and then partitioned into training, validation, and testing datasets. A deep learning model was developed to make predictions that supported decision making; subsequently, model agnostic explanation techniques were embedded, post hoc, to uncover and feed-back the most salient features that lead to each prediction. Predictive performance was assessed using accuracy, precision, recall, F1-score and area under the receiver operating characteristic curve compared to an existing deep learning model without explanatory mechanisms. The consistency and the relevance of generated explanations were then evaluated and 160 participants were then exposed to AI-driven decisions with or without explanations to test the impact on trust, perceived understanding, decision confidence, and willingness to trust the system. The findings showed that the explainable model can predict as well as the conventional model, the explanations produced by the two different interpretability techniques were very similar, and participants who received the explanations of the decisions they made were significantly more confident, understood, trusted and willing to rely on the decision made by the explainable model than those who were not given the explanations. Further, the quality of explanations was also determined to be significantly and positively correlated with user trust. The results indicate that interpretability can be integrated into the deep learning-based decision support for IoT without compromising its predictive power and effectively enhance the integration between humans and AI.
Comparative Analysis of Existing UNet-Based Medical Image Segmentation Models and an Optimized ResAttTransUNet Framework
Despite the popularity of medical image segmentation in computer-aided diagnosis (CAD), the choice of an effective architecture for deep learning is still a difficult task as the datasets, network architectures, activation functions and the assessment approaches are different. In this work we compare in detail the segmentation models based on UNet recently developed and we propose a new architecture based on UNet, called ResAttTransUNet, for improving the segmentation accuracy, robustness and generalization across several imaging modalities. In the proposed model, the UNet architecture is used to integrate the residual blocks, dual attention mechanisms and transformer bridge, while the effects of GELU, PReLU and Leaky ReLU activation functions are evaluated. A set of experiments were performed on three benchmark datasets, namely the brain MRI dataset, BRATS2020, the liver CT data, LiTS17, and the breast ultrasound data, BUSI, with a common experimental setup. The Dice coefficient, Intersection over Union (IoU), qualitative analysis, ablation studies, and comparative evaluation were used to evaluate the performance. The results show that GELU always performed best in all datasets. The proposed ResAttTransUNet achieved the best performance on LiTS17 with Dice score and IoU of 0.9619 and 0.9299, respectively, and 0.8278/0.7470 on BRATS2020 and 0.7466/0.6074 on BUSI. The proposed method shows an excellent performance in segmenting multimodal medical images and also highlights its segmentation accuracy and generalization capabilities over the existing state-of-the-art methods, by making optimal use of the residual learning, attention mechanism, transformer-based modeling, and optimization of the activation functions.
Copyright (c) 2026 Swapnil Justin, Akhilesh A. Waoo
Agentic Business Intelligence Architecture for Autonomous Data Exploration, Insight Generation, and Strategic Decision Orchestration
Business Intelligence (BI) is critical for supporting tactical, operational, and strategic decision-making in enterprises. Traditionally, BI solutions have focused on creating data use dictionaries that allow decision leaders to collect and reference those data narratives that are most suited to their decision making. Contemporary digital business ecosystems generate immense data streams, and the creation of dictionaries for these streams is impractical and insufficient. Autonomous BI that allows autonomous data exploration and narrative generation is therefore a necessity. To meet this need, the Decision-Orchestration Adaptive Business Intelligence (DOABi) architecture enables the formation of autonomous decision-supporting BI nodes. These nodes connect to existing data streams with little or no dependence on human or administrative processing. Decisions needing support are inquired through BI agent entities that serve the interest of the decision-maker. A formal exploration model specifies the autonomy and supporting mechanisms of such service providers. These service BI agents operate at three cognitive levels and can ingest and explore data under a controlled-adaptive mechanism. The architecture is supported by an illustrative live enterprise case study and is further strengthened by literature analysis and industrial, academic, and entrepreneurship expert opinions. Data exploration is not new to data science. Making discovery easier, faster, and more fully realized is the question of the day. Meyer et al. [2018] highlight the shortcomings of traditional BI systems, which have focused on creating use dictionaries for data resources. Data streams generated by information systems, IoT devices, and social media, along with imagery and video captured by drones and mobile devices, evolve too rapidly for such dictionaries to be a fitting solution. The exploration of such uncharted data sources is better left to intelligent agents and the AI behind them. In a framework for autonomous business decision orchestration, autonomous exploration is a major enabler.
Evaluting BERT and Neural Network Approaches for Tweet Sentiment Analysis
Deep Learning is being explored as a prominent tool in machine learning in the current era by researchers to a great extent. A lot of work being done in this area is opening avenues for consumers. Utilization and growth of Artificial Intelligence have leveraged the power of deep learning for its applicability. Since its inception, a lot of algorithms have been proposed and implemented in the area of deep learning. In this paper, BERT (Bidirectional Encoder Representations from Transformers) a model of Google, LSTM, DistilBERT and CNN models have been discussed. Deep learning models have a major challenge of low performance, which restricts their use in real-time applications. In this work, BERT, LSTM, DistilBERT and CNN have been implemented separately using the same dataset of Twitter to perform sentiment analysis and compared their performance and accuracies as a first step to make a new model that will not only provide good accuracy but will also have better accuracy in future.
Copyright (c) 2026 Amit Kumar Yadav, Mukta Bhatele, Akhilesh A. Waoo
From Followers to Traders: Finfluencer Credibility, Digital Trust, and Investment Decision-Making among Retail Investors
Purpose: The purpose of this study is to explore how financial content creators and social media influencers impact individual trading behaviours, especially among youth, by shaping risk perceptions, promoting online trading and betting apps, and influencing impulsive financial decisions through unverified advice, potentially increasing vulnerability to scams and financial missteps. Design/Methodology/Approach: The study adopts a quantitative approach using primary data from 237 young individual traders and investors active on platforms like YouTube and Telegram. Responses were collected through purposive sampling. Data analysis was conducted using SPSS and AMOS, employing descriptive statistics, correlation, regression, factor analysis, and Structural Equation Modeling (SEM). Findings: The findings reveal that frequent exposure to financial influencers on YouTube and Telegram significantly impacts individual trading decisions. Influencer promotion of betting/trading apps increases the likelihood of investing in high-risk platforms. Trust in unverified social media financial advice strongly correlates with impulsive, often misinformed, trading and betting behaviour among youth. Conclusion: The study concludes that financial content creators and social media influencers significantly shape individual trading behaviours, especially among youth. Their promotions drive risk-taking and susceptibility to scams. Psychological factors like trust and frequent exposure to unverified advice contribute to impulsive and misinformed financial decisions, highlighting the need for regulatory oversight and financial literacy. Originality/Value: This study provides valuable insights into the influence of financial influencers on individual traders' investment behaviours, particularly among youth. It highlights the psychological and behavioural factors driving impulsive decisions, offering critical implications for financial literacy, regulation, and investor protection strategies.
Copyright (c) 2026 Vodnala Shivalingam, M. Gangadhar, Veeragantala Vinay
Strengthening Digital Security Resilience: A Framework Based on Cybersecurity, Data Privacy, and Cyber Governance
Digital Security Resilience (DSR) shines a spotlight on its importance as the digital technologies continue to evolve and grow, providing organizations with additional opportunities to address cyber threats, data breach and privacy concerns. The current study explores how important the practice of Cyber security (CSP), Data privacy protection (DPP), Cyber Governance Framework (CGF) and Cyber security Awareness (CSA) are for Digital Security Resilience (DSR) in India. The quantitative approach was taken, where the data collected from 486 respondents which include students, IT professionals, academicians, research scientist and employees from different sectors and adopted both Protection Motivation Theory and Socio-Technical Systems Theory. The proposed relationships were tested with the software Partial Least Squares Structural Equation Modeling (PLS-SEM) version 3 (SmartPLS). The findings reveal the Cybersecurity Awareness's effects on Digital Security Resilience, with Cybersecurity Practices and Data Privacy Protection coming next. But the effect of Cyber Governance Framework was not statistically significant. However, this accounts for 41.9% of the variance of Digital Security Resilience; it emphasises the critical need to meld technology, privacy and consciousness into all elements of digital systems, which have strong implications for policy makers and organisations as they strive to develop digital economies.
The AI-Driven Development Lifecycle: Replacing Over-Planning with Rapid Iteration and Agentic Execution
The fast development of AI has provided opportunities to reshape the traditional approaches to software engineering beyond the automation of tasks. The Software Development Lifecycle (SDLC) practices that have existed in the past are still based on massive planning, orderly sequential execution, and decision-making processes, which lead to sluggish delivery and adaptation capabilities. This study presents the AI-Driven Development Lifecycle (AI-DLC), a new approach to software development that substitutes planning-filled processes with fast development and agentic behavior. The suggested model introduces an AI maturity spectrum that includes traditional SDLC, AI-assisted development, AI-driven development and AI-managed development models. Moreover, AI-DLC includes five-stage lifecycle that includes business intent capture, collected contextual knowledge, AI-supported planning, deployment, and continuous improvement. It suggests a multi-agent architecture, which combines design, development, and security, testing, DevOps, and mail industry-specific agents. The framework provides a human-AI operating model wherein human controls are made in strategic decisions but operational processes are run through AI. The assessment system evaluates the speed of delivery, efficiency in engineering, preparedness, software reliability and automation potential. The study is the base of the future self-governing software engineering environments.
THE DATA CONTRACT THAT SAVED MY PIPELINES: ENFORCING THE OPEN DATA CONTRACT STANDARD FOR RELIABLE CROSS-TEAM DATA PIPELINES
This study discusses the failure mode where the pipeline's successful completion and no meaningful output is produced due to schema modifications at the upstream that are not poised to be detected by a downstream system. The structural issue is that it is architecturally flawed in decentralized data platforms more generally, the agreement between a producing team with its consumers teams who consume that data is encoded in a place where a machine can verify. The paper suggests integrating that rather informal contract by a formal, versioned, machine-readable document, and considers the Open Data Contract Standard (ODCS), the YAML document standard under the management of the Bitol Project of the Linux Foundation, as the means. It is a typical ODCS v3 contract for a daily orders dataset that is designed and annotated that tends to combine schema, data quality rules such as domain business logic, and service-level properties and ownership into a single document to be reviewed. The following two-gate enforcement architecture is then discussed: structural gate running in continuous integration at the pull-request time, and behavioral gate running on the same data between transformation and publication that is materialized. Experience with adoption of production changes suggests that an incidence of breaking-change moves to producer build-time and that dataset onboarding effort declines, and that quality responsibility moves to the producing realm. Boundary situations where contract overhead cannot be justified are determined and a minimum adoption way has been suggested.
Copyright (c) 2026 Santosh Nagnath Mehtre
Generalized Logarithmic Similarity Measure for Intuitionistic Fuzzy Sets and Applications for Medical Diagnosis, Pattern Recognition and Decision-Making
Intuitionistic fuzzy set theory is a modified version of fuzzy set theory, first introduced by Atanassov. The studies show that several similarity measures between Intuitionistic fuzzy sets are obtained, some of which are inappropriate for use in the current situation. In this study, we will create a novel and adaptable approach under the Intuitionistic fuzzy environment for investigating the decision-making process. Additionally, it suggested new logarithmic similarity and weight similarity measures under Intuitionistic fuzzy sets and clarified their validity. The proposed measure satisfies several exquisite features, allowing for implementation in various domains. The proposed similarity measure has been offered for performance in pattern recognition and medical diagnosis problem with several illustrative examples. Some unexpected scenarios have been discussed and analysed by using existing similarity measures. Using an example, illustrate the comparison analysis's effectiveness and adaptability. The study findings might offer vital information to the required stakeholders in various disciplines. A graph of the developed study is displayed to illustrate the challenges in medical diagnosis between patients and diseases. Finally, we demonstrated the MADM method for enhancing the college selection problem by drawing numerical illustration and making a graphical representation between them.
Copyright (c) 2026 Manish Kumar, Vijay Prakash Tomar, Pawan Gora, Bhagwan Dass
Vision-Based Hand Gesture Keyboard for Game Control Using OpenCV
A vision-based system is designed to enable real-time hand gesture control in gaming, without any need for physical accessories. The system uses a standard webcam to record and track hand gestures that mimic steering-wheel movements. A virtual steering indicator is also displayed on the screen, which rotates based on hand tilt to reflect direction and intensity of steering. These gestures are mapped to keyboard keys such as W (acceleration), A (left), D (right), and in certain configurations, S (reverse). The system is developed entirely in Python, using OpenCV for real-time image processing and MediaPipe for precise 21-point hand landmark detection. By programmatically simulating keyboard events, PyAutoGUI enables interaction with almost any PC game that supports conventional keyboard input. The system is able to recognize gestures with low latency and does not require any external equipment or integration with the game. To establish an initial steering reference, users align their hands in a straight position during a short calibration step, simulating a virtual steering wheel. After calibration, the system calculates tilt angles in real time and converts them into keyboard inputs. This touchless interface provides a new way of interaction, especially for users interested in immersive technologies, and also offers an alternative to traditional controllers. The system was tested on the kart racing game (Smash Karts) and demonstrated smooth performance. It also shows potential as a Human-Computer Interaction (HCI) technique in gaming, with scope for further improvements and application in more complex game environments.
Copyright (c) 2026 Pradnya Jadhav, Poorvi K. Joshi, Aarti R. Salunke, Rishi Pande, Harish Khandelwal, Aditya Arvikar, Aayush Pande
Strategic Agility in the AI Era: Examining the Role of Intelligent Business Systems
With the continuous evolution of AI technology, businesses are increasingly leveraging its capabilities to make informed decisions about their strategies, adapting to significant market changes, and staying competitive in the ever-evolving digital landscape. In today's world, strategic agility is more essential than ever as businesses have to make rapid decisions, modify business models and adapt to the trends in the conditions of the customer and competition. AI, machine learning, predictive analytics, automation, and real-time data processing enable intelligent business systems to deliver capabilities that can help today's organizations bolster strategic responsiveness and organizational adaptability. In today's business landscape, where artificial intelligence is a big player, this paper explores the significant role of intelligent business systems in attaining maximum strategic agility. It explores the possibility of AI based systems that can be flexible, innovative, reconfigure resources, scan the environment, and make real time decisions and respond to customers. Furthermore, the organizational barriers that affect the success of intelligent systems are discussed, including the technical readiness, employee competencies, support of top management, data quality, organizational culture and ethical governance. It's about the human element and the need to strike a balance between AI recommendations and managerial choices; the risk of over-automation is a real possibility, and privacy, transparency, bias and accountability are issues. The paper takes a conceptual approach and discusses the latest research in the area and developments within AI in business management. They indicate that intelligent business systems, when complemented with organizational capabilities, and backed by proper governance, are more than just a technology to implement and use, they can be a strategic enabler. The study also suggests that those organizations that build strategic agility that is AI-informed, coupled with human expertise, organisational flexibility and a culture of continuous learning, are more likely to create sustainable strategic agility that may assist them in adapting to uncertainty in the evolving digital economy.
Copyright (c) 2026 Ayush Gupta, Esmita Gupta
Vision-Threatening Diabetic Retinopathy Detection Using EfficientNetB4: A Two-Stage Transfer Learning Approach
Vision-Threatening Diabetic Retinopathy (VTDR) marks a stage of diabetic eye disease that is characterized by a need for urgent medical treatment to avoid permanent sight impairment. In this paper, we have proposed an automated deep learning framework for binary classification of retinal fundus images in to Non-VTDR (Healthy/Mild DR) and VTDR(Moderate/Severe/Proliferate DR) categories. The transfer learning employs a two-stage strategy with EfficientNetB4 backbone architecture with weights pretrained on ImageNet. The dataset has 2,750 retinal images, and it is split into training (70%), validation (20%), and test (10%) subsets with classes balanced as best as possible. By using data augmentation strategies such as rotation, zoom, brightness, and horizontal flip, our approach increases generalization and helps the model generalize better. The model first does feature extraction with frozen base layers (10 epochs), and then fine-tunes the top 120 layers (20 epochs) with class weighted binary cross-entropy loss. Experimental results suggest that summary accuracy of 86.18% supports our method potential for automating VTDR screening. This system has potential to be used as a part of the real- time clinical workflow to help ophthalmologists detect sight-threatening conditions at an early stage mainly in the resource-limited settings with no access to specialists.
Copyright (c) 2026 Yamini Chouhan, SIDDOJU DIVYA, VNS Manaswini, J. Priyanka
Point Set Domination in Uniform Single-Valued Neutrosophic Graphs for Energy-Efficient Backbone Design in Wireless Sensor Networks
This paper introduces a novel framework for Point Set Domination in Uniform Single-Valued Neutrosophic Graphs (PS-USVNGs) and investigates its application to energy-efficient backbone design in Wireless Sensor Networks (WSNs). A Point Set Neutrosophic Strength (PNS) function, incorporating truth-membership, indeterminacy-membership, and falsity-membership values, is proposed to model domination under uncertainty. Based on this strength function, new concepts of Point Set USVNG domination, dominating sets, and domination numbers are formulated. Several membership-based properties are established to analyze the effects of truth, indeterminacy, and falsity on domination behavior. Furthermore, graph operation results involving union, join, Cartesian product, and complement operations are developed within the PS-USVNG framework. A uniformity theorem is also derived to characterize domination in uniform neutrosophic environments. The proposed model is applied to WSN backbone formation, enabling reliable and uncertainty-aware node selection. Comparative analysis demonstrates that the PS-USVNG approach provides a more comprehensive representation of uncertain communication networks than existing domination models.
Copyright (c) 2026 R. Poornavalli, P. Solairani
Hybrid Deep Learning Framework for Real-Time Phishing Website Detection and Cybersecurity Enhancement
Phishing sites are still one of the most dangerous online threats as they lure people to hand over their confidential details like passwords, bank information or other sensitive personal info. Conventional methods of catching phishing scanners mainly rely on a set of rules, and features designed manually and because of this often miss very recent and sophisticated phishing attacks. Authors of this paper first designed a Hybrid Deep Learning Structure for Real-Time Phishing Website Detection and Cybersecurity Enhancement based on a combination of CNN and LSTM. The newly-developed mechanism tries to automatically recognize and locate spatial as well as sequential patterns in URLs, webpage contents, and security-related attributes for detecting Phishing websites from safe ones. Having a detection system working in real-time presents the possibility of quickly spotting harmful websites, which will ultimately minimize the risks of cyberattacks and at the same time protect the users effectively. Proposed model was tested against an established dataset for phishing websites, and its performance measures like accuracy precision recall, and F1-score were compared to other models. Experimental results show that performance measures of the new model are: accuracy 98.76%, precision 98.42%, recall 98.65%, and F1-score 98.53%, which are even better than those of standard machine learning models and deep learning models separately. Besides, the model even managed to keep the detection delay very low during the live examples. In short, this combination of deep learning approaches brings an effective, flexible, and automatically upgrading way to identify phishing, and a great step towards a safer digital world.
Copyright (c) 2026 Amol Rajmane, Netra Patil, Pramod Ganjewar, Poonam Lambhate, Manisha Dhage, Vaishali Wangikar, Usha Verma
AI-Driven Intrusion Detection System for Edge Computing Environments Using Lightweight Deep Learning Models
The increasing deployment of Internet of Things (IoT) devices and edge computing services has introduced significant cybersecurity challenges due to the distributed nature and resource constraints of edge environments. Conventional intrusion detection systems (IDSs), particularly computationally intensive deep learning approaches, may introduce excessive latency and memory requirements, limiting their suitability for real-time edge deployment. This study proposes a Lightweight Attention-Based Convolutional Neural Network (LA-CNN) for efficient intrusion detection in edge computing environments. The proposed framework combines data preprocessing, feature optimization using correlation analysis, Mutual Information (MI), and Recursive Feature Elimination (RFE), depthwise separable convolution, and channel-attention-based feature learning to improve detection performance while reducing computational requirements. The proposed model is evaluated using the CICIoT2023 dataset under a multi-class intrusion detection setting and compared with conventional machine learning and deep learning approaches. Experimental results demonstrate that LA-CNN achieves 98.34% accuracy, 98.12% precision, 98.41% recall, and 98.26% F1-score, with a false alarm rate of 1.59%. Furthermore, the proposed model requires only 8.7 ms inference time and 91 MB memory, representing approximately 52.7% lower inference latency and 37.2% lower memory consumption than the conventional CNN baseline. These results demonstrate that integrating feature optimization, lightweight convolution, and attention-based feature representation can provide an effective balance between intrusion detection performance and computational efficiency. The proposed framework therefore offers a promising solution for real-time cybersecurity monitoring in resource-constrained edge computing environments.
Copyright (c) 2026 Pramod Ganjewar, Poonam Lambhate, Manisha Dhage, Netra Patil, Amol Rajmane, Vaishali Wangikar, Usha Verma
Compliance-as-Code, Grounded in Observability: Continuous Controls Monitoring in AI-Driven Banking
Banks increasingly place machine learning at the point of decision, and those systems change continuously through retraining, threshold tuning and challenger promotion. Control assurance has not kept pace. Controls are still validated discretely, at a quarterly review, an annual validation or a single audit sample, so an institution can hold a clean validation opinion and still be unable to demonstrate that a control operated on every decision taken between checkpoints. This paper presents a reference model for compliance-as-code grounded in observability, and a proof of concept that instantiates part of that model and is evaluated empirically. The reference model contributes a three-layer obligation to control to signal taxonomy with layered ownership, a machine-readable control signal descriptor that is the executable control rather than documentation about one, a many-to-many obligation mapping that makes coverage gaps visible by inspection, and a five-level maturity model. The proof of concept instruments a credit decisioning service with distributed tracing, evaluates five controls against live telemetry, and writes every verdict to a hash-chained, signed, append-only ledger. Across 1,220 decisions it produced 5,722 signed evidence records with no gaps, control observability coverage of 1.0, a baseline false-positive rate of 0.0, full-chain integrity verification, correct detection of a deliberately altered record, and per-control detection latency of 0 to 66 decisions. All evaluation used public and synthetically generated data.
Behind-the-Meter Solar at Hyperscale Data-Center Campuses: A Resource-Graded Carbon-Price Threshold Across Eleven U.S. Markets
Grid interconnection and delivery capacity, rather than generation cost, have become the binding constraint on serving hyperscale electricity loads in many U.S. markets. We show that the carbon price required to bring on-site solar into a cost-optimal campus portfolio can be read directly from the local solar resource: across eleven U.S. data-center market archetypes the threshold falls from approximately $230/ton at an annual solar capacity factor of 0.147 to $110/ton at 0.238, a near-linear relationship (R²=0.97) that gives developers a single-parameter screening rule for siting. The result comes from a reproducible framework built only from public data, coupling a levelized-cost and Monte Carlo screening layer to a temporally-resolved capacity-expansion and dispatch optimization that co-optimizes investment and hourly operation and prices battery storage explicitly, run on real hourly NREL PVWatts V8 profiles. Because dispatchable gas capacity is retained for firmness in every scenario, on-site solar competes against gas’s variable cost rather than its full levelized cost; this is why the threshold is graded by resource and why it lies well above current U.S. carbon prices, with gas remaining least-cost firm supply in every market at carbon prices up to at least $100/ton. The relationship survives more favorable PV assumptions and a temperature-responsive cooling load. All code, inputs, and outputs are openly archived.
Beyond Checkout: A Payment-Centric Scalability Framework for Autonomous Retail
Autonomous retail is often described as a computer vision and sensor-fusion breakthrough that removes checkout lines. This paper argues that such a framing is incomplete. Perception accuracy is necessary for autonomous retail, but it is not sufficient: durable scalability also requires mature payment orchestration, customer trust, merchant economics, and ecosystem alignment among issuers, acquirers, processors, schemes, wallets, and merchants. Through a practitioner-informed, design-science-oriented analysis of stadiums and arenas, airports, corporate and university campuses, hospitals, convenience stores, and hotels, this paper offers three contributions: a format-specific Autonomous Retail Scalability Matrix with an explicit scoring rubric, a Frictionless Commerce Payments Maturity Model, and a payment-centric reference architecture. The conceptual analysis suggests that autonomous retail scales best when shopping missions are time-sensitive, baskets are simple, customer identity can be bound at entry, and payment flows can support initial/pre-authorization, incremental authorization, partial reversal, final capture, refunds, chargebacks, and reconciliation without undermining customer trust. The paper also explains why large grocery is more difficult and that real-time basket visibility and shopping-control expectations matter. The paper concludes that the future of autonomous retail is not a single universal checkout model, but a portfolio of format-specific systems in which payments become the invisible backbone of frictionless commerce.
HELIX: Human-Agent Embedded Lifecycle for Healthcare Integration Engineering
The increasing complexity of healthcare integration engineering—managing hundreds of HL7 and FHIR interfaces across distributed integration engine environments—demands development methodologies that scale beyond traditional manual workflows. While general-purpose agent-driven software development lifecycle (SDLC) frameworks have been proposed for software engineering, no formal framework exists for systematically embedding AI agents across every phase of the healthcare integration development lifecycle under the clinical safety, regulatory compliance, and semantic precision constraints unique to this domain. This paper introduces HELIX (Human-Agent Embedded Lifecycle for Healthcare Integration Engineering)—a formal framework for AI-driven development lifecycle (AI-DLC) in healthcare integration engineering, defined as a 6-tuple comprising lifecycle phases, a 12-role agent taxonomy, an institutional knowledge architecture, orchestration protocols, constitutional constraints, and evaluation metrics. The framework positions specialized AI agents as embedded participants in every lifecycle phase—from requirements analysis through operations—with the human engineer as the authoritative decision-maker. Six of twelve defined agent roles are retrospectively validated through published healthcare-specific empirical research, three are partially validated by general-domain AI and DevOps research requiring healthcare adaptation, and three are identified as open research gaps requiring new investigation. A preliminary empirical evaluation using a within-subjects counterbalanced design with eight integration analysts developing 48 healthcare interfaces (24 per condition) demonstrates that HELIX orchestration reduces mean development time by 57.1% (paired t-test: t(7) = 8.33, p < 0.001, Cohen's d_z = 2.95), analyst cognitive workload by 33.4% (NASA-TLX: t(7) = 7.78, p < 0.001), and defect density by 30.6% (t(7) = 3.84, p < 0.01) compared to manual baseline. HELIX provides the first domain-specific formal foundation for a research program advancing AI-embedded healthcare integration engineering.
Development of Accurate Soil Profile Prediction for the Agriculture Sector using Machine Learning
India is one of the largest agricultural countries in the world. It contributes major production of pulses and spices and is considered one of the powerhouses of agriculture. However, the agriculture sector is facing the challenges such as soil degradation, low productivity, variations in the monsoons, lack of agriculture infrastructure, water scarcity and irrigation. There are several methods to find soil profile prediction such as field observation, soil sampling analysis, Pedogenesis model, remote sensing, and machine learning algorithms. Among these model, machine learning algorithm is one of the emerging trends because of scalability, adaptability, versatility, robustness and continuous improvement. In this regard, an Improved Regression by Discretization Algorithm (IRDA) is used to predict the soil profile of an agriculture unit for the better cultivation. The accurate value of PH, presence of essential nutrients , and the nature of soil are determined using IRDA algorithms. The performance of IRDA algorithm is compared with other popular machine learning algorithms using cover coefficient, mean absolute error, and root relative square errors.
Copyright (c) 2026 P. Soubhagyalakshmi, Savitha H. P, Sandhya S, Shriya Sinha
Integrating Marketing Automation and Commerce Platforms into a Unified Customer Engagement Architecture
Marketing automation and digital commerce are usually described as adjacent systems that need connecting. They are better understood as two systems that keep time differently. Commerce runs at the speed of a customer who is browsing, authenticating, ordering and paying, and needs the current state of identity, entitlement and account access to answer the request in front of it. Marketing automation runs on segments, journeys, campaign audiences, consent rules and scheduled engagement, and is designed to reason about populations over intervals rather than about one customer in one moment. The two also carry different data models: marketing thinks in contact keys and communication permissions, commerce in accounts, roles, pricing and ordering rights. This paper argues that most integration failure between them lives in that gap, not in the field mappings usually blamed for it. It contributes four artefacts: a source of truth ownership matrix assigning each of nine customer data domains to one owning platform; a treatment of the common customer key, the governed identity relationship across platforms, as the precondition without which marketing and commerce cannot stay aligned at all; a classification of which flows across the boundary must be real time and which may be scheduled, with the consequence of misclassifying each; and a divergence detection and reconciliation loop governed by the rule that every important data flow must have one defined source of truth, because without it teams repair the symptom in one system while the cause sits in another. The paper is explicit about being part reference architecture and method proposal, part practitioner account of one estate at a large North American consumer brand enterprise running both business to business and direct to consumer commerce. That estate supports more than 150,000 customers, more than 100,000 annual customer orders and approximately 110,000 customer registrations, with approximately 35 enterprise application programming interfaces, approximately 15 real time integrations and approximately 10 scheduled integration processes. Registration processing improved to approximately 800 milliseconds and page load performance to around one second. No campaign, conversion, email engagement or marketing lift measurement is available for this work, and none is claimed anywhere in the paper.
Freedom of expression strengthens democracy, but on many occasions, in history freedom of expression, was used to crush the same democracy and populism and authoritarianism were born. In 21st century the role of social media in relation to freedom of expression is posing a serious threat to functional democracy in various countries. Populist leaders are using social media to spread false and misleading news in the context of politics under the guise of freedom of expression has given rise to populism and totalitarianism in many countries of the world. In this research paper, we have analysed the relationship between populism and social media with particular reference to the rise of populist leaders, Nazis from the Weimar Republic and AfD party in Germany, Victor Orban in Hungary, Erdogan in Turkey, Narendra Modi in India, Rodrigo Duterte in the Philippines, Jean Marie La Pen in France, Nigel Farage in Britain and Donald Trump in the United States of America.
Copyright (c) 2026 Kamala Yadav, Alok Kumar Kashyap
The Impact of Anthropomorphism on AI Chatbot Usage Intention: The Mediating Role of Customer Experience in a Modified UTAUT Model
The precipitous trajectory of artificial intelligence has hastened, within university career services, the integration of chatbot systems. However, technical sophistication alone does not guarantee the efficacy of AI chatbot deployment; user acceptance and the quality of the interaction experience are equally critical. This study identifies the mediating role of customer experience within the association between anthropomorphism, the constructs of a modified Unified Theory of Acceptance and Use of Technology (UTAUT)—namely perceived ease of use, performance expectancy, initial trust, and social influence as well as the usage intention toward the Patriot Yupi chatbot at a university Career Center. A quantitative survey was administered to 403 university alumni, and the collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that all hypothesized direct correlations were positive and statistically significant. Furthermore, customer experience meaningfully mediated the associations between anthropomorphism, the modified UTAUT constructs, and usage intention. Specifically, anthropomorphism cultivates favorable user perceptions, while customer experience serves as the pivotal mechanism translating these perceptions into a sustained intention to use the chatbot. This research extends the scope within which the UTAUT framework can be meaningfully applied within the domain of AI-driven career service chatbots and provides practical insights for designing chatbots that effectively enhance the user experience.
Copyright (c) 2026 Arista Pratama, Faisal Muttaqin, Siti Mukaromah, Eristya Maya Safitri, Belinda Ingridina
A Novel Machine Learning Approach for Analyzing Subtly Expressed Opinions in Diverse Online Platforms
Various applications have grown to rely on comprehending the ideas and feelings conveyed in user-generated content because to the exponential expansion of this type of data on internet platforms. But it's not easy to identify and understand sarcasm and implicit emotions and other nuanced statements of opinion across different language modes. This study introduces a fresh method using machine learning to tackle this problem. We present a model that integrates deep learning, multi-modal analysis, and natural language processing to pick up on subtle sentiments conveyed in visual content like photos and films shared online. We show that our method is effective in sarcasm detection, emotion identification, and opinion analysis tasks by evaluating it on several datasets from different web platforms. In addition, we go over some of the possible uses and consequences of our research in domains including online content regulation, social media monitoring, and product review analysis. The suggested multi-modal and multi-task framework demonstrated good results in sarcasm detection, emotion recognition, and sentiment analysis whose F1-score ratings were of more than 0.80 in all the tasks. Attention based fusion showed the need to dynamically weight the importance of the text, audio, and visual modalities and ablation experiments were used to prove the fact that a combination of multiple modalities enhances the predictive accuracy greatly. This implies that the model is effective in the ability to elicit subtle and nuanced opinions in various online platforms.
Copyright (c) 2026 Amit Kumar Das, Dinesh Mishra
Sustainable Finance and Strategic Management Practices for Long-Term Business Growth: An Integrated Framework for Resilience, Innovation, and Value Creation
Sustainable finance is no longer a niche-interest issue for a few sustainability-oriented investors and regulators but a key management issue related to capital allocation policy, processes of innovations, risk governance, reputational value, and long-term viability. The present paper provides an insight into ways that sustainable finance can be embedded into management decision-making processes. The conceptual framework was developed, within the context of which the study was conducted. Based on the findings, it was found that financial discipline, strategic intent, digital competence, sustainable innovations, stakeholder management, supply chain agility, and learning processes are critical success factors in achieving sustainable business growth. The research method applied was qualitative in nature, and thematic analysis of the topic was undertaken. The themes identified were long-term growth, sustainable finance, capital allocation, information technology, strategic orientation, sustainable innovations, and firm performance. The study found that investment criteria, operating targets, innovations, risk management practices, and performance indicators that underpin long-term growth are all influenced by sustainability considerations. Information technology and supply-chain agility were found to be critical to strategic priorities, including stakeholder responsiveness. In addition, corporate reputation and sustainable innovation promote competitive advantage and credibility. The study framework views sustainable business growth as a strategic consequence arising from the convergence of capital, capabilities, and stakeholder value-creation, and not a solo action. Therefore, the paper concludes that long-term sustainable business growth can be realized by embedding strategic management concerns in a company’s capital, technology, resources, governance, and performance-measurement systems. The study highlights the importance of a strategic-level approach to long-term business growth in the current business environment where businesses can experience heightened levels of instability and competition. Sustainability issues play a crucial role in corporate viability, strategic decision-making, and gaining access to new capital, and should be the main concern in reputation management, regulatory oversight, resource-productivity prospects, and overall performance.
Copyright (c) 2026 Rovilyn Katalbas - Dagondon, Femmy Sofie Schouten, J Sabitha, Pranami Chakravorty, Poongavanam S, Siti Maemunah
Financial Literacy, Welfare Scheme Utilization, and Economic sustainability among Leather Industry Workers in Uttar Pradesh, India
Leather industry workers constitute an important segment of Uttar Pradesh’s informal labour force, yet many remain economically vulnerable due to low incomes, unstable employment, limited financial capability, and uneven access to welfare support. The study addresses the problem of how financial literacy and welfare scheme utilization influence the economic sustainability of leather workers. The objectives were to assess financial literacy, examine welfare awareness, registration, and benefit receipt, and analyze their relationship with economic sustainability. The study used a quantitative, descriptive, and analytical design based on primary survey data collected from 100 leather workers. Data were analyzed using descriptive statistics, a Financial Literacy Index, welfare participation counts, Spearman correlation, and regression analysis in SPSS. The findings show that financial literacy significantly improves welfare registration and, to some extent, benefit access. However, financial literacy alone does not strongly predict economic sustainability in simple models. Income emerged as the strongest determinant of economic sustainability, while welfare utilization did not show a clear positive effect on resilience. The study concludes that financial literacy is necessary for institutional access but insufficient without stable earnings and effective welfare delivery. Policy should therefore combine financial literacy interventions with stronger labour protection, income support, and improved implementation of welfare schemes for workers.
Copyright (c) 2026 Faraz Khan, Asma Farooque
Comparative Analysis of Aerial Photogrammetric and Real-Time Kinematic (RTK) Methodologies in Topographic Surveys for Designing a Potable Water System in Uyuntza, Suntsuntza, and Pikiur Communities, Sevilla Don Bosco Parish, Morona Santiago Canton, Ecuado
The aim of this study is to compare the results of two topographic survey methodologies in the design of a potable water system for the communities of Uyuntza, Suntsuntza, and Pikiur in the parish of Sevilla Don Bosco in the Morona Santiago canton, Ecuador, with an intervention area of 468 hectares. In this context, the results were compared between the methodology employing an unmanned aerial vehicle (drone) and the second utilizing a Real Time Kinematic (RTK) GNSS receiver or kinematic differential GPS; the analysis involved post-processing the information collected with the drone and differential GPS, contrasting parameters such as cost, time, and precision through statistical tests. The outcome of this method comparison demonstrated that the drone-based process is secure, feasible, and efficient in terms of time and cost; however, significant differences in altimetry precision (Z-axis) were found. Therefore, local ground control points (GCP) will continue to be crucial in studies of environments incorporating data collection with airborne sensors.
Copyright (c) 2026 Mauro José Jiménez Granizo, Jessica Paulina Brito Noboa, Victor Elias Molina Ruiz, Nelson Estuardo Patiño Vaca, Gonzalo Paúl Oviedo Salas, Roberto David Vallejo Campos
Saudi Arabia's Strategy in Associated Natural Gas Investment and its Impact on Industrial Transformation (1980-1985): A Historical Analytical Study
Natural gas is a cornerstone of national economies and a primary source of clean energy that supports sustainable development. Saudi Arabia's Vision 2030 has spearheaded serious efforts to optimize investment in this sector. This study aims to review Saudi Arabia's strategy for investing in associated natural gas and its impact on industrial transformation (1980-1985). It serves as a historical and analytical study of the Kingdom's ambitious natural gas investment strategy, examining its impact on maintaining a clean environment and supporting decision-makers in promoting sustainable economic development. The study focuses on analyzing the policies and strategies adopted by the Kingdom to develop the natural gas sector, enhance integration between the energy and industry sectors, support industrial cities, measure the industrial impact of this strategy, and evaluate its role in achieving sustainability. The study's main idea stemmed from recognizing the importance of natural gas investment in achieving the desired industrial transformation in the Kingdom and the resulting economic diversification. This involves encouraging a reduction in reliance on oil as the primary source of manufacturing and revenue, and exploring vital alternatives such as associated natural gas. The study raises the question of the positive and effective relationship between natural gas investment and industrial growth, examining the period between 1980 and 1985 and whether there are benefits to investing in it within industry. It also explores the extent to which natural gas contributes to reducing production costs and creating job opportunities. The study focused on analyzing the period between 1980 and 1985, which represents a turning point in the Kingdom's policy towards associated natural gas. Therefore, the descriptive and inductive methods were used to gather data. The results demonstrated the Kingdom's significant and distinctive interest in investing in associated natural gas as a crucial development resource and an alternative energy source for achieving economic prosperity and sustainable development. The study recommends working on the regional connection of the natural gas transmission network due to its economic and political importance. It also recommends urging the intensification of government initiatives to involve the private sector in the energy and petrochemical industries.
Copyright (c) 2026 Abeer Fahd Abdulaziz Almuhailib
An Efficient Image Segmentation Approach Using Fully Convolutional Neural Networks with Attention Fusion for Ophthalmic Disease Classification
The process of segmenting retinal images and classifying ophthalmic diseases establishes a crucial pathway for both early disease identification and clinical treatment choices. Deep Learning (DL) methods demonstrate effective results however, previous research suffers from three critical issues, which include their failure to extract sufficient features, their inability to generalize well and their weak connection between preprocessing and augmentation methods. This research develops an Attention-Based Fully Convolutional Neural Network (FCNN) framework to solve existing problems in ophthalmic disease classification through Ocular Image Analysis – Ophthalmic Disease Intelligent Recognition (OIA-ODIR) retinal fundus image dataset analysis. The proposed methodology applies image preprocessing methods, which include resizing, denoising and histogram equalization, together with data augmentation techniques, which include rotation, scaling, normalization and cropping to generate new dataset samples and enhance the quality of images. The attention-based FCNN architecture classifies processed retinal images through its ability to extract retinal features that distinguish different eye conditions. The experimental results show that the developed model reaches an accuracy of 99.20% with a precision of 98.17% and a recall of 99.35%, and an F1-score value of 98.74%, which surpasses multiple current algorithms. The study concludes that the integration of preprocessing, augmentation, and attention mechanisms significantly enhances segmentation and classification performance. The proposed framework delivers accurate results that demonstrate strong performance and dependable outcomes for classifying ophthalmic diseases through retinal fundus image analysis.
Copyright (c) 2026 Sachin Malviya, Prajeet Sharma
Impact Of Employee Engagement On Employee Productivity: A Study With Reference To The Cement Industry In Kadapa, Andhra Pradesh
Employee engagement has become an important issue in organizational research because the performance of an industrial organization depends not only on machinery, capital and technology but also on the extent to which employees are willing to invest effort in their work. The issue is particularly relevant to cement manufacturing, where production is technically intensive, safety-sensitive and dependent on coordination among production, maintenance, quality, logistics and support functions. This study examines the relationship between employee engagement and employee productivity with specific reference to the cement industry in Kadapa, Andhra Pradesh. The research deliberately adopts a descriptive research design based exclusively on secondary data. No questionnaire survey, interview or primary employee-level dataset is used. Evidence is drawn from government publications, industry reports, published academic studies and credible institutional sources. The study examines employee engagement dimensions including recognition, empowerment, communication, teamwork, supervisory support, career development, organizational involvement and meaningful work, and considers productivity through work efficiency, quality, target achievement, timely completion, attendance, problem-solving, error reduction and individual contribution. Current Gallup data show that 23% of employees in India were engaged at work in 2025, compared with 59% who were not engaged and 18% who were actively disengaged. Cement production reached about 453 million tonnes in FY2025, demonstrating the scale and continuing expansion of the sector. Kadapa possesses substantial limestone resources suitable for cement manufacture, providing a strong regional industrial context. The secondary evidence reviewed in the study indicates that employee engagement is consistently associated with favourable performance outcomes and those organizational resources such as recognition, support, development opportunities and empowerment can strengthen engagement.
Copyright (c) 2026 Medida Ananth Kumar, P.V.Vara Prabhakar
A Comprehensive Survey on Light weight block Ciphers with Authentication approaches for low resource IoT devices
Resource limited devices like IoT nodes, RFID tags, wireless sensor networks, and embedded edge technologies can now be safeguarded with the help of lightweight cryptography. Traditional cryptographic primitives are not suitable for these devices because they work under stringent conditions on power, area, storage, and latency. With emphasis on hardware implementations, this review offers an in depth and methodical analysis of existing lightweight block ciphers and authentication techniques. High-profile journal articles published between 2018 and 2026 are scrutinized and analyzed using a PRISMA-based methodology. The study includes techniques that are focused on authentication as well as traditional and new lightweight block ciphers. Metrics at the ASIC and FPGA levels, including LUTs, power usage, frequency, slices, latency, throughput, and hardware efficiency, are used to perform quantitative comparisons. Significant design trade-offs between area-optimized, throughput-oriented, and energy-efficient designs are highlighted in the analysis, along with the increasing trend of integrated encryption–authentication approaches. The survey also finds inadequacies in side-channel resilience, scalability across various heterogeneous platforms, security–efficiency co-optimization, and uniform benchmarking. Finally, future research scope is described for leading the creation of emerging lightweight cryptographic architectures that strike a undermine between hardware efficiency and robustness for contexts with limited resources.
Copyright (c) 2026 Chaitra P, Shobha I Hugar
The Paradox of Micro-Capital Fungibility: Evaluating Credit Utilization and its Impact on Women’s Empowerment
Purpose: This study evaluates micro-credit’s contribution to women’s empowerment in rural West Bengal. It specifically examines the “paradox of micro-capital fungibility” by analyzing how the utilization of accessed impacts economic independence, mobility, decision-making, and social recognition. Design/methodology/approach: Using a quantitative approach, primary data was collected from 450 women beneficiaries in the West Medinipur district. Statistical analyses were employed to identify patterns in credit utilization from both Commercial Banks and Microfinance Institutions (MFIs). Findings: Results indicate that while micro-credit is a transformative tool for financial inclusion, the actual utilization (fungibility) of credit is the critical determinant of the socio-economic empowerment levels achieved by rural women. Research limitations/implications: The geographical focus on rural West Bengal may limit generalizability. Future research should investigate the digital divide’s impact on credit utilization among less tech-savvy populations. Practical implications: Financial institutions must move beyond mere credit delivery to monitor and support effective fund utilization, ensuring long-term entrepreneurial success for women. Social implications: This research informs policymakers on designing robust support systems that bridge the gap between credit access and effective utilization, enhancing women's social status and community resilience. Originality/value: This paper addresses the under-explored “fungibility” of micro-capital, bridging the gap between financial access and actual empowerment outcomes.
Copyright (c) 2026 Arpita Dhal, Pijus Kanti Bhuin, Shantanu Chakraborty, Prasanta Guha
A multistage video shot boundary detection approach: robust to motion and illumination effects
In this today’s modern era because of increasing digitization and excess growth of multimedia data, video indexing and retrieval tool have become very essential in the field of video retrieval based on content, where video shot boundary detection is regarded as a stage of develop-ment for sudden changes in motion and brightness. A robust multi stage Shot Boundary Detection (SBD) technique is proposed in this present work using Uniform LBP, Canny Edge Detector and Lab Color Differ-ence. The proposed system can identify abrupt shot transition in a video, which are invariant to motion and intensity effects. For extracting fea-tures from each of the frames of a video uniform Local Binary Patterns (LBP) is used. Adaptive threshold δ1 is used to identify possible tran-sition frames. Adaptive threshold δ2 and LAB color difference is used to extract and identify actual transition frames. Motion effects and illu-mination effects are handled in two different phases. At the first stage, effects of brightness and motion effects are reduced by Canny edge de-tection and in the second stage, Lab color difference is used to handle illumination effects. TRECVid 2001 and TRECVid 2007 datasets are used for observation purposes. The proposed method provides good F 1 score compared to other recent shot boundary detection approaches.
Copyright (c) 2026 Debjani Bhowmik, Saptarshi Chakraborty, Arnab Roy, Hriteek Roy
ADAPTIVE CONTEXT-AWARE MULTIMODAL TRANSFORMER FRAMEWORK FOR ROBUST RESPIRATORY DISEASE DIAGNOSIS
Early detection and accurate diagnosis of respiratory diseases are remaining critical for the timely clinical interference and treatment. Many of the conventional diagnostic systems are relying on the unimodal datasets or static multimodal fusion strategies that tends in limiting the ability in capturing the respiratory diseases. Despite the success of recent multimodal learning approaches, where the practical limitations are remaining unresolved. This study proposes an Adaptive Context-Aware Multimodal Transformer Network (AMCT-Net) that is combining the chest X-ray images, respiratory acoustic signals, and structured clinical parameters for the respiratory disease diagnosis. The proposed architecture is employing three modality-specific encoders: a convolutional neural network that tends to extract the radiographic features, where a spectrogram-based audio transformer that is capturing the respiratory sound patterns, and a tabular transformer that is processing clinical variables. The extracted representations are passing through the Adaptive Modality Weighting mechanism that is assigning the dynamic importance scores to each of the modality. A context-aware multimodal transformer fusion module is then modelling the cross-modal dependencies in generating the diagnostic representations. The fused features are subsequently processed through the classification layer that tends to predict the respiratory disease categories. The experimental evaluation is showing that the proposed Adaptive Context-Aware Multimodal Transformer Network (AMCT-Net) is achieving a superior performance compared with the conventional multimodal diagnostic frameworks. The model is reaching 88.72% classification accuracy, 88.10% precision, 88.45% recall, and 88.30% F1-score, which significantly is exceeding the performance of conventional architectures that includes MDT, E-RespiNet, Med-MLLM, FATE, DREAM-OSA, CAMAF, and RMT with the mask attention.
Copyright (c) 2026 R. SARADHA, M. SUNDARA RAJAN
Hybrid LaBSE Semantic and Handcrafted Feature Fusion with Machine Learning for Fake Review Detection in Roman Marathi Code-Mixed Text
Fake review detection in low-resource and code-mixed languages remains challenging due to informal writing styles, transliterated regional expressions, linguistic variability, and the limited availability of annotated datasets. This paper presents a hybrid LaBSE semantic and handcrafted feature fusion approach with machine learning for fake review detection in Roman Marathi code-mixed text. A real-time dataset comprising 2,287 Roman Marathi reviews collected from multiple online platforms is utilized to evaluate the proposed approach. The framework integrates opinion-mining features with multilingual semantic representations generated using Language-agnostic BERT Sentence Embedding (LaBSE) and handcrafted linguistic, behavioural, contextual, temporal, and metadata features to construct a comprehensive hybrid feature representation. The dataset is balanced using random oversampling and subsequently partitioned into training and testing subsets using an 80:20 ratio. Four machine learning classifiers, namely Random Forest, XGBoost, Support Vector Machine, and K-Nearest Neighbour, are evaluated using accuracy, precision, recall, and F1-score. Experimental results demonstrate that XGBoost achieves the best performance with 94.60% accuracy, 95.63% precision, 93.47% recall, and 94.54% F1-score, outperforming the other evaluated classifiers. The findings demonstrate the effectiveness of combining multilingual semantic information with explicit linguistic and contextual characteristics for identifying deceptive reviews in Roman Marathi code-mixed environments and establish an initial benchmark for fake review detection in this low-resource setting.
Copyright (c) 2026 Swapnil S. Nehar, Ranjit R. Keole, Pravin P. Karde
A Resource-Aware Hybrid Quantum-Classical Framework for Network Intrusion Detection with Selective Quantum Processing
Quantum machine learning (QML) is increasingly investigated for network intrusion detection, but the practical value of current quantum models remains uncertain because many studies rely on small simulated data sets, idealized execution, weak classical baselines, or isolated accuracy comparisons. This study develops and evaluates a resource-aware hybrid quantum-classical intrusion detection framework that combines fixed fidelity quantum kernels, repaired Quantum Kernel Alignment (QKA), a Variational Quantum Classifier (VQC), LightGBM, prototype-based kernel reduction, and probabilistic stacking. Experiments use the UNSW-NB15 benchmark under a leakage-aware preprocessing protocol and repeated evaluation across five random seeds. The experimental program covers 4- and 6-qubit representations, multiple training sizes, prototype budgets, and finite-shot configurations, yielding 260 experimental configurations/runs. The strongest quantum-assisted model, a 6-qubit QKA-VQC-LightGBM stack at 100 samples per class, obtains mean F1=0.8480, MCC=0.7092, ROC-AUC=0.9398, and PR-AUC=0.9302. Repaired QKA improves F1 from 0.8043 for the corresponding fixed kernel to 0.8089, while 50-prototype QKA attains F1=0.8257 with a more favorable kernel-workload trade-off than larger prototype budgets. Finite-shot experiments between 1,024 and 4,096 shots remain stable in simulation. However, full-data LightGBM remains stronger overall (F1=0.8957, MCC=0.8068, ROC-AUC=0.9755, PR-AUC=0.9704), and corrected statistical tests do not establish general quantum superiority. These findings motivate a Selective Quantum Intrusion Detection System (S-QIDS), in which classical inference handles high-confidence traffic and quantum processing is reserved for uncertain, novel, or high-risk observations. The study therefore reframes near-term quantum intrusion detection from unconditional quantum advantage toward measurable quantum utility, quantum-classical complementarity, and resource-aware quantum intervention.
Copyright (c) 2026 Rahul R. Bhoge, Ranjit R. Keole, Pravin P. Karde
Beyond Code Correctness: A Hybrid Behavioral-Cognitive Model for Student Assessment in Programming Education
Traditional programming assignment assessments primarily focus on evaluating the correctness of the final output, often neglecting critical aspects such as engagement, collaboration, and debugging skills. As programming education increasingly shifts toward project-based and collaborative learning, there is a growing need for more holistic evaluation methods. This paper proposes a hybrid conceptual framework that integrates cognitive correctness metrics with behavioral analytics derived from Source Code Management (SCM) systems, such as GitHub. The framework captures authentic student behaviors during assignment development and leverages machine-learning models combined with explainable AI techniques to transparently predict grades. This conceptual study advocates a paradigm shift from purely product-based assessments to a more process-oriented, learning-centered evaluation model that promotes deeper and more meaningful educational experiences in programming education.
Copyright (c) 2026 Amitkumar Patel, Jaykumar Patel, Sandipkumar Surati
AI-Assisted Dynamic RSU Deployment Using Real-Time Traffic Density Estimation in VANETs
With the rapid advancement of vehicular communication technologies, maintaining reliable connectivity in Vehicular Ad Hoc Networks (VANETs) has become a critical challenge due to high mobility, dynamic topology, and uneven traffic distribution. Frequent disconnections in Vehicle-to-Vehicle (V2V) communication lead to increased latency and reduced network performance. To address these issues, this research proposes an AI-assisted dynamic Roadside Unit (RSU) deployment framework that leverages real-time traffic density estimation to optimize communication infrastructure. The proposed system utilizes deep learning-based vehicle detection models to analyze real-time traffic images and estimate vehicle density across different road segments. The extracted traffic information is further processed using machine learning techniques to predict communication demand and identify potential connectivity gaps. Based on these predictions, the system dynamically activates, deactivates, or repositions RSUs to ensure continuous network coverage and reduce dependency on unstable V2V links. The optimization model focuses on minimizing communication delay, enhancing packet delivery ratio, and improving overall network reliability through adaptive RSU placement. Additionally, a hybrid communication approach combining V2V and Vehicle-to-Infrastructure (V2I) is employed to overcome connectivity loss in sparse or highly dynamic traffic conditions. Simulation results demonstrate that the proposed AI-driven framework significantly improves network throughput, reduces communication latency, and ensures stable connectivity compared to traditional static RSU deployment strategies. The system effectively adapts to varying traffic patterns, making it suitable for next-generation intelligent transportation systems and smart city applications.
Copyright (c) 2026 Sayyada Fahmeeda, Shashank, Jyoti, Soumya M A, Amareshwari Patil, Priyadarshini c Patil, Bhagyashri Patil
MindPhone AI: Intelligent Prediction of Smartphone Addiction
Excessive smartphone use is increasingly framed as a behavioral addiction, traditionally assessed through validated self-report instruments such as the Smartphone Addiction Scale. Machine-learning approaches promise a more scalable, passively-collected alternative to survey-based screening, but most published systems still rely on self-reported psychometric ground truth for model training. Methods. We describe a Flask-based system that extracts Android Digital Wellbeing usage counters like usage minutes, app opens, notification counts, screen-time percentage, either from a connected device via ADB or from a generated fallback dataset and classifies each usage profile into one of five heuristically defined addiction-risk tiers such as MINIMAL, LOW, MODERATE, HIGH, SEVERE using a Random Forest classifier where n_estimators=100 with Standard Scaler feature normalization and LabelEncoder target encoding. The risk labels themselves are produced by a deterministic weighted formula over the same usage counters rather than an independently measured outcome. Results is on the project's static 14-app sample, the classifier reached 100% training accuracy with no held-out test set; illustrative single predictions for example an Instagram usage pattern were classified SEVERE with 86% predicted-class probability. These figures reflect fit to a formula-derived label on training data, not validated predictive performance, and must not be reported as generalization accuracy.
Copyright (c) 2026 Jayashree Agarakhed, Bhimaraya Patil, Siddarama Patil, Chinnakonda Chenchu Tejesh Redddy
OCCUPATIONAL STRESS AND BURNOUT AMONG NURSES: A COMPARATIVE ANALYSIS OF PUBLIC AND PRIVATE HOSPITAL
Nursing is an extremely challenging occupation. In many cases, nursing causes stress and leads to burnout, which affects nurses’ wellbeing and quality of patient care. The study investigates the differences in nurses’ stress levels in public hospitals and private hospitals, analyzes the influence of workload and job requirements on burnout and evaluates the effect of the support system of employment on stress levels. The results of this research illustrate that stress levels of nurses in public hospitals are slightly higher than those of nurses in private hospitals because public hospitals have more patients and experience a lack of many resources. Work load and work hours were found to be the most important predictors of burnout. Burnout was less pronounced in the presence of the support system, though it was not effective in resolving the issue of burnout crisis without large-scale reforms of the health care system.The implementation of these measures is crucial to minimize employee turnover, promote employee job satisfaction, and maintain improvements in patient care quality. According to the findings of the study, nurse burnout is a problem that needs to be taken care of on a policy level and from the point of view of an institution rather than an individual. Future studies should concentrate on longitudinal studies, effectiveness of interventions, and global assessment of the phenomenon.
Copyright (c) 2026 L. VENKATRAMAN, M. MUTHUKUMAR
Numerical optimization of VRM Cyclone Geometry for Enhanced Energy efficiency
The cement industry, which is a highly energy-intensive industry as well as a rapidly growing sector, is grappling with various challenges like increasing power, fuel, and raw material costs which are leading to diminishing profit margins. In order to survive and sustain and expand profit margin, plant went for capacity enhancement by increasing the feed of the Vertical roller mill. However, it led to an increased pressure drop of 200-210 mmWG across the cyclone against the typical value of 120-130 mmWG. This paper unveils the deliberation of reduction in pressure drop with the help of Computational Fluid Dynamics (CFD) across the cyclone to reduce power consumption of raw mill fan. CFD was employed to understand the flow patterns across the cyclone separator. Non-uniform flow of flue gas at the inlet of the cyclone was observed. To make the uniform flow pattern, guide plates were installed and the cyclone’s inlet area was enlarged, and the modification resulted in the pressure drop reduction of 40 mmWG and savings of 0.18 kWh/t material in the raw mill fan.
Copyright (c) 2026 Rajpal Singh Shekhawat, Pushpendra Kushwaha, Dr. Madhu Agarwal
Spatio–Temporal Environmental Monitoring Using IoT Sensor Networks with ConvLSTM and Graph Neural Networks
The rapid expansion of smart cities has increased the demand for reliable environmental monitoring systems capable of detecting dynamic changes in urban climate and potential hazards such as pollution accumulation and fire outbreaks. Internet of Things (IoT) technologies enable continuous environmental sensing through distributed sensor networks. However, the complex spatial and temporal dependencies within environmental data pose significant challenges for traditional analytical models. This paper proposes a hybrid spatio-temporal deep learning framework that integrates Convolutional Long Short-Term Memory (ConvLSTM) networks with Graph Neural Networks (GNN) to analyze environmental data collected from IoT sensors. The ConvLSTM module captures temporal dependencies in multivariate sensor readings, while the GNN component models spatial relationships among sensor nodes. Experimental results demonstrate improved prediction accuracy compared with conventional models. The framework supports smart city applications including environmental monitoring, urban heat analysis, and early fire detection.
Copyright (c) 2026 R.SUNDARESH, K.LAKSHMI PRIYA
INTENT-GUIDED RETRIEVAL-AUGMENTED GENERATION WITH LORA-BASED INTENT ROUTING FOR INTELLIGENT CUSTOMER SUPPORT
Automated customer support technologies based on large-scale language models (LLMs) suffer from three classic failure modes: false creation of non-existing policies, inconsistent answers to the same questions, and misleading interpretations of user intentions due to confusing, joking, or vague inquiries. Large-scale language model (LLM)-based automated customer support technologies fail due to false policy creation, inconsistent answers to the same questions, and misleading interpretations of user intentions due to confusing, joking, or vague enquiries. Instant-Guided Retrieval-Augmented Generation with LoRA-based Intent Routing, a novel customer assistance system with intent-based retrieval, was successfully created. The Baseline pre-trained Llama-3-8B-Instruct model with no external grounding, a Naive Retrieval-Augmented Generation pipeline (Solution V1) that embeds SOP passages with sentence-transformers/all-MiniLM-L6-v2, indexes them in ChromaDB, and augments the prompt with the top-retrieved passage at inference time, and a Hybrid, The same externally held test partition was striated and an adversarial partition was screened for hedging and frustration language to evaluate all three procedures. Evaluations used ROUGE-1, ROUGE-L, BLEU, Format adherence rate, exact match, and fuzzy match accuracy. Based on 50 samples, retrieval increased ROUGE-1 from 0.2116 to 0.277, a 31.4% increase over the ungrounded benchmark, and BLEU from 0.0030 to 0.0328, an almost tenfold gain. With a fine-tuned intent router added to retrieval, ROUGE-1 would rise 3.9% to 0.2887 and BLEU to 0.0478 (+45.4% over V1). For the entire and adversarial sets, the intent router adheres to JSON Format 100% with 25-36% exact match and 42-46% fuzzy match accuracy.
Copyright (c) 2026 Chethana T V, Payal Koolwal, R. Jennie Bharathi, Tamilarasan S, Rakhi Dua
An Explainable Hybrid Machine Learning Framework for Enhanced Early Detection and Comprehensive Staging of Breast Cancer
Breast cancer remains a major global health challenge, requiring accurate and interpretable diagnostic systems for reliable clinical deployment. This paper proposes an Explainable Hybrid Machine Learning (XML) framework that integrates advanced feature extraction with interpretable classification techniques for early breast cancer detection and staging. Using benchmark datasets including WDBC, BreakHis, and CBIS-DDSM, the framework applies CLAHE-based preprocessing, PCA, and SHAP-driven Recursive Feature Elimination (SHAP-RFE) to generate an optimized Hybrid Feature Vector (HFV). Experimental results demonstrate a classification accuracy of 96.84% and sensitivity of 97.12%, outperforming conventional machine learning models while reducing overfitting. The framework further supports multi-class staging aligned with AJCC TNM criteria using a dual Explainable AI subsystem combining Grad-CAM++ and SHAP for visual and mathematical interpretability. Inclusion of molecular pathways such as MAPK and PI3K-Akt improved predictive reliability by 9.2%. The proposed system offers a robust, transparent, and clinically auditable solution for personalized breast cancer diagnosis and treatment planning.
Copyright (c) 2026 Shubhangi, Sanjeev Sharma, Akhtar Husain
Digital Entrepreneurial Ecosystems and Women-Led Tourism Enterprises in the Himalayan Region
Purpose: This study examines digital platform adoption, revenue outcomes and structural barriers among women-led tourism enterprises in Ladakh's emerging platform economy. Design/methodology/approach: Using survey data from 346 women entrepreneurs across Leh, Nubra, Zanskar and Kargil collected between March and May 2025, the study employs descriptive statistics, regression analysis, ANOVA and structural equation modelling. Findings: Instagram and WhatsApp Business emerged as the dominant digital business platforms, significantly improving visibility and customer engagement. However, connectivity limitations, digital skill gaps and language barriers continue to constrain entrepreneurial growth, particularly in remote areas such as Zanskar. Research limitations/implications: The cross-sectional design and self-reported revenue data restrict causal inference and findings apply to a single Union Territory. Practical implications: Connectivity investment should precede digital skills training, curricula should be multi-platform and Hindi and Ladakhi portal interfaces are priority interventions. Originality/value: The study provides the quantitative analysis of women-led digital entrepreneurship in a peripheral Himalayan tourism economy and contributes evidence for platform localisation, digital literacy and connectivity-focused policy interventions. It further contributes to digital entrepreneurship literature by conceptualising digital platforms as infrastructural substitutes enabling entrepreneurial participation within geographically peripheral tourism economies.
Copyright (c) 2026 Mohd Rafee, Tsultim Zangmo, Tsewang Dolma, Stanzin Khenrab, Tsering Lanzes
Generative AI and Technical Debt in Software Development: Productivity Gains versus Code Quality and Security Risk
Generative Artificial Intelligence (GenAI) coding assistants like GitHub Copilot, ChatGPT, Amazon CodeWhisperer, and new agentic development environments have taken the software industry by storm, with the promise of enhancing developer productivity. But there has been increasing evidence to the contrary, that since these benefits come with a parallel increase in what has been dubbed "technical debt"—quality, maintainability and security problems only become apparent at a later point in the software lifecycle. This paper empirically analyzes the impact of GenAI coding assistants on developer productivity, code quality and maintainability and security posture from two perspectives, that of IT companies. Concurrent mixed method (triangulation) design is used with structured survey of 412 software professionals and repository-level analysis of 1240 software code commits with the authorship of each commit identified, and 22 semi-structured practitioner interviews. Two methods are used to test the hypothesis of the ‘productivity paradox' – structural equation modelling and non-parametric artefact analysis. The results suggest that the use of GenAI can statistically significantly improve productivity in the short term (β = 0.48, p < .001) particularly when using GenAI for the routine aspect of work, but benefits appear to be lower for more experienced developers when considering the maintenance burden (β = 0.06, p > .05). Lacking good governance, AI-assisted code has substantially higher technical-debt density (37%) and security-vulnerability density (41%) than do codes written by humans. The governance maturity is the key moderator that has significant increase in the value of Δ R^2 = 0.14. The study suggests a governance model that consists of these three dimensions: velocity, maintainability, and security, and extends the concept of technical debt in the field of information systems and software engineering, adding the concept of technical debt as a socio-technical risk instead of automated.
Copyright (c) 2026 Rishu Roy, Vishal Khasgiwala, Vanita Joshi
Digital Forensics, Informational Privacy and Constitutional Dignity: A Critical Study of Cybercrime Investigative Framework under BNSS and DPDP Act
The rapid expansion of cybercrime investigation has brought fundamental changes in the manner in which criminal investigations are used to be conducted in India, especially with increased usage of digital forensic technologies during extraction, preservation, and analysis of electronic devices seized during cybercrime investigations. However, these modern digital forensic technologies help in increasing the technological capabilities of our law enforcement agencies. On the other hand, it also raises several constitutional concerns as well, such as breach of informational privacy, constitutional dignity, procedural fairness, and protection against self-incrimination guaranteed under Article 21 and 20(3) of the Indian Constitution, due to unrestricted extraction of personal information by investigative authorities. Therefore, it can be said that technological upgrades in criminal investigations have expanded faster than constitutional and procedural safeguards. This article attempts to evaluate critically India’s legal and constitutional framework governing digital forensic investigations, with special focus on the investigation stage of cybercrime investigation. This article includes analysis of various constitutional provisions protecting the right to privacy, along with India’s procedural framework regulating investigative data practices, forensic extraction, and state access to personal digital information, such as the Bharatiya Nagarik Suraksha Sanhita 2023 and the Digital Personal Data Protection Act, 2023. This article argues that though India’s legal framework has been updated recently to accommodate electronic investigation within in its criminal procedure, but the absence of comprehensive statutory framework specifically regulating digital forensic governance, forensic laboratories, extraction limitation, electronic device search and seizure practices, protocols for cloning the device, data minimization requirement during cybercrime investigation has substantially impacted the dignity as well as fail to secure informational privacy of the suspect of an alleged offence.
Copyright (c) 2026 Puneet Sharma, Archana Sarma
LEGAL GOVERNANCE OF CRITICAL INFORMATION INFRASTRUCTURE IN INDIA: A CRITICAL AND COMPARATIVE ANALYSIS
In today’s digital world, the Critical Information Infrastructure (CII) is the foundation for many essential services. CII also forms the backbone for national security, economic stability, and public welfare. The increasing dependence on digital technology and even faster-evolving cyber threats has made the protection of CII a matter of utmost importance. In India, the protection of CII is primarily recognized under the IT Act, 2000. Sections 70, 70A and 70B of the IT Act, 2000 provide the statutory basis of identification and protection of CII. In addition to these, the sector-specific guidelines and the Digital Personal Data Protection Act, 2023 (DPDP Act) also contribute to a broader cybersecurity framework. In an era of digitalization where dependence on technology is increasing, cyber threats have also become more sophisticated. This article aims to critically examine India’s existing legal framework for the protection of CII. Starting with the primary law – the IT Act 2000, it then covers various institutional mechanisms, sector-based regulations, and the relevance of the DPDP Act 2023. A comparative analysis with the legal approach adopted by various international jurisdictions – USA and UK – for protection of CII is also made. The methodology adopted during the study is doctrinal, which is supplemented by comparative analysis. The major legal and institutional issues and challenges are being identified during this study, which are affecting the effective protection of CII in today’s digital era. These include the absence of updated legal provisions, limited regulatory coordination, limited public-private cooperation, overlapping responsibilities, low incident reporting, etc. The article concludes by briefly suggesting the need for a more coherent, coordinated, and comprehensive legal framework for strengthening India’s legal regime governing CII.
Copyright (c) 2026 Puneet Sharma, Divya arora
A Lightweight CNN-GRU Model for Human Activity Recognition with Efficient Edge Deployment Using TFLite
Wearable sensor-based human activity recognition (HAR) has become increasingly popular for applications in health monitoring, fitness, and smart living. But the use of deep learning models on edge devices is still challenging due to limited memory and computational power. In this paper, we develop a resource-constrained CNN-GRU hybrid model for HAR on the WISDM dataset. This architecture uses convolutional layers for spatial learning and gated recurrent units (GRU) for sequence learning. For deployment on the edge, the model is quantized to TensorFlow Lite (TFLite) using float16. Our experiments show that the model achieves an accuracy of 94.07%, while the size of the model is substantially smaller and suitable for deployment on edge devices. Importantly, the TFLite model maintains the same accuracy as the original model, ensuring its suitability for real-time deployment. The extensive assessment through confusion matrices, ROC curves and classification metrics confirms the effectiveness of the model across various activities. The proposed approach offers a balance between accuracy and computational efficiency, enabling real-time HAR on edge devices.
Copyright (c) 2026 Ganesha H S, Harsha K G, Roshini P, Shabana Sultana, Sharada Guptha M N, Manu Y M
Seismic Response of (G+10) R.C.C Building with Floating Column In Different Zones
In the current situation, majority of the buildings are usually erected on the steep ground owing to the limitations of the terrain. The behaviour of building on sloping ground during earthquake is dependent on distribution of stiffness and mass in vertical and horizontal plane, which are different in the case of building resting in sloping ground. Short columns are very stiff, and thus they absorb a lot of structural energy. Some of the manuals and regulations like the Earthquake regulations to this phenomenon require an additional attention to be paid to it. The building that was considered in this study has a G + 10 storeys reinforced concrete (RC) building with variable ground slope as 0 0, 5, 10, 15 and 20 without shear walls and with shear walls regular on plan and at corner as well. The constructions are planned according to the IS 456:2000 and subjected to the earthquake loads. Liner Static and Linear Dynamic analysis (Response Spectrum and Linear Time History analysis) has been done using structure analysis tool SAP 2000, on the modelling and analysis of the building. The primary purpose is to determine the behaviour of the building on sloping ground due to different height of column used in bottom storey and different location of shear walls and to investigate the effectiveness of shear wall on the sloping ground. The building models have been found to check performance of the building by studying the dynamic properties such as maximum forces in columns, Base shear, Roof displacement, Bending Moments and Fundamental natural periods. It is noted that step back buildings with shear walls which are symmetrically arranged in plan are observed to be more appropriate as compared to step back buildings which have shear walls arranged in peripheral corners and buildings which do not have shear wall.
Copyright (c) 2026 V. Kavitha, G. Anil Kumar, P. Rohit
Artificial Intelligence in FinTech: Catalyzing Financial Innovation and Entrepreneurial Venture Creation
Artificial Intelligence (AI) is transforming the FinTech industry, revolutionizing financial solutions, enhancing information-driven choices, and ushering in new chances for entrepreneurial venture development. The FinTech sector is experiencing a transformative influence from Artificial Intelligence (AI), with its role in innovation and shaping the future of financial solutions, data-driven decision-making, and entrepreneurial venture development. This research aims to explore the potential of using Artificial Intelligence (AI) in the FinTech industry to drive innovation and entrepreneurial growth opportunities. It delves into the ways that technologies like machine learning, predictive analytics, natural language processing, intelligent automation and AI-based decision systems are redefining traditional financial services and enabling the growth of technology-based business models. The study aims to understand the potential of using AI for efficient operations, customer personalization, risk assessment, fraud prevention, and provision of financial services, and its implications for entrepreneurial experimentation and venture formation. It also explores the significance of AI-driven fintech solutions in terms of opportunities identification, resources optimization, penetration, and scaling financial products. At the same time, the concepts of data privacy, algorithmic bias, cybersecurity, regulatory uncertainty, technological dependence and accessibility of AI capabilities are examined. The research method used in the study is qualitative, conceptual and analytical based on nature problem and aspect analysis. This study uses a conceptual and analytical research method that uses secondary data (literature review) in order to understand the relationship between the implementation of AI, financial innovations, and entrepreneurial activities. The results suggest that AI can be leveraged as a tech tool to boost efficiency and productivity, but most importantly as a tool to generate new businesses, reduce the hurdles of entrepreneurship and innovate new financial products and services. The study indicates that the potential of AI-powered FinTech ecosystems to transform will require responsible use of AI, supportive laws and regulations, digital infrastructure, and entrepreneurial competence.
Copyright (c) 2026 Vijayalakshmi S, Skanda Moda Gururajarao, Ankitha Sharma, Midhun. M, Neeti Misra, T. Mohanapriya
Hybrid Deep learning framework for automated Diabetic retinopathy classification using rential fundus images
DR is a major cause of blindness. It is particularly crucial to make an early and accurate diagnosis of DR. The manual examination of retinal fundus images is time-consuming, subjective and prone to error and hence there is a motivation for automated computer aided solutions. Kaggle APTOS 2019 is used as the dataset. Images are scaled, normalised, augmented and feature extracted in order to setup parallel pipelines for classification and detection tasks. The range of models used include basic models (ResNet50, MobileNetV2 and DenseNet121), proposed models (SVM-RBF, Modified EfficientNetV2, EfficientNet–SVM hybrid) and additional hybrid or ensemble models (Xception, NASNet-Large and voting classifiers using several models). We use YOLOv5, YOLOv8, YOLOv9 and YOLOv11 as detectors in this work. Accuracy, precision, recall and F1-score are used for classification. DenseNet121 was 99.6 % for all of them. YOLOv5 had the highest precision (0.713) and mAP (0.788) for object detection and YOLOv9 had the highest recall for localising lesions. XAI approaches such as Grad-CAM can point out the important regions inside an image where the AI model based its conclusion on, thereby increasing the interpretability of the model. The web interface developed using flask provides the end to end inference and displays the real-time classified results to the user along with the annotated photographs which have the bounding boxes. It is a very accurate and interpretable hybrid system to detect and classify DR.
Copyright (c) 2026 Goka Tejaswini Nivedhitha, Naga Malleswari Dubba
Agriculture Land Classification Using MobileNetV2 on Euro SAT RGB Dataset
LULC classification from the remote sensing imagery is an important and critical task for sustainable agricultural planning, environmental monitoring, and precision farming. In this paper, we propose an efficient deep learning framework for classifying agriculture land from high-resolution satellite using the Euro SATRGB dataset that cover ten land cover classes. We used light weight CNN MobileNetV2 with Transfer learning, Extensive data augmentation, Mixed precision training and cosine decay learning rate scheduler. The proposed model achieved a test accuracy of 98.67%, which is a state-of-the-art among newer models tested on the same dataset and was trained on 19,504 images, validated on 3,446 images and tested on 4,050 images. We have shown that MobileNetV2 is not only the fastest model but also competitive in performance, demonstrating its ability as a light-weight but highly accurate model with potential for large-scale agricultural land classification tasks. This study lays the foundation for lightweight deep learning models for scalable and real-time practical applications in precision agriculture and builds on the rapidly growing body of literature in this area.
Copyright (c) 2026 B Anil, Polapally Vishnuvardhan Reddy, G Praveen, R Uma
Transfer Learning with MobileNetV3 for High-Accuracy Classification of Tomato Plant Diseases
This research proposes a deep learning based transfer learning approach for automatic identification of tomato plant diseases. The system utilizes MobileNetV3Large as a backbone for feature extraction and is further fine-tuned on a custom dataset based on the Plant Village dataset with total of 14,529 images divided over 10 classes (nine disease classes and one healthy class). The method used for training was a two-step approach where we first froze the base network and trained a new classifier head and then we fine-tuned the last 40 layers of the feature extractor. Data augmentation techniques such as rotation and horizontal flipping were employed during training to facilitate generalization. We present experimental results that show a remarkable classification performance, with a test set accuracy of 98.91% with a loss of 0.0361. When the fine-tuning happened it produced a validation accuracy of 97.35% This study validates MobileNetV3 as a powerful light-weight backbone for real-time, vision-based diagnosis of plant diseases, making a promising contribution to precision agriculture applications.
Copyright (c) 2026 G Praveen, S Anusha, K Suma, S. Sudharshan
MB-EFF: A Multi-Branch Environmental Feature Fusion Deep Learning Model for Crop Classification and Recommendation
The motive of this project is to develop an accurate, reliable and scalable toward nature crop classification and recommendation system which examines the scenarios envisaged in the traditional machine learning modeling and integrates the multi heterogeneous soil and environmental data attributes handling capablity of deep learning. However, given the non-normality of this kind of heterogeneities of soil nutrients and climatic factors, the conventional nature models fail to characterize these complicated nonlinear connections as effectively as they can, which may lead to low prediction accuracy and poor generalization. To respond to these challenges this paper proposes a new deep learning architecture: Multi-Branch Environmental Feature Fusion (MB-EFF), which unites chemical soil features and environmental parameters into individual learning branches. The extracted representations are then recalibrated via a squeeze-and-excitation based feature recalibration mechanism, and subsequently subjected to deep nonlinear reasoning layers for enhanced discriminative learning. The model adopts a standardization on input features as pre-processing, trained with adam optimizer + early stopping applied to achieve stable convergence and robustness. This paper proposes an experimentally validated solution based on publicly available crop dataset and achieves 98% accuracy along with macro averaged & weighted averaged precision, recall and F1-score, being 0.98. As it shows an excellent performance near the perfect scores (1.0) for most of the crop categories reflects the strong generalization of the model in providing class-wise performance. The results showed that MB-EFF is a systematic scalable solution to farm precision ag applications with simulation-level precision and can be fused with mechanistic knowledge with data-driven ag decision support with expertise in determining crop recommendation.
Copyright (c) 2026 VNS Manaswini, Chithanoor Arjun, Yamini Chouhan, V. Sumalatha