As classical computation approaches its fundamental limits related to power dissipation, reversible logic, which theoretically achieves zero energy loss, is becoming a critical technology for future low-power and quantum computing. However, most research in this field remains theoretical, lacking practical, hardware-verified implementations. This paper bridges this gap by presenting the complete hardware implementation and rigorous fault-tolerance validation of a reversible encryptor based on extended Fredkin gates. First, we detail the full realization of the encryptor on an Altera Cyclone IV Field-Programmable Gate Array. This implementation is not just a simulation but a complete, interactive hardware prototype, featuring real-time data input via a standard keyboard and output to a video graphics array monitor. Second, since functional verification is insufficient for cryptographic hardware and exhaustive testing is computationally infeasible, we introduce a novel validation methodology. This core contribution utilizes a metaheuristic ant colony optimization algorithm, not for synthesis, but for the intelligent generation of an optimal and compact set of test vectors. This test set is designed to achieve maximum fault coverage for the industry-standard "stuck-at fault" model. The algorithm successfully generated a minimal test set achieving 100% coverage for the considered single stuck-at fault model. We then experimentally validated this methodology by manually injecting a stuck-at fault into the hardware description language, recompiling the faulty circuit into the device, and confirming that the metaheuristically generated test vector successfully detected the physical fault. Thus, this work demonstrates the full cycle from theory to a practically validated and reliable hardware implementation of a reversible system. 

Part-of-Speech (POS) tagging is an essential and important pre-processing activity for many Natural Language Processing (NLP) applications, this is particularly more evident for morphologically rich languages such as Marathi. This research investigates POS tagging for Marathi using the Maximum Entropy Markov Model (MEMM). MEMM combines the strengths of conditional probability modelling and sequence prediction, allowing the integration of rich contextual features. Features used include word forms, suffixes, prefixes, and neighboring tags, effectively tackling the challenges presented by inflectional variations and ambiguity in Marathi. Experimental results demonstrate that the MEMM-based POS tagger achieves an accuracy of 83.72%. This performance marks a notable advancement in Marathi POS tagging, given the linguistic diversity and the scarcity of annotated data. Error analysis enhances the issues like ambiguity in homonyms and out-of-vocabulary words, providing methods for further improvement through enriched datasets and sophisticated modelling techniques. This study enhances NLP applications such as machine translation, spell checking, and sentiment analysis for Indian languages and offers a solid foundation for future research in Marathi POS tagging.

Most of the existing data center allocation mechanisms contribute either user centric or service provider centric not for both ends but in reality, both have different objectives. For example, the objective of a user is minimization of cost, response time as well as processing time whereas the objective of service provider is to maximize the profit and processing time and minimization of response time, bandwidth, energy consumption and computing overhead with subject to effective resource utilization and load balancing. To address this challenge, this paper introduces a Cost Denigration-Based Data Center Allocation Policy (CD-BDAP) utilizing Particle Swarm Optimization (PSO), which simultaneously considers economic cost, response time, and energy consumption in the selection of data centers. In contrast to conventional PSO-based broker policies, CD-BDAP integrates a workload similarity-aware allocation strategy by calculating a dissimilarity index among user requests, thereby facilitating enhanced consolidation and energy efficiency. A weighted objective function is developed to balance user-centric metrics (cost and response time) with provider-centric metrics (profit and energy consumption), explicitly capturing their trade-offs. The proposed mechanism is assessed utilizing CloudAnalyst, which is constructed on CloudSim. The experimental results indicate that CD-BDAP achieves a reduction in VM cost, a decrease in response time, and an enhancement in energy efficiency, while simultaneously increasing the overall profit for service providers. The findings suggest that the integration of energy-aware cost modeling and workload similarity into PSO-based allocation can enhance both economic and performance efficiency in the selection of cloud data centers. The outcomes of CD-BDAP are compared with the existing PSO-based mechanisms and found enhanced performance.

Education is crucial for personal and economic growth, but financial challenges in developing countries hinder equitable academic success. NGOs administer scholarship programs to empower underprivileged individuals, a crucial step towards the attainment of Sustainable Development Goal 4, which aims to provide inclusive and equitable quality education for all. This study proposes a novel Scholarship Award Recommendation System that leverages predictive modelling and ensemble learning to identify deserving students for scholarship awards. The system utilizes a robust ensemble model that combines the strengths of Quadratic Discriminant Analysis (QDA), Random Forest (RF), and Extra Trees (ET) to predict students' academic performance. Additionally, we incorporate answers from the General Mental Health Questionnaire (GHQ-12). The GHQ-12 responses are pre-processed using a binary scoring approach (0-0-1-1) and integrated as predictive variables alongside academic and demographic features. We apply this framework to a case study of Nigerian university students in partnership with Springtime Development Foundation. The results indicate that incorporating GHQ-12 features significantly enhances prediction accuracy, with QDA, RF, and ET achieving accuracy scores of 0.90, 0.86, and 0.89, respectively. Statistical analysis using a t-test confirms the relevance of GHQ-12 features, with a p-value of 0.0013 establishing a significant correlation between student performance and mental health status. The study showed the effectiveness of the ensemble model to accurately predict students’ academic performance. It highlights the significance of incorporating variables from the (GHQ-12) into the predictive model, indicating mental health as a crucial factor for predicting academic performance which in turn enhances the performances of the Classification Models considered.

The rapid integration of conversational Artificial Intelligence (AI) into instant messaging platforms has transformed how younger generations interact with digital technology. This study investigates Generation Z's engagement with Meta AI on WhatsApp by employing a modified Technology Acceptance Model (TAM) that partitions End-User Computing Satisfaction (EUCS) dimensions to ensure measurement validity. Specifically, 'content' and 'accuracy' reflect Perceived Usefulness, while 'format' and 'timeliness' reflect Perceived Ease of Use. A quantitative survey involving 272 Generation Z respondents in Indonesia was analyzed using Covariance-Based Structural Equation Modeling (CB-SEM) with AMOS. The results reveal that Perceived Ease of Use significantly influences both Perceived Usefulness and Attitude Toward Using. Consequently, Perceived Usefulness acts as a partial, rather than full, mediator between ease of use and user attitude. Furthermore, Attitude Toward Using emerged as a powerful determinant of Actual System Use, with the proposed model explaining 89% of its variance (R2 = 0.89). These findings suggest a synergistic effect for "digital natives": while an intuitive format and fast response times directly foster positive attitudes, the epistemic quality and accuracy of the AI remain the dominant drivers of sustained engagement. This study contributes theoretically by validating a robust, multicollinearity-resistant modified TAM for conversational AI, providing practical insights for developers to maintain frictionless interfaces while prioritizing algorithmic accuracy to enhance user adoption.

In the Internet of Things (IoT) environment, a Distributed Denial-of-Service (DDoS) attack in the network causes poor performance and resource-limited issues to users. Existing systems do not provide real-time adaptability, leading to delayed mitigation. Also, centralized storage systems suffer from breaches and tampering. To tackle these issues, a secure and intelligent IoT DDoS detection and mitigation framework is presented that utilizes hybrid encryption, blockchain storage, ensemble deep learning (DL), and reinforcement learning (RL) to improve the accuracy, security, and efficiency of IoT networks against several cyber-attacks. The developed technique collects data from a dataset and pre-processes it for handling missing values and normalizes it for further analysis. Secondly, a hybrid encryption method combining Homomorphic Encryption (HE) and ChaCha20 is adopted for data encryption with optimal key selection using Dingo Optimizer (DOX). Then, the encrypted data is securely stored in blockchain through off-chain storage and on-chain hash storage to ensure data integrity and tamper-proof security. DDoS attack detection is performed using an ensemble model called TriGuard-Net that combines AlexNet, LSTM, and PSPNet, with optimizing hyperparameters using Fire Hawks Optimizer (FHO). Finally, an RL-based mitigation system using Deep Q-Network (DQN) helps in real-time attack mitigation and enhances IoT security. Experimental results reveal that the presented model offers superior performance by achieving an accuracy of 99%, a kappa score of 98%, an R2 Score of 97%, an MCC of 98%, a Jaccard Score of 98%, and a Hamming Loss of 0.006, thereby outperforming other current models.

Requirements change management is one of the core business analyst's activities, directly affecting change impact analysis, stakeholder communication, and the long-term system maintainability. While research on this topic examines in detail change processes, tracking methods, and change type classification, the problem of systematically documenting requirements changes remains underexplored. Existing research lacks a unified classification of change documentation approaches and context-sensitive recommendations for their selection, which limits their effectiveness in managing requirements.
To address this gap, this study develops a context-oriented framework for selecting approaches to requirements change documentation. The framework integrates three components: a conceptual model based on the Baseline–Delta–Target State triad, a taxonomy of documentation approaches, and a context-driven selection mechanism grounded in empirical evidence. A systematic literature review was combined with an analysis of the survey of 324 practicing business analysts from Ukrainian and international companies. Statistically significant associations between selected project context attributes and documentation practices were identified using the Chi-square test of independence and Cramer's V, while additional dimensions were supported through evidence from the literature.
The framework incorporates six documentation approaches: Full Target State, Delta-only, Target-driven Delta, Delta-driven Target, Parallel Use, and Hybrid Cycle. Four contextual dimensions emerge as key factors: project, environment, resources, and stakeholders. To support context-based selection of the change documentation approach, a matrix was developed that integrates the identified dependencies.
The results position requirements change documentation as a context-sensitive knowledge management mechanism rather than a universal procedural standard.

Machine learning (ML) has made it much easier to find and estimate the risk of early stage of cardiovascular illnesses by making it possible to analyses massive, various clinical datasets quickly and easily. In these kinds of datasets, demographic information, lifestyle characteristics, medical history, and diagnostic measurements are all included. These are all things that may not be easy to see through standard clinical examination. This study examines heart disease prediction through a series of hybrid ML models that integrate neighborhood-based classifiers, swarm intelligence-driven optimization, and ensemble learning, motivated by existing obstacles. There are four hybrid models being proposed: MSMO-KE and MSMO-KM, which combine Modified Spider Monkey Optimization (MSMO) with K-Nearest Neighbour classifiers that use Euclidean and Minkowski distance measures, respectively. There are also two ensemble variants, MSMO-KECB and MSMO-KMCB, which add CatBoost as a final prediction layer. To make sure it is strong and can be used in other situations, the proposed framework is tested on three separate cardiovascular datasets using a cross-validation method. The experimental findings show that the performance is always better than the baseline and the best models that are already used. The MSMO-KMCB model performs the best overall out of all the approaches tested. It has a cross-validated accuracy of 98.2% on Dataset-3 while keeping a high sensitivity. The comparative research demonstrates that the proposed MSMO-based ensemble models surpass current methodologies in predictive accuracy and recall, underscoring their promise for dependable and efficient heart disease risk prediction in clinical decision-support systems.

Recommender system commonly suffers from data sparsity and cold-start problems, where user-item interactions hinder reliable preference learning. While recent Graph Neural Network based models such as LightGCN and NGCF effectively capture higher-order collaborative signals, they primarily rely on interaction-derived embeddings and remain sensitive to sparse environments. This paper Attribute Enabled Graph Neural Framework (AE-GNF) proposes a Semantic-Aware Graph Refinement Framework that integrates attribute-driven representation learning with graph-based collaborative propagation to address these limitations. The proposed method first encodes heterogeneous user and item attributes using semantic embedding modules to generate informative initial representations independent of interaction density. Dense sematic embeddings are generated using modality specific neural encoders including transformer based text encoder for descriptive attributes, a recurrent attention network for behavioral interaction sequences and temporal contextual feature encoder for metadata signals. These embeddings are then refined through a normalized graph propagation mechanism that jointly models structural connectivity and semantic similarity, enabling robust higher-order preference learning. Unlike conventional recommenders, the framework preserves attribute semantics during message passing and enables inductive cold-start recommendations, where embeddings for newly introduced users or items are generated directly from attributes without requiring prior interaction edges. Experimental evaluation conducted on publicly available benchmark datasets including MovieLens-1M, Amazon Electronics, Amazon Books, and Amazon Prime Movies and LastFM360 demonstrates consistent performance improvements over Matrix Factorization, content-based models, GraphSAGE and Neural Graph Collaborative Filtering (NGCF). Results show notable gains in ranking accuracy, diversity and robustness under varying sparsity levels. The proposed AE-GNF achieves improved recommendation performance reducing RMSE by 3.5 to 6.2% and improving NDCG@10 by 6-11% compared to graph-based baselines across benchmark datasets. The findings confirm that integrating semantic attribute encoding with graph refinement provides a scalable and effective solution for next-generation recommendation systems operating in sparse and heterogeneous environments.

Sinusitis is an inflammation of the paranasal sinus mucosa, which is an infection caused by a bacterium, fungus or virus. Therefore, for earliest and accurate prediction of sinusitis from Computed Tomography (CT) image, this research introduces a novel Artificial Intelligence (AI) based technique. The developed research is initiated with preprocessing using a Gabor filter to improve the quality of an image. After, segmentation using Gaussian Mixture Model (GMM) is exploited for effective isolation of sinus regions affected by inflammation. For acquiring the crucial features from the segmented regions, Gray-Level Co-occurrence Matrix (GLCM) based feature extraction is utilized which offers clinically meaningful features that improve transparency. Consequently, the hybrid Harmony Search Algorithm (HSA)-Grey Wolf Optimizer (GWO) feature selection is utilized to choose the most relevant features. This hybrid method outperforms traditional selection techniques by effectively identifying the most discriminative and non-redundant features, enhancing classification accuracy while reducing computational complexity. For accurate classification of sinusitis into various severity levels, the modified Artificial Neural Network (ANN) is employed. Unlike end-to-end deep learning models, this modular approach allows for fine-grained control at each stage, ensuring that critical medical insights are not lost in abstraction. This structured pipeline allows each phase to be optimized individually, improving transparency, reliability and ultimately, diagnostic performance. The performance of the research is analyzed via python software and it reveals that the developed classifier achieves an accuracy of 96.41%.

With the rapid proliferation of electronic devices, the volume and sophistication of malware have surged, posing critical cybersecurity threats. Traditional malware detection approaches face challenges such as limited generalization, unbalanced datasets, and high computational costs. To address these issues, this study introduces the LLM-Powered Transformer Framework for Multi-Class Malware Detection, an image-based approach integrating Large Language Models (LLMs) and transformer architectures with Convolutional Neural Networks (CNNs). The proposed framework enhances malware classification by leveraging data visualization, balanced sampling, and data augmentation techniques, achieving over 98.86% accuracy across four open-source datasets. Furthermore, this study makes two key contributions: first, it provides granular insights into malware classification performance using confusion matrix analysis, aiding cybersecurity professionals in refining detection strategies. Second, the balanced sampling approach eliminates the need for additional datasets, minimizes hardware overhead, and dynamically adjusts sampling weights for optimal learning. Additionally, data augmentation techniques mitigate overfitting, enhancing the model's adaptability to diverse malware variants. Comparative analysis with state-of-the-art methods demonstrates the proposed framework's efficiency in achieving high accuracy while maintaining computational feasibility. These advancements establish a robust foundation for real-world malware detection and cybersecurity applications.

Emotion detection from text plays a pivotal role in applications such as sentiment analysis, social media insights, and customer experience management. This study introduces a multi-model fusion approach for emotion detection using the Kaggle Emotion Text Dataset, a widely recognized benchmark that captures a variety of emotions across diverse textual inputs. The proposed framework employs a combination of machine learning classifiers, including Random Forest (RF), Logistic Regression (LR), Decision Trees (DT), Stochastic Gradient Descent (SGD) and Support Vector Machine (SVM). To maximize predictive performance, these models are integrated using two ensemble strategies: Stacking and Majority Voting. Stacking combines base models with a meta-classifier, enabling the system to learn intricate patterns in the data, while Majority Voting provides a simpler yet effective method for decision consolidation by leveraging collective model predictions. Performance evaluation is conducted using metrics such as accuracy, precision, recall, F-measure, False Positive Rate (FPR), and False Negative Rate (FNR). The results demonstrate that the Stacking approach achieves the highest accuracy of 99.92%, with precision of 99.68 %, recall of 99.19% and f-measure of 99.43%, respectively with Micro FPR of 0.0001, Micro FNR of 0.0007, Macro FPR of 0.0002 and Macro FNR of 0.0081. Majority Voting, while slightly less accurate, excels in reducing FPR and FNR, making it a valuable alternative in scenarios where minimizing misclassification is critical. This work underscores the potential of ensemble learning in addressing the complexities of emotion detection in text. The integration of diverse classifiers enhances prediction robustness and highlights the trade-offs between model complexity and real-world feasibility. By delivering a comprehensive evaluation and actionable insights, this single-author study contributes to advancing the field of emotion analysis and its practical applications.