Journal of Trends in Computer Science and Smart Technology
type: channel
publisher: Inventive Research Organization
language: en
publication name: Journal of Trends in Computer Science and Smart Technology
issn: 2582-4104
items: Integrating Clinical Indicators and DaTSCAN SPECT Biomarkers Using Transfer Learning Ensemble Models for Early Parkinson’s Disease Diagnosis with SWEDD Cohort
items: Comprehensive Seven-Lens Evaluation Framework for Remote Access Laboratory Systems: Implementation and Performance Optimization
items: Deployment-Oriented Evaluation of Healthcare Reimbursement Cost Prediction
items: Adaptive Gated Multimodal Fusion for Robust and Generalized Human Activity Recognition
items: Hybrid Graph Mamba Framework for Histopathological Image Classification
items: Enhancing Rail Surface Defect Detection Using a Hybrid YOLOv11–Vision Transformer Framework
items: Guardian-Based Anonymous Password Management with Privacy Preservation Using Threshold Cryptography
items: Dual-Metric Detection of Synthesis-Masked Hardware Trojans in AES-256: Correlating Power Signatures with Switching Activity
items: Machine Learning Approach for Adaptive Data Protection in Document Database Systems
items: Federated Attention‑Capsule CNN with Bio‑Optimization for Coronary Artery Disease Screening
items: A Slot-Aware Semantic Signaling Framework for Multi-intent Classification
items: Emotion and Cognition Based Mental Health Analysis from Social Media
items: Graph Neural Network and Reinforcement Learning Framework for Test Case Prioritization, Selection, and Reduction
items: A Machine Learning Framework for Evaluating MSME Incubation Dynamics
items: Adaptive Markov Decision Process-Based Congestion Control Using Deep Deterministic Policy Gradient in Distributed MQTT Brokers
items: Energy–Cost–SLA Aware Cloud Scheduling via Adaptive Non-dominated Sorting Genetic Algorithm-III and Neighborhood Refinement
items: A Federated Learning Autoencoder CNN Hybrid Approach for Privacy Preserving Network Traffic Classification
items: Live Virtual Machine Migration Using Xen Server
items: Digital Twin based Data Pattern Analysis for IoT-Based Pharmaceutical Production Monitoring
items: Review on Blockchain-Based Offline Payment Solutions
items: A Comprehensive Review of Architectural, Algorithmic, and Technological Innovations for Energy-Efficient 6G Communication Networks
items: Evaluating Random Forest and Decision Tree Algorithms for Resolving Lexical Ambiguity in the Gujarati Language
items: Multi-Scale Semantic Fusion Network with Adaptive Attention for Remote Sensing Image Captioning
items: Explainable Hybrid Artificial Intelligence Boosting–Shapley Framework for Cardiac Disease Diagnosis
items: The Role of Digital Intangible Culture Heritage Resources in Promoting Students' Innovative Ability in Higher Education Courses
items: Design and FPGA Based Realization of an SMC-ESO Enhanced Nonlinear Fractional Order PID Controller for BLDC Motor Speed Control
items: Energy-Efficient Neuromorphic Architectures Enabled by Resistive Memory
items: UPS-Tailored Next-Generation Battery Technologies: Variability, Sustainability, Comparative Analysis and a Technology Roadmap
items: AI-Driven Malware Detection and Prevention using Hybrid Machine Learning and Blockchain for Secure Cyber Threat Intelligence
items: Effectiveness of Farmers’ Professional Cooperatives in Helping Rural Revitalization Using Big Data Analysis

Integrating Clinical Indicators and DaTSCAN SPECT Biomarkers Using Transfer Learning Ensemble Models for Early Parkinson’s Disease Diagnosis with SWEDD Cohort
type: item
description: One of the most crucial issues nowadays is the initial detection of Parkinson's disease (PD) to improve patient treatment and diagnosis. The objective of this research is to classify PD patients at an early stage among the healthy controls (HC) and the Scan without Evidence of Dopaminergic Deficit (SWEDD) group, which has a combination of symptoms from the PD and HC control groups. Accurate classification is necessary and challenging to identify Parkinson's disease in these hybrid cases. To address the aforementioned issues, the current study focuses on a novel deep neural network (DNN) architecture that can distinguish between participants in SWEDD, healthy controls, and people with PD. The data consists of clinical information and DaTSCAN single-photon emission computed tomography (SPECT) scans from 589 participants, including 62 SWEDD subjects, 135 healthy controls, and 392 PD patients. The original PPMI datasets were used. The proposed framework incorporates DNN and numerous transfer learning models, including ResNet50, DenseNet121, Xception, ResNet152, InceptionV3, VGG16, and EfficientNetV2B0.We have used Random Forest (RF) and Support Vector Machine (SVM) to create ensemble models that are comparable to all of the previously mentioned transfer learning models to improve optimization. In comparison to the estimations from hybrid model practices and conventional clustering algorithms like DBSCAN and K-Means, DNN produced distinguishing scores of 91.8% accuracy, ResNet50 with SVM produced 0.94 accuracy, ResNet152 with SVM produced 0.97 accuracy, and Xception with SVM produced 0.99 accuracy. The Xception with SVM model also yielded better results, with F1 scores of 0.98 for class 0 (HC), 1.00 for class 1 (PD), and 0.93 for class 2 (SWEDD) subjects. These results show that the proposed deep learning and transfer learning ensemble model configuration is valid for identifying PD cases alongside healthy ones in the SWEDD population.
subject: Articles
  topic: section
subject: Transfer Learning
  topic: keywords
subject: Parkinson Disease Progression
  topic: keywords
subject: SWEDD
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Ensemble Model
  topic: keywords
creator: Bakaraniya Parul V.
creator: Mamta C. Padole
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-05-18
publication date: 2026-05-18
starting page: 389
ending page: 412
doi: 10.36548/jtcsst.2026.2.010

Comprehensive Seven-Lens Evaluation Framework for Remote Access Laboratory Systems: Implementation and Performance Optimization
type: item
description: Remote access labs are an important tool for teaching engineering students’ hands-on skills that they may use in their future careers. To ensure that remote labs will deliver education effectively, they need to be evaluated systematically on many different operational factors. The Seven Lens Assessment Framework is a comprehensive assessment method that assesses remote access lab systems using seven different lenses or operational factors including Affordability, Portability, Scalability, Compatibility, Maintainability, Usability and Universality. Our LabVIEW-based implementation is one of the first demonstrations of how to apply this assessment framework in practice. The implementation reduced the Total Cost of Ownership (TCO) by 55.4%, simplified the deployment process by 89.2%, provided sub-2.5 second latency for up to 70 concurrent users, made the system compatible with eight different platforms, increased the Mean Time Between Failures (MTBF) by 253%, improved the System Usability Scale (SUS) score by 31.4%, and achieved 95% browser universality. The proposed system utilises native Windows services along with secure Virtual Private Network (VPN) connections, Web Real-Time Communication (WebRTC) streaming, and Erlang-C queuing models to optimize the utilization of resources. Systematic assessments were performed against 35 other reviewed remote access labs to evaluate the effectiveness of the Seven Lens Assessment Framework. The Seven Lens Index achieved 0.847 when compared to the baseline systems’ index of 0.472, which represents a 79.4% overall improvement.
subject: Articles
  topic: section
subject: Remote Access Laboratories
  topic: keywords
subject: Seven-Lens Framework
  topic: keywords
subject: VPN-Based Architecture
  topic: keywords
subject: WebRTC Streaming
  topic: keywords
subject: Performance Optimization
  topic: keywords
subject: Multi-User Pipelining
  topic: keywords
subject: LabVIEW Integration
  topic: keywords
creator: Niket Amoda
creator: Lochan Jolly
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-05-06
publication date: 2026-05-06
starting page: 368
ending page: 388
doi: 10.36548/jtcsst.2026.2.009

Deployment-Oriented Evaluation of Healthcare Reimbursement Cost Prediction
type: item
description: The use of healthcare cost prediction models to assist with resource allocation and risk management is on the rise, but their use is limited by the requirement for reliable, easily interpretable explanations. Although there are multiple methods of providing these explanations, their operational use in healthcare settings remains inadequately evaluated. This research analyses four of the available explainable models (i.e., Permutation Feature Importance, Tree Importance, Local Interpretable Model Explanations, and Shapley-based explanations) using a Random Forest healthcare cost prediction model developed on a real-world dataset (comprising 2,302 aggregated patient segments and 54 features) with a coefficient of determination of 0.9957. The analysis used three criteria that are critical to the deployment of these models: steadiness of explanation under data perturbation, correspondence of feature importance across methods, and computational latency. Results show that each of the global explainable models has relatively high steadiness and relatively low latency, making them applicable for real time and regulatory use; however, the local explainable models provide more intuitive instance-level explanations at the expense of lower steadiness and greater computational demands. A tiered deployment framework for method selection is proposed based on these results as guidance for selecting methods based on clinical, regulatory, and operational needs. This research provides insight into the practical use of explainable healthcare cost prediction systems within real-world environments.
subject: Articles
  topic: section
subject: Explainable AI
  topic: keywords
subject: Healthcare Cost Prediction
  topic: keywords
subject: Feature Importance Analysis
  topic: keywords
subject: Deployment Feasibility
  topic: keywords
subject: Stability Interpretability Trade-Off
  topic: keywords
creator: Hayat Ouadi
creator: Ilhame El Farissi
creator: Ilham Slimani
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-04-29
publication date: 2026-04-29
starting page: 347
ending page: 367
doi: 10.36548/jtcsst.2026.2.008

Adaptive Gated Multimodal Fusion for Robust and Generalized Human Activity Recognition
type: item
description: In this paper, an adaptive gated multimodal fusion framework for generalized and robust multimodal Human Activity Recognition (HAR) systems with heterogeneous sensing modalities like skeletal pose estimation and inertial measurement units (IMUs) is proposed. The proposed framework employs deterministic data harmonization through anterior harmonization and a reliability-based gated multimodal fusion mechanism to improve the robustness and generalization capability of multimodal HAR systems. The proposed gated multimodal fusion mechanism has been mathematically derived to approximate the inverse-variance weighting mechanism to obtain stability in the presence of modality-dependent noise and avoid posterior domain adaptation techniques. To improve temporal alignment between multimodal data streams, frequency domain analysis has been used to justify resampling at a unified 30 Hz rate to meet the Nyquist criterion. The proposed framework has been evaluated using the NTU RGB+D 120, UTD-MHAD, and PAMAP2 datasets with statistically significant results over static baselines (p<0.05, d=2.1), and low computational costs to meet edge-constrained IoT sensing requirements.
subject: Articles
  topic: section
subject: Human Activity Recognition
  topic: keywords
subject: Multimodal Learning
  topic: keywords
subject: Skeleton-Based Recognition
  topic: keywords
subject: IMU Sensing
  topic: keywords
subject: Cross-Dataset Evaluation
  topic: keywords
subject: Reproducible Systems
  topic: keywords
creator: Swati Gautam
creator: Ankush Shrivastava
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-04-27
publication date: 2026-04-27
starting page: 324
ending page: 346
doi: 10.36548/jtcsst.2026.2.007

Hybrid Graph Mamba Framework for Histopathological Image Classification
type: item
description: Digital pathology has already been shown to be useful in disease diagnosis; however, the computational power required to use high-resolution digital pathology is substantial, making it unrealistic in many clinical laboratories due to limitations in hardware capabilities. Accordingly, this paper presents a new architecture, MFLA-Graph-Mamba-NCA, which addresses these limitations. The main point of MFLA-Graph-Mamba-NCA is to develop a lightweight model for tissue classification while maintaining speed and optimal accuracy. Four complementary learning paradigms are incorporated into the architecture: a morphological frontend, a frequency-domain branch based on Functional Kernel Adaptive Networks (FunKAN), feature stabilization over time using Liquid Neural Ordinary Differential Equations, and global sequence modelling using a Graph-based Mamba module. The model was tested on a harmonized benchmark of 125,000 images, based on the NCT-CRC-HE-100K and LC25000 datasets, and divided into 12 different biological classes to expressly avoid cross-dataset lab leakage. It has been shown to provide 99.89%accuracy and a Macro: F1 score of 99.78%. These techniques were condensed to 1.46 million parameters, forming a hybridized model that can be deployed to standard clinical workstations.
subject: Articles
  topic: section
subject: Computational Pathology
  topic: keywords
subject: Histopathological Image Analysis
  topic: keywords
subject: Selective State-Space Models (Mamba)
  topic: keywords
subject: Liquid Neural ODEs
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
subject: Spectral Representation Learning
  topic: keywords
subject: Functional Kernel Adaptive Networks (KAN)
  topic: keywords
subject: Explainable-AI(XAI)
  topic: keywords
creator: Dave Machnaim
creator: Shawn Jaison
creator: Naveen Sundar
creator: Narmadha Naveen D.
creator: Sai Midhun
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-04-24
publication date: 2026-04-24
starting page: 304
ending page: 323
doi: 10.36548/jtcsst.2026.2.006

Enhancing Rail Surface Defect Detection Using a Hybrid YOLOv11–Vision Transformer Framework
type: item
description: Railway track surface defects cause severe challenges to the safety of humans, trains, and transported goods. Modern YOLO-based detectors efficiently detect rail surface defects in real time. However, they mainly focus on local features and also confuse defects that look similar. On the other hand, Vision Transformers are good at modeling global context using self-attention, but they do not localize objects properly when used alone. To overcome these issues, this study introduces a hybrid YOLOv11 and Vision Transformer (YOLOv11a and ViT) framework for enhanced rail surface defect detection. The approach integrates a lightweight Vision Transformer module into the YOLOv11 backbone so that the model learns both detailed local features and captures global dependencies. Experiments are conducted on a merged public railway surface defect computer vision dataset comprising 8,177 labeled images of five defect categories. The results show that the new method achieves an mAP@0.5 of 0.951 with a precision of 0.899 and recall of 0.921, outperforming the baseline YOLO model. The most significant advancements have been made in identifying elongated defects such as cracks and light bands.The framework also maintains real-time performance, making it practical for use in railway safety inspections.
subject: Articles
  topic: section
subject: Rail Surface Defect Detection
  topic: keywords
subject: YOLOv11
  topic: keywords
subject: Vision Transformer
  topic: keywords
subject: Hybrid Detection Framework
  topic: keywords
subject: Global Context Modelling
  topic: keywords
creator: Baburao Markapudi
creator: Lahari Gundiga
creator: Venkata Ramesh Babu Kondeti
creator: Sravanthi Kanumuri
creator: Yamini Maddala
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-04-24
publication date: 2026-04-24
starting page: 291
ending page: 303
doi: 10.36548/jtcsst.2026.2.005

Guardian-Based Anonymous Password Management with Privacy Preservation Using Threshold Cryptography
type: item
description: Most password managers are designed as a compromise between providing a secure way to handle difficult login credentials and creating a system that keeps user information private. Most password managers store users’ sensitive information in a central location, leaving users vulnerable to hacking attacks. The proposed work creates a better way to manage passwords and provide more protection to users based on their private information through a new concept called Guardian-Anonymous Password Management (GAPM). The idea is to create a unique architecture that stores passwords in decentralized locations using guardian anonymity, creating a hybrid architecture of secret sharing with post-quantum encrypted wraps. Accordingly, GAPM separates the act of recovering user passwords from a person, using a set of anonymous guardians who securely recover users’ passwords without putting any of them at risk of being hacked or located through social engineering techniques like phishing. This is achieved by using a Shamir-style secret sharing scheme combined with verifiably reassured commitments, where none of the guardians know each other, and they are required to reach a certain threshold of agreement to combine their shares into an easily accessible password recovery key. The GAPM system supports multiple guardian sets, allowing participants to be added or removed, and there is no need to reissue all the shares each time users make a change. The user can also change the recovery threshold in real-time. Finally, the shares are further secured through the use of a post-quantum Key Encapsulation Mechanism (KEM) to ensure that, no matter what kind of attack (classical or quantum), the password recovery process will remain strong and secure.
subject: Articles
  topic: section
subject: Cryptography
  topic: keywords
subject: Password Manager
  topic: keywords
subject: Privacy
  topic: keywords
subject: Secret Sharing
  topic: keywords
subject: Post Quantum Cryptography
  topic: keywords
subject: Security
  topic: keywords
creator: Harivignesh K.S.
creator: Venkatesan R.
creator: Selvarathi M.
creator: Jasmine David D.
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-04-24
publication date: 2026-04-24
starting page: 267
ending page: 290
doi: 10.36548/jtcsst.2026.2.004

Dual-Metric Detection of Synthesis-Masked Hardware Trojans in AES-256: Correlating Power Signatures with Switching Activity
type: item
description: Hardware Trojans can be accidentally hidden due to synthesis optimizations performed by electronic design automation tools in cryptosystems, leaving important detection gaps in the process. In this study, the impact of synthesis on the power signatures of AES-256 cores has been analyzed based on power/net switching correlations. Four hardware Trojans have been synthesized targeting the 7nm Versal technology of Xilinx and have been simulated over the course of 10,000 clock cycles across 714 different key/plaintext combinations. The results showed that three hardware Trojans exhibited unexpected negative power deviations, although they incorporated malicious logic in their structures. Two separate categories of synthesis interactions can be observed from switching distribution histograms over 646 nets and KL divergence statistics (0.03–0.42): synthesis-masked dormant Trojans with lower toggles and leftward-shifting distributions, and synthesis-neutralized active Trojans with 40% more active nets and lower power. This two-fold correlation method allows for detecting synthesis-affected Trojans that cannot be identified through conventional power-overhead analysis methods, achieving reproducible structural detection supported by dual-metric cross-validation (Z-scores -0.62 to -0.77), despite individual signatures falling below the conventional 3σ detection threshold.
subject: Articles
  topic: section
subject: Hardware Trojans
  topic: keywords
subject: AES-256
  topic: keywords
subject: Power Analysis
  topic: keywords
subject: Synthesis Masking
  topic: keywords
subject: Negative Power Signature
  topic: keywords
subject: Switching Activity Analysis
  topic: keywords
creator: Rahimunnisa K.
creator: Aadhitya G.
creator: Abhyjeet J.
creator: Dharnesh S.
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-04-24
publication date: 2026-04-24
starting page: 243
ending page: 266
doi: 10.36548/jtcsst.2026.2.003

Machine Learning Approach for Adaptive Data Protection in Document Database Systems
type: item
description: With the growing dependence of cloud applications and enterprise organizations on NoSQL databases, it is necessary to ensure data protection while maintaining efficiency and performance. Traditional static encryption systems deliver strong regulatory protection but are unable to react to changing contexts in dynamic and zerotrust environments, which limits their ability to address abnormal behaviour, malicious insiders, and advanced attackers. This work proposes AdaptiCrypt-ML, a lightweight proxy based on machine learning, which aims to implement domain-level adaptive encryption in NoSQL database security systems. The framework utilizes the LightGBM model to classify 14 contextual, behavioural, and data-sensitivity features to determine immediately the most appropriate encryption level across four security categories. When data is entered, the encryption level is dynamically determined according to the risk level, whereas a risk-based decryption policy controls the extent to which data is revealed when retrieved. Empirical results, derived from a statistically validated synthetic dataset of 50,000 examples, demonstrate strong predictive performance, with an overall accuracy of 99.1%, an F1-macro score of 0.963, and a low generalization gap of 0.0018. The average inference time ranged between 0.5 and 0.8 milliseconds, and the total response time stabilized at 3.25 milliseconds (P95 = 4.10 milliseconds), with an average of 3,120 queries per second. A 5% noise robustness test validated 96% performance stabilization. These findings emphasize the possibility of integrating context-aware adaptive encryption into NoSQL frameworks without sacrificing real-time requirements.
subject: Articles
  topic: section
subject: LightGBM
  topic: keywords
subject: NoSQL Security
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Adaptive Encryption
  topic: keywords
subject: Noise
  topic: keywords
subject: Real-time
  topic: keywords
creator: Abdelilah Belhaj
creator: Soumia Ziti
creator: Khalil Ladrham
creator: Souad Najoua Lagmiri
creator: Karim El Bouchti
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-04-20
publication date: 2026-04-20
starting page: 219
ending page: 242
doi: 10.36548/jtcsst.2026.2.002

Federated Attention‑Capsule CNN with Bio‑Optimization for Coronary Artery Disease Screening
type: item
description: Coronary artery disease (CAD) remains the leading cause of death around the globe and hence requires reliable, non-invasive, and secure diagnostic methods. In order to diagnose CAD in its early stages, we present a novel architecture of a Federated Attention-Capsule Convolutional Neural Network (FAC-CNN) integrated with Bio-Inspired Optimization Algorithms (BIOA) in dealing with multiple modalities of physiological data such as Electrocardiography (ECG), Photoplethysmogram (PPG), and Blood Pressure (BP). The model is evaluated using unimodal (only ECG signals) and multimodal physiological datasets to validate the proposed algorithm for different data distributions. To improve accuracy, the architecture of FAC-CNN uses capsule networks to recognize the hierarchical structure within the data fed into it. Through attention mechanisms, the network is capable of selecting the most important features related to cardiac diseases. To ensure data security, the federated learning (FL) method is applied as a solution to the problem of local model training using patients' private data stored in edge computing devices. A novel hybrid optimization method combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is used to update learning rates, the total number of capsule layers, and routing iterations. Three databases, including the PTB diagnostic ECG database, the MIT-BIH Arrhythmia database, and the MIMIC-III Waveform database, which comprises authentic ICU recordings of ECG, PPG, and blood pressure data from more than 40,000 patients were employed to evaluate the model. It can be seen from the results that FAC-CNN outperformed the other models used in the experiment, including CNNs and LSTMs by 4-7%, obtaining a mean accuracy of 97.4%, an F1-score of 96.8%, and an AUC score of 0.985. Moreover, the scalability of the proposed algorithm for applications in remote healthcare settings improved, and the more effective training method contributed to time-savings for th…
subject: Articles
  topic: section
subject: Coronary Artery Disease
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Capsule Networks
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Genetic Algorithm
  topic: keywords
subject: Particle Swarm Optimization
  topic: keywords
creator: Angelin Jeba P.
creator: Jasmine David D.
creator: Selvarathi M.
creator: Kenneth Bryan Nesh
creator: Alan Biju Varkey
creator: Jemima Jebaseeli T.
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-04-17
publication date: 2026-04-17
starting page: 195
ending page: 218
doi: 10.36548/jtcsst.2026.2.001

A Slot-Aware Semantic Signaling Framework for Multi-intent Classification
type: item
description: Multi-Intent Detection (MID) and Slot Filling (SF) are fundamental tasks in Natural Language Understanding (NLU) for goal-oriented dialogue systems. Despite advances in various joint models, which improve performance by learning interactions between intents and slots, existing models might not adequately capture intricate and complex relationships between intents in multi-intent utterances, especially if slot-level semantic information is not adequately utilized during intent detection (ID). To this end, this paper proposes a novel framework, namely Slot-Aware Semantic Signaling for Multi-Intent Classification (S3MIC), which is a joint framework that leverages slot-level semantic information to improve intent prediction in multi-intent dialogue systems. We conduct experiments with the proposed framework on benchmark datasets, namely MixATIS and MixSNIPS, and report consistent improvements over existing benchmark models in terms of Slot F1, Intent Accuracy, and Semantic Frame Accuracy (SeFr Acc). Specifically, our proposed framework achieves 52.5% SeFr Acc on MixATIS and 86.28% SeFr Acc on MixSNIPS, thereby validating the efficacy of slot-aware semantic signaling in improving joint MID and SF performance in goal-oriented dialogue systems.
subject: Articles
  topic: section
subject: Slot-Aware Semantic Signaling
  topic: keywords
subject: Multi-Intent Detection
  topic: keywords
subject: Slot Filling
  topic: keywords
subject: Joint Learning
  topic: keywords
creator: Juhi Shah
creator: Priyank Thakkar
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-03-25
publication date: 2026-03-25
starting page: 176
ending page: 194
doi: 10.36548/jtcsst.2026.1.009

Emotion and Cognition Based Mental Health Analysis from Social Media
type: item
description: Social media websites are popular, and it is now possible to collect a significant amount of behavioral data in real time. This has made it possible to conduct research on mental health. This study proposes a machine learning framework to identify and predict mental health issues, such as emotion and cognition, related to social media with respect to depression, anxiety, and bipolar disorder. In this study, three social media datasets were used, each with more than 1.6 million posts: Sentiment140, Twitter Depression Dataset, and Facebook Sentiment. The data was tokenized and lemmatized, and stopwords were removed. We used BERT, a transformer model, to view the data using emotional traits. This was done using the sentimental polarity score and emotions such as grief, anger, joy, fear, and insignificance. We used Latent Dirichlet Allocation (LDA) and Linguistic Inquiry and Word Count (LIWC) to infer cognitive distortions and psychological indices. We used Recursive Feature Elimination (RFE) for feature selection and a Random Forest classifier to predict mental health across multiple classes. The proposed method is more effective than deep learning and machine learning techniques, achieving an accuracy of 95.0%, precision of 94.0%, recall of 93.7%, F1-score of 94.8%, and AUC-ROC of 98.9%. The sentiment polarity score improved the prediction by 28%, the emotional scores improved the prediction by 35%, the cognitive themes improved the prediction by 22%, and the LIWC features improved the prediction by 15%. The results indicate that the use of cognitive linguistics and emotional representation facilitates the identification of early symptoms of severe depression and bipolar disorder.
subject: Articles
  topic: section
subject: Mental Health Disorders
  topic: keywords
subject: Social Media
  topic: keywords
subject: Emotion
  topic: keywords
subject: Cognitive
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Twitter
  topic: keywords
subject: Facebook
  topic: keywords
subject: Depression
  topic: keywords
subject: Anxiety
  topic: keywords
creator: Angelin Jeba P.
creator: Jemima Jebaseeli T.
creator: Selvarathi M.
creator: Achal Shaji
creator: Ivine Thomas
creator: Agilesh Vigram S.
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-03-20
publication date: 2026-03-20
starting page: 155
ending page: 175
doi: 10.36548/jtcsst.2026.1.008

Graph Neural Network and Reinforcement Learning Framework for Test Case Prioritization, Selection, and Reduction
type: item
description: The process of regression testing is generally done under critical time and resource constraints. In the existing approaches, Test Case Prioritization (TCP), Test Case Selection (TCS), and Test Suite Reduction (TSR) have been treated as different optimization problems. This is based on simple heuristics that do not use valuable information that can be extracted from the program under test. Moreover, they do not allow for making internally consistent budget-conscious decisions. This paper introduces a unified approach that combines various variants of Graph Neural Networks (GNNs) and Reinforcement Learning (RL) for the solutions of TCP, TCS, and TSR. In our proposal, the representation of regression artifacts such as test cases, code entities, and faults is used as nodes in the typed graph, while the edges are used to show the relations between these artifacts. The relation-aware GNN is used for generating test case embeddings, reflecting the distance between the test cases and the changed areas as well as the areas known to have issues in the past. The actor-critic RL agent uses these test case embeddings and the budget to decide whether to run, skip, or discard test cases. The performance of our proposal outstrips coverage-based heuristics, history-based ranking, a GA-based search, an enhanced QPSO, and two learning-based ablations, as validated by our experiments on four Java projects from Defects4J. In terms of various programs and budgets, using GNN–RL has led to an improvement in the Average Percentage of Faults Detected (APFD) and Cost-cognizant Average Percentage of Faults Detected (APFDc) by approximately 6–9 percentage points on lower budgets. In addition, it is possible to obtain a 42% suite reduction and a 48% cost reduction while still retaining 98% of all fault-revealing tests in the final suite. The results show that it is possible to obtain promising solutions for adaptive regression testing using budget-aware reinforcement learning and graph-based re…
subject: Articles
  topic: section
subject: Regression Testing
  topic: keywords
subject: Test Case Prioritization
  topic: keywords
subject: Test Suite Reduction
  topic: keywords
subject: Test Case Selection
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Search-Based Software Engineering
  topic: keywords
creator: Sowmyadevi S.
creator: Shashi Mehrotra
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-03-19
publication date: 2026-03-19
starting page: 130
ending page: 154
doi: 10.36548/jtcsst.2026.1.007

A Machine Learning Framework for Evaluating MSME Incubation Dynamics
type: item
description: The Business Incubation is an important way to assist entrepreneurs in transferring their innovative ideas into viable businesses, offering resources, expertise, and connecting them with available resources for assistance. In India, the MSME Hackathons represent one type of competition to generate new and exciting ideas by identifying the best solutions across different technology and industry sectors. This study will develop a data-based framework for understanding innovation patterns from the MSME business incubation framework by using the officially approved ideas generated in multiple MSME Idea Hackathons. This research will also use an innovation analytics perspective as the basis for developing results, rather than using traditional descriptive approaches for understanding MSME incubator policy development; therefore, the results will identify how many institutions, states, and knowledge bases are in the overall data set and relate to the overall structure of the innovation network. A machine learning–based analytical approach is employed to examine innovation domains and structural relationships among different innovation ecosystems. Supervised ensemble modeling is used to evaluate the influence of institutional affiliation and geographical location on thematic specialization, while unsupervised clustering and dimensionality reduction techniques are applied to explore structural patterns within the dataset. The classification model achieved an accuracy of 0.672, precision of 0.599, recall of 0.672, and an F1-score of 0.624, indicating moderate predictive capability while confirming that institutional and geographical attributes provide meaningful signals for innovation domain classification. The results further indicate that the structure of institutional networks and regional environments significantly influences innovation-specific focus areas, while also revealing the coexistence of both organized and distributed forms of innovation activity reflected in h…
subject: Articles
  topic: section
subject: MSME Incubation
  topic: keywords
subject: Innovation Ecosystems
  topic: keywords
subject: Hackathon Analytics
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Clustering Analysis
  topic: keywords
subject: Innovation Policy
  topic: keywords
creator: Taflin S Raj
creator: Radhika R.
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-03-11
publication date: 2026-03-11
starting page: 114
ending page: 129
doi: 10.36548/jtcsst.2026.1.006

Adaptive Markov Decision Process-Based Congestion Control Using Deep Deterministic Policy Gradient in Distributed MQTT Brokers
type: item
description: The publish-subscribe variant of the Message Queuing Telemetry Transport (MQTT) protocol is lightweight enough to permit the asynchronous sending of messages. Nonetheless, the protocol is experiencing issues related to congestion, which is caused by the IoT traffic characteristics in the distributed broker networks in the IoT environment. As a result, delay occur resulting in a degradation of QoS. However, using classical AIMD and static rate-limiting techniques for congestion control in the IoT environment is becoming difficult. The proposed work provides an adaptive Reinforcement Learning (RL)-based system to handle congestion in the IoT environment. This system can be represented using a Markov Decision Process (MDP). The DDPG algorithm will allow the RL agent to discover optimal strategies in the IoT environment, and it is beneficial in managing non-stationary IoT traffic. Based on the agent clusters’ simulation of the brokers, the RL-based system outperforms the traditional system in the IoT setting. It achieves this as the message drop rate is reduced by 38-40%, the latency is reduced by 27-32%, and the fairness of the load is increased by 18-24%.
subject: Articles
  topic: section
subject: MQTT
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Distributed Broker Networks
  topic: keywords
subject: Congestion Control
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Markov Decision Process
  topic: keywords
subject: Quality of Service
  topic: keywords
subject: Traffic Management
  topic: keywords
creator: Snowlin Preethi Janani
creator: Immanuel Johnraja J.
creator: Getzi Jeba Leelipushpam P.
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-03-09
publication date: 2026-03-09
starting page: 89
ending page: 113
doi: 10.36548/jtcsst.2026.1.005

Energy–Cost–SLA Aware Cloud Scheduling via Adaptive Non-dominated Sorting Genetic Algorithm-III and Neighborhood Refinement
type: item
description: The optimization of resource allocation in a cloud computing environment is a problem that has been challenging due to heterogeneous tasks with varying resource requirements and different optimization objectives for execution time, energy consumption, service level agreements (SLAs) and so on. In this paper, a hybrid multi-objective optimization algorithm called AH-NSGAIII-VND is proposed for solving a multi-objective optimization problem in a cloud computing environment. The proposed algorithm integrates a global search process using a variant of a multi-objective evolutionary algorithm called Non-Dominated Sorting Genetic Algorithm III (NSGA-III) and a local search process using a variant of a local search algorithm called Variable Neighborhood Descent (VND). The problem of resource allocation in a cloud computing environment is formulated as a multi-objective optimization problem considering makespan, energy consumption, cost, service level agreements (SLAs) and resource utilization. Researchers compared the results of the PSO, GA, and GWO optimization methods to the baseline using CloudSim-based model conditions on the changing workload scale. Experimental results indicate that cloud supply governance efficiency is enhanced with the AH-NSGAIII-VND architecture. It achieves around 11.3%, 12.8%, and 28% lower costs than the baseline NSGA-III approach while increasing overall asset utilization by over 5 percentage points. Moreover, with increasing workload, the proposed model exhibits improved convergence behavior and scalability. These results confirm that global evolutionary optimization supported by adaptive local search successfully reinstates or enhances the efficiency of resource allocation.
subject: Articles
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Resource Allocation
  topic: keywords
subject: Multi-Objective Optimization
  topic: keywords
subject: Hybrid Optimization
  topic: keywords
subject: NSGA-III
  topic: keywords
subject: SLA Management
  topic: keywords
creator: Vijayasekaran G.
creator: Sathya V.
creator: Sumathi S.
creator: Lavanya M.
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-03-07
publication date: 2026-03-07
starting page: 63
ending page: 88
doi: 10.36548/jtcsst.2026.1.004

A Federated Learning Autoencoder CNN Hybrid Approach for Privacy Preserving Network Traffic Classification
type: item
description: Federated learning (FL) is an essential technique for classifying network traffic in a decentralized setting while maintaining privacy. In this paper, the Federated Learning Autoencoder Convolutional Neural Network (FL-AECNN), a hybrid federated model, is proposed. For unsupervised feature learning, the FL-AECNN combines a Convolutional Neural Network (CNN) classifier with an autoencoder. This combination improves classification accuracy, generalization, and feature representation quality. Furthermore, it works in environment where the data is Non-Independent and Identically Distributed (non-IID). In the model, the CNN is used for supervised classification, while the autoencoder transforms the traffic features into latent features. This model is hybrid due to its two functionalities. For the experiment, a customized Android traffic dataset with ten classes and the ISSC VPN2016 benchmark dataset with five classes are used to test the proposed model. In the experiment, the SMOTE algorithm is employed to balance the classes, and log transformation is used to normalize all the datasets to address the skewed features. The Federated Averaging algorithm, or FedAvg, is used to aggregate the model globally, while the local model is trained independently by the ten clients and the central server. The average training accuracy of the FL-AECNN model is 90.24%, and the range of the testing accuracy of the model is between 77.01% and 82.54%. These results show that the FL-AECNN model performs better in terms of accuracy and consistency compared to the Federated Learning Convolutional Neural Network (FLCNN). These results indicate the possibility of applying federated supervised classification and federated unsupervised representation learning to develop a new method of safe traffic assessment.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Autoencoder
  topic: keywords
subject: CNN
  topic: keywords
subject: Network Traffic Classification
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Non-IID Data
  topic: keywords
subject: FL-AECNN
  topic: keywords
creator: Apurva Bhalchandra Parandekar
creator: Pritish A. Tijare
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-02-20
publication date: 2026-02-20
starting page: 47
ending page: 62
doi: 10.36548/jtcsst.2026.1.003

Live Virtual Machine Migration Using Xen Server
type: item
description: Live VM migration is an indispensable part of the cloud for load balancing availability, resource management and reduced service disruption. Current migration techniques present a high memory demand, complex page patterns, varying workloads, and aggressive SLAs. We have designed an efficient live VM migration system including hybrid pre-copy and post-copy, workload prediction, and memory optimization. The transfer between pre-copy and post-copy migration can be switched in a dynamic manner depending on real time memory write type, and therefore service disruption and redundant retransmission will be reduced. To this end, we build an LSTM-based model to predict dirty page production and CPU consumption for proactive migration and adaptive phase switching according to the temporal restrictions of these two variables. It also uses clustering of the most modified pages in Lustre data access frequency to lower useless date transmission and speed up the migration. Experiments on a private cloud system under XenServer demonstrate that, as compared to other methods service disruption time is reduced by 55%, total migration time is increased by 25% and improvement of SLA satisfaction rate is recorded up to18%. This is, in a nutshell, an efficient, scalable and low-overhead approach for live migration of VMs over heterogeneous clouds by embracing predictive workload awareness through adaptive memory clustering.
subject: Articles
  topic: section
subject: Live Virtual Machine Migration
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Optimization
  topic: keywords
subject: Hybrid Pre-copy/Post-copy Migration
  topic: keywords
subject: Workload Prediction
  topic: keywords
subject: Dirty-Page Clustering
  topic: keywords
creator: Akashbhai Ashokbhai Dave
creator: Jayna Donga
creator: Jaykumar Vala
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-01-29
publication date: 2026-01-29
starting page: 28
ending page: 46
doi: 10.36548/jtcsst.2026.1.002

Digital Twin based Data Pattern Analysis for IoT-Based Pharmaceutical Production Monitoring
type: item
description: A major step forward in pharmaceutical production has been the merging of Artificial Intelligence (AI) with the Internet of Things (IoTs), which successfully connects the digital and physical realms. Improving the production process and quality control through the integration of AI algorithms into IoT sensors leads to higher overall efficiency. Digital Twins (DT) are made possible by the creation and implementation of new Industry 4.0 technologies, which help to make manufacturing smarter and more agile. DTs are digital representations of real-world systems that mimic their operation and dynamics. Complete DT implementation in pharmaceutical production has not yet occurred, despite the pharmaceutical sector is currently undergoing a digital transformation to incorporate industry 4.0 and has adopted Quality-by-Design (QbD) programs. Therefore, it is crucial to assess the pharmaceutical industry's progress in implementing DT solutions. Data obtained from digital twins can also provide a full image of a product or process's lifetime, which can help with supply chain management, product quality control, and workflow optimization for manufacturing individual parts. This research proposes a Digital Twin enabled Data Pattern Analysis with Diminutive Difference Trigger using Probabilistic Regression (DT-DPA-DDT-PR) model for accurate monitoring of pharmaceutical production operations. The proposed model when contrasted with traditional methods performs better in pharmaceutical production monitoring.
subject: Articles
  topic: section
subject: Digital Twins
  topic: keywords
subject: Pharmaceutical Production
  topic: keywords
subject: Quality-by-Design
  topic: keywords
subject: Data Pattern Analysis
  topic: keywords
subject: Diminutive Difference Trigger
  topic: keywords
subject: Probabilistic Regression
  topic: keywords
creator: Narne Hemanth Chowdary
creator: Prasad Reddy P.V.G.D.
creator: Suresh Chittineni
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-01-24
publication date: 2026-01-24
starting page: 1
ending page: 27
doi: 10.36548/jtcsst.2026.1.001

Review on Blockchain-Based Offline Payment Solutions
type: item
description: Blockchain technology has proven to be a revolutionary base technology for the development of digital payment systems, which include properties like decentralization, transparency, and strong cryptography. However, most existing blockchain-based payment systems require always-connected scenarios, limiting their applicability in environments where no or intermittent internet connectivity is available. Hence, there is increased research in existing studies on blockchain-based offline payment systems, particularly from the viewpoints of financial inclusion and Central Bank Digital Currencies. The objective of this paper is to carry out a detailed review of existing blockchain-based offline payment systems, which includes discussions on design, security, and assumptions. The proposed work will group the prominent existing payment systems along the lines of state channel networks, hardware-based payment systems, and offline token payment systems, which will be analyzed for their pros and cons. In addition, it also raises awareness of prominent security aspects like authentication using cryptography, trusted execution environments, and delayed reconciliation, considering this study to assume certain conditions when examining the offline environment. Finally, based on the major existing work, it will outline important research opportunities in double spending, scalability, privacy, interoperability, and usability. The purpose of this study is, through synthesis, to provide a structured view of the offline blockchain payment platform and help focus R&D efforts on effective, secure, and scalable offline digital payment systems.
subject: Articles
  topic: section
subject: Blockchain
  topic: keywords
subject: Offline Payment
  topic: keywords
subject: Secure Transaction
  topic: keywords
subject: E-Commerce
  topic: keywords
subject: Central Bank Digital Currencies
  topic: keywords
subject: Decentralized Architecture
  topic: keywords
subject: Digital Wallet
  topic: keywords
subject: Key Management
  topic: keywords
subject: Smart Cards
  topic: keywords
creator: Surekha Lanka
creator: Phothiwong Suwannatat
creator: Nalinnee Pumhiran
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-01-02
publication date: 2026-01-02
starting page: 768
ending page: 779
doi: 10.36548/jtcsst.2025.4.008

A Comprehensive Review of Architectural, Algorithmic, and Technological Innovations for Energy-Efficient 6G Communication Networks
type: item
description: Due to the evolution of 6th generation (6G) communication networks, it has been witnessing an increasing need for ultra-high data rates, massive connection, low latency, and intelligence. However, some tasks need to be performed for fulfilling this demand through preserving the energy consumption. In this article, it is providing an extensive assessment of technological, computational, and architectural developments for optimizing energy efficiency during developing 6G communication networks. In context of architectural advancement, it is assessing intelligent reflecting surfaces, Green cell-free massive MIMO, and hierarchical edge-cloud computing architectures. They help decrease energy consumption during processing, device, and transmission, according to those architectures. The capabilities of AI-driven network management, energy-conscious resource allocation, and reinforcement learning optimization for optimizing energy efficiency in an effective manner through computing capabilities of AI have been described. The role of technological innovations like energy harvesting, integrated sensing and communication, ultra-low-power transceivers, and terahertz transmission in lowering operational network costs has been discussed too. In this review article, it is discussing THz wave communication and Green cell-free massive MIMO technology particularly.
subject: Articles
  topic: section
subject: 6G Generation Networks
  topic: keywords
subject: Energy Efficient
  topic: keywords
subject: Network Management
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: CF-mMIMO
  topic: keywords
subject: THz Waves
  topic: keywords
creator: Nellore Kapileswar
creator: Judy Simon
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2025-12-31
publication date: 2025-12-31
starting page: 753
ending page: 767
doi: 10.36548/jtcsst.2025.4.007

Evaluating Random Forest and Decision Tree Algorithms for Resolving Lexical Ambiguity in the Gujarati Language
type: item
description: Word sense disambiguation is the task of determining the exact meaning of a word based on its context. This task is crucial in natural language processing. The lack of labeled datasets and the complex structure of the language, which includes idiomatic usage and subtle semantic changes, contribute to the poor outcomes of earlier attempts to solve word sense disambiguation in Gujarati. As a result, various models have shown low accuracy. To address this issue, we have created a new dataset that is manually sense-annotated for unclear Gujarati words. The corpus contains 50 ambiguous words, and each word has been assigned to the appropriate context. This makes it a valuable starting point for evaluating supervised learning models. With this newly compiled corpus, we carry out a systematic study of two supervised machine learning algorithms-Decision Tree and Random Forest-using 3-fold and 5-fold cross-validation. Our results show that Random Forest obtains the highest accuracy, highlighting which supervised methods are best suited for this particular task. The main contributions of this work include the development of a much-needed annotated corpus and sufficient evidence to prove that supervised learning can be quite effective in improving WSD for Gujarati when proper data is integrated.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Word Sense Disambiguation
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Sense Annotated Corpus
  topic: keywords
subject: Gujarati Language
  topic: keywords
creator: Avani N Dave
creator: Sanjay M Shah
creator: Nakul R Dave
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2025-12-02
publication date: 2025-12-02
starting page: 727
ending page: 752
doi: 10.36548/jtcsst.2025.4.006

Multi-Scale Semantic Fusion Network with Adaptive Attention for Remote Sensing Image Captioning
type: item
description: The aim of remote sensing image captioning (RSIC) is to obtain insightful and detailed textual description of satellite images and aerial images. However, traditional methods are not able to achieve this aim effectively due to a lack of contextual awareness caused by variations in scale, viewpoint and scene complexity. In this paper, we propose a method, the Multiscale Region-Aware Captioning Network (MSR-CapNet), which helps to achieve the aim of RSIC by generating relevant and semantically correct textual descriptions for scenes in satellite images (and aerial images). We train and test our method for the purpose of RSIC on the RSICD and UCM caption datasets. In our MSR-CapNet method, we have integrated Feature Pyramid Encoding (used for local and global visual characteristics representation), Adaptive Attention (which helps in dynamic prioritization of relevant regions) and Topic-Sensitive Embeddings (to generate semantically consistent captions). To show the effectiveness of the proposed method (MSR-CapNet), we compared it with existing techniques (recent transformer and graph-based baselines) using BLEU-4, METEOR, and CIDEr measures, where it shows consistent improvement over existing techniques.
subject: Articles
  topic: section
subject: Remote Sensing Image Captioning (RSIC)
  topic: keywords
subject: Attention Mechanism
  topic: keywords
subject: Topic-Sensitive Word Embeddings
  topic: keywords
subject: Satellite Images
  topic: keywords
creator: Chandrashekhar Pawar
creator: Ashwin Makwana
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2025-11-21
publication date: 2025-11-21
starting page: 700
ending page: 726
doi: 10.36548/jtcsst.2025.4.005

Explainable Hybrid Artificial Intelligence Boosting–Shapley Framework for Cardiac Disease Diagnosis
type: item
description: Heart disease is the leading cause of illness and death worldwide. Thus, there is an increasing demand for developing appropriate diagnostic techniques. This paper presents an explainable artificial intelligence hybrid model using a combination of SHapley Additive ExPlanations and Extreme Gradient Boosting to perform interpretable feature explanation and heart disease prediction. In this context, a dataset including 4,240 patient records with 15 clinical parameters was employed to validate the proposed model. XGBoost hyperparameters were optimized for the best setting using a grid-based search combined with 10-fold cross-validation. The conducted experiments showed that the proposed approach outperforms traditional classifiers dealing with the same data. Thus, the F1-score, AUC, recall, accuracy, and precision of the proposed methodology are 99.3%, 98.8%, and 0.97, respectively. SHAP analysis provided both local and global feature attributions, revealing that cardiovascular risk is strongly linked to age, systolic blood pressure, smoking status, and cholesterol level. This hybrid design enhances the reliability of predictions and works well in physician decision-support systems for the early detection and treatment of cardiovascular diseases. It also supports clarity in clinical information and renders it more trustworthy.
subject: Articles
  topic: section
subject: Heart Disease
  topic: keywords
subject: Explainable Artificial Intelligence
  topic: keywords
subject: XGBoost
  topic: keywords
subject: AI Model
  topic: keywords
subject: Healthcare
  topic: keywords
subject: Cardiovascular
  topic: keywords
subject: Cardiac Disease
  topic: keywords
creator: Karuppuchamy V.
creator: Nallusamy C.
creator: Sridhar S.R.
creator: Azhagesan M.
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2025-11-08
publication date: 2025-11-08
starting page: 678
ending page: 699
doi: 10.36548/jtcsst.2025.4.004

The Role of Digital Intangible Culture Heritage Resources in Promoting Students' Innovative Ability in Higher Education Courses
type: item
description: The study is aimed at incorporating Web-AI (IESWS-LGBM) systems into the higher education curricula, specifically focusing on the development of creative thinking and innovation based on the use of digital ICH assets. The research was conducted on 52 undergraduates in the experiential learning program at the School of Ethnology and Sociology at Guangxi Minzu University. Additionally, 240 undergraduates from various branches of science, engineering, and arts were taught at a university in Southeast Asia. Specialized innovation courses focused on an AI application that provided audio for the digital assets of ICH. Although the strategy is unique to one institution, it is likely to be more beneficial and assist other colleges attempting to improve student creativity through the application of cultural elements in addition to the primary focus of the strategy. The research employed a mixed-methods design including pre-and post-tests, behavioral observation, questionnaires, interviews, and evaluations of projects. Quantitative data indicate a 26.4% increase in cognitive knowledge of ICH. Behavioral data shows that 92% of students reported increased levels of cultural motivation with 86% of the students being actively engaged. One of the most important aspects of the data is that 76% of the students in rubric-based exams evaluated the creativity criterion. The proposed model IESWS-LGBM has also demonstrated an increased accuracy (91.5%) in evaluating students’ creativity compared to previous models. These results suggest that ICH with the help of technology is quite effective at promoting critical thinking, originality, and multicultural learning among students in institutions of higher education.
subject: Articles
  topic: section
subject: Intangible Cultural Heritage
  topic: keywords
subject: Innovation Ability
  topic: keywords
subject: Higher Education
  topic: keywords
subject: Experiential Learning
  topic: keywords
subject: IESWS-LGBM
  topic: keywords
subject: Digital Resources
  topic: keywords
subject: Student Engagement
  topic: keywords
creator: Jing Ji
creator: Terry Lucas
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2025-10-28
publication date: 2025-10-28
starting page: 658
ending page: 677
doi: 10.36548/jtcsst.2025.4.003

Design and FPGA Based Realization of an SMC-ESO Enhanced Nonlinear Fractional Order PID Controller for BLDC Motor Speed Control
type: item
description: Extended State Observe Nonlinear Fractional-order PID (NLFOPID) algorithm and Sliding Mode Control (SMC) are utilized for controlling the speed of a BLDC motor in this research study. The slow response time of the traditional PID controller makes it inefficient. FOPID controller is optimized to control the speed of the BLDC motor utilizing the SMC-ESO alongside it to overcome the shortcomings of traditional controllers. BLDC motors are suitable for many applications because they use permanent magnets to provide high torque over a broad range of speeds. The FOPID controller offers improved precision in control, SMC provides immunity to disturbance, and ESO provides real-time estimation of the state. The controller under consideration achieves ±1 RPM constant speed accuracy, under 5 % overshoot, and approximately 0.3 s settling time with 2 150 look-up tables, 1 800 flip-flops, 10 DSP slices and 2 BRAMs in the FPGA, demonstrate better accuracy and efficiency compared to standard PID and FOPID controllers. For a real-time system, it is implemented on a Field Programmable Gate Array (FPGA) due to its high processing capacity.
subject: Articles
  topic: section
subject: Proportional Integral Derivative Controller
  topic: keywords
subject: Extended State Observer
  topic: keywords
subject: Sliding Mode Control
  topic: keywords
subject: Field Programmable Gate Array
  topic: keywords
creator: Kamatchi Kannan V.
creator: Muniraj C.
creator: Balaji Periasamy
creator: Divya N.
creator: Rekha P.
creator: Ponmurugan P.
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2025-10-22
publication date: 2025-10-22
starting page: 641
ending page: 657
doi: 10.36548/jtcsst.2025.4.002

Energy-Efficient Neuromorphic Architectures Enabled by Resistive Memory
type: item
description: Their excellent performance and compatibility with CMOS technology, while the OXRAM (Oxide-based CMOS Resistive Random Access Memory) technique used in CMOS transistors presents challenges in terms of device unpredictability and scalability, also offers potential advantages such as higher endurance, lower power consumption, and quicker reading and writing operation speeds when compared to traditional Flash memory. The inability of conventional SRAMs to store data after powering off limits their use in battery-operated mobile devices and other applications where non-volatility related to zero leakage currents is required. In the article, a new OXRAM-based Non-Volatile SRAM (NVSRAM) device is presented. It is suggested to compare the performance of SRAM with NVSRAM at the memory and cell levels. Learning is crucial for the brain's ability to adapt to changing conditions. A synaptic connection table in an external memory at a local routing node is used to learn a rule in the address domain for neuromorphic architecture. A number of parameters are compared, including design complexity, leakage current values (SRAM cells are 3.4µA, 7.4nA) and (NVSRAM-based OXRAM are 2.7 µA, 5.9nA) at 180nm and 90nm, and energy saving or power usage values (SRAM cells cell are 5.5 µA, 10.5nA) and (NVSRAM based OXRAM are 4.9 µA, 9.8nA) at 180nm and 90nm. The circuits that are being described can be realized using far-above ground voltage CMOS Cadence tools at 180 nm and 90 nm.
subject: Articles
  topic: section
subject: Static Random CMOS Access Memories (SRAMs)
  topic: keywords
subject: Non-Volatile CMOS SRAM (NVCSRAM)
  topic: keywords
subject: OXRAM
  topic: keywords
subject: Power Dissipation
  topic: keywords
subject: Technology
  topic: keywords
creator: Joshika Sharma
creator: Shyam Akashe
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2025-10-07
publication date: 2025-10-07
starting page: 622
ending page: 640
doi: 10.36548/jtcsst.2025.4.001

UPS-Tailored Next-Generation Battery Technologies: Variability, Sustainability, Comparative Analysis and a Technology Roadmap
type: item
description: The global uninterruptible power supply forecast market is estimated at nearly USD 4.04 billion in 2024 owing to the growth in demand at data centers and other crucial sectors. Despite the great deal of information available in battery research, most of the literature on uninterruptible power supply does not consider the variability (temperature, depth of discharge, duty cycle) and mode of operation (grid-interactive vs. Standalone) performance during other operational modes. This review overcomes these deficiencies by providing three contributions: (1) variability in uninterruptible power supply specific quantitative performance metrics; (2) a growth map predicting the adoption of various battery chemistries in UPS on a short, medium, and long-term basis; (3) a sustainability assessment through the lens of primary materials, recyclability, and costs from the life cycle. Representative outcomes include: low-cost standby solutions from VRLA batteries, but limited cycle life (500–1,500 cycles); continuous UPS applications are dominated by LFP and LTO chemistries, which have a high tolerance to deep cycles; Li–S batteries have a high energy density (350–600 Wh/kg), but poor cycle life (<1,000); flow batteries have limited energy power density, but excel in longevity (>10,000 cycles) and are still too bulky for compact uninterruptible power supply systems. These conclusions provide a reasonable assessment of the cross technology trade-offs and the underlying research gaps to derive a pathway towards next generation reliable UPS batteries.
subject: Articles
  topic: section
subject: UPS
  topic: keywords
subject: Battery Technologies
  topic: keywords
subject: Lithium-Ion
  topic: keywords
subject: Sodium-Ion
  topic: keywords
subject: Solid-State Batteries
  topic: keywords
subject: BMS
  topic: keywords
subject: Sustainability
  topic: keywords
subject: Hybrid Systems
  topic: keywords
subject: Thermal Management
  topic: keywords
creator: Manoharan S.
creator: Mahalakshmi B.
creator: Sindhu M P.
creator: Vidyalakshmi R
creator: Anandhi K.
creator: Gaayathry K.
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2025-09-30
publication date: 2025-09-30
starting page: 608
ending page: 621
doi: 10.36548/jtcsst.2025.3.016

AI-Driven Malware Detection and Prevention using Hybrid Machine Learning and Blockchain for Secure Cyber Threat Intelligence
type: item
description: The ever-evolving cyber threat landscape has enabled sophisticated and intelligent malware detection techniques. False positive rates and inadequate attack pattern adaptation are common limitations of conventional intrusion detection algorithms. In an effort to improve security, this work offers a combined malware detection method based on artificial intelligence. Additionally, it integrates deep learning and machine learning with blockchain technology. By using Random Forest and Long Short-Term Memory for feature selection and anomaly detection, the suggested system can identify cyber threats with greater accuracy. A blockchain ledger also facilitates the recording of attack indicators, enhancing threat intelligence. The proposed method outperformed the standalone ML/DL results with 99.7% accuracy, 99.5% precision, 99.4% recall, and a 99.5% F1-score on the chosen dataset. Blockchain technology further enhances incremental trust in security by ensuring confidence in cybersecurity agencies by eliminating data manipulation.
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Malware Detection
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Blockchain
  topic: keywords
creator: Bharti Ahuja Salunke
creator: Sharad Salunke
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2025-09-26
publication date: 2025-09-26
starting page: 590
ending page: 607
doi: 10.36548/jtcsst.2025.3.015

Effectiveness of Farmers’ Professional Cooperatives in Helping Rural Revitalization Using Big Data Analysis
type: item
description: Farmers' Professional Cooperatives have been increasingly recognized as organizational forms facilitating economic development, agricultural modernization, and rural social capital construction as part of China's national rural revitalization strategy. Based on a big data study method, the study estimates the performance trajectory of FPCs and their impacts on social capital construction, income increase, poverty alleviation, agricultural restructuring, and employment in urban-rural areas. A combined approach of the Back Propagation Neural Network and the Mayfly Optimization Algorithm was put forward in an attempt to promote feature selection and accuracy of prediction for the intricate and multi-layered socioeconomic case. Big data and the proposed predictive model will be compared by the proposed study using the data of 820 members in 14 cooperatives. The outcome reveals that the suggested model achieved a 93.4 % success rate, a 26.4 % increase in earnings, an 18.7 % rise in technology uptake, and a 31.2 % rise in training attendance and engagement. Among cooperative members, descriptive statistics demonstrated a significant improvement in household income, market access, training engagement, and the application of technology. To facilitate generalization, the model was trained on 70% of the dataset, tested on 15% of the dataset, and validated on the other 15% through the use of cross-validation methods. The proposed model was found to be more accurate than traditional models with a 93.4% correctness rate and an RMSE value of 2.13. Other significant factors determining performance in cooperatives, including farm size, years of experience, and education, were also discovered by the model. FPCs strategically facilitate resource concentration, implementation of policies, and rural institutional integration along with enhancing farmers' economic welfare, as revealed by the findings.
subject: Articles
  topic: section
subject: Farmers’ Professional Cooperatives
  topic: keywords
subject: Bigdata Analysis
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subject: Agricultural Modernization
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subject: Rural Revitalization
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subject: Cooperative Effectiveness
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creator: Ruiqi Zhang
creator: Wong Sing Yun
creator: Siti Hajar Samsu
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2025-09-23
publication date: 2025-09-23
starting page: 568
ending page: 589
doi: 10.36548/jtcsst.2025.3.014