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: Tri-Level Network Attack Risk Stratification Using IDS-Contextual Ensemble Learning
items: XAI-Guided Features with Neural Learning Architecture for Anti-Money Laundering Detection in Financial Transactions
items: Confidence-Weighted Ensemble Learning and Graph-Based Shapley Theory for Refined Emotion Prediction in Autistic Children
items: A Symmetry-Aware Shape Descriptor Framework for Automated Defect Detection in Chocolate Images using Geometric Feature Engineering
items: Hybrid Channel-Spatial Attention Enhanced EfficientNet for Multi-Class Brain Tumor Classification Using MRI
items: Comparative Multi Domain Vibration Analysis of Dual Sensors for Structural Health Monitoring
items: Improving Directed Acyclic Graph-Based IoT Consensus through a Hybrid Stake-Weighted Fast Probabilistic Consensus Framework
items: Efficient Skeleton-Based Human Action Recognition for IoT Edge Devices Using Lightweight LSTM Networks
items: CAAMF: Conflict-Aware Adaptive Multimodal Fusion for QR Code Phishing Attack Detection
items: UGBCF-Net: Uncertainty-Guided BEV Cross-Attention Fusion Network for Robust Radar-Camera Object Detection
items: Adversarial Attack Detection in Wireless Networks Using Deep Learning Based Capsule Networks
items: A Multifaceted Framework for Predicting Ambient Air Pollution in the National Capital Region
items: Design and Validation of an IoT-Enabled Safety Monitoring System for Agricultural UAV Operations
items: Affective Student Engagement Detection using Machine Learning: A Systematic Survey on Methods, Modalities and Learning Environments
items: Trustworthy Multimodal Depression Screening via Cross-Attention Fusion and Calibrated Uncertainty
items: Detecting Intrusions in MongoDB NoSQL Databases Using Machine Learning
items: A Hybrid Approach for Transforming Retail and Customer Experience with User Motion Analytics Using Yolov4 and Pattern Mining
items: MVRODC: Multi-Variate Regression based Outlier Detection and Classification on IoT Sensor Data — A Temporal Node Level Approach
items: An Asynchronous Tri-Node IoT Architecture for Real-Time Environmental Mitigation in Bedridden Patients
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

Tri-Level Network Attack Risk Stratification Using IDS-Contextual Ensemble Learning
type: item
description: Advanced Persistent Threats (APTs) remain among the most damaging risks to enterprise networks: they evade signature-based defenses, persist inside compromised environments, and cause substantial harm. Most machine learning approaches to intrusion detection treat the problem as binary classification and therefore offer little support for the risk prioritisation that Security Operations Centre (SOC) analysts actually require. This paper presents an Intrusion Detection System (IDS)-contextual ensemble learning framework that assigns network traffic to three operationally meaningful risk tiers: High, Medium and Low. Sixteen domain-knowledge engineered features are combined with IDS-generated contextual annotations, raw behavioural flow measurements and temporal components to form a 36-dimensional input representation. A one-way analysis of variance (ANOVA) with eta-squared effect sizing, performed before any model is trained, shows that none of the nine raw behavioural features carries meaningful discriminating power for risk-tier assignment, which establishes the need for IDS context. Five classifiers are evaluated on a balanced dataset of 125,000 APT flow records. Extra Trees performs best, reaching 89.35% accuracy, a Macro F1 of 0.8941, a Matthews Correlation Coefficient of 0.8404 and a macro Area Under the Curve (AUC) of 0.9829, at an inference cost of approximately 42 microseconds per record. A six-configuration ablation study shows that removing the IDS annotation alone reduces Macro F1 from 0.8941 to 0.3341, the chance level for three balanced classes. Robustness is confirmed by five-fold stratified cross-validation and by a fairness audit across the four network protocol types present in the data.
subject: Articles
  topic: section
subject: Advanced Persistent Threats (APTs)
  topic: keywords
subject: Ensemble Learning
  topic: keywords
subject: Intrusion Detection System (IDS)-Contextual Features
  topic: keywords
subject: Security Operations Centre (SOC) Alert Triage
  topic: keywords
subject: Tri-Level Risk Stratification
  topic: keywords
creator: Reeta Mishra
creator: Neelu Chaudhary
rights: Copyright (c)
date: 2026-08-04
publication date: 2026-08-04
starting page: 791
ending page: 814
doi: 10.36548/jtcsst.2026.3.018

XAI-Guided Features with Neural Learning Architecture for Anti-Money Laundering Detection in Financial Transactions
type: item
description: Anti-Money Laundering (AML) involves concealing the origin of illegal money through financial crime and the necessity for nations and institutions to develop and implement AML measures. Machine learning (ML) have shown efficiency in detecting suspicious transactions; nonetheless, they do not capture complex non-linear behavior from transactions while maintaining transparency and reliability. In addition, the black box nature of deep learning techniques makes them unsuitable for real-time implementation in financial security applications, as they require transparent decision-making. To address this problem, the current research proposes XAIGFL-NN for Robust AML Detection. The model is designed for integrating domain-aware feature engineering, minority class-preserving sampling, and explainable neural learning to detect suspicious transactions in a large-scale financial environment. A neural network that has been fine-tuned through RMSprop is used to learn discriminatory transaction representations, and SHAP-based interpretability is applied to discover significant transaction features to improve the learning of feature significance. Different from conventional XAI models that utilize explainability for interpretability purposes only, the proposed approach leverages SHAP-based feature engineering to improve both model interpretability and anomaly detection capability. The approach is tested on two popular benchmark datasets, namely IBM AMLSim and PaySim, both of which have heavily skewed class distributions. Extensive comparative outcomes report the encouraging performance of the XAIGFL-NN algorithm over recent methodologies. Therefore, the proposed model is found to be an accurate, interpretable, and robust solution for next-generation AML and financial transaction intelligence systems.
subject: Articles
  topic: section
subject: Anti-Money Laundering
  topic: keywords
subject: Class Imbalance
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Explainable Artificial Intelligence
  topic: keywords
subject: Feature Learning
  topic: keywords
subject: Baseline Model
  topic: keywords
creator: Ravi N.
creator: Sathiamoorthy S.
rights: Copyright (c)
date: 2026-07-29
publication date: 2026-07-29
starting page: 768
ending page: 790
doi: 10.36548/jtcsst.2026.3.017

Confidence-Weighted Ensemble Learning and Graph-Based Shapley Theory for Refined Emotion Prediction in Autistic Children
type: item
description: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by variations in brain function and structure that affect emotion perception, communication, and social interaction. Traditional machine learning (ML) algorithms suffer from bias-variance imbalance and redundant feature representations, leading to inaccurate emotion classification in autistic children due to heterogeneous facial cues and limited annotated data. To address these challenges, this research proposes a Mutual-Information-weighted Shapley Graph (MISG) and Confidence Weighted Aggregation with Ensemble ML (CWAEML) for emotion classification in autistic and non-autistic facial datasets. MISG performs structured graph-based feature refinement by modelling inter-feature dependencies using mutual information and estimating marginal contribution via Shapley theory. Deterministic feature selection removes semantic redundancy while preserving discriminative attributes, thereby improving classifier robustness. The EML integrates multiple base learners, including Multi-Support Vector Machine (MSVM), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Decision Tree (DT), and Random Forest (RF), through confidence-weighted fusion to ensure adaptive learning across emotion domains. The proposed MISG-CWAEML achieves accuracies of 98.75%, 97.95%, and 68.72% on AffectNet, Autistic Children Emotions - Dr. Fatma M. Talaat, and Young AffectNet HQ, respectively, which are superior to conventional SVM and other benchmark models in cross-domain evaluations. The findings demonstrate the effectiveness of graph-based feature selection combined with adaptive ensemble learning in handling complex and imbalanced emotion datasets. The proposed MISG-CWAEML provides practical implications for developing reliable systems, thereby enabling enhanced emotion recognition for children with ASD.
subject: Articles
  topic: section
subject: Ensemble Machine Learning
  topic: keywords
subject: Emotion Prediction
  topic: keywords
subject: Feature Refinement
  topic: keywords
subject: Shapley Graph Theory
  topic: keywords
subject: Autism Children
  topic: keywords
creator: Sujatha Hanumantharayappa
creator: Manjula R. Bharamagoudra
rights: Copyright (c)
date: 2026-07-20
publication date: 2026-07-20
starting page: 745
ending page: 767
doi: 10.36548/jtcsst.2026.3.016

A Symmetry-Aware Shape Descriptor Framework for Automated Defect Detection in Chocolate Images using Geometric Feature Engineering
type: item
description: In recent years, developments in food quality and safety regulations have placed greater importance on high-quality food production. Foods that are inspected for quality defects include chocolates that are susceptible to defects during their manufacturing process. Currently, there are various techniques for detecting defects in chocolates, but most focus on global properties of the shape of chocolates. In this study, a new technique called Symmetry Aware Shape Descriptor (SASD) approach was proposed for automatic defect detection in chocolates. This algorithm is composed of two descriptors; Symmetry Difference Index (SDI), which measures the global symmetry properties of a shape, and Symmetry Residual Descriptor (SRD), which measures the local asymmetry properties of the shape. After the extraction of these two descriptors, they form a feature vector, which is then classified using Support Vector Machine (SVM) Classifier. To perform a comparative analysis, classical shape-based approaches, basic shape features, Hu moments, and convex hull-based approaches were also implemented. The dataset used in this study consists of 225 chocolate images from 14 different chocolate brands. The suggested framework exhibited improved performance when compared to conventional methods, where the classification accuracy and precision achieved by this framework were 77.78% and 78.95%, respectively. These results show that the suggested SASD framework is an effective, transparent, and computationally efficient method for automated defect detection in chocolates.
subject: Articles
  topic: section
subject: Chocolate Defect Detection
  topic: keywords
subject: Shape Analysis
  topic: keywords
subject: Symmetry-Aware Descriptors
  topic: keywords
subject: Support Vector Machine (SVM)
  topic: keywords
subject: Image Processing
  topic: keywords
subject: Quality Inspection
  topic: keywords
subject: Geometric Features
  topic: keywords
creator: Ruth Moly Benjamin
creator: Abraham Chandy D.
creator: Hepzibah Christinal A.
rights: Copyright (c)
date: 2026-07-20
publication date: 2026-07-20
starting page: 728
ending page: 744
doi: 10.36548/jtcsst.2026.3.015

Hybrid Channel-Spatial Attention Enhanced EfficientNet for Multi-Class Brain Tumor Classification Using MRI
type: item
description: Brain tumor classification from Magnetic Resonance Imaging (MRI) remains challenging because of tumor heterogeneity, the overlapping intensity distribution of tumors, and the limited availability of medical images. This study introduces a lightweight deep learning model by integrating a Hybrid Channel–Spatial Attention (HCSA) module into the EfficientNet-B0 architecture for multi-class brain tumor classification. The HCSA module processes channels and spatial features sequentially to enhance feature representation by focusing on tumor-relevant information and better localizing the tumor region while maintaining computational efficiency. The proposed framework was evaluated on a publicly available Kaggle MRI brain tumor dataset, comprising 5,600 MRI images across 4 classes – glioma, meningioma, pituitary tumor and no-tumor. Comparative experiments were conducted with ResNet50, DenseNet121, EfficientNet-B0, and attention-enhanced EfficientNet models with identical preprocessing, data augmentation and training conditions. The proposed HCSA-EfficientNet-B0 model achieved a classification accuracy of 98.57%, an F1-score of 0.99, and an ROC-AUC value of 0.999 on the internal hold-out test set. Ablation results indicate that the combination of channel attention and spatial attention consistently outperforms each of the two attention mechanisms in isolation. Moreover, the proposed framework effectively captures the tumor-relevant regions, as suggested by Grad-CAM visualizations, which provide qualitative evidence supporting the interpretability of the classification results. The proposed framework is effective for multi-class brain tumor classification using MRI images and is able to improve classification performance while achieving computational efficiency.
subject: Articles
  topic: section
subject: EfficientNet-B0
  topic: keywords
subject: Hybrid Attention
  topic: keywords
subject: Brain Tumor Classification
  topic: keywords
subject: Grad-CAM; Multi-Class Classification; Attention Mechanism
  topic: keywords
creator: Kiran Kumar M.
creator: Dheeraj Kumar K V.
creator: Gunji. Sreenivasulu
creator: Kusuma S.
rights: Copyright (c)
date: 2026-07-18
publication date: 2026-07-18
starting page: 703
ending page: 727
doi: 10.36548/jtcsst.2026.3.014

Comparative Multi Domain Vibration Analysis of Dual Sensors for Structural Health Monitoring
type: item
description: Vibration-based Structural Health Monitoring (SHM) is a powerful technique to monitor the health of engineering structures and identify possible anomalies. In this paper, the vibration signal received using HG24HS and HG07U sensors in Python on Google Colab is thoroughly analyzed. The approach combines time-domain, frequency-domain, statistical, and clustering-based techniques to assess how similar sensor responses are and identify possible structural changes. The time-domain and frequency-domain analyses indicate that the signals have similar amplitudes and spectral properties. Statistical testing, T-test and Mann-Whitney U test, show that there is no significant difference in mean and median behavior, whereas the Kolmogorov-Smirnov test demonstrates on the level of distribution that there are slight variations. The analysis of variance indicates that there is a slight difference in the dispersion of vibration energy in the HG07U sensor. The similarity in vibration patterns with a partial overlap is further supported by clustering analysis. Overall, the findings indicate that the system works in stable conditions and there is no strong indication of structural damage, but slight variations can be noticed that can be explained by localized effects or differences in the sensitivity of the sensors.
subject: Articles
  topic: section
subject: Cluster
  topic: keywords
subject: Sensors
  topic: keywords
subject: Structural Health Monitoring
  topic: keywords
subject: Statistical Testing
  topic: keywords
subject: Vibration Signals
  topic: keywords
creator: Gowri Shankar Manivannan
creator: Guruprasath Jeevajothi
creator: Sanjoy Deb
creator: Venkateswara Rao Kolli
creator: Deepika K C.
creator: Santhosh Kumar K B.
rights: Copyright (c)
date: 2026-07-16
publication date: 2026-07-16
starting page: 687
ending page: 702
doi: 10.36548/jtcsst.2026.2026.3.013

Improving Directed Acyclic Graph-Based IoT Consensus through a Hybrid Stake-Weighted Fast Probabilistic Consensus Framework
type: item
description: The emergence and expansion of the Internet of Things (IoT) have created an increasing need for distributed, secure, and scalable consensus protocols that can validate transactions in such volatile and resource-limited environments. DAG-based ledger systems, in combination with Fast Probabilistic Consensus (FPC) offer high throughput with minimal communication cost for conflict resolution. However, traditional FPC does not have any logical means of assigning weight to different validators in an adversarial setting. In this context, this article attempts to present a hybrid Proof-of-Stake and Fast Probabilistic Consensus (PoS-FPC) protocol for DAG-based IoT systems. The proposed framework consists of transaction attachments on a DAG graph, Ed25519 signatures, BLAKE2b-256 hashing, a weighted quorum for non-conflicting transactions, and a stake-weighted FPC algorithm for the resolution of conflicting transactions. The weight of validators in the proposed framework is computed based on an adaptive weighting scheme that uses the normalized weight of stake and mana, dynamically tunes their weights depending on network traffic, and implements reward and penalty mechanisms to ensure honest participation and prevent malicious attacks. The proposed framework was tested by conducting discrete event simulations of 20,000 transactions in different adversarial situations. The experimental evaluation yielded a throughput of 5,128 transactions per second, a decision accuracy of 99.6%, adversary resistance of 97.81%, a quorum latency of 69.782 ms, FPC conflict latency of 690 ms and average convergence time of 3.73 rounds of the FPC algorithm. When compared to a mana-based DAG-FPC framework that was evaluated under the same simulation setup, the proposed framework outperforms it in terms of decision accuracy, conflict latency, faster convergence, and robustness to adversarial participation of up to 40%.
subject: Articles
  topic: section
subject: Directed Acyclic Graph
  topic: keywords
subject: Distributed Ledger Technology
  topic: keywords
subject: Fast Probabilistic Consensus
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Proof-of-Stake
  topic: keywords
subject: Weighted Consensus
  topic: keywords
creator: Basil Q. Abdulkareem
creator: Suad A. Alasadi
rights: Copyright (c)
date: 2026-07-16
publication date: 2026-07-16
starting page: 665
ending page: 686
doi: 10.36548/jtcsst.2026.3.012

Efficient Skeleton-Based Human Action Recognition for IoT Edge Devices Using Lightweight LSTM Networks
type: item
description: Human Action Recognition (HAR) is becoming increasingly important in areas such as intelligent surveillance, healthcare monitoring, and facilitating human-computer interaction. However, most current techniques use RGB-based deep learning, which is computationally intensive and cannot run on low-resource edge devices. This study presents a lightweight, skeleton-based Human Action Recognition (HAR) framework. The framework utilizes MediaPipe pose estimation, spatiotemporal normalization, and LSTM-based temporal modeling to efficiently recognize kinetically distinct human movements on resource-limited Internet of Things (IoT) edge devices. It combines MediaPipe pose estimation with Long Short-Term Memory (LSTM) networks to model temporal motion efficiently in resource-constrained IoT edge computing environments. Twelve kinetically unique actions from the UCF50 dataset were rendered into skeletal representations by temporally interpolating and spatially normalizing the action sequences. The HAR framework achieved 85.14% classification accuracy, indicating that sufficient motion-related information can be inferred from skeletal topology, allowing for reliable recognition at a reduced computational cost. The results demonstrate a practical balance between efficiency and performance, positioning the framework as an effective tool for edge-oriented HAR applications. From an IoT perspective, the framework has the potential to support IoT-enabled smart surveillance, assisted living, and intelligent monitoring systems by making it suitable for connected cameras and edge devices for local action recognition, even with limited computing power and communication bandwidth. This makes it a promising candidate for deployment across diverse real-world environments while preserving privacy through skeleton-based representations.
subject: Articles
  topic: section
subject: Human Action Recognition (HAR)
  topic: keywords
subject: IoT
  topic: keywords
subject: Skeleton-based Classification
  topic: keywords
subject: Long Short-Term Memory (LSTM)
  topic: keywords
subject: MediaPipe
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Skeletal Representation
  topic: keywords
creator: Shubbar Salman Baqer Alkhafaji
creator: Ali Abdulazeez Mohammed Baqer Qazzaz
creator: Yousif Samer Mudhafar
rights: Copyright (c)
date: 2026-07-16
publication date: 2026-07-16
starting page: 649
ending page: 664
doi: 10.36548/jtcsst.2026.3.011

CAAMF: Conflict-Aware Adaptive Multimodal Fusion for QR Code Phishing Attack Detection
type: item
description: QR codes are widely utilized in digital transactions; however, substantial security risks are increased by QR-based phishing (quishing) attacks. Existing attack detection techniques depend on a single modality-either analysis of URLs or visual inspection of QR codes causing high false-positive rates. Furthermore, attackers exploit mechanisms that bypass either of these techniques. This paper proposes CAAMF, a Conflict-Aware Adaptive Multimodal Fusion framework which combines different layers for URL risk assessment, visual integrity analysis, and conditional anomaly resolution into a unified pipeline. The URL scoring layer utilizes LightGBM with threat intelligence, the visual layer uses lightweight Convolutional Neural Networks (CNN) to detect tampering, and Isolation Forest acts as a conditional conflict resolver among disagreements between detection modalities. The results demonstrate an accuracy of 95.9 %, an AUC of 0.982, and a false-positive rate of 2.4 %, outperforming the evaluated baseline models. The conflict-aware adaptive fusion framework is significantly optimized for deployment in real-time (less than 25 ms inference) applications in UPI payment and enterprise security.
subject: Articles
  topic: section
subject: QR Phishing Detection (Quishing)
  topic: keywords
subject: Multimodal Security
  topic: keywords
subject: Adaptive Fusion
  topic: keywords
subject: Conflict-Aware Detection
  topic: keywords
subject: UPI Payment
  topic: keywords
subject: Enterprise Security
  topic: keywords
creator: Nidhi Nigam
creator: Rajat Bhandari
rights: Copyright (c)
date: 2026-07-15
publication date: 2026-07-15
starting page: 627
ending page: 648
doi: 10.36548/jtcsst.2026.3.010

UGBCF-Net: Uncertainty-Guided BEV Cross-Attention Fusion Network for Robust Radar-Camera Object Detection
type: item
description: Object detection under adverse weather remains a major challenge for autonomous driving systems. Cameras do not work well during rain, fog, and darkness, while mmWave radar works accurately in all of these conditions, regardless of illumination and precipitation. In this paper, we propose UGBCF-Net – the Uncertainty-Guided Bird's-Eye-View Cross-Attention Fusion Network, which incorporates radar confidence estimation based on physical laws and adaptive multimodal fusion. Our proposed architecture estimates radar confidence from radar cross-section measurements, maintains the Doppler channel by means of four-channel BEV representation, and uses uncertainty-guided weighting with a lightweight pooled cross-attention network for efficient feature fusion. Experimental results on the nuScenes v1.0-trainval dataset have shown that our UGBCF-Net reaches 0.683 mAP with 8.9 M parameters, operating at 56 FPS on a single NVIDIA RTX 3050 GPU. UGBCF-Net has achieved an accuracy similar to large-fusion networks and showed great robustness, reaching night recall equal to 0.884 with recall rates of 93.1%, 95.9% and 97.4% for vehicles, pedestrians, and cyclists, respectively.
subject: Articles
  topic: section
subject: Autonomous Driving
  topic: keywords
subject: 3D Object Detection
  topic: keywords
subject: Radar–Camera Fusion
  topic: keywords
subject: Bird's-Eye-View (BEV)
  topic: keywords
subject: Uncertainty Estimation
  topic: keywords
subject: Cross-Attention Networks
  topic: keywords
subject: Millimeter-Wave Radar
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Bhaskar S V.
creator: Roja Reddy B.
rights: Copyright (c)
date: 2026-07-14
publication date: 2026-07-14
starting page: 603
ending page: 626
doi: 10.36548/jtcsst.2026.3.009

Adversarial Attack Detection in Wireless Networks Using Deep Learning Based Capsule Networks
type: item
description: The vulnerability of wireless networks to misclassification and security oversight is increased by adversarial attacks using Deep Learning (DL)-based Intrusion Detection Systems (IDS). Because they are unable to distinguish small fluctuations in signal data and retain spatial hierarchy, traditional neural networks prefer Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which are highly prone to adversarial perturbations. In this research, a Capsule Network (CapsNet)-based framework detecting adversarial attacks in wireless networks is proposed. The network uses RF-aware capsule representations and dynamic routing to improve hierarchical feature learning and robustness against adversarial perturbations. To successfully search for adversarial distortion, the proposed model extracts significant network traffic features, encodes spatial hierarchy via primary and digit capsules, and uses a reconstruction loss function. To improve the model's robustness against sophisticated attack tactics, an exploratory quantum-assisted CapsNet implementation for preliminary investigation is also included. Experimental evaluation on benchmark wireless intrusion datasets shows that CapsNets outperform conventional CNNs and Long Short-Term Memory (LSTM) models, achieving 98.3% accuracy under normal conditions and maintaining 91.3% precision even under adversarial attack, compared to 87.5% and 72.4% for CNNs and 90.2% and 78.6% for LSTMs, respectively. In addition, compared with traditional DL models, CapsNets show a 34% improvement in robustness metrics. The proposed CapsNet system achieved 98.3% detection accuracy and a reduced False Positive Rate across several adversarial attack scenarios.
subject: Articles
  topic: section
subject: Adversarial Attacks
  topic: keywords
subject: Wireless Networks
  topic: keywords
subject: Capsule Networks
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Dynamic Routing
  topic: keywords
creator: Sneha George
creator: Priscilla Joy R.
creator: Karuppasamy K.
rights: Copyright (c)
date: 2026-07-10
publication date: 2026-07-10
starting page: 579
ending page: 602
doi: 10.36548/jtcsst.2026.3.008

A Multifaceted Framework for Predicting Ambient Air Pollution in the National Capital Region
type: item
description: Air quality forecasting models anticipate and control pollution concentrations quickly. In this research, two diversified datasets are analyzed and modeled using the proposed novel Hybrid Forecasting Model (HFM). PM10, PM2.5, NO, and NO2 pollutant concentrations for the National Capital Region, Delhi, were collected from the Central Pollution Control Board as Dataset 1. A Raspberry Pi hardware integrated with an MCP3008 Analog-to-Digital Converter and pollutant sensors like MQ135 and GP2Y1010AU0F was assembled to collect PM2.5, CO, and NH3 pollutant concentrations at the study site as Dataset 2. The results obtained for the proposed HFM are compared with three existing algorithms in terms of R-squared and Mean Squared Log Error. The novelty of this work is to test the performance of the algorithm by executing it on a computer with an Intel 8265U processor and also on a Raspberry Pi integrated with an MCP3008 Analog-to-Digital Converter. The experimental results indicate that the Raspberry Pi requires approximately 3.37% of the power consumed by the Intel 8265U processor. Conversely, the Intel 8265U processor achieves approximately 86.67% lower latency, thereby delivering significantly faster computational performance compared to the Raspberry Pi.
subject: Articles
  topic: section
subject: Long Short-Term Memory
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Auto Regressive Integrated Moving Average
  topic: keywords
subject: R Squared
  topic: keywords
subject: Mean Squared Log Error
  topic: keywords
subject: National Capital Region
  topic: keywords
creator: Dhanalakshmi Subramanian
creator: Poongothai Marimuthu
creator: Nithyalakshmi Ramadoss
rights: Copyright (c)
date: 2026-07-09
publication date: 2026-07-09
starting page: 558
ending page: 578
doi: 10.36548/jtcsst.2026.3.007

Design and Validation of an IoT-Enabled Safety Monitoring System for Agricultural UAV Operations
type: item
description: The challenges faced by Unmanned Aerial Vehicles (UAVs) used in precision agriculture are growing. Operational risks arise from harsh conditions, such as abnormal flight behaviors, extended missions, and environmental circumstances. Current flight controllers primarily focus on navigation and stabilization, with only limited ability to monitor real-time safety. This study presents a low-cost, real-time safety monitoring platform for agricultural UAVs using an ESP32 microcontroller integrated with an MPU6050 inertial measurement unit (IMU) and a DHT11 temperature sensor. The system continuously measures roll, pitch, and on-board temperature, sending telemetry data to a cloud-based IoT platform for visualization and alerts. An empirically derived, threshold-based anomaly detection plan, based on over 100 normal flight data points, more than 20 Pixhawk crash log analyses, and observations of climatic conditions from 28 Indian states, allows for timely detection of abnormal operating conditions. The monitoring module has a sampling rate of 30-40 Hz and uses persistence-based detection logic to ensure that sustained anomalies are present over a 125-165 ms timespan. It was experimentally verified that roll and pitch estimates based on an atan2-based approach have a mean absolute error of 7.62° and 13.52°, respectively, compared to reference flight controller measurements. Temperature validation shows that the DHT11 sensor's temperature readings do not exceed the ±4.6°C deviation limit relative to the reference readings. These findings reveal that the suggested framework offers a scalable, cost-effective, and easily implemented solution for safety monitoring and early detection of abnormal flight conditions in agricultural UAV delivery.
subject: Articles
  topic: section
subject: DHT11 and MPU6050 Sensors
  topic: keywords
subject: IoT-Based Monitoring
  topic: keywords
subject: Safety Monitoring System
  topic: keywords
subject: Threshold-Based Anomaly Detection
  topic: keywords
subject: Agricultural UAVs
  topic: keywords
creator: Girijalaxmi G.
creator: Ravindra S. Hegadi
rights: Copyright (c)
date: 2026-07-08
publication date: 2026-07-08
starting page: 538
ending page: 557
doi: 10.36548/jtcsst.2026.3.006

Affective Student Engagement Detection using Machine Learning: A Systematic Survey on Methods, Modalities and Learning Environments
type: item
description: Student engagement is one of the essential factors an academic success, but tracking the internal affective conditions of learners is a complicated problem in the modern educational environment. The results show that there has been a major shift from using simple behavioral monitoring to more complex affective computing, though visual modalities (63%) are the most used because of their scalability and non-invasive nature. Nevertheless, the results highlight an increased use of multimodal fusion and deep learning systems, particularly, hybrid models (e.g. CNN-LSTM), to effectively learn fine-grained academic emotions (e.g. frustration, boredom, and enjoyment) in online, traditional, and hybrid classes. Furthermore, the review shows a methodological shift towards “in-the-wild” study designs with an emphasis on ecological validity in authentic and uncontrolled learning settings. Although there is significant technical advancement, major gaps remain in establishing standardized benchmark datasets, explaining AI models, and deploying ethical, privacy-protecting frameworks. Overall, this survey highlights that future developments should focus on promoting more responsive, inclusive, and empathetic learning through the development of real-time, pedagogically validated intervention systems.
subject: Articles
  topic: section
subject: Student Engagement Detection
  topic: keywords
subject: Emotion Recognition
  topic: keywords
subject: Affective Computing
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Multimodal
  topic: keywords
subject: Online and Hybrid Learning
  topic: keywords
creator: Shruthi H K.
creator: Jayapriya J.
creator: Vinay M.
creator: Raju Ramakrishna Gondkar
rights: Copyright (c)
date: 2026-07-04
publication date: 2026-07-04
starting page: 517
ending page: 537
doi: 10.36548/jtcsst.2026.3.005

Trustworthy Multimodal Depression Screening via Cross-Attention Fusion and Calibrated Uncertainty
type: item
description: Conventional automated systems for screening for depression leverage speech/text-based features, which makes such systems sensitive to external noises, potential mistakes in automatic speech recognition (ASR), and other modality-related limitations. Besides, most current approaches fail to provide any form of uncertainty estimates, properly calibrated output probabilities, and explanation capabilities for their predictions, limiting the use of these tools within a clinical environment. In this work, we present an approach for building trustworthy depression screening systems based on a fusion of acoustic and linguistic features using a novel cross-attention-based method. Specifically, self-supervised learning techniques like wav2vec 2.0 and HuBERT models are used for extracting acoustic features from raw audio recordings. For text processing, our framework leverages DistilBERT and RoBERTa language representation models. By employing a multi-head cross-attention module, we allow our model to effectively exploit interactions between linguistic content and acoustic features. Predictive uncertainty estimates are produced by incorporating Monte Carlo dropout into the model architecture. Temperature scaling is applied for proper calibration of output probabilities. Token-level attributions are used for explaining predictions made for linguistic input, while attention coefficients for segments of audio signal correspond to explanation. Experiments conducted on a dataset of clinical interviews from the DAIC-WOZ corpus show that our method significantly outperforms audio-only, text-only, and fusion baselines, reaching an accuracy, Macro-F1, Weighted-F1, AUROC, and ECE of 0.82, 0.80, 0.81, 0.87, and 0.034 respectively. Our system also shows increased robustness against noisy audio conditions, ASR-based transcripts, and missing data.
subject: Articles
  topic: section
subject: Multimodal Depression Detection
  topic: keywords
subject: Cross-Attention Fusion
  topic: keywords
subject: Trustworthy AI
  topic: keywords
subject: Speech and Language Processing
  topic: keywords
subject: Uncertainty-Aware Deep Learning
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: DAIC-WOZ
  topic: keywords
subject: Mental Health Screening
  topic: keywords
creator: Aakash Gupta
creator: Umme Salma M. Pirzada
rights: Copyright (c)
date: 2026-06-19
publication date: 2026-06-19
starting page: 494
ending page: 516
doi: 10.36548/jtcsst.2026.3.004

Detecting Intrusions in MongoDB NoSQL Databases Using Machine Learning
type: item
description: Due to their flexibility and scalability, NoSQL databases, most notably MongoDB, are widely adopted in the modern cloud environment. However, this popularity introduces new security vulnerabilities, such as NoSQL injections, brute-force attacks, and denial-of-service attacks, which traditional Intrusion Detection Systems (IDS) struggle to detect. In this paper, machine learning is adapted to detect five types of attacks targeting MongoDB including NoSQL injections, XSS through the $where operator, brute-force attacks, denial-of-service attacks through expensive queries, and preparatory scanning. For this purpose, the ToN_IoT dataset is adapted to the MongoDB context by selecting 150,000 representative samples and 9 native features, and implementing four composite features. Three models are evaluated: Random Forest, XGBoost, and MLP Classifier. XGBoost achieves the best performance with an inference time of 0.019 ms, an accuracy of 98.9% and a macro F1-score of 0.9889. Performance per class indicates that scanning attacks and brute-force attacks are the easiest to detect (F1 > 0.98). However, injection attacks and XSS attacks are more difficult to detect (F1 < 0.98) due to their signatures associated with the application level, which are difficult to detect. The present method outperforms previous methods reporting 96-98% accuracy while offering MongoDB specificity and interpretability. These experiments reveal that real-time detection of attacks on MongoDB is achievable with high efficiency, allowing it to be deployed in cloud environments.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Intrusion Detection System
  topic: keywords
subject: Random Forest
  topic: keywords
subject: XGBoost
  topic: keywords
subject: MongoDB
  topic: keywords
subject: Multi-Class Cloud Environment
  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: 2026-06-05
publication date: 2026-06-05
starting page: 473
ending page: 493
doi: 10.36548/jtcsst.2026.3.003

A Hybrid Approach for Transforming Retail and Customer Experience with User Motion Analytics Using Yolov4 and Pattern Mining
type: item
description: Advances in technology, particularly the tracking and analyzing of customer movements and behavior within retail stores, are leading to a massive revolution within the retail industry. This paper suggests a model that combines object detection, pattern mining, improved preprocessing techniques, and analysis of user motion to boost the retailing process and improve the customer experience. In the proposed method, video data preprocessing is accomplished using the Lucas-Kanade approach. The success of this technique is attributed to its ability to create a solid base for future study by successfully tracking and recording movements made by users. For object detection, the most suitable algorithm is YOLOv4, known for its high accuracy in detecting objects in real time. The motion data acquired through this approach will be utilized for pattern mining. This proposed approach enables extensive analysis of consumer behavior in retail stores. In this study, DBSCAN and K-means clustering methods are used to analyze the clusters. While K-means creates clusters from the dataset based on consumer behavior, DBSCAN is used because of its flexibility in handling different densities, including the presence of noise in the cluster. The accuracy of the proposed system is 98%, while the recall score is 99.02%, and the F1 score is 96.7%.
subject: Articles
  topic: section
subject: User Motion Analytics
  topic: keywords
subject: YOLOv4
  topic: keywords
subject: Pattern Mining
  topic: keywords
subject: Retail Transformation
  topic: keywords
subject: Object Detection
  topic: keywords
subject: Cluster Analysis
  topic: keywords
creator: Angelin Jeba P.
creator: Jasmine David D.
creator: Esther Alice Mathew
creator: Marvin Mathew
creator: Harish M.
creator: Jenin Joel M.
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-06-05
publication date: 2026-06-05
starting page: 452
ending page: 472
doi: 10.36548/jtcsst.2026.3.002

MVRODC: Multi-Variate Regression based Outlier Detection and Classification on IoT Sensor Data — A Temporal Node Level Approach
type: item
description: The reliability and correctness of data detected by sensors are essential for the efficient use of Internet of Things (IoT) and Wireless Sensor Network (WSN) technologies. However, sensor readings are often affected by errors due to hardware failure or actual environmental events. This causes outliers that can affect decision-making and system efficiency. To address these problems, the present study proposes a novel outlier detection and classification technique called Multivariate Regression-based Outlier Detection and Classification (MVRODC). MVRODC uses similarity measures derived from Multiple Linear Regression (MLR) along with an adaptive buffer to model temporal relationships. This ensures that outliers are detected and classified into two different categories in real-time into two categories: errors and actual events. Inter-sensor feature correlations across multiple sensor streams (temperature, humidity, air quality, and light) are exploited along with temporal prediction consistency to enable robust real-time outlier detection and classification. The MVRODC technique ensures that relevant outliers caused by actual events are retained, allowing for the detection of environmental changes while ignoring erroneous data. This filtering technique saves energy because sending erroneous data consumes as much energy as sending legitimate data. Experimentally, MVRODC performs better than existing outlier detection techniques, achieving superior results in terms of detection rate, false alarm rate, accuracy, error detection rate, and event detection rate.
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: Outliers
  topic: keywords
subject: Errors
  topic: keywords
subject: Events
  topic: keywords
subject: Detection Rate
  topic: keywords
subject: Multiple Linear Regression
  topic: keywords
creator: Veera Brahmam M.
creator: Gopikrishnan S.
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-06-05
publication date: 2026-06-05
starting page: 429
ending page: 451
doi: 10.36548/jtcsst.2026.3.001

An Asynchronous Tri-Node IoT Architecture for Real-Time Environmental Mitigation in Bedridden Patients
type: item
description: Although IoT has enhanced health monitoring among immobile subjects, conventional cloud computing-based frameworks are riddled with perilous network latency, separating biometrics from environmental settings and hindering timely physical intervention. This paper discusses a unique tri-node, asynchronous edge-computing structure that shifts from passive monitoring to proactive, local intervention. Biometric abnormalities are constantly detected by the Smart Glove (Telemetry Node), which includes an ESP32-S3 microcontroller, an MPU-6050 gyroscope, and a CJMCU-6701 galvanic skin response sensor. Concurrently, the Dispenser Node, based on Arduino Nano, evaluates environmental factors using DHT22, GY-302, and MQ-135 sensors and operates as a robotized emergency pillbox driven by servos. The system then initiates a physical mitigation operation by activating exhaust fans, emergency LED lights, and window servos to improve air quality when biometric and environmental anomalies overlap, with an Automation Box, based on Arduino Nano ESP32, serving as the central processing unit. In order to maintain clinical documentation, all data is permanently stored in a centralized database. As a result, the bi-modal paradigm attains a 96.0% detection rate and a latency of only 72 milliseconds.
subject: Articles
  topic: section
subject: Internet of Medical Things (IoMT)
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Asynchronous Architecture
  topic: keywords
subject: Multi-Modal Biosensing
  topic: keywords
subject: Real-Time Anomaly Detection
  topic: keywords
subject: Automated Mitigation
  topic: keywords
creator: Nitin Babu Daniel
creator: Sebastian Terence
creator: Jude Immaculate
rights: Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
date: 2026-05-30
publication date: 2026-05-30
starting page: 413
ending page: 428
doi: 10.36548/jtcsst.2026.2.011

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
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subject: Slot Filling
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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