Journal of Trends in Computer Science and Smart Technology
Comparative Benchmarking Study of Deep Learning Models for Geomagnetic Storms Prediction
Geomagnetic storms have been known to have a substantial impact on the performance of satellites, communication systems, navigation systems, and other space weather-based systems. This research is concerned with developing a data-driven forecast of geomagnetic activity using deep learning and traditional machine learning models. This study focuses on comparing deep learning and machine learning models for predicting the Disturbance Storm Time (Dst) index through solar wind and geomagnetic parameters. For the analysis, an hourly dataset for 2000-2024 has been utilized resulting 219,168 observations. From these, eight parameters including magnetic field components, solar wind speed, geomagnetic indices, and past Dst values have been selected. Additionally, Random Forest, XGBoost, LightGBM, and persistence were employed as traditional baseline models. The performance measures considered included RMSE, MAE, R², and Pearson correlation, alongside training time, inference time, number of parameters, and a feature ablation study. Among the evaluated deep-learning architectures, LSTM achieved the best average performance across the five independent seeds. The RMSE of LightGBM and persistence baselines was 3.77459 nT and 4.63338 nT, respectively. The ablation analysis shows that AE, Kp10, and solar-wind variables substantially influence forecasting performance, whereas historical Dst and a longer 48-hour window do not necessarily improve the reported error. Overall, the results have shown that it is possible to model nonlinear temporal dependencies in geomagnetic observations using recurrent architectures. However, the use of traditional baselines along with repeated-seeds and ablation tests makes the evaluation more thorough.
Semantic Adaptive Fusion with Trust Signals for Fake News Detection
Due to the rapid proliferation of deceptive information disseminated via digital platforms, there is a need to construct scalable, robust and mathematical models to detect fake news automatically. Even though Large Language Models (LLMs) show remarkable ability to solve contextual semantic reasoning tasks, the current state-of-the-art hybrid models require static approaches to feature fusion, which cannot handle different claim structures adaptively. In this paper, we introduce AQFND (Adaptive and Trust-aware Fake News Detector), an end-to-end model that combines dense contextual semantic features provided by a frozen LLM encoder (Qwen2.5) with statistical lexical features (TF-IDF) using a complexity-aware dynamic gating mechanism. With the aim to increase the level of reliability of the decision-making process, we incorporate the idea of a trust-aware inference engine based on Shannon Entropy and confidence thresholding (τ = 0.65). The proposed framework is evaluated on three widely-used benchmark datasets covering complex political claims, extensive fact-checking articles, and entertainment news: LIAR, PolitiFact, and GossipCop. With the LIAR dataset, AQFND attains an F1-score of 76.20% (accuracy 71.86%, ROC-AUC 76.96%, and MCC 0.4215), while on the PolitiFact dataset, AQFND gets an F1-score of 71.31% (accuracy 73.01%, ROC-AUC 81.42%, and MCC 0.4668). Also, on the GossipCop dataset, AQFND has an F1-score of 97.19% (accuracy 97.17%, ROC-AUC 97.85%, and MCC 0.9435).
Multi-Source Generalization-Aware Phishing URL Detection Using Calibrated Stacked Ensemble and False-Positive Control
Phishing remains a persistent cybersecurity threat, with over 1.3 million attacks reported in a single quarter of 2023. Despite strong benchmark performance, many machineand deep-learning models exhibit limited deployment reliability because they are evaluated using balanced, single-source datasets with randomized splits. This paper addresses this gap in two phases. First, it presents a controlled multi-source empirical study of four baseline phishing URL detection models–Logistic Regression, Support Vector Machine, Random Forest, and XGBoost–under four increasingly realistic evaluation conditions, including cross-source generalization, temporal drift, and class imbalance. Second, it introduces GAFPNet (Generalization-Aware and False-Positive Controlled Framework Network), a five-module stacked-ensemble framework. GAFPNet uses dataset-neutral lexical and structural URL feature extraction, SMOTE-based imbalance correction, Platt scaling for ensemble calibration, and a tunable false-positive control scheme. Experiments using a consolidated 13,000-sample set from PhishTank, OpenPhish, and Tranco Top-Sites show that baseline accuracy decreases by 13.16 to 20.22 percentage points in cross-source testing and by 2.41 to 8.14 percentage points in standard testing. GAFPNet achieves 99.12% accuracy, a 98.97% F1-score, an AUC-ROC of 0.9943, an MCC of 0.988, and a false positive rate of 0.74%, outperforming the evaluated baselines in all four scenarios. An ablation study confirms the contribution of each module. These results position GAFPNet as a generalization-aware and deployment-oriented phishing URL classifier for real-time filtering applications.
Hybrid Quantum-Classical Deep Learning Model for Global Cotton Yield Forecasting
Global cotton yield forecasting is critical for food security planning, supply chain management, and climate adaptation policy, yet remains challenging due to the non-linear interactions among satellite vegetation indices, meteorological variables, and agronomic factors across heterogeneous growing regions. This paper proposes the Hybrid Quantum-Classical Deep Learning (HQC-DL) framework, the first systematic empirical study integrating parametric quantum circuit (PQC)-based feature enhancement into a CNN-BiLSTM and gradient-boosted regression pipeline for global crop yield prediction. When tested on the FAOSTAT-MODIS-CRU dataset with 1,142 country-year observations of 59 countries between 2001 and 2020, the proposed method exhibits RMSE of 0.769±0.004 t/ha and R2 of 0.690± 0.004, and is characterized by the lowest cross-run variance compared to all other neural network architectures, which demonstrates its better prediction stability. The ablation study shows that all architectural stages have a positive contribution: when gradient boosted regressors are trained on features generated via deep learning methods, they show better results than models trained on raw data, regardless of the quantum module, and PQC expressibility analysis proves that the ansatz is capable of exploring a wide range of states in Hilbert space.
Tri-Level Network Attack Risk Stratification Using IDS-Contextual Ensemble Learning
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.
Intrusion Detection System (IDS)-Contextual Features
Security Operations Centre (SOC) Alert Triage
XAI-Guided Features with Neural Learning Architecture for Anti-Money Laundering Detection in Financial Transactions
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.
Confidence-Weighted Ensemble Learning and Graph-Based Shapley Theory for Refined Emotion Prediction in Autistic Children
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.
A Symmetry-Aware Shape Descriptor Framework for Automated Defect Detection in Chocolate Images using Geometric Feature Engineering
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.
Hybrid Channel-Spatial Attention Enhanced EfficientNet for Multi-Class Brain Tumor Classification Using MRI
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.
Grad-CAM; Multi-Class Classification; Attention Mechanism
Comparative Multi Domain Vibration Analysis of Dual Sensors for Structural Health Monitoring
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.
Improving Directed Acyclic Graph-Based IoT Consensus through a Hybrid Stake-Weighted Fast Probabilistic Consensus Framework
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%.
Efficient Skeleton-Based Human Action Recognition for IoT Edge Devices Using Lightweight LSTM Networks
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.
CAAMF: Conflict-Aware Adaptive Multimodal Fusion for QR Code Phishing Attack Detection
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.
UGBCF-Net: Uncertainty-Guided BEV Cross-Attention Fusion Network for Robust Radar-Camera Object Detection
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.
Adversarial Attack Detection in Wireless Networks Using Deep Learning Based Capsule Networks
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.
A Multifaceted Framework for Predicting Ambient Air Pollution in the National Capital Region
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.
Auto Regressive Integrated Moving Average
Design and Validation of an IoT-Enabled Safety Monitoring System for Agricultural UAV Operations
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.
Affective Student Engagement Detection using Machine Learning: A Systematic Survey on Methods, Modalities and Learning Environments
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.
Trustworthy Multimodal Depression Screening via Cross-Attention Fusion and Calibrated Uncertainty
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.
Detecting Intrusions in MongoDB NoSQL Databases Using Machine Learning
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.
Copyright (c) 2026 Journal of Trends in Computer Science and Smart Technology
A Hybrid Approach for Transforming Retail and Customer Experience with User Motion Analytics Using Yolov4 and Pattern Mining
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%.
MVRODC: Multi-Variate Regression based Outlier Detection and Classification on IoT Sensor Data — A Temporal Node Level Approach
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.
An Asynchronous Tri-Node IoT Architecture for Real-Time Environmental Mitigation in Bedridden Patients
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.
Integrating Clinical Indicators and DaTSCAN SPECT Biomarkers Using Transfer Learning Ensemble Models for Early Parkinson’s Disease Diagnosis with SWEDD Cohort
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.
Comprehensive Seven-Lens Evaluation Framework for Remote Access Laboratory Systems: Implementation and Performance Optimization
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.
Deployment-Oriented Evaluation of Healthcare Reimbursement Cost Prediction
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.
Adaptive Gated Multimodal Fusion for Robust and Generalized Human Activity Recognition
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.
Hybrid Graph Mamba Framework for Histopathological Image Classification
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.
Functional Kernel Adaptive Networks (KAN)
Enhancing Rail Surface Defect Detection Using a Hybrid YOLOv11–Vision Transformer Framework
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.
Guardian-Based Anonymous Password Management with Privacy Preservation Using Threshold Cryptography
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.