Journal of Advances in Information Technology
Journal of Advances in Information Technology is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology. Our aim is to publish experimental and theoretical results in strategy, infrastructure, human resources, system development and implementation, IT risk, IT management, data science, communications, technology development and futures, national policies and standards, software engineering, etc. In particular, we encourage a multidisciplinary/convergent approach based on the following broadly based branches of computer science for the application areas highlighted below: · Artificial Intelligence · Machine Learning · Data Science · Natural Language Processing · Cloud Computing · Internet of Things · Image Processing · Information Security · Networked Control Systems · Communication Systems Technologies · Digital Signal Processing in Communications · Neural Networks, Fuzzy Logic, Reasoning · Human-Computer Interaction (HCI) · Software Engineering · Non-technical but relevant topics such as Information Policy, Ethics and Legal issues as appropriate.
For articles published in JAIT, the copyright will be retained by the authors. Articles are licensed under the Creative Commons Attribution License (CC BY 4.0), which means all papers can be downloaded, shared, and reused without restriction, as long as the original authors are properly cited. These conditions allow for maximum use and exposure of the work, while ensuring that the authors receive proper credit. In exceptional circumstances, articles may be licensed differently. If you have a specific condition (such as one linked to funding) that does not allow this license, please mention this to the editorial office of the journal at submission.
Machine Learning Approaches for Imbalanced Post-Disaster Building Damage Classification: A Case Study of the 2022 Cianjur Earthquake
Quick and accurate assessment of building damage after a disaster is essential for effective recovery and reconstruction planning. While traditional field surveys are reliable, they are often time-consuming, resource-intensive, and can be affected by human bias. This study proposes a comparative evaluation of eight machine learning algorithms: LightGBM, Extreme Gradient Boosting (XGBoost), Synthetic Minority Oversampling Technique (SMOTE) + Random Forest (RF), Balanced Random Forest (BRF), Random Forest, Logistic Regression, Support Vector Machine (SVM) (Radial Basis Function (RBF)), and Naive Bayes, for multiclass classification of post-earthquake building damage utilizing real-world data on the 2022 Cianjur earthquake. The dataset comprises 3,063 records detailing geospatial and environmental characteristics, comprising epicentral distance, elevation, slope type, soil type, and vulnerability zone. To ensure reliable model performance on imbalanced data, a 10-fold cross-validation strategy was applied, evaluated across five metrics: accuracy, precision, recall, F1-Score, and Area Under the ROC Curve (AUC). The results indicate that ensemble-based models, particularly XGBoost (AUC = 0.860), LightGBM (AUC = 0.858), and BRF (AUC = 0.857), outperform traditional classifiers in both predictive accuracy and sensitivity toward severely damaged buildings. The outcomes underscore the effectiveness of gradient boosting and balanced ensemble methods in managing complex, imbalanced datasets, highlighting their potential for integration into automated post-disaster damage assessment systems. This research contributes to the development of intelligent, data-driven decision-support tools that can help accelerate recovery efforts in earthquake-prone regions.
Topic: Machine Learning in Computer and Information Systems
Copyright (c) 2026 Eka Rahmawati, Catur Edi Widodo, Sorja Koesuma https://creativecommons.org/licenses/by/4.0
Region-Aware Attention with Hybrid Angular-Cosine Margin Loss for Prosopagnosia Face Recognition
Prosopagnosia diminishes an individual’s ability to recognize familiar faces, under pose variations, occlusions, and when faces appear visually similar. This study develops a deep learning framework, ResRAMACL, with ResNet-101, Region-Aware Attention Mechanism (RAAM), and a novel Hybrid Angular-Cosine Margin Loss (HACML), for supporting prosopagnosia patients to recognize human faces. Facial images are aligned using 2D facial landmark plotting, which separates key semantic regions from the images. ResNet-101 extracts feature maps for each region, which are weighted by RAAM to highlight cognitively crucial areas. These features are fused into a single embedding vector and trained using HACML, and the facial embeddings are stored in a Structured Query Language (SQL) database. The prediction phase generates an embedding using a trained model, checks the similarity with the stored embeddings, and predicts the final result. Compared to the existing loss functions, the proposed HACML achieved up to 2.16 units higher inter-class separation and up to 0.44 units lower intra-class variance. Ablation study proved higher performance compared with variations of the pipeline. The generalizability analysis proved minimal variation in performance when tested with other datasets. Overall, this proves the significance of the approach for integration with Augmented Reality (AR)-based or mobile cognitive assistance technologies, enabling prosopagnosia patients to recognize faces.
Copyright (c) 2026 Bhavana Nagaraj, Rajanna Muniswamy https://creativecommons.org/licenses/by/4.0
A Novel Lightweight Multi-Octave Dilated Temporal Convolutional Network with Explainable AI and Meta-Learning Approach Based Digital Financial Fraud Detection
The number of online banking and financial services through mobile apps is growing steadily. The number of customers using these apps for their monetary transactions is drastically increasing day by day. However, the increasing use of these apps on smart devices raises security concerns. Therefore, it is becoming a significant process to implement effective mechanisms to prevent fraud and protect personal data. In today’s financial world, the use of credit cards for online purchases has increased exponentially, and with it, the fraud that accompanies it. It is very difficult to detect fraudulent transactions in banking transactions. Therefore, the proposed work has introduced a novel deep learning framework based on a complexity-reduced Multi-Octave Dilated Temporal Convolutional Network (MO-DTCN) with Explainable Artificial Intelligence (XAI) approaches, including Lime and Shap. With these advancements, the proposed model can detect fraudulent financial transactions. To reduce the complexity of the architecture, the proposed work has utilized a Weighted Pruning and Quantization approach. In addition, meta-learning helps to fine-tune the performance model. Experimental evaluation on this dataset proves that the proposed MO-DTCN framework is effective, as it attains an accuracy of 99.13%, precision of 99.48%, recall of 99.35%, and F1-Score of 99.60% for an 80% training split with a very high Matthews Correlation Coefficient (MCC) of 0.9945, reflecting strong robustness on highly imbalanced data.
Financial Fraud Detection, Explainable Artificial Intelligence , Meta-Learning,
Copyright (c) 2026 Mohd Abdul Rahim Khan, Yasir Hashim Naif, Salima Sarahan ALMughairi https://creativecommons.org/licenses/by/4.0
Enhancing Heart Rate Estimation with POS-SSA Remote Photoplethysmography
Remote Photoplethysmography (rPPG) enables non-contact heart rate monitoring from facial videos but it is highly susceptible to motion artefacts, illumination changes, and sensor noise. A framework combining the Plane Orthogonal-to-Skin (POS) method and Singular Spectrum Analysis (SSA) was proposed in this work to address these challenges by first projecting normalized Red Green Blue (RGB) signals onto a skin-tone–orthogonal subspace to suppress illumination and motion distortions and then decomposing the resulting signal into components that isolate physiologically meaningful oscillations. Evaluation on the PFF and UBFC-Phys dataset demonstrates that this approach consistently outperforms conventional single-channel, statistical, and chrominance-based methods by achieving a mean absolute error of 4.99 beats per minute (bpm) and correlation of 0.76 on PFF, and a mean absolute error of 4.11 bpm with correlation of 0.86 on UBFC-Phys. Furthermore, the comparison with results reported in the existing literature indicates that the proposed framework achieves competitive accuracy relative to popular learning-based rPPG approaches. These findings indicate that integrating chrominance projection with adaptive temporal decomposition significantly improves robustness and accuracy for contact-free heart rate estimation.
Copyright (c) 2026 Ruixuan Wang, Wei Quan, Bogdan Matuszewski, Nick A Heywood, Christopher Gaffney, Katie Hoad https://creativecommons.org/licenses/by/4.0
Enhancing Aggregation Efficiency and Training Stability in Heterogeneous Federated Learning Using the Adaptive LoRA FL Framework
In recent years, Low-Rank Adaptation (LoRA) has emerged as a highly effective parameter-efficient fine-tuning technique for Transformer-based models. This paper extends the applicability of LoRA to traditional Deep Neural Networks (DNNs) by integrating it into a Federated Learning (FL) framework. The principal aim of this study is to facilitate data privacy protection while simultaneously minimizing weight transfer overhead in heterogeneous environments. To address the challenges of Non-Independent Identically Distribution (Non-IID) data, this study proposes the Adaptive LoRA FL framework, which incorporates feature mapping and category unification mechanisms to harmonize highly heterogeneous datasets. By integrating LoRA fine-tuning with feature mapping and category unification mechanisms, it effectively integrates highly heterogeneous datasets (e.g., CIC-IDS2017, Edge-IIoTset, and TON-IoT datasets). Furthermore, the performance of Adaptive LoRA FL framework is evaluated against state-of-the-art FL methods and demonstrate its generalizability across diverse application domains. Experimental results demonstrate that compared to full-weight aggregation, the Adaptive LoRA FL framework significantly reduces client communication costs while maintaining improved global accuracy. By integrating an early stopping mechanism, the training process terminates once convergence is reached, effectively minimizing redundant computation and communication overhead.
Topic: Internet of Things (IoT) in Smart Systems and Applications
Copyright (c) 2026 Lin-Huang Chang, Yu-Sheng Hsieh, Tsung-Han Lee https://creativecommons.org/licenses/by/4.0
AgroExpense-OCR: Vision Transformer-Based Optical Character Recognition for Agricultural Receipt Digitization
This study concentrates on the development of AgroExpense-OCR, an Optical Character Recognition (OCR) system for agricultural expense receipts, employing the Vision Transformer (ViT) architecture to improve the recognition of both printed and handwritten text. Utilizing a dataset of over 15,000 systematically prepared items, experimental results demonstrate that FastViT achieves the fastest convergence and lowest Character Error Rate (CER), despite transient instability during handwritten sample training. The best character-level performance is achieved by FastViT, with a test loss of 0.03144 and Certificate of Analysis (CER) of 0.0086 using greedy decoding and 0.0091 with beam search. Evaluation results demonstrated superior performance, with printed receipts attaining an accuracy of 96.80% and handwritten receipts 92.40%, surpassing traditional CNN-RNN methodologies. The AgroExpense-OCR application was created for both mobile and web platforms, including features such as receipt scanning, automatic expense categorization, cost analysis, and budget notifications. User satisfaction assessments indicated a high level of acceptance, thereby confirming the practical applicability of the system. This research makes a significant contribution to the advancement of digital agriculture and establishes a foundation for integration with future smart farm management systems.
Copyright (c) 2026 Wongpanya S. Nuankaew, Chanapa Phikhason, Watthana Kayowaen, Tatsaneewan Yenwattana, Pratya Nuankaew https://creativecommons.org/licenses/by/4.0
Optimization of Task Offloading and Resource Allocation in the Internet of Everything: A Comprehensive Survey
The Internet of Everything (IoE) enables ubiquitous connectivity among heterogeneous devices but poses significant challenges for task offloading and resource allocation due to high dynamics and strict resource constraints. To address these complexities, optimization plays a pivotal role in efficiently orchestrating these resources. This survey comprehensively reviews optimization methodologies for IoE task offloading. Methodologically, this study relies on a comprehensive literature synthesis to distill evolutionary algorithmic trends and evaluate their applicability to specific IoE scenario constraints. The review is structured around six major paradigms: classic, Lyapunov-based, heuristic, game-theoretic, AI-native, and hybrid optimization. For each, representative techniques, assumptions, and performance characteristics are summarized. Comparative analysis highlights the evolution from model-based formulations toward intelligent, adaptive, and collaborative frameworks. Key challenges are identified, including scalability bottlenecks, learning instability in non-stationary environments, and complex multi-objective trade-offs. Finally, emerging research directions are outlined. This work bridges theoretical foundations with intelligent methodologies, offering a unified perspective for advancing cognitive and sustainable IoE ecosystems.
Adaptive scheduling, decision making, internet of everything, resource allocation, scheduling optimization, task offloading
Copyright (c) 2026 Wang Dayong, Liping Lei, Zhen Wang, Cui Cui https://creativecommons.org/licenses/by/4.0
A Stacking Ensemble Approach Integrating XGBoost and Deep Neural Networks with SMOTE for Financial Fraud Detection
The detection of financial fraud is one of the most serious challenges in modern financial systems and keeps on growing with increasingly complex and dynamic fraudulent activities. The paper proposes a robust hybrid model by integrating Extreme Gradient Boosting (XGBoost) and Deep Neural Networks within a stacking ensemble framework to improve the accuracy and scalability of fraud detection. It is evaluated on the pre-processed Credit Card Fraud Detection Dataset by balancing using the Synthetic Minority Over-sampling Technique (SMOTE) technique to handle the inherent class imbalance. The model achieved near-perfect performance, with a cross-validated average accuracy of 99.7% and consistently high precision, recall, and F1-scores across folds. The comparison done with previous works underlines how well the model overcomes some of the traditional challenges like data imbalance and evolving fraud patterns while scalability and adaptability for real-time applications are kept intact. Besides, the combination of advanced preprocessing techniques with ensemble learning ensures the robustness of the model in detecting fraudulent transactions. Though the model is promising, this study recognizes its limitations, such as computational complexity and dataset dependency, and proposes future research directions to optimize the model for diverse datasets and real-world constraints. Therefore, this study gives a great boost to financial fraud detection with its scalable, interpretable, and highly accurate solution and paves the path for further development in securing financial systems against fraud.
Copyright (c) 2026 Noor Ahmed, Fadi Al-Turjman https://creativecommons.org/licenses/by/4.0
Are Emergent Misaligned Models Self-Aware of their Misalignment? Latent Introspection in Activation Spaces of Niche Misaligned LLM
Large language models can exhibit emergent misalignment when finetuned on narrowly malicious tasks. Whilst this misalignment is widespread across models of all sizes, the question of whether it overrides the model’s safety training or suppresses it post-finetuning remains poorly understood. In this, we investigate this question for the Qwen2.5 32B model organism finetuned on risky financial advice, which shows broad misalignment. We evaluate our hypothesis that an emergent misaligned model is self-aware of its activation-space alignment by conducting four experiments using linear probing and causal tracing. Our results on linear probing suggest that diffuse “risk” representations exist across all layers. We also observe, through latent introspection analysis, a strong alignment between misaligned activations and base-model “refusal vectors” using causal tracing and activation patching. By further leveraging this idea, we conduct a more granular mechanistic interpretability analysis using mean ablation and direct logit attribution to identify which components L62H12 and L62H28 may contribute most to suppressing safety training. We validate our findings by evaluating the model’s attack success rate on the standard Jailbreak bench dataset before and after mean ablation of the suppression heads. Our findings underscore the importance of representational consistency of evaluations in misaligned models for assessing the role of misaligned finetuning in undermining the model’s safeguards. Broadly, our work shows that misaligned models exhibit a quantitative conflict: latent safety representations are computed across the network, but a few late-layer heads may override them.
Copyright (c) 2026 Ajay Agarwal, Tatsuhito Hasegawa https://creativecommons.org/licenses/by/4.0
Evaluating Machine Learning Models for DDoS Detection in SDNs
With the increased usage of internet-enabled devices in the contemporary networked world, vulnerability to security attacks has significantly widened. Distributed Denial of Service (DDoS) attacks are particularly critical, as they impact service availability and diminish valuable computing and internet resources. The study aims to enhance fine-grained DDoS identification by evaluating and comparing the performance of machine learning algorithms over Software-Defined Networks (SDNs), to improve accuracy and reduce false positives. We assessed machine learning algorithms, namely Extreme Gradient Boosting (XGBoost), Random Forest (RF), Naive Bayes (NB), and the Hidden Markov Model (HMM), against the labelled DDoS datasets, the LR-HR DDoS 2024 and InSDN. We employed a vaccine-based binary wolf grey optimization feature selection approach to rank data attributes by their respective levels of importance. The measures employed for the purpose included time delay, accuracy, false positive rate, and true positive rate. From the models compared, XGBoost showed the most effective detection, with the best accuracy and reduced false positive instances, especially when employed with an enhanced vaccine-based feature selection. Experimental results confirm that XGBoost, especially when combined with vaccine-based Binary Grey Wolf Optimization (BGWO) feature selection, gives a highly effective solution for detecting DDoS attacks in a Software Defined Network environment.
Copyright (c) 2026 Özgür Tonkal, Jeremia Anthony Mgungile https://creativecommons.org/licenses/by/4.0
DeepARTS: A Graph-Transformer Continual Learning Framework for Adaptive Regression Test Sequence Recommendation
Continuous Integration (CI) pipelines require fast regression feedback under strict time budgets, making full regression suites impractical for large systems. Deep Adaptive Regression Test Sequencer (DeepARTS) generates an ordered regression test sequence per CI cycle under a budget K. The approach combines a Graph Attention Network (GAT) for code–test dependency encoding, a Transformer encoder for context-aware modeling of candidate tests, and a deep Q-network for sequential test selection using a detection-oriented reward with an execution mask. Continual learning based on entropy-guided replay and Maximum Mean Discrepancy (MMD) drift detection updates model parameters across cycles without full retraining. Evaluation uses Regression Test Prioritization Torrent (RTPTorrent), a dataset containing 20 open-source Java projects and more than 100,000 TravisCI build logs, with chronological per-project splits to reduce temporal leakage. Under identical candidate sets and budget \mathbit{K}, DeepARTS improves early fault detection compared with Linkage Learning-based Non-Dominated Sorting Genetic Algorithm (L2-NSGA), achieving Average Percentage of Faults Detected (APFD) = 0.94 with Precision@K = 99.2% and Recall@K = 98.7%, while maintaining per-cycle recommendation latency near 2.5 s on Central Processing Unit (CPU). Generalization beyond Java projects and TravisCI logs remains to be validated.
Adaptive Test Sequence Recommendation System
Copyright (c) 2026 Srinivasa Rao Kongarana, Ananda Rao A, Radhika Raju P https://creativecommons.org/licenses/by/4.0
A Metrics-Based Framework for Model Selection in Object Detection Systems
Object detection models, such as the You Only Look Once (YOLO) family, have been widely deployed in agriculture, surveillance, and industrial inspection. However, selecting an appropriate model remains challenging due to trade-offs between precision, recall, and efficiency. Conventional evaluation approaches often rely on a single metric such as an F1-score, which may obscure critical domain-specific requirements. This study proposes a metrics-based framework that integrates multiple evaluation indicators into a unified composite index using Multi-Criteria Decision-Making (MCDM) principles. Precision, recall, and F1-score were normalized and weighted according to use-case priorities across four experimental scenarios: indoor/non-molted, indoor/molted, outdoor/non-molted, and outdoor/molted. Composite scores and rankings were computed under three operational contexts: security/surveillance, real-time edge applications, and quality inspection. Results show that Scenario 3 (outdoor/non-molted) consistently achieved the highest composite performance across all contexts, while Scenario 2 (indoor/molted) and Scenario 4 (outdoor/molted) varied in ranking depending on use-case weights. Sensitivity analysis further confirmed the robustness of Scenario 3 under shifting recall weight assignments. Compared to single-metric evaluation, the proposed framework offered finer-grained differentiation, highlighting trade-offs that are critical for real-world deployment. The findings demonstrate the value of multi-metric integration for systematic model selection and provide practical guidance for applying object detection in diverse operational environments.
YOLO; object detection; model selection; multi-criteria decision-making; precision–recall trade-off; automated object counting
Copyright (c) 2026 Sudasawan Ngammongkolwong, Rungtiva Saosing https://creativecommons.org/licenses/by/4.0
Explainable Hybrid Modeling of Stock Market Turning Points: An Integrated ARIMA-LSTM-SHAP Approach
In recent years, global shocks such as the COVID-19 pandemic, geopolitical conflicts, and trade tensions have substantially intensified volatility in financial markets, thereby increasing the frequency of price turning points. This study proposes a novel approach that addresses the growing demand for artificial intelligence models that are not only highly predictive but also interpretable. Traditional econometric models, such as Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH), are effective at capturing linear dependencies but fail to adequately represent nonlinear market dynamics. Meanwhile, machine learning methods, despite their strong predictive power, often exhibit limited interpretability. To bridge this gap, this paper presents an explainable hybrid framework that integrates ARIMA, Long Short-Term Memory (LSTM), and Shapley Additive exPlanations (SHAP). Using daily data from ten major global stock indices from January 1, 2010 to September 30, 2025, the study employs a hybrid labeling approach that combines price extrema, Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD) indicators. Forecasting accuracy is further enhanced by incorporating ARIMA residual–based feature fusion. The model’s performance is evaluated using both statistical (F1-Score, Precision-Recall Area Under the Curve (PR-AUC), Root Mean Squared Error (RMSE), and economic (Sharpe ratio) metrics within a walk-forward validation framework. The results show that the ARIMA-LSTM-SHAP model achieves an optimal balance between predictive accuracy and economic efficiency, achieving an F1-Score of 0.60 and a Sharpe ratio exceeding 1.5. The findings highlight the framework’s practical relevance for investors and policymakers by facilitating the early detection of turning cycles and supporting risk-informed decision-making.
Copyright (c) 2026 Tsolmon Sodnomdavaa https://creativecommons.org/licenses/by/4.0
AI-Powered Detection of Advanced Persistent Threats (APTs): A Decision Tree Model for Intrusion Detection Using MITRE ATT&CK Behavioral Analysis
Advanced Persistent Threats (APTs) demand intrusion detection systems that are not only highly accurate but also operationally transparent and aligned with analyst workflows. This paper presents Decision Tree–Based Intrusion Detection System (DTB-IDS), a decision tree–based intrusion detection system that performs multi-level classification of network flows into (i) benign versus malicious traffic, (ii) MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) tactics, and (iii) primary ATT&CK techniques. DTB-IDS is trained on a behaviorally enriched dataset obtained by carefully merging and harmonizing UWF-ZeekData24 and NF-UQ-NIDS-v2, yielding 628,415 Zeek-style flow records mapped to 13 tactics and 24 techniques. Using 33 engineered, semantically meaningful flow features and 3 specialized Classification and Regression Tree (CART) trees, DTB-IDS achieves strong performance on the merged dataset, with 99.2% binary accuracy, 99.1% tactic F1, and 99.4% technique micro-F1, together with a very low Hamming loss (≈10⁻³). Cross-dataset validation on Canadian Institute for Cybersecurity Intrusion Detection Systems 2017 (CICIDS 2017) confirms the robustness of the learned decision boundaries, with 99.9% binary accuracy and 99.2% tactic F1 without retraining. Temporal and community-based splits further demonstrate that performance is sustained under stricter generalization regimes. Compared with Random Forest, Support Vector Machine (SVM), Deep Neural Networks (DNNs), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Explainable Boosting Machine baselines, validated by McNemar’s and paired t-tests. Feature-importance analysis and rule-path inspection show that a small set of interpretable, ATT&CK-aligned features and decision paths account for most predictions, making DTB-IDS a practical and transparent APT detection solution for security operations centers.
Advanced Persistent Threat (APT), Intrusion Detection System (IDS), Decision Tree, MITRE ATT&CK, Interpretability.
Copyright (c) 2026 asem daoud, Prof.Mohamed Hamdi https://creativecommons.org/licenses/by/4.0
Graph Neural Network Based Personalized Recommendation with Edge Enrichment Using Ratings and Review Sentiments
Visual features and user-item interaction graphs enable recommendation systems to model complex relationships and improve accuracy. We propose Heterogeneous Graph Recommender Network with Bayesian Personalized Ranking for Fashion (HGRN-BPR-F), a Graph Neural Network that places multimodal edge features, explicit ratings and review sentiments, directly on user-item interactions. Unlike prior work aggregating features at nodes, our edge-centric design preserves interaction-specific context. The graph data is extracted from a real-world dataset of Amazon Fashion and exhaustive preprocessing is done to include relevant features for both users and products. Model training employs the Bayesian Personalized Ranking (BPR) loss function, which optimizes the ranking between positive and negative user–item pairs, with hard negatives used to enhance learning effectiveness. Our model achieves AUC of 0.885 ±0.010 evaluated over three seeds, demonstrating strong ranking performance with a 10.2% improvement over the rating-only baseline. Ablation experiments further demonstrate that combining ratings and sentiment as edge features substantially outperforms single-modality and node-feature-based configurations, thereby validating our edge-centric multimodal architecture.
Bayesian Personalized Ranking (BPR) Loss
Copyright (c) 2026 Anu Mathews, Sheba Selvam https://creativecommons.org/licenses/by/4.0
A Comparative Analysis of Hypertension Risk Detection Using Machine Learning and Deep Learning Techniques
A major global health concern that raises health risks considerably is hypertension. Improving early diagnosis and management of hypertension and improving patient outcomes represent the 2 benefits of utilizing Machine Learning (ML) algorithms for early detection. A lot of attention has been paid to them. This work tackles the important issue of hypertension identification by utilizing a sizable dataset from Kaggle that contains a range of physiological and demographic features. To accurately evaluate this dataset, the suggested model incorporates a number of ML classifiers, including Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Random Forest (RF), Decision Tree Classifier (DT), Artificial Neural Network (ANN) and Covering Number 2 (CN2) rule inducer, to investigate their prediction potential. Performance criteria such as accuracy, precision, sensitivity, specificity, and F1-score were used to assess each classifier’s effectiveness. According to the results, the GB approach had the highest accuracy (99.2%), followed by AdaBoost (96.4%). The 2 algorithms that performed exceptionally well were RF and CN2 rule inducers, which had respective accuracy rates of 93.8% and 93.5%. These results demonstrate the possibility of accurately predicting hypertension with state-of-the-art ML techniques, offering useful information to support healthcare providers in making knowledgeable decisions about patient care.
Artificial intelligence, classification, gradient boosting, hypertension., Kaggle, machine learning
Copyright (c) 2026 Eng.Areen Arabiat, Dr.Hamza Abu Owida, Eng.MuneeraAltayeb https://creativecommons.org/licenses/by/4.0
Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluation
Federated Learning (FL) has emerged as a transformative approach for distributed machine learning, particularly in edge computing environments where data privacy, low latency, and bandwidth efficiency are critical. This paper presents a systematic review and performance evaluation of FL techniques tailored for edge computing. It categorizes state-of-the-art methods into four dimensions: optimization strategies, communication efficiency, privacy-preserving mechanisms, and system architecture. Using benchmarking datasets such as MNIST, CIFAR-10, FEMNIST, and Shakespeare, it assesses five leading FL algorithms across key performance metrics including accuracy, convergence time, communication overhead, energy consumption, and robustness to non-Independent and Identically Distributed (IID) data. Results indicate that SCAFFOLD achieves the highest accuracy (0.90) and robustness, while Federated Averaging (FedAvg) excels in communication and energy efficiency. Visual insights are provided by a taxonomy diagram, dataset distribution chart, and a performance matrix. Problems including data heterogeneity, energy limitations, and repeatability still exist despite advancements. To enable the creation of more robust and scalable FL systems for edge-based intelligence, this analysis identifies existing gaps and provides an organized research agenda in the future.
Copyright (c) 2026 Sales Aribe Jr., Gil Nicholas Cagande https://creativecommons.org/licenses/by/4.0
Considering Cluster Validity in Attribute Extension for Small Data Set Predictions
Cluster validity has been widely used in determining the optimal clusters with a huge data sample size in recent years. However, there is less discussion of the validity cluster in small data sizes. This study presents a new approach, which considers the cluster validity to improve predictive ability for small data set problems. The first step of the proposed method is the use of the K-means data clustering technique, with seven cluster validity indices to determine the optimal number of clusters; and the second step is to build up the attribute extending function for each attribute in clusters to generate new attributes by computing the membership possibility. Finally, cross-validation and t-tests are used on two real manufacturing cases of Thin-Film Transistor Liquid Crystal Display (TFT-LCD) quality and Photo-Spacer Height (PSH) to verify the effectiveness of the proposed method with backpropagation neural networks (BPNN) and support vector machine for regression (SVR) forecasting methods. The results show that the combinations of C-index and attribute extension yields significantly lower forecasting errors, reduced variance, and statistically validated superiority over other cluster-validity indices and over baseline BPNN and SVR and linear regression (LR) models without attribute extension.
Copyright (c) 2026 LUU-LY TRAN, Chih-Chieh Chang, HSIANG-AN YU https://creativecommons.org/licenses/by/4.0
Intelligent (LLM) Knowledge Management System with Asynchronous Technology and Text-to-Speech Using PHP Language
This paper introduces a sophisticated knowledge management system developed to address the growing demand for real-time information retrieval and interaction. The system architecture leverages a robust Hypertext Preprocessor (PHP) backend to integrate Google’s Gemini Application Programming Interface (API) for advanced Large Language Model (LLM) functionalities, such as complex querying and content summarization. A key innovation is the implementation of PHP Fibers to manage I/O-bound tasks asynchronously, effectively overcoming the limitations of conventional synchronous processing and preventing application bottlenecks. For data retrieval, the system utilizes SQLite’s FTS5 extension for high-speed, indexed full-text search across large local datasets. To enhance accessibility, the Web Speech API is incorporated for text-to-speech functionality. Quantitative performance evaluation reveals that this asynchronous architecture achieves an improvement of up to 87% terms of information retrieval speed and reduces the average response latency by 35% compared to synchronous counterparts. Furthermore, the text-to-speech feature not only promotes inclusivity for users with disabilities but also facilitates more efficient cognitive processing of large volumes of information.
Topic: Software Design and Program Development
Copyright (c) 2026 Pinyaphat Tasatanattakool, Taskeow Srisod, Prachyanun Nilsook, Panita Wannapiroon, Jira Jitsupa https://creativecommons.org/licenses/by/4.0
Stock Market Prediction Using Machine and Deep Learning Models: Taxonomy and Comprehensive Analysis
Stock market prediction is a key objective in financial engineering, requiring advanced analytical methods to model complex market behavior. As global markets grow more dynamic, Machine Learning (ML) and Deep Learning (DL) methods increasingly outperform traditional statistical approaches. This study introduces a structured taxonomy of forecasting techniques and evaluates major regression-based models, including linear regression, logistic regression, and neural architectures such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and hybrid Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) networks. Five predictive models—Linear Regression (LR), Logistic Regression, RNN, LSTM, and CNN-LSTM—were implemented. LR achieved the strongest performance, with Root Mean Square Error (RMSE) values of 0.334 (training) and 0.304 (testing), while Logistic Regression performed the worst with RMSE values of 0.463 (training) and 0.487 (testing). RNN and LSTM produced higher errors than LR (RMSE 0.355 and 0.383), showing that increased model complexity does not guarantee higher predictive accuracy. The approach applies advanced preprocessing, including Z-score normalization and temporal sequence structuring. Results indicate that predictive performance depends heavily on data characteristics and market conditions. While previous studies exist, this work offers novelty by applying a structured ML-DL taxonomy to an underexplored emerging market (Saudi Aramco/Tadawul), implementing a reproducible preprocessing framework, and demonstrating that simpler models can outperform more complex architectures in markets with limited volatility and data depth.
deep learning, machine learning, prediction, AI methods, stock market, regression, taxonomy
Copyright (c) 2026 DR. Hadi S Alqahtani, Prof. Mohammed Alhaddad, DR. Mutasem Jarrah https://creativecommons.org/licenses/by/4.0
Deep Learning-Based Stock Price Forecasting for the Saudi Telecommunication Sector: A Comparative Evaluation with Baseline Models
Accurately predicting stock prices is a long-standing challenge due to the volatility and nonlinear structure of financial markets. To address this, the present study conducts a comprehensive evaluation of Deep Learning (DL) architectures for univariate stock price forecasting within the Saudi telecommunications sector. Four DL models—Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and a hybrid CNN-LSTM—are benchmarked against the Naïve, SMA-5, and Autoregressive Integrated Moving Average (ARIMA) models using five years of daily closing prices for Saudi Telecom Company (STC), Mobily, and Zain. All models performances were assessed using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) on inverse-scaled predictions. The findings clearly show that DL models capture complex temporal dependencies more effectively than traditional baselines. LSTM consistently achieved the lowest test RMSE across all datasets (Mobily: 1.169705; STC: 0.708495; Zain: 0.271470), confirming its superior predictive capability. CNN and CNN-LSTM delivered competitive accuracy, while RNN exhibited the weakest performance. Overall, the results demonstrate the strong potential of deep learning—particularly LSTM—for improving short-term stock price prediction and advancing data-driven financial analytics in the Saudi stock market.
Deep learning, Stock market, Prediction, Methods, regression, time series
Copyright (c) 2026 Hadi S. Alqahtani, Mohammed J. Alhaddad, Mutasem Jarrah https://creativecommons.org/licenses/by/4.0
Implementation of GRU Model in Badminton Time Series Data for Movement Trajectory Prediction
This study tackles the challenge of image recognition in badminton due to its small size and complex movement trajectory by developing a new dataset and predicting shot sequences. The experimental procedure involves two main steps: detailed pre-processing of original data (extracting key features, normalizing, and dividing into training and test sets) and using deep learning algorithms to predict future shots. We validated the model with 20 repeated experiments and multiple evaluation metrics (Accuracy, Precision, Recall, F1-Score) and confirmed statistical significance and stability through T-tests. The results demonstrate that the Gated Recurrent Unit (GRU) effectively predicts shot sequences, benchmarking with Long Short-Term Memory (LSTM) and One-Dimensional Convolutional Neural Networks (1D-CNNs). The proposed architecture model with joint Rectified Linear Unit (ReLU) achieved 59.77% accuracy compared to a random guessing baseline of 16.67%, showcasing superior performance and promising applications. Therefore, this research provides a new tool for badminton data analysis and lays the groundwork for broader sports data analysis and behavior prediction.
Copyright (c) 2026 MING-HUNG LIN, LUU-LY TRAN, HAN-YU CHEN, Chih-Chieh Chang https://creativecommons.org/licenses/by/4.0
Deep Learning-Based Occupancy Detection in Unmarked Parking Zones
This study proposes a method for automated detection of parking space occupancy in environments with absent or unclear markings. The system uses a U-Net11-based image segmentation model supported by a preprocessing pipeline that includes contrast correction, perspective transformation, and Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement to improve feature visibility under varying lighting conditions. The model was trained on a combined dataset from surveillance cameras and open-source images. On the test set, it achieved an accuracy of 74%, with Precision of 75.4%, Recall of 70.1%, F1-score of 72.6%, and Intersection over Union (IoU) of 57%. The Precision-Recall operating point (Precision = 75.42%, Recall = 70.27%) confirms reliable classification in challenging scenes. Despite the moderate segmentation accuracy, the achieved 74% is sufficient for practical parking-space detection because the final occupancy decision is based on aggregated spatial predictions rather than pixel-level precision. Local segmentation errors do not significantly affect the binary classification of “occupied/vacant”, making the system robust to noise, weather variability, and imperfect markings. Experiments confirm that occupancy determination remains stable even when segmentation boundaries are slightly distorted. The proposed approach improves the efficiency of identifying available parking spaces and can be integrated into smart city infrastructure to support automated parking monitoring and urban mobility optimization.
Copyright (c) 2026 Irina Dyomina , Natalya Denissova, Mukhamed Tolegenov, Saule Rakhmetullina, Aizhan Tlebaldinova, Zarina Khassenova https://creativecommons.org/licenses/by/4.0
Towards Intelligent Ageing-In-Place: A Literature Review on Personalized Assistive Healthcare and Behavioral Data
By the year 2030, more than one in six people worldwide will be 60 years old or above. This elderly population is projected to grow to approximately 2.1 billion individuals by mid-century. This demographic shift presents significant challenges and opportunities for healthcare systems worldwide. Ageing in place, which enables older adults to remain safely and independently in their homes, is increasingly recognized as a cost-effective and quality-enhancing alternative to institutional care. However, its success hinges on intelligent, adaptive technologies that support personalized healthcare delivery. Despite advances in Artificial Intelligence (AI), machine learning, and digital health, current systems often rely on generic, one-size-fits-all solutions that fail to address the complex, individualized needs of ageing populations. This paper presents a comprehensive literature review examining the use of personalized assistive healthcare technologies and behavioral data in aging-in-place settings. It reveals key gaps, including poor adaptability to individual needs, limited integration of real-time data, and a lack of long-term evidence from real-world deployments. Challenges such as usability, ethical concerns, and insufficient system interoperability further limit effectiveness. To address these issues, we argue for a paradigm shift toward person-centered, adaptive systems that evolve with users over time. We outline the need for hybrid monitoring approaches, scalable personalization frameworks, and inclusive design strategies to ensure sustainable and impactful solutions for ageing populations globally.
Copyright (c) 2026 Nazlena Mohamad Ali, Muhammad Aiman Md Zuki, Shuhadah Othman, Lana Halaseh, Ahmad Salaymeh https://creativecommons.org/licenses/by/4.0
Self-Supervised Semantic Learning for Trustworthy Anomaly Detection in 6G-Enabled Smart Grid Communication
Sixth Generation (6G)-enabled smart grid communication requires a robust installation and secure framework to manage high-speed and low-speed data communication. Existing models face challenges such as a lack of labeled data, poor temporal modeling, weak interpretability, inadequate behavioral profiling, and difficulties with edge-deployment. Additionally, the massive influx of data increases vulnerabilities to faults, cyberattacks, and zero-day anomalies. This study presents an innovative deep learning architecture that utilizes semantic encoding and self-supervised learning to detect both known and unknown anomalies within a 6G-enabled smart grid communication system. The proposed approach begins with cleaning and normalizing the electrical sensor logs. Features are extracted using a One-Dimensional Convolutional Neural Network (1D CNN), and a Multi-Modal Vision Transformer (MM-ViT) transforms sensor sequences into semantic event tokens. The self-supervised model combines Contrastive Predictive Coding (CPC), Temporal Convolutional Networks (TCN), and Attention mechanisms to enable robust temporal anomaly detection from sensor data. While TrustNet combines TCN and Attention, it models network session behavior and supports dynamic trust evaluation. Compared to the existing anomaly detection methods, the proposed semantic and self-supervised methods perform extremely well across many metrics, achieving a 99.34% accuracy. This demonstrates its effectiveness and suitability for reliable anomaly detection in 6G edge-enabled smart grid infrastructures.
Copyright (c) 2026 S Arockia Babi Reebha, P.C. Karthik, J. Umamageswaran, J. Shobana https://creativecommons.org/licenses/by/4.0
Confidence-Aware BLSTM with CDLS for Crop Recommendation and Yield Prediction
The field of agriculture continues to face challenges in terms of predicting crop yields, which is a critical factor for decision-making at international, regional, and local levels. Crop yield prediction depends on factors such as climatic conditions, soil properties, crop-specific traits and environmental influences. Therefore, selecting the most appropriate crop for maximizing crop yield plays a vital role in improving real-life farming scenarios. In this manuscript, a Class Dependent Label Smoothing-Bayesian Long Short-Term Memory (CDLS-BLSTM) approach is developed for an efficient crop recommendation and yield prediction. The CDLS regularization method is incorporated in the traditional BLSTM technique to prevent the bias and enhance both the recommendation and prediction process. The BLSTM learns distribution across LSTM weights instead of fixed weights and predicts the uncertainties in data. In pre-processing phase, the min-max normalization technique is utilized to scale data into a uniform range of 0 to 1. The developed CDLS-BLSTM acquired an accuracy of 99.78% in Crop Recommendation Dataset (CRD, 2000 samples) dataset and an accuracy of 95.32% in Crop Yield Prediction (CYP, 4000 samples) dataset when compared with conventional algorithms.
Copyright (c) 2026 Nandini Geddlehally Renukaradya, Kishore Gopala Rao, Raviprakash Madenur Lingaraju https://creativecommons.org/licenses/by/4.0
Deformable Sub-Pixel Convolutional Layer with Convolutional Neural Network for Land Use and Land Cover Classification
Image classification using Remote Sensing (RS) or satellite images provides data about Land Use and Land Cover (LULC), which is also used in several applications such as environmental monitoring and urban planning. In recent times, Deep Learning (DL)-based approaches have been used for LULC classification and have achieved high effectiveness. However, it remains challenging because of class similarity and mixed-pixel issues, which lead to misclassification and reduced classification performance. In this manuscript, the Deformable Sub-Pixel Convolutional Layer with Convolutional Neural Network (DSPCL with CNN) is developed to effectively classify LULC by mitigating class similarity and mixed-pixel issues. The deformable convolutional layer is incorporated instead of the traditional convolutional layer, which adaptively changes the filters and captures deep features. Then, the sub-pixel convolutional layer is incorporated into the output layer, which separates the pixels into multiple pixels and classifies the classes more effectively. The developed DSPCL with CNN method achieved 98.62% accuracy on the EuroSAT dataset and 99.01% accuracy on the NWPU-RESISC45 dataset compared to conventional techniques.
multiple pixels and sub-pixel convolutional layer
Copyright (c) 2026 Kishore Raju Kalidindi, Murty S V V S N Ch, A Srinivasa Reddy, Sridevi Gadde, RAMBABU PEMULA https://creativecommons.org/licenses/by/4.0
Smart Crop Recommendation for Precision Agriculture: A Comparative Analysis of Ensemble and Deep Learning Models Using Soil and Environmental Data
Precision agriculture increasingly requires reliable, data-driven tools to assist farmers in selecting crops suited to local soil and climate conditions, thereby enhancing productivity and sustainability. This study presents a comprehensive comparative analysis of six advanced machine learning models for smart crop recommendation, leveraging soil and environmental data to identify optimal crops under specific growing conditions. The research addresses a multi-class classification problem using the Kaggle Crop Recommendation Dataset, which comprises 2200 observations across 22 crop types and seven key environmental parameters: nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall. Results demonstrate exceptional performance from gradient boosting methods, with Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) achieving 99.77% accuracy, followed by Random Forest at 96.36%. Deep learning approaches (Gated Recurrent Unit (GRU): 91.82%, Long Short-Term Memory (LSTM): 90.45%) and a hybrid Random Forest–LSTM model (88.41%) performed competitively but less effectively. Feature-importance analysis proved that soil nutrients (NPK) were dominant predictors, contributing approximately 60% to the overall importance in ensemble models. Statistical testing confirmed significant performance differences (p < 0.001), while computational analysis favored LightGBM, with the shortest training time (1.8 min) and lowest memory usage (198 MB). These findings provide evidence-based guidance for agricultural technology developers and practical insights for deploying robust crop recommendation systems across diverse farming contexts. Ethical considerations regarding data bias and equitable access to Artificial Intelligence (AI) tools are discussed, emphasizing inclusive and sustainable development practices.
Copyright (c) 2026 Kanda Sorn-In, Assoc. Prof. Dr. Wirapong Chansanam, Asst. Prof. Dr. Pathamakorn Netayawijit https://creativecommons.org/licenses/by/4.0
Intelligent Malware Detection through Bio-Inspired Optimization and Gradient Boosting
Malware continues to pose a critical threat to cybersecurity, necessitating intelligent detection systems capable of adapting to evolving attack strategies. This paper introduces an enhanced malware detection framework that integrates bio-inspired feature selection with advanced gradient boosting classifiers to achieve high accuracy and efficiency. Two metaheuristic algorithms—Harris Hawks Optimization (HHO) and the Bat Algorithm (BA)—are independently applied to extract compact and discriminative feature subsets from the ISCX-URL2016 malware dataset. The reduced feature sets are subsequently evaluated using Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LightGBM) classifiers. Performance is rigorously assessed using Accuracy as the primary evaluation metric. Experimental results demonstrate that LightGBM combined with BA achieves the highest performance, reaching an accuracy of 99.52%, precision of 99.48%, recall of 99.48%, and F1-Score of 99.48%. These findings underscore the effectiveness of bio-inspired optimization for feature selection, showing that the proposed framework not only improves predictive performance but also offers a scalable and reliable solution for real-world malware detection.
Copyright (c) 2026 Adeeb Alsaaidah, Mosleh M. Abualhaj, Sumaya N. Al-Khatib, Ahmad Shalaldeh, Mahran Al-Zyoud, Mohammad Sh. Daoud, Hani Al-Mimi, Mohamad Anbar https://creativecommons.org/licenses/by/4.0
Feature Selection for High-Dimensional Data: A Case Study of NFT Valuation
In this study, we propose hedonic models for valuing Non-Fungible Tokens (NFTs) from the Azuki collection. We first analyze the NFT’s metadata and introduce a market volatility-robust dependent variable. Specific information of Azuki attributes is encoded via Term Frequency-Inverse Document Frequency (TF-IDF) to reflect both presence and collection-wide scarcity, yielding hundreds of features for each token. Two hedonic models are considered: a linear model and a squared model. To address high dimensionality, we tailor three variable-selection procedures—forward, backward, and stepwise—and compare them with regularization benchmarks and machine-learning methods. Using actual Azuki transaction data, we evaluate performance on a train-validation partition. The squared model overfits out of sample, while the linear model generalizes better and is adopted as the baseline. Applying variable selection to the linear baseline improves both parsimony and predictive performance. Machine-learning models exhibit very high training fit but notable performance degradation on the validation set, indicating overfitting in this setting. Overall, carefully specified hedonic models combined with principled variable selection offer competitive, interpretable, and more generalizable NFT valuation.
Copyright (c) 2026 Geun-Cheol Lee, Heejung Lee, Hoon-Young Koo https://creativecommons.org/licenses/by/4.0