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.
Optimizing Next-generation Cloud and Data Center Networks: A Review of Routing, Resource Management, and Emerging Technologies
As cloud computing and data centers become integral to global Information Technology (IT) infrastructure, optimizing routing and resource management in these networks is critical for maintaining performance, scalability, and energy efficiency. This paper presents a structured review of optimization models in cloud and data center environments using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided methodology covering literature from 2016 to 2025. Key advancements include adopting Software-Defined Networking (SDN), machine learning-based routing algorithms, and energy-efficient resource allocation strategies. This paper critically analyzes the challenges posed by scalability, latency, energy consumption, security, and interoperability, alongside the opportunities presented by Artificial Intelligence (AI)-driven autonomous networks, edge computing, and the integration of 5G technologies. Comparative evaluation highlights key trade-offs between performance gains and real-world feasibility, particularly for machine learning and deep reinforcement learning approaches. Furthermore, the study examines emerging trends, including cloud-edge collaboration and multi-objective optimization frameworks. The findings reveal that the adaptive methods can improve throughput, reduce latency, and enhance energy efficiency under specific datasets, simulation settings, traffic models, and network configurations. Overall, this review provides a consolidated perspective on current approaches, open challenges, and promising research directions for next-generation cloud and data center network optimization.
Copyright (c) 2026 Sultan Alanazi https://creativecommons.org/licenses/by/4.0
GCN-Mamba: A Semantic-guided Graph Convolutional Network with Mamba State Space Models for Skeleton-based Action Recognition
Human Action Recognition (HAR) has seen significant advancements with Graph Convolutional Networks (GCNs), which effectively model skeletal motion dynamics. In this work, we propose a novel HAR framework that integrates GCNs with an Adaptive Adjacency Matrix for spatial modeling and the Mamba State Space Model (SSM) for temporal feature extraction. This hybrid approach aims to balance accuracy and efficiency. It does so by leveraging the structural expressiveness of GCNs for spatial modeling, while harnessing the sequential modeling power of Mamba-SSM for temporal dynamics. The central goal of this paper is to evaluate GCN-Mamba against our own previous model, ImpSGN (Improved Semantic-Guided Network), with a specific focus on reducing model complexity while preserving recognition accuracy. GCN-Mamba achieves competitive performance with an approximately 72% reduction in parameter count (from 4.0 M to 1.1 M), making it a lightweight yet effective architecture. Our findings highlight the trade-offs between accuracy and efficiency in HAR models and demonstrate the potential of state-space modeling in skeletal action recognition. The code is publicly available on GitHub.
Copyright (c) 2026 Dr. Amine Mansouri, Dr. Abdellah Elzaar, Prof. Toufik Bakir, Prof. Smain Femmam https://creativecommons.org/licenses/by/4.0
Countering Paraphrasing and Style Transfer in Educational Settings: A GAN-Based Approach to Identifying AI-Generated Academic Essays
The generation of content by Artificial Intelligence (AI) in educational settings represents a growing consequence of the irresponsible use of generative AI that threatens academic integrity. Mitigating this phenomenon through AI-based detection remains a major challenge, given that generative AI tools, such as Large Language Models (LLMs), evolve rapidly. Classical detection tools such as anti-plagiarism software are no longer capable of tracking or curbing the spread of this phenomenon. Furthermore, the existing AI-based tools fail to detect the content generated, paraphrased, or humanized by bots that alter sentence structure. In this article, we propose a novel architecture based on Generative Adversarial Networks (GANs). This framework is a generative model composed of two main components trained through a minimax game. The first is the generator; it generates or reformulates text, while its opponent, the discriminator, tries to detect whether the text is human-generated or produced by the generator. We used DistilGPT-2 as the generator, trained simultaneously with DistilRoBERTa, the discriminator. Our framework was evaluated on 44,868 academic text samples and achieved 99% precision on unmodified texts. Moreover, our approach demonstrates strong reliability, maintaining a precision of 92% despite paraphrasing and humanization, as well as noise injection attacks that render other detection tools ineffective. The use of GANs enables our discriminator to more effectively identify deep semantic patterns and reformulated structures. Finally, we discuss future research directions to improve the adaptability and long-term relevance of the proposed approach in light of rapid advances in artificial intelligence.
Copyright (c) 2026 Zohair Elmourabit, Asmaâ Retbi https://creativecommons.org/licenses/by/4.0
Design and Evaluation of a Dual-Layer Emotion—Personality Framework for Adaptive Conversational Robots
This paper presents a dual-layer emotional framework for human–robot conversational interaction that integrates internal emotion, representing the robot’s intrinsic affective state, and social emotion, representing outward emotional expression adapted for interpersonal alignment. Unlike conventional dialogue systems that rely primarily on semantic and contextual interpretation, the proposed framework processes user input through three complementary dimensions: content, context, and emotion, while supporting multimodal interaction through voice and physical actions. Emotion computation is regulated using two personality-modulated parameters. Sensitivity controls how strongly external stimuli influence internal emotional states, whereas consideration governs the degree to which the robot aligns its social expression with the user’s affect. A robotic prototype was developed to implement the framework, integrating multimodal sensors and expressive actuators for interactive operation. Preliminary experiments were conducted to evaluate the temporal evolution of emotional states, system response latency, and the influence of personality parameters on emotional behavior. The results illustrate that the system can update internal and social emotional states dynamically, adapt responses according to personality parameters, and generate emotionally coherent multimodal outputs. From a Human–Computer Interaction (HCI) perspective, the proposed framework provides a system-level approach for designing conversational interfaces capable of emotionally adaptive multimodal interaction in embodied robotic systems. The findings suggest that personality-modulated emotional regulation can support more flexible and context-dependent conversational behavior compared with conventional reactive emotional dialogue mechanisms.
Topic: Human-Computer Interaction (HCI) in Modern Technological Systems
Copyright (c) 2026 Shitara Kaede, Kantawatchr Chaiprabha, Pimolkan Piankitrungrean, Kosuke Takano, Ratchatin Chancharoen, Gridsada Phanomchoeng https://creativecommons.org/licenses/by/4.0
AI-Driven Forecasting of Operating Room Quality Indicators Using LSTM Neural Networks: A Data-Driven Framework for Smart Surgical Management
Operating Room (OR) efficiency directly influences patient safety, resource allocation, and overall hospital performance. Despite the routine monitoring of operating room quality indicators, most analyses remain retrospective and provide limited support for forward-looking management. This study develops a forecasting framework based on Long Short-Term Memory (LSTM) neural networks and integrates the prediction results into a Business Intelligence (BI) visualization environment. Monthly specialty-level operational data from a medical center in Taiwan covering 2021 to 2024 were analyzed, including seven key operating room quality indicators. Different historical input windows ranging from 3 to 24 months were examined to evaluate their effects on predictive performance. To assess whether deep learning was necessary for this task, model performance was compared with traditional statistical approaches, including Moving Average, automated Autoregressive Integrated Moving Average (auto-ARIMA), and Naïve forecasting models, using a strictly chronological training and testing split. Indicators characterized by short-term operational variability, such as cancellation and scheduling delay rates, were more accurately predicted using shorter historical windows, whereas structurally stable metrics including utilization and occupancy rates showed improved performance with longer input sequences. While conventional statistical models performed competitively for some stable indicators, LSTM provided more consistent performance across heterogeneous metrics. The forecasting outputs were incorporated into an interactive dashboard to facilitate real-time monitoring and threshold-based management. Although the analysis was conducted using data from a single medical center, the results demonstrate the feasibility of integrating predictive analytics into routine surgical quality management.
Copyright (c) 2026 Wen-Shin Hsu, Pei-Ju Wang https://creativecommons.org/licenses/by/4.0
Methodological Rigor in EHR-based Prediction: A Leakage-free Evaluation Framework for Diabetes Medication Initiation
Prevailing studies employing the UCI Diabetes 130-US Hospitals dataset report classification performance exceeding Area Under the Curve (AUC) > 0.99; however, such estimates are systematically inflated by data leakage the inadvertent incorporation of information unavailable at prospective prediction time. This study introduces a rigorous, leakage-free evaluation framework for predicting diabetes medication initiation among hospitalized patients. Analyzing 101,766 inpatient encounters (1999–2008) from 71,290 unique patients, we enforced strict calendar-based temporal separation and excluded all 24 medication-specific features to eliminate both target and temporal leakage sources. Six models were evaluated spanning logistic regression, random forests, Extreme Gradient Boosting (XGBoost), One-Dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), and a CNN-LSTM hybrid under identical leakage-free conditions. The 1D-CNN achieved the highest discrimination (AUC = 0.824), surpassing LSTM and all baselines, while demonstrating superior probability calibration (Brier score = 0.149; ECE = 0.028) and 3.2× faster inference. Primary predictors included HbA1c > 7%, elevated admission glucose > 180 mg/dL, advanced age (≥70 years), and frequent prior hospitalizations. These results confirm that lightweight convolutional architectures can outperform recurrent models on sparse, short-sequence Electronic Health Record (EHR) data a dataset-specific finding that should not be generalized without qualification. Dataset characteristics, scope constraints, and generalizability limitations are addressed fully in the methods and discussion sections.
Topic: Machine Learning in Computer and Information Systems
diabetes prediction, electronic health records, data leakage, temporal validation, model calibration, clinical decision support, convolutional neural networks.
Copyright (c) 2026 Adedeji Olugboja https://creativecommons.org/licenses/by/4.0
A CNN-BiLSTM Hybrid for Plant Leaf Disease Classification: Comparative Performance Evaluation of Deep Learning Algorithms
The existence of plant leaf diseases is a big problem for farmers all over the world because they make crops less healthy and less plentiful, which puts global food security at risk. The most common problems with diagnosing plant leaf diseases are a lack of experience, different ways of undertaking visual assessments, and image overlaps, all of which can lead to wrong diagnoses. This study introduces a hybrid deep learning architecture that integrates squeeze-and-excitation residual blocks, capsule networks, bidirectional long short-term memory, and attention mechanisms. The design utilizes convolutional operators for effective feature extraction, Squeeze-and-Excitation (SE) block for channel reweighting, capsule networks for spatial relationship capture Bidirectional Long Short-Term Memory (BiLSTM) for sequential dependencies, and attention mechanisms for emphasizing prominent features. Experiments were performed on 2 empirical datasets: the Corn Leaf Disease Dataset (CLDD) and the Rice Leaf Disease Dataset (RLDD). The data were divided into 60% for training, 20% for validation, and 20% for testing. The proposed method attained 99.88% training accuracy on CLDD and 100% on RLDD. During testing, the class-wise accuracies were 99.29% for blight and 100% for the other CLDD categories. In the case of RLDD, the accuracies attained were 78.95% for bacterial leaf blight, 85.53% for brown spot, 89.77% for healthy samples, 77.27% for leaf blast, 100% for leaf scald, and 97.73% for narrow brown spot. This work highlights practical potential for deployment in terms of mobile applications, enabling farmers to obtain rapid, reliable, and cost-effective field diagnoses, thereby improving agricultural productivity and sustainability.
Copyright (c) 2026 Aekkarat Suksukont, Ekachai Naowanich https://creativecommons.org/licenses/by/4.0
Predicting Students’ Attrition for the Information Systems Program Using Machine Learning Algorithms
Student attrition, a crucial factor in determining institutions’ financial resources, is also a measure of an institution’s academic standing, with a direct impact on its revenue. This study, which examined the factors influencing students’ departure from the Information Systems (IS) program, has significant implications for higher education. The research was conducted in 2 stages. Initially, we experimented with 13 machine learning algorithms to identify the most accurate method for predicting student attrition. Our findings revealed that Extra Trees, Light Gradient Boosting Machine, and Random Forests are the top 3 predictors in that order. In the second stage, we used these algorithms to identify the most influential factors driving student attrition. Our results indicated that the 3 classifiers identified the Last Grade Point Average (Last GPA) as the most significant factor affecting students’ decision to leave the IS program. Performance in mathematics and gender are the other 2 factors affecting dropout. We have found that, as a percentage, more male than female students are leaving the program. Moreover, female students are primarily transferring to more academically demanding majors, such as data analytics and information technology, while male students are transferring to less technology-oriented majors, such as management and law. This finding is reinforced by the observation that female students’ mathematics performance among students leaving the IS program is significantly higher than that of male students. Our results can have significant policy implications for admission procedures and academic advising, positively affecting the IS program’s attrition rate.
Machine learning, random forests, student attrition, extra trees
Copyright (c) 2026 Elfadil Abdalla Mohamed, Mirna Nachouki, Riyadh Mehdi, Yara Mohammad https://creativecommons.org/licenses/by/4.0
A Hybrid Metaheuristic Optimization Framework for Multi-Scale Time Series Forecasting Using AO and PGRO
Time series forecasting plays a pivotal role in domains ranging from finance and healthcare to multimodal analytics, yet achieving robust accuracy across heterogeneous datasets and varying prediction horizons remains a significant challenge. In this paper, we propose a novel hybrid forecasting framework that integrates advanced metaheuristic optimization with deep learning architectures for multi-domain, multi-scale prediction. The core of our approach is a dual-stage optimization strategy that combines the global exploration capability of the Aquila Optimizer (AO) with the guided local refinement of the Proposed Guided Remora Optimization Algorithm (PGRO). This hybrid optimization mechanism is applied to fine-tune model hyperparameters, fusion strategies, and incremental learning settings for architectures such as Long Short Term Memory (LSTM), Transformer, and multimodal fusion networks. The framework is further enhanced by an incremental learning module with memory replay, enabling continual adaptation without catastrophic forgetting. We evaluate the proposed approach on 6 diverse datasets (National Association of Securities Dealers Automated Quotations/New York Stock Exchange (NASDAQ/NYSE) Historical Stock, Financial Question Answering (FiQA), Financial PhraseBank, Reddit Financial News, Fourth Makridakis Forecasting Competition Time series (M4), and Video and Text (VaTeX) Multimodel), spanning structured numerical, textual, social media, and multimodal time series. Experimental results show that our AO + PGRO-enhanced models consistently outperform classical statistical models, deep learning baselines, and state-of-the-art transformer-based forecasters in both short- and long-horizon prediction tasks. Ablation studies confirm the contribution of each component, and detailed horizon-wise analysis highlights the method’s adaptability to varying temporal dependencies. The proposed framework delivers state-of-the-art accuracy, stability, and generalization across domains, offering a powerful and extensible approach for real-world time series forecasting challenges.
Copyright (c) 2026 SARVANI ANANDARAO, Hemasundara Reddy Lanka, Nagaraju Devarakonda, Nagha Harish Vundavalli https://creativecommons.org/licenses/by/4.0
Optimized Deep Learning Model for Concrete Crack Classification Monitoring on Resource Constrained Devices
Structural cracks are commonly caused by factors such as high loading, fatigue, degradation, thermal expansion, and humidity. Detection of cracks in early stages is important. If neglected, these cracks can spread and weaken the building as well as other civil infrastructure. To enhance safety and prevent catastrophic structural failures, accurately detecting cracks is essential in Structural Health Monitoring (SHM) systems, thus enabling early maintenance. The latest developments in Deep Learning (DL) have facilitated the automatic detection of structural cracks through images taken with the help of cameras, drones, or mobile devices. Such methods minimize the manual inspection and enhance monitoring efficiency. In this paper, a systematic optimization of deep learning models through hyperparameter tuning with random search was implemented to achieve a balance between classification performance and computational efficiency for resource-constrained environments. Custom architecture, (Convolutional Neural Network (CNN) and CNN-Long Short-Term Memory (LSTM)), as well as transfer learning models (MobileNetV2, ResNet101, and DenseNet201), are evaluated on the Middle East Technical University (METU) concrete crack dataset (40,000 labelled images). The performance measures, such as accuracy, precision, recall, F1-Score, inference time, and computational complexity in Million Floating-Point Operations (MFLOPs), are evaluated and compared on the models. From the results, MobileNetV2 shows a classification accuracy of 99.8% with a reduction of 50% computational complexity, thus making it suitable for deployment on constrained devices in real-world applications.
Copyright (c) 2026 Vanusha, Karthikeyan, Naga Malleswari TYJ, Ushasukhanya https://creativecommons.org/licenses/by/4.0
Enhancing Genetic Algorithms Optimization Using Epigenetic-Inspired Mechanisms
Genetic Algorithms (GA), developed several decades ago, are widely used to solve optimization problems. Since the invention of genetic algorithms, significant progress has been made in genetics, leading to the discovery of many new mechanisms of heredity that were not previously incorporated into genetic algorithms. One such group is epigenetic processes. The article presents two epigenetic-based operations used in genetic algorithms: cytosine methylation and allele inactivation (gene silencing). This article presents two original versions of genetic algorithms that use epigenetic processes. It presents the results of research on the impact of the introduced modifications on the genetic algorithm's performance. The study examined two optimization problems: the loading optimization problem and the outlier detection problem. The experiments examined the optimal probability of occurrence of the introduced modifications, the average number of iterations of the genetic algorithm with and without modifications required to obtain the optimal result, and compared execution times. The research confirmed that introducing new operations into genetic algorithms can improve their performance.
Copyright (c) 2026 Kornel Chrominski, Malgorzata Przybyla-Kasperek, Rafal Skinderowicz https://creativecommons.org/licenses/by/4.0
LLM-Based Multi-agent Collaborative Spectrum Cognition Method
Current spectrum cognition methods based on mathematical models and deep learning excel at single tasks with clear inputs and outputs, such as signal detection and classification. However, they generally lack autonomous task decomposition, logical reasoning, and tool coordination capabilities. Consequently, it is difficult for them to directly transform raw signal data into valuable information in complex and dynamic wireless communication environments. Multi-Agent Systems (MAS), due to their autonomy, distributivity, and collaborative nature, show broad application prospects in interdisciplinary fields like communications and artificial intelligence. This paper proposes an MAS-based spectrum cognition method, implemented through a “master-slave” MAS architecture jointly driven by the Qwen3 and MiniCPM4 Large Language Models (LLMs). The system consists of a Master Agent, a Signal Perception Sub-Agent, a Knowledge Retrieval Sub-Agent, and a Cognitive Reasoning Sub-Agent, working together to accomplish collaborative spectrum cognition tasks. In experiments, focusing on Wi-Fi signal analysis, we constructed a signal sample database using Universal Software Radio Peripheral (USRP) platforms. The proposed method automated data analysis, feature extraction, historical data querying, and preliminary signal emitter direction estimation for the database. The results demonstrate that the proposed method achieves autonomous task decomposition, signal sample feature identification, and spectrum cognition for Wi-Fi signal emitters. It highlights the potential of MAS in communication signal analysis and offers a new technical approach for intelligent communication detection, automated signal monitoring, and smart emitter identification.
Copyright (c) 2026 Lihao Liu, Jianhui Wang, Jingjing Li, Nan Hu, Wenlin Zhang, Da Zhang https://creativecommons.org/licenses/by/4.0
Integrating Whole Blood Gene Expression and Clinical Metadata into a Machine Learning Pipeline for Predictive Coronary Artery Disease Diagnosis
Timely and accurate identification of signs of Coronary Artery Disease (CAD) remains a central issue in modern preventive cardiology. Traditional diagnostic methods overlook the molecular alterations that drive disease occurrence and progression. This study proposes a multimodal machine learning framework integrating whole-blood gene expression profiles with clinical variables to support molecularly informed CAD diagnostics. We employed a Machine Learning framework which prioritizes robustness and interpretability over maximal predictive performance. Models based on the combined microarray and clinical data achieved the highest internal discrimination (AUC ≈ 0.76). The confounding analyses identified age as the dominant predictor, whereas gene expression features contributed an independent and biologically meaningful signal. The final selected gene set was enriched in immune and inflammatory pathways relevant to CAD pathophysiology. External validation revealed a decrease in performance, consistent with expected cross-platform domain shifts. The study underscores both the potential and the challenges of transcriptomic prediction of CAD, highlighting the importance of rigorous validation and data interpretation in high-dimensional biomedical modeling.
Coronary Artery Disease, Functional Enrichment, Gene Expression, Machine Learning, Random Forest, Statistical Analysis
Copyright (c) 2026 Zhilbert Tafa, Bilgin Demir, Djansel Bukovec https://creativecommons.org/licenses/by/4.0
Towards Compliant and Private EHR Sharing: An Experimental Evaluation of ZKP-Blockchain Integration for Healthcare Data
The digitization of health records has enhanced clinical efficiency, but amplified risks related to data privacy, integrity, and auditability. While permissioned blockchains offer immutability and traceability, they often fail to reconcile transparency with confidentiality—either exposing sensitive data or obscuring it beyond regulatory scrutiny. To address this gap, this paper presents an integrated framework that combines Zero-Knowledge Proofs (ZKPs) with a permissioned blockchain to enable verifiable yet private healthcare transactions. A visit centric Electronic Health Record (EHR) model supports three real-world use cases: medication validity, procedure confirmation, and demographic verification. A four-layer architecture decouples data, application logic, cryptographic trust, and audit logging, allowing end-to-end validation without raw data disclosure. Experimental evaluation across three ZKP libraries (snarkJS, ZoKrates, and gnark) on a synthetic dataset of 1,000 patient visits demonstrates sub-500 ms verification latency, with snarkJS selected for its ecosystem compatibility despite slower raw performance. End-to-end pipeline latency averages 1.35 s, confirming feasibility for batch workflows such as insurance claims. The system further includes a web-based auditor interface that validates tamper-evidence under off-chain attacks, bridging cryptographic guarantees with operational compliance.
Copyright (c) 2026 Yan Watequlis Syaifudin, Vipkas Al Hadid Firdaus, Imam Fahrur Rozi, Chandrasena Setiadi, Suryani Dyah Astuti, Nobuo Funabiki, Maskur, Kadek Suarjuna Batubulan https://creativecommons.org/licenses/by/4.0
A Neural-Symbolic Approach to Automated Essay Scoring: Integrating BERT and Hidden Markov Models for Interpretable Assessment
Automated Essay Scoring (AES) systems have achieved notable success in evaluating semantic content using deep learning models. However, they often fail to provide explicit, interpretable feedback on essay structure. This paper presents a hybrid neural-symbolic approach that integrates Bidirectional Encoder Representations from Transformers (BERT) for semantic analysis with a Hidden Markov Model (HMM) for explicit structural modeling. Unlike ensemble methods, our system employs a tightly coupled, weighted integration scheme (70% content, 30% structure) optimized through validation experiments. The HMM component models essay organization as a sequence of rhetorical states Introduction, Body, and Conclusion offering transparent feedback on logical flow. We explicitly position this work as a methodological proof-of-concept validation study.
Automated Essay Exams Scoring, BERT, Hidden Markov Model, Natural Language Processing, Educational Technology.
Copyright (c) 2026 Ahmed Amin https://creativecommons.org/licenses/by/4.0
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.
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
BFS-ZAT: Blockchain-Enabled Federated Security with Zero-Knowledge Adaptive Trust in Multi-Cloud Environments
The proposed system introduces a blockchain-federated security system and a zero-knowledge adaptive trust system that would allow secure and auditable access control on multi-cloud platforms. There is a need to overcome the issues of heterogeneity of infrastructure, dynamic threats, and uninteroperable and privacy-sensitive trust management in securing access in a multi-cloud environment. The suggested model incorporates a federated identity management system, which combines biometric authentication and cryptographic identity that runs on blockchain to provide non-repudiable identity management. The system involves a blockchain trust authority that handles cryptographic keys and a dynamic on-chain trust score, which facilitates privacy-preserving authentication employing zero-knowledge proofs. Hybrid cryptography provides secure data access and sharing by combining fine-grained access control with homomorphic encryption, based on attribute-based encryption and homomorphic encryption, respectively. The combination of a blockchain and differential privacy allows executing queries in an auditable and privacy-guaranteed manner with clear data owner policies. To achieve cross-cloud interoperability, a trust-aware blockchain-aided proxy re-encryption scheme permits protected information sharing with adjustable trust levels, and trust re-assessment and blockchain-aided key refreshing schemes are resistant to dynamic threats. Experiments conducted on a federated multi-cloud testbed have shown that the proposed framework obtains the following security guarantee and efficiency, as compared to existing schemes authentication delay of 180 ms, a transaction throughput of 220 tps, and an accuracy of trust evaluation of 94%. Results of the experiment indicate that the proposed framework is scalable, privacy-protecting, and reliable in terms of access control to federated multi-cloud systems.
Copyright (c) 2026 Prabakaran K 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
Detection the Ripeness of Oil Palm Fresh Fruit Bunch Using Pretrained and Improved Models of YOLOv8
The ripeness of oil palm fresh fruit bunches significantly impacts the quality and economic value of palm oil production. This study proposes a novel approach using deep learning, particularly You Only Look Once (YOLO)v8, to classify and detect the ripeness of oil palm fruit bunches. By leveraging pretrained models and fine-tuning techniques, this study aims to improve detection accuracy while reducing inference time. The methodology involves data preparation, model training, and evaluation, utilizing a dataset comprising 427 images of oil palm fruit bunches classified into raw, half-ripe, and ripe categories. Additionally, the images were augmented to increase the dataset size. The YOLOv8 architecture, known for its scalability and efficiency, is applied to improve the classification process. Results demonstrate that YOLOv8 provides a balance between accuracy and processing speed, making it a suitable tool for real-time applications in the palm oil industry. This study contributes to reducing the reliance on manual techniques, lowering operational costs, and increasing the overall efficiency of the oil palm industry.
Copyright (c) 2026 Pattanapong Saeleung, Yutthapong Pianroj, Teerasak Punvichai, Piyanart Chotikawanid, Saysunee Jumrat, Doungrat Chitcharoen, Jirapond Muangprathub https://creativecommons.org/licenses/by/4.0
Secure and Intelligent DAWN-MANET Routing via AI and IoT Integration for Disaster Response
The Disaster Area Wireless Mesh Mobile Ad Hoc Network (DAWN-MANETs) face many challenges such as: moving topology, lack of pre-determined infrastructure and vulnerable to a variety of attacks which make it quite difficult in establishing the reliable and secure communication. Although current Ad hoc On-Demand Distance Vector (Secure-AODV) and AI-based MANET schemes are designed for addressing the security or adaptability problems only and do not have a disaster-aware decision-making model in place. In this paper, we present an integrated Artificial Intelligence-Internet of Things (AI-IoT) secure routing framework for DAWN-MANETs. The proposed approach successfully integrates management of IoT-assisted situational awareness and AI-based decision-making engine along with secure AODV routing protocol. This approach assures adaptive and secured routing under dynamic and adverse disaster situations. As network and environmental data can be monitored and analyzed in real-time, the approach can dynamically calculate routing paths which best balancing between reliability and security under the stochastic environment. Experimental results demonstrate that the proposed framework significantly improves the important network performance parameters as compared to traditional AODV and Secure-AODV protocols. The robustness of packet delivery, communication delay and security against attack scenarios have been well improved. With these strengths, it is believed that the integration disaster awareness and intelligent decision-making into secure routing protocols can greatly enhance the robustness and fitness for DAWN-MANETs. Despite focusing on simulation-based validation in the present research, the framework provides a scalable basis for actual application in disaster responses.
Copyright (c) 2026 Khanista Namee https://creativecommons.org/licenses/by/4.0
FedPrivEngine: A Federated Distributed Data Engineering Framework for Privacy-Preserving Analytics in Healthcare and Finance
Privacy-preserving analytics is essential in healthcare and financial domains where sensitive data must comply with regulations such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the Payment Card Industry Data Security Standard (PCI-DSS). Although Federated Learning (FL) enables collaborative model training without sharing raw data, existing frameworks often lack compliance-aware orchestration, efficient encrypted query execution, domain flexibility, and scalability under Homomorphic Encryption (HE). This paper presents FedPrivEngine, a federated and distributed data engineering framework designed for regulation-aware analytics across institutional silos. The framework integrates Homomorphic Encryption (HE), optional Differential Privacy (DP), Spark-based distributed orchestration, and a compliance-aware query planner to enforce policy constraints during federated execution. Two domain-specific models are implemented: HealthPrivNet, a Gated Recurrent Unit (GRU)-based model evaluated on the Open Safely healthcare dataset, and FinanceRiskNet, a Multi-Layer Perceptron (MLP)-based model assessed on the FICO HELOC credit risk dataset. Experimental results show that FedPrivEngine achieves high classification accuracy (93.8% in healthcare and 91.2% in finance), ensures zero policy violations, and supports encrypted query execution with success rates above 98%, while maintaining sublinear scalability and moderate system overhead. These results demonstrate the feasibility of secure, accurate, and regulation-aware federated analytics in policy-sensitive environments.
Copyright (c) 2026 Srinivas Lakkireddy 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
Explainable Deep Learning Models for Nutmeg Seed Image Quality Assessment
Nutmeg seed quality plays a key role in determining the economic value and export potential of Indonesian spice products. Manual assessment of quality is often subjective, time-consuming, and depends on the evaluator’s experience. This study introduces an explainable deep learning method designed to automatically and transparently evaluate nutmeg image quality. The model employs the EfficientNet-B0 architecture trained on 300 nutmeg images categorized into three quality levels: good, moderate, and rotten. To enhance interpretability, Explainable Artificial Intelligence (XAI) techniques—specifically Gradient-Weighted Class Activation Mapping (Grad-CAM) and local Interpretable Model-Agnostic Explanations (LIME)—were applied to highlight the image regions most influential in the model’s decisions. Model performance was evaluated using accuracy, precision, recall, F1-score, and mean Average Precision (mAP). The EfficientNet-B0 model, coupled with XAI methods, achieved an overall accuracy of 78% and a mAP of 70.29%, with Grad-CAM and LIME visualizations consistently highlighting the key visual features that determine nutmeg quality. These findings demonstrate that integrating deep learning with XAI can produce an objective, efficient, and dependable quality assessment system, which offers potential applications to other agricultural products.
Explainable Deep Learning, EfficientNet-B0, Grad-CAM, LIME, Nutmeg Seed Quality, Artificial Intelligence, Smart Agriculture
Copyright (c) 2026 Manuel Soares Dos Reis Pacheco, Hadiyanto Hadiyanto, Ridwan Sanjaya https://creativecommons.org/licenses/by/4.0
A Verifiable Credential Framework for Traceable and Scalable Data Sharing: Application to Product Carbon Footprint
Verifiable Credentials (VCs) technology is gaining traction as a means of transmitting trustworthy credentials in the digital world. One is its leading use cases currently being explored is sharing Product Carbon Footprint (PCF) data among companies in a supply chain. Advancing the application of VCs is expected to reveal technical challenges and opportunities for improvement, thereby fostering further technological development. This paper proposes a new model to enable data to be securely and efficiently shared using VCs. In our model, we propose two schemes that are independent and combinable. The first scheme is to share data that includes a watermark in a verifiable form. This scheme’s main advantage is that it allows shared data to be tracked and revoked. The second scheme is to make data sharing scalable in a manner in which downstream-product companies can verify that the data was created by certified upstream-product companies. To assess feasibility, we design, implement, and evaluate our proposed system for sharing PCF data. The results show that the proposed model significantly enhances security and efficiency. The proposed schemes, inspired by the challenges in sharing PCF data, are designed with general applicability, enabling them to be adapted to a wide range of scenarios.
Copyright (c) 2026 Nobuaki Endou https://creativecommons.org/licenses/by/4.0