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.
A Practical Protocol and Governance Framework for Immersive In-vehicle HCI Usability Evaluation Using Commercial XR Analytics
Virtual reality combined with embedded eye tracking offers a promising alternative to traditional in-vehicle usability assessment; however, integrating commercial extended reality analytics platforms into immersive workflows raises methodological challenges related to standardization, reproducibility, metric transparency, and version control. This study proposes a repeatable evaluation protocol and governance framework for the use of commercial extended reality analytics in immersive in-vehicle usability assessment. The framework was operationalized in an exploratory proof-of-concept study involving an immersive cockpit simulation, embedded eye tracking, a proprietary spatial analytics platform, and a physical vehicle mockup aligned with the virtual scene. Ten participants performed structured driving-related tasks, and gaze data were converted into spatial visualizations, behavioral metrics, and platform-derived presence indicators, which were descriptively compared with subjective questionnaire responses to support multimodal interpretation. The findings suggest that commercial analytics platforms can support the early identification of ergonomic inconsistencies and attention-distribution patterns in immersive cockpit evaluation. At the same time, the study highlights structural dependencies introduced by proprietary infrastructures, including metric opacity, software version instability, and limited auditability, which may affect longitudinal comparability and interpretive reliability. Practically, the study provides a reusable workflow for collision and area-of-interest validation, version documentation, multimodal benchmarking, and independent data archival to support more transparent, auditable, and repeatable immersive usability evaluation.
Topic: Application of Virtual Reality and Augmented Reality Technologies in Inte
Copyright (c) 2026 Felipe Leão Dias, Rafael Miguez, Catharina Ramos, Daniela Rodrigues, João Teixeira, Sanval Santos, Yasmim Thasla, Ingrid Winkler https://creativecommons.org/licenses/by/4.0
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
Advancing Machine-generated Text Detection: A Comprehensive Evaluation of Transformer-based Models
Improved fluency in large language models has intensified the need for accurate detection of machine-generated text. This study evaluates transformer-based models using an improved version of the Conference on Computational Linguistics 2025 (COLING 2025), Generative Artificial Intelligence (GenAI) Content Detection Task 1 dataset, which was carefully preprocessed to enhance label quality and balance. All models were trained under a unified protocol to ensure fair comparison and robust evaluation. Test set results show that Decoding-Enhanced Bert with Disentangled Attention (DeBERTa) achieves the highest macro F1−Score of 85.48%, surpassing the previously top-ranked Multi-Task Learning (MTL) system, which attains a macro F1 of 83.07%. These results highlight the effectiveness of advanced transformer architectures for distinguishing human-written and machine-generated text. Despite these gains, performance degradation under domain shift and highly paraphrased inputs remains a challenge.
Topic: Machine Learning in Computer and Information Systems
Copyright (c) 2026 Batyr Sharimbayev, Dr. Shirali Kadyrov https://creativecommons.org/licenses/by/4.0
Strategy for Countering Phishing Attacks within the Endless Antagonistic Game Framework
A mathematical model has been developed for the computational core of the decision support system taken in the course of ensuring information security of exchange sessions for the sale and purchase of digital cryptocurrencies. The model contributes to making rational decisions to ensure the information security of players who may face the threat of phishing attacks against them. The model was developed on the basis of an infinite antagonistic game and is designed to find the optimal strategies for players. The solution of such a problem contributes to the analytical search not only for the value of the game, but also makes it possible to find the characteristics of the risk degree of achieving the goal by the players when they use optimal mixed strategies. The described solution contributes to the analysis of the situation with counteraction to the party attacking with the help of phishing the participant of the trading session for buying and selling digital cryptocurrencies. Computational experiments were carried out using the Anaconda distribution kit. The results obtained will be useful for analysts dealing with information security issues of trading platforms, specializing in the purchase and sale of digital cryptocurrencies, and counteracting the threats phishing attacks. During research, three specific zones in the resource space have been identified: “Safe Zone”, “Strategic Parity Zone”, and “Critical Risk Zone”. Mathematically proven, the “Safe Zone” of cryptocurrency exchange is not constant. It deforms nonlinearly as market trends change. The obtained results give informational security analysts a tool for the flexible redistribution of defense budgets during periods of high market volatility.
Copyright (c) 2026 Valery Lakhno, Volodimir Malyukov, Inna Malyukova, Bakhytzhan Akhmetov, Zhuldyz Alimseitova, Atkeldi Ogan 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
Cross-Dataset Generalization Framework for Cybercrime Detection Using CIC-IDS2017, CIC-Phishing2019, and Malicious Uniform Resource Locator (URL) Data
Proposed cybercrime detection models demonstrate satisfactory performance on specific benchmark datasets; however, they are not always robust across diverse, practical scenarios. This paper examines the generalization performance of a cross-dataset measure for cybercrime detection across network traffic, phishing email, and malicious Uniform Resource Locator (URL) domains. The work combines three popular security benchmarks Canadian Institute for Cybersecurity Intrusion Detection System Dataset2017 (CIC-IDS2017), Canadian Institute for Cybersecurity Phishing Dataset2019 (CIC-Phishing2019), and Malicious URL 2020 in a single preprocessing and learning pipeline to avoid dataset-specific bias. One or more datasets are used to train models, which are then directly evaluated on previously unseen datasets to verify transferability across distribution shifts. We will discuss performance while considering accuracy, F1−Score, Receiver Operating Characteristic (ROC), and fold-to-fold stability. Experiments demonstrate that direct training with a single random source results in significant performance deterioration, with a 23% decrease in F1−Score when applied to unseen datasets. In contrast, the degradation caused by the proposed framework is kept below 10% and maintains Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) values consistently above 0.90. Paired significance testing demonstrates that the gains in robustness are highly significant (p < 0.01). The results indicate that cross-dataset evaluation is essential for the development of deployable cybercrime detection systems, providing empirical insights for developing generalization-oriented security analytics.
Cross-Dataset Generalization; Cybercrime Detection; Network Traffic Analysis; Phishing Email Detection; Malicious URL Classification; Security Benchmark Datasetss
Copyright (c) 2026 R. Adinarayana, G. Vamsi Krishna 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
Modelling Security Threats of Live Streaming Monetization Pipelines
Live-streaming platforms increasingly turn real-time payments, including cheers, Bits, gifts, and highlighted chat, into public signals of status and support. This article examines anomalous cheering as a security problem across the full monetization pipeline, asking how unusual spikes in volume, value, timing, or attribution can be related to system assets, trust boundaries, and controls. We develop a reference data-flow model of a monetized cheering pipeline and apply Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege (STRIDE) to each system element across identity, payments, wallet and ledger services, event streaming, anomaly detection, and trust and safety operations. A simple ordinal Damage Potential, Reproducibility, Exploitability, Affected Users, and Discoverability (DREAD) rubric is used for prioritization, with a worked example showing how one spike can support several competing hypotheses. The analysis highlights five recurring risks: scalable identity abuse and account takeover, payment and ledger exploitation, replay or duplication of canonical events, latency-sensitive denial of service, and misuse of privileged detection or enforcement tools. The paper concludes that monetized cheering should be protected as an end-to-end pipeline, with safeguards placed at the relevant trust boundaries: server-side event generation, idempotency and replay resistance, adaptive throttling, graceful degradation, tamper-evident audit trails, privacy-aware measurement, and least-privilege operational access.
Copyright (c) 2026 Emil Eminov, Stephen Flowerday 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
Ultra-light Multi-scale Human Parsing with Attention-guided Grouping
Human parsing in large and dynamic environments is commonly handled by large-scale models that deliver high segmentation accuracy but require hundreds of millions of parameters and substantial computational resources. This level of complexity makes such approaches difficult to deploy on power- and resource-constrained devices, particularly in real-time edge Artificial Intelligence (AI) settings. In this paper, we introduce Fast Dilated Spatial Pyramid Pooling + Parsing Grouping Network + Attention (Fast DSPP + PGN + ATTN), an efficient human parsing framework designed to balance segmentation performance with computational efficiency. The model employs a MobileNetV2 encoder with DSPP module for contextual modeling and a PGN-style grouping decoder integrated with light-weight spatial attention and Squeeze-and-Excitation (SE) attention. With 2.14 M parameters and 5.70 Giga Floating Point Operations (GFLOPs), it attains 40.67% mean Intersection over Union (mIoU). In ablation results, we observe that DSPP has the most significant effect (0.0151 mIoU), while spatial attention and SE yield smaller improvements. The accuracy of the grouping decoder is retained while the number of computations can be reduced. The model achieves 0.2490 mIoU on Look into Person (LIP) dataset, with variants to 0.2640, thus confirming robustness over domain shift. This indicates that integrating structured pixel grouping and efficient contextual aggregation together can solve the task of human parsing, even with severe computational constraints, which could render the proposed method particularly appealing to real-world applications such as embedded vision systems/mobile perception and real-time surveillance.
Copyright (c) 2026 abderrahim ouza, Mohamed El Ghmary, Ali Choukri https://creativecommons.org/licenses/by/4.0
Data-Driven Detection of Multi-vector IoT DDoS Attacks across Network Layers
As cyberattacks targeting Internet of Things (IoT) networks grow more sophisticated, the demand for models capable of accurately detecting and mitigating these threats becomes increasingly urgent existing detection systems often concentrate on a single attack surface which leads to critical blind spots in IoT network monitoring. This research introduces an intensive IoT attack-detection framework that addresses internal and external attack sources, leveraging multi-layer attack vectors across the application, transport, network, and data link layers. The proposed framework was evaluated in two phases. In the first phase, three datasets that simulate a distinct attack source were created: outbound, including Dynamic Host Configuration Protocol (DHCP) amplification and DHCP starvation; inbound attacks, including application-layer attacks, Synchronize (SYN) flood, User Datagram Protocol (UDP) flood, Internet Control Message Protocol (ICMP) Smurf, and ICMP direct; and internal Address Resolution Protocol (ARP) spoofing. Three machine learning models; Random Forest, Light Gradient-Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) were evaluated using accuracy, precision, recall, F1-Score, and Receiver Operating Characteristic (ROC) curve. In the second phase, a new dataset was generated by combining all datasets from the first phase. On the combined dataset, LightGBM achieved the highest performance, with accuracy: 94.08%, precision: 93.94%, recall: 94.08%, and F1-Score: 93.90%. Random Forest and CatBoost showed comparable performance, with all metrics ranging from approximately 93.2% to 93.9%. LightGBM demonstrates a slight edge in overall detection performance compared to other models, which highlights its effectiveness in detecting diverse and complex attack patterns across multiple traffic directions.
Topic: Internet of Things (IoT) in Smart Systems and Applications
Copyright (c) 2026 Rania A. Al-Ali, Mohammed M. Alnabhan, Qasem Abu Al-Haija 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.
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 Smart Business Intelligence Model Based on HGO Discovery Approach for Precision in Inpatient Care in Regional General Hospitals
In Bangka Belitung Archipelago Province, the Crude Death Rate (CDR), which represents the general mortality rate per 1000 discharged patients, exceeded 100. This figure indicates a significantly high mortality rate compared to the government standard of fewer than 45. Similarly, the Net Death Rate (NDR), defined as the mortality rate occurring after 48 h of hospitalization per 1000 discharged patients, was also above the national standard of fewer than 25. These findings highlight fundamental issues within hospital healthcare management system. As a potential solution, this study proposes the application of smart Business Intelligence (BI) through the Hierarchy, Governance, Outlook (HGO) Discovery approach. This approach is designed to optimize smart business intelligence in inpatient services in regional general hospitals, with the primary objective of addressing multidimensional challenges, including patient attribute management, human resource management as a strategic element, and the enhancement of inpatient care quality. The HGO Discovery method functions as an information system that identifies hidden patterns within a sequence of events (sequential patterns) by reorganizing existing hospital workflows. Thus, this approach facilitates accelerated inpatient care processes, improves operational efficiency, and provides more interactive support for decision-making. The implementation of HGO Discovery-based business intelligence is expected to produce a more integrated healthcare service system by combining hierarchical service structures, transparent governance, and strategic outlooks in every stage of decision-making, supported by the integration of smart business intelligence, data warehouse and data mining techniques.
Topic: Software Design and Program Development
Business Intelligence, HGO Discovery, Inpatients Care, Hierarchy, Governance, Outlook, Hospital
Copyright (c) 2026 Hengki Hengki, Rahmat Gernowo, Oky Dwi Nurhayati 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
HYBERT-X: A Hybrid Deep Learning Framework for Cyberbullying and Cyber Threat Detection
Cyberbullying and online threat detection are the new vulnerabilities of cybersecurity, and the conventional tools are unable to keep pace with the development of harmful content. The paper discusses HYBERT-X as a hybrid deep learning model that integrates Bidirectional Long Short-Term Memory (BiLSTM), Graph Convolutional Network (GCN) based learning, Bidirectional Encoder Representations from Transformers (BERT) and Extreme Gradient Boosting (XGBoost) to detect threats with high accuracy and interpretability. The proposed framework uses an ensemble classification layer to boost the accuracy and interpretability of detection based on semantic, temporal, and structural representation. The experimental assessment done on the typical benchmark datasets has shown that HYBERT-X is up to 95.53% accurate in comparison with the currently available baseline models both in generalization ability and explainability. The findings suggest that this work is computationally effective, scalable, and can be adapted to real-time cybersecurity applications and is applicable to next-generation cybersecurity systems against online threats and cyberbullying.
Cyberbullying detection; Cybersecurity; Hybrid deep learning; BERT; XGBoost
Copyright (c) 2026 JAYAPRIYA J, Kavitha S, Manimekala B, Vinay M, Nileem Kaveramma 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
An Enhanced AlexNet-VGG Hybrid with SiLU Activation and Multi-dimensional Slicing for Lung Infection Classification from 3D CT-scans
The use of deep learning for automatic classification has expanded rapidly, particularly through Convolutional Neural Network (CNN) architectures. AlexNet, a widely used CNN, offers a simple and stable structure. However, its performance on Computed Tomography (CT) scan images is limited by challenges in processing high-dimensional data and the scarcity of 3D CT scan datasets, often resulting in suboptimal classification outcomes. The Multi-Dimensional Slicing (MDS) method addresses this issue by converting 3D CT scan images into 2D representations along the axial, coronal, and sagittal planes. This technique enables comprehensive analysis of lung structures using more manageable datasets while preserving critical information. In this study, MDS is integrated with the ALVS-Net architecture, an improved version of AlexNet that incorporates a Visual Geometry Group (VGG) based structure in the final layer to reduce parameter complexity and enhance training stability. The Sigmoid-weighted Linear Unit (SiLU) activation function replaces the traditional Rectified Linear Unit (ReLU) function to improve nonlinear learning capabilities. The results of this study show that the proposed combination of MDS and ALVS-Net achieves excellent performance in classifying lung infections into four classes. This model achieves an accuracy, precision, recall, and F1−Score, all reaching 94%, a G-mean value of 93%, and a Cohen’s kappa value of 92%. These results suggest that the proposed method is effective and reliable for classifying lung infections based on CT scan images.
Copyright (c) 2026 Anita Desiani, Bambang Suprihatin, Yuli Andriani, Siti Rusdiana Puspa Dewi, Muhammad Arhami, Deshinta Arrova Dewi, Silfani Cahaya Putri 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