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.
Enhancing Precision: Leveraging Diverse Acquisition Functions in Dynamic Data Segmentation Using GPR-KNN Approach for Improved Nominal Current Forecasting
Managing energy consumption use at airports is becoming more critical, but there’s not much research on airfield lighting systems. This research proposes a hybrid machine learning framework designed to estimate the nominal output current demand by considering weather conditions and time of day. The hybrid model combines two techniques, Gaussian Process Regression (GPR) and K-Nearest Neighbors. This allows it to recognize overall trends and specific changes in energy use. This study also uses data segmentations (25%, 50%, 75% and 100%) and various acquisition functions such as Expected Improvement (EI), Expected Improvement Plus (EIP), Expected Improvement Per Second (EIPS), Expected Improvement Per Second Plus (EIPSP), Lower Confidence Bound (LCB), and Probability of Improvement (POI) to improve predictions. To identify the most effective model, metrics including accuracy, precision, recall, and F1-Score were employed for evaluation. Findings from the study indicate that both data segmentation and the selected acquisition function play a crucial role in influencing model performance. Notably, the model is able to work well even with limited data, meaning it can still be effective even if only a portion of the data is available. This makes it efficient, reducing both costs and time needed to train the model without giving up on accuracy. The solution is lightweight model, making it a practical choice compared to complex models that are hard to interpret. Ultimately, this research presents a new, scalable approach to optimizing energy use in airfield lighting, which can help create more innovative and sustainable airport operations.
Copyright (c) 2025 Wan Mohammed Rais Bin Jamaludin, Wan Mohamed. W.M., N.H. Nik Ali https://creativecommons.org/licenses/by/4.0
Behavioral Authentication Model for Smartphone Users Based on Gyroscope and Accelerometer Data for Mobile Educational Resources
With the continuous rise in the popularity of mobile devices and the growing volume of data related to education, the protection of users’ personal information is becoming increasingly important. This study presents a behavioral authentication model that can be effectively utilized to enhance security when accessing intelligent educational resources, based on the processing of gyroscope and accelerometer data. The paper reviews existing authentication methods, highlights the advantages of behavioral authentication, and proposes a new model. The behavioral authentication model analyzes users’ behavioral patterns to identify and protect user accounts. This approach is particularly crucial in the context of intelligent educational resources, where users frequently interact with various types of content that require the safeguarding of personal data and the preservation of the educational process’s integrity.
smartphone, mobile application, behavioral authentication, gyroscope, accelerometer, security, education
Copyright (c) 2026 Aray Kassenkhan, Vasily V. Serbin https://creativecommons.org/licenses/by/4.0
Ontology-Based Topic Model for Document Retrieval Systems in Information Technology
Most current academic document retrieval systems for topic-based search rely on simple keyword matching or statistical topic modeling. In these methods, topics are formed either from sets of frequent keywords or from statistical clusters. While these approaches work in some contexts, they cannot fully capture the rich semantic meaning of topics as understood by human experts. This often leads to search results that fail to match the intended meaning of the topic, causing gaps between what users need and what the system returns. This study aims to overcome these limitations by developing a topic-based retrieval system that represents topics in a more semantically rich and human-aligned way. The system is designed to help Information Technology (IT) students search for topic-relevant materials—specifically English-language ebooks and research papers—from a curated faculty repository. We introduce C-ONTO, a structured knowledge model that includes topic names, descriptions, learning objectives, real-world application scenarios, and concept graphs describing internal semantic relationships. Documents are also modeled as concept graphs, enabling accurate semantic similarity calculations. An intelligent query analysis module interprets user intent and maps it to the system’s semantic structure. Testing with 425 real student queries in four IT domains shows that the system achieves 81.18% accuracy, outperforming keyword-based and statistical methods in precision, recall, coverage, and F1-Score. By aligning computational topic modeling with human understanding, the proposed system improves accuracy, semantic consistency, and educational relevance in academic document retrieval for information technology and related fields.
Topic: Software Design and Program Development
Copyright (c) 2025 NGUYEN THANH DIEN, Assoc. Prof. Dr. DO VAN NHON, Dr. TRAN HOANG TUNG https://creativecommons.org/licenses/by/4.0
A Hybrid Machine Learning Model in Diagnosing Brain Strokes
Strokes can occur suddenly and unexpectedly, especially brain strokes, which can be fatal for individuals over the age of fifty. Survivors of a stroke may experience severe paralysis or weakness, posing a significant challenge for healthcare professionals to treat. However, artificial intelligence and Machine Learning (ML) have been proven promising in addressing these critical issues. Despite the high incidence of strokes in countries like Qatar, there is limited research on stroke risk in the Middle East. This study is the first to use a dataset that combines multiple open-source datasets from the region. In this research, several machine learning and ensemble learning algorithms, including Decision Trees (DT), Multiple Layer Perceptron (MLP), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM), ensemble stacking, and Random Forest (RF) classifier have been investigated. All the algorithms were comprehensively analyzed by tuning their respective hyperparameters using the Grid Search approach and extracting the best features from the dataset through statistical analysis. The proposed ensemble stacking model achieved the highest accuracy and an F1-Score of 98% and 98.29%, respectively. The outcome indicates substantial improvement compared to current approaches in literature with similar datasets.
Copyright (c) 2025 Muhammd Imran Basheer Ahmad , Rim Zaghdoud, Atta Rahman, FARHAN Ali, Hussain Alhashim, Mohammed Almubarak, Mohammed Albasheer, Abdul Wahab Alaqel, Ahmed Almaskeen, Dina A. Alabbad, Danah Aljaafari, Aishah Albakr https://creativecommons.org/licenses/by/4.0
Enhancing IoT Attack Detection with Explainable AI: A Robust Evaluation of LIME and SHAP Interpretability
As Internet of Things (IoT) sensors became more common in weather monitoring systems, the need for classification models that are both accurate and interpretable has grown, particularly for applications such as smart city infrastructure, natural disaster prediction, and cyber threat detection. In IoT weather attack classification, this need is even more critical, as detecting malicious activity alongside normal environmental variations requires both precision and transparency. Although Machine Learning (ML) models such as Random Forest (RF) and Extreme Gradient Boosting (XGBoost) have demonstrated excellent prediction accuracy, their ‘black box’ nature makes it difficult to understand how they reach decisions, which can undermine trust in their results. This research addresses the transparency challenge by developing a dual explanation framework that combines SHapley Additive exPlanations (SHAP) for understanding a model’s overall behavior and Local Interpretable Model-agnostic Explanations (LIME) for explaining individual predictions, providing both global and local insights. Using a pre-processed IoT-sensor weather dataset, six classifiers were trained and evaluated, RF, Gradient Boosting, AdaBoost, XGBoost, Logistic Regression, and Support Vector Machine (SVM), across four performance metrics. RF and XGBoost achieved the highest performance, with RF obtaining 99.67% accuracy, precision, recall, and F1-Score, and an Area Under the Curve (AUC) of 1.00 for nearly all classes. Across all models, the “label” feature consistently emerged as the most influential predictor. SHAP identified it as the dominant driver of overall decisions, while LIME confirmed its significance in specific cases, strengthening confidence in the model’s reasoning. The framework also introduced a novel cross-verification step, comparing SHAP and LIME outputs to ensure alignment, akin to having two independent experts validate the model. Beyond delivering high predictive accuracy, this approach provided clear, actionable insights that meteorologists and security teams can use for forecasting storms and detecting anomalous or malicious sensor activity. While SHAP and LIME offer complementary interpretability, their computational trade-offs must be considered for real-time IoT deployments.
Copyright (c) 2025 Yousef Qawqzeh https://creativecommons.org/licenses/by/4.0
An Adaptive Multi-Scale Feature Fusion Framework for Detecting Depression and Assessing Its Severity from Speech Signals
Depression is a chronic mental health disorder characterized by psychological, physical, and social impairments, often resulting in diminished interest in daily activities and, in severe cases, suicidal ideation. Recent physiological studies have revealed measurable differences in vocal attributes between depressed and non-depressed individuals, motivating the use of speech signal analysis for automatic depression detection. However, conventional approaches relying on Mel-frequency or Fourier-based spectrograms often fail to capture discriminative features for effective emotional classification. To address this limitation, this study proposes an optimized deep learning framework, Adaptive Multi-scale Fused Feature-based Deep Network (AMF2-DNet) for both depression detection and severity assessment. Speech signals from the Distress Analysis Interview Corpus with Wizard-of-Oz (DAIC-WOZ) benchmark dataset undergo noise reduction and normalization, followed by feature extraction, including Mel-Frequency Cepstral Coefficients (MFCCs), spectral features, and deep features learned through a Sparse Autoencoder (SAE). These multimodal features are integrated within a residual Bidirectional Recurrent Neural Network (Bi-RNN) architecture enhanced with a novel loss function for improved convergence. Further, the Refined Attacking Strategy of Giant Armadillo Optimization (RASGAO) dynamically optimizes network parameters and scale weights. Experimental results demonstrate that AMF2-DNet achieves 94.88% accuracy, an F1-Score of 84.50%, and a Mathews Correlation Coefficient (MCC) of 0.82, surpassing state-of-the-art baselines by up to 7.44% in accuracy. The proposed framework also exhibits strong robustness across noise levels and speaker variability. Future work will focus on real-time deployment using lightweight architectures, cross-lingual validation, and multimodal fusion with facial and physiological cues for enhanced clinical applicability.
Copyright (c) 2025 Ms. Raminder Kaur Nagra, Dr. Vikram Kulkarni https://creativecommons.org/licenses/by/4.0
Sentiment Analysis with Deep Learning: Benchmarking CNNs vs. Prompt-Tuned BERT Models
With the growth of user-generated content on platforms like social networks and e-commerce, effective sentiment analysis has become increasingly important. This paper presents a comparative study of deep learning models for sentiment analysis, focusing on both Convolutional Neural Networks (CNNs) and prompt-based Bidirectional Encoder Representations from Transformers (BERT) models. We evaluated three CNN variants and multiple prompt strategies for BERT (hand-crafted, adaptive, and hybrid) across five diverse datasets. The results show that the hybrid Gated Recurrent Units_Attention Mechanism (GRU_ATT) BERT model outperforms all others. Prompt-based approaches, particularly adaptive and hybrid designs, offer strong performance and surpass CNN-Non-Static. Moreover, dataset characteristics, such as input length, class distribution, and language structure, significantly impact the performance of the models. Lastly, practical guidelines are proposed for selecting appropriate models based on dataset characteristics.
Copyright (c) 2025 Bo Huang, Fei Song https://creativecommons.org/licenses/by/4.0
Experiential-Adaptative Hybrid Model for Hospitality Based on CNN Neural Networks, Case-Based Reasoning (CBR) and 360° VR
In this work, an Experiential-Adaptive Hybrid model for hospitality based on Convolutional Neural Networks (CNN) neural networks, Case-Based Reasoning (CBR) and 360° VR, is proposed for generating experiences in hotel marketing and promotion activities. The model is based on CBR, which considers user profiles and immersive 360° videos. The work also improves and automates the video tagging process, using CNN, transitioning from a manual to an automated process. The 360° Virtual Reality (VR) experiences can elicit responses across behavioral, affective, cognitive, and attitudinal dimensions; therefore, they are evaluated within an immersive 360° VR environment. To validate the results obtained from this approach, a quasi-experimental study was conducted using various visual experiences, including surrounding landscapes, facilities, environments, and services of a boutique hotel. This study employed a random sample of young participants. This work aims to contribute to the use of 360° VR videos for visualizing the characteristics, environments, and services of hotels in a tour tailored to user profiles, with the goal of evaluating attitudinal and behavioral, cognitive and affective responses as well, and the potential influence on booking and purchase intentions. Conclusions and recommendations for future research are established.
Topic: Machine Learning in Computer and Information Systems
Copyright (c) 2025 Samuel Quecara, Dr. Luis Alfaro https://creativecommons.org/licenses/by/4.0
A Conflict-Reduced Task-to-Host Matching Scheme for Task Offloading in Edge Computing-Enabled IoT Network
The continuous evolution of applications and the introduction of novel service models have significantly intensified the computational demands on Internet of Things (IoT) Terminal Devices (TDs). In order to improve service capability, edge computing-enabled IoT shifts the computational burden from TDs to edge computing hosts through task offloading. However, current task-to-host matching approaches exhibit significant limitations, particularly in their inability to anticipate tasks that are nearing the completion of data transmission and are about to arrive at the edge computing network. This shortcoming results in matching conflicts between tasks offloaded horizontally across sites and those directly offloaded from terminal devices (TDs). To address this challenge, a Two-layer Asynchronous Conflict-reduced Task-to-host Matching (TACTM) scheme is proposed. The proposed scheme jointly considers the data upload progress of tasks and the dynamic load of edge computing hosts, aiming to reduce matching conflicts while optimizing task-to-host assignment. Simulation results demonstrate that the proposed TACTM scheme outperforms existing methods by achieving a 20.79% reduction in average conflict ratio, an 8.26% decrease in average processing time, a 4.30% drop in average task failure ratio, and a 4.05% improvement in average Quality of Experience (QoE).
Topic: Internet of Things (IoT) in Smart Systems and Applications
Copyright (c) 2025 Wang Dayong, Kamalrulnizam Bin Abu Bakar, Babangida Isyaku, Lei Liping https://creativecommons.org/licenses/by/4.0
Performance Peaks: Monitoring and Optimizing Systems for Efficiency
Ensuring operations run smoothly and systems are reliable is essential in the fast-paced and vital energy sector, but it can be hard to do because of limited resources. This case study looks at how an open-source monitoring solution called Zabbix was used in a multi-plant energy company with limited Information Technology (IT) resources and infrastructure spread out geographically. Implementing Zabbix is a deliberate move away from traditional proprietary systems and toward a cheaper and more flexible option, showing how well it can be used in various business settings. The company significantly improved essential system monitoring and uptime tracking, even though they couldn’t use all of Zabbix’s optimization tools because they needed more staff. The study stresses the importance of using even the most basic tracking methods to keep performance stable and operations running smoothly in a complex energy production setting. Even though there were problems, like integrating Zabbix with old systems and balancing tracking duties with other IT tasks, it made it easier for the company to find and fix big problems in many places. The results clarify the pros and cons of using open-source tracking tools in areas with few resources. These give companies with similar issues important information when running effective IT operations across geographically dispersed infrastructures.
Real-time Systems Management, Infrastructure Reliability, Open-source Monitoring Tools, IT System Optimization, Network Performance, Scalability, Operational Efficiency, Proactive Management, Cost Efficiency, Systems Customization
Copyright (c) 2025 William Rey, Edward Anthony G. Cudilla https://creativecommons.org/licenses/by/4.0
A Model for Financing the Process of Education Informatization, Taking into Account Computer Security within the Framework of a Differential Quality Game
This article proposes a game-theoretic model for financing education informatization, incorporating Cyber Security (CS) considerations within a university context. The model formalizes the financial interactions between stakeholders, addressing the critical interdependence between educational quality and cybersecurity. This interdependence necessitates optimized resource allocation to establish a secure and innovative educational environment. The core challenge investigated is the strategic balance between investments in digitalization and the costs of associated information security measures. The novelty of our approach lies in applying a differential quality game with a bilinear structure, where the participants’ financial states are governed by a system of differential equations. Classical methods for solving linear differential games are inapplicable here, as the bilinear structure prevents the use of the Cauchy formula to solve the system. Furthermore, the positional differential game approach is also unsuitable, as the model permits the use of non-measurable controls by an opponent. These mathematical challenges underscore the work’s significance, contributing not only as a tool for CS policy but also through its theoretical advancements. Within this framework, the model identifies player preference sets and optimal strategies, findings which are validated through a computational experiment. The visualization of the preference set for the first player—the Computer Security Center (CSC)—demonstrates the model’s practical utility in resolving financing dilemmas for education informatization amid cybersecurity constraints. The study’s impact lies in providing a robust analytical tool for developing optimal resource allocation strategies. This contributes significantly to the sustainable development of Higher Education (HE) systems navigating digital transformation and escalating cyber threats.
higher education, informatization, cybersecurity, game theory, scenarios, resources, optimal strategy
Copyright (c) 2025 Bagdat Yagaliyeva, Arkadii Chikrii , Kaiyrbek Makulov , Berik Akhmetov , Valerii Lakhno, Inna Malyukova, Volodimir Malyukov https://creativecommons.org/licenses/by/4.0
A Multi-Agent Framework for Autonomous Process Mining and Optimization
Process mining has emerged as a powerful approach for analyzing business processes through event logs, but it often requires specialized expertise to interpret results and lacks autonomous decision-making capabilities for implementing improvements. This paper presents a novel framework integrating process mining with agentic artificial intelligence to create an autonomous system for continuous process discovery, analysis, and optimization. We introduce a specialized multi-agent architecture where five distinct agents collaborate, generating quantifiable improvement recommendations. Unlike previous approaches that focus on isolated aspects of process mining or require significant human intervention, our framework enables end-to-end analysis and optimization with minimal manual effort. Our experiment demonstrates that our approach achieves 98.7% accuracy in data preprocessing, generates process models with 0.94 fitness and 0.86 precision scores, and identifies bottlenecks with 92.3% recall. The framework produced optimization recommendations resulting in a 23.7% reduction in process cycle time, 18.4% improvement in resource utilization, and 15.9% estimated cost reduction. Our contribution bridges a gap between technical process mining capabilities and business-oriented decision-making, making process optimization more accessible to non-experts while delivering measurable business value.
Copyright (c) 2025 Dr. Sonali Sen Baidya, Mr. Abhishek Baidya https://creativecommons.org/licenses/by/4.0
Advancing Sentiment Analysis for Colloquial Arabic: A Comparison of Machine Learning and Lexicon-Based Approaches for the Palestinian Dialect
Formal Arabic is less commonly used in everyday communication, especially on social media, where colloquial Arabic dominates. However, most Arabic Natural Language Processing (NLP) tools, including sentiment analysis systems, are primarily developed for Modern Standard Arabic (MSA), making it difficult to analyze dialectal content. This study addresses this gap by focusing on the Palestinian dialect, which is widely used across the Levant region. Two main approaches were explored: machine learning and lexicon-based methods. A manually labeled dataset of Facebook telecom service reviews was collected, cleaned, and split into training and testing sets. Five machine learning algorithms and a neural network model (Multilayer Perceptron) were evaluated, while the lexicon-based approach used a predefined sentiment lexicon for colloquial Arabic. The machine learning models outperformed the lexicon-based method, achieving accuracy scores between 92.2% and 94.0%, with Naïve Bayes yielding the highest F1-Score (95.0%). The neural network model achieved 95.0% in both accuracy and F1-Score, while the lexicon-based method scored 81.0% accuracy and a 67.0% F1-Score. Although no hybrid model was implemented, the findings suggest future research could explore combining lexical and machine learning approaches.
Language, Social Media, Colloquial Arabic, Machine Learning, Lexicon-Based Approach, Sentiment Analysis, Multilayer Perceptron Classifier.
Copyright (c) 2025 Khalid Rabaya, Ahmad Hasasneh, Khalil Rantisi https://creativecommons.org/licenses/by/4.0
Classification of Batik Patterns Using Inception-ResNetV2 with Data Augmentation
One of Indonesia’s cultural heritages with significant artistic and historical value is batik. The background of this research is the diversity of cultures and customs in various regions of Indonesia, which is reflected in the diverse batik patterns currently used. Classifying batik patterns is very important in preserving, recognizing, and promoting batik as a valuable cultural asset of the country. The problem statement is how classification facilitates the identification of the history, significance, and characteristics of each batik pattern currently in use. This problem can be overcome by applying deep learning to help identify batik patterns throughout the archipelago. The key conclusion is that a deep learning strategy is necessary, which involves training with extensive visual data. The methods used include deep learning by utilizing the Inception-ResNetV2 architecture. Its contribution is adopting an architecture that is designed to minimize the number of parameters and improve computational performance. The network can perform more efficiently overall when combined with residual connections from ResNetV2. Various types of batik and classes from the archipelago form the dataset. After the calculation was carried out for 9 min and 6 s, a batik pattern model was obtained with an average accuracy of 98.19%, precision of 98.20%, recall of 98.19%, and F1-Score of 98.16%. Mean Squared Error (MSE) 0.0023, Root Mean Squared Error (RMSE) 0.0483, Mean Absolute Error (MAE) 0.0035. The experimental data were then used to test the confidence level, achieving an average accuracy of 76–99%.
Copyright (c) 2026 Budi Dwi Satoto, Wahyudi Agustiono, Suraya Binti Hamid, Novia P. Ramadhani, Fiola L. Rafelina, Chafi A. R. Zakiy, Budi Irmawati, Deshinta Arrova Dewi https://creativecommons.org/licenses/by/4.0
Advanced Techniques for Spatio-Temporal Data Management in Graph Databases—A Systematic Review
A high-dimensional representation is required to represent connected information that reflects real events and caters to spatio-temporal dimension. Graph data structures have shown potential for integration into smart city data management frameworks and have evolved to handle spatio-temporal data. To investigate the advanced techniques used in managing spatio-temporal data in graph databases, a systematic literature review of related research papers published from 2019 to 2024 was conducted. The review examines the evolution from basic graphs to specialized structures like dynamic attributed graphs and fuzzy spatio-temporal Resource Description Framework (RDF) and also summarizes algorithms used—including graph representation learning, pattern matching, clustering, and centrality algorithms—that enable sophisticated multi-domain analyses. The research provides five key contributions: (1) the state of graph data structure development and algorithms across various fields; (2) insights on spatio-temporal data inputs used in graph structures; (3) algorithms for spatio-temporal data management and analytics; (4) spatio-temporal analyses conducted using graph-structured databases; and (5) future research trajectories. From the review, we identify challenges in graph-based implementation with spatio-temporal data such as structural graph complexity, temporal representation, semantics, and data quality, while outlining future directions in graph representation techniques, temporal-semantic innovations, scalability solutions, and comprehensive data management.
Copyright (c) 2026 Farah Ilyana Hairuddin, Suhaibah Azri, Uznir Ujang https://creativecommons.org/licenses/by/4.0
Smart Firewall for Phishing Detection Powered by Bio-Inspired Algorithms
Phishing attacks continue to pose significant risks to digital security by exploiting user vulnerabilities through deceptive methods. This paper presents a smart firewall model for phishing detection that leverages bio-inspired algorithms to enhance threat identification and response. The model utilizes the Whale Optimization Algorithm (WOA) and Dragonfly Algorithm (DA) independently for effective feature selection, thereby reducing data dimensionality while retaining critical phishing indicators. These optimized features are then processed by advanced Machine Learning (ML) classifiers—Extra Trees (ET), Random Forest (RF), and K-Nearest Neighbors (KNN)—to rigorously evaluate detection accuracy. Experimental results on the ISCX-URL2016 dataset demonstrate that the combination of WOA with the ETs classifier achieves a superior detection accuracy of 98.86%, precision of 99.50%, recall of 99.50%, F1-Score of 99.50%, outperforming alternative configurations and recent methods. This result highlights the potential of bio-inspired optimization combined with ML to develop intelligent, adaptive firewalls capable of effectively mitigating phishing threats.
Copyright (c) 2025 Mosleh Abualhaj, Sumaya N. Al-Khatib, Ahmad A. Abu-Shareha, Abdallah Hyassat https://creativecommons.org/licenses/by/4.0
Optimizing 5G Resource Allocation with Reinforcement Learning: A Q-Learning and Deep Q-Network (DQN) Approach
Simulation using a Network Simulator-3 (NS-3)-based setup enables realistic testing under varying network densities and channel conditions. The proposed Deep Q-Network (DQN)-based approach is benchmarked against recent deep reinforcement learning methods, achieving performance improvements of up to 25.7% in throughput, 31.5% latency reduction, and 27.4% energy savings. Q-Learning offers simpler scalability and faster convergence in low-complexity scenarios. These findings confirm the promise of reinforcement learning as a core enabler for autonomous and adaptive 5G resource management and as an efficient and scalable method for intelligent 5G management. The success of DQN in this domain can be attributed to its integration of deep neural architectures that can learn intricate interactions among channel quality, user demand, and interference patterns, in conjunction with experience replay and target networks. Notably, this method attained significant fairness and energy efficiency without the need for explicit fairness constraints in the reward formulation, underscoring the robustness of the generalizability of the learned policies.
Copyright (c) 2025 Basem Alrifai, Loiy Alsbatin, Firas Zawaideh https://creativecommons.org/licenses/by/4.0
A Comprehensive Study on Deep Learning Techniques for IoT Security
The present paper looks at the security problems caused by the fast growth of the Internet of Things (IoT) in areas like industry, healthcare, and agriculture. As IoT systems become more common, they face more threats from cyberattacks like Brute Force, Denial of Service (DoS), Botnets, and so ones. To deal with these security issues, we studied recent papers to review different intrusion detection systems made for IoT. The goal was to see how well they work and find ways to make them better. Hence, we have selected 63 relevant papers among 1200 find papers. These selected papers were published between 2020 and 2024. Our study shows that deep learning-based intrusion detection systems can improve the manner how online threats are detect. These systems, especially when they use neural networks, are better at spotting and reacting to harmful activities. Combining machine learning with Intrusion Detection Systems (IDS) seems to help boost the security of internet of things networks, offering stronger protection against cyber-attacks. One of the best algorithms we found was the combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. This deep learning model showed very high accuracy in protecting IoT networks, especially when tested with different datasets. This proves that using advanced algorithms is important to keep up with the growing challenges of cyber-threats targeting IoT systems.
Copyright (c) 2025 Abdeslem Blali, Souhayla Dargaoui, Mourade Azrour, Azidine Guezzaz, Abdulatif Alabdulatif, Fatima Amounas https://creativecommons.org/licenses/by/4.0
YOLOv8nGM: An Improved Speed of YOLOv8n Object Detection
The rapid advancement of technology has significantly enhanced object detection tasks that require high speed and accuracy. Although the YOLOv8n model is known for its efficient and accurate detection capabilities, its detection process still involves multiple complex stages, limiting its deployment in challenging environments. This research investigates how the YOLOv8n architecture can be modified to enhance inference speed without compromising detection accuracy. The objective is to propose a modification to the YOLOv8n backbone by integrating a gating mechanism on top of the C2f module. The model, YOLOv8nGM, utilizes a sigmoid activation function to implement gating, converting network outputs into probabilities that enable selective computation depending on input complexity. This adaptive mechanism enables the network to bypass unnecessary calculations, thereby reducing computational overhead. Experiments on the benchmark dataset reveal that YOLOv8nGM achieves an average inference time of 8.72 ms per image, representing an approximately 5.4% improvement in speed compared to the original YOLOv8n. Additionally, the GPU memory consumption was reduced from 0.27 GB to 0.21 GB, signifying more efficient resource use. The mAP50 metrics on the validation and test datasets showed that the proposed model achieved minor improvements of 0.02% and 0.008%, respectively, compared to the original model, indicating that accuracy was maintained despite faster computation. These findings demonstrate that the gating mechanism effectively balances inference speed and precision, making the YOLOv8nGM model suitable for real-time object detection applications such as warehouse inventory counting. This study advances object detection technology by providing a practical and computationally efficient solution for environments that require rapid and accurate recognition.
Copyright (c) 2025 Chandra Hermawan Heruatmadja, Harjanto Prabowo, Harco Leslie Hendric Spits Warnars, Yaya Heryadi https://creativecommons.org/licenses/by/4.0
Enhancing Career Counseling for Baccalaureate Students in Morocco through Retrieval-Augmented Generation Technology: A RAG-Based Chatbot
Career guidance is important for helping students make informed choices about their studies and future careers. Yet many students lack access to resources that match their personal needs, leaving them uncertain about their options. This paper presents a chatbot built with Retrieval-Augmented Generation (RAG) to provide tailored career advice for baccalaureate students in a Moroccan high school. We compared multiple embedding models (OpenAI text embedding, Bidirectional Encoder Representations from Transformers (BERT), and Mistral) and large language models (GPT, LLaMA, and Mistral) within the same retrieval–generation framework. The evaluation showed that while all models achieved strong performance, the combination of OpenAI embeddings with Elasticsearch for retrieval and GPT for generation produced the most consistent and contextually appropriate responses. These findings show that RAG-based systems can support students with timely and personalized career guidance that connects their academic choices to available career options.
Copyright (c) 2025 CHAIMAE OUHADDOU, Asmaâ Retbi, Samir Bennani https://creativecommons.org/licenses/by/4.0
Indo-ASR+: Enhancing Indonesian Automatic Speech Recognition by Fine-Tuning Wav2Vec2 with FAdam
Automatic Speech Recognition (ASR) has become a key technology in human-machine interaction, especially in supporting languages with limited resources such as Bahasa Indonesia. Although deep learning-based models such as Wav2Vec2 have shown good performance in speech recognition, further optimization is still needed to improve training accuracy and efficiency, especially in data-constrained and noisy environments. This research focuses on optimizing the Wav2Vec2 model for Indonesian ASR by applying the Fisher Adam optimizer (FAdam). FAdam combines Natural Gradient Descent (NGD) with Fisher Information Matrix (FIM) to improve learning stability, accelerate convergence, and reduce sensitivity to noise in the data. The model was trained using the Indonesian Common Voice dataset and evaluated based on Word Error Rate (WER) of 5.59% and Character Error Rate (CER) of 1.76% on the validation set. Experimental results show that this approach not only improves accuracy over previous methods, also enhances training efficiency and improves the stability of model convergence compared to state-of-the-art models such as XLSR-53 and XLS-R 300m for Indonesian ASR. In addition, FAdam is shown to provide increased inference speed, making it a more optimal solution for ASR implementation in real-world scenarios. This research contributes to the development of a more adaptive and efficient ASR technology for Indonesian, while opening up further optimization opportunities in self-supervised learning-based models.
Copyright (c) 2025 Irfan Darmawan, Alam Rahmatulloh, Rohmat Gunawan, R. Wahjoe Witjaksono, Ghatan Fauzi Nugraha https://creativecommons.org/licenses/by/4.0
Towards Accurate SDG Research Categorization: A Hybrid Deep Learning Approach Using Scopus Metadata
The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Transformers (BERT), and FastText embeddings follows our approach, which comprises exhaustive preprocessing operations including stemming, stopword deletion, and ways to address class imbalance. Training and evaluation of the hybrid BiLSTM-CNN model on several benchmark datasets, including SDG-labeled corpora and relevant external datasets like GoEmotion and Ohsumed, help provide a complete assessment of the model’s generalizability. Moreover, this study utilizes zero-shot prompt-based categorization using GPT-3.5/4 and Flan-T5, thereby providing a comprehensive benchmark against current approaches and doing comparative tests using leading models such as Robustly Optimized BERT Pretraining Approach (RoBERTa) and Decoding-enhanced BERT with Disentangled Attention (DeBERTa). Experimental results show that the proposed hybrid model achieves competitive performance due to contextual embeddings, which greatly improve classification accuracy. The study explains model decision processes and improves openness using interpretability techniques, including SHapley Additive exPlanations (SHAP) analysis and attention visualization. These results emphasize the need to incorporate rapid engineering techniques alongside deep learning architectures for effective and interpretable SDG text categorization. With possible effects on more general uses in policy analysis and scientific literature mining, this work offers a scalable and transparent solution for automating the evaluation of SDG research.
Copyright (c) 2025 Jalal Al-bayati, Furat N. Tawfeeq, Moahmmed Al-Shammaa https://creativecommons.org/licenses/by/4.0
Large Language Models in a Local Environment for Generative AI-Based Automated Event Log Processing in Process Mining Techniques
Process mining is vital for enhancing the operational efficiency and supporting strategic decision-making of organizations because it identifies bottlenecks and optimizes workflows. However, its analytical success depends fundamentally on the quality of event logs, and preparing these logs is a highly complex task. This complexity stems from diverse data storage formats across organizational systems, including varied log structures and inconsistent timestamps. Thus, analysts utilize public generative Artificial Intelligence (AI) services for data preparation, which demonstrates functional capabilities within usage limitations. However, the transmission of sensitive data to external servers in public generative AI systems poses data leakage risks. Moreover, manual intervention required in compiling the AI model-exported data is prone to errors and time consuming. Token limit violations in such models also cause service interruptions. To address these challenges, a local generative AI system was developed for automated event log transformation utilizing Ollama Llama 3.1. The system employed Python with the Streamlit framework, Pandas library, and Ollama integration. It thus implemented rule-based format detection algorithms, AI-assisted semantic column mapping, and a data transformation engine that managed complex conversions, including regular expression algorithms for timestamp standardization. This system was tested on real data obtained from a hospital in Thailand (128,917 events from 10,217 cases), during which it processed 4,194 events per second. It transformed complex data formats into standard event logs, which were then imported into a process mining software, Disco. Results indicated that this system addressed data security concerns by establishing automated processes, enabling organizations to leverage the benefits of AI in preparing data for process mining without data leakage risks.
Copyright (c) 2025 Poohridate Arpasat, Wichian Premchaiswadi https://creativecommons.org/licenses/by/4.0
Analysis of External Factors on the Performance of Cryptographic Algorithms in Medical Systems Based on ESP32
This paper investigates the influence of real-time physiological and environmental variables on the computational performance of cryptographic algorithms deployed in wearable medical systems powered by the ESP32 microcontroller. The proposed system integrates biometric sensors to capture heart rate, skin temperature, and galvanic skin response, from which a composite Stress Index (SI) is calculated. This SI dynamically modulates encryption behavior to adapt to the user’s physiological state. An experimental dataset comprising 300 samples from 10 participants was collected over ten days under semi-controlled environmental conditions. Six widely used cryptographic algorithms—AES-256, HMAC-SHA256, SHA-256, SHA-3, BLAKE3, and ChaCha20—were evaluated based on execution time, CPU load, and estimated energy consumption under varying stress levels. To quantify algorithmic robustness, we introduced the Crypto Stress Tolerance Score (CSTS), a custom metric combining performance stability, stress correlation, and resource efficiency. The findings reveal that elevated body temperature and stress index values significantly affect cryptographic performance, with execution time increasing by up to 35% under high-stress conditions. AES-256 exhibited the highest sensitivity and variability, whereas BLAKE3 and ChaCha20 delivered consistent, low-latency performance with minimal fluctuation. A comparative analysis and scoring table are provided to guide optimal algorithm selection for constrained medical Internet of Things (IoT) environments. This work contributes a novel framework for adaptive, stress-aware encryption in embedded healthcare devices, offering improved reliability, energy efficiency, and security personalization in patient-centric monitoring systems.
ESP32; cybersecurity; cryptographic algorithms; medical systems; temperature; pressure; pulse; performance analysis; threat model; algorithm optimization Wearable cryptographic systems, physiological stress index
Copyright (c) 2025 Dana Sybanova, Saltanat Adilzhanova, Gulnur Tyulepberdinova, Murat Kunelbayev, Raкhysh Aigerim https://creativecommons.org/licenses/by/4.0
Intelligent Expert System to Optimize Nutritional Care: Analysis Based on Machine Learning and Clinical Rules
This study developed and validated an intelligent expert system based on machine learning and clinical rules to optimize nutritional care for patients in the San Martín region of Peru. The approach specifically addresses challenges posed by limited access to specialized dietary guidance. A dataset consisting of 615 patient records, each with 45 clinical, anthropometric, and lifestyle variables, was used to train and implement a hybrid architecture that combined unsupervised clustering (K-means) with a rule-based inference engine. The model achieved optimal segmentation into five nutritional profiles, supported by dimensionality reduction through Principal Component Analysis (PCA) and validated using the Silhouette (0.1229), Davies-Bouldin (1.3238), and Calinski-Harabasz (47.8740) indices. The expert system’s dietary recommendations showed a global concordance of 77% with those of the clinical nutritionists, achieving up to 83% accuracy in standard metabolic clusters. These results confirm the system’s ability to generate personalized and clinically coherent dietary plans, thereby improving the efficiency of nutritional services. Overall, the findings highlight the potential of intelligent systems to enhance healthcare delivery in resource-limited settings and pave the way for broader applications of AI in public health and clinical decision support.
Copyright (c) 2025 Miguel Angel Valles-Coral, Jorge Raul Navarro-Cabrera, Cristian García-Estrella, Jorge Valverde-Iparraguirre, Sarita Saavedra, Luz Karen Quintanilla-Morales https://creativecommons.org/licenses/by/4.0
ML and DL Approaches for Drug Review Classification Using a Composite Effectiveness–Satisfaction Score
With the increasing availability of patient-generated content on platforms like WebMD, sentiment analysis has emerged as a powerful tool to understand user experiences with pharmaceuticals. This study presents a comparative evaluation of traditional machine learning (ML) and deep learning (DL) models for classifying drug reviews based on a novel composite metric—the Drug Effectiveness-Satisfaction Score (DESS)—calculated as the average of user-rated effectiveness and satisfaction. A diverse set of ML algorithms (Random Forest, SVM, XGBoost) and DL architectures (LSTM, CNN-LSTM, DistilBERT, and BERT) were tested using multiple feature extraction and embedding methods, including CountVectorizer, TF-IDF, GloVe, and transformer-based embeddings. Results show that BERT achieved the highest F1-score (87.22%), while XGBoost offered a strong balance between accuracy and computational efficiency. Despite robust results, performance was slightly below benchmarks reported in other domains, likely due to the complexity and ambiguity introduced by the DESS metric and the unstructured nature of real-world medical reviews. This work underscores the potential of combining ML and DL for healthcare sentiment analysis and highlights future opportunities in domain-specific fine-tuning, ensemble modeling, and explainable AI.
Natural Language Processing; Transformer Models; User Feedback; Sentiment Modeling; Healthcare Data; Neural Networks; Word Embeddings; Pharmacovigilance; Comparative Evaluation.
Copyright (c) 2026 Blessing Nwogu, Essia Hamouda, Khouloud Safi Eljil https://creativecommons.org/licenses/by/4.0
AI for Medical Informatics: The Application of Optical Character Recognition Technology in the Transcription of Pharmaceutical Labels
The growing elderly population in Thailand faces significant challenges in managing their medications, primarily due to vision-related issues that affect proper adherence. This study presents a novel approach that utilizes Optical Character Recognition (OCR) technology integrated into a mobile platform to automate the extraction of pharmaceutical label information. The developed system operates in four key phases: capturing images, enhancing image quality, extracting textual information, and processing data. The text extraction employs advanced methods, including drug name verification through fuzzy matching using Levenshtein distance, dosage validation with regular expressions, medication timing confirmation through dictionary matching, and condition identification using named entity recognition. The system converts all processed information into JSON format to facilitate integration with healthcare information systems. Testing demonstrated high accuracy rates, achieving 97.60% in dosage identification and 95.65% in timing extraction, although improvements are needed in drug name and indication recognition. The application has been developed through systematic Software Development Life Cycle protocols and serves various stakeholders, including medical staff, administrators, and caregivers. This technological solution appears promising in enhancing medication management safety, particularly benefiting elderly individuals with visual impairment.
Utilization of Optical Character Recognition (OCR)
Copyright (c) 2025 Wongpanya S. Nuankaew, Fapratan Jailangka, Atthakit Khampraphai, Apatsara Kamka, Patchara Nasa-Ngium, Pratya Nuankaew https://creativecommons.org/licenses/by/4.0
An automated Visual Acuity test system using Vietnamese speech recognition for answer selection
Visual Acuity (VA) testing traditionally requires an ophthalmologist, limiting accessibility in non-clinical settings. This paper presents an automated VA test system designed for precise vision assessment without professional supervision, which leverages the Early Treatment Diabetic Retinopathy Study (ETDRS) eye chart for higher accuracy compared to the conventional Snellen chart. The system uses a computer with an Liquid Crystal Display (LCD) monitor and incorporates automated scoring with 0.02 LogMAR precision. To facilitate remote operation, we implement speech recognition in Vietnamese via a microphone, utilizing Azure Speech API, which is enhanced with a correction function and noise classification for improved accuracy. An experiment with 80 participants (N = 80) demonstrated a speech recognition accuracy of 93.1%, with a mean response time of 4.6 s per optotype. The VA scores from our system closely matched those from standard printed ETDRS charts, with 95.6% of measurements differing by ≤0.1 LogMAR. Our automated VA test system provides a reliable, low-cost solution for vision assessment in non-clinical environments, combining high accuracy with user-friendly remote operation.
Topic: Human-Computer Interaction (HCI) in Modern Technological Systems
Copyright (c) 2025 Hoang Minh Pham, Duc Thanh Nguyen, Hong Duong Pham https://creativecommons.org/licenses/by/4.0
An AI-Powered Public Health Kiosk for Context-Aware Over-the-Counter Medication Guidance: An Experimental Pilot Study
Background: The HERMES Kiosk (Healthcare Enhanced Recommendations through Artificial Intelligence (AI) & Expertise System) is designed to provide personalized Over-the-Counter (OTC) medication recommendations, addressing the limitations of traditional health kiosks. It integrates an advanced GAMENet model enhanced with Graph Attention Networks (GAT) and Multi-Head Cross-Attention (MHCA) while ensuring user privacy through federated learning. This paper outlines the conceptual design and architecture of HERMES, with a focus on deployment in high-traffic public areas. Methods: HERMES analyzes self-reported symptoms and anonymized medical histories using AI algorithms to generate context-aware OTC medication recommendations. The system was initially trained using Electronic Health Records (EHR) from the MIMIC-III dataset (6,350 patients) and Drug-Drug Interaction (DDI) data from the TWOSIDES database, incorporating the top 90 severity DDI types. Real-time DDI checks and Anatomical Therapeutic Chemical (ATC)-mapped drug codes further improve safety. The kiosk is designed for accessibility, offering multilingual support, large fonts, voice commands, and Braille compatibility. A built-in health education library promotes preventive care and health literacy. A survey was conducted among 10 medical professionals to evaluate its potential applications in medicine. Results: Preliminary results show that the enhanced GAMENet model achieved a Precision-Recall Area Under the Curve (PRAUC) of 0.74, outperforming the original model. These findings suggest a strong potential for delivering accurate and secure healthcare recommendations in public settings. Conclusion: HERMES demonstrates how AI-driven, privacy-preserving kiosks can enhance public health access, empower users, and alleviate burdens on healthcare systems. Future work will focus on real-world deployment, usability testing, and scalability for broader adoption.
Over-the-Counter (OTC) Medication Recommendation
Copyright (c) 2025 Sonya falahati, Morteza Alizadeh, Fatemeh Ghazipour, Zhino Safahi, Navid Khaledian, Mohammad R. Salmanpour https://creativecommons.org/licenses/by/4.0
X-CoffeeNet: A Novel Framework for Coffee Bean Image Classification Using Explainable Artificial Intelligence (XAI) and MobileNet
This paper presents X-CoffeeNet, a novel framework for classifying coffee bean images utilizing Explainable Artificial Intelligence (XAI) and MobileNetV2. The proposed model is designed to accurately classify three types of coffee beans—Arabica, Robusta, and Liberica—by processing images through advanced deep learning techniques. The framework integrates image augmentation techniques, such as rotation, flipping, zoom, and brightness shifting, to enhance training data diversity and reduce overfitting. MobileNetV2, an efficient convolutional neural network architecture, is employed for feature extraction, significantly reducing computational resources while maintaining high performance. Furthermore, the model is enhanced by integrating LIME (Local Interpretable Model-agnostic Explanations) to provide transparency in the decision-making process, allowing users to understand the reasoning behind the model's predictions. Experimental results demonstrate that X-CoffeeNet achieves 100% accuracy in classifying coffee beans, with perfect precision, recall, and F1-scores across all classes. The combination of cutting-edge deep learning methods and explainable AI offers a robust and transparent solution for coffee bean classification, with potential applications in agricultural technology and food industry automation
Coffee Bean Classification, Deep Learning, XAI, MobileNet, Image Processing, Computer Vision
Copyright (c) 2025 Mohamad Jamil, Suratman Sudjud , Sherly Asriany , Muhammad Said https://creativecommons.org/licenses/by/4.0