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.
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
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 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
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
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.
Topic: Machine Learning in Computer and Information Systems
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
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
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
Development of a Data Collection and Data Transmission System using Drones and IoT Sensors
This paper presents a low-cost and energy-efficient system for environmental data collection in remote areas using Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) sensor nodes based on ESP32 microcontrollers. The sensor nodes, powered by solar energy, collect environmental data and store it locally. A drone equipped with an ESP32 device periodically flies over the monitored area, retrieving data via the ESP-NOW protocol. Onboard edge computing is performed using the Typicality and Eccentricity Data Analytics (TEDA) algorithm to detect and filter outliers before the data is transmitted to the cloud. The system integrates with Amazon Web Services (AWS) IoT Core for secure data transmission, processing, and visualization through Grafana dashboards. Experimental results demonstrate up to 2.66 days of battery-only autonomy, extended by solar charging, and reliable wireless communication up to 100 m. The entire system was built for under USD 320, with each sensor node costing approximately USD 36. This architecture proves to be a practical and scalable solution for real-time environmental monitoring in remote or infrastructure-constrained environments.
Copyright (c) 2025 Eng. Mateus, Eng. Rafael, Juan Moises Mauricio Villanueva https://creativecommons.org/licenses/by/4.0
A Real-Time Deep Neural Network-Based System's Ability to Identify and Classify Active Assaults on Networks
In the context of computer science and technology, a group of linked devices, or nodes, that exchange data, resources, or services with one another is referred to as a network. An active network attack refers to a malicious activity in which an attacker deliberately attempts to disrupt, manipulate, or gain unauthorized access to a computer network or its resources. In today’s digital context, network security is of vital importance as cyber threats continue to evolve in terms of sophistication and frequency. Active network attacks pose significant challenges to traditional detection methods, necessitating the exploration of advanced techniques such as deep learning. This research proposes a novel approach for the identification and classification of active network attacks based on deep learning methodologies. To achieve this, an experimental research analysis design was used. A comprehensive review of deep learning approaches appropriate for network attack detection was undertaken. The proposed methodology involves the development of a model based on deep learning that was learned using a dataset comprising diverse network traffic data which is Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD). This study utilizes a comprehensive preprocessing pipeline, including data cleaning, feature selection for categorical variables and standardization of numerical features to prepare the dataset for modeling. To extract the pertinent information, preprocessing approaches are used. Metrics like as accuracy, precision, recall, F1-Score, and confusion matrix are used to evaluate performance as a result from deep learning models Deep Neural Network (DNN), Convolution Neural Networks (CNN), Long Short Term Memory(LSTM), Bi-Long Short Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU) experiments done, Bi-LSTM model scored the best result of 99.15% and 99.12% accuracy for binary and multi classification, respectively.
Copyright (c) 2025 Dr. Karthikeyan Kaliyaperumal, Prof. Raja Sarath Kumar Boddu, Prof. Sai Kiran Oruganti https://creativecommons.org/licenses/by/4.0
Spam Detection Using an Advanced Hybrid Model
Emails are now widely used across various fields, from business to education. However, the rise of spam presents a constant challenge for users, as it wastes time, drains resources, and compromises data privacy. As spam volume continues to increase, traditional detection methods, such as blacklists and content-based filtering, are becoming inadequate against sophisticated fraudulent techniques. To enhance spam identification, we provide a novel hybrid model in this study that incorporates a Transformer, Multilayer perceptron (MLP), and Bidirectional Encoder Representations from Transformers (BERT). The model stands out due to its capacity to comprehend the rich context of communications, improve classification accuracy, and uncover intricate patterns from lengthy texts. Additionally, the model's capacity to generalize and identify new threats was improved by testing it on a huge and varied dataset. The findings demonstrate that the suggested model strikes a compromise between precision and adaptability, making it more successful in real-world settings than earlier approaches that might have relied on sparse data sets or had generalization issues. In addition to being a classification tool, the model is an integrated system that can be updated continuously, which makes it a viable choice for improving the security of contemporary email systems; it achieves a high accuracy of 94%. In addition to highlighting the significance of using hybrid and advanced models to combat spam, this study aims to establish a solid basis for future research centered on increasing effectiveness, adaptability, and lessening the negative effects of spam on users and organizations.
Copyright (c) 2025 Ahmad Hasasneh, Tahany Kmail, Marah Hawa https://creativecommons.org/licenses/by/4.0
Improving Leaf Disease Classification using Dynamic Hyper Parameter Optimization of CNN Models
Early leaf diseases are hard to identify based on crop type, environment, or symptoms. While there have been a number of deep learning algorithms applied to this topic, the vast majority of them are region-only and unable to differentiate between different kinds of crop types or diseases. To address this, this work employs an improved CNN model to recognize and classify plant leaf diseases. The improved CNN model uses leaf pictures and hyperparameters like learning rate and optimizer to identify illnesses. The technique was tested using the New Plant Disease Dataset. The study also showed how learning rate and optimizer affect model training. On the dataset for leaf disease, new optimized CNN model with enhanced reduced LR feature got the accuracy of 99.58% when the learning rate is 0.0001and got 95.59% accuracy where the learning rate is 0.0005 with a multi-class average 99.02%. The proposed model was also evaluated on New Plant Disease Dataset, other sequential CNN models, and transfer learning models and established higher accuracy and reduced the computational complexity. Precision from the model can be predicted as 97.53 % and Recall also as 97.53%, whereas, the F-measure can be predicted as 97.33%. According to the intrinsic analysis, it can be concluded that the impact of the optimizer option lies in the fact that the model reaches approximately the same performance level when it is set on the corresponding batch size and learning rate. Furthermore, it was found that lower learning rates provided a better performance than the higher learning rates indicating that using small batch size in conjunction with a small learning rate, produced the most effective model.
Copyright (c) 2025 Prashanthi Bh, A.V. Praveen Krishna, Ch. Mallikarjuna Rao https://creativecommons.org/licenses/by/4.0
Feature Selection Using Memetic Salp Swarm Algorithm with Symmetrical Uncertainty Ranking for Arabic Text Classification
Dealing with high-dimensional data has made it very challenging to find the best subset of chosen features because of the limitation in reducing the exponential growth of the search process. Text feature selection is a dimensionality reduction technique that tends to reduce the extra text features for better classification accuracy. Feature selection is an Nondeterministic Polynomial-time hard (NP-Hard) optimization. Additionally, many feature selection models overlook the interactions between or between features and the decision class. To produce the suggested Memetic Salp Swarm Algorithm (MSSA), the SSA is combined with Symmetrical Uncertainty (SU). The proposed technique intends to pinpoint the most valuable characteristics of Arabic text by decreasing computing complexity and enhancing classification accuracy. MSSA, drawing inspiration from the innate behaviour of salps and the concepts of memetic algorithms, effectively navigates the search space to locate the most optimum subsets of features. Symmetrical Uncertainty ranking enhances the selection process by precisely measuring the significance of the characteristics of the classification objective. The suggested strategy has been proven successful in Arabic text datasets through experimental findings, surpassing existing classification accuracy and feature subset size approaches.
Copyright (c) 2025 Mohammed Ghassan Abdulkareem, Alhasan Amer Ibrahim, Ibrahem Amer Hammed, Alsultan Abdulkareem https://creativecommons.org/licenses/by/4.0
EC-EBMs: Skin Lesion Classification with EBMs
Skin lesion classification presents significant challenges in the medical field, primarily due to the increasing complexity and diversity of datasets. Traditional methods often struggle with issues like class imbalance and unseen distribution of disease types, which are common in medical data. In this paper, we introduce a novel approach: the integration of the Energy Correlation (EC) method into Energy-Based Models (EBMs), hereafter abbreviated as EC-EBMs, which combines EBMs with EC to enhance the classification of skin lesion images. This method effectively addresses the challenges of class imbalance and data heterogeneity by optimizing the relationship between features and labels, minimizing intraclass energy, and maximizing interclass differences. Experimental results from two datasets demonstrate the effectiveness of EC-EBMs. On the ISIC 2020 dataset, our approach achieved an accuracy of 98.24% in distinguishing between malignant melanoma and benign non-melanoma lesions. Additionally, it showed promising results on the ISIC 2019 dataset, successfully classifying eight diagnostic labels with an accuracy of 72.33%, sensitivity of 55.09%, and specificity of 95.18%. These findings highlight the potential of EC-EBMs to improve classification performance and provide a new solution to complex challenges in medical classification.
Copyright (c) 2025 Quyen Vo Van, An Cong Tran, Hiep Xuan Huynh https://creativecommons.org/licenses/by/4.0
Development And Research of a Simulation Model for a Wearable Device
The article presents a simulation model of a wearable stress monitoring device developed in the MATLAB/Simulink environment. The model includes 10 sensors that register physiological signals in the range from -1.2 to +1.2 standard units, with an adaptive polling frequency from 1.5 to 10 Hz. Over a period of 10 seconds, the battery charge level decreased from 100% to 57% at maximum load to 7 units. Alarm notifications were activated when the threshold of 0.8 was exceeded in the time intervals 0-1.5 and 5.5–7.3 seconds. PCA analysis, thermal mapping, and spectral analysis were performed, confirming the synchronicity and activity of the sensors. The results demonstrate the effectiveness of the proposed device architecture for personalized stress monitoring without using machine learning algorithms on board.
Wearable device, Stress monitoring, Sensory signals, MatLab(Simulink, Adaptive polling frequency
Copyright (c) 2025 Gulnur Tyulepberdinova, Sulu Issabayeva, Murat Kunelbayev, Gulshat Amirkhanova, Ardak Tolepberdinova https://creativecommons.org/licenses/by/4.0
A Novel Change Detection Method with Feature Alignment and Channel Attention
Change detection plays a crucial role in various fields such as environmental monitoring and disaster management. In recent years, deep learning based change detection methods have made significant progress in accuracy and efficiency. However, the limitations of multi-scale feature alignment, feature fusion, and cross scale semantic consistency still constrain its application in complex real-world scenarios such as geological hazard prevention. This paper proposes an improved change detection method that introduces ChannelAttention and NonLocalBlock modules into the feature extraction stage, enhances channel information expression through adaptive attention mechanisms, and captures global non local features. At the same time, the feature displacement alignment and fusion module is integrated in the decoding stage, aligning multi-scale features through an adaptive mechanism based on the flow field. By combining multi-layer feature fusion and semantic consistency optimization, the proposed method effectively improves the model's ability to detect small changes and computational efficiency. The experimental results on the benchmark dataset and the Three Gorges rock mass dataset show that this method achieves the latest technological level in complex changing scenarios and demonstrates good application potential in geological hazard monitoring and early warning systems.
Copyright (c) 2025 Qin Bao, Yong Yang, Li-Chuan Chen, Yun-Ping Liao, Chang Yang, Peng Liu, Li-Wen Zhang https://creativecommons.org/licenses/by/4.0
Steganalysis in the Spatial Domain: Improving VGG19 Performance Using Particle Swarm Optimization Algorithm
Steganography, or hiding information within digital media, is one of the most important challenges in digital security in terms of detecting hidden content for both various embedding processes and under different payload sizes. This study proposes an enhanced deep learning methodology that combines the VGG19 convolutional neural network with particle swarm optimization to optimize key hyperparameters, improving its ability to detect steganographic content more effectively. Our proposed approach was tested using the BOSSBase 1.01 dataset and a combined dataset with BOWS2, focusing on stego-images generated by the S-UNIWARD and WOW algorithms. The results clearly indicate that our proposed methodology outperforms state-of-the-art models such as Xu-Net, Ye-Net, Yedroudj-Net, and VGG16Stego, achieving accuracy of 0.8816 and 0.8900 for payloads of 0.2Bpp and 0.4Bpp, respectively. These findings show the significance of our approach, highlighting its potential to become a leading solution for steganography detection in digital security applications.
Steganalysis, Particle Swarm Optimization, VGG19, Spatial Domain, Image Security, Data Hiding.
Copyright (c) 2025 Rahmeh Ibrahim, Ashraf M. Al-Qadomi https://creativecommons.org/licenses/by/4.0
Multimodal Emotion Detection and Analysis from Conversational Data
Emotion recognition is gaining traction due to its wide range of potential applications across different fields. With the rise of social media, chat platforms, and voice assistants, there is a vast increase in data through which humans implicitly and explicitly carry emotional cues. With new algorithms being developed to understand the nuances of human language and emotion, businesses can tailor more personalized and empathetic services. Sentiment analysis expresses a positive, negative, or neutral viewpoint that laid the foundation for emotion classification. Emotion classification in conversations represents the most advanced stage of classification. It is also challenging due to the existence and expression of human emotions, which change from person to person in daily life. Several techniques can be used to detect emotions in text and conversations, like analyzing lexical features, machine learning, and hybrid approaches. Capturing contextual and temporal dependencies can help in the accurate prediction of Emotions in conversation. This research explores emotion classification in conversations with textual, audio, and multimodal approaches. We experiment with the effectiveness of analyzing contextual dependencies in text-based emotion detection with BERT models and also extract the acoustic features, such as Mel-frequency spectral coefficients, Chroma, and 10 others, for recognizing emotions from audio data. Additionally, four different fusion techniques that combine textual and audio information were implemented to enhance the accuracy of emotion classification in conversations with various deep-learning models.
Copyright (c) 2025 Abhinay Jatoth, Faranak Abri, Tien Nguyen https://creativecommons.org/licenses/by/4.0
Evolving Information Retrieval: From Traditional Models to Emerging Paradigms
This article delves into the evolution of Information Retrieval (IR) techniques, tracing their trajectory from foundational methods to cutting-edge approaches. IR encompasses the extraction of relevant information from diverse sources based on user queries, involving the indexing, organization, and analysis of data. However, traditional models such as the Boolean Retrieval Model (BRM), Vector Space Model (VSM), and Term Frequency-Inverse Document Frequency (TF-IDF) struggle with semantic comprehension and are hindered by data noise. Additionally, the article examines advanced techniques like Latent Semantic Analysis (LSA), PageRank (PR), and BM25, as well as Machine Learning (ML) innovations such as Learning to Rank (LTR) and Neural IR (NIR), all of which significantly enhance search relevance and personalization. Furthermore, it explores emerging paradigms like Reinforcement Learning (RL) and graph-based models, highlighting their potential for future breakthroughs. By critically analyzing the strengths and weaknesses of various methodologies, the article proposes solutions to common IR challenges and categorizes algorithms into four groups: Foundations of IR, Modernizing IR, ML Innovations in Search, and Enhancing Personalization in IR. While NIR and RL demonstrate remarkable effectiveness, they also necessitate substantial computational resources and data investments, which presents a challenge for widespread adoption.
Copyright (c) 2025 Ibrahim A. Atoum https://creativecommons.org/licenses/by/4.0