Journal of Advances in Information Technology
Journal of Advances in Information Technology is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology. Our aim is to publish experimental and theoretical results in strategy, infrastructure, human resources, system development and implementation, IT risk, IT management, data science, communications, technology development and futures, national policies and standards, software engineering, etc. In particular, we encourage a multidisciplinary/convergent approach based on the following broadly based branches of computer science for the application areas highlighted below: · Artificial Intelligence · Machine Learning · Data Science · Natural Language Processing · Cloud Computing · Internet of Things · Image Processing · Information Security · Networked Control Systems · Communication Systems Technologies · Digital Signal Processing in Communications · Neural Networks, Fuzzy Logic, Reasoning · Human-Computer Interaction (HCI) · Software Engineering · Non-technical but relevant topics such as Information Policy, Ethics and Legal issues as appropriate.
For articles published in JAIT, the copyright will be retained by the authors. Articles are licensed under the Creative Commons Attribution License (CC BY 4.0), which means all papers can be downloaded, shared, and reused without restriction, as long as the original authors are properly cited. These conditions allow for maximum use and exposure of the work, while ensuring that the authors receive proper credit. In exceptional circumstances, articles may be licensed differently. If you have a specific condition (such as one linked to funding) that does not allow this license, please mention this to the editorial office of the journal at submission.
A 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
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
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
Topic: Machine Learning in Computer and Information Systems
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
Bridging Language Barriers in AI Education: A Smart Platform Integrating Speech-to-Text, Translation, and Real-Time Knowledge Delivery
This paper presents a specialized AI-powered education platform designed to improve accessibility and engagement in multilingual learning environments. The system integrates core technologies such as real-time speech recognition using the Whisper model, multilingual translation via Google’s API, automated news aggregation through web crawling, and an OpenAI-based interactive chatbot. The platform enables learners to access translated transcripts of global lectures, receive timely updates on AI advancements, and interact with a virtual assistant for personalized support. System evaluation demonstrates a 4% Word Error Rate in speech recognition and a BLEU score of 85, confirming high performance in transcription and translation tasks. User feedback highlights strong satisfaction with the platform’s usability, particularly regarding its ability to reduce language barriers and centralize educational content. The study underscores the effectiveness of combining automation, AI-driven interaction, and knowledge retrieval into one cohesive educational tool. Future work aims to expand language support, enhance context-aware translation, and optimize system responsiveness. This research contributes to the evolution of inclusive, scalable, and intelligent education systems for a globally diverse audience.
Topic: Software Design and Program Development
Multilingual education, speech recognition, AI chatbot, Speech to Text Translation technology, Automation Crawling and Parsing, real-time learning, AI-powered platform
Copyright (c) 2025 illchul Doo, JAE Young. WOO https://creativecommons.org/licenses/by/4.0
Degradation of Lidar-Based 3D Object Detection under the Influence of Artificial Rain
Ensuring robust and safe 3D object detection in outdoor environments requires addressing the challenges posed by adverse conditions such as rain, fog, snow, and varying lighting. However, the scarcity of diverse, labeled training, and test data reflecting these conditions hinders progress in this area. Synthetic data augmentation offers a promising solution to bridge this gap. In this paper, we evaluate the effectiveness of an existing physical rain model for augmenting lidar-based datasets. First, we validate the model by comparing its results to those generated by state-of-the-art simulation software, AURELION. Next, we apply the rain model to augment the KITTI 3D object detection dataset with varying rain intensities and assess the impact on a lidar-based object detection framework. Our results demonstrate that the physical rain model produces outputs nearly identical to AURELION. Furthermore, the augmented data reveal a significant degradation in detection performance across all evaluated object classes under increasing rain intensities. Retraining the detection model with the augmented data set substantially improves its robustness, even under heavy rainfall. These findings highlight the potential of synthetic data augmentation for enhancing the resilience of lidar-based 3D object detection systems in adverse weather conditions.
Copyright (c) 2025 Christoph Rohmann, Bashar Hoary, Silas Scholz, Prof. Dr.-Ing. Harald K. Bachem https://creativecommons.org/licenses/by/4.0
Optimization Techniques for Dealing with Small Dataset for Sentiment Analysis
Sentiment analysis is crucial for many organizations, including those in the transportation industry which use it to gain insights into current issues and improve services provided by public transport operators. However, industries such as transportation face difficulties in fully utilizing AI tools due to the lack of annotated, domain-specific datasets. This scarcity often stems from challenges such as the sensitive nature of the data and a shortage of manpower dedicated to data annotation. Although there are numerous technologies designed for sentiment analysis, including state-of-the-art (SOTA) transformer-based pretrained models, most of these technologies rely on the availability of extensive datasets. This creates a gap in solutions for scenarios characterized by limited and imbalanced data. Our research aims to address this gap by systematically exploring strategies for optimizing sentiment analysis with small, imbalanced datasets. We consider constraints posed by data privacy and resource limitations, proposing methodologies that enhance sentiment analysis accuracy without the need for large datasets or extensive annotation efforts. Using RoBERTa, a transformer-based pretrained model designed for sentiment analysis, and a combination of optimization and data augmentation techniques, we aim to extend the capabilities of sentiment analysis models to perform effectively in data-sparse situations. This approach not only mitigates the challenges associated with small datasets but also contributes to the broader field of sentiment analysis by offering scalable solutions adaptable to various domain-specific data environments. Our experimentation has achieved significant improvements in prediction accuracy, demonstrating the feasibility and effectiveness of our approach. By integrating theoretical insights with practical applications, our study sheds light on the untapped potential of small datasets in sentiment analysis. It provides a roadmap for leveraging advanced optimization techniques and innovative data processing strategies to achieve high levels of accuracy, thus opening new avenues for research and application in areas where data is inherently limited.
Copyright (c) 2025 Isfaque AL Kaderi Tuhin, Zhengkui Wang, Li Xiaorong, Wei Zhang https://creativecommons.org/licenses/by/4.0
Automated Sleep Apnea Detection Using CNNs: Insights into the Impact of FFT Feature Extraction on EEG Signals
Sleep apnea, a serious sleep disorder characterized by interrupted breathing during sleep, poses significant health risks such as cardiovascular disease and diabetes. Traditional diagnostic methods, such as polysomnography, are cumbersome and expensive, creating a demand for automated solutions. This study investigates sleep apnea detection using Electroencephalogram (EEG) signals and deep learning techniques. A Convolutional Neural Network (CNN) model was designed to classify EEG signals, with Fast Fourier Transform (FFT) employed for feature extraction to enhance performance. Two versions of the model were developed: one trained on raw EEG data and the other on FFT-processed data. The Physionet Sleep-EDF Database served as the source for EEG recordings, labeled as “normal” or indicative of sleep-disordered breathing. The data preprocessing pipeline involved splitting the dataset into training, validation, and testing subsets. The CNN architecture consists of convolutional layers, ReLU activations, max pooling, and fully connected layers optimized for classification tasks. Performance metrics, including accuracy, precision, recall, and F1 score, were used to evaluate the models. The CNN trained on raw EEG data achieved superior results, with 92% accuracy, a precision of 0.86, recall of 1.00, and an F1 score of 0.92, outperforming previous studies utilizing Multi-Layer Perceptrons (MLP). These findings highlight the importance of preserving raw EEG features for effective sleep apnea detection, while FFT preprocessing may introduce complexities. The results demonstrate the potential of CNNs in developing low-cost, accessible diagnostic tools. Future efforts should address dataset limitations and explore alternative feature extraction methods to improve generalizability.
Copyright (c) 2025 Mera Kartika Delimayanti, Asep Taufik Muharram, Ayres Pradiptyas, R. Ahmad Ryanari, R. Arya Prasetyo, Rizky Adi, Mohammad Reza Faisal, Rizqi Fitri Naryanto, Haralampos Hatzikirou https://creativecommons.org/licenses/by/4.0
EfficientNet Deep Learning Model for Lungs Cancer Early Diagnosis from Computed Tomography Scan Images with Transfer Learning
Lung cancer is a lethal ailment which has a significant fatality rate among individuals affected by the disease. Timely detection and precise determination lung cancer phase can notably surge the likelihoods of saving the patient's life. Computed Tomography (CT) scans are usually employed for diagnosing lung cancer, but manual examination can be slow and error-prone. To address this issue, deep learning techniques are being utilized to speed up and improve the accuracy of detecting cancerous and non-cancerous CT scans. Therefore, this study introduced an innovative transfer learning method aimed at improving the precision of lung cancer classification. The proposed method was build based on EfficientNet model modified with additional CNN customs layers and attention mechanism for accurate classification of lung cancer. Experimental analysis was conducted, utilizing eight variants of the modified EfficientNet (B0-B7) using three lung cancer CT scan datasets, comprising IQ-OTH/NCCD, Chest-CT scan, and LIDC-IDRI, grouped into 3, 4, and 2 classes respectively. Various data augmentation techniques were utilized to address the problem of class imbalance and mitigate any biases present. The model achieved accuracies of 99.5%, 98.0%, and 90.3% on the IQ-OTH/NCCD, Chest-CT scan, and LIDC-IDRI datasets, respectively. The results depict that the modified EfficientNetB1 performed better than other presented approaches with respect to both accuracy, sensitivity, f-score, and precision. The outcome also indicates that the presented method is more appropriate for multi-class classification of lung cancer.
Copyright (c) 2025 Amjad Rehman Khan, Noor Ayesha, Ibrahim Hayatu Hassan, Abeer Rashad Mirdad https://creativecommons.org/licenses/by/4.0
Retrieving Math Information Based on Equation Detection and Recognition within Digital Images
In the USA, math proficiency levels these days are lower than ever before, which is problematic, since math is commonly used throughout life and math enables people to better solve problems, understand patterns, quantify relationships, and make predictions of the future. While the area of study in Math Information Retrieval (MIR) is relatively new, it is essential and provides a means to search for relevant sources of math information to those who are studying math, something which is difficult to do for people without prior knowledge of a specific math subject area they are looking for. In order to develop a robust MIR system, designers must be able to process math equations (MEs) to a format that the system can use, which is difficult due to various formats, including visual images and document texts, math information are stored. In solving this problem, we propose a ME extraction system that (i) applies a one shot object detector to identify math equations in digital images using an efficient neural architecture search method and (ii) employs a sequence-to-sequence (Seq2Seq) encoder-decoder system to recognize math equation symbols based on the Bayesian Neural Network (BNN) row encoding. The proposed system balances speed and accuracy of a Math IR system.
Image detection, image recognition, math ques tions, math answers
Copyright (c) 2025 Angel Wheelwright, Yiu-Kai Ng https://creativecommons.org/licenses/by/4.0
An Exergame System for Tenosynovitis Prevention by Softening Thumb Base
With the increasing prevalence of tenosynovitis that can be caused by repetitive use of smartphones, effective rehabilitation methods have become crucial. To address this issue, we have proposed an exergame system for softening hand wrist while playing video games using hand gestures for wrist exercises. It is known that softening thumb base is often effective for preventing tenosynovitis in addition to hand wrist. In this paper, we present an exergame system for softening thumb base by extending our previous work. We newly design hand gestures with a rubber band that increases exercise loads to improve the effectiveness. The gestures are recognized by a Python program using Mediapipe library. Besides, to enhance accessibility and usability, we implement a web application for this system using Node.js. To evaluate the proposal, we asked 10 persons including a tenosynovitis patient, to play four video games installed in the system, and measured the maximum thumb movement angle of each person to investigate the finger flexibility. Besides, we evaluate their engagement levels through a questionnaire with the system usability score (SUS). The results observe the significant improvement in the thumb base flexibility and the positive feedback on user experiences.
Copyright (c) 2025 YANQI XIAO, Nobuo Funabiki, Irin Tri Anggraini, Haruya Fujiwara, Jun-You Liu, Chih-Peng Fan https://creativecommons.org/licenses/by/4.0