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.
Smart Crop Recommendation for Precision Agriculture: A Comparative Analysis of Ensemble and Deep Learning Models Using Soil and Environmental Data
Precision agriculture increasingly requires reliable, data-driven tools to assist farmers in selecting crops suited to local soil and climate conditions, thereby enhancing productivity and sustainability. This study presents a comprehensive comparative analysis of six advanced machine learning models for smart crop recommendation, leveraging soil and environmental data to identify optimal crops under specific growing conditions. The research addresses a multi-class classification problem using the Kaggle Crop Recommendation Dataset, which comprises 2200 observations across 22 crop types and seven key environmental parameters: nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall. Results demonstrate exceptional performance from gradient boosting methods, with Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) achieving 99.77% accuracy, followed by Random Forest at 96.36%. Deep learning approaches (Gated Recurrent Unit (GRU): 91.82%, Long Short-Term Memory (LSTM): 90.45%) and a hybrid Random Forest–LSTM model (88.41%) performed competitively but less effectively. Feature-importance analysis proved that soil nutrients (NPK) were dominant predictors, contributing approximately 60% to the overall importance in ensemble models. Statistical testing confirmed significant performance differences (p < 0.001), while computational analysis favored LightGBM, with the shortest training time (1.8 min) and lowest memory usage (198 MB). These findings provide evidence-based guidance for agricultural technology developers and practical insights for deploying robust crop recommendation systems across diverse farming contexts. Ethical considerations regarding data bias and equitable access to Artificial Intelligence (AI) tools are discussed, emphasizing inclusive and sustainable development practices.
Copyright (c) 2026 Kanda Sorn-In, Assoc. Prof. Dr. Wirapong Chansanam, Asst. Prof. Dr. Pathamakorn Netayawijit https://creativecommons.org/licenses/by/4.0
Feature Selection for High-Dimensional Data: A Case Study of NFT Valuation
In this study, we propose hedonic models for valuing Non-Fungible Tokens (NFTs) from the Azuki collection. We first analyze the NFT’s metadata and introduce a market volatility-robust dependent variable. Specific information of Azuki attributes is encoded via Term Frequency-Inverse Document Frequency (TF-IDF) to reflect both presence and collection-wide scarcity, yielding hundreds of features for each token. Two hedonic models are considered: a linear model and a squared model. To address high dimensionality, we tailor three variable-selection procedures—forward, backward, and stepwise—and compare them with regularization benchmarks and machine-learning methods. Using actual Azuki transaction data, we evaluate performance on a train-validation partition. The squared model overfits out of sample, while the linear model generalizes better and is adopted as the baseline. Applying variable selection to the linear baseline improves both parsimony and predictive performance. Machine-learning models exhibit very high training fit but notable performance degradation on the validation set, indicating overfitting in this setting. Overall, carefully specified hedonic models combined with principled variable selection offer competitive, interpretable, and more generalizable NFT valuation.
Copyright (c) 2026 Geun-Cheol Lee, Heejung Lee, Hoon-Young Koo https://creativecommons.org/licenses/by/4.0
Mapping Continental Water Bodies in the Peruvian Andes Using Machine Learning and Sentinel-2 Imagery
Identifying and characterizing inland water bodies is essential for water resource management and ecological monitoring, especially in dry and drought-prone areas. In this study, we applied land cover classification through remote sensing imagery in the southern Andean region of Arequipa, Peru. Machine learning algorithms were employed to identify inland water bodies within a heterogeneous landscape, facilitating their monitoring, conservation, and sustainable management. The study used the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which comprises six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Sentinel-2 multispectral satellite imagery was processed. Three supervised machine learning algorithms (Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN)) were trained and tested to classify landscape features into distinct categories and map inland water bodies. Cross-validation and random sampling validation points were used to evaluate model performance. Among the tested models, KNN showed the best performance in detecting lakes and lagoons, with an F1-Score of 0.741, an Overall Accuracy (OA) of 0.409, and a Kappa coefficient (κ) of 0.248, outperforming RF and SVM for this specific task. The results demonstrate the feasibility of using high-accuracy machine learning techniques to classify and map inland water bodies, supporting informed decision-making for the conservation and sustainable management of aquatic ecosystems in the Arequipa region.
Copyright (c) 2026 José Sulla Torres, Luis Barrios Lipa, Bryan Toribio-Obando, Enrique Zúñiga-Portilla, Manuel Zúñiga-Carnero, Karina Rosas-Paredes, Gwendolyn Peyre https://creativecommons.org/licenses/by/4.0
Toward an LLM Semantic Reasoning Database: From Similarity Search to Semantic Discovery
While Large Language Models (LLM) excel at semantic reasoning across concepts and domains, existing database systems, including those with vector capabilities and knowledge graphs, only support similarity search and graph traversal. They cannot perform inter-domain discov-ery, analogical reasoning, or causal chain discovery. This paper proposes a semantic reasoning database (ReasonDB), a novel database paradigm with a functional prototype that validates a couple of core capabilities. ReasonDB is the first system that treats embeddings as primary data and implements multiple reasoning modes as first-class opera-tions. Through three core innovations: vector-native storage with probabilistic semantics, semantic reasoning primitives, and machine learning-driven adaptive indexing, ReasonDB transforms databases from passive storage systems into active discovery partners. Experimental validation demonstrates two breakthrough results: 7× performance improvement in inter-domain analogical reasoning over similarity search, and successful causal chain discovery where vector similarity fundamentally fails (0.708 causal chain strength despite 0.074 cosine similarity). This paper investigates four key areas. First, how vector-native storage transforms query capabilities to enable discovery-based reasoning that uncovers novel rela-tionships across domains. Second, what new query classes and reasoning capabilities become possible. Third, how adaptive indexing improves performance over fixed strategies. Fourth, how these innovations integrate into a cohesive architecture. ReasonDB demonstrates that semantic reasoning requires fundamentally different database primitives. This enables entirely new classes of discovery operations impossible with current database architecture.
Vector databases, knowledge graphs, semantic reasoning, analogical reasoning, cross-domain discovery, vector-native storage, reasoning primitives, adaptive indexing, probabilistic semantics
Copyright (c) 2026 Gerry Wolfe, Ashraf Elnashar, William Schreiber https://creativecommons.org/licenses/by/4.0
Enhancing Gold Price Forecasting Using Machine Learning Models Optimized with Metaheuristic Algorithms
Forecasting gold prices is essential for supporting informed decision-making among investors, policymakers, and financial analysts. However, due to their non-linear and volatile behavior influenced by complex economic and geopolitical factors, predicting gold prices remains a significant challenge. This study evaluates the forecasting performance of three traditional machine learning models—Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost—on a monthly dataset spanning from January 1991 to December 2023, using macroeconomic and commodity-related indicators obtained from IndexMundi. To enhance predictive accuracy, RF and MLP were optimized using metaheuristic algorithms including Particle Swarm Optimization (PSO), Differential Evolution (DE), Simulated Annealing (SA), and Genetic Algorithm (GA), while XGBoost was fine-tuned using Grid Search. Two ensemble strategies were developed to further improve performance: a weighted ensemble based on inverse error metrics and a boosting ensemble that sequentially combined top-performing models. The results show that combining traditional models with metaheuristic optimization significantly improves forecasting accuracy. The best performance was achieved by the boosting ensemble integrating RF-PSO and optimized XGBoost, attaining an R² of 0.9654 and a Root Mean Square Error (RMSE) of 0.0433, representing an improvement of 11.1% in RMSE over the best single optimized model. This research demonstrates that effective and scalable financial forecasting systems can be developed using established machine learning techniques, offering valuable decision-support tools in dynamic financial markets.
Copyright (c) 2026 Alaa Nasrallah Sawalha, Mohammed Azmi Al-Betar, Sharif Naser Makhadmeh https://creativecommons.org/licenses/by/4.0
Novel Human Object Detection Method Based on YOLO Deep Learning Optimization Strategy
Along with the development of volleyball match video analysis, this paper introduces a novel human target detection method based on an optimized You Only Look Once (YOLO) deep learning strategy. Firstly, shot segmentation (identifying scene boundaries) is performed on the volleyball videos to extract key frames. Then, using semantic annotation techniques (classifying segments to filter non-game content), the videos are described as sequences of shots composed of long shots, medium shots, close-ups, replays, and off-court shots. Secondly, to address the issue of slow speed in target detection algorithms, the backbone network of YOLOv8s is optimized by implementing lightweighting through the GhostNet network and enhancing semantic information with the Convolutional Block Attention Module (CBAM) module to improve model accuracy. Experimental results on the PascalVisual Object Classes (PASCAL VOC), Common Objects in Context (COCO), and the Volleyball datasets, as well as real volleyball match videos, demonstrate that the proposed algorithm achieves a 1.5% higher mAP@50 with 64.2% fewer computational load Giga Floating- point Operations Per Second (GFLOPs) compared to the baseline YOLOv8s, achieving an optimal balance between accuracy and efficiency, making it suitable for real-time tactical analysis and automated player performance statistics in coaching and broadcasting.
semantic annotation, object detection, deep learning, lightweight network, volleyball video
Copyright (c) 2026 Ting Zhang, degan zhang, Shu-qi Niu, Lu Chen, Chen-hui Dou, Tao Liu https://creativecommons.org/licenses/by/4.0
Enhancing Precision: Leveraging Diverse Acquisition Functions in Dynamic Data Segmentation Using GPR-KNN Approach for Improved Nominal Current Forecasting
Managing energy consumption use at airports is becoming more critical, but there’s not much research on airfield lighting systems. This research proposes a hybrid machine learning framework designed to estimate the nominal output current demand by considering weather conditions and time of day. The hybrid model combines two techniques, Gaussian Process Regression (GPR) and K-Nearest Neighbors. This allows it to recognize overall trends and specific changes in energy use. This study also uses data segmentations (25%, 50%, 75% and 100%) and various acquisition functions such as Expected Improvement (EI), Expected Improvement Plus (EIP), Expected Improvement Per Second (EIPS), Expected Improvement Per Second Plus (EIPSP), Lower Confidence Bound (LCB), and Probability of Improvement (POI) to improve predictions. To identify the most effective model, metrics including accuracy, precision, recall, and F1-Score were employed for evaluation. Findings from the study indicate that both data segmentation and the selected acquisition function play a crucial role in influencing model performance. Notably, the model is able to work well even with limited data, meaning it can still be effective even if only a portion of the data is available. This makes it efficient, reducing both costs and time needed to train the model without giving up on accuracy. The solution is lightweight model, making it a practical choice compared to complex models that are hard to interpret. Ultimately, this research presents a new, scalable approach to optimizing energy use in airfield lighting, which can help create more innovative and sustainable airport operations.
Copyright (c) 2025 Wan Mohammed Rais Bin Jamaludin, Wan Mohamed. W.M., N.H. Nik Ali https://creativecommons.org/licenses/by/4.0
Behavioral Authentication Model for Smartphone Users Based on Gyroscope and Accelerometer Data for Mobile Educational Resources
With the continuous rise in the popularity of mobile devices and the growing volume of data related to education, the protection of users’ personal information is becoming increasingly important. This study presents a behavioral authentication model that can be effectively utilized to enhance security when accessing intelligent educational resources, based on the processing of gyroscope and accelerometer data. The paper reviews existing authentication methods, highlights the advantages of behavioral authentication, and proposes a new model. The behavioral authentication model analyzes users’ behavioral patterns to identify and protect user accounts. This approach is particularly crucial in the context of intelligent educational resources, where users frequently interact with various types of content that require the safeguarding of personal data and the preservation of the educational process’s integrity.
smartphone, mobile application, behavioral authentication, gyroscope, accelerometer, security, education
Copyright (c) 2026 Aray Kassenkhan, Vasily V. Serbin https://creativecommons.org/licenses/by/4.0
PolyVision: Optimising Retinal Disease Detection Through Collaborative Neural Networks
Diabetic Retinopathy (DR) remains one of the leading causes of preventable blindness worldwide, and early, reliable diagnosis is essential for reducing vision loss. Deep learning has shown promise in this domain, but single models often suffer from limited generalizability, sensitivity–specificity imbalance, and high computational demand. To address these challenges, we present PolyVision, a modular ensemble framework designed for robust and equitable DR screening. PolyVision integrates three complementary backbones—ResNet50, EfficientNet-B2, and Vision Transformer—each capturing different levels of spatial and contextual retinal features. Their predictions are combined through a dual fusion mechanism based on mean and maximum voting, which balances diagnostic sensitivity and specificity while minimizing variance across models. To further enhance robustness, the models are trained with diverse augmentation strategies, and hyperparameters are tuned for optimal performance. Evaluated on ultra-widefield fundus images, PolyVision achieved an AUC-ROC of 0.953, an AUPRC of 0.975, and an inference latency of 110 ms per image, demonstrating both high diagnostic accuracy and clinical efficiency. Beyond accuracy, the framework incorporates fairness evaluation across imaging subgroups, supporting more equitable diagnostic outcomes. Its lightweight design also facilitates deployment in resource-constrained clinical settings without compromising reliability. These results highlight the potential of ensemble learning to provide scalable, accurate, and fair DR screening. However, additional validation on multi-institutional datasets and real-world clinical environments remains necessary before broad clinical adoption.
Copyright (c) 2026 Sultan Ahmad, Swathi Kalam, Eali Stephen Neal Joshua, Hikmat A. M. Abdeljaber, Hessa Alfraihi https://creativecommons.org/licenses/by/4.0
Ontology-Based Topic Model for Document Retrieval Systems in Information Technology
Most current academic document retrieval systems for topic-based search rely on simple keyword matching or statistical topic modeling. In these methods, topics are formed either from sets of frequent keywords or from statistical clusters. While these approaches work in some contexts, they cannot fully capture the rich semantic meaning of topics as understood by human experts. This often leads to search results that fail to match the intended meaning of the topic, causing gaps between what users need and what the system returns. This study aims to overcome these limitations by developing a topic-based retrieval system that represents topics in a more semantically rich and human-aligned way. The system is designed to help Information Technology (IT) students search for topic-relevant materials—specifically English-language ebooks and research papers—from a curated faculty repository. We introduce C-ONTO, a structured knowledge model that includes topic names, descriptions, learning objectives, real-world application scenarios, and concept graphs describing internal semantic relationships. Documents are also modeled as concept graphs, enabling accurate semantic similarity calculations. An intelligent query analysis module interprets user intent and maps it to the system’s semantic structure. Testing with 425 real student queries in four IT domains shows that the system achieves 81.18% accuracy, outperforming keyword-based and statistical methods in precision, recall, coverage, and F1-Score. By aligning computational topic modeling with human understanding, the proposed system improves accuracy, semantic consistency, and educational relevance in academic document retrieval for information technology and related fields.
Topic: Software Design and Program Development
Copyright (c) 2025 NGUYEN THANH DIEN, Assoc. Prof. Dr. DO VAN NHON, Dr. TRAN HOANG TUNG https://creativecommons.org/licenses/by/4.0
A Hybrid Machine Learning Model in Diagnosing Brain Strokes
Strokes can occur suddenly and unexpectedly, especially brain strokes, which can be fatal for individuals over the age of fifty. Survivors of a stroke may experience severe paralysis or weakness, posing a significant challenge for healthcare professionals to treat. However, artificial intelligence and Machine Learning (ML) have been proven promising in addressing these critical issues. Despite the high incidence of strokes in countries like Qatar, there is limited research on stroke risk in the Middle East. This study is the first to use a dataset that combines multiple open-source datasets from the region. In this research, several machine learning and ensemble learning algorithms, including Decision Trees (DT), Multiple Layer Perceptron (MLP), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM), ensemble stacking, and Random Forest (RF) classifier have been investigated. All the algorithms were comprehensively analyzed by tuning their respective hyperparameters using the Grid Search approach and extracting the best features from the dataset through statistical analysis. The proposed ensemble stacking model achieved the highest accuracy and an F1-Score of 98% and 98.29%, respectively. The outcome indicates substantial improvement compared to current approaches in literature with similar datasets.
Copyright (c) 2025 Muhammd Imran Basheer Ahmad , Rim Zaghdoud, Atta Rahman, FARHAN Ali, Hussain Alhashim, Mohammed Almubarak, Mohammed Albasheer, Abdul Wahab Alaqel, Ahmed Almaskeen, Dina A. Alabbad, Danah Aljaafari, Aishah Albakr https://creativecommons.org/licenses/by/4.0
Enhancing IoT Attack Detection with Explainable AI: A Robust Evaluation of LIME and SHAP Interpretability
As Internet of Things (IoT) sensors became more common in weather monitoring systems, the need for classification models that are both accurate and interpretable has grown, particularly for applications such as smart city infrastructure, natural disaster prediction, and cyber threat detection. In IoT weather attack classification, this need is even more critical, as detecting malicious activity alongside normal environmental variations requires both precision and transparency. Although Machine Learning (ML) models such as Random Forest (RF) and Extreme Gradient Boosting (XGBoost) have demonstrated excellent prediction accuracy, their ‘black box’ nature makes it difficult to understand how they reach decisions, which can undermine trust in their results. This research addresses the transparency challenge by developing a dual explanation framework that combines SHapley Additive exPlanations (SHAP) for understanding a model’s overall behavior and Local Interpretable Model-agnostic Explanations (LIME) for explaining individual predictions, providing both global and local insights. Using a pre-processed IoT-sensor weather dataset, six classifiers were trained and evaluated, RF, Gradient Boosting, AdaBoost, XGBoost, Logistic Regression, and Support Vector Machine (SVM), across four performance metrics. RF and XGBoost achieved the highest performance, with RF obtaining 99.67% accuracy, precision, recall, and F1-Score, and an Area Under the Curve (AUC) of 1.00 for nearly all classes. Across all models, the “label” feature consistently emerged as the most influential predictor. SHAP identified it as the dominant driver of overall decisions, while LIME confirmed its significance in specific cases, strengthening confidence in the model’s reasoning. The framework also introduced a novel cross-verification step, comparing SHAP and LIME outputs to ensure alignment, akin to having two independent experts validate the model. Beyond delivering high predictive accuracy, this approach provided clear, actionable insights that meteorologists and security teams can use for forecasting storms and detecting anomalous or malicious sensor activity. While SHAP and LIME offer complementary interpretability, their computational trade-offs must be considered for real-time IoT deployments.
Copyright (c) 2025 Yousef Qawqzeh https://creativecommons.org/licenses/by/4.0
An Adaptive Multi-Scale Feature Fusion Framework for Detecting Depression and Assessing Its Severity from Speech Signals
Depression is a chronic mental health disorder characterized by psychological, physical, and social impairments, often resulting in diminished interest in daily activities and, in severe cases, suicidal ideation. Recent physiological studies have revealed measurable differences in vocal attributes between depressed and non-depressed individuals, motivating the use of speech signal analysis for automatic depression detection. However, conventional approaches relying on Mel-frequency or Fourier-based spectrograms often fail to capture discriminative features for effective emotional classification. To address this limitation, this study proposes an optimized deep learning framework, Adaptive Multi-scale Fused Feature-based Deep Network (AMF2-DNet) for both depression detection and severity assessment. Speech signals from the Distress Analysis Interview Corpus with Wizard-of-Oz (DAIC-WOZ) benchmark dataset undergo noise reduction and normalization, followed by feature extraction, including Mel-Frequency Cepstral Coefficients (MFCCs), spectral features, and deep features learned through a Sparse Autoencoder (SAE). These multimodal features are integrated within a residual Bidirectional Recurrent Neural Network (Bi-RNN) architecture enhanced with a novel loss function for improved convergence. Further, the Refined Attacking Strategy of Giant Armadillo Optimization (RASGAO) dynamically optimizes network parameters and scale weights. Experimental results demonstrate that AMF2-DNet achieves 94.88% accuracy, an F1-Score of 84.50%, and a Mathews Correlation Coefficient (MCC) of 0.82, surpassing state-of-the-art baselines by up to 7.44% in accuracy. The proposed framework also exhibits strong robustness across noise levels and speaker variability. Future work will focus on real-time deployment using lightweight architectures, cross-lingual validation, and multimodal fusion with facial and physiological cues for enhanced clinical applicability.
Copyright (c) 2025 Ms. Raminder Kaur Nagra, Dr. Vikram Kulkarni https://creativecommons.org/licenses/by/4.0
Sentiment Analysis with Deep Learning: Benchmarking CNNs vs. Prompt-Tuned BERT Models
With the growth of user-generated content on platforms like social networks and e-commerce, effective sentiment analysis has become increasingly important. This paper presents a comparative study of deep learning models for sentiment analysis, focusing on both Convolutional Neural Networks (CNNs) and prompt-based Bidirectional Encoder Representations from Transformers (BERT) models. We evaluated three CNN variants and multiple prompt strategies for BERT (hand-crafted, adaptive, and hybrid) across five diverse datasets. The results show that the hybrid Gated Recurrent Units_Attention Mechanism (GRU_ATT) BERT model outperforms all others. Prompt-based approaches, particularly adaptive and hybrid designs, offer strong performance and surpass CNN-Non-Static. Moreover, dataset characteristics, such as input length, class distribution, and language structure, significantly impact the performance of the models. Lastly, practical guidelines are proposed for selecting appropriate models based on dataset characteristics.
Copyright (c) 2025 Bo Huang, Fei Song https://creativecommons.org/licenses/by/4.0
Experiential-Adaptative Hybrid Model for Hospitality Based on CNN Neural Networks, Case-Based Reasoning (CBR) and 360° VR
In this work, an Experiential-Adaptive Hybrid model for hospitality based on Convolutional Neural Networks (CNN) neural networks, Case-Based Reasoning (CBR) and 360° VR, is proposed for generating experiences in hotel marketing and promotion activities. The model is based on CBR, which considers user profiles and immersive 360° videos. The work also improves and automates the video tagging process, using CNN, transitioning from a manual to an automated process. The 360° Virtual Reality (VR) experiences can elicit responses across behavioral, affective, cognitive, and attitudinal dimensions; therefore, they are evaluated within an immersive 360° VR environment. To validate the results obtained from this approach, a quasi-experimental study was conducted using various visual experiences, including surrounding landscapes, facilities, environments, and services of a boutique hotel. This study employed a random sample of young participants. This work aims to contribute to the use of 360° VR videos for visualizing the characteristics, environments, and services of hotels in a tour tailored to user profiles, with the goal of evaluating attitudinal and behavioral, cognitive and affective responses as well, and the potential influence on booking and purchase intentions. Conclusions and recommendations for future research are established.
Topic: Machine Learning in Computer and Information Systems
Copyright (c) 2025 Samuel Quecara, Dr. Luis Alfaro https://creativecommons.org/licenses/by/4.0
A Comprehensive Study of Image-Based 3D Reconstruction Using Deep Learning
The fast progress of Three-Dimensional (3D) reconstruction has led to the emergence of advanced Deep Learning (DL) approaches and techniques. Leveraging the technology of computers to produce realistic three-dimensional representations of objects has grown into an essential component of extensive study in a variety of domains. This review article investigates the cutting-edge methodologies, difficulties, and potential in this research field. The state-of-art study follows the development of Deep learning techniques with graphics expertise, which strengthens the requirement for good efficacy with better performance of 3D reconstruction. The research work begins by discussing classic strategies for 3D reconstruction with active and passive techniques that emphasizes their limitations with the need for cutting-edge practices. The various types of neural network architectures employed, like Convolutional Neural Networks (CNNs), autoencoders, and Generative Adversarial Networks (GANs) are explored with auxiliary information. This review aims to provide researchers and practitioners with a thorough understanding of the advancements, problems, and prospects in image-based 3D reconstruction while opting for the progressions in Deep Learning. Further, this research study presents the development in Neural Radiance Fields (NeRF) which is revolutionizing image-based rendering for efficient 3D reconstructions.
3D reconstruction, Deep Learning (DL), Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), Transfer Learning
Copyright (c) 2026 Ajit Aher, Dr. R. A. Kapgate https://creativecommons.org/licenses/by/4.0
A Conflict-Reduced Task-to-Host Matching Scheme for Task Offloading in Edge Computing-Enabled IoT Network
The continuous evolution of applications and the introduction of novel service models have significantly intensified the computational demands on Internet of Things (IoT) Terminal Devices (TDs). In order to improve service capability, edge computing-enabled IoT shifts the computational burden from TDs to edge computing hosts through task offloading. However, current task-to-host matching approaches exhibit significant limitations, particularly in their inability to anticipate tasks that are nearing the completion of data transmission and are about to arrive at the edge computing network. This shortcoming results in matching conflicts between tasks offloaded horizontally across sites and those directly offloaded from terminal devices (TDs). To address this challenge, a Two-layer Asynchronous Conflict-reduced Task-to-host Matching (TACTM) scheme is proposed. The proposed scheme jointly considers the data upload progress of tasks and the dynamic load of edge computing hosts, aiming to reduce matching conflicts while optimizing task-to-host assignment. Simulation results demonstrate that the proposed TACTM scheme outperforms existing methods by achieving a 20.79% reduction in average conflict ratio, an 8.26% decrease in average processing time, a 4.30% drop in average task failure ratio, and a 4.05% improvement in average Quality of Experience (QoE).
Topic: Internet of Things (IoT) in Smart Systems and Applications
Copyright (c) 2025 Wang Dayong, Kamalrulnizam Bin Abu Bakar, Babangida Isyaku, Lei Liping https://creativecommons.org/licenses/by/4.0
Performance Peaks: Monitoring and Optimizing Systems for Efficiency
Ensuring operations run smoothly and systems are reliable is essential in the fast-paced and vital energy sector, but it can be hard to do because of limited resources. This case study looks at how an open-source monitoring solution called Zabbix was used in a multi-plant energy company with limited Information Technology (IT) resources and infrastructure spread out geographically. Implementing Zabbix is a deliberate move away from traditional proprietary systems and toward a cheaper and more flexible option, showing how well it can be used in various business settings. The company significantly improved essential system monitoring and uptime tracking, even though they couldn’t use all of Zabbix’s optimization tools because they needed more staff. The study stresses the importance of using even the most basic tracking methods to keep performance stable and operations running smoothly in a complex energy production setting. Even though there were problems, like integrating Zabbix with old systems and balancing tracking duties with other IT tasks, it made it easier for the company to find and fix big problems in many places. The results clarify the pros and cons of using open-source tracking tools in areas with few resources. These give companies with similar issues important information when running effective IT operations across geographically dispersed infrastructures.
Real-time Systems Management, Infrastructure Reliability, Open-source Monitoring Tools, IT System Optimization, Network Performance, Scalability, Operational Efficiency, Proactive Management, Cost Efficiency, Systems Customization
Copyright (c) 2025 William Rey, Edward Anthony G. Cudilla https://creativecommons.org/licenses/by/4.0
A Model for Financing the Process of Education Informatization, Taking into Account Computer Security within the Framework of a Differential Quality Game
This article proposes a game-theoretic model for financing education informatization, incorporating Cyber Security (CS) considerations within a university context. The model formalizes the financial interactions between stakeholders, addressing the critical interdependence between educational quality and cybersecurity. This interdependence necessitates optimized resource allocation to establish a secure and innovative educational environment. The core challenge investigated is the strategic balance between investments in digitalization and the costs of associated information security measures. The novelty of our approach lies in applying a differential quality game with a bilinear structure, where the participants’ financial states are governed by a system of differential equations. Classical methods for solving linear differential games are inapplicable here, as the bilinear structure prevents the use of the Cauchy formula to solve the system. Furthermore, the positional differential game approach is also unsuitable, as the model permits the use of non-measurable controls by an opponent. These mathematical challenges underscore the work’s significance, contributing not only as a tool for CS policy but also through its theoretical advancements. Within this framework, the model identifies player preference sets and optimal strategies, findings which are validated through a computational experiment. The visualization of the preference set for the first player—the Computer Security Center (CSC)—demonstrates the model’s practical utility in resolving financing dilemmas for education informatization amid cybersecurity constraints. The study’s impact lies in providing a robust analytical tool for developing optimal resource allocation strategies. This contributes significantly to the sustainable development of Higher Education (HE) systems navigating digital transformation and escalating cyber threats.
higher education, informatization, cybersecurity, game theory, scenarios, resources, optimal strategy
Copyright (c) 2025 Bagdat Yagaliyeva, Arkadii Chikrii , Kaiyrbek Makulov , Berik Akhmetov , Valerii Lakhno, Inna Malyukova, Volodimir Malyukov https://creativecommons.org/licenses/by/4.0
SymLink: Multi-Agent NLP System for Medical Triage Optimization and Symptom Association Discovery
Medical triage is the key in healthcare systems around the world, as there is a rising demand in healthcare systems against the available resources to accommodate them. The available automated symptom checkers and triage systems lack accuracy, over-triage, and inadequate performance on rare conditions. In order to address these difficulties, we propose TRIage AGent ENhanced Technology (TRIAGENT), a new multi-agent application to optimize medical triage, which evaluates patient symptom reports through a set of hierarchical structure with specific dialogue agent, symptom agent, and decision agent. A Dynamic Symptom Relationship Graph (DSRG) algorithm used by TRIAGENT builds individual symptom networks in real-time during patient interaction and is based on two knowledge graphs: general medical knowledge graph based on Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) and the International Classification of Diseases, Tenth Revision (ICD-10) and a patient-specific dynamic graph constructed in response to reported symptoms and depends on symptom temporal relationships. The system applies contrastive learning-based rare disease detection and quantifications of uncertainties to risk-aware decision making. The overall analysis frequency of 10,000 clinical cases showcases that TRIAGENT reports the comparable triage classification mean of 89.7%, which is statistically superior by a margin of 17.2% points to the most accurate commercial systems (p < 0.001). The system shows unequivocal performance within different tested population (age: 18–85, diverse ethnicities) and symptom typology, especially in emergency management (F1 = 0.93) and self-care prescriptions (F1 = 0.91) and is capable of generating 2 (2X), 3 (3X), and up to 10 (10X) times less over-triage (7.3% vs. 15.2%) and under-triage (3.0% vs. 8.0%) rates than other leading commercial tool. It is also noteworthy that TRIAGENT does not degrade to the extent of known or rare conditions equal to <0.1%, with baselines as high as 70% accuracy rating, vastly exceeding existing implementations that tend to diminish to <45%, suggesting the potential of the system in increasing the access of healthcare opportunities and making sure that life-threatening medical conditions would not be overlooked.
Copyright (c) 2026 seethalakshmi palaparthi, Dhawaleshwara Rao Ch, Swaroopa K https://creativecommons.org/licenses/by/4.0
A Multi-Agent Framework for Autonomous Process Mining and Optimization
Process mining has emerged as a powerful approach for analyzing business processes through event logs, but it often requires specialized expertise to interpret results and lacks autonomous decision-making capabilities for implementing improvements. This paper presents a novel framework integrating process mining with agentic artificial intelligence to create an autonomous system for continuous process discovery, analysis, and optimization. We introduce a specialized multi-agent architecture where five distinct agents collaborate, generating quantifiable improvement recommendations. Unlike previous approaches that focus on isolated aspects of process mining or require significant human intervention, our framework enables end-to-end analysis and optimization with minimal manual effort. Our experiment demonstrates that our approach achieves 98.7% accuracy in data preprocessing, generates process models with 0.94 fitness and 0.86 precision scores, and identifies bottlenecks with 92.3% recall. The framework produced optimization recommendations resulting in a 23.7% reduction in process cycle time, 18.4% improvement in resource utilization, and 15.9% estimated cost reduction. Our contribution bridges a gap between technical process mining capabilities and business-oriented decision-making, making process optimization more accessible to non-experts while delivering measurable business value.
Copyright (c) 2025 Dr. Sonali Sen Baidya, Mr. Abhishek Baidya https://creativecommons.org/licenses/by/4.0
Advancing Sentiment Analysis for Colloquial Arabic: A Comparison of Machine Learning and Lexicon-Based Approaches for the Palestinian Dialect
Formal Arabic is less commonly used in everyday communication, especially on social media, where colloquial Arabic dominates. However, most Arabic Natural Language Processing (NLP) tools, including sentiment analysis systems, are primarily developed for Modern Standard Arabic (MSA), making it difficult to analyze dialectal content. This study addresses this gap by focusing on the Palestinian dialect, which is widely used across the Levant region. Two main approaches were explored: machine learning and lexicon-based methods. A manually labeled dataset of Facebook telecom service reviews was collected, cleaned, and split into training and testing sets. Five machine learning algorithms and a neural network model (Multilayer Perceptron) were evaluated, while the lexicon-based approach used a predefined sentiment lexicon for colloquial Arabic. The machine learning models outperformed the lexicon-based method, achieving accuracy scores between 92.2% and 94.0%, with Naïve Bayes yielding the highest F1-Score (95.0%). The neural network model achieved 95.0% in both accuracy and F1-Score, while the lexicon-based method scored 81.0% accuracy and a 67.0% F1-Score. Although no hybrid model was implemented, the findings suggest future research could explore combining lexical and machine learning approaches.
Language, Social Media, Colloquial Arabic, Machine Learning, Lexicon-Based Approach, Sentiment Analysis, Multilayer Perceptron Classifier.
Copyright (c) 2025 Khalid Rabaya, Ahmad Hasasneh, Khalil Rantisi https://creativecommons.org/licenses/by/4.0
Classification of Batik Patterns Using Inception-ResNetV2 with Data Augmentation
One of Indonesia’s cultural heritages with significant artistic and historical value is batik. The background of this research is the diversity of cultures and customs in various regions of Indonesia, which is reflected in the diverse batik patterns currently used. Classifying batik patterns is very important in preserving, recognizing, and promoting batik as a valuable cultural asset of the country. The problem statement is how classification facilitates the identification of the history, significance, and characteristics of each batik pattern currently in use. This problem can be overcome by applying deep learning to help identify batik patterns throughout the archipelago. The key conclusion is that a deep learning strategy is necessary, which involves training with extensive visual data. The methods used include deep learning by utilizing the Inception-ResNetV2 architecture. Its contribution is adopting an architecture that is designed to minimize the number of parameters and improve computational performance. The network can perform more efficiently overall when combined with residual connections from ResNetV2. Various types of batik and classes from the archipelago form the dataset. After the calculation was carried out for 9 min and 6 s, a batik pattern model was obtained with an average accuracy of 98.19%, precision of 98.20%, recall of 98.19%, and F1-Score of 98.16%. Mean Squared Error (MSE) 0.0023, Root Mean Squared Error (RMSE) 0.0483, Mean Absolute Error (MAE) 0.0035. The experimental data were then used to test the confidence level, achieving an average accuracy of 76–99%.
Copyright (c) 2026 Budi Dwi Satoto, Wahyudi Agustiono, Suraya Binti Hamid, Novia P. Ramadhani, Fiola L. Rafelina, Chafi A. R. Zakiy, Budi Irmawati, Deshinta Arrova Dewi https://creativecommons.org/licenses/by/4.0
Advanced Techniques for Spatio-Temporal Data Management in Graph Databases—A Systematic Review
A high-dimensional representation is required to represent connected information that reflects real events and caters to spatio-temporal dimension. Graph data structures have shown potential for integration into smart city data management frameworks and have evolved to handle spatio-temporal data. To investigate the advanced techniques used in managing spatio-temporal data in graph databases, a systematic literature review of related research papers published from 2019 to 2024 was conducted. The review examines the evolution from basic graphs to specialized structures like dynamic attributed graphs and fuzzy spatio-temporal Resource Description Framework (RDF) and also summarizes algorithms used—including graph representation learning, pattern matching, clustering, and centrality algorithms—that enable sophisticated multi-domain analyses. The research provides five key contributions: (1) the state of graph data structure development and algorithms across various fields; (2) insights on spatio-temporal data inputs used in graph structures; (3) algorithms for spatio-temporal data management and analytics; (4) spatio-temporal analyses conducted using graph-structured databases; and (5) future research trajectories. From the review, we identify challenges in graph-based implementation with spatio-temporal data such as structural graph complexity, temporal representation, semantics, and data quality, while outlining future directions in graph representation techniques, temporal-semantic innovations, scalability solutions, and comprehensive data management.
Copyright (c) 2026 Farah Ilyana Hairuddin, Suhaibah Azri, Uznir Ujang https://creativecommons.org/licenses/by/4.0
Smart Firewall for Phishing Detection Powered by Bio-Inspired Algorithms
Phishing attacks continue to pose significant risks to digital security by exploiting user vulnerabilities through deceptive methods. This paper presents a smart firewall model for phishing detection that leverages bio-inspired algorithms to enhance threat identification and response. The model utilizes the Whale Optimization Algorithm (WOA) and Dragonfly Algorithm (DA) independently for effective feature selection, thereby reducing data dimensionality while retaining critical phishing indicators. These optimized features are then processed by advanced Machine Learning (ML) classifiers—Extra Trees (ET), Random Forest (RF), and K-Nearest Neighbors (KNN)—to rigorously evaluate detection accuracy. Experimental results on the ISCX-URL2016 dataset demonstrate that the combination of WOA with the ETs classifier achieves a superior detection accuracy of 98.86%, precision of 99.50%, recall of 99.50%, F1-Score of 99.50%, outperforming alternative configurations and recent methods. This result highlights the potential of bio-inspired optimization combined with ML to develop intelligent, adaptive firewalls capable of effectively mitigating phishing threats.
Copyright (c) 2025 Mosleh Abualhaj, Sumaya N. Al-Khatib, Ahmad A. Abu-Shareha, Abdallah Hyassat https://creativecommons.org/licenses/by/4.0
Optimizing 5G Resource Allocation with Reinforcement Learning: A Q-Learning and Deep Q-Network (DQN) Approach
Simulation using a Network Simulator-3 (NS-3)-based setup enables realistic testing under varying network densities and channel conditions. The proposed Deep Q-Network (DQN)-based approach is benchmarked against recent deep reinforcement learning methods, achieving performance improvements of up to 25.7% in throughput, 31.5% latency reduction, and 27.4% energy savings. Q-Learning offers simpler scalability and faster convergence in low-complexity scenarios. These findings confirm the promise of reinforcement learning as a core enabler for autonomous and adaptive 5G resource management and as an efficient and scalable method for intelligent 5G management. The success of DQN in this domain can be attributed to its integration of deep neural architectures that can learn intricate interactions among channel quality, user demand, and interference patterns, in conjunction with experience replay and target networks. Notably, this method attained significant fairness and energy efficiency without the need for explicit fairness constraints in the reward formulation, underscoring the robustness of the generalizability of the learned policies.
Copyright (c) 2025 Basem Alrifai, Loiy Alsbatin, Firas Zawaideh https://creativecommons.org/licenses/by/4.0
Evaluation of Databases for Digital Twins and Industrial Internet of Things: A Comparative Analysis
The adoption of Digital Twins (DT) and Industrial Internet of Things (IIoT) systems necessitates efficient database solutions for real-time data ingestion and analytics. This study evaluates the performance of time-series databases, Influx Database (InfluxDB) and Timescale Database (TimescaleDB), alongside Not only Structured Query Language (NoSQL) database Mongo Database (MongoDB). Through comprehensive benchmarking, including write throughput and query latency under simulated IIoT workloads, the study identifies trade-offs between write-intensive and read-intensive operations. The results highlight the suitability of InfluxDB for high-frequency data ingestion and TimescaleDB for complex analytical queries. The findings provide actionable recommendations for database selection in digital twin architectures, offering insights for practitioners in industrial applications. Key features and differences, such as data write/read speed and scalability, are analysed. Special attention was given to load testing using Go language, which allowed running parallel threads and achieving write speeds up to 300,000 records per second in InfluxDB. TimescaleDB showed stable performance when executing complex SQL queries, providing 40 ms per query when sampling 50,000 and 250,000 rows. Examples of using time series databases for storing and processing real-time data from IoT sensors are considered. A brief analysis of the OpenTwins architecture, its databases, and internal components related to database operations has been conducted. It is concluded that the choice of technology should be based on specific requirements for data processing speed, analytics, and long-term storage.
Copyright (c) 2026 Bauyrzhan Amirkhanov , Azim, Murat Kunelbayev , Gulshat Amirkhanova , Timur Ishmurzin , Dinara Zhaisanova https://creativecommons.org/licenses/by/4.0
A Comprehensive Study on Deep Learning Techniques for IoT Security
The present paper looks at the security problems caused by the fast growth of the Internet of Things (IoT) in areas like industry, healthcare, and agriculture. As IoT systems become more common, they face more threats from cyberattacks like Brute Force, Denial of Service (DoS), Botnets, and so ones. To deal with these security issues, we studied recent papers to review different intrusion detection systems made for IoT. The goal was to see how well they work and find ways to make them better. Hence, we have selected 63 relevant papers among 1200 find papers. These selected papers were published between 2020 and 2024. Our study shows that deep learning-based intrusion detection systems can improve the manner how online threats are detect. These systems, especially when they use neural networks, are better at spotting and reacting to harmful activities. Combining machine learning with Intrusion Detection Systems (IDS) seems to help boost the security of internet of things networks, offering stronger protection against cyber-attacks. One of the best algorithms we found was the combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. This deep learning model showed very high accuracy in protecting IoT networks, especially when tested with different datasets. This proves that using advanced algorithms is important to keep up with the growing challenges of cyber-threats targeting IoT systems.
Copyright (c) 2025 Abdeslem Blali, Souhayla Dargaoui, Mourade Azrour, Azidine Guezzaz, Abdulatif Alabdulatif, Fatima Amounas https://creativecommons.org/licenses/by/4.0
YOLOv8nGM: An Improved Speed of YOLOv8n Object Detection
The rapid advancement of technology has significantly enhanced object detection tasks that require high speed and accuracy. Although the YOLOv8n model is known for its efficient and accurate detection capabilities, its detection process still involves multiple complex stages, limiting its deployment in challenging environments. This research investigates how the YOLOv8n architecture can be modified to enhance inference speed without compromising detection accuracy. The objective is to propose a modification to the YOLOv8n backbone by integrating a gating mechanism on top of the C2f module. The model, YOLOv8nGM, utilizes a sigmoid activation function to implement gating, converting network outputs into probabilities that enable selective computation depending on input complexity. This adaptive mechanism enables the network to bypass unnecessary calculations, thereby reducing computational overhead. Experiments on the benchmark dataset reveal that YOLOv8nGM achieves an average inference time of 8.72 ms per image, representing an approximately 5.4% improvement in speed compared to the original YOLOv8n. Additionally, the GPU memory consumption was reduced from 0.27 GB to 0.21 GB, signifying more efficient resource use. The mAP50 metrics on the validation and test datasets showed that the proposed model achieved minor improvements of 0.02% and 0.008%, respectively, compared to the original model, indicating that accuracy was maintained despite faster computation. These findings demonstrate that the gating mechanism effectively balances inference speed and precision, making the YOLOv8nGM model suitable for real-time object detection applications such as warehouse inventory counting. This study advances object detection technology by providing a practical and computationally efficient solution for environments that require rapid and accurate recognition.
Copyright (c) 2025 Chandra Hermawan Heruatmadja, Harjanto Prabowo, Harco Leslie Hendric Spits Warnars, Yaya Heryadi https://creativecommons.org/licenses/by/4.0
Enhancing Career Counseling for Baccalaureate Students in Morocco through Retrieval-Augmented Generation Technology: A RAG-Based Chatbot
Career guidance is important for helping students make informed choices about their studies and future careers. Yet many students lack access to resources that match their personal needs, leaving them uncertain about their options. This paper presents a chatbot built with Retrieval-Augmented Generation (RAG) to provide tailored career advice for baccalaureate students in a Moroccan high school. We compared multiple embedding models (OpenAI text embedding, Bidirectional Encoder Representations from Transformers (BERT), and Mistral) and large language models (GPT, LLaMA, and Mistral) within the same retrieval–generation framework. The evaluation showed that while all models achieved strong performance, the combination of OpenAI embeddings with Elasticsearch for retrieval and GPT for generation produced the most consistent and contextually appropriate responses. These findings show that RAG-based systems can support students with timely and personalized career guidance that connects their academic choices to available career options.
Copyright (c) 2025 CHAIMAE OUHADDOU, Asmaâ Retbi, Samir Bennani https://creativecommons.org/licenses/by/4.0