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.
Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluation
Federated Learning (FL) has emerged as a transformative approach for distributed machine learning, particularly in edge computing environments where data privacy, low latency, and bandwidth efficiency are critical. This paper presents a systematic review and performance evaluation of FL techniques tailored for edge computing. It categorizes state-of-the-art methods into four dimensions: optimization strategies, communication efficiency, privacy-preserving mechanisms, and system architecture. Using benchmarking datasets such as MNIST, CIFAR-10, FEMNIST, and Shakespeare, it assesses five leading FL algorithms across key performance metrics including accuracy, convergence time, communication overhead, energy consumption, and robustness to non-Independent and Identically Distributed (IID) data. Results indicate that SCAFFOLD achieves the highest accuracy (0.90) and robustness, while Federated Averaging (FedAvg) excels in communication and energy efficiency. Visual insights are provided by a taxonomy diagram, dataset distribution chart, and a performance matrix. Problems including data heterogeneity, energy limitations, and repeatability still exist despite advancements. To enable the creation of more robust and scalable FL systems for edge-based intelligence, this analysis identifies existing gaps and provides an organized research agenda in the future.
Copyright (c) 2026 Sales Aribe Jr., Gil Nicholas Cagande https://creativecommons.org/licenses/by/4.0
Intelligent (LLM) Knowledge Management System with Asynchronous Technology and Text-to-Speech Using PHP Language
This paper introduces a sophisticated knowledge management system developed to address the growing demand for real-time information retrieval and interaction. The system architecture leverages a robust Hypertext Preprocessor (PHP) backend to integrate Google’s Gemini Application Programming Interface (API) for advanced Large Language Model (LLM) functionalities, such as complex querying and content summarization. A key innovation is the implementation of PHP Fibers to manage I/O-bound tasks asynchronously, effectively overcoming the limitations of conventional synchronous processing and preventing application bottlenecks. For data retrieval, the system utilizes SQLite’s FTS5 extension for high-speed, indexed full-text search across large local datasets. To enhance accessibility, the Web Speech API is incorporated for text-to-speech functionality. Quantitative performance evaluation reveals that this asynchronous architecture achieves an improvement of up to 87% terms of information retrieval speed and reduces the average response latency by 35% compared to synchronous counterparts. Furthermore, the text-to-speech feature not only promotes inclusivity for users with disabilities but also facilitates more efficient cognitive processing of large volumes of information.
Topic: Software Design and Program Development
Copyright (c) 2026 Pinyaphat Tasatanattakool, Taskeow Srisod, Prachyanun Nilsook, Panita Wannapiroon, Jira Jitsupa https://creativecommons.org/licenses/by/4.0
Stock Market Prediction Using Machine and Deep Learning Models: Taxonomy and Comprehensive Analysis
Stock market prediction is a key objective in financial engineering, requiring advanced analytical methods to model complex market behavior. As global markets grow more dynamic, Machine Learning (ML) and Deep Learning (DL) methods increasingly outperform traditional statistical approaches. This study introduces a structured taxonomy of forecasting techniques and evaluates major regression-based models, including linear regression, logistic regression, and neural architectures such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and hybrid Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) networks. Five predictive models—Linear Regression (LR), Logistic Regression, RNN, LSTM, and CNN-LSTM—were implemented. LR achieved the strongest performance, with Root Mean Square Error (RMSE) values of 0.334 (training) and 0.304 (testing), while Logistic Regression performed the worst with RMSE values of 0.463 (training) and 0.487 (testing). RNN and LSTM produced higher errors than LR (RMSE 0.355 and 0.383), showing that increased model complexity does not guarantee higher predictive accuracy. The approach applies advanced preprocessing, including Z-score normalization and temporal sequence structuring. Results indicate that predictive performance depends heavily on data characteristics and market conditions. While previous studies exist, this work offers novelty by applying a structured ML-DL taxonomy to an underexplored emerging market (Saudi Aramco/Tadawul), implementing a reproducible preprocessing framework, and demonstrating that simpler models can outperform more complex architectures in markets with limited volatility and data depth.
Topic: Machine Learning in Computer and Information Systems
deep learning, machine learning, prediction, AI methods, stock market, regression, taxonomy
Copyright (c) 2026 DR. Hadi S Alqahtani, Prof. Mohammed Alhaddad, DR. Mutasem Jarrah https://creativecommons.org/licenses/by/4.0
Self-Supervised Semantic Learning for Trustworthy Anomaly Detection in 6G-Enabled Smart Grid Communication
Sixth Generation (6G)-enabled smart grid communication requires a robust installation and secure framework to manage high-speed and low-speed data communication. Existing models face challenges such as a lack of labeled data, poor temporal modeling, weak interpretability, inadequate behavioral profiling, and difficulties with edge-deployment. Additionally, the massive influx of data increases vulnerabilities to faults, cyberattacks, and zero-day anomalies. This study presents an innovative deep learning architecture that utilizes semantic encoding and self-supervised learning to detect both known and unknown anomalies within a 6G-enabled smart grid communication system. The proposed approach begins with cleaning and normalizing the electrical sensor logs. Features are extracted using a One-Dimensional Convolutional Neural Network (1D CNN), and a Multi-Modal Vision Transformer (MM-ViT) transforms sensor sequences into semantic event tokens. The self-supervised model combines Contrastive Predictive Coding (CPC), Temporal Convolutional Networks (TCN), and Attention mechanisms to enable robust temporal anomaly detection from sensor data. While TrustNet combines TCN and Attention, it models network session behavior and supports dynamic trust evaluation. Compared to the existing anomaly detection methods, the proposed semantic and self-supervised methods perform extremely well across many metrics, achieving a 99.34% accuracy. This demonstrates its effectiveness and suitability for reliable anomaly detection in 6G edge-enabled smart grid infrastructures.
Copyright (c) 2026 S Arockia Babi Reebha, P.C. Karthik, J. Umamageswaran, J. Shobana https://creativecommons.org/licenses/by/4.0
Confidence-Aware BLSTM with CDLS for Crop Recommendation and Yield Prediction
The field of agriculture continues to face challenges in terms of predicting crop yields, which is a critical factor for decision-making at international, regional, and local levels. Crop yield prediction depends on factors such as climatic conditions, soil properties, crop-specific traits and environmental influences. Therefore, selecting the most appropriate crop for maximizing crop yield plays a vital role in improving real-life farming scenarios. In this manuscript, a Class Dependent Label Smoothing-Bayesian Long Short-Term Memory (CDLS-BLSTM) approach is developed for an efficient crop recommendation and yield prediction. The CDLS regularization method is incorporated in the traditional BLSTM technique to prevent the bias and enhance both the recommendation and prediction process. The BLSTM learns distribution across LSTM weights instead of fixed weights and predicts the uncertainties in data. In pre-processing phase, the min-max normalization technique is utilized to scale data into a uniform range of 0 to 1. The developed CDLS-BLSTM acquired an accuracy of 99.78% in Crop Recommendation Dataset (CRD, 2000 samples) dataset and an accuracy of 95.32% in Crop Yield Prediction (CYP, 4000 samples) dataset when compared with conventional algorithms.
Copyright (c) 2026 Nandini Geddlehally Renukaradya, Kishore Gopala Rao, Raviprakash Madenur Lingaraju https://creativecommons.org/licenses/by/4.0
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
Intelligent Malware Detection through Bio-Inspired Optimization and Gradient Boosting
Malware continues to pose a critical threat to cybersecurity, necessitating intelligent detection systems capable of adapting to evolving attack strategies. This paper introduces an enhanced malware detection framework that integrates bio-inspired feature selection with advanced gradient boosting classifiers to achieve high accuracy and efficiency. Two metaheuristic algorithms—Harris Hawks Optimization (HHO) and the Bat Algorithm (BA)—are independently applied to extract compact and discriminative feature subsets from the ISCX-URL2016 malware dataset. The reduced feature sets are subsequently evaluated using Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LightGBM) classifiers. Performance is rigorously assessed using Accuracy as the primary evaluation metric. Experimental results demonstrate that LightGBM combined with BA achieves the highest performance, reaching an accuracy of 99.52%, precision of 99.48%, recall of 99.48%, and F1-Score of 99.48%. These findings underscore the effectiveness of bio-inspired optimization for feature selection, showing that the proposed framework not only improves predictive performance but also offers a scalable and reliable solution for real-world malware detection.
Copyright (c) 2026 Adeeb Alsaaidah, Mosleh M. Abualhaj, Sumaya N. Al-Khatib, Ahmad Shalaldeh, Mahran Al-Zyoud, Mohammad Sh. Daoud, Hani Al-Mimi, Mohamad Anbar 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
Dual Metric Learning for Few-Shot Weakly-Supervised Optic Disc and Cup Segmentation on Fundus Images
Segmentation of the Optic Disc (OD) and Optic Cup (OC) on fundus images is vital for glaucoma diagnosis. Deep learning has been utilized to automate this segmentation task. However, it typically requires a large number of labeled images. To address this issue, we propose Dual Metric Learning for OD and OC Segmentation (DMLOS), which requires only a few images with some of their pixels labeled. It consists of a neural network for embedding extraction, followed by dual branches to obtain prototypes and predictions. The Omni Training algorithm is used to improve data utilization and use a diverse number of shots. Meanwhile, DeepLabv3+ and miniUNet are the neural network used. We extensively evaluated DMLOS on the DRISHTI-GS, REFUGE, and RIM-ONE r3 datasets using various numbers of shots and label densities. Using 15 shots with 0.1 grid density, DMLOS achieved an Intersection over Union of 92.56% for OD and 73.08% for OC on DRISHTI-GS, surpassing other less-supervised methods. The results demonstrate the potential of DMLOS as an effective approach in low-label scenarios.
Copyright (c) 2026 Pandega Abyan Zumarsyah, Igi Ardiyanto, Kazuhiko Hamamoto, Hanung Adi Nugroho https://creativecommons.org/licenses/by/4.0
HaloMed: Efficient Recursive Zero-Knowledge Proofs Using Pasta Curves for Privacy-Preserving Healthcare Data Verification
Proofs Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARK) have emerged as a promising solution for safeguarding privacy in healthcare data systems. However, existing implementations still face challenges related to trusted setup requirements and computational inefficiencies in recursive proof composition. This paper presents HaloMed, an innovative solution implementing the Halo2 proof system without trusted setup for healthcare data verification. By leveraging the Pasta curves (Pallas/Vesta) in a 2-cycle configuration, HaloMed recursive composition achieves O(1) constant constant verification complexity regardless of batch size—enabling scalability from individual records to 10,000-record batches without performance degradation. The system employs Inner Product Arguments (IPA) to eliminate trusted setup and implements a Merkle tree structure for recursive proof aggregation. Experimental evaluation demonstrates that through recursive proof aggregation, HaloMed achieves 890 ms proof generation for large datasets with 8× throughput improvement over individual proofs, while maintaining constant 15 ms verification and 82% proof size reduction—enabling real-time healthcare operations at scale. HaloMed addresses the limitations of existing ZK-SNARK systems—namely, trusted setup requirements (Groth16, PLONK), recursive composition inefficiency, and performance bottlenecks. A five-layer architecture enforces Health Insurance Portability and Accountability Act compliance through cryptographic guarantees of minimum disclosure and role-based access control without trusted intermediaries. The main contributions include the first Halo2 implementation in healthcare, recursive proof composition via Pasta curves enabling linear-time aggregation of up to 10,000 medical records with constant O(1) verification, and practical performance suitable for real-time operations at scale. This system paves the way for widespread adoption of zero-knowledge technology in digital healthcare ecosystems.
Copyright (c) 2026 Linda Handayani, S.T., MMSI., Prof. Dr. Eri Prasetyo Wibowo, S.Si, MMSI., Dr. rer. nat. Avinanta Tarigan, S.T., Dr. Asep Juarna, S.Si., M.Kom. 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
Hybrid Table Formats for Lakehouse Systems: A Functional Benchmark of Iceberg, Hudi, and Delta
The swift development of the Lakehouse architectures has brought hybrid table formats such as Apache Iceberg, Apache Hudi, and Delta Lake that enmesh the transactional guarantees of data warehouses with the scalability of data lakes. At the same time, they are increasingly used in enterprise data pipelines; existing benchmarking studies typically do not include an integrated comparison of these formats across workload and functional dimensions. Previous studies mainly focus on individual performance metrics or narrow case-prone analysis, and hence lack a comprehensive overview of feature support and suitability for hybrid workloads. The current study aims to fill this gap by providing a functionality-driven benchmark framework for hybrid table formats that evaluates ingestion throughput, schema evolution, streaming support, compaction efficiency, and concurrency. It presents a Functional Capability Score (FCS) algorithm that uses qualitative output from 15 specific functional test cases to estimate format-specific capabilities. What is it: A reproducible Docker and Apache Spark-based benchmarking testbed that simulates both batch and streaming operations over synthetic and semi-realistic datasets in a fair and scalable manner. According to the experimental results, Delta Lake outperforms in both streaming and batch read speeds. At the same time, Iceberg offers improved schema evolution through metadata-driven table versioning mechanisms, while Hudi provides high ingestion throughput for incremental and streaming workloads and high ingestion throughput. The suggested framework enables a one-to-one mapping of workload types to suitable table formats, thereby enhancing practical Lakehouse deployment efforts. Therefore, this work provides a reproducible, expandable benchmark model that helps data engineers and architects choose suitable table formats, tune for better performance, and achieve future-proof scalability in the most versatile hybrid data lake environments.
Copyright (c) 2026 Srinivas Lakkireddy https://creativecommons.org/licenses/by/4.0
Hybrid Deep Learning Approach with Attention Mechanism for Predicting Student Success in Higher Education Institutions
Higher education institutions are focusing increasingly on targeted interventions by supporting students with academic challenges, improving their overall learning outcomes. In this context, hybrid Deep Learning (DL) approaches have emerged as leaders in the construction of recommendation systems capable of predicting students-at-risk by analyzing patterns of historical academic data. This paper proposes a hybrid DL model integrating an attention mechanism, specifically tailored to predict the success of students and to identify students-in-need of academic support. Our primary dataset, sourced from the Majmaah University of the Kingdom of Saudi Arabia, contained 146,989 records and 26 features, including a wide variety of student-related features crucial for making academic achievement predictions. Comparison with the current models like Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Neural Network Random Weight (NNRW), Long Short-Term Memory (LSTM), and NNRW-LSTM demonstrates the exemplary effectiveness of the proposed NNRW-LSTM model integrated with an attention mechanism. The proposed model achieved remarkable accuracy of 93.53%, 92.85% of the recall rate, 92.6% of precision, and 92.55 of the F1-Score. These results document the prospect of this hybrid DL model integrated with an attention mechanism to assist schools in identifying students-at-risk proactively, facilitating interventions well in time and helping curb academic underperformance and dropouts.
deep learning; student success; higher study; NNRW-LSTM; attention mechanism
Copyright (c) 2026 Muhammad nazir, Noraziah Ahmad, Abdullah Alsaleh, Rahmah Mokhtar https://creativecommons.org/licenses/by/4.0
Testing the Limits: Evaluating AI Detectors’ Accuracy and the Impact of Obfuscation Techniques on AI-Generated Text
The rise of Artificial Intelligence (AI)-generated text has led to the development of numerous detection tools to distinguish between human and machine-authored content. However, the effectiveness of these tools, especially against manipulated texts, remains uncertain. This study evaluates nine widely used AI detection tools—Turnitin, ZeroGPT, Detecting-AI.com, GPTZero, QuillBot, Grammarly, Sapling, Copyleaks, and Originality.ai—using texts from four large language models—ChatGPT, DeepSeek, Gemini, and Grok—as well as human-written samples. Initial findings indicate that commercial tools, such as Copyleaks and Originality.ai, achieved near-perfect detection rates, while free tools, including Grammarly and QuillBot, performed less reliably, with some as low as 63.0%. On the other hand, paraphrasing and Non-Native English Speakers (NNES)-style rewriting techniques reduced detection accuracy across most detectors. Turnitin dropped to 45.7%, while Grammarly fell to 19.0% in some cases. Only Copyleaks, GPTZero, and Sapling maintained strong performance under obfuscation. The study highlights three issues: inconsistent detector performance, the impact of obfuscation, and ethical risks, including bias and false positives. The study suggests that while some detectors offer robust baseline performance, combining them with pedagogical strategies and policies is essential to uphold academic integrity.
Topic: Human-Computer Interaction (HCI) in Modern Technological Systems
Copyright (c) 2026 Alfira Makhmutova, Batyr Sharimbayev, Altynbek Amirzhanov , Ardak Shalkarbay-uly https://creativecommons.org/licenses/by/4.0
Digital Representations in IoT: Cryptographic Tools for Improved Security
This study addresses the pressing challenge of developing efficient and secure cryptographic solutions for Internet of Things (IoT) systems, where devices operate under constraints of limited computing power, memory, and energy consumption. Against the backdrop of the rapid proliferation of IoT devices based on Embedded Systems (ESP32) microcontrollers and the growing cybersecurity threats, a systematic analysis of hardware and software encryption methods is conducted. The paper investigates the implementation of cryptographic protection at the microcontroller level in IoT networks, with particular emphasis on execution time, energy efficiency, and hardware resource utilization. The scientific novelty of this work lies in the integration of machine learning techniques for the automatic selection of the most efficient cryptographic algorithm Advanced Encryprion Standard (AES), Secure Hash Algorithm (SHA) or Hash-based Message Authentication Code (HMAC), based on data volume, execution time, and power consumption. Unlike prior studies that focus on isolated implementations, this research proposes a universal adaptive encryption selection system tailored for resource-constrained microcontroller environments. A key contribution is the implementation and testing of this system on the ESP32 and ESP32-S3 platforms. The results demonstrate that hardware-based encryption significantly outperforms software-based methods in terms of execution speed and energy efficiency. The developed logistic regression model achieved 100% classification accuracy on the ESP32 platform, outperforming alternative algorithms and confirming its applicability for energy-efficient real-time IoT applications. Finally, the paper identifies promising avenues for future work, including the development of secure digital twins for healthcare systems and autonomous devices.
Topic: Internet of Things (IoT) in Smart Systems and Applications
Copyright (c) 2026 Aigerim Rakhysh, Saltanat Adilzhanova, Murat Kunelbayev, Gulshat Amirkhanova, Dana Sybanova 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
FSTS Fog-Based Smart Transportation System a Novel Approach: Navigating towards Future of Urban Mobility
The number of lives and possessions lost because of ineffective and dangerous traffic management has risen significantly in recent years. Innovative solutions are required for effective transportation management due to the accelerated urbanisation and rising demands on the transportation infrastructure. We propose a Fog-Based Smart Transportation System (FSTS) that utilizes fog computing to enhance traffic management, environmental monitoring, and road safety. In contrast to traditional centralized cloud solutions, the FSTS combines fog nodes, proxy servers, and cloud servers to facilitate low-latency, energy-efficient, and dynamic execution of data. The proposed framework was validated by iFogSim2, simulating up to 60 fog nodes together with various smart cameras and sensors. A comparison is conducted by utilizing two parameters, namely latency and energy consumption. The results indicated that the FSTS decreases latency by as much as 82% and energy consumption by approximately 75% in comparison to leading methodologies like Scalable Microservices Placement (SMP) and Reliable Resource Allocation and Management (R2AM). These enhancements underscore the system’s capacity to reduce congestion, facilitate real-time accident identification, and optimize energy use. The suggested architecture prioritizes data security and privacy through the integration of encryption and safe communication between fog nodes and the cloud. Therefore, experimental results validate that the proposed FSTS approach outperforms alternatives by reducing latency, energy consumption, and optimized traffic efficiency.
Fog Commuting, Smart Transportation System, iFogsim, Smart City, Cloud Computing, Internet of Things (IoT)
Copyright (c) 2026 Deepa Arora, Oshin Sharma https://creativecommons.org/licenses/by/4.0
Enhanced Sentiment Analysis Using Extended Corpus and Co-Occurrence Graph
Lexicon-based sentiment analysis often faces challenges due to the limited coverage and adaptability of existing sentiment lexicons, particularly in domain-specific or resource-scarce contexts. Constructing new lexicons from scratch is labor-intensive and rarely feasible for large-scale applications. To address this gap, we introduce the Enhanced Sentiment Analysis using Extended Corpus and Co-Occurrence Graph (ECG) framework, which leverages co-occurrence graph construction, triadic closure theory, and PageRank-inspired weighting to propagate sentiment values from a small seed set to a broader vocabulary. Our study focuses on nouns extracted from the Harry Potter corpus, which are typically underrepresented in standard sentiment resources. Experimental results demonstrate that ECG achieves effective sentiment propagation within 1500 iterations, reaching stable convergence. Evaluation using the Coefficient of Variation (COV) indicates improved sentiment differentiation, with broader coverage compared to benchmark lexicons such as SentiWordNet, Valence Aware Dictionary and Sentiment Reasoner (VADER), and National Research Council (NRC). For instance, ECG consistently assigned interpretable sentiment values to narrative-specific entities (e.g., “Slytherin”, “Voldemort”), which were often missed by conventional lexicons. These findings highlight ECG’s scalability, interpretability, and suitability for low-resource scenarios. Beyond literary analysis, the framework can be extended to other domains such as social media, customer feedback, and specialized corpora. Overall, ECG represents a resource-efficient alternative that bridges the limitations of static lexicons and enhances sentiment inference in diverse contexts.
Sentiment Analysis, Co-occurrence graph, Sentiment propagation
Copyright (c) 2026 Nirach Romyen, Asst. Prof. Dr. Maleerat Maliyaem, Prof. Dr.-Ing. habil. Dr. h.c. Herwig Unger https://creativecommons.org/licenses/by/4.0
Enhancing Facility Asset Management through Graph-Based Asset Lifecycle Modeling: A Multi-Dimensional Approach
Current asset management approaches struggle to represent asset information involving multidimensional dependencies namely spatial, temporal, and semantic, where it is often stored in isolation rather than interconnected elements. These dependencies are crucial in knowledge modelling which appear as challenges in the current state of research due to inflexible database structures struggling to adapt to dynamic asset lifecycles, limiting comprehensive understanding and strategic decision-making. To address these challenges, this research proposes an innovative asset lifecycle event model using Labelled Property Graph (LPG) to integrate spatial, temporal, and semantic dependencies. The study develops a graph database approach enabling comprehensive asset lifecycle representation. The model was evaluated through lifecycle event queries across similar and diverse assets, examining temporal sequence retrieval and multidimensional asset dependencies analysis. Key findings demonstrate the framework’s ability to capture complex asset dependencies, support lifecycle visualisation and facilitate efficient data retrieval. Performance evaluation comparing Neo4j and PostgreSQL revealed the graph database approach outperformed traditional relational databases by 1.2 to 80 times across various query types. This research contributes to a novel conceptualization of integrating spatial, temporal, and semantic dependencies within a unified graph data structure in a graph database for asset management. In pursuit of comprehensive asset management for systematic decision-making, this approach introduces a novel data modeling method capable of storing multidimensional information interconnected with asset entities through graph database technology that advances the knowledge modelling within asset information by transforming the stored multidimensional data into comprehensive insights.
Copyright (c) 2026 Farah Hairuddin, Dr. Suhaibah Azri, Dr. Uznir Ujang https://creativecommons.org/licenses/by/4.0
GCTformer: Generalizable Spatio-Temporal Traffic Forecasting Using Graph and Transformer Models
Precise traffic prediction is paramount for optimised city mobility and smart transportation systems. Current models, though, are based on deep multi-layer structures, which are resource exhaustive and limit real-time implementation. This paper introduces Graph-Convolutional Temporal Transformer (GCTformer), a light spatio-temporal model that combines a one-layer Graph Convolutional Network (GCN) with a Transformer encoder. The innovation of GCTformer is that it can learn the adjacent matrix dynamically so that it accommodates adaptive spatial dependency modeling regardless of static or pre-designed graphs. Comprehensive experiments on three benchmark datasets Los Angeles Metropolitan Region (METR-LA), San Francisco Bay Area (PEMS-BAY), and a traffic speed dataset for the Northeast–Beijing (NE-BJ) Traffic Dataset demonstrate that GCTformer achieves accuracy equal to or superior to state-of-the-art methods while saving parameters by 30–40%. When compared to multi-layer GCN-based models, GCTformer shows resistance to long-term forecasting, reducing Mean Absolute Percentage Error (MAPE) by up to 2.3% at Horizon-12. The results establish GCTformer as a practical and efficient solution for real-time traffic forecasting in intelligent transportation systems. The implementation is available at: https://github.com/imagrawalpalak/GCTformer
Copyright (c) 2026 Pravinkumar Sonsare, Palak Agrawal, Nithya Rekha Sivakumar, Prakash N.B, Murugappan M 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.
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
Hybrid ANN-LSTM with Attention: Predictive Analytics and Data Visualization for Solar Energy Systems
This study proposes and evaluates a hybrid Artificial Neural Network–Long Short-Term Memory (ANN-LSTM) model integrated into a Data Visualization Tool (DVT) for solar energy forecasting and management. Using meteorological data from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) Project in the Davao Region (1,956 daily records), the DVT combines a hybrid model with LSTM for sequential data and ANN for static feature analysis, which was further enhanced by attention mechanisms and normalization. The model achieves a Mean Absolute Error (MAE) of 0.82 and Root Mean Squared Error (RMSE) of 1.15, outperforming single-model baselines. Key contributions include demonstrating the effectiveness of the hybrid approach, incorporating data quality into training, and clarifying feature roles in temporal modeling. While the model performs well in tropical conditions, its applicability to low-irradiance regions remains a limitation, highlighting opportunities for future research, adaptation, and implementation.
Copyright (c) 2026 Johann Gabriel, Reynette Micah, Kathlyn Anne, Genevieve Pilongo 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