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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 2,057 Documents
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Enhancing early detection of autism spectrum disorder through ensemble-based machine learning classifiers Shabeena Lylath; Laxmi B. Rananavare
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3732-3744

Abstract

Autism spectrum disorder (ASD) is a developmental disability characterized by significant social, communication, and behavioral challenges. Machine learning is a practical approach for autism detection. The proposed ensemble-based machine learning classifier methodology presented in this study seeks to revolutionize the diagnosis of ASD by harnessing the collective power of various machine learning algorithms. This ensemble approach is designed to enhance diagnostic precision, mitigate the subjectivity associated with traditional diagnostic methods, and accelerate the detection process. This methodology addresses the urgent need for early and accurate ASD identification, enabling timely interventions. Leveraging complex data analysis, it offers deeper diagnostic insights, facilitating informed clinical decisions and advancing ASD research. The methodology's accessibility across healthcare settings marks a significant step forward in making early ASD detection more universally available, showcasing the transformative potential of machine learning in healthcare. In deploying the “ensemble-based machine learning classifier” for ASD diagnosis, this study utilizes an extensive dataset comprising behavioral and medical profiles from diverse demographics, including toddlers, children, adolescents, and adults with ASD. Upon the preliminary analysis, the dataset enables the methodology to learn from a wide array of ASD manifestations, ensuring its robustness and applicability across different age groups and severity levels.
Bridging gaps in health artificial intelligence: challenges in MDPI research articles Irwan Bastian; Aqilla Rahman Musyaffa; Lukman Nulhakim; Novia Putri Bahirah; Dewi Agushinta R.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3053-3067

Abstract

Technological advancements in artificial intelligence (AI) have transformed healthcare by improving early disease detection, personalized treatment, predictive analytics, and clinical decision support systems. However, AI adoption in healthcare faces critical challenges, including data privacy concerns, algorithmic bias, regulatory barriers, usability issues, and system interoperability. Addressing these issues requires standardized regulations, ethical frameworks, and interdisciplinary collaboration to ensure responsible AI integration. This study systematically analyzes research trends in health AI over the past six years through a systematic literature review (SLR) and a bibliometric analysis using VOSviewer. The review focuses on Multidisciplinary Digital Publishing Institute (MDPI) journal articles to identify key contributors, emerging trends, and research gaps in AI-driven healthcare. Findings highlight dominant research areas, including machine learning for diagnosis, AI-driven hospital management, and predictive analytics, while exposing persistent challenges such as a lack of standardized AI models, ethical concerns, and accessibility disparities. By mapping the research landscape, this study provides evidence-based insights and recommendations to address AI adoption barriers, improve transparency, and guide future research in healthcare AI. The results contribute to developing a more equitable, efficient, and trustworthy AI-driven healthcare system.
Forecasting income inequality in Vietnam’s regions using machine learning T. Thai-Phuong; L. Nguyen-Son
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3464-3475

Abstract

This study applies artificial neural networks (ANN) and support vector regression (SVR) to forecast the Gini coefficient across Vietnam’s seven socio-economic regions using limited data from 2014–2022 (N = 63). Despite the small dataset, robust techniques including repeated cross-validation, temporal backtesting, regularization, and bootstrapping were employed to mitigate overfitting. The results show a modest continued decline in income inequality through 2025, with faster improvements in urban regions (Red River Delta and Southeast) than in rural and highland areas. ANN outperformed SVR with an average cross-validation coefficient of determination (R²) of 0.93. The novelty lies in its granular regional forecasting and region-specific policy recommendations beyond national-level analyses. Key drivers—per capita income and simple housing—support targeted interventions such as housing credits in the Mekong River Delta and agricultural investments in the Central Highlands. The findings provide an evidence-based foundation for regionally tailored strategies to reduce income disparities in Vietnam.
Data-driven analysis of growth factors in oyster mushroom cultivation: a case study from Indonesia’s market Yosef Budiman; Gilang Adi Prasetyo; Asma’ Khoirunnisa’; Hanifah Mar’atush Shalihah; Muhamad Riyan Maulana; Yanuar Agung Fadlullah; Sugiri Sugiri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3568-3580

Abstract

The oyster mushroom is one of the potential agricultural products that can be developed as an alternative to other agricultural products, to maintain Indonesia's economic condition. However, the production of oyster mushrooms remains low and falls short of the minimum amount of market demand. This study employs a machine learning (ML)–based approach to identify the key parameters influencing oyster mushroom production rates. Recursive feature elimination (RFE) was applied to reduce the initial 19 features to nine, enabling faster processing while maintaining high predictive accuracy. The results showed that agricultural features showed a high contribution rather than environmental, economic, and demographic features. Furthermore, these parameters were related to the train-test analysis to visualize the statistical analysis shown by the best method, adaptive boosting (AdaBoost), with coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE) values of 0.997575, 0.009841, and 0.085884, respectively. Related research relevant to the research findings was analyzed to validate that agricultural product features affect the decline of oyster mushroom production. Other supported research conducted by integrating real-time analysis and twin digital models, which can enhance substrate quality.
YOLO-based deep learning for tooth detection, segmentation, and numbering in panoramic radiographs Sri Oktamuliani; Luqyana Mahdiyah; Haritsul Haq; Nesa Perdana Putri; Wulandani Liza Putri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3591-3602

Abstract

Panoramic dental radiographs are essential for diagnosis and treatment planning, but are difficult to interpret due to overlapping anatomical structures and limited image quality. Automating tooth detection, segmentation, and numbering can enhance clinical efficiency and reduce observer variability. This study developed a you only look once version 8 (YOLOv8)-based deep learning model for automatic detection and segmentation of teeth in panoramic radiographs. A dataset of 302 images containing 9,009 annotated teeth was divided into 70% training, 20% validation, and 10% testing sets. Teeth were labeled using the Federation Dentaire Internationale (FDI) two-digit numbering system with bounding boxes and segmentation masks. Model performance was assessed using precision, recall, F1-score, and mean average precision (mAP) at intersection over union (IoU) thresholds of 0.5 and 0.5–0.95. The model achieved bounding box precision, recall, and F1-score of 0.9075, 0.9352, and 0.9212, respectively, and segmentation scores of 0.9078, 0.9344, and 0.9209. Bounding box mAP@0.5 reached 0.9510 and mask mAP@0.5 was 0.9504, while stricter thresholds lowered performance to 0.7763 and 0.6896. Class-wise analysis showed high accuracy overall but reduced performance on posterior teeth due to anatomical overlap and peripheral image quality. YOLOv8 enables fast, accurate, and robust tooth detection and segmentation, supporting real-time computer-aided diagnosis in orthodontics, prosthodontics, and forensic dentistry.
Analysis of normalization technique on multi objective preference analysis method Fristi Riandari; Gabriel Ardi Hutagalung; Ferry Fachrizal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3827-3835

Abstract

Normalization is a critical step in multi-criteria decision analysis (MCDA) because it influences ranking consistency and decision reliability. This study evaluates the effects of four normalization techniques linear max, linear max-min, linear sum, and semi-linear vector, within the multi objective preference analysis (MOPA) framework using a tourism development case involving 18 alternatives and 8 decision criteria with both cost and benefit attributes. The techniques were compared based on ranking behavior, discriminative capability, and robustness using statistical and non-parametric validation. The results show that linear max-min normalization provides the strongest discriminative performance and the most significant statistical results, while semi-linear vector demonstrates high ranking stability and balanced sensitivity. In contrast, linear sum and linear max exhibit lower discriminative capability under the evaluated conditions. Kendall's tau and robustness analyses further confirm that normalization choice significantly affects ranking consistency and decision reliability. These findings provide practical guidance for selecting appropriate normalization techniques and support the development of more reliable MCDA-based decision-making models for complex applications, including sustainable tourism planning.
Hybrid deep learning model for the task scheduling in cloud computing Kavita Rani; Om Prakash Sangwan; Ritu Garg
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3683-3691

Abstract

Task scheduling plays a crucial role in optimizing performance, reducing costs, and enhancing system reliability by efficiently allocating resources to workloads. Traditional task scheduling methods lack the ability to efficiently manage workloads and resource distribution, leading to potential inefficiencies in performance and energy consumption. To address these limitations, advanced techniques leveraging deep learning and reinforcement learning are explored. This study proposes a deep learning-based model for task scheduling in cloud computing. The model employs a convolutional neural network (CNN) to predict the optimal machines for task allocation. Additionally, Q-learning is integrated with CNN to facilitate load shifting between machines, ensuring efficient utilization of resources. The dataset used in this work consists of task attributes, such as execution time, resource requirements, which were loaded from a CSV file. Comparative analysis with existing models shows that the proposed approach achieves approximately 94% accuracy and consumes less energy than other models, demonstrating its effectiveness in cloud task scheduling.
Performance evaluation of YOLOv11-based vehicle detection and tracking for urban intelligent transportation systems Thang C. Vu; Dung T. Nguyen; Minh T. Nguyen; Long Q. Dinh; Mui D. Nguyen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3518-3527

Abstract

This paper proposes and evaluates an integrated vehicle detection and tracking framework based on you only look once (YOLO)v11 for intelligent transportation systems (ITS). The combination of the deep simple online and real-time tracking (DeepSORT) algorithm helps maintain vehicle identity across consecutive frames, thereby enhancing the stability of the multi-object tracking system. Additionally, the slicing-aided hyper inference (SAHI) technique is integrated to improve the detection efficiency of small vehicles in remote sensing imagery and urban surveillance video data collected in Thai Nguyen, Vietnam. The system's performance is comprehensively evaluated through several key quantitative indicators, including mean average precision (mAP), multiple objects tracking accuracy (MOTA), and identification F1-score (IDF1), across realistic urban traffic scenarios. The results show that the framework significantly improves detection accuracy, tracking consistency, and small object recognition efficiency in real-world urban traffic scenarios. This paper provides useful insights for selecting appropriate detection and tracking configurations in ITS applications.
A novel embedded approach to face recognition using multi-threaded controller based on weightless neural network Ahmad Zarkasi; Hadipurnawan Satria; Anggina Primanita; Deris Stiawan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3903-3918

Abstract

This research presents a high efficiency embedded face recognition system based on the weightless neural network-face recognition algorithm (WNN-FRA) integrated with a multi-thread controller to enhance execution time and recognition accuracy under limited hardware resources. The system implements a center-scan feature alignment model to address resolution discrepancies between reference and input facial images. The multi-threaded architecture divides processing into three concurrent threads front, left, and right facial orientations each handling approximately 20 facial patterns. Experimental evaluation on a dataset of 60 facial images demonstrated a maximum recognition accuracy of 96.83% and an average execution time ranging from 16 to 34 milliseconds per dataset, confirming real-time performance. Comparative analysis shows that the multi-threaded approach reduced the execution time by over 67% compared to single-thread processing 0.09 second vs. 0.271 second, while maintaining balanced workload distribution across threads. Memory analysis revealed that the entire system required only 24 KB from the available 512 KB flash capacity, indicating efficient resource utilization. The results confirm that integrating WNN-FRA with multi-threading provides a robust, low-cost, and scalable solution for real-time facial pattern recognition in embedded environments.
Enhancing pedestrian detection in adverse conditions using YOLOv5s with adaptive weighted fusion efficient channel attention module Oumayma Rachidi; Badr Bououlid Idrissi; Chafik Ed-dahmani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3299-3308

Abstract

Pedestrian detection constitutes a critical task within advanced driver assistance systems (ADAS), where reliable identification of pedestrians is essential for ensuring vehicular safety. Although deep learning has substantially improved detection performance, existing state-of-the-art models continue to exhibit notable degradation in adverse weather conditions and low-light. To mitigate these challenges, this study introduces an enhanced pedestrian detection framework based on you only look one version 5 (YOLOv5s), retrained on an augmented common object in context (COCO) dataset focused on the person class. Additionally, a novel, lightweight, and adaptive attention mechanism called: the weighted fusion efficient channel attention (WF-ECA) module is incorporated into the detection architecture. The WF-ECA module selectively focusses on important features without compromising computational efficiency or inference speed. Comparative experiments demonstrate a 5% increase in mean average precision (mAP) in comparison to the baseline model, thereby demonstrating the efficacy of the proposed attention module in improving detection robustness under challenging environmental conditions. These findings highlight the potential of attention-based mechanisms to enhance pedestrian detection performance in real-world ADAS applications.

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