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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 2,143 Documents
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Evaluating YOLOv8-pose variants for soccer keypoint detection and homography mapping Durrotun Nashihin; Yohanes Yohanie Fridelin Panduman; Dwi Cahyo Kartiko; Anna Noordia; Khoirul Islam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4819-4829

Abstract

Soccer analytics intensely relies on accurate pitch keypoint detection and homography mapping, yet soccer broadcast data poses challenges caused by camera angle variation and partial pitch coverage. This study systematically evaluates you only look once version 8 (YOLOv8)-pose variants (nano, small, medium, large, and x-large) for keypoint detection and homography mapping. These models were retrained on broadcast and scouting datasets, while homography mapping was computed using direct linear transform (DLT) and random sample consensus (RANSAC). Three metrics were used for evaluation: detection accuracy, computational efficiency, and homography reliability. Results showed that the medium variant achieved the best balance, with the lowest mean pixel error (35.67 px), competitive inference speed 49.6 frames per second (FPS), and moderate model size (52.2 MB). Furthermore, it also reached the highest homography success rate (96.2%), surpassing all other variants. These findings suggest that the medium variant is the most effective balance for real-time soccer analytics, providing robust performance in accuracy, efficiency, and reliability under diverse broadcast data. Future studies would focus on enlarging the dataset with additional keypoints, incorporating temporal smoothing, and integrating a player tracking system. For reproducibility, the dataset and code are openly available on GitHub.
Confidence-driven feature selection and ensemble classification using ResNet, AlexNet, and DenseNet for scene recognition Sumathi Krishnamurthy; Shetty Pramod Kumar; Halasur Rudrappa Mahadevaswamy; Ujwala Bagepalli Satishbabu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4523-4539

Abstract

Indoor scene recognition is a significant problem because of its structural complexity, illumination variations, and visual similarities between the various scene classes. Recent approaches use convolutional neural networks (CNNs) and transfer learning to extract features for classification. In these methods, all the features were considered for classification, which leads to an increase in computational complexity and training time. This leads to more time consumption for processing. To address these challenges, this study proposed a hybrid indoor scene recognition framework that integrates illumination-aware pre-processing, multi-model deep feature extraction, and confidence-based feature fusion. The pre-processing technique uses contrast-limited adaptive histogram equalization (CLAHE) and Retinex enhancement. Deep features are extracted using Alex Krizhevsky network (AlexNet), densely connected convolutional network (DenseNet), and residual network 101 layers (ResNet101) to capture spatial and semantic information. The features extracted are fed to confidence-driven world cup optimization (CD-WCO) to extract the significant features. The final representation is classified using a deep liquid state machine (DLSM) classifier to enhance class discrimination. Our experiments, conducted on the NYU indoor scene dataset with 20 scene categories and the MIT Indoor-15 class subset results indicate the effectiveness of the proposed framework, achieving an overall classification accuracy of 96.5% and 95.2%, respectively.
Optimizing cloud energy with deep reinforcement learning: an artificial bee colony-Q-learning hybrid approach Mohammed Ziaur Rahman; Anandaraj Shanthi Pichandi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4645-4655

Abstract

Load balancing in the cloud is a crucial characteristic for distributing resources from data centers. However, load balancing in a cloud environment remains a challenging issue due to the influx of numerous incoming tasks, particularly in an infrastructure as a service (IaaS) cloud environment. To address the complexity and high dimensionality of the process, machine learning (ML) techniques combined with optimization algorithms are used, which fasten the process and minimize execution time. The deep reinforcement learning (DRL) – artificial bee colony with Q-learning (ABCQ) approach is proposed to solve load balancing in a cloud environment. The agent in DRL makes intelligent decisions to handle a large number of tasks efficiently at lesser time and increasing speed. The artificial bee colony (ABC) algorithm uses a global guiding factor to initialize position updates, enhancing the searchability and performance of the algorithm. The proposed DRL-ABCQ approach optimizes scheduling and resource utilization, increases the throughput of virtual machines (VMs), and balances the load between VMs. The DRL-ABCQ approach achieves a makespan of 125.52 s, energy utilization of 99.81 kJ, and a throughput of 2.39 jobs/s, compared to existing approaches.
Navigating cybersecurity paradox: opportunities and risks of artificial intelligence integration in healthcare systems Rani Tiyas Budiyanti; Roro Isyawati Permata Ganggi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp3981-3990

Abstract

Artificial intelligence (AI) has been widely used in healthcare services, such as assisting in diagnosis and clinical decision-making. AI can potentially improve cybersecurity within healthcare services. Nevertheless, AI can also pose new threats that are dangerous to the system. The objective of this research is to investigate the contradictory roles that AI plays in the context of healthcare security. This study was a bibliometric study and systematic review from the Scopus database that were published in 2015-2025. This research shows that AI can bring benefits for security, such as enhanced threat detection, automation, and efficiency, as well as improved patient care. However, AI has many risks and challenges in healthcare security, such as new vulnerabilities, data privacy concerns, and ethical and bias issues. Integration of AI in healthcare services, especially to strengthen cybersecurity, needs to be accompanied by clear regulations, robust data protection standards, and ongoing supervision. In addition, principles of patient data security and privacy must be carefully observed to balance the benefits of using AI in healthcare security with the potential risks that may arise.
MatchIA: a semantic artificial intelligence solution for interoperability in schema matching Mohamed Raoui; Moulay Hafid El Yazidi; Ahmed Zellou
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4213-4223

Abstract

Schema matching plays a crucial role in data integration by aligning attributes from heterogeneous sources. Traditional rule-based and statistical techniques often lack the flexibility to adapt to diverse schema structures, requiring extensive manual effort. To address these challenges, this paper introduces MatchIA, a machine learning solution that combines word embeddings and random forest to enhance the accuracy of schema matching. The approach accounts for both structural dependencies and semantic relationships between attributes, enabling more automated, and reliable correspondence identification. Unlike traditional methods, MatchIA leverages pretrained embeddings to capture contextual similarities and integrates learned patterns to generalize across various domains. Experimental evaluations on standard datasets demonstrate that MatchIA achieves a precision of 0.94, a recall of 0.92, and an overall matching accuracy of 0.93, significantly outperforming state-of-the-art methods. These results highlight the potential of MatchIA for real-world applications requiring scalable and automated data interoperability.
Dynamic sports environments: automatic feature players tracking and re-identification with similar appearances Adil Abdulhur Abushana; Riyadh Alsaeedi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4292-4303

Abstract

Tracking multiple players in sports videos remains challenging due to frequent occlusions, similar appearances among players, and rapid motion. This paper presents a comprehensive player tracking framework that integrates deep player re-identification (DPRI), an individual probabilistic overlapping bounding box occupancy mapping (IPOM) for robust spatial localization under severe overlap, and an identity-aware k-shortest path optimization algorithm (KSP-ID) for improved trajectory association. The DPRI module combines pose-based embeddings, jersey appearance cues, and facial features to generate discriminative identity representations. Experimental evaluation on the autonomous production of images based on distributed and intelligent sensing (APIDIS) and Shantou University (STU) datasets demonstrates strong performance, achieving 85.5% multiple object tracking accuracy (MOTA), 69.0% generalized multiple object tracking accuracy (GMOTA), and 90.2% rank-1 re-identification accuracy. Furthermore, the proposed framework significantly reduces identity switches (IDS) compared with conventional k-shortest path (KSP) approaches, probabilistic occupancy models, and recent deep learning-based tracking methods. These results demonstrate that integrating semantic identity information with spatial reasoning enables robust and efficient player tracking in highly dynamic sports environments.
Exploring Arabic non-sibilant fricative consonants: a comprehensive classification Youssef Elfahm; Nesrine Abajaddi; Badia Mounir; Laila Elmaazouzi; Ilham Mounir; Abdelmajid Farchi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4878-4892

Abstract

Voice interaction serves as an efficient tool for improving communication between humans and machines, enabling quicker exchanges without the need for physical contact with devices. This study introduces a methodology aimed at identifying Arabic non-sibilant fricative consonants and recognizing their significance within this context. Our approach involves analyzing the normalized energy distribution within consonant-vowel phonetic syllables, which serves as the acoustic input signal for our speech recognition system. Notably, the relatively limited attention given to non-sibilant consonants in existing literature underscores the importance of this research endeavor. The evaluation of our classification method applied to our dataset highlights the pivotal role of vocal signal energy in characterizing Arabic non-sibilant fricative consonants. Achieving a classification accuracy of 90% for distinguishing voiced and voiceless non-sibilant consonants, with a rate exceeding 85% for other categories, underscores the effectiveness of our approach. Noteworthy is the outperformance of our classification technique compared to current algorithms based on support vector machines (SVM) and artificial neural networks (ANNs), indicating its potential to enhance Arabic automatic speech recognition (ASR) systems. Overall, this research contributes to advancing speech recognition technology, particularly in the realm of Arabic linguistics, offering promising implications for future developments in this field.
Performance analysis of convolutional neural network-based image classifiers for agricultural farmland mapping Anjela C. Tolentino; Anazel P. Gamilla; Amir Ledesma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4403-4411

Abstract

Precise farmland mapping is essential for crop monitoring, land-use inventory, and subsidy audits. Particularly, the office of the municipality’s assessor or the registry of deeds needs adequate image classifiers to categorize farmland on a large scale across different seasons and regions. The study evaluated three convolutional neural network (CNN) models, such as efficient network baseline 3 (EfficientNetB3), residual network 50 layers version 2 (ResNet50V2), and inception version 3 (InceptionV3), to identify the model that best balances accuracy and efficiency for classifying high-resolution farmland imagery. EfficientNetB3 proved to be the most suitable model, demonstrating higher accuracy, robustness, and a stronger ability to identify rice farmland patterns. While ResNet50V2 and InceptionV3 are widely used for image analysis, their performance declined when processing farmland images that exhibit substantial differences in visual characteristics. EfficientNetB3 delivered more consistent results for farmland image classification in large-scale agricultural applications. These findings highlight its potential for supporting data-driven agricultural management and policy implementation.
Traffic flow optimization using reinforcement and thread-based V2X communication for smart traffic management Parul Arora; Ritika Wason; Devansh Arora; M. N. Hoda; Sumit Gupta
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4061-4070

Abstract

This research proposes an advanced framework that integrates reinforcement learning techniques with thread-based vehicle-to-everything (V2X) communication protocol to address urban traffic jams, which lead to increased delay, inefficient fuel consumption, and deteriorating air quality, posing a significant challenge to sustainable living. This framework empowers cars to “talk” directly to each other as well as with traffic signals, adapting to the real-time flow on the roads. By placing the decision-making process closer to where it’s needed, right at the network’s edge, the system is able to respond quickly to changes in traffic without relying on the usual pre-set signal cycles. Unlike many traditional traffic management solutions that are slow to adjust, the proposed framework allows vehicles and intersections to coordinate in real time. This reduces unnecessary waiting, smooths out traffic flow, and helps cut down on emissions. Through detailed simulations using the simulation of urban mobility (SUMO) traffic model, it was found that this system led to significant improvements: average waiting time was reduced by over 40%, queue lengths were cut by more than half, and vehicle throughput increased by nearly 30%. Additionally, fuel consumption dropped by approximately 24%, and carbon dioxide (CO2) emissions were reduced by more than 25% compared to existing systems.
Quantitative chest computed tomography-based hybrid deep learning for chronic lung disease detection and staging Garima Jain; Amit Kumar Goel; Rahul Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4568-4591

Abstract

This research proposes a hybrid deep learning model to accurately detect chronic lung diseases in chest computed tomography (CT) scans. Current deep learning methods, such as convolutional neural networks (CNNs) and transformer models, often suffer from high computational requirements and low interpretability. This research combines a pre-trained residual network 50 layers (ResNet50) for deep feature extraction with hand-crafted texture features. The features are combined via hybrid concatenation, and the model is classified using an optimized extreme gradient boosting (XGBoost) ensemble with hyperparameter tuning to improve performance and reduce overfitting. This research compared the proposed model with radiomics-only, ResNet50 CNN, vision transformer (ViT-B16), and Swin transformer methods. This research used a public dataset containing adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and normal classes. The classification accuracy of the hybrid framework was 92.38%, with better precision, sensitivity, specificity, F1-score, and receiver operating characteristic-area under the curve (ROC-AUC) than the baselines. A dual-mode explainable artificial intelligence (XAI) framework integrating gradient-weighted class activation mapping (Grad-CAM) and Shapley additive explanation (SHAP) was implemented to enable spatial lesion localization and feature attribution for clinical transparency. Grad-CAM was able to localize the lesions successfully, and SHAP revealed the influence of entropy-based texture features and semantic embeddings on the predictions. Overall, the framework develops advanced XAI systems for chest CT analysis.

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