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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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Application of adaptive artificial intelligence for personalizing learning paths in e-learning Mohammed Salihoun; Abdelali Elmounadi
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.pp4992-5000

Abstract

The field of education and online education in particular, has undergone radical change thanks to digital transformation. The integration of adaptive artificial intelligence (AI) into the latter has given a further significant evolution, enabling the online personalization of learning paths. The result is clearly effective, engaging and specific education for every student. This article analyzes how adaptive AI can improve e-learning, with a specific accentuation on algorithms and tools—such as machine learning and deep learning that enable this level of customization. Technical and ethical challenges will be discussed, as we go over their benefits, precisely with regard to improving learning outcomes and promoting student involvement and engagement. Additionally, the paper examines the potential applications of adaptive AI in online learning, stressing the anticipated advancements and the significance of a thoughtful strategy that considers moral and legal concerns. The paper highlights the ways in which adaptive AI can revolutionize distance learning and stimulates critical thought about the implications of its broad implementation.
Throughput maximization and overhead-aware quality link scheduling for heterogeneous wireless networks Nataraja Nagarajappa; Naveen Kalenahalli Bhoganna
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.pp4497-4511

Abstract

Next-generation heterogeneous wireless networks (HWNs) require advanced scheduling solutions to guarantee high quality of service (QoS) while sustaining throughput, minimizing latency, and efficiently utilizing spectrum resources. Traditional link scheduling and admission control techniques are constrained by interference complexities and dynamic mobility patterns, leading to suboptimal connectivity and reduced network performance. To address these limitations, this paper introduces a throughput maximization and link overhead minimization-aware scheduling (TMLOMS) strategy. The proposed mechanism embeds adaptive backoff-time optimization into resource allocation and incorporates a priority-driven call admission control process powered by an optimized deep reinforcement learning (ODRL) framework. The TMLOMS-ODRL is mathematically modeled to balance trade-offs between spectral efficiency, throughput maximization, and latency minimization, thereby enhancing QoS across diverse user requirements. Performance is validated using the Stanford University Interim (SUI) channel model under both urban and highway propagation environments, capturing realistic interference and mobility dynamics. Comparative evaluation against the hybrid beamforming (HBF) optimized through DRL method demonstrates that TMLOMS-ODRL significantly reduces resource access failures, lowers delay, and improves delivery ratio and throughput. The proposed strategy establishes a scalable and intelligent link scheduling mechanism for HWNs, ensuring resilient QoS-driven performance under complex real-world conditions.
Why simple neural networks outperform traditional classifiers in cyberbullying detection Rini Anggrainingsih; Firdaus Ashiddqi Noor; Gita Bangun Prakoso; Wiharto Wiharto; Dewi Wardani; Hasan Dwi Cahyono
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.pp4852-4861

Abstract

Cyberbullying on social media, particularly in Bahasa Indonesia, has become a pressing problem. Detecting abusive language in Bahasa Indonesia poses unique challenges due to the prevalence of informal expressions, slang, code-mixing, and implicit aggression. Recently pre-trained language models, such as Indonesian bidirectional encoder representations from transformers (IndoBERT), can generate contextual word embeddings that have shown strong potential for understanding complex linguistic patterns. However, it remains unclear which classification models can best exploit these rich embeddings. This study aims to enhance the performance of cyberbullying detection and investigate why simple neural networks outperform traditional machine learning classifiers, such as support vector machine (SVM) and na¨ıve Bayes (NB) when applied to contextual embeddings in Bahasa Indonesia. This study conduct both theoretical and empirical comparisons of fully connected neural networks (FCNN), SVM, and NB classifiers using IndoBERT embeddings. The results demonstrate that even shallow neural architectures can leverage contextual semantic information more effectively than traditional methods, yielding higher accuracy and robustness. Besides performance improvement, the study also explains the underlying reasons for this superiority, highlighting the adaptability of neural models to complex embedding spaces. These findings provide insights for developing more effective abusive language detection systems in low-resource languages and contribute to understanding the interaction between modern embeddings and classifier architectures.
Empirical evaluation of generative fusion decoding for Indonesian general and medical speech recognition Asril Jarin; Lyla Ruslana Aini; Agung Santosa; Gunarso Gunarso; Mohammad Teduh Uliniansyah; Elvira Nurfadhilah; Siska Pebiana
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.pp4893-4906

Abstract

This study empirically evaluates generative fusion decoding (GFD) with open-weight large language models for Indonesian automatic speech recognition (ASR) under two controlled offline conditions: general read speech (FLEURS-ID) and medical dictation (Harkit). The training-free byte-level fusion integrates three frozen language-model backends—SEA-LION, Sahabat-AI, and Mistral—with frozen Whisper-large-v3 during decoding. A development partition was used to select the static fusion weight and prompting condition, and a held-out test partition was used for final evaluation. On FLEURS-ID, the best GFD configuration reduced corpus-level word error rate (WER) from 5.91% to 4.17%, corresponding to a 29.36% relative reduction. On Harkit, it reduced WER from 15.89% to 12.30%, a 22.59% relative reduction. Paired utterance-level tests yielded p < 0.01 for both datasets. Several non-prompted configurations already improved over the unfused Whisper-large-v3 baseline, suggesting that the external language-model prior contributed more consistently than explicit prompting. Prompting produced limited changes on FLEURS-ID and larger but backend-dependent changes on Harkit. The results support GFD as a training-free decoding strategy under the evaluated conditions, while broader validation is required for noisy, spontaneous, streaming, and clinical deployment settings.
Machine learning-based prediction model for high-value waste types to support the circular economy Andi Irmayana; Nurul Aini; Ramlah P.; Muhammad Husnul Yaqin; Zalsabila Sabir
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.pp4199-4212

Abstract

The management of high-value waste is a strategic challenge in supporting the implementation of a circular economy at the city level. This study aims to develop and compare machine learning-based prediction models to classify the economic value of waste into low, medium, and high categories based on the characteristics of transaction types and quantities. The dataset was obtained from waste bank transactions in Makassar City from January 2023 to June 2025. The research stages included data preprocessing, category labeling using the k-means and quantile-based classification algorithms, and then creating classification modeling using random forest and gradient boosting. The evaluation results show that both models perform very well with an accuracy rate above 92%, where gradient boosting consistently shows superior performance in terms of F1-score and the ability to reduce classification errors in high-value categories. Confusion matrix analysis shows that the boosting approach is more adaptive in handling class boundary distributions, while random forest tends to be more stable against data variations. These findings confirm that the ensemble learning approach is effective in supporting the classification of waste economic value based on historical transaction data.
Road surface defect segmentation using the U-Net deep learning model Quang-Quy Tran; Thi-Lien Pham; Huy-Manh Trieu; Minh-Tien Dang; Anh-Kiet Lam; Quoc-Trung Ha
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.pp4743-4754

Abstract

The rapid expansion of transportation infrastructure in Vietnam has introduced new challenges in road maintenance, particularly in detecting and managing surface defects. Early identification and precise localization of these defects are essential to ensuring traffic safety and optimizing maintenance operations. To address these issues, this study proposes a U-Net–based deep learning model integrated with an internet of things (IoT) architecture for real-time road surface defect detection and monitoring. The proposed system operates on an embedded Raspberry Pi platform equipped with a camera, global positioning system (GPS) module, and auditory alert unit, enabling on-device inference with minimal latency. A mobile web application is also developed to visualize detected defects, record their geolocations, and support road authorities in timely maintenance planning. Experimental results on the pothole dataset demonstrate a training accuracy of 97.7%, while the model achieves a test accuracy of 89.49% on unseen data, confirming the robustness of the model under various environmental conditions. Unlike previous studies focusing solely on image-based detection, this work introduces a fully integrated IoT-driven ecosystem that bridges deep learning, embedded computing, and cloud-based visualization—enhancing automation, scalability, and real-time responsiveness in intelligent transportation systems.
Lightweight deep learning framework for autism spectrum disorder detection from children's facial images Prafulla Kumari Kannangala Siddaiah; Gayathri Koti Madhusudhana
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.pp4602-4611

Abstract

Autism spectrum disorder (ASD) refers to a neurodevelopmental disorder that affects children’s social interaction, responsiveness, and behavioral patterns. Early identification of ASD is necessary for prompt treatment of children. The existing diagnosis procedures are subjective, take a lot of time, and require interpretation by an expert. In this study, an efficient network baseline 0 (EfficientNetB0)-based deep learning (DL) approach is developed for automatic ASD detection from children's facial images. The model is trained on enhanced training data by using a preprocessing-aware data augmentation strategy. The proposed augmentation method includes image processing operations such as face alignment, Gaussian smoothing, hue, saturation, value (HSV) normalization, contrast-limited adaptive histogram equalization (CLAHE), and horizontal flipping. The experimental analysis demonstrated the effectiveness of the proposed predictive model against existing methods on the same dataset, where the model achieved 93% test accuracy and an area under the curve (AUC) score of 0.97. Additionally, gradient-weighted class activation mapping (Grad-CAM)-based interpretation demonstrated that the model focused on semantically relevant facial regions, which offers visual support and transparency for the classification. The proposed predictive system is lightweight and provides a non-invasive and scalable solution to support ASD screening using facial images.
Leveraging machine learning for fetal health classification Ravi Kumar Sachdeva; Priyanka Bathla; Rohit Lamba; Pooja Rani; Sanjoy Kumar Debnath; Wai Yie Leong
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.pp4315-4324

Abstract

Early diagnosis of fetal health complications is crucial to ensure maternal and infant safety. However, conventional tabular machine learning approaches often suffer from severe majority-class bias and a lack of clinical interpretability. In this research, an optimized Python-based framework was developed using a 2,126-record dataset containing 21 predictive features to classify fetal health status. To resolve structural class imbalance without data leakage, the synthetic minority over-sampling technique (SMOTE) was dynamically applied strictly within stratified 5-fold cross-validation loops. Six configurations—naive Bayes, logistic regression, support vector machines, a multi-tiered deep multi-layer perceptron (deep MLP), random forest, and extreme gradient boosting (XGBoost)—were systematically tuned using randomized grid searches. Among the benchmarked models, the upgraded XGBoost ensemble demonstrated the highest performance, leading with a diagnostic accuracy of 94.87%, a macro F1-score of 91.37%, and an area under the curve (AUC) of 98.77%. While the deep MLP required a high training latency of 13.8179 seconds, the optimal XGBoost layout achieved superior power with a training runtime of only 1.8325 seconds and an inference speed of 0.0046 seconds. Game-theoretic Shapley additive explanations (SHAP) values confirmed abnormal short-term variability as the primary risk driver, ensuring a transparent, high-throughput decision-support tool.
Electroencephalography-based epileptic seizure classification using a sparse iterative recursive-gated recurrent unit Santhosh Kumar Gorva; Mohan Gowda Venkateshappa; Pradeep Kesagodu Rajashekar; Narayan Naik; Shivamma Devanna; Navya Rajashekar
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.pp4540-4555

Abstract

Epileptic seizure classification using electroencephalography (EEG) plays a significant role in clinical diagnosis, long-term patient monitoring, and treatment planning. However, accurate seizure classification remains challenging due to its high complex and non-stationary nature of brain signal, which leads to misdiagnosis. This research proposes a sparse iterative recursive (SIR)-gated recurrent unit (GRU) to classify epileptic seizures accurately. By incorporating GRU into SIR, the model effectively learns and retains significant temporal patterns in EEG signals over long sequences. The gated structure minimizes the vanishing gradient, resulting in effective learning from sequential seizure patterns. Sparsity and recursion enhance the feature representation with lesser computational complexity for accurate seizure classification. Time-and frequency domain feature extract with sparsity and iterative recursion enables the model to selectively concentrate on seizure relevant temporal patterns, which minimizes redundant and noisy information. Hence, SIR-GRU achieves higher accuracies of 99.75% and 99.54% on the Bonn EEG as well as Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, respectively, compared to the existing method including spatio-temporal feature fusion dual attention (STFFDA). These findings indicate that SIR-GRU provides rapid and reliable performance, which improves accuracy in clinical practice.
Advanced synchronization strategies in metacloud systems for reliable distributed data storage Ghassan Adnan Hammoodi Al-Bdairi; Saad Talib Hasson
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.pp4980-4991

Abstract

The exponential growth of distributed data storage and the limitations of single-provider cloud infrastructures have motivated the emergence of metacloud systems, integrated environments that connect multiple independent cloud platforms to enhance data reliability and security. This paper presents an advanced synchronization framework for metacloud systems, addressing the challenge of maintaining consistent and secure data across heterogeneous cloud nodes. Three synchronization strategies are proposed and analyzed: the primary source synchronization algorithm (PSSA), which ensures rapid one-way replication from a central node; the merge synchronization algorithm (MSA), which intelligently reconciles concurrent updates from multiple sources; and the bi-directional synchronization algorithm (BDSA), which provides complete decentralization through peer-to-peer synchronization. A detailed comparative discussion highlights the computational efficiency, reliability, and scalability trade-offs among the three approaches. The findings demonstrate that adaptive integration of these models can significantly improve system fault tolerance and performance. Finally, future research directions are outlined, emphasizing the role of artificial intelligence (AI), blockchain validation, and quantum-safe security in advancing next-generation metacloud synchronization frameworks.

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