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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 86 Documents
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Search results for , issue "vol 15, no 5: october 2026" : 86 Documents clear
Explainable deep learning framework for classifying pathogen-induced chest infections with fuzzy severity reasoning Nirmala Bai Limba Naik; Manju Devi
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.pp4755-4765

Abstract

Accurate classification of pathogen-induced chest infections is a vital step in enabling rapid diagnosis in the context of a high clinical burden scenario. This paper proposes an explainable deep learning (DL) framework that employs a hybrid approach by integrating an efficient classification model with both decision-level and feature-level interpretability. A neural search architecture is adopted to develop a fine-tuned EfficientNet model for multi-class classification of chest radiographs subjected to seven classes. To enhance clinical trust, a Mamdani-type fuzzy-logic-based severity reasoning layer is introduced to provide contextual assessments of disease severity and possible co-infections. Furthermore, feature-level explanations are enabled using gradient-weighted class activation mapping (Grad-CAM) to visualize important regions that contribute to each prediction. The model is validated on a comprehensive dataset, and the experimental outcome demonstrated the effectiveness of the proposed framework with classification accuracy of 96% and sensitivity of 95%.
Satellite imagery and deep learning framework for highway construction with minimal vegetation impact Meenakshi Meenakshi; Rajesh Thalwagal Matha
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.pp4933-4945

Abstract

Constructing roads through vegetated terrain forces a trade-off between minimizing ecological harm and preserving constructional ease. This work presents a unified, end-to-end framework that reconciles both objectives, with three major contributions. First, propose a hybrid perception model that fuses a graph convolutional network (GCN)—capturing scene-level spatial context—with a capsule network (CapsNet) that encodes part-whole relationships for reliable detection of small, clustered vegetation; this configuration attains an average precision (AP) of 0.925, average recall (AR) of 0.948 (intersection over union (IoU) = 0.5), and 98% land-cover classification accuracy on unseen data. Second, we introduce an ecological-sensitivity cost map fusing vegetation normalized difference vegetation index (NDVI) with terrain slope, over which we evaluate Dijkstra, A*, a novel Eco-Aware A* embedding a vegetation-avoidance penalty, and an non-dominated sorting genetic algorithm II (NSGA-II) multi-objective search that jointly minimizes path length and vegetation damage. Third, on a benchmark modeled on the Bengaluru–Kanakapura road (NDVI = 0.7, slope = 0.3), the eco-sensitive route lowers segment NDVI by roughly 69% and eliminates all high-canopy exposure while adding only about 8% to path length versus an optimized non-vegetated route. Together, these components enable automated, ecologically aware road planning directly from imagery.
Evaluating conditional convolution for magnification-aware histopathological image classification Wissal El Habti; Abdellah Azmani
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.pp4612-4623

Abstract

Histopathological breast cancer images exhibit multi-scale variability and class imbalance, challenging automated classification. This study integrates conditionally parameterized convolutions (CondConv) into convolutional neural networks (CNNs) to enhance accuracy and robustness across heterogeneous magnifications. This study proposes CondConv-augmented mobile network version 2 (MobileNetV2) and densely connected convolutional network 121 (DenseNet121), which dynamically adapt convolutional kernels to input characteristics, improving generalization across spatial resolutions. This is the first evaluation of CondConv-based architectures for multi-magnification histopathological classification. This study evaluates these architectures at 40×, 100×, 200×, and 400× using imbalanced and class-rebalanced datasets. Performance was measured via convergence analysis, macro-averaged metrics, and statistical validation with Friedman tests, Nemenyi post-hoc comparisons, and Wilcoxon signed-rank tests. CondConv-enhanced networks matched or surpassed static-kernel variants, with notable gains at 100× and 200× where scale variability peaks. These improvements persisted under class imbalance, demonstrating resilience to distributional skew. Although differences narrowed at 400× due to feature saturation, statistical analyses confirmed significant enhancements at lower magnifications, especially for CondMobileNetV2.
Fine-tuning OpenAI’s Whisper model for Kazakh speech recognition Bakdaulet Kynabay; Aimoldir Aldabergen; Shirali Kadyrov; Ardak Shalkarbay-uly
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.pp4053-4060

Abstract

Speech recognition remains a challenging task, especially for low-resource languages such as Kazakh. This study explores the effectiveness of fine-tuning OpenAI’s Whisper model on Kazakh speech data for improving speech recognition accuracy. The Kazakh speech corpus (KSC) is used as a benchmark dataset and to conduct experiments to evaluate how the adapted Whisper framework measures up against current leading speech recognition models, including deep neural network (DNN)-hidden Markov model (HMM), end-to-end long short-term memory (E2E-LSTM), and end-to-end transformer (E2E-transformer). Research outcomes indicate that the Whisper model outperforms all baselines after fine-tuning, showing a result with a word error rate (WER) of 12.5%, a character error rate (CER) of 3.15%, a character-level F1-score (chrF) of 93.72, and a bilingual evaluation understudy (BLEU) score of 76.05. These findings suggest that the Whisper model that is fine-tuned on Kazakh audio data can be a promising approach for improving Kazakh speech recognition accuracy. This work advances the development of speech recognition for the Kazakh language by demonstrating the effectiveness of transfer learning in low-resource languages. Subsequent research must prioritize collecting larger and more diverse speech data for Kazakh, including noisy and accented speech, to further improve speech recognition accuracy.
Feature-engineered stacking and AutoGluon framework for deep learning-based autism detection Savita Rani; Chandra Shakher Tyagi; Devendra 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.pp4260-4272

Abstract

Autism spectrum disorder (ASD) is a brain development disorder that distracts a person’s perceptions and social interactions with others. Current studies emphasize conventional machine learning and typical ensemble methods and little focus have been placed on merging automated machine learning (AutoML) systems with stacking ensemble structures. To bridge this gap, this study evaluates stacking ensembles with an artificial neural network (ANN)-based meta-classifier and the AutoGluon framework. For enhancing model stability and generalizability across various ASD datasets, systematic RandomizedSearchCV along with 10-fold stratified cross-validation is utilized. The stacking model outperformed the AutoGluon framework and all individual classifiers on dataset-I, achieving 99.50% accuracy, 99.53% precision, 99.53% recall, and a 99.97% receiver operating characteristic-area under the curve (ROC-AUC). With 97.28% accuracy, precision 96.33%, 95.38 F1-score, and a 99.76% ROC-AUC on dataset-II, the model demonstrated great generalization and continuously outperformed traditional ensemble and boosting techniques. Stacking model perform well on both datasets and maintain consistence, whereas AutoGluon give competitive performance on dataset-1 and it exhibited a notable decline on dataset-II. K-fold internal and external cross-validation were used to access model’s generalization and consistency. The outcomes of stacking ensemble model indicate that model offers higher accuracy, greater resilience, better consistency across difference datasets and offers a robust approach to early ASD prediction.
LLM-FS-Agent: a deliberative role-based large language model architecture for transparent feature selection Mohamed Bal-Ghaoui; Fayssal Sabri
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.pp4026-4038

Abstract

High-dimensional data can reduce interpretability and increase computational cost in machine learning (ML) pipelines. Large language models (LLMs) offer a promising approach for dimensionality reduction through feature selection (FS), but existing LLM-based methods lack structured reasoning and do not provide clear justifications for their choices. This study propose LLM-FS-Agent, a multi-agent architecture where specialized LLM roles collaborate through a deliberative process to evaluate and rank features. Each selection decision is accompanied by an explicit rationale. We tested our method on the CIC-DIAD 2024 internet of things (IoT) intrusion detection dataset and compared it against LLM-select and traditional techniques like principal component analysis (PCA) across multiple subset sizes. LLM-FS-Agent achieves comparable or better accuracy while reducing classifier training time by 46% on average (statistically significant, 0.094 s, p = 0.028 for extreme gradient boosting (XGBoost)). These results show that multi-agent deliberation improves transparency and computational efficiency in FS.
Hybrid feature selection strategies for strong network intrusion detection Josephine Rathinam; 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.pp4366-4380

Abstract

High dimensional network traffic datasets pose great challenges to network intrusion detection systems (NIDS) due to redundant and irrelevant features, which decrease detection accuracy and increase the computational cost. Traditional feature selection methods often cannot achieve a balance between classification performance and processing efficiency. This paper proposes hybrid feature selection for network intrusion detection systems (HybFS-NIDS), a hybrid feature selection framework using filter- and wrapper-based optimization methods to detect the most significant intrusion detection features to do this. The proposed model was evaluated on the CIC-IDS2017 dataset using performance metrics such as accuracy, precision, recall, F1-score, and Matthew’s correlation coefficient (MCC). The experimental results selected 18 optimal features from the initial 78 features of the dataset and achieved 90.68% accuracy, 91.74% precision, 90.64% recall, 91.19% F1-score, and an MCC of 0.9148. The proposed method also achieved a 41.5% reduction in training time compared with the full-feature baseline model while improving the overall classification performance. The results show that HybFS-NIDS can successfully improve the efficiency of intrusion detection and fit for real-time cybersecurity applications with a large amount of network traffic data.
A feature-enhanced deep learning model for multi-class paddy ailment prediction in smart farming Santhosh Byramangala Jayaramaiah; Balarengadurai Chinnaiah; Boddu Ravi Prasad
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.pp4470-4481

Abstract

Paddy is an essential staple food crop eaten by more than 50% of the world population, particularly in Asia, where countries like China and India account for over 50% of both production and consumption. However, paddy cultivation is frequently threatened by numerous ailments that badly impact crop production and quality. Normal ailment identification approaches are often manual, time-consuming, and error-prone. To address these challenges, deep learning (DL)-based approaches have been widely adopted for automated paddy ailment detection. While these models have achieved commendable accuracy, many fail to focus on effective feature extraction, which is vital for accurate classify across diverse ailment categories. Hence, this study proposes an advanced convolutional neural network (CNN) based approach associated with convolutional block attention module (CBAM) for enhancing feature representation by focusing on both spatial and channel-wise importance. The approach was trained and executed on the widely used “paddy doctor” dataset, covering 2-class, 10-class, and 13-class classify scenarios. The proposed CNN-CBAM achieved outcomes with 99.75%, 99.93%, and 99.92% accuracy, respectively, outperforming existing models. These outcomes show the model’s effectiveness in capturing discriminative features and improving classify performance. This model contributes a foundation for future extensions in precision agriculture, including pest detection and real-time monitoring.
Deep reinforcement learning-based credit risk prediction model for multi-attribute data Al-Khowarizmi Al-Khowarizmi; Ferry Fachrizal; Romi Fadillah Rahmat; Arif Ridho Lubis; Hariyadi Hariyadi
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.pp4624-4634

Abstract

This paper aims to develop a multi-attribute data-based credit risk prediction model using the deep reinforcement learning (DRL) approach. In the context of creditworthiness evaluation, a model is needed that is not only accurate but also adaptive to complex data dynamics and risk patterns. This study uses a dataset of 5,000 entries that have gone through a preprocessing process, with features such as credit score, income, age, loan amount, and employment history. Initial correlation analysis through heatmap visualization shows that credit score has a strong negative relationship with credit risk, while other features show weak to moderate correlations. The evaluation of model performance is shown through the area under the curve (AUC) value on the receiver operating characteristic (ROC) curve of 0.88, which indicates excellent discrimination ability. The final evaluation results show that the DRL model has an accuracy of 91%, precision of 89%, recall of 90%, and F1-score of 89.5%, indicating that the model not only has high accuracy but is also balanced in its sensitivity and specificity. Thus, the application of DRL has proven effective in predicting credit risk adaptively and accurately, and can be implemented as an intelligent solution in an automated credit decision-making system based on multi-attribute data.
Interpretable machine learning framework for congestive heart failure risk prediction Sakchai Tangprasert; Nalinpat Bhumpenpein; Siranee Nuchitprasitchai; Yuenyong Nilsiam
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.pp4345-4355

Abstract

Congestive heart failure (CHF) is a leading cause of morbidity and mortality worldwide and is influenced by multiple clinical risk factors, including age, sex, blood pressure, cholesterol levels, and electrocardiographic characteristics. This study employs publicly available heart disease datasets compiled from populations in Cleveland, Hungary, Switzerland, and Long Beach repositories (2015–2020) and analyzes 11 predictor attributes and one binary outcome using the cross-industry standard process for data mining (CRISP-DM) framework. Four supervised machine learning (ML) models—decision tree, random forest, gradient boosted trees, and naïve Bayes—were developed and evaluated using confusion matrix–based performance metrics, with results showing that the decision tree model offers balanced predictive performance and clear interpretability, making it suitable for heart failure risk prediction. The findings demonstrate the potential of ML approaches to support clinical risk assessment and enhance decision-making in heart failure management.

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