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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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Energy loss prediction using least absolute shrinkage and selection operator regression and SHAP explainability Nur Diana Izzani Masdzarif; Siti Azirah Asmai; Yogan Jaya Kumar; Muhammad Hafidz Fazli Md Fauadi
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.pp3103-3119

Abstract

Technical energy loss estimation in power distribution systems is essential for improving operational efficiency and cost-effectiveness. However, distribution-level datasets are often failed to cope with the nonlinear behavior and sparse, low-resolution data typical in modern grid environments. This study proposes an interpretable artificial intelligence (AI) framework based on least absolute shrinkage and selection operator (LASSO) regression integrated with Shapley additive explanations (SHAP) to estimate and explain technical energy losses at the feeder level. An exploratory multicollinearity assessment using correlation analysis and variance inflation factor (VIF) revealed severe redundancy among operational variables, justifying the adoption of L1-regularized regression. Hyperparameter tuning via LassoCV identified an optimal regularization parameter, resulting in strong predictive performance. Comparative evaluation with nonlinear models, including random forest and gradient boosting, demonstrated that LASSO achieves competitive or superior generalization performance while preserving interpretability. Feature importance analysis and SHAP-based explanations confirmed that operational loading variables particularly infeed energy, load factor, and maximum demand are the dominant drivers of technical losses. SHAP dependence and interaction analyses further revealed context-dependent behavior among correlated predictors, enriching interpretability beyond coefficient-based rankings. The results demonstrate that regularized linear modeling, when combined with explainable AI techniques, provides a robust, transparent, and practically deployable solution for technical loss estimation in distribution networks.
Deep learning for categorizing microsatellite stability in colorectal cancer Sofyan El Idrissi; Yassine Drider; Ikram Ben Abdel Ouahab; Mohammed Bouhorma; Fatiha El Ouaai
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.pp3761-3769

Abstract

Cancer remains a significant global health challenge, with its incidence rising steadily in recent decades. In colorectal cancer (CRC), microsatellite instability (MSI), and microsatellite stability (MSS) are important biomarkers that influence treatment decisions and patient outcomes. Accurate MSI classification is critical but traditional methods can be costly and time-consuming. This study explores the potential of deep learning to classify MSI and MSS in CRC. A large dataset of CRC patients with confirmed MSI and MSS status was utilized, obtained through standard testing images. Deep learning models were applied to histopathological images, analyzing tissue features from digital slides. Convolutional neural network (CNN) and residual network (ResNet)-18 models demonstrated high accuracy in distinguishing between MSI and MSS CRCs. The best-performing model, which integrated genomic and histopathological data, achieved an area under the curve (AUC) receiver operating characteristic (ROC) of 0.85, indicating strong discrimination capability. The findings suggest that deep learning could be a valuable tool for clinical decision-making and personalized medicine in CRC.
Hybrid quantum-classical neural networks for brain computed tomography scan diagnosis Bilal R. Altamer; Muhamad Azhar Abdilatef Alobaidy; Aws Hazim Saber Anaz; Zahraa Tarik AlAli
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.pp3342-3351

Abstract

Medical image classification is considered as very important field of diagnosis and treatment of neurological disorders, which include stroke, tumors, and hemorrhages, it can used to facilitate timely medical intervention. A hybrid quantum-classical convolutional neural network (QCNN) is presented by combining quantum information processing with classical deep learning techniques for improved feature extraction and classification accuracy. The model combines convolutional neural network (CNN), which handles initial feature extraction with a PennyLane and TensorFlow based layer that employs quantum entanglement and superposition principles to enhance classification performance. The model is trained and tested over a computed tomography (CT) scan image dataset which has four classes (normal, stroke, tumor, and hemorrhage). Various learning rates are tested and employed a hybrid backpropagation method to improve efficiency. Confusion matrices, region of conversion receiver operating characteristic (ROC) curves, and plots for the training convergence that all pointed to promising classification accuracy were derived with a detailed analysis accordingly. Overall, the results indicate that combining quantum computing with deep learning architectures could improve classification performance at a lowest computational cost. The results suggest the viability of hybrid quantum-classical models for medical imaging applications and indicate that quantum computing is a promising direction for enhancing diagnostic accuracy in the area of radiology.
Context-aware AgriBot using dual intent and entity transformer and hybrid deep learning model Binod Deka; Ridip Dev Choudhury; Utpal Barman
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.pp3637-3645

Abstract

The agriculture sector has gone through vast technological improvement, leading to increased productivity and sustainability. This research introduces AgriBot, a context-aware virtual assistant (VA) that helps farmers in rice cultivation and identifies diseases while providing instant suggestions for subsequent task. Using the RASA framework, AgriBot has been designed to understand the farmer's queries. Dual intent and entity transformer (DIET) classifier has been used for entity classification, achieving training and testing accuracy of up to 98% and 97%, respectively. Additionally, the system incorporates machine learning (ML) models for rice disease detection, utilizing a dataset of 4,624 images covering three major rice diseases: bacterial blight, brown spot, and blast. Among the tested models—neural network (NN), random forest (RF), support vector machine (SVM) and naive Bayes (NB) achieved an accuracy of up to 99.9%, demonstrating excellent classification performance. Using text-based query handling with image-based disease identification and instant suggestion makes it a more robust support system for the farmers.
Exploring cognitive patterns in children with autism spectrum disorder using correlation and cluster analysis Hana Bezzih; Muna Darweesh; Amjad Gawanmeh
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.pp3164-3175

Abstract

This paper investigates the relationships between age and five cognitive abilities in children diagnosed with autism spectrum disorder (ASD). Data from 210 children aged 6 to 12 years who completed measures of visual motor precision (VM), facial memory (FM), spatial vision (SV), drawing memory (DM), and orientation (OR) were analyzed. Data preparation, descriptive statistical analysis, correlation analysis, cluster analysis, and factor analysis were conducted. The results showed that age was not significantly associated with the cognitive measures. The clearest association was a moderate positive correlation between VM and FM (0.34). Cluster analysis identified three groups. However, the low silhouette score (0.1368) indicated weak separation and substantial overlap between cognitive profiles. Factor analysis suggested three latent dimensions: factor 1 was strongly associated with VM and FM; factor 2 was negatively related to DM and moderately positively related to OR; and factor 3 was primarily driven by SV. These findings suggest that cognitive abilities in children with ASD operate relatively independently, with some shared mechanisms between certain skills. It can be concluded that individualized approaches in assessment and intervention strategies are warranted.
Genetic algorithm-optimized BERTopic with SHAP explainability for institutional research trend analysis Muhammad Dedi Irawan; Yustria Handika Siregar; Hewa Majeed Zangana; Ali Ikhwan
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.pp3818-3826

Abstract

Institutional research grant titles constitute short-text grey literature characterized by heterogeneous semantic structures, making topic identification and research trend analysis challenging. This study proposes an integrated bidirectional encoder representation from transformers-based topic modeling (BERTopic) framework combining genetic algorithm (GA)-based hyperparameter optimization and Shapley additive explanations (SHAP)-based interpretability to improve semantic topic quality and model transparency. GA was applied to optimize dimensionality-reduction and density-based clustering parameters, while SHAP was used to estimate the contribution of bigram features to the surrogate classifier’s predictions of BERTopic-generated topic labels. Experimental results demonstrated that the proposed framework improved topic coherence from 0.367 to 0.543 while reducing the outlier ratio from 21.12% to 13.55%. In addition, the number of topics decreased from 46 to 10, resulting in a more compact and less fragmented topic structure. The resulting topic structure revealed dominant themes related to higher education, religious moderation, Islamic counseling, halal tourism, and sharia banking. Overall, the proposed framework contributes to the development of more coherent, interpretable, and semantically robust topic modeling for institutional short-text grey literature analysis.
Leveraging artificial intelligence in education a comprehensive review of the Arab literature Mohamed Abdelraouf Elsayed; Taleb Saleh Alattas
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.pp2985-2998

Abstract

This descriptive analytical study relied on the systematic literature review (SLR) addressing the role of artificial intelligence (AI) in improving education management and delivery, empowering teaching and teachers, learner learning, and learning assessment. The SLR sample was determined in 125 articles from Almandumah and Scopus databases. The analysis results revealed: 39.2% of articles were related to the Saudi Arabia context, 34.4% were published in 2024, and 68.8% were quantitative. The most prominent AI applications used were: ChatGPT, Alexa, Grammarly, Duolingo, Chatbot, and Coursera. The results also showed that the most areas of the educational process in which AI applications can be implemented were: empowering teaching and teachers (62.4%), and improving learner learning (48.8%). The SLR also came up with some practices that can be applied to enhance the AI use in improving the educational process. An opinion poll was applied on 37 academic experts to identify their agreement on the potential of implementing these developed practices in the areas of the educational process. The results showed that the experts' agreement on the potential of implementing these practices in the areas of the educational process was very high. The study recommended some useful mechanisms for implementing these practices.
AI-driven hybrid neural network for electrocardiogram-based authentication and predictive health monitoring Swati Lakshmi Boppana; Velicheti Anantha Lakshmi; Padala SriKavitha; Venkata Ashok Kalaga; Venkateswara Rao Naramala; Suneetha Thalluru; Gunturi S. Raghavendra
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.pp3309-3317

Abstract

The increasing adoption of digital healthcare systems demands secure and reliable patient authentication mechanisms. Traditional methods such as passwords and PINs are vulnerable to security breaches, motivating the use of biometric-based solutions. Among various biometrics, the electrocardiogram (ECG) signal is distinctive, stable, and non-invasive, making it suitable for secure authentication. This paper proposes CardioGuard, an artificial intelligence (AI)–based authentication framework that employs a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to extract discriminative features from ECG signals and classify users as genuine or impostors. In addition to access control, the system analyzes ECG patterns to support early detection of potential cardiovascular abnormalities. Experimental results demonstrate that CardioGuard achieves improved authentication accuracy and enhanced predictive health insights compared to conventional approaches, highlighting its effectiveness for secure and intelligent healthcare monitoring.
A holistic energy expenditure tracking framework: fuelling wellness with holistic energy tracking Prachi Kadam; Gagandeep Kaur; Leena Manojkumar Panchal; Smita Nirkhi
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.pp3546-3555

Abstract

Effective public health management is essential for a country’s social and economic development. A strong public health system reduces the strain on healthcare infrastructure by encouraging preventive measures. With significant lifestyle changes in recent years, monitoring both energy intake (EI) (diet) and energy expenditure (EE) (physical activity (PA)) has become crucial to maintaining public health. Existing methods primarily track exercise-related EE using self-assessment tools or wearable devices, often neglecting occupation-related activities. This results in an underestimation of total EE. To address this limitation, we propose the holistic energy expenditure tracking (HEET) framework, which aims to provide a comprehensive estimate of daily EE by including occupational activities. The framework was applied to data from a dietitian who collected 180 data points from working professionals across three different occupations. Exploratory data analysis revealed a 41% increase in metabolic equivalent of task (MET) values and a 27% rise in calorific values when occupational EE was considered. A categorical scoring system was developed based on the new calorific values, enabling dietitians to offer more personalized dietary recommendations. The study highlights the need for a standardized framework that captures all daily activities, offering a more accurate and holistic approach to public health monitoring and intervention.
Multi-stage hybrid YOLO-driven and MobileNetV2-CNN variants for robust fish freshness classification Raseeda Hamzah; Rosniza Roslan; Amni Munira Khidir; Lala Septem Riza
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.pp3660-3671

Abstract

This study presents a multi-stage hybrid representation learning framework for robust fish freshness classification to address the critical challenge of reliable quality assessment in unpreserved food supply chains. The proposed pipeline operates in two stages: stage-1 employs you only look once (YOLO)v8n as feature-gated detector to validate inputs and eliminate non-fish images, while stage-2 leverages transfer-learned MobileNetV2 variants enhanced with convolutional neural network (CNN) layers, fine-tuning, adaptive learning rate schedulers, and expanded fully connected layers for hierarchical classification into three freshness classes i.e., highly fresh, fresh, and not fresh. The framework has been trained and evaluated on two curated datasets comprising 4,500 fish and non-fish images and 11,111 freshness-labeled including augmented dataset to increase diversity. The experimental results showed significant improvements. The best performance model transfer learning (TL)-MobileNetV2 + CNN + fine-tuning achieved 98.07% training accuracy and 67.72% validation accuracy on 80:20 split, and training accuracy of 97.57%, validation accuracy of 97.21% through 10-fold cross-validation. The comparative benchmarking confirmed that dual-stage design outperformed baseline MobileNetV2 and YOLOv5s models across precision, recall, and F1-score. The findings highlighted significant value of integrating detection-driven validation with transfer-learning classification, and propose new benchmark for intelligent freshness monitoring. For future work, this study aims to explore attention-based models, data integration, and species diversity.

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