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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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A reinforcement learning-based intrusion detection system for internet of things with zero-day resilience Sowmya Somanath; Usha Banavikal Ajay; Sangeetha Kodlipet Nanjundaswamy
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.pp4691-4699

Abstract

The conventional intrusion detection systems (IDS) are often found to be ineffective when faced with zero-day attacks that are novel compared to previously learned behavior patterns. This paper presents the development of a robust and adaptive IDS for the internet of things (IoT) using a deep Q-network (DQN) agent. A custom environment is designed in which state abstraction is applied to convert high-dimensional feature vectors into discrete state spaces. The proposed agent model is trained to detect normal and malicious traffic using a reward and penalty mechanism to encourage the agent to take correct decisions. The validation of the proposed methodology is carried out using the UNSW-NB15 dataset under both known attack and zero-day attack scenarios. The results showed detection accuracies of 99.56% for known attacks and 99.11% for zero-day attacks.
Stacking ensemble for multilingual aspect-based sentiment analysis of tourism reviews Basworo Ardi Pramono; Rahmat Gernowo; Aghus Sofwan
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.pp4381-4391

Abstract

Multilingual tourism reviews often express opinions about multiple destination attributes within a single text, making aspect-level sentiment classification challenging. This study compares classical machine learning and ensemble methods for multilabel aspect-based sentiment analysis of amenities, attractions, and accessibility. The dataset comprises 13,085 Indonesian- and English-language Google Reviews labeled through a rule-based procedure. Reviews were represented using term frequency-inverse document frequency features and classified using logistic regression, multinomial naive Bayes, random forest, soft voting, adaptive boosting (AdaBoost), and stacking under a one-vs-rest framework. The stacking classifier combined logistic regression and random forest as base learners with logistic regression as the meta-learner. It achieved the best performance among the evaluated models, with a micro-F1 of 0.8626, macro-F1 of 0.7285, Hamming loss of 0.0633, and subset accuracy of 0.6825. These findings indicate that stacking effectively integrates complementary decision patterns and provides a reproducible framework for multilingual multilabel analysis of tourism reviews.
Deep multi-block convolutional neural network for quantitative near-infrared spectral analysis Hilmy Abidzar Tawakal; Yohannes Aris Purwanto; Sony Hartono Wijaya; Shelvie Nidya Neyman
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.pp4512-4522

Abstract

Near-infrared (NIR) spectroscopy is a powerful analytical technique, but its effectiveness is often hindered by instrumental noise and a dependency on expert-driven preprocessing and calibration. This study introduces a novel, intelligent deep learning architecture, a deep multi-block convolutional neural network (CNN) that addresses these challenges by performing end-to-end quantitative analysis directly on raw spectral data. By segmenting the NIR spectrum into multiple blocks and processing them in parallel, the model mimics multi-scale feature learning, automatically identifying and leveraging localized spectral regions relevant to target properties. This approach eliminates the need for manual preprocessing, a significant step toward creating autonomous analytical systems. This study evaluated the model on four rice quality datasets (moisture, protein, fatty acids, and total fat) and benchmarked it against traditional partial least squares regression (PLSR) and other deep learning models. The proposed multi-block CNN consistently achieved superior performance, demonstrating its ability to handle raw spectral data robustly. For instance, it achieved coefficient of determination (R2) values of 0.996 ±0.003 for total fat and 0.997 ± 0.001 for fatty acids. Statistical tests confirm that the model’s performance on raw data is not significantly different from its performance with optimized preprocessing, validating its potential for real-world artificial intelligence applications in agriculture, food quality monitoring, and biosystems engineering where rapid, automated analysis is critical.
Classifying Arabic handwriting using convolutional neural network Anas Quteishat; Asharaf Al Sharah; Ahmed Qtaishat; Tareq Alawneh
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.pp4015-4025

Abstract

This paper presents a classification of Arabic handwritten letters using convolutional neural networks (CNNs). The paper aims to address the unique challenges of recognizing Arabic letters due to their special characteristics such as dots and similarities. Leveraging deep learning, particularly CNNs, the project employs an architecture specifically designed for image classification tasks, which is well-suited for optical character recognition (OCR) of Arabic handwritten letters. The dataset used consists of images collected from the "Arabic handwritten alphabets, words, and paragraphs" (AHAWP) and "Arabic handwritten character database" (AHCDB), which have been carefully processed to ensure uniformity and diversity. The model was built using deep network designer, a tool in MATLAB, and trained over three epochs to prevent overfitting, achieving an accuracy of 89.2%.
Combining depthwise separable convolution and transfer learning for pneumonia classification Puspita Kartikasari; Rukun Santoso; Suparti Suparti; Rizwan Arisandi; Vikri Haikal
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.pp4792-4805

Abstract

Pneumonia represents a prevalent pulmonary illness within the general population, significantly impacting diverse demographic groups, most notably infants. Therefore, identifying the disease at an early stage is essential to manage the condition effectively and boost the likelihood of a complete recovery via proper therapeutic interventions. This research endeavors to construct a robust early diagnostic framework based on medical imaging of pneumonia. The methodology introduced evaluates the synergy of depthwise separable convolution, transfer learning, and soft voting mechanisms, where each predictive model undergoes rigorous optimization via particle swarm optimization (PSO). For this study, the dataset is strictly categorized into two distinct groups: normal and pneumonia-infected. The analytical findings demonstrate that the PSO-optimized soft voting ensemble achieves a peak accuracy rate of 92.11%. Such a substantial accuracy level suggests that the developed framework can function as a dependable supplementary diagnostic instrument in clinical environments, which could minimize human diagnostic errors and substantially reduce the time radiologists spend screening critical cases. Moreover, embedding these computational methodologies into interactive, user-friendly web dashboard framework highlights the model’s practical feasibility for delivering real-time diagnostic assistance in resource-constrained environments.
Investigating the use of near infrared in household object recognition Muhammad Attamimi; Muhammad Ammar Aiman Majid; Hendra Kusuma; Muhammad Rivai; Rudy Dikairono
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.pp4946-4960

Abstract

Household object recognition remains a challenging task in robotics due to environmental disturbances like varying illumination conditions. This study investigates the utilization of standalone near-infrared (NIR) imaging to mitigate household object recognition challenges across 20 distinct classes under diverse lighting scenarios. Crucially, this research focuses on evaluating the isolated, single-channel baseline capacity of the NIR spectrum to explicitly establish its independent robustness prior to introducing complex multimodal architectural fusion frameworks. Based on a total dataset of 73,269 synchronized images (36,631 red, green, blue (RGB) and 36,638 NIR) captured across variable illumination states, this research modeled the recognition process using a convolutional neural network (CNN) with transfer learning based on the efficient network (EfficientNet) architecture. To evaluate the model's capabilities, we tested performance across variations of dim white and RGB light. This research reveals that the NIR-based model significantly outperforms the standard RGB baseline, which achieved an average accuracy of (64.55 ± 24.32)%. In contrast, the investigative NIR model maintained high robustness, achieving an average accuracy of (95.85 ± 3.08)% across all testing scenarios. This research establishes a robust foundation for implementing NIR spectrum analysis in household robotics to enhance operational reliability in real-world environments.
Enhanced diabetes prediction using SMOTE-Tomek and F1-optimized machine learning models Chaitra Nayak Janasale; Rajanikanta Mohanty; Sarappadi Narasimha 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.pp4283-4291

Abstract

This study presents a systematic and effective strategy for the early prediction of diabetes, utilizing a hybrid machine learning framework designed to address prevalent issues found in conventional diagnostic systems. A significant challenge in medical datasets, particularly in the context of diabetes, is the issue of class imbalance, characterized by a disproportionate representation of non-diabetic cases compared to diabetic cases. The proposed framework addresses this issue by implementing the synthetic minority oversampling technique (SMOTE)-Tomek resampling in conjunction with a model selection strategy that emphasizes the F1-score for the diabetic class. This approach ensures a heightened focus on accurately identifying at-risk individuals, rather than solely optimizing for overall accuracy. A variety of supervised learning models were assessed, comprising logistic regression (LR), decision trees (DT), support vector machines (SVM), random forest (RF), k-nearest neighbors (KNN), extreme gradient boosting (XGBoost), and artificial neural networks (ANN). A 10-fold cross-validation technique was utilized to ensure a robust evaluation of model performance. The evaluated models, ANN and XGBoost, demonstrate notable performance, with F1-scores of 0.87 and 0.84, respectively. The ANN model achieves the highest accuracy, recorded at 94.3%. SMOTE-Tomek has enhanced recall and decreased false negatives, thereby increasing the model's sensitivity to diabetic cases.
Predicting willingness to pay for digital waste services using class-balanced machine learning Rahmat Taufik R. L. Bau; Sri Nilawaty Lahay; Dedy Abdianto Nggego
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.pp4335-4344

Abstract

As urban waste generation continues to increase, digital waste management applications offer a promising solution, yet their scalability is often constrained by low user adoption and uncertain willingness to pay (WTP). This study proposes a supervised machine learning (ML) framework to predict individual WTP for digital waste services using survey data from 400 residents of Gorontalo City, Indonesia, where fewer than 60% of households receive reliable waste collection. A random forest (RF) classifier with class-balancing and F2 score optimization was developed to prioritize recall of potential payers. The model achieved strong performance (F2 score: 0.81) and revealed occupation, app usability perception, and cost tolerance as top predictors. Feature importance analysis, correlation assessment, and boxplot visualization further clarified the behavioral and socio-economic drivers underlying payment decisions. By emphasizing recall over precision, the framework minimizes the risk of excluding willing participants and supports inclusive service planning. Beyond the local case study, the proposed approach applies to other cities with similar waste management constraints and emerging digital service ecosystems, offering a transferable decision-support tool for data-driven pricing, outreach, and policy design.
A deep learning approach to object detection in quadcopter based reconnaissance Morakot Thongprom; Manit Inkamchuer; Banjerd Saengchandr; Rabin Palee; Viroch Sukontanakarn
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.pp4392-4402

Abstract

This paper presents a reconnaissance system utilizing a remote-controlled quadcopter drone, designed to transmit long-range, and real-time video feeds for target object detection. The system enables continuous surveillance and rapid identification of targets in remote or high-risk areas. It employs high-resolution imaging and video capture through a camera mounted on a stabilized gimbal, along with long-range radio frequency communication to ensure high-quality video transmission over extended distances. For object detection and classification, the system integrates the CiRA CORE software, which processes video input from a universal serial bus website camera (USB webcam) quickly and efficiently. Field tests demonstrate the system's effectiveness in dynamic and changing environments, highlighting its strong capability to detect and distinguish targets. The results confirm the feasibility of reliable, real-time target detection from a long-range unmanned aerial surveillance platform.
Deep attention transformer models for chronic kidney disease stage prediction Subashini N. J.; Venkatesh Kaliyamurthy
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.pp4711-4723

Abstract

Chronic kidney disease (CKD) is a growing health problem that demands accurate stage prediction for timely clinical intervention. Conventional machine learning (ML) models, although widely used, often struggle to capture complex, non-linear relationships in clinical data. To address this limitation, this study proposes a deep learning (DL) framework based on two tabular architectures: attention network (AttentionNet) and tabular transformer (TabTransformer). AttentionNet assigns higher importance to clinically relevant features through an attention mechanism, while TabTransformer uses self-attention to model dependencies among clinical variables. These models were evaluated against baseline approaches, including residual multilayer perceptron (ResMLP), wide and deep (WnD), and a standard multilayer perceptron (MLP). The dataset comprised 1,446 patient records with 26 validated features after leakage removal, spanning five CKD stages. Preprocessing involved cleaning, categorical encoding, normalization, and evaluation using 10-fold cross-validation. Results showed that ResMLP and TabTransformer consistently achieved the highest accuracy and F1-scores, both exceeding 99%, with minimal variation across folds. Ablation analysis revealed the critical role of glomerular filtration rate (GFR) and serum creatinine (SC), as their removal led to notable performance degradation. High area under the curve (AUC) values further confirmed model robustness. Overall, DL-based tabular models offer a reliable foundation for CKD stage prediction and clinical decision support systems.

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