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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 83 Documents
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Search results for , issue "Vol 14, No 5: October 2025" : 83 Documents clear
Optimizing diabetes prediction: unveiling patient subgroups through clustering Ganguly, Rita; Singh, Dharmpal; Bose, Rajesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3681-3692

Abstract

Diabetes is a significant global health concern, leading to numerous deaths annually and affecting many individuals who remain undiagnosed. As its prevalence rises, the importance of early detection becomes increasingly vital. The rising diabetes epidemic demands data-driven strategies to catch health problems sooner and identify them clearly. This study utilizes the Pima Indians diabetes dataset (PIDD) to compare three powerful clustering schemes such as k-means, fuzzy C-means, and hierarchical. Uncontrolled diabetes, arising from the body's struggle to manage blood sugar due to insulin deficiency, can lead to devastating complications. Early detection and intervention are the cornerstones of effective management and improved patient outcomes. This study breaks new ground by meticulously evaluating the performance of each clustering algorithm using advanced metrics like silhouette score and adjusted Rand index. The goal is to identify the method that generates the most accurate and well-defined clusters for diabetes-related attributes. This, in turn, has the potential to revolutionize diabetes diagnosis, enabling earlier interventions and ultimately leading to better disease management and patient care. By providing a comprehensive comparison of these clustering techniques, this research offers a significant contribution to the fight against diabetes.
User acceptance of the gender and development mobile app with a rating checklist using a modified technology acceptance model Perea, Rossian V.; Miranda, Abigael M.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3906-3914

Abstract

Resource centers of gender and development (GAD) in local government use the traditional method of disseminating information about GAD awareness, such as distributing printed campaign materials and conducting gender sensitivity training (GST) on faculty and staff, students, and selected barangay communities in the Philippines. Some recipients of campaign materials are text-heavy and unappealing to read, which makes them less interested. However, faculty and students conducting research are not aware if their study is gender-responsive or if GAD is invisible. Hence, this study examines the user acceptance of the GAD app mobile application using the modified technology acceptance model (TAM) with a machine learning (ML) algorithm applied. The results of statistics and analyses from the intended users (N=100) were presented including data-driven modeling using a support vector machine (SVM) to show precise findings for the research on how this technology was used and accepted. The study’s findings show widespread acceptance among experts and users of the mobile application employing external factors like self-efficacy (SE) and specific anxiety (SA) and moderating variables such as age, sex, highest educational attainment (HEA), and knowledge in GAD implementation, which are crucial for predicting and understanding the consequences of the research made clear.
Designing an intelligent system for vibration diagnosis of centrifugal water-cooling pumps using Bayesian networks Suprihatiningsih, Wiwit; Romahadi, Dedik; Genetu Feleke, Aberham
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4390-4402

Abstract

Implementing monitoring methods is a viable method to reduce substantial damage to cooling water centrifugal pumps. Engaging in manual vibration analysis requires considerable time and a requisite level of competence. Small datasets pose challenges when applying classification systems that utilize linear classification models and deep learning. Given these issues, our proposal entails developing a system capable of autonomously, precisely, and accurately diagnosing vibrations using a limited dataset. The system is anticipated to possess the capability to detect multiple categories of mechanical defects, such as static imbalance, dynamic imbalance, misalignment, cavitation, looseness, and bearing corrosion. The Bayesian network (BN) structure was constructed using the MATLAB software. The input data parameters comprise vibration signals measured in the frequency domain and values representing phase differences. The constructed intelligent system was subsequently assessed using a dataset including 120 samples. The smart system can rapidly anticipate and precisely identify every form of harm with exceptional accuracy and sensitivity, relying on test outcomes. The test data analysis reveals that the intelligent system attained an average accuracy of 94.74%, precision of 95.32%, sensitivity (recall) of 93.67%, and F-score of 94.36%. 
Two-steps feature selection for detection variant distributed denial of services attack in cloud environment Kurniabudi, Kurniabudi; Winanto, Eko Arip; Sharipuddin, Sharipuddin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3945-3957

Abstract

The prevalence of cloud computing among organizations poses a significant problem in ensuring security. Specifically, distributed denial of services (DDoS) attacks targeting cloud computing networks can lead to financial losses for consumers of cloud computing services. This assault has the potential to render cloud services inaccessible. The detection system serves as a remedy to prevent more substantial losses. This research aims to enhance the efficacy of the system detection model by integrating feature selection with three machine learning algorithms: decision tree (DT), random forest (RF), and naïve Bayes (NB). Therefore, our study suggests combining two phases of feature selection into the DDoS attack detection procedure. The first phase uses the information gain (IG) feature selection technique approach, and the second phase uses the principal component analysis (PCA) feature extraction approach. The technique is referred to as two-step feature selection. The test findings indicate that the implementation of two-step feature selection can enhance the performance of the DT and RF detection models by around 9%.
Temporal context of lightweight network model for detecting boats approaching the tsunami early warning system Yogantara, Wayan Wira; Suprijanto, Suprijanto; Kusuma, Anak Agung Ngurah Ananda; Istianto, Yuki
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3542-3553

Abstract

The tsunami early warning system (TEWS) is a device that detects potential tsunamis. However, a boat that approaches TEWS is a source of communication disturbance. A convolutional neural network (CNN), as part of intelligent computer vision, is one solution for detecting boats and providing a warning to move away from the TEWS area. Water segmentation and refinement-temporal (WaSR-T), as the current advanced CNN network, exhibits impressive performance in detecting object obstacles in the marine domain, although it requires a powerful computational device. In the paper, we propose a modification of WaSR-T, replacing the most computationally intensive stages with a lightweight version called lightweight WaSR-T. On the proposed lightweight WaSR-T, the previous encoder of WaSR-T was replaced with MobileNetV3, and some feature layer maps were reduced as input to the decoder. For training and validating the lightweight WaSR-T, the image dataset representing the open sea and our extended dataset from Indonesia's ocean region were used. Based on the quantitative results and evaluation of the computational load, the sensitivity to detect a boat for WaSR-T and lightweight WaSR-T is 95.71% and 90.00%, respectively. The lightweight WaSR-T required less memory at 32.57%, resulting in a 0.0761% reduction in total processing time compared to the original WaSR-T. Therefore, our proposed lightweight WaSR-T is promising for use as the central part of an intelligent maritime computer vision system in TEWS.
A novel method for examining promoters using statistical analysis and artificial intelligence learning Sheet, Sinan Salim Mohammed; Al-Hatab, Marwa Mawfaq Mohamedsheet; Qasim, Maysaloon Abed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4006-4016

Abstract

Accurately classifying promoters has become a significant focus in bioinformatics research. Although numerous studies have attempted to address this challenge, the performance of existing methods still leaves room for improvement this study, statistical feature analysis has been applied to the features that have been developed in our previous work. This approach extracted additional informative features from basic sequence characteristics and then used them together with the original and newly engineered features. Utilizing statistical feature analysis enhanced key patterns, which lead to an improvement in the accuracy of the promoter classification. Results demonstrated that our proposed method outperforms other models that use only basic features. The value of the area under the curve (AUC) of 0.83958 achieved when using the combined feature set confirmed the effectiveness of our approach. Furthermore, the AUC value reached 1 when these optimized features were used with naive Bayes (NB) classifier, referring to the strength of incorporating statistical analysis into feature design.
Inverse-Mel scale spectrograms for high-frequency feature extraction and audio anomaly detection in industrial machines Tajuddin Shaikh, Kader Basha; P. Jawarkar, Naresh; Ahmed, Vasif; Ali Charniya, Nadir Nizar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3656-3666

Abstract

Unlike humans, the energies in industrial machine sounds (IMS) vary across a wide range of frequencies. Mel scales, which are developed for the perception of human audio, fail to capture the complete information present in IMS. To improve performance, we propose using an inverse-Mel scale, along with the concatenation and combination of Mel and inverse-Mel scale based spectrograms, as feature vectors for audio anomaly detection (AAD) in industrial machines. Adaptation in the Librosa Python package and the DCASE 2022 Challenge Task 2 baseline system is pursued for the construction of inverse-Mel scale spectrograms. Experiments are conducted using the malfunctioning industrial machine investigation and inspection for domain generalization (MIMII DG) datasets. Systems based on the inverse-Mel scale achieve a maximum improvement of up to 37% in the bearing machine and an average improvement of up to 9% in the area under the curve (AUC) score across all machines in the MIMII DG datasets. The proposed features also enhance DG, overcoming the effects of environmental and operational domain shifts caused by variations in recording setup, load, background noise, and operational patterns. Challenge official evaluator assessed the proposed system against the evaluation datasets, ranking it three positions higher than the baseline system.
Lung sound classification using YAMNet, neural network, and augmentation Arifin, Jaenal; Sardjono, Tri Arief; Kusuma, Hendra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4101-4112

Abstract

Globally, lung disease occupies a significant position as one of the main contributors to mortality rates. The characteristics of human respiratory sound signals can show a wide spectrum, ranging from normal patterns to indications of lung abnormalities. The proposed lung sound classification system is based on YAMNet as a pre-trained neural network model for medical audio recognition, which is then refined using artificial neural networks (ANN). This study presents the integration of multiple datasets and advanced pre-processing approaches. A total of 1,363 lung sound recordings from Kaggle, ICBHI, and Mendeley. This reflects the variety of clinical conditions, and differences in recording devices are combined. In order to increase the diversity of lung sound signal input, the pre-processing process is carried out through several stages, including adjusting the sampling frequency to 4 kHz, segmenting for 6 seconds, signal filtering with wavelet, min–max normalization, and data augmentation using window warping, jittering, cropping, and padding. A fold cross-validation scheme is employed to comprehensively evaluate the model's effectiveness. The evaluation results indicate that the model achieves an accuracy of 93.64%, a precision of 93.60%, a recall of 93.64%, and an F1-score of 93.52%, collectively reflecting outstanding classification performance. This work may incorporate deep learning technology into clinical practice, ultimately improving diagnosis accuracy and efficiency in the hospital setting.
Enhanced deepfake detection using an ensemble of convolutional neural networks Ralhen, Yeeshu; Sharma, Sharad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4043-4049

Abstract

Digital media integrity and authenticity have been seriously challenged with the rise of deepfakes. The challenge is to automatically detect this artificial intelligence (AI) generated manipulations. These manipulations or forgeries can cause harmful consequences such as spreading fake news in politics, scamming people online and invading privacy. Convolutional neural networks (CNN) models are found to be good at classification tasks, but the performance could not reach high accuracy, especially when they were tested on more challenging deepfake datasets. In this paper we present a deepfake detection system based on an ensemble of CNN architectures, ResNet50 and EfficientNet, capable of distinguishing between real and deepfake videos with high accuracy. For the experiment, we have chosen Celeb-DF version 2, as it has emerged to be one of the most challenging deepfake dataset. The ensemble model achieved an F1-score of 94.69% and an accuracy of 90.58%, outperforming the individual CNN models. This study shows that ensemble learning can increase the reliability and accuracy of deepfake detection systems on challenging datasets.
Laurent series intelligent multidimensional object optimization classification for crop disease detection Karunanithi, Anandhan; Singh, Ajay Shanker
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4050-4060

Abstract

Rice crop disease detection and its diagnosis methods are vitally important for the agriculture field to be sustainable. Traditional methods suffer from paddy yield, complex issues, and crop diseases, leading to inefficiencies in the agriculture domain. Our research provides space for a novel approach, combining the Laurent series with an intelligent multidimensional object optimization (LIMO) classification framework based on generative adversarial networks (GANs) to recognize various types of crop diseases in agricultural fields. Through our proposed research work, IoT nodes sense the values of the field crop, and gathered information is shared with processing units through base station communication. Multi-objective and cognitive learning routing (MOCLEAR) protocol supports choosing the optimal path for data transmission improvement. Then, for image segmentation, GAN combined with cognitive residual convolution network (CRCNet) is modified to segment values from input images. After receiving segment input images, perform feature extraction and classification using significant attributes. The proposed Laurent series with IMO is newly formulated by integrating the Laurent series with Intelligent IMO algorithms. Through extensive experimentation and analysis, the proposed LIMO-based GAN network provides effective and improved performance metrics with overall accuracy, sensitivity, and specificity values at 91.5%, 92.6%, and 92.41%, respectively. 

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