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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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Multi-class gas pipeline leak detection using Hilbert transform and a lightweight convolutional neural network Dedik Romahadi; Aberham Genetu Feleke; Nur Indah; Dafit Feriyanto; Muhamad Fitri; Hadi Pranoto; Rachmat Muwardi
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.pp4556-4567

Abstract

Ensuring the safety and sustainability of gas distribution networks depends heavily on the timely detection of pipeline leaks, since leaks that go unnoticed can cause substantial losses and create serious safety risks. When applied to multi-class leak types, however, conventional detection approaches often prove insufficiently accurate. To address this limitation, the present study proposes a novel multi-class gas pipeline leak detection approach based on acoustic signal analysis, aiming to enhance both detection accuracy and processing efficiency. In this approach, features are first extracted from leak-generated acoustic signals through application of the Hilbert transform, after which classification is performed using a lightweight convolutional neural network (CNN) architecture referred to as LightCNN. The method was tested using the gas pipeline leakage assessment-12 (GPLA-12) dataset, comprising 12 distinct leak categories. According to the evaluation outcomes, the proposed approach peaked at 96.18% accuracy while employing a more streamlined model architecture than earlier techniques. Overall, this work contributes meaningfully to advancing leak detection systems that are more reliable, faster, and more efficient, with potential for broad application across the gas pipeline industry and in signal processing technologies more generally.
Improving image processing and noise removal using generative deep learning techniques Firas Abdulrahman Yousif; Shamil Kh. Ramadhan; Ahmed A. Mostfa
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.pp4667-4678

Abstract

Denoising plays a crucial role in satellite and medical images. The classical methods struggle to find out the noisy values and might lead to some loss in image metadata’s essitals. In this study, a generative adversarial network (GAN) is introduced along with the U-Net architecture. GAN algorithm has the capability to provide a powerful estimated fine image compared with a regular neural network approach. The Modified National Institute of Standards and Technology (MNIST) dataset is injected with synthetic random noise. The proposed work demonstrated noise reduction capabilities and significant improvement in peak signal to noise ratio (PSNR), from 11.70 dB for noise image to 19.41 dB for the estimated image. The structural similarity index (SSIM) moved from 0.61 to 0.93, showing a big restoration with stable convergence for the image details. The GAN visual analysis based on deep learning showed effective output detail recovery.
Diabetic prediction using ridgelet process neural network with noise contrastive estimation Parashiva Murthy Basavanapura Muddumadap; Pruthvi Palahalli Ramachandra; Manjunatha Basavaraju; Nandeesh Hallimysore Devaraj
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.pp4133-4143

Abstract

Globally, diabetes is the most prevalent disease and a major public health concern, often leading to serious complications like kidney disease, vision loss, and coronary issues. Deep learning (DL) and data mining techniques have emerged as reliable and highly effective methods for predicting diabetes, offering significant support to the medical field in accurate diagnosis. However, challenges like missing data and difficulties in handling high-dimensional data significantly impact diabetes prediction, resulting in poor performance. This research proposes a ridgelet process neural network (RPNN) with noise contrastive estimation (NCE) to mitigate the effects of noise and remove irrelevant features, enhancing prediction accuracy. In the pre-processing, standard scaling is involved to ensure data categorization, and missing data is handled using the interquartile range (IQR) method to improve prediction performance. The feature selection using partial least angle regression (PLAR) is utilized to handle high-dimensional data and minimize multicollinearity among features efficiently. The RPNN using a ridgelet transformer maps the data into higher-dimensional space, enabling capture of complex features and effective handling of linear patterns. The proposed method achieves better accuracy of 99.89%, 98.63%, and 99.37% on datasets like diabetes, PIMA, and diabetes 130-US when compared with existing methods like guided artificial neural network (G-ANN).
An explicit multi-aspect sentiment analysis using bidirectional encoder representation transformers Mohammed Ziaulla; Arun Biradar
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.pp4839-4851

Abstract

Aspect-based sentiment analysis (ABSA) is a vital natural language processing (NLP) task that extends traditional sentiment analysis by identifying sentiments associated with specific aspects within a sentence. Although recent deep learning (DL) models have improved ABSA performance, most concentrate on extracting only a single aspect and predicting sentence-level polarity, thereby overlooking multiple explicit aspects and their individual sentiments. This reveals a research gap in fine-grained sentiment detection. The main problem addressed in this work is the absence of robust models capable of extracting multiple explicit aspects and accurately predicting aspect-wise polarity. To address this, the primary objective was to design a model supporting multi-aspect extraction and sentiment classification. Accordingly, this work proposes explicit aspect-aware bidirectional encoder representations from transformers (EA-BERT), a BERT-based architecture enhanced with a multi-head attention (MHA) mechanism to capture aspect-specific contextual features and predict their corresponding polarities. The model was trained and evaluated on the SemEval 2014 restaurant (Res2014) and laptop (Lap2014) datasets. EA-BERT achieved accuracies of 90.86% and 90.23% and macro-F-scores of 91.89% and 90.28%, respectively. Experimental comparisons demonstrate that EA-BERT consistently outperforms several recent ABSA models, confirming its effectiveness for multi-aspect sentiment classification.
Impact of self-efficacy, digital and financial literacy, and family support on Indonesian entrepreneurs’ performance Md. Nahidul Islam; Maheran Zakaria; Wan Noraswaniaty Wan Ahmad; Erna Hernawati; Yudi Nur Supridi; Afif Deni Irawan
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.pp4961-4968

Abstract

Although micro, small, and medium enterprises (MSMEs) are easy to establish, many fail due to a lack of capabilities and an inability to keep pace with recent digital and financial developments. Even worse, some lack support from family members. As a result, they struggle to compete with larger institutions for sustainability. Despite these challenges, a notable subset succeeds, prompting researchers to investigate the factors contributing to their performance. Grounded in self-efficacy theory and existing literature, this study examines the impact of self-efficacy, digital and financial literacy, and family support on entrepreneurial performance. The researchers collected data from 237 entrepreneurs in West Java, Indonesia, and analyzed it using structural equation modeling–partial least squares (SEM-PLS). The findings indicate that all predicted factors, self-efficacy, digital literacy, financial literacy, and family support, positively influence entrepreneurial performance. These insights are valuable for policymakers, entrepreneurship agencies, and academics in formulating policies and training programmers that emphasize self-efficacy, digital and financial literacy, and family support. Strengthening these aspects will help entrepreneurs succeed, ultimately driving inclusive and sustainable economic growth.
Automated convolutional neural network-based reject–repeat decision system for chest radiographs Dwi Rochmayanti; Kusworo Adi; Catur Edi Widodo
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.pp4121-4132

Abstract

Image quality in radiography is essential for diagnostic accuracy, yet reject–repeat analysis (RRA) is still mostly performed manually and depends on subjective radiographer judgment. Inter-operator variability leads to inconsistent quality data and limits workflow optimization. While many studies evaluate deep learning models for disease classification, research using convolutional neural networks (CNNs) to assess image quality and automate RRA remains limited. This study introduces a novel approach by applying CNNs to classify the causes of image rejection in chest radiography: positioning, exposure, artefacts, and accepted images, integrating these outcomes into a quality-management framework. The aim is to develop and evaluate CNN models (visual geometry group (VGG)-16, residual neural network (ResNet)-101, and dense convolutional network (DenseNet)-201) to support automated RRA using digital imaging and communications in medicine (DICOM)-based radiographs. The dataset comprised DICOM chest images annotated by a panel of radiologists, radiographers, and medical physicists into four technical classes. The CNNs were trained using supervised learning with structured training, validation, and testing splits, and evaluated using accuracy, precision, recall, and F1-score. Results show VGG-16 delivers the most stable and accurate performance across categories. Overall, CNNs effectively automate RRA with greater objectivity and consistency, reducing repeat rates, improving workflow efficiency, and strengthening radiology quality-management systems to enhance patient safety.
Perspectives of Indonesian dental practitioners on application of artificial intelligence in dentistry Ivan Djuarsa; Vania Ewitrawati; Andry Sentosa; Hilmy Irsyadi Hanif; Santhia Pramanthi
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.pp3991-4000

Abstract

Limited evidence exists about the knowledge, attitudes, and perceptions of Indonesian dental practitioners with regard to artificial intelligence (AI). A cross-sectional survey was performed among 235 licensed dentists throughout Indonesia, assessing their attitudes on the potential benefits and barriers of AI in dentistry, and its perceived influence on their profession. A “positive attitude – knowledge gap” was observed: attitudes towards AI were very positive (79.6% good), with comparatively low levels of basic AI knowledge (54.3% good). Dentists are positive towards the impact of AI on their professional efficiency (86.4%) and accuracy (82.1%), but remain concerned over the potential barriers of its implementation, specifically the ambiguity in legal liability (82.1%), high cost (77.0%), and data security issues (71.9%). The majority of dentists do not believe that AI will replace them (65.5%), but most worry that the use of AI may erode their skills (54.9%). There is strong support for curricular change (83.4%). Overall, Indonesian dentists are amenable to AI but are currently limited by low knowledge and many AI-related barriers. To increase its adoption, future efforts should focus on promoting practitioner education, as well as increasing confidence in legal and regulatory structures.
Automated government document analysis and classification using robotic process automation-ChatGPT model Adisorn Phonsupharak; Rattanawadee Panthong
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.pp4862-4877

Abstract

Government offices still process most official documents manually, and the cost is familiar: slow turnaround, inconsistent filing, and data-entry mistakes that are easy to make when every document must be read, classified, and re-keyed. This study tackles that problem with an automated document-processing system for the University of Phayao Document Management System (UP-DMS) that brings together robotic process automation (RPA), optical character recognition (OCR), and the chat generative pre-trained transformer application programming interface (ChatGPT API). A software bot retrieves each document, OCR pulls out the text, ChatGPT classifies the document and summarizes its contents, and the bot writes the result back into UP-DMS. On the OCR pilot, the system reached 89.00% accuracy, enough to support the later stages reliably. Tested on 105 authentic administrative documents, it classified the three main document types at 95.24% accuracy (macro-F1 = 0.952) and the fifteen sub-categories at 82.86%. Automation also cut the average handling time from roughly 4.5 minutes per document to 0.38 minutes, a 91.57% reduction. When 24 administrative staff trialed the system, they rated it highly for usability (x̄ = 4.49), performance (x̄ = 4.49), and practical impact (x̄ = 4.60). The findings point to a workable way of reducing workload while improving both the speed and the accuracy of document handling in public and academic institutions.
Formation of structural model of objects of power systems on the basis of artificial neural networks Oksana Porubay; Isamiddin Siddikov
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.pp4113-4120

Abstract

The article presents an approach to optimizing the processes of the technical re-equipment and reconstruction (TRR) of power grid facilities using artificial neural networks (ANN). A two-level decision-making structure is proposed, including the tactical level (selection of preferred alternatives for individual facilities) and the strategic level (prioritization for the regional power system or RPS). Mathematical models of private criteria have been developed to evaluate alternatives: economic costs, technical efficiency, reliability, socio-ecological consequences, and equipment unification. The concept of “nomenclature unification criterion” was introduced, which allows clustering of solutions based on the typing of equipment parameters. Training of neural networks was carried out using the algorithm of error back propagation, which provided high accuracy of classification of alternatives (F-measure up to 0.979). Experiments showed the effectiveness of the Levenberg-Marquardt method for training networks with three hidden layers. The integration of ANN into automated decision support systems (ADSS) allows minimizing subjective factors, increasing the adaptability of power systems to changing conditions, and optimizing resource allocation. Results demonstrate the ability to reduce risks and improve infrastructure reliability by processing noisy and incomplete data, as well as real-time analysis. The approach applies to large-scale power systems, balancing short-term and long-term modernization goals.
Facial emotion recognition through artificial ‎intelligence Anas Mohammad Quteishat; Mohammad Hassan; Ashraf Al Sharah; Ahmed Qtaishat
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.pp4700-4710

Abstract

This paper presents a fuzzy min-max deep neural network (FMMDNN) for facial emotion recognition (FER). ‎The fuzzy min-max neural network (FMMNN) will replace the classification layer in the convolutional neural network (CNN). The‎ fuzzy min-max (FMM) exhibits appealing features such as online adaptation, fast training, interpretability, and noise robustness. ‎The fully connected softmax layer in the CNN is replaced by the FMMNN, where hyperboxes will ‎be created and used for classification purposes. The FER2013 dataset is used ‎to test the performance of the proposed method. The empirical results show that adding an FMMNN ‎layer to the CNN network improves the performance.

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