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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 81 Documents
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Search results for , issue "vol 15, no 3: june 2026" : 81 Documents clear
Yarn inspection and sorting system using robotic vision and machine learning Emmanuel Agung Nugroho; Joga Dharma Setiawan; Deni Kurnia; Nanang Roni Wibowo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2325-2336

Abstract

The increasing demand for automation in the textile industry, particularly in quality inspection processes, underscores the need for intelligent and cost effective solutions. Conventional methods of yarn classification and sorting remain labor-intensive, time-consuming, and susceptible to human error, resulting in inconsistent quality control. This study introduces an automated system for yarn inspection and sorting that integrates robotic vision, machine learning, and position-based visual servoing (PBVS) for real-time motion control. The proposed system combines Raspberry Pi-based machine learning with computer vision utilizing a 4-degree-of-freedom (4-DOF) robotic manipulator and a webcam, enabling precise pick-and-place operations based on yarn classification into four categories: good, striped, moldy, and dirty. Experimental results validate the system’s effectiveness, achieving an average deviation of 0.375 mm along the x-axis, 0.69 mm along the y-axis, and 0.675 mm along the z-axis, resulting in an overall position error of 0.58 mm. These results demonstrate the system’s robustness and reliability in dynamic industrial environments. The novelty of this research lies in leveraging a low-cost embedded architecture with advanced visual servoing for textile automation, reducing operational errors, improving efficiency, and supporting industry 4.0 adoption.
Navigating the new frontier: large language models and their implications for education Laila Boullous; Mustapha Hain; Adil Chergui; Brahim Elbhiri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2141-2152

Abstract

This survey characterizes the contributions of large language models (LLMs) to technology enhanced learning by relating their capabilities to actual educational functions, making comparisons with traditional models of language. The contributions for this study are: i) introduce an education centered taxonomy that classifies LLM use by four key functions personalization and adaptivity, assessment and evaluation, profiling and prediction, and intelligent tutoring with illustrations from deployed systems and tools; ii) give a domain-based comparison of where LLMs outperform traditional models (sentiment analysis with sarcasm, context-aware question answering, and abstractive summarization) and why those advantages will mean something to e-learning practice; iii) synthesize six cross-cutting risks, including computational cost/carbon, privacy, bias and hallucination, labor displacement, interpretability, and the limits of human-like judgment, and provide practical design/research implications; and iv) report on a transparent review protocol that got the initial corpus down to 50 key articles, allowing for modifications and future updates from other interested researchers. In sum, the discussion about LLMs in education has been pushed past the broad strokes to a situation where there is a comprehensive vocabulary for what LLMs can do, and how they may or may not responsibly improve learning experiences, educator workflows, and systems/learning design in e-learning.
Advanced inferential statistics and data mining for chlorophyll distribution clustering Felix Reba; Toha Saifudin; Rimuljo Hendradi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2081-2091

Abstract

This study proposes an integrated statistical framework to analyze chlorophyll distribution in marine environments by combining probability distribution modeling, goodness-of-fit (GoF) evaluation, and machine learning-based clustering. Eight probability distribution models—half normal, inverse Gaussian, Rician, Birnbaum–Saunders, Nakagami, extreme value, t location-scale, and stable—were evaluated using observational chlorophyll-a data from the Copernicus Marine Service. Model performance was assessed through the Kolmogorov–Smirnov (KS) and Anderson Darling (AD) GoF tests, along with five statistical information criteria. The results indicate that the inverse Gaussian and extreme value distributions consistently offered the best statistical fit and ecological relevance across varying sample sizes. Clustering analysis, performed using the k-means algorithm and validated via the silhouette index, further confirmed the robustness of these two models in forming stable and well-separated clusters. In contrast, the half-normal distribution showed poor performance and instability, especially with smaller sample sizes. The proposed taxonomy and spatial visualizations enable empirical classification of model behavior and support integration into real-time marine decision support systems (DSS) for ecosystem monitoring. Overall, the study contributes to the development of accurate, data-driven analytical tools that aid sustainable marine resource management, aligned with sustainable development goal (SDG) 14 on marine ecosystem protection.
Architectural design of an internet of things-based framework for road bike speed optimization Tigor Hamonangan Nasution; Opim Salim Sitompul; Fahmi Fahmi; Muhammad Anggia Muchtar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2125-2140

Abstract

This research aims to develop an internet of things (IoT) system framework to predict cyclists’ optimal speed in road cycling using multisensor data and machine learning. The primary issue raised is the lack of an intelligent system capable of integrating physiological, performance, and environmental data in real-time speeds for cyclists. The designed framework consists of four functional layers: data acquisition layer; data processing and feature layer; predictive modeling layer; and recommendations and output layer. Modeling is carried out using gradient boosting regression (GBR), performed end-to-end with validation on real cyclist activity data. The test results demonstrate that the system can provide precise optimal speed estimates and offer pacing zone recommendations that positively impact athlete performance strategies. This research contributes novelty in the form of an adaptive multivariate prediction approach and a modular IoT architecture design that can be implemented on cloud and edge platforms.
Mapping global ethical AI principles into Indonesian higher education: a framework for responsible institutional implementation Zaqqi Yamani; Sarifah Putri Raflesia; Dinda Lestarini; Ghita Athalina; Purwita Sari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2053-2061

Abstract

Artificial intelligence (AI) is an essential component of higher education's digital revolution. But its application unlocks a chain of ethics issues that must be resolved in an organized manner. This study uses mapping between United Nations Educational, Scientific, and Cultural Organization (UNESCO) and Organisation for Economic Co-operation and Development (OECD) guidelines and illustrates whether both guidelines can actually be implemented by Indonesian universities. In this study, a literature review and analysis of the content of the AI policy framework at the international level were conducted which were then applied to understand the operating environment in higher education. The findings in this study emphasize eight contextually meaningful ethical norms such as fairness, transparency, accountability, data protection, sustainability, inclusion, AI literacy, and ethical governance. Each of these values is combined with real-world practices such as algorithmic audits, multidisciplinary coordination, regulations for data encryption, and the formation of an AI ethics committee. In addition, this study produces a strategic narrative that can serve as a guide for universities in Indonesia when developing AI systems. The contribution of this study is the creation of a framework that can be applied to provide information to stakeholders on how to align AI-based applications with international standards while remaining oriented towards local values and laws in Indonesia.
Cutting-edge algorithms for reliable failure prediction in metro train systems Sana Chakri; Naoual Mouhni; Faouzia Ennaama
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2269-2277

Abstract

This study investigated various machine learning algorithms on dataset for failure prediction within metro train systems. The data indicated strong linear relationships within the dataset, making linear models such as support vector machines (SVMs) viable, as well as logistic regression analysis. For example, the least absolute shrinkage and selection operator (LASSO) regularization method used in feature selection had profound implications, leading to enhanced performance through the identification of pertinent attributes. Some advanced models like gradient boosting machines (GBMs), convolutional neural networks (CNNs), and kernel SVMs were found to outperform the conventional methods because they are capable of recognizing any complicated trends or non-linear relationships present in data sets. Combining strong learners can produce an ensemble model that improves forecast performance, while top-performing models are used in the ensemble method to enhance prediction accuracy. These findings would help professionals in the metro train industry choose appropriate machine learning methods to support preventive maintenance strategies, minimizing costs while enhancing operational effectiveness and safety.
Vision transformer and hybrid models for Malayalam handwritten word recognition Anju Arangil Thazhath; Binu Poothakuzhiyil Chacko; Mohamed Basheer Kizhakke Parambath
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2655-2663

Abstract

Transformer-based architectures and attention mechanisms have revolutionized the field of image recognition. This study focuses on offline handwritten Malayalam word recognition, addressing the lack of publicly available datasets for this low-resource language. A new Malayalam word dataset (MWD) comprising 20,850 samples across 139 classes was developed to support research in this domain. The vision transformer (ViT) was employed for advanced feature extraction, and multiple recognition models—feed-forward neural network (FFNN), global average pooling (GAP), bidirectional long short-term memory (BiLSTM), and attention based feed-forward neural network (AFFNN)—were evaluated. Among these, AFFNN achieved the highest accuracy of 98.56%, establishing the proposed vision transformer-based attention handwritten word recognition (ViTA-HWR) model as a robust framework for handwritten Malayalam word recognition and valuable contribution to regional language processing.
Prediction of new student admissions to higher education using support vector machines Neni Purwati; Windya Harieska Pramujati; A. Aviv Mahmudi; Mira Febriana Sesunan; Yahya Yahya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2484-2493

Abstract

Higher education institutions across various regions operate using systems that generate large amounts of data. This data is stored and utilized for strategic decision-making, providing significant business value to these institutions. Support vector machine (SVM) has become popular due to its strong generalization capability, high prediction accuracy, and faster training speed. SVM employs kernels as tuning parameters. This study aims to enhance the accuracy of student admissions prediction in higher education institutions using the SVM classification model. The SVM model was applied to a dataset comprising 5,936 records with four attributes and was evaluated using the use training set, 10-fold cross-validation, and percentage splits of 70%–30% and 80%–20%. Initially, the SVM-kernel model achieved high accuracy but failed to identify any true positive instances, indicating its inability to detect the minority “not accepted” class due to severe class imbalance. After applying class balancing techniques, the model’s performance improved significantly in terms of area under the curve (AUC), F-measure, and Matthews correlation coefficient (MCC), reflecting a more balanced classification between majority and minority classes. The SVM with Pearson VII function-based universal kernel (PUK) and classifier version 4.5 (C4.5) models achieved the best performance, indicating that class balancing effectively enhances both sensitivity and fairness in predictive classification.
Identification of areas of influence using a thematic modeling approach and belief function theory Fatima-Zahrae Sifi; Wafae Sabbar; Amal El Mzabi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2838-2848

Abstract

Social networks have become essential platforms for the dissemination of information and the exchange of ideas. They are transforming the way influencers interact and exert influence over their peers. On platforms such as Twitter, Facebook and Instagram, influence is expressed through social interactions such as retweets, likes, mentions, comments and shares. These activities play a key role in amplifying messages and shaping opinions. Studying the practices of influencers allows for a more precise identification of the domains in which their impact is particularly significant. However, this task is complex. It requires rigorous methods capable of integrating various forms of social engagement while managing the uncertainties associated with heterogeneous data. In this context, we propose a method to identify the domain of influence of a social media influencer. This approach combines thematic modeling Latent Dirichlet Allocation (LDA) with Belief Function Theory (BFT) to analyze social interactions and dominant topics of interest. By incorporating indicators such as retweets, likes and mentions, the method provides a robust framework for evaluating the influencer's impact across different domains. It thus offers precise tools for researchers, practitioners and decision-makers aiming to better understand these complex dynamics.
Ensuring effective cervical cancer diseases diagnosis system using ensemble machine learning model Oluwatobi Akinlade; Jafar Abdollahi; Misan Paul Etchie; Sunday Adeola Ajagbe; Oluwaseyi Omotayo Alabi; Bambo Ayo Adeyanju
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2410-2422

Abstract

Worldwide, cervical cancer is a major public health concern, and early detection is essential for successful management and treatment. Ensemble machine learning (ML) has emerged as a promising approach for improving the accuracy and reliability of cervical cancer diagnosis systems. This study evaluated the performance of various ensemble ML models for cervical cancer diagnosis using a large dataset of cervical cell images. The performance of different ensemble models was compared, including random forest (RF), gradient boosting (GB), and stacking, with conventional ML models, such as logistic regression and support vector machine (SVM). The bagging ensemble model developed reported training of 0.991667, 0.827586, 1.0, and 0.90566 for accuracy, precision, recall, and F-measure (F1-score), respectively. Furthermore, the interpretability of ensemble models was investigated using feature importance and partial dependence plots. The interpretability analysis revealed that ensemble models can provide valuable insights into the key features and factors that contribute to cervical cancer diagnosis. In conclusion, the findings suggest that ensemble ML is a promising approach for developing accurate and reliable cervical cancer diagnosis systems and improving the interpretability of these models.

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