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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 1,974 Documents
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A sequential attention-enhanced deep learning framework for robust potato leaf disease diagnosis under real field conditions Yoochomboon, Watcharkorn; Mhuadthongon, Nithizethe; Krachodnok, Piyaporn
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1790-1803

Abstract

Diagnosing potato leaf diseases from images collected in real-life field settings is challenging, mainly because of uneven lighting, complex backgrounds, and disease symptoms that are often subtle or visually inconsistent. In this study, a deep learning-based framework was developed to support potato leaf disease diagnosis, with particular attention given to improving generalization and interpretation. Several convolutional neural network (CNN) architectures were first examined under the same experimental conditions, and ResNeXt-50 showed the most stable overall performance. The model was then extended by applying efficient channel attention (ECA), followed by spatial attention adapted from the convolutional block attention module (CBAM). Test results indicate that this sequential attention design performs better than the baseline model as well as variants using only a single attention mechanism. Additional evaluation using 300 real-field images collected under different field conditions suggests improved robustness, while visualization results from gradient weighted class activation mapping (Grad-CAM) show clearer focus on lesion-related regions. Overall, the findings suggest that combining channel wise and spatial attention can improve both prediction reliability and interpretability, making the approach suitable for practical agricultural use.
TMA-Net: a transformer-based multi-modal attention network for abnormal behavior detection Doan, Huong-Giang; Nguyen, Ngoc-Trung
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1441-1450

Abstract

Abnormal behavior detection in crowded environments remains challenging due to complex motion patterns, occlusions, and domain variability. This paper presents transformer-based multi-modal attention network (TMA-Net), a unified framework that integrates red, green, and blue (RGB), optical flow (OF), and heat map (HM) modalities through a dual-stage attention fusion mechanism. The system employs you only look once version 11 (YOLOv11) for human localization and vision transformer (ViT)-B/16 for feature encoding, followed by intra-modal self-attention and cross-modal fusion to capture fine-grained spatial–temporal and motion energy dependencies. Extensive experiments on six public benchmarks as UMN, Crowd-11, UBNormal, ShanghaiTech, CUHK Avenue, UCSD Ped2, and EPUAbN dataset, demonstrate that TMA-Net achieves up to 97.5% area under the curve (AUC) and 96–100% accuracy, outperforming previous other state-of-the-art approaches. These results highlight the framework’s strong generalization and robustness across both single- and cross-dataset evaluations, underscoring its potential for reliable deployment in real intelligent surveillance systems.
Automated classification of apple bruises from hyperspectral images: an approach for fruit quality assessment Venkateswara Reddy, Peddireddy; Parivazhagan, Alaguchamy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1381-1389

Abstract

Apple bruise detection plays a crucial role in post-harvest quality control; however, conventional manual inspection remains labor-intensive, subjective, and unsuitable for large-scale industrial deployment. This study proposes an automated classification framework for identifying bruised regions in apples using hyperspectral imaging combined with deep learning and adaptive optimization techniques. The proposed model integrates a long short-term memory (LSTM) network optimized using an adaptive sand cat swarm optimization (ASCSO) algorithm, along with a ResNet-50 feature extraction backbone. The adaptive behavior embedded within ASCSO dynamically adjusts the optimization parameters to enhance convergence and prevent premature stagnation during LSTM hyperparameter tuning. Hyperspectral images were processed to extract relevant spectral–spatial features, which were subsequently fed into the optimized classifier. Experimental evaluations demonstrate that the proposed hybrid model significantly outperforms conventional and baseline deep learning approaches, achieving a classification accuracy of 98.0% while maintaining robustness across varying bruise patterns and intensity levels. The results highlight the effectiveness of combining hyperspectral imaging with adaptive deep learning optimization for high-precision fruit quality assessment. This research contributes a reliable, scalable solution for automated bruise detection and quality grading in the fruit supply chain, offering strong potential to reduce post-harvest losses and improve operational efficiency in the agro-food industry.
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.

Page 190 of 198 | Total Record : 1974

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