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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 1,893 Documents
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Recognition system based on artificial vision using OpenCV for discarding and detecting ceramics with defects Alvarado, Fernando; Yauri, Ricardo
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.pp1166-1173

Abstract

Early detection of defects through preventive maintenance is important in industry to avoid economic losses, as in the case of ceramic tile manufacturing, where manual inspection allows defective parts to advance in production, causing delays. The research review shows that computer vision enables the automation of object detection, classification, and elimination tasks in industrial processes, using solutions based on Python, OpenCV, and MATLAB. For this reason, the design of a computer vision recognition system with OpenCV is proposed, which allows automatic discarding of ceramics with defects using an algorithm for detecting ceramics with a camera and Arduino-based hardware, comparing the captured images with a standard image on a conveyor belt. The machine vision system was integrated with a camera connected to a computer running OpenCV, achieving effective automatic detection with a threshold of 25% difference from the standard part. This percentage was calculated by comparing the grayscale pixel values with a reference image. The system calculates the proportion of pixels that exceed the similarity threshold. The conclusion is that the developed system contributes to production, highlighting the possibility of future industrial integration.
Real-time detection of rider fatigue: a comparative study of black-box and glass-box artificial intelligence approaches Hayat, Cynthia; Soenandi, Iwan Aang; Harsono, Budi
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.pp1409-1417

Abstract

Rider fatigue poses a critical safety challenge in two-wheeled vehicle operation due to limited physical protection, increased balance demands, and prolonged exposure to environmental stressors. Effective real-time fatigue detection is essential to mitigate accident risks, particularly in high-traffic regions such as Indonesia. This study presents a comparative analysis of black-box and glass-box artificial intelligence (AI) models for real-time detection of rider fatigue, evaluated through a human factor’s lens emphasizing interpretability, intrusiveness, and cognitive compatibility. Multimodal data comprising physiological signals, behavioral indicators, and environmental context were collected using wearable sensors and rider telemetry to train and assess the models. Experimental results reveal that black-box models, including convolutional neural network (CNN) + long short-term memory (LSTM), random forest (RF), and support vector machine (SVM), achieve superior predictive accuracy (94.3%, 91.5%, and 88.2%, respectively) but lack inherent transparency. Conversely, glass-box models such as decision tree (DT) and logistic regression (LR) offer greater interpretability, a critical factor in safety-sensitive applications, though with reduced accuracy (approximately 83–85%). These findings underscore the trade-off between predictive performance and explainability, highlighting the need to tailor model choice to specific operational requirements. This research advances the design of intelligent, human-centered rider support systems that balance accuracy, transparency, and user trust, fostering safer two-wheeled transportation.
A reinforcement-guided multi-phase hybrid architecture for threat profiling and defense towards IoT handheld device Narayana Singh, Pushpa Rajput; Siddalingaiah, Neelambike
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.pp1497-1504

Abstract

The contribution of artificial intelligence (AI) towards offering proactive security in handheld devices of internet of things (IoT) is in evolving stage. Review of literature showcases noteworthy attempts of machine learning (ML) and deep learning (DL) models; however, they are a large scope of improvement towards bridging the trade-off between security and computational-communication efficiency. This problem is addressed in this manuscript by presenting a unique and innovative solution where reinforcement learning (RL) has been hybridized with standalone ML and DL models. The model reads the permission-based data in cloud, followed by vulnerability prediction carried out by hybridization of RL and logistic regression (LR). Further, RL is integrated with deep neural network (DNN) for exploring a secure path to facilitate data transmission. The proposed model witnessed 97.9% accuracy, 67.35% of higher accuracy, 55.14% of reduced latency, and 52.54% of faster response time in contrast to baselines.
Unified voting-based ensemble learning for rice leaf disease detection using improved pretrained models Subburaman, Govindarajan; Selvadurai, Mary Vennila
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.pp1646-1663

Abstract

As a staple food for a large portion of the global population, rice is particularly susceptible to leaf diseases that adversely affect its yield and overall quality. This study utilizes four pretrained convolutional neural network (CNN) models to construct a unified voting-based ensemble approach for rice leaf disease classification. The models include VGG16, DenseNet121, InceptionV3, and Xception. The dataset used in this study was collected from Kaggle and further enriched with images obtained from Google sources. It comprises a total of 4,000 images categorized into six classes: bacterial leaf blight, brown spot, leaf blast, leaf scald, narrow brown spot, and healthy leaves. It was split into training (327 images/class), validation (140 images/class), and testing (200 images/class). Images were normalized to [0,1] and augmented through rotation, flipping, shifting, shear, zoom, brightness, and channel adjustments to improve generalization. Individually, the fine-tuned models achieved accuracies of 91.3% (VGG16), 95.6% (DenseNet121), 92.1% (InceptionV3), and 89.8% (Xception). The ensemble leveraged majority voting (93.6%), weighted voting (96.5%), and soft voting (97%), yielding an absolute gain of 1.4% over the best individual model and 4.8% over the average of all models. To our knowledge, this is the first ensemble combining these four architectures with unified voting for identifying diseases in rice leaves, delivering a scalable and computationally efficient solution suitable in advance diagnosis and timely execution in agricultural settings with limited resources.
IoT-enabled smart nutrition scale using fuzzy logic for dietary assessment and recommendation Widiyanto, Wahyu Wijaya; Susanto, Edy; Suparti, Sri
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.pp1194-1201

Abstract

Childhood malnutrition, particularly stunting, remains a major public health challenge that requires preventive and technology-supported nutritional interventions. This study presents an IoT-enabled smart nutrition scale integrated with fuzzy logic to support real-time dietary assessment and personalized recommendation. The system combines IoT-based sensing, mobile and web applications, and a fuzzy inference engine that evaluates child profiles and food composition data to generate nutritional adequacy scores and tailored dietary guidance. Experimental validation demonstrates high measurement accuracy of the sensing system, achieving a strong linear correlation (R² ≈0.9995). Comparison with expert nutritionist assessments shows strong agreement, supported by low error values (mean absolute error (MAE) =2.96; root mean square error (RMSE) =3.41), and Bland–Altman analysis. Usability evaluation involving community health workers and caregivers yields an excellent system usability scale (SUS) score, indicating strong acceptance for practical deployment. By integrating IoT sensing with fuzzy reasoning, the proposed system shifts nutritional monitoring from retrospective assessment toward proactive dietary intervention. This work highlights the potential of intelligent nutrition technologies to enhance decision-making in community-based stunting prevention programs and provides a scalable foundation for preventive digital health applications.
Enhancing imbalanced dataset diagnosis using class-based input image composition Hlali, Azzeddine; Ben Yakhlef, Majid; El Hazzat, Soulaiman
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.pp1613-1622

Abstract

Deep learning models often falter when faced with small, imbalanced datasets or degraded image quality, leading to unacceptably high false prediction rates. To bridge this gap, we introduce class-based image composition. This technique reformulates training inputs by fusing multiple intra-class images into unique composite input images (CoImg). By concentrating information density and amplifying intra-class variance, CoImg forces the model to discern subtle, nuanced disease patterns that might otherwise be lost. We validated this approach using the optical coherence tomography dataset for image-based deep learning methods (OCTDL), a collection of seven imbalanced retinal disease scan categories. From this, we engineered Co-OCTDL: a perfectly balanced variant where each training sample exists as a 3×1 layout composite. To measure the impact of this new representation, we benchmarked the original dataset against its composite counterpart using a VGG16 architecture. Precision was paramount. We maintained identical hyperparameters and model structures across all experiments to ensure a rigorous, fair comparison. The results were transformative. While baseline datasets struggled, the enhanced Co OCTDL achieved a near-perfect F1-score of 0.995 and an AUC of 0.9996. The method effectively neutralized the risks of class imbalance. It didn't just improve the numbers; it refined the diagnostic reliability of the model.
Novel convolution neural network model for dysgraphia affected handwriting classification Vanjari, Nisha Ameya; Shete, Prasanna J.
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.pp1418-1427

Abstract

It is estimated that 10% of the population in the world suffers from learning disabilities like dyslexia, dysgraphia, and dyscalculia. Learning disabilities are neurological disorders in which children struggle with reading, writing and mathematical skills. Dysgraphia disorder impacts on writing abilities of students and thus may be a hurdle in their learning and evaluation of subject matter. Hence early detection/prediction of learning disability (LD) in school going children will greatly help in providing necessary accommodations so as to ease their future learning curve. In recent years researchers have used several deep learning algorithms that produce automated and trained models which can be useful in the handwriting classification. To properly capture the distinct handwriting inconsistencies linked to dysgraphia, this study contains experiments that determine how various convolution neural network (CNN) model layers contribute to performance. To address it, this research focused on the improved novel model based on CNN and targeted dysgraphia English handwriting classification with 98% accuracy with 102,691 trainable parameters. The model is trained on both normal and dysgraphia-affected handwriting, increasing its accuracy in identifying individual differences.
Research themes and trends in the field of blockchain engineering: a topic modelling analysis Zhaisanova, Dinara; Mansurova, Madina
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.pp1863-1875

Abstract

This study employed topic modeling to identify key research themes in blockchain engineering and examined how these themes have evolved over time. The dataset of collected abstracts from 3,665 relevant papers of Web of Science (WoS) core collection for the period from 2019 to 2024 was analyzed with latent Dirichlet allocation (LDA) approach. Based on the results of the topic development trends analysis, the topics collectively highlight the evolving landscape of technologies such as blockchain, smart contracts, the internet of things (IoT), and edge computing, focusing on their integration and impact across sectors like finance, healthcare, supply chain management, and energy systems. It offers valuable insights and implications for research related to blockchain engineering. Latent semantic indexing (LSI) provided further understanding by highlighting strong connections between specific topics, such as energy trading, supply chains, and medical applications. A comparison of LDA and LSI topics revealed overlapping themes, which supports the reliability of the topic structure identified by LDA.
Transformer-based Hindi image description and storytelling using enhanced attention and FastText embeddings Sharma, Anjali; Aggrwal, Mayank; Khanna, Jitin
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.pp1771-1782

Abstract

This work presents a novel image description generation framework that combines a Transformer-based encoder-decoder architecture with a custom squeeze-and-excitation (SE) attention block integrated into an EfficientNet feature extractor. The decoder uses FastText embeddings specifically trained for Hindi and is evaluated on the Microsoft common objects in context (MS-COCO) dataset. To improve the captioning process, the model incorporates a generative pre-trained transformer (GPT) module to generate narrative descriptions based on the initial captions and applies multiple similarity metrics to assess output quality. The proposed system significantly outperforms existing methods, achieving high bilingual evaluation understudy (BLEU) scores (BLEU-1 to BLEU-4: 83.24, 73.17, 64.56, and 58.22), a consensus-based image description evaluation (CIDEr) score of 81.41, an F1 score of 90.29, and a metric for evaluation of translation with explicit ordering (METEOR) score of 81.18, indicating strong caption accuracy. Furthermore, the model achieves low error rates, with a word error rate (WER) of 15% and a character error rate (CER) of 11%. This work highlights the challenges of applying large-scale datasets like MS-COCO to resource-limited languages and demonstrates the effectiveness of integrating FastText embeddings with transformer-based models for Hindi image captioning.
Heart disease detection and classification using grid search with random forest Badveli, Ramakrishna Reddy; Siddappa, Nijaguna Gollara; Kanipakapatnam, Sundeep Kumar
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.pp1300-1315

Abstract

Cardiovascular disease (CVD) is basically stated as heart disease, is a significant impact of mortality rate in worldwide. Diagnosing heart disease is challenging because of the complexity of patient data, which establishes multiple categories of the disease and also irrelevant features, making it difficult to achieve classification accuracy. This research proposed a grid search with random forest (GS-RF) approach, which effectively identifies heart disease and significantly enhances classification accuracy by fine tuning the random forest (RF) approach. It optimizes key hyperparameters like number of trees and greater number of features, improving model performance. The chaotic maps-based dwarf mongoose optimization (CMDMO) is used for feature selection, which efficiently selects the relevant feature and prevents the algorithm from getting trapped in local minima. The classification using grid search’s effectiveness ensures that resources are spent on finding the best model rather than performing random, less efficient tuning. The proposed GS-RF model demonstrates high classification performance, achieving 99.43% accuracy on Cleveland dataset, while also attaining 99.10% accuracy on Statlog dataset, thereby confirming its robustness and effectiveness across different datasets. The proposed approach is evaluated in comparison with existing classification techniques, such as support vector machine (SVM), to demonstrate its greater effectiveness with respect to accuracy.

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