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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
Automated vial defect inspection using Gabor wavelets and k-means clustering C. R., Vishwanatha; Asha, V.; Channabasava, Channabasava; Rallapalli, Sreekanth
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.pp4279-4289

Abstract

This study proposes a machine vision-based defect inspection system for pharmaceutical vials, aiming to ensure the quality and safety of medicinal fluids. The system employs a series of image processing techniques, including denoising, feature extraction using the Gabor wavelet transform, segmentation, clustering with the K-means algorithm, and precise defect identification using the Canny edge operator. Experimental results demonstrate high performance, with recall, precision, accuracy, and F1-score exceeding 98%. Additionally, the proposed method achieves area under the curve-receiver-operating characteristic curve (AUC-ROC) and AUC-precision-recall (PR) values of approximately 98%. The system's average computational time is 355 microseconds, indicating its potential for real-time defect detection. Overall, this approach offers an effective solution for identifying various cosmetic defects such as scratches, bruises, cracks, and black spots, in pharmaceutical vials without the need for vial classification training. 
Enhanced solar panels fault detection approach using lightweight YOLO Yanboiy, Naima El; Khala, Mohamed; Elabbassi, Ismail; Elhajrat, Nourddine; Eloutassi, Omar; El Hassouani, Youssef; Messaoudi, Choukri
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.pp3554-3562

Abstract

Artificial intelligence (AI)-driven fault detection improves the reliability of solar energy by reducing the chances of system failures. However, existing single-stage object detection methods excel in accuracy but demand high computational resources, preventing seamless integration into embedded systems. This paper introduces a lightweight approach using YOLOv5, which incorporates a multi-backbone design, specifically tailored for accurate fault detection in solar cells. It evaluates YOLOv5 and TinyYOLOv5. The findings emphasize the effectiveness of YOLOv5l with Ghost backbone, particularly notable for its precision rates of 96% for faulty and 93% for non-faulty instances. Additionally, it showcases commendable mean average precision (mAP) scores, achieving 78% at an intersection over union (IoU) threshold of 0.5 and 72% at an IoU of 0.95. Additionally, YOLOv5_Ghost emerges as the optimal selection, showcasing competitive precision, processing speed of 42.1 giga floating point operations per second (GFLOPS), and remarkable efficiency with 2.4 million parameters. This evaluation underscores the effectiveness of YOLOv5 models, thereby leading to advanced solar energy technology significantly.
Robust 3D finger knuckles biometric identification with hierarchical featureNet architecture Gangachannaiah, Divya; Shivaraj, Mamatha Aruvanalli; Nagaraj, Honganur Chandrasekharaiah
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.pp4181-4191

Abstract

A novel biometric identifier known as the 3D finger knuckle pattern provides highly discriminative characteristics for the finger knuckle-based personal identification. This paper addresses the challenge of 3D finger knuckle recognition, aiming to enhance precision and overcome limitations in existing approaches. Leveraging neural network technology, it introduces a novel neural network framework for this purpose. Recent research has made significant progress in 3D finger knuckle recognition, particularly in the areas of matching schemes, feature representations, and specialized deep neural networks. Challenges such as limited training data and dataset heterogeneity are discussed. The proposed 3D hierarchical featureNet (HFN) methodology involves a multi-stage pre-processing process for 3D images, encompassing detection, cropping, smoothing, and hole-filling. Feature similarity is evaluated with nearest neighbor distance ratios, enabling precise recognition. The key contribution of this work is the introduction of a new feature vector that incorporates curvature data, improving the state-of-the-art. The methodology employs statistical distribution analysis for feature similarity and 3D geometry for accurate curvature representation. Overall, this research offers a comprehensive solution for 3D finger knuckle recognition, enhancing accuracy and efficiency through innovative pre-processing, feature extraction, and similarity evaluation methods.
Integrating IndoBERT and balanced iterative reducing and clustering using hierarchies of BERTopic in Indonesian short text Muhajir, Muhammad; Gunardi, Gunardi; Danardono, Danardono; Rosadi, Dedi
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.pp4192-4201

Abstract

Short text topic modeling remains challenging due to data sparsity, limited word co-occurrences, and unstable clustering results, particularly for Indonesian texts. This study proposes an improved BERTopic framework that integrates IndoBERT embeddings, best match 25 (BM25)-based topic representation, and balanced iterative reducing and clustering using hierarchies (BIRCH) clustering to address these issues. IndoBERT generates contextual embeddings adapted to Indonesian linguistic features, and BM25 weighting improves keyword relevance by considering document length and term saturation. BIRCH clustering minimizes outliers by assigning most documents to valid clusters, which enhances data utilization and topic stability. Experiments on Indonesian datasets from X (formerly Twitter), Google Reviews, and YouTube demonstrate that the proposed approach consistently achieves higher topic coherence. The proposed method yields stable topic diversity values between 0.91 and 0.94, maintains embedding density from 0.60 to 0.66, and achieves intra-topic similarity between 0.39 and 0.41 across increasing dataset sizes. The proposed framework successfully reduces outlier proportions to 1-5%, which significantly outperforms standard BERTopic and K-Means. Furthermore, the model maintains stable topic counts as the data volume grows, confirming robustness and scalability for sparse short text modeling. Overall, integrating IndoBERT, BM25, and BIRCH provides a more coherent, stable, and effective solution for Indonesian short text topic modeling.
Comparison among search algorithms for hyperparameter of support vector machine optimization Nghien, Nguyen Ba; Cong, Cuong Nguyen; Thi, Nhung Nguyen; Dung, Vuong Quoc
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.pp3802-3815

Abstract

Support vector machine (SVM) is widely used in machine learning for classification and regression tasks, but its performance is highly dependent on hyperparameter tuning. Therefore, fine-tuning these parameters is key to improving accuracy and generality. Recently, many researchers have focused only on applying different algorithms to optimize these parameters. There is a shortage of studies that compare the performance of these methods. Hence, research is needed to compare the performance of these algorithms for the hyperparameters of the SVM optimization problem. This paper compares five optimization algorithms for tuning SVM hyperparameters: grid search (GS), random search (RS), Bayesian optimization (BO), genetic algorithm (GA), and the novel chemical reaction optimization (CRO) algorithm. Experimental results on benchmark datasets such as iris, digits, wine, breast cancer Wisconsin, and credit card fraud demonstrate that CRO consistently outperforms other methods in terms of classification scoring metrics and computational time. It achieves improvements in accuracy, precision, recall, and F1-score of up to 1% on balanced datasets and up to 10% on highly imbalanced datasets such as credit card fraud. It also reduces computation time by up to 50% compared to GS, BO, and RS. These findings suggest that CRO is a promising approach for hyperparameter optimization (HPO) of SVM models.
Artificial intelligence applications in agriculture: a systematic review of literature Cabanillas-Carbonell, Michael; Zapata-Paulini, Joselyn
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.pp3503-3519

Abstract

Artificial intelligence (AI) is transforming agriculture by offering innovative solutions to persistent challenges. This systematic literature review explores the most studied AI applications in agriculture, emphasizing crop management, agronomic decision-making, early detection of diseases and pests, and climate change adaptation. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 700 publications were retrieved from databases such as Scopus, ScienceDirect, and IEEE Xplore, with 104 relevant articles selected after applying strict inclusion and exclusion criteria. The findings underscore the importance of machine learning and image processing in tailoring agronomic practices to specific plot conditions and microclimates. These tools enable early identification and control of plant diseases and pests, reducing crop losses and dependence on chemicals. Nonetheless, challenges remain, particularly regarding accessibility for smallholder farmers, high implementation costs, and limited data infrastructure. While AI offers significant potential to enhance agricultural productivity, sustainability, and resilience, addressing these limitations is crucial. A balanced, inclusive approach is essential to ensure AI’s benefits are widely distributed and contribute to long-term food security and environmental sustainability.
A competitive learning approach to enhancing teacher effectiveness and student outcomes Tammouch, Ilyas; Nouna, Soumaya; Elouafi, Abdelamine; Nouna, Assia
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.pp3647-3655

Abstract

Machine learning has found extensive application and improvement in the field of education. Nevertheless, there remains a lack of research studies focusing on unsupervised learning within this domain. To address this gap, our study aims to investigate the relationship between teacher attributes and student achievement in Morocco while identifying regions requiring attention and intervention, using a novel clustering approach based on unsupervised competitive learning, specifically the 'Centroid neural network', to cluster Moroccan teachers based on their qualities and qualifications. Teacher qualities and qualifications are operationalized as initial teaching qualifications, completion of training programs, and employment status. To achieve our objective, we utilize the program for international student assessment (PISA) dataset, which provides comprehensive responses from individual students, including information on parental backgrounds, socio-economic positions, and school conditions. Additionally, we incorporate data from the teacher questionnaire, which encompasses background information, initial education, professional development, teaching practice, and teacher beliefs and attitudes. Consistent with previous research, our findings suggest that teachers' qualities and qualifications significantly influence student performance. Furthermore, our clustering approach identifies regions where there is a pronounced prevalence of attributes negatively impacting student achievement. Urging academicians to incorporate resilience-building measures into the design of policies in these regions to improve students' educational outcomes.
Comparative analysis of gender classification methods using convolutional neural networks Pamungkasari, Panca Dewi; Asfandima, Ilhan Alim; Rifai, Achmad Pratama; Huu Tho, Nguyen
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.pp3634-3646

Abstract

Gender classification has become an important application in the fields of system automation and artificial intelligence, having important implications across various fields. The main challenge in this classification task is the variation in illumination that affects the quality of facial images. This study presents a method for identifying genders with Convolutional Neural Networks (CNNs). To address this issue, various preprocessing methods are applied, including Self Quotient Image (SQI), Histogram Equalization, Locally Tuned Inverse Sine Nonlinear (LTISN), Gamma Intensity Correction (GIC), and Difference of Gaussian (DoG), to stabilize the effects of illumination variations before the images are processed by the CNN. The CNN architecture used consists of 5 convolutional blocks and 2 fully connected blocks, which have proven effective in image recognition. The results of the study show that the model trained with the DoG method achieved an accuracy of 91.07%, making it the best preprocessing technique compared to other methods such as SQI and HE, which achieved accuracies of 90.39% and 88.76%, respectively. These findings demonstrate that the application of SQI in CNN can improve the accuracy of gender classification on facial images, providing better performance than previous methods. These findings are expected to serve as a foundation for further developments in facial image classification and its applications in various fields.
Automatic essay scoring: leveraging Jaccard coefficient and Cosine similarity with n-gram variation in vector space model approach Dwi Cahyani, Andharini; Fathoni, Moh. Wildan; Rachman, Fika Hastarita; Basuki, Ari; Amin, Salman; Khotimah, Bain Khusnul
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.pp3599-3612

Abstract

Automated essay scoring (AES) is a vital area of research aiming to provide efficient and accurate assessment tools for evaluating written content. This study investigates the effectiveness of two popular similarity metrics, Jaccard coefficient, and Cosine similarity, within the context of vector space models (VSM) employing unigram, bigram, and trigram representations. The data used in this research was obtained from the formative essay of the citizenship education subject in a junior high school. Each essay undergoes preprocessing to extract features using n-gram models, followed by vectorization to transform text data into numerical representations. Then, similarity scores are computed between essays using both Jaccard coefficient and Cosine similarity. The performance of the system is evaluated by analyzing the root mean square error (RMSE), which measures the difference between the scores given by human graders and those generated by the system. The result shows that the Cosine similarity outperformed the Jaccard coefficient. In terms of n-gram, unigrams have lower RMSE compared to bigrams and trigrams.
Voting classifier in pain points identification Miftahuddin, Yusup; Firdaus, Muhammad Alif
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.pp3915-3925

Abstract

A successful app understands and addresses the needs of its users. Pain points-specific difficulties and frustrations that users experience while using an application-are crucial for understanding user expectations and improving user experience. Google Play Store reviews can be a valuable source for identifying these pain points, but this raw data requires processing to be useful for developers. This study develops a model to automatically classify reviews as either containing pain points or not. We chose the voting classifier as our primary algorithm because of its proven ability to produce models with high accuracy through combining the strengths of multiple classifiers. After evaluating 5 different classifier methods, our research shows that the optimal model combines XGradient boosting, multinomial naïve Bayes, and logistic regression-with each contributing unique strengths in text classification. This combination achieves 90% accuracy and a 90% F1-Score, outperforming previous studies that used neural networks (which achieved 80% accuracy). The model successfully identifies user frustrations from app reviews, providing developers with actionable insights to improve their applications. 

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