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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
Comparison of HSV-color and ANN-HSV-color segmentation for detecting soybean adulteration Rahmat Abadi, Farid; Evi Masithoh, Rudiati; Sutiarso, Lilik; Rahayoe, Sri
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.pp3734-3743

Abstract

Soybeans are an important food crop, but their quality is often compromised by contamination with other materials, a process known as adulteration. Conventional methods for detecting adulteration are slow; therefore, there is a need for rapid and non-invasive alternatives. This study aimed to assess the capability of hue-saturation-value (HSV) color segmentation and its combination with artificial neural networks (ANN) to identify adulteration in soybean samples. This research employed image processing and machine learning to segment soybeans mixed with adulterants at concentrations of 5%, 10%, 15%, 20%, and 25%. The HSV method successfully distinguished soybeans and other materials, but some challenges were observed in shadow regions and areas with similar colors. The HSV-ANN model with six hidden layers performed well with a calibration accuracy of R² value of 0.97 and root-mean-square error (RMSE) of 2.16%, which provided more detailed segmentation, although it still had some problems in shadow regions and undetected corn embryo parts. The validation results indicated that the HSV model had an R² value of 0.98 and RMSE of 4.48%, while the HSV-ANN model had an R² value of 0.96 and RMSE of 1.3%. Both models were capable of predicting the levels of adulteration, and the HSV-ANN model proved to be more accurate. It is concluded that both methods are efficient; however, there is a need for more work on modeling and sampling to increase the segmentation precision and decrease the biases, especially in the shadow and overlapped color.
The effectiveness of ChatGPT in extracting architectural patterns and tactics Milhem, Hind; Al-Jawabrah, Naderah; Abu Wadi, Raghad
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.pp4363-4370

Abstract

This work investigates the potential of ChatGPT, a cutting-edge large language model (LLM), for software design analysis specifically in detecting architectural patterns and tactics. The evaluation involves comparing ChatGPT’s performance with that of Archie, a traditional Eclipse plugin designed for architectural analysis. The study uses the source code of five open-source software systems as the testing ground. Results reveal that ChatGPT achieves noteworthy performance in both pattern and tactic detection tasks. Specifically, for pattern detection, ChatGPT demonstrates an accuracy of up to 47.06%, while for tactic detection, it achieves a precision of 28.25%. While ChatGPT’s current capabilities are not yet a replacement for specialized tools like Archie, it offers significant potential as a complementary tool in architectural analysis workflows. By bridging the gap between natural language understanding and software engineering, ChatGPT could pave the way for more intelligent and automated solutions in the field. However, a key limitation is its difficulties in handling foundational or traditional tactics, resulting in a lower detection rate in certain areas. This research contributes valuable insights into the application of LLMs in software engineering, highlighting both the strengths and the limitations of ChatGPT in addressing complex architectural tasks.
Multiclass instance segmentation optimization for fetal heart image object interpretation Syaputra, Hadi; Nurmaini, Siti; Partan, Radiyati Umi; Roseno, Muhammad Taufik
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.pp4137-4150

Abstract

This research aims to develop a multi-class instance segmentation model for segmenting, detecting, and classifying objects in fetal heart ultrasound images derived from fetal heart ultrasound videos. Previous studies have performed object detection on fetal heart images, identifying nine anatomical classes. Further, these studies have conducted instance segmentation on fetal heart images for six anatomical classes. This research seeks to expand the scope by increasing the number of classes to ten, encompassing four main chambers left atrium (LA), right atrium (RA), left ventricle (LV), right ventricle (RV); four valves tricuspid valve (TV), pulmonary valve (PV), mitral valve (MV), and aortic valve (AV); one aorta (Ao), and the spine. By developing an instance segmentation method for segmenting ten anatomical structures of the fetal heart, this research aims to make a significant contribution to improving medical image analysis in healthcare. It also aims to pave the way for further research on fetal heart diseases using AI. The instance segmentation approach is expected to enhance the accuracy of segmenting fetal heart images and allow for more efficient identification and labeling of each anatomical structure in the fetal heart.
Optimizing nitik batik classification through comparative analysis of image augmentation Suprapto, Suprapto; Tentua, Meilany Nonsi; Maulana, Ahmad Rizki
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.pp3970-3981

Abstract

Nitik batik is one of the most intricate and culturally significant motifs in Yogyakarta's batik tradition, characterized by its complex, geometric dot-based patterns. The unique challenges of automatically classifying nitik batik motifs stem from the high variability within the class and the limited availability of training data. This study investigates how different image data augmentation techniques can enhance the performance of a random forest classifier for nitik batik motifs. Techniques such as geometric transformations (flip, rotate, and scaling), intensity transformations (cut-out, grid mask, and random erasing), non-instance level augmentation (pairing samples), and unconditional image generation (deep convolutional generative adversarial network (DCGAN)) were used to expand the dataset and improve the model's ability to generalize. The results show that specific techniques, notably flip, cut-out, and DCGAN, significantly improved classification accuracy, with flip achieving the highest accuracy improvement of 20.20%, followed by cut-out at 19.27% and DCGAN at 16.25%. Moreover, DCGAN demonstrated the lowest standard deviation (0.78%), indicating high stability and robustness in classification performance across multiple validation folds. These findings suggest that augmentation techniques effectively improve classification accuracy and enhance the model's ability to generalize from limited and complex datasets.
Backpropagation neural networks for solving gas flow equations in porous media Adrianto, Adrianto; Syihab, Zuher; Sutopo, Sutopo; Marhaendrajana, Taufan
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.pp3744-3756

Abstract

This study proposes a backpropagation neural network (BPNN) as an alternative solver for nonlinear equations in gas flow simulation through porous media. Conventional solvers like the Newton-Raphson (N-R) method are accurate but may become inefficient for large-scale or heterogeneous systems. We develop a feedforward BPNN architecture with adaptive learning rates to solve discretized residual equations from the one-dimensional gas flow model. The methodology includes finite difference discretization and mapping the nonlinear algebraic system into a four-layer neural network. The BPNN solver is validated against the Newton method across various grid sizes and heterogeneous permeability-porosity distributions. Results show that BPNN achieves high accuracy, with maximum absolute errors (MAE) of only 0.241 psi in the homogeneous model and 0.0418 psi in the heterogeneous model. While the BPNN requires more iterations and longer computation time, especially for finer grids, it exhibits the ability to learn pressure patterns and improve efficiency over time. This approach demonstrates that BPNN can serve as a viable nonlinear solver in reservoir simulation, offering flexibility in handling nonlinearities while maintaining accuracy.
Optimized ensemble framework for predicting hydroponic stock and sales using machine learning Pranatawijaya, Viktor Handrianus; Priskila, Ressa; Putra, Putu Bagus Adidyana Anugrah; Sari, Nova Noor Kamala; Christian, Efrans; Geges, Septian; Kristianti, Novera
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.pp3879-3886

Abstract

The increasing global demand for food necessitates the adoption of sustainable agricultural practices. Hydroponic farming, while efficient in resource utilization, faces challenges in accurately predicting stock levels and sales due to dynamic, ever-changing factors. This research presents an optimized ensemble framework for forecasting hydroponic stock levels and sales by integrating linear regression (LR), random forest (RF), and XGBoost, further enhanced through an evolutionary algorithm (EA). The proposed framework is evaluated using root mean square error (RMSE) and mean absolute error (MAE), demonstrating significant accuracy improvements over individual models. The ensemble model achieves an RMSE reduction of 43.82% for stock prediction and 55.3% for sales forecasting compared to the best-performing individual model. Additionally, local interpretable model-agnostic explanations (LIME) are employed to offer stakeholders clear insights into decision-making processes, such as identifying "number of harvested crops" and "sales data" as key drivers of prediction outcomes. This framework supports sustainable development goals (SDGs) 9.3, 12.3, and 12.C by promoting resource efficiency, reducing food waste, and improving small-scale farmer market access. Future research will explore real-time data integration for dynamic adaptation and further model enhancements.
Object detection for indoor mobile robot: deep learning approaches review Messbah, Hind; Emharraf, Mohamed; Saber, Mohamed
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.pp3520-3527

Abstract

Efficient object detection is crucial for enabling autonomous indoor robot navigation. This paper reviews current methodologies and challenges in the field, with a focus on deep learning-based techniques. Methods like you only look once (YOLO), region-based convolutional neural networks (R-CNN), and Faster R-CNN are explored for their suitability in real-time detection in dynamic indoor environments. Deep learning models are emphasized for their ability to improve detection accuracy and adaptability to varying conditions. Key performance metrics such as accuracy, speed, and scalability across different object types and environmental scenarios are discussed. Additionally, the integration of object detection with navigation systems is examined, highlighting the importance of accurate perception for safe and effective robot movement. This study provides insights into future research directions aimed at advancing the capabilities of indoor robot navigation through enhanced deep learning-based object detection techniques.
Ensemble reverse knowledge distillation: training robust model using weak models Reswara, Christopher Gavra; Cenggoro, Tjeng Wawan
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.pp4162-4170

Abstract

To ensure that artificial intelligence (AI) can be aligned with humans, AI models need to be developed and supervised by humans. Unfortunately, it is possible for an AI to exceed human capabilities, which is commonly referred to as superalignment models. Thus, it raised the question of whether humans can still supervise a superalignment model, which is encapsulated in a concept called weak-to-strong generalization. To address this issue, we introduce ensemble reverse knowledge distillation (ERKD), which leverages two weaker models to supervise a more robust model. This technique is a potential solution for humans to manage a super-alignment of models. ERKD enables a more robust model to achieve optimal performance with the assistance of two weaker models. We tried to train a more robust EfficientNet model with weaker convolutional neural network (CNN) models in a supervised fashion. With this method, the EfficientNet model performed better than the model trained with the standard transfer learning (STL) method. It also performed better than a model that was supervised by a single weaker model. Finally, ERKD-trained EfficientNet models can perform better than EfficientNet models that are one or even two levels stronger.
BonoNet: a deep convolutional neural network for recognizing bangla compound characters Ahmed, Kazi Rifat; Jahan, Nusrat; Masud, Adiba; Tasnim, Nusrat; Sharmin, Sazia; Mim, Nusrat Jahan; Mahmud, Imran
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.pp4171-4180

Abstract

The bangla alphabet includes vowels, consonants, and compound symbols. The compound nature of bangla is a product of combining two or more root bangla characters into one graph. They are difficult to differentiate because they have a sophisticated geometric shape and an immense variety of scripts used by different places and individuals. This is one of the greatest challenges in creating effective optical character recognition (OCR) systems for bangla. In this paper, a deep convolutional neural network (DCNN)-based system is presented to identify bangla compound characters with high precision. The model was trained using the AIBangla dataset. It has about 171 classes of bangla compound characters. A DCNN system, BonoNet, was designed to classify compound characters. BonoNet outperformed all other state-of-the-art architecture on the test set and improved over current state-of-the-art architecture methods. BonoNet will greatly improve the automation and analysis of the bangla language by accurately identifying these compound complex characters.
AI-driven hyper-personalization and transfer learning for precision recruitment Alqudah, Nour; Abuein, Qusai Q.; Shatnawi, Mohammed Q.
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.pp4271-4278

Abstract

The research study demonstrates how artificial intelligence (AI)-powered models can transform the hiring process by maximizing the match between candidates and jobs, leading to better hiring options and increased worker productivity. Our research develops highly personalized AI-powered recruitment applications. By using hyper-personalization to tailor job recommendations based on job compatibility and big five personality traits, this study leverages AI to improve job matching. Unlike traditional recruitment models that depend only on complex skill matching, hyper-personalization combines soft skills and personality dimensions to achieve a more precise candidate-job alignment. Transformer-based models, including bidirectional encoder representations from transformers (BERT), RoBERTa, and cross-lingual language model (XLM)-RoBERTa, have shown exceptional performance in natural language processing (NLP) and classification tasks; thus, we apply them. Transfer learning helps us to fine-tune these models to improve the accuracy of personality classification. Compared to conventional models, experimental data achieves up to 80% accuracy in binary classification and 72% in multi-class classification. By demonstrating job-candidate compatibility, this study emphasizes the potential of AI-driven models to transform recruitment, leading to better hiring decisions and workforce productivity. Our outcomes play a crucial role in advancing hyper-personalized AI applications in talent.

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