IAES International Journal of Artificial Intelligence (IJ-AI)
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.
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<![CDATA[Adaptive kernel integration in visual geometry group 16 for enhanced classification of diabetic retinopathy stages in retinal images ]]>
<![CDATA[Hiri, Mustafa]]>;
<![CDATA[Ourdani, Nabil]]>;
<![CDATA[Chrayah, Mohamed]]>;
<![CDATA[Alsadoon, Abeer]]>;
<![CDATA[Aknin, Noura]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i2.pp1484-1495
<![CDATA[Diabetic retinopathy (DR) is a major cause of vision impairment globally, with early detection remaining a significant challenge. The limitations of current diagnostic methods, particularly in identifying early-stage DR, highlight a pressing need for more accurate diagnostic technologies. In response, our research introduces an innovative model that enhances the visual geometry group 16 (VGG16) architecture with adaptive kernel techniques. Traditionally, the VGG16 model deploys consistent kernel sizes throughout its convolutional layers. In this study, multiple convolutional branches with varying kernel sizes (3×3, 5×5, and 7×7) were seamlessly integrated after the 'block5_conv1' layer of VGG16. These branches were adaptively merged using a softmax-weighted combination, enabling the model to automatically prioritize kernel sizes based on the image's intricate features. To combat the challenge of imbalanced datasets, the synthetic minority over-sampling technique (SMOTE) was employed before training, harmonizing the distribution of the five DR stages. Our results are promising, showing a training accuracy above 94.17% and a validation accuracy over 90.24%, our model significantly outperforms traditional methods. This study represents a significant stride in applying adaptive kernels to deep learning for precise medical imaging tasks. The model's accuracy in classifying DR stages highlights its potential as a valuable diagnostic tool, paving the way for future enhancements in DR detection and management.]]>
<![CDATA[Developing a website for English-speaking practice to English as a foreign language learners at the university level ]]>
<![CDATA[Fauzi, Iwan]]>;
<![CDATA[Asi, Natalina]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i2.pp1642-1653
<![CDATA[This study explored the adaptation of the ADDIE instructional model in designing and developing a website for the speaking practice of EFL students at the university level. The feasibility of the website was measured through the evaluation of independent experts from three aspects of rating: web design, instructional content, and language usage. Six lecturers and 64 EFL students were invited to evaluate the website. Two lecturers have expertise in multimedia and informatics, while the four others are two experts in instructional content of English teaching and two lecturers in English linguistic expertise. The assessments exposed that the web is easy to use by students and very practical in supporting students for learning; the content of learning material in the website has manifested the syllabus of English-speaking skill on the specified level; and the language used by the website is matched with the level of students’ language proficiency. Therefore, this study successfully developed a prototype of a web-based language learning product that helps students practice English speaking at the intermediate level.]]>
<![CDATA[Accuracy of neural networks in brain wave diagnosis of schizophrenia ]]>
<![CDATA[Sukemi, Sukemi]]>;
<![CDATA[Cahyadi, Gabriel Ekoputra Hartono]]>;
<![CDATA[Samsuryadi, Samsuryadi]]>;
<![CDATA[Akbar, Muhammad Agung]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i2.pp1311-1325
<![CDATA[This research explores the application of a modified deep learning model for electroencephalography (EEG) signal classification in the context of schizophrenia diagnosis. This study aims to utilize the temporal and spatial characteristics of EEG data to improve classification accuracy. Four popular convolutional neural network (CNN) architectures, namely LeNet-5, AlexNet, VGG16, and ResNet-18, are adapted to handle 1D EEG signals. In addition, a hybrid architecture of CNN-gated recurrent unit (GRU) and CNN-long short-term memory (LSTM) is proposed to capture spatial and temporal dynamics. The model was evaluated on a dataset consisting of EEG recordings from 14 patients with paranoid schizophrenia and 14 healthy controls. The results show high accuracy and F1 scores for all modified models, with CNN-LSTM and CNN-GRU achieving the highest performance with scores of 0.96 and 0.97, respectively. Receiver operating characteristic (ROC) curves demonstrate the model's ability to distinguish between healthy controls and schizophrenia patients. The proposed model offers a promising approach for automated schizophrenia diagnosis based on EEG signals, potentially assisting clinicians in early detection and intervention. Future work will focus on larger data sets and explore transfer learning techniques to improve the generalization ability of the model.]]>
<![CDATA[An enhanced cascade ensemble method for big data analysis ]]>
<![CDATA[Izonin, Ivan]]>;
<![CDATA[Muzyka, Roman]]>;
<![CDATA[Tkachenko, Roman]]>;
<![CDATA[Gregus, Michal]]>;
<![CDATA[Korzh, Roman]]>;
<![CDATA[Yemets, Kyrylo]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i2.pp963-974
<![CDATA[In the digital age, the proliferation of data presents both challenges and opportunities, particularly in the realm of big data, which is characterized by its volume, velocity, and variety. Machine learning is a crucial technology for extracting insights from these vast datasets. Among machine learning methods, ensemble methods, and especially cascading ensembles, are highly effective for big data analysis. While it is true that the training procedures for cascade ensembles can be time-consuming and may have limitations in terms of accuracy, this paper proposes a solution to enhance their performance. Our method involves using stochastic gradient descent (SGD) classifiers, an improved training data separation algorithm, and integrating principal component analysis (PCA) at each ensemble level. We are confident that these enhancements lead to improved results and accuracy. The proposed approach is designed to enhance both the generalization properties and accuracy of the ensemble (3%), while also reducing its training time. Results from modelling on a real-world biomedical dataset demonstrate significant reductions in training duration, improvements in generalization properties, and enhanced accuracy when compared to other possible implementations of the ensemble.]]>
<![CDATA[Automatic detection of dress-code surveillance in a university using YOLO algorithm ]]>
<![CDATA[Tantra, Benjamin Jason]]>;
<![CDATA[Widjaja, Moeljono]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i2.pp1568-1575
<![CDATA[Dress-code surveillance is a field that utilizes an object detection model to en- sure that people wear the proper attire in workplaces and educational institutions. The case is the same within universities, where students and staff must adhere to campus clothing guidelines. However, campus security still enforces univer- sity student clothing manually. Thus, this experiment creates an object detection model that can be used in the campus environment to detect if students are wear- ing appropriate clothing. The model developed for this research has reached an f1-score of 45% with an overall 51.8% mean accuracy precision. With this, the model has reached a satisfactory state with room for further improvements.]]>
<![CDATA[Detecting road damage utilizing retinanet and mobilenet models on edge devices ]]>
<![CDATA[Mahmudah, Haniah]]>;
<![CDATA[Aisjah, Aulia Siti]]>;
<![CDATA[Arifin, Syamsul]]>;
<![CDATA[Prastyanto, Catur Arif]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i2.pp1430-1440
<![CDATA[A particular form of road digitalization produces a system that detects road damage automatically and in real time, employing the device to detect road damage as an edge device. The application of RetinaNet152 and MobileNetV2 models for road damage detection on edge devices necessitates a trade-off between high system performance and efficiency. Currently, edge devices have limited storage. In this paper, we explore how tuning hyperparameters with batch size and several optimizers improves system performance on RetinaNet152 and MobileNet models, as well as how they are implemented on edge devices. After tuning hyperparameters in the batch size of the optimizer, the Adam optimizer displayed enhanced performance with mean average precision (mAP), average recall (AR), and F1-score. This implies a positive impact on overall model performance. The MobileNetV2 model's hyperparameter tuning technique significantly improves performance, resulting in faster inference times and overall system performance. This demonstrates that the MobileNetV2 model could be used directly on edge devices to identify road damage. However, the RetinaNet152 model has a lower inference time, which cannot be deployed directly to edge devices. The RetinaNet152 model can be deployed on edge devices; however, a technique for speeding up inference time is essential.]]>
<![CDATA[Artificial intelligence algorithms to predict customer satisfaction: a comparative study ]]>
<![CDATA[Berrada Chakour, Othman]]>;
<![CDATA[Ettaoufik, Abdelaziz]]>;
<![CDATA[Aissaoui, Khalid]]>;
<![CDATA[Maizate, Abderrahim]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i2.pp1654-1662
<![CDATA[Customer satisfaction is the key for every business successful. Therefore, keeping the current customer portfolio and expanding it over time is the main goal for any business. Hence, we need first to satisfy these clients. The customer satisfaction helps to retain consumers of its products, increase the life value of the customer, also make known its brand through positive word of mouth to get a better reputation and thus increase turnover. For this reason, several studies have been conducted on this subject to explore all tools and technologies that will help retain customers and reduce their churn rate. Based on various customer satisfaction studies for different types of businesses, this paper shows the review of promising research areas and artificial intelligence (AI) application models in predicting customer satisfaction. The results of this study allowed the identification of the best algorithms with the highest score of performance metrics that can be applied as part of the customer satisfaction prediction, through a detailed benchmark performed. The result shows that random forest (RF) and gradient boost (GB) algorithms in machine learning (ML) and convolutional neural network - long short-term memory (CNN-LSTM) in deep learning (DL) are giving the best performance. The most used metrics are accuracy andF1-score. In addition, DL models outperform ML models in most cases.]]>
<![CDATA[A portfolio optimization model for return trend rate and risk trend rate based on machine learning ]]>
<![CDATA[Zhu, Chunman]]>;
<![CDATA[Yahya Dawod, Ahmad]]>;
<![CDATA[Xi, Yu]]>;
<![CDATA[Chen, Gongsuo]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i2.pp933-944
<![CDATA[This paper presents a machine learning-based portfolio optimization model alongside a trading strategy algorithm. There are two distinct steps to the approach. Firstly, the long short-term memory (LSTM) neural network model was used to predict the closing price of stocks in the following 4 days. The average rise and fall rate over these four days is then calculated as the stock's return trend rate, which can measure the direction and intensity of the stock's rise and fall. The same method is used to predict the average of the industry index's rise and fall rate over the next four days as the risk trend rate. In the second step, the improved mean–variance model (IMV) model is used to provide customers with the stock portfolio purchasing strategy based on the return trend rate and risk trend rate. The experimental results demonstrate that the approach has a certain application value and outperforms the traditional method in terms of annual returns and Sharpe ratio, using the Shanghai Stock Exchange and the Shenzhen Stock Exchange as study samples. The model shows approximately 1% improvement in prediction accuracy. The latest advancements in machine learning provide substantial prospects for tactics involving the purchase of portfolios.]]>
<![CDATA[Diabetes mellitus diagnosis method based random forest with bat algorithm ]]>
<![CDATA[Anam, Syaiful]]>;
<![CDATA[Deny Tisna Amijaya, Fidia]]>;
<![CDATA[Hadi Wijoyo, Satrio]]>;
<![CDATA[Eka Ratnawati, Dian]]>;
<![CDATA[Ayu Dwi Lestari, Cynthia]]>;
<![CDATA[Ilyas, Muhaimin]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i2.pp1140-1149
<![CDATA[Diabetes mellitus (DM) is a very dangerous disease and can cause various problems. Early diagnosis of DM is essential to avoid severe effects and complications. An affordable DM diagnosis method can be developed by applying machine learning. Random forest (RF) is a machine learning technique that is applied to develop a DM diagnosis method. However, the optimization of RF hyperparameters determines the performance of RF approach. Swarm intelligence (SI) could be used to solve the hyperparameter optimization problem on RF. It is robust and simple to be applied and doesn’t require derivatives. Bat algorithm (BA) is one of SI techniques that gives a balance between exploration and exploitation to find a global optimal solution. This article proposes developing an RF-BA-based technique for diagnosing DM. The results of the experiment demonstrate that RF-BA can diagnose DM more accurately than conventional RF. RF-BA has higher performance compared to RF-particle swarm optimization (PSO) in terms of computational time. The RF-BA also are able to solve the overfitting problem in the conventional RF. In the future, the proposed method has a high chance of being implemented for helping people with early DM diagnosis with high accuracy, low cost, and high-speed process.]]>
<![CDATA[Abstractive summarization using multilingual text-to-text transfer transformer for the Turkish text ]]>
<![CDATA[Alipour, Neda]]>;
<![CDATA[Aydın, Serdar]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i2.pp1587-1596
<![CDATA[Today, with the increase in text data, the application of automatic techniques such as automatic text summarization, which is one of the most critical natural language processing (NLP) tasks, has attracted even more attention and led to more research in this area. Nowadays, with the developments in deep learning, pre-trained sequence-to-sequence (text-to-text transfer converter (T5) and bidirectional encoder representations from transformers (BERT) algorithm) encoder-decoder models are used to obtain the most advanced results. However, most of the studies were done in the English language. With the help of the recently emerging monolingual BERT model and multilingual pre-trained sequence-to-sequence models, it has led to the use of state-of-the-art models in languages with fewer resources and studies, such as Turkish. This article used two datasets for Turkish text summarization. First, Google multilingual text-to-text transfer transformer (mT5)-small model was applied on multilingual summarization (MLSUM), which is a large-scale Turkish news dataset, and success was examined. Then, success was evaluated by first applying BERT extractive summarization and then abstractive summarization on 1010 articles collected on the Dergipark site. Rouge measures were used for performance evaluation. This study is one of the first examples in the Turkish language and it is considered to provide a basis for future studies with good results.]]>
