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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 1,722 Documents
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Design and analysis of reinforcement learning models for automated penetration testing Jaganathan, Suresh; Latha, Mrithula Kesavan; Dharanikota, Krithika
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.pp4061-4073

Abstract

Our paper proposes a framework to automate penetration testing by utilizing reinforcement learning (RL) capabilities. The framework aims to identify and prioritize vulnerable paths within a network by dynamically learning and adapting strategies for vulnerability assessment by acquiring the network data obtained from a comprehensive network scanner. The study evaluates three RL algorithms: deep Q-network (DQN), deep deterministic policy gradient (DDPG), and asynchronous episodic deep deterministic policy gradient (AE-DDPG) in order to compare their effectiveness for this task. DQN uses a learned model of the environment to make decisions and is hence called model-based RL, while DDPG and AE-DDPG learn directly from interactions with the network environment and are called model-free RL. By dynamically adapting its strategies, the framework can identify and focus on the most critical vulnerabilities within the network infrastructure. Our work is to check how well the RL technique picked security vulnerabilities. The identified vulnerable paths are tested using Metasploit, which also confirmed the accuracy of the RL approach's results. The tabulated findings show that RL promises to automate penetration testing tasks.
A comprehensive artificial intelligence framework for reducing patient rehospitalizations Khekare, Ganesh; Janarthanan, Midhunchakkaravarthy
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.pp3827-3834

Abstract

The role of artificial intelligence (AI) in the healthcare sector is increasing daily. Readmissions of patients have become a significant challenge for the medical sector, adding unnecessary burden. Governments and public sectors are continuously working on the hospital readmissions reduction program (HRRP). In this research work, an AI framework has been developed to reduce patient readmissions. The accuracy of the framework has been increased by continuous refinement in feature engineering, incorporating several complex datasets. The framework analyses the different algorithms like bidirectional long short-term memory (BiLSTM), convolutional neural network (CNN), and XGBoost for prediction. This framework has shown a 92% accuracy rate during testing, showing a 37% reduction in 40-day rehospitalization rates. This reduces the overburden on hospital systems by avoiding unnecessary readmissions of patients. The system’s real-time development, scalability, management of things in an ethical manner, and long-term viability will remain as future scope.
Students’ perceptions and effect of ChatGPT on research proposal quality across gender in Indonesia Otoluwa, Moon Hidayati; Salim, Arhanuddin; Kadir, Kadir; Saud, Indah Wardaty; Darise, Gina Nurvina; Asma, Andi
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.pp3613-3623

Abstract

The use of ChatGPT in improving students' academic writing abilities has been extensively studied, but how students perceive the use of ChatGPT affecting the quality of thesis proposals remains unclear becomes the novelty of this research. To address this gap, this study examines final semester students (N=55) utilizing ChatGPT in preparing final assignments across universities in East Indonesia. Employing a mixed-methods research design, this study collected data through surveys and short essays. Descriptive statistics, bivariate correlations, independent t-tests, linear regression, and thematic analysis were used to analyze the data. Findings indicate that i) perceptions of ChatGPT positively correlate with the quality of students' proposals, ii) perceptions of ChatGPT use predict the quality of research proposals, iii) gender does not influence perceptions of ChatGPT use or the quality of students' proposals, and iv) ChatGPT has a positive impact as research reference model, source of ideas, framework reference, translation tool, and paraphraser, but it also has limitations, particularly in providing accurate responses and posing a risk of reducing critical thinking abilities. ChatGPT has proven effective in helping students prepare research proposals, developing ideas, and research frameworks. However, institutions must provide appropriate guidance to prevent the decrease of critical thinking abilities.
Heart disease prediction optimization using metaheuristic algorithms Nouna, Zaid; Bouyghf, Hamid; Nahid, Mohammed; Sabiri, Issa
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.pp4332-4341

Abstract

This study explores metaheuristics hyperparameter tuning effectiveness in machine learning models for heart disease prediction. The optimized models are k-nearest neighbors (KNN) and support vector machines (SVM) using metaheuristics to identify configurations that minimize prediction error. Even though the main focus is utilizing metaheuristics to efficiently navigate the hyperparameter search space and determine optimal setting, a pre-processing and feature selection phase precedes the training phase to ensure data quality. Convergence curves and boxplots visualize the optimization process and the impact of tuning on model performance using three different metaheuristics, where an error of 0.1188 is reached. This research contributes to the field by demonstrating the potential of metaheuristics for improving heart disease prediction performance through optimized machine learning models.
Optimizing nonlinear autoregressive with exogenous inputs network architecture for agarwood oil quality assessment Roslan, Muhammad Ikhsan; Ahmad Sabri, Noor Aida Syakira; Noramli, Nur Athirah Syafiqah; Ismail, Nurlaila; Mohd Yusoff, Zakiah; Almisreb, Ali Abd; Tajuddin, Saiful Nizam; Taib, Mohd Nasir
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.pp3493-3502

Abstract

Agarwood oil is highly valued in perfumes, incense, and traditional medicine. However, the lack of standardized grading methods poses challenges for consistent quality assessment. This study proposes a data-driven classification approach using the nonlinear autoregressive with exogenous inputs (NARX) model, implemented in MATLAB R2020a with the Levenberg-Marquardt (LM) algorithm. The dataset, sourced from the Universiti Malaysia Pahang Al-Sultan Abdullah under the Bio Aromatic Research Centre of Excellence (BARCE) and Forest Research Institute Malaysia (FRIM), comprises chemical compound data used for model training and validation. To optimize model performance, the number of hidden neurons is systematically adjusted. Model evaluation uses performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R²), epochs, accuracy, and model validation. Results show that the NARX model effectively classifies agarwood oil into four quality grades which is high, medium-high, medium-low, and low. The best performance is achieved with three hidden neurons, offering a balance between accuracy and computational efficiency. This work demonstrates the potential of automated, standardized agarwood oil quality grading. Future research should explore alternative training algorithms and larger datasets to further enhance model robustness and generalizability.
Fuzzy risk assessment system for indoor air quality and respiratory disease prevention Nunes, Éldman de Oliveira; Barrera, Ariel Isaac Posada; Peralta, Laura Margarita Rodríguez; Sampaio, Paulo Nazareno Maia; Astudillo, Fabián Leonardo Cuesta
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.pp3693-3701

Abstract

This study addresses the evaluation of indoor air quality, with a focus on mitigating respiratory diseases and sick building syndrome (SBS). Recognizing that different pollutants exhibit variable behavior depending on environmental factors and human activity, the objective was to develop a fuzzy logic-based classification system that integrates environmental variables such as temperature, relative humidity, and pollutant concentrations‒particulate matter (PM10, PM2.5), carbon dioxide (CO₂), and total volatile organic compound (TVOC)‒into a unified model. The method involved defining risk levels as low, moderate, high, and very high, and implementing 54 fuzzy rules to dynamically and accurately categorize these risks, based on measurements taken between 2022 and 2024 in the states of Morelos and Puebla under various relative humidity and temperature scenarios. The analysis of the results demonstrated robust system performance, with an overall accuracy of 94.08%, but also revealed challenges in distinguishing between adjacent risk classes. This research contributes to a better understanding of the complex impacts of air quality on health and reinforces efforts to mitigate respiratory problems and SBS in densely populated indoor environments.
FaceSynth: text-to-face generation using CLIP and its variants with generative adversarial networks Ravisankar, Priyadharsini; Dhanvanth, Shruthi; Jenane Padmanabhan, Vaishnave
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.pp3588-3598

Abstract

In recent years, there have been massive developments in the field of generative AI, especially in generative adversarial networks (GANs). GANs generate original images that haven't been seen during training and have had several advancements like StyleGAN, StyleGAN2, and StyleGAN2-adaptive discriminator augmentation (ADA). Contrastive language-image pre-training (CLIP), by OpenAI, is a visual linguistic model that has been trained to associate texts with images. Recently, new CLIP variants were developed, such as metadata-curated language-image pre-training (MetaCLIP), released by Facebook and trained on a larger dataset, and Multilinigual-CLIP, which adapts CLIP to multiple languages. We compare CLIP and its variants in text-to-face synthesis with a custom StyleGAN2-ADA model and a pre-trained StyleGAN2 model. Our training-free algorithm starts with an initial image latent code that is iteratively manipulated to match a given text description. It achieves this by minimizing the distance between the text and image embedding in the multi-modal embedding space of the CLIP models. An examination of CLIP and its variants showed that MetaCLIP outperformed its competitors in LPIPS similarity and closeness of the synthesized image to the actual prompt. CLIP produced the most realistic images with the best FID score and multilingual-CLIP presented a choice of input text language and generated decent images.
Data-driven clustering and prediction of high school graduation rates in Indonesia (2015-2023) using machine learning Salman Arrosyid, Muhammad; Marzuki, Marzuki; Widihastuti, Widihastuti; Haryanto, Haryanto; Fransiska Mbari, Maria Angelina
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.pp3771-3780

Abstract

This study aims to analyze the graduation rate of senior high school education in 34 Indonesian provinces during the period 2015-2023 and identify patterns of educational disparities between regions. To achieve the objectives, this study applies a neural network to predict education completion patterns based on historical data, then the prediction results are analyzed using K-means clustering technique utilizing the elbow method to select the ideal number of clusters. The clustering results show three categories of provinces based on education completion rates: high, medium, and low. The provinces with high completion rates, generally, supported with good education infrastructure and effective policies, while the medium category faces challenges in resource distribution, but still potentially improve. In contrast, the low category suffers from limited access, geographical constraints, and socio-economic disparities. This research contributes to education policy-making by offering a machine learning-based approach to understanding education disparities between regions. The new insight offered by this study lies in the integration of neural network and K-means clustering in mapping education completion rates to support strategies for improving access and quality of education in Indonesia.
Transformation of Islamic values in the era of artificial intelligence Faizin, Nur; Ma`ali, Abul; Hidayatullah, Muhammad Fahmi; Nasih, Ahmad Munjin; Faizah, Rohmatul; Fauzan, Moh.
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.pp4353-4362

Abstract

The emergence of artificial intelligence (AI) such as ChatGPT has brought significant changes in the way humans’ access and understand information, including in the religious field. This research aims to examine how the transformation of Islamic values occurs through ChatGPT responses in the aspects of educational ethics, Islamic law, da'wah, and Qur'anic interpretation. This study applied a qualitative case study method and data was collected from indexed scientific articles from academic databases, ChatGPT responses, and online news articles. The study findings show that the use of ChatGPT in the context of Islam requires caution. While technology can answer a variety of questions, there are fundamental flaws related to the accuracy of citations, unverified sources of information, and a lack of understanding of the sharia context. In fact, there are errors in the mention of Qur'anic verses that have the potential to cause confusion. This emphasizes the importance of the sanad principle in Islamic scholarship as a valid reference. The paper proposes the need to develop more ethical and contextual AI systems in understanding religious questions, as well as the involvement of scholars and academics in training machines to conform to Islamic values.
Recommendation system for football player recruitment using k-nearest neighbor Maukar, Maukar; Rodiah, Rodiah
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.pp3847-3857

Abstract

In modern professional football, achieving a competitive edge depends not only on on-field performance but also on effective off-field strategies, particularly in player recruitment. This study proposes a machine learning-based recommendation system to support talent identification and optimal player placement using statistical performance data. The model analyzes a wide range of features, including shots, expected goals, expected assists, pass types, offensive contributions, and defensive actions across field zones. The dataset undergoes preprocessing steps such as normalization (per 90 minutes) and dimensionality reduction. A key innovation of this research is the use of principal component analysis (PCA) to reduce feature dimensionality, minimizing redundancy while retaining essential information, which improves model efficiency and scalability. The refined data is then processed using the k-nearest neighbors (KNN) algorithm with cosine similarity, allowing the system to identify players with similar performance profiles based on directional similarity in a high-dimensional space. This combination enhances recommendation accuracy by focusing on performance structure rather than raw values. The resulting system provides actionable insights into player suitability and potential, offering clubs a data-driven tool for informed scouting and recruitment decisions. The approach demonstrates the effectiveness of combining PCA and KNN in optimizing football player recommendation systems.

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