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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
Unveiling critical features for failure prediction in green internet of things applications Khattach, Ouiam; Moussaoui, Omar; Hassine, Mohammed
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.pp4308-4318

Abstract

The rapid growth of the green internet of things (GIoT) in recent years signifies a transformative shift in internet of things (IoT) solution development. This evolution is driven by technological advancements, heightened environmental awareness, and a global imperative to combat climate change. Ensuring the reliability of GIoT applications is crucial for their success. This study identifies critical features for predicting IoT device failures, enabling early detection and intervention. Using datasets from industry, energy, and agriculture sectors, we employ a feature selection strategy to analyze extensive data from diverse GIoT deployments. Our analysis identifies significant features and integrates key insights from existing literature. Our findings support enhanced predictive maintenance strategies, reduced downtime, and improved overall performance of sustainable IoT solutions.
Learning assistance module based on a small language model Jinete, Marco Antonio; Jiménez-Moreno, Robinson; Espitia-Cubillos, Anny Astrid
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.pp4202-4210

Abstract

This paper presents the development of a low-cost learning assistant embedded in an NVIDIA Jetson Xavier board that uses speech and gesture recognition, together with a long language model for offline work. Using the large language model (LLM) Phi-3 Mini (3.8B) model and the Whisper (model base) model for automatic speech recognition, a learning assistant is obtained under a compact and efficient design based on extensive language model architectures that give a general answer set of a topic. Average processing times of 0.108 seconds per character, a speech transcription efficiency of 94.75%, an average accuracy of 9.5/10 and 8.5/10 in the consistency of the responses generated by the learning assistant, a full recognition of the hand raising gesture when done for at least 2 seconds, even without fully extending the fingers, were obtained. The prototype is based on the design of a graphical interface capable of responding to voice commands and generating dynamic interactions in response to the user's gesture detection, representing a significant advance towards the creation of comprehensive and accessible human-machine interface solutions.
Translation-based image steganography system utilizing autoencoder and CycleGAN Jawad, Thakwan Akram; Mohasefi, Jamshid Bagherzadeh; Reda Abdelghany, Mohammed Salah
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.pp3958-3969

Abstract

Traditional image steganography involves embedding secret information into a cover image, a process that requires modification of the carrier and potentially leaves detectable marks. This paper proposes a novel method of coverless image steganography based on generative models. Initially, a CycleGAN model is constructed and trained to learn the features of different image domains. Subsequently, an Autoencoder model is trained using two sets of images to achieve a precise one-to-one mapping. Once the models are trained, the autoencoder is used on both the sender and receiver sides to convert the cover image (also known as the stego image) into the secret image and vice versa. The CycleGAN model is then utilized to enhance the visual quality of the images generated by the autoencoder. Experimental results demonstrate that this method not only effectively secures secret information transmission but also improves efficiency and increases the capacity for information hiding compared to similar methods.
Detection of chronic kidney disease based on ensemble approach with optimal feature selection using machine learning Ajalkar, Deepika Amol; Deshmukh, Jyoti Yogesh; Shelke, Mayura Vishal; Wankhade, Shalini Vaibhav; Patil, Shwetal Kishor
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.pp4017-4031

Abstract

Chronic kidney disease (CKD) poses a significant health risk globally, necessitating early and accurate detection to ensure timely intervention and effective treatment. This study presents an advanced ensemble machine learning (ML) approach combined with optimal feature selection to enhance the detection of CKD. Using five baseline ML classifiers like gradient boosting (GB), random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), and decision tree (DT), and utilizing grid search for hyperparameter tuning, the proposed ensemble model capitalizes on the strengths of each algorithm. Our approach was tested on a public benchmark CKD dataset from Kaggle. The experimental results demonstrate that the ensemble model consistently outperforms individual classifiers and existing methods, achieving 97.5% accuracy, precision, recall, and an F1-score of 97.4%. This superior performance underscores the ensemble model's potential as a reliable early CKD detection tool. Integrating ML into CKD diagnostics enhances accuracy. It facilitates the development of automated, scalable diagnostic tools, aiding healthcare professionals in making informed decisions and ultimately improving patient outcomes.
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.

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