cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Imam Much Ibnu Subroto
Contact Email
imam@unissula.ac.id
Phone
-
Journal Mail Official
ijai@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 84 Documents
 5   6   7   8   9 
Search results for , issue "Vol 14, No 4: August 2025" : 84 Documents clear
Using the ResNet-50 pre-trained model to improve the classification output of a non-image kidney stone dataset Oyebode, Kazeem; Odoh, Anne Ngozi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3182-3191

Abstract

Kidney stone detection based on urine samples seems to be a cost-effective way of detecting the formation of stones. Urine features are usually collected from patients to determine if there is a likelihood of kidney stone formation. There are existing machine learning models that can be used to classify if a stone exists in the kidney, such as the support vector machine (SVM) and deep learning (DL) models. We propose a DL network that works with a pre-trained (ResNet-50) model, making non-image urine features work with an image-based pre-trained model (ResNet-50). Six urine features collected from patients are projected onto 172,800 neurons. This output is then reshaped into a 240 by 240 by 3 tensors. The reshaped output serves as the input to the ResNet-50. The output of this is then sent into a binary classifier to determine if a kidney stone exists or not. The proposed model is benchmarked against the SVM, XGBoost, and two variants of DL networks, and it shows improved performance using the AUC-ROC, Accuracy and F1-score metrics. We demonstrate that combining non-image urine features with an image-based pre-trained model improves classification outcomes, highlighting the potential of integrating heterogeneous data sources for enhanced predictive accuracy.
Artificial intelligence of things: society readiness Yuniarto, Dwi; Subiyakto, A'ang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2590-2600

Abstract

The convergence of artificial intelligence (AI) and the internet of things (IoT), known as the artificial intelligence of things (AIoT), represents a transformative leap in technology. This study investigated societal readiness for AIoT adoption and identified key factors influencing the readiness. The researchers used technology readiness index (TRI) model and broken down the model into the online survey’s instrument. The study used about 129 samples for examining the used variables, i.e., perceptions of innovation, technological skills, social and cultural influences, regulatory factors, and digital literacy. The authors employed partial least squares structural equation modeling (PLS-SEM) method using SmartPLS 3.0 to analyze the relationships between the variables of the model. The results highlighted innovation as a significant driver of societal readiness, while factors like discomfort have a lesser impact. Security and optimism also played moderate roles in shaping readiness. These findings offer crucial insights for stakeholders of the AIoT implementation by providing a foundation for strategies that promote the successful integration of AIoT into society. The study contributes to the broader discourse on technology adoption, offering a roadmap for enhancing societal preparedness.
Performance analysis and comparison of machine learning algorithms for predicting heart disease Bhadu, Neha; Singh, Jaswinder
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2849-2863

Abstract

Heart disease (HD) is a serious medical condition that has an enormous effect on people's quality of life. Early as well as accurate identification is crucial for preventing and treating HD. Traditional methods of diagnosis may not always be reliable. Non-intrusive methods like machine learning (ML) are proficient in distinguishing between patients with HD and those in good health. The prime objective of this study is to find a robust ML technique that can accurately detect the presence of HD. For this purpose, several ML algorithms were chosen based on the relevant literature studied. For this investigation, two different heart datasets the Cleveland and Statlog datasets were downloaded from Kaggle. The analysis was carried out utilizing the Waikato environment for knowledge analysis (WEKA) 3.9.6 software. To assess how well various algorithms predicted HD, the study employed a variety of performance evaluation metrics and error rates. The findings showed that for both the datasets radio frequency is a better option for predicting HD with an accuracy and receiver operating characteristic (ROC) values of 94% and 0.984 for the Cleveland dataset and 90% and 0.975 for the Statlog dataset. This work may aid researchers in creating early HD detection models and assist medical practitioners in identifying HD.
Optimized ensemble modeling approach for student cumulative grade point average prediction using regression models Gunasekaran, Hemalatha; Arokiarag Amalraj, Rex Macedo; Jesudoss, Angelin Gladys; Kanmani, Deepa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3074-3088

Abstract

This research focuses on developing models to accurately predict student’s cumulative grade point average (CGPA) in the early stages of their study to tackle the problem of dropout rates in educational institutions. The state-of-the-art methods address CGPA prediction as a classification problem, providing only an approximate prediction where precise prediction is essential. In this research, six regression models, namely linear regression, support vector regression (SVR), decision tree (DT), random forest (RF), lasso regression (LR), and ridge regression (RR) are developed without optimization and later fine-tuned using Bayesian optimization (BO) and GridSearchCV. BO efficiently searches the hyper-parameter space using probabilistic distribution’s function, whereas GridSearchCV exhaustively searches the hyper-parameter space. These techniques significantly improved the model's performance; SVR achieved an R² score of 94.11% through BO. Ensemble techniques, such as stacking, voting, and boosting, can further enhance the predictive capability of the model. The stacking ensemble model achieved the highest R² score of 94.45%, providing a 0.50% improvement in the R2 score. The findings of this study suggest that advanced optimization and ensemble techniques can substantially enhance the predictive capability of the model, thus enabling institutions to support students at risk of academic probation proactively.

Page 9 of 9 | Total Record : 84

 5   6   7   8   9 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 15, No 1: February 2026 Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 4: December 2020 Vol 9, No 3: September 2020 Vol 9, No 2: June 2020 Vol 9, No 1: March 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 More Issue