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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 1,639 Documents
 158   159   160   161   162 
A hybrid model for handling the imbalanced multiclass classification problem Alshdaifat, Esra'a; Hussein, Fairouz; Al-shdaifat, Ala'a; Al-Hassan, Malak; Altarawneh, Enshirah
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.pp3982-3993

Abstract

Data in many application domains is imbalanced. In machine learning, addressing imbalanced data is crucial to prevent bias towards the dominant class label and ensure that prediction models can learn and predict the minority class proficiently. This paper proposes a hybrid imbalanced classification model (HICD) to address the multiclass imbalanced data problem. The primary idea is to combine effective methods to construct a classification model that can handle multiclass imbalanced data effectively. Four methods are employed: an oversampling method to balance the data, a decomposition method to convert the multiclass problem into a set of binary problems, ensemble classification to integrate base classifiers to improve prediction, and a boosting method to encourage the classifier to pay more attention to misclassified samples. To evaluate the proposed model, seventeen imbalanced datasets from various application domains, featuring different numbers of classes, instances, features, and imbalance ratios, are assessed. The experimental results and statistical significance tests demonstrate that the proposed hybrid model significantly outperforms the standard one-vs-one (OVO) approach and the OVO combined with oversampling technique (SMOTE), both considered state-of-the-art for addressing imbalanced multiclass datasets, in terms of F1-score.
Anisa: artificial intelligence companion for elderly care with empathetic conversations and health management Karegoudra, Shilpa; Hegde, Pawan; Poojary, Sinchana C.; Shetty, Pranitha P.; Kotian, Sahana M.; Kallianpur, Saanvi; Koti, Veeresha R.
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.pp4260-4270

Abstract

This study introduces Anisa, an advanced artificial intelligence (AI) companion designed to enhance elderly care by addressing the multifaceted needs and challenges of older adults. The system integrates the Llama 3.2 model, powered by Groq, to facilitate context-aware dialogues and empathetic interactions. This capability helps alleviate loneliness and provides essential companionship. Agenda.js is used for scheduling and managing reminders, ensuring timely notifications for medications and appointments. Additionally, Twilio enables emergency alerts when distress signals are detected. Anisa promotes physical activity, tracks daily routines, and generates activity reports shared with caregivers and healthcare providers. Expo CLI implements step-tracking and document-sharing features. By integrating these functionalities, Anisa improves the quality of life for seniors, eases caregiver responsibilities, and fosters a safer, more supportive environment.
Accuracy of long short-term memory model in predicting YoY inflation of cities in Indonesia Leipary, Harfely; Setiawan, Adi
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.pp3887-3896

Abstract

Our  research  evaluates  the  effectiveness  of  the long  short-term  memory (LSTM) model in forecasting annual year-on-year (YoY) inflation across 82 cities in Indonesia based on time series data from BPS economic reports for 2014-2024. This study tests the accuracy of the model in reconstructing past inflation patterns, then evaluates the capabilities and limitations of the model in  various  urban  area  contexts  with  the root  mean  square  error (RMSE), mean  absolute  percentage  error (MAPE),  and coefficient  of  determination(R2)  metrics.  The  findings  show  that  LSTM  performs  well  in  metropolitan areas  such  as  Jakarta,  Bandung,  and  Surabaya  with R2values  >0.8  and  the lowest  MAPE  of  10.91%  in  Jakarta.  However,  in  small  cities  with  higher economic  volatility  such  as  Tanjung  Pandan,  the  model  shows  significant prediction   errors   (R²<0.50   and   MAPE   up   to   283.11%).   Moderate performance  (0.50≤ R²≤0.80)  was  found  in  cities  such  as  Palembang, Semarang, and Makassar, reflecting the model's adaptive ability to moderate inflation  patterns.  These  results  emphasize  the  important  role  of  structured economic data in improving the reliability of predictions, so that the policy implications  of  this  study  include  the  use  of  the  LSTM  model  as  an  early warning system by fiscal and monetary authorities, as well as the need for a data-based  inflation  control  strategy  to  strengthen  regional  and  national economic    resilience    in    supporting    sustainable    development    towards Indonesia Emas 2045.
Enhancing challenge-based immersion in cultural game using appreciative fuzzy logic Muljono, Muljono; Haryanto, Hanny; Andono, Pulung Nurtantio; Nugroho, Raden Arief; Yakub, Fitri; Sukmono, Indriyo K.
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.pp3702-3714

Abstract

Many traditional games in Indonesia are considered cultural heritage and are in serious decline; young generations no longer know about them. Serious games have been considered a potential educational tool for cultural heritage preservation. Lack of immersive experience due to over-focus on the learning content is a common problem in those games. Very little research also discusses cultural heritage serious game design frameworks. This study uses the appreciative fuzzy logic system (AFLS) to enhance the challenge-based immersive experience (CBIE) in the Joglosemar cultural heritage game. The AFLS provides autonomous challenges, such as enemy numbers and aggressive behavior, and the frequency of item appearances in the games using fuzzy logic with respect to the appreciative serious games (ASG) concepts. The ASG is the design guide for serious games that divides the game activities into 4-D: discovery, dream, design, and destiny. We use three ASG-based serious games to evaluate the CBIE produced by AFLS. The game experience questionnaire (GEQ) is used to measure the player experience, while the cross-validation is used to measure the AFLS performance. Results show that the AFLS enhances the CBIE. The study contributes mainly to provide reliable intelligent system for automated serious game design.
Grid graph convolutional network-cyclical learning rate EfficientNet for liver tumor segmentation classification Narasimhulu, Sangi; Rao, Ch D V Subba
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.pp4235-4249

Abstract

Liver tumors are identified in computed tomography (CT) images, which are crucial for accurate disease diagnosis and treatment planning as they enable clear delineation of tumors. Hence, it is vital in the field of medical radiology to segment and classify CT images of liver tumors effectively. However, liver tumor locations are not captured accurately at the boundaries in terms of size and depth within the liver due to downsampled images, leading to reduced segmentation and classification results. This research proposes a grid-graph convolutional network-based cyclical learning rate EfficientNet (GGCN-CLREN) to accurately segment and classify liver tumors. GGCN addresses inaccurate liver tumor segmentation due to downsampled images, which capture spatial relationships effectively and preserve tumor boundaries as well as depth information. For classification, CLREN optimizes classification by adjusting the learning rate, which enhances convergence and accuracy. Therefore, GGCN-CLREN ensures enhanced segmentation and classification by addressing size and depth inaccuracies. Golden sine gray wolf optimization (GSGWO) selects the most appropriate features effectively. The GGCN-CLREN achieves commendable accuracies of 99.80% and 99.96%, respectively, for the LiTS17 and CHAOS datasets when compared to the existing techniques: enhanced swim transformer network with adversarial propagation (APESTNet) and adding inception module-UNet (AIM-UNet).
Educational data mining approach for predicting student performance and behavior using deep learning techniques Ramaraj, Muniappan; Dhendapani, Sabareeswaran; Chembath, Jothish; Srividhya, Selvaraj; Thangarasu, Nainan; Ilango, Bhaarathi
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.pp4113-4122

Abstract

Educational Data Mining (EDM) uncovers insights from large datasets collected from various educational platforms, such as online learning systems, student information databases, and classroom tools. EDM helps educators identify hidden patterns that improve teaching strategies, personalize learning experiences, and predict student performance. Predicting student success has become a key focus of EDM, allowing institutions to implement targeted interventions and personalized support. The dataset included academic achievement grades from 1,001 students enrolled in various courses during the fall semester across multiple years, to demonstrate how proposed models provide more accurate predictions compared to traditional machine learning methods. Models such as YOLO, Fast R-CNN, Artificial Neural Networks (ANNs), and Long Short-Term Memory (LSTM) networks are used to capture complex, non-linear relationships within the data. The comparative analysis shows that these deep learning models significantly outperform traditional techniques, such as decision trees and support vector machines (SVMs). The results indicate that proposed method offers improved predictive accuracy, enabling educational institutions to identify at-risk students and deliver tailored interventions. This study highlights the potential of enhanced method to transform personalized education and enhance student success by better understanding individual learning needs and behaviors.
Optimizing diabetes prediction: unveiling patient subgroups through clustering Ganguly, Rita; Singh, Dharmpal; Bose, Rajesh
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.pp3681-3692

Abstract

Diabetes is a significant global health concern, leading to numerous deaths annually and affecting many individuals who remain undiagnosed. As its prevalence rises, the importance of early detection becomes increasingly vital. The rising diabetes epidemic demands data-driven strategies to catch health problems sooner and identify them clearly. This study utilizes the Pima Indians diabetes dataset (PIDD) to compare three powerful clustering schemes such as k-means, fuzzy C-means, and hierarchical. Uncontrolled diabetes, arising from the body's struggle to manage blood sugar due to insulin deficiency, can lead to devastating complications. Early detection and intervention are the cornerstones of effective management and improved patient outcomes. This study breaks new ground by meticulously evaluating the performance of each clustering algorithm using advanced metrics like silhouette score and adjusted Rand index. The goal is to identify the method that generates the most accurate and well-defined clusters for diabetes-related attributes. This, in turn, has the potential to revolutionize diabetes diagnosis, enabling earlier interventions and ultimately leading to better disease management and patient care. By providing a comprehensive comparison of these clustering techniques, this research offers a significant contribution to the fight against diabetes.
User acceptance of the gender and development mobile app with a rating checklist using a modified technology acceptance model Perea, Rossian V.; Miranda, Abigael M.
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.pp3906-3914

Abstract

Resource centers of gender and development (GAD) in local government use the traditional method of disseminating information about GAD awareness, such as distributing printed campaign materials and conducting gender sensitivity training (GST) on faculty and staff, students, and selected barangay communities in the Philippines. Some recipients of campaign materials are text-heavy and unappealing to read, which makes them less interested. However, faculty and students conducting research are not aware if their study is gender-responsive or if GAD is invisible. Hence, this study examines the user acceptance of the GAD app mobile application using the modified technology acceptance model (TAM) with a machine learning (ML) algorithm applied. The results of statistics and analyses from the intended users (N=100) were presented including data-driven modeling using a support vector machine (SVM) to show precise findings for the research on how this technology was used and accepted. The study’s findings show widespread acceptance among experts and users of the mobile application employing external factors like self-efficacy (SE) and specific anxiety (SA) and moderating variables such as age, sex, highest educational attainment (HEA), and knowledge in GAD implementation, which are crucial for predicting and understanding the consequences of the research made clear.
Designing an intelligent system for vibration diagnosis of centrifugal water-cooling pumps using Bayesian networks Suprihatiningsih, Wiwit; Romahadi, Dedik; Genetu Feleke, Aberham
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.pp4390-4402

Abstract

Implementing monitoring methods is a viable method to reduce substantial damage to cooling water centrifugal pumps. Engaging in manual vibration analysis requires considerable time and a requisite level of competence. Small datasets pose challenges when applying classification systems that utilize linear classification models and deep learning. Given these issues, our proposal entails developing a system capable of autonomously, precisely, and accurately diagnosing vibrations using a limited dataset. The system is anticipated to possess the capability to detect multiple categories of mechanical defects, such as static imbalance, dynamic imbalance, misalignment, cavitation, looseness, and bearing corrosion. The Bayesian network (BN) structure was constructed using the MATLAB software. The input data parameters comprise vibration signals measured in the frequency domain and values representing phase differences. The constructed intelligent system was subsequently assessed using a dataset including 120 samples. The smart system can rapidly anticipate and precisely identify every form of harm with exceptional accuracy and sensitivity, relying on test outcomes. The test data analysis reveals that the intelligent system attained an average accuracy of 94.74%, precision of 95.32%, sensitivity (recall) of 93.67%, and F-score of 94.36%. 
Two-steps feature selection for detection variant distributed denial of services attack in cloud environment Kurniabudi, Kurniabudi; Winanto, Eko Arip; Sharipuddin, Sharipuddin
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.pp3945-3957

Abstract

The prevalence of cloud computing among organizations poses a significant problem in ensuring security. Specifically, distributed denial of services (DDoS) attacks targeting cloud computing networks can lead to financial losses for consumers of cloud computing services. This assault has the potential to render cloud services inaccessible. The detection system serves as a remedy to prevent more substantial losses. This research aims to enhance the efficacy of the system detection model by integrating feature selection with three machine learning algorithms: decision tree (DT), random forest (RF), and naïve Bayes (NB). Therefore, our study suggests combining two phases of feature selection into the DDoS attack detection procedure. The first phase uses the information gain (IG) feature selection technique approach, and the second phase uses the principal component analysis (PCA) feature extraction approach. The technique is referred to as two-step feature selection. The test findings indicate that the implementation of two-step feature selection can enhance the performance of the DT and RF detection models by around 9%.

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