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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 1,808 Documents
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Explainable hybrid models for cardiovascular disease detection and mortality prediction Al-Ataby, Ali; Attia, Hussain
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp191-212

Abstract

The impact of cardiovascular diseases (CVDs) is devastating, with 20.5 million deaths annually. Early detection and prediction tools exist, but current approaches struggle to balance predictive performance with clinical interpretability. In this work, a two-stage machine learning (ML) framework is proposed for heart disease detection and mortality prediction in heart failure patients. Logistic regression (LR), random forest (RF), and gradient boosting (GB) models were trained using the publicly available heart failure datasets, and their performance was compared, then a stacked ensemble approach was employed to enhance prediction accuracy. Model interpretability was achieved through Shapley additive explanations (SHAP), which provide global feature rankings and specific patient attributes, supporting explainable artificial intelligence (XAI) in clinical practice. The GB model achieved the highest performance in the first stage with a receiver operating characteristic area under the curve (ROC AUC) of 96% and an accuracy of 89% on internal testing, while external validation confirmed strong generalization (ROC AUC of 94%). In the second stage, stacked ensemble model was employed and achieved marginal improvements. Two interactive web applications were developed to enable real-time predictions with SHAP visualizations. The results demonstrate that combining high-performance ML models with interpretable outputs can significantly improve trust in real-world healthcare environments.
Comparison between ensemble and linear methods for website phishing detection Rashid, Saba Hussein; Abdulwahhab, Saba Alaa; Abdulaziz, Farah Amer
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp681-694

Abstract

In the current digitalized world, the notion of cybersecurity has become crucial in everyday life, and the issue of privacy takes the leading role in the technological agenda of the global community. One such social engineering attack that is currently prevalent is phishing, which is a common technique used by cybercriminals to intercept sensitive data. Despite the presence of certain limitations which can restrict its usefulness, machine learning (ML) has evolved into an interesting approach to identify phishing attacks. Cloud ML is an effective solution that uses cloud computing solutions to create, train, and deploy models that provide a faster and more accurate result as well as support large datasets. This paper compares the ensemble method of Amazon SageMaker’s AutoML tool, AutoGluon, with the linear method of SageMaker’s linear learner algorithm for website phishing detection. Key factors examined include training techniques, training time, batch transform time, endpoint prediction time, and model accuracy. The results demonstrate that while AutoGluon outperforms linear learner in terms of accuracy and prediction speed, linear learner is faster in training and batch transform processes.
A comprehensive survey of cyberbullying on social media: challenges, detection, and AI-based prevention Odeh, Ammar; Hassan, Osama Alhaj; Abu Taleb, Anas; Aboshgifa, Abobakr; Belhaj, Nabil
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp86-96

Abstract

Cyberbullying is a pervasive issue in the digital landscape, particularly on social media platforms, where individuals engage in online harassment, intimidation, and abuse. Unlike traditional bullying, cyberbullying has a broader reach, anonymity, and persistence, making it a growing concern for mental health, social well-being, and online safety. This paper provides a comprehensive survey of cyberbullying trends, its psychological and social impacts, and the role of social media in amplifying the problem. It explores existing detection and prevention strategies, including artificial intelligence (AI)-driven approaches, policy frameworks, and platform-based moderation techniques. Furthermore, it discusses challenges in enforcement, the limitations of automated detection systems, and the need for improved legal measures. This paper uniquely contributes an integrated perspective on cyberbullying detection and prevention by synthesizing current research across psychological, sociocultural, and technical dimensions. It emphasizes underexplored gaps such as multilingual detection, real-time moderation, and cross-platform enforcement, and proposes a layered framework to guide future research and policy.
Serious game intelligent transportation system based on internet of things Nugroho, Fresy; Buditjahjanto, I Gusti Putu Asto; Pebrianti, Dwi; Hammad, Jehad A. H.; Fachri, Moch; Lestari, Tri Mukti; Maharani, Dian; Nurrahma’N, Alfina
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp177-190

Abstract

This research examines the implementation of the preference ranking organization method for enrichment evaluation (PROMETHEE) approach for multi-criteria decision-making in a character recommendation system for serious games. The method calculates character skill values across multiple criteria and generates rankings of the best characters according to game environment conditions derived from closed-circuit television (CCTV) based traffic detection. Image processing algorithms were applied to classify congestion levels into quiet, moderate, and busy categories, which directly influence gameplay modes. Experimental results show that PROMETHEE rankings vary across maps (e.g., A6 ranked highest in quiet mode, while B2 dominated in busy mode), demonstrating the system’s contextual adaptability. Usability testing with 50 participants yielded an average system usability scale (SUS) score of 78.9, while expert evaluation using game design factor questionnaire (GDFQ) produced a mean of 4.19/5, both indicating high acceptance and positive user experience. These findings confirm that PROMETHEE is effective in generating context-aware recommendations, providing both strategic depth and engagement. The study concludes that integrating traffic data into serious game design can enrich intelligent transportation systems (ITS) education and awareness, with future improvements possible through real-time player feedback adaptation and machine learning–based traffic prediction.
Bridging hybrid deep learning detection and lightweight handcrafted features for robust single sample face recognition Nastiti, Faulinda Ely; Sopingi, Sopingi; Hariyadi, Dedy; Sumarlinda, Sri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp888-900

Abstract

Single sample face recognition (SSFR) remains a challenging task due to the limitation of having only one reference image per identity, which reduces embedding diversity and decreases robustness under variations of pose, expression, and illumination. This study proposed a hybrid framework that integrates deep learning-based detection through anchor box optimization and non-maximum suppression (NMS) with lightweight handcrafted feature extraction using local binary pattern (LBP). The detection stage leverages deep learning to ensure robust face localisation, while LBP maintains computational efficiency under limited-sample conditions. The training process showed accuracy improvement from 47.5% at the initial epoch to 98.0% at epoch 72, while testing accuracy stabilized at 85-88% with the best value of 87.9%. Evaluation on 48 new facial images achieved 89.6% accuracy, 95.3% precision, 91.1% recall, 93.1% F1-score, and 0.94 area under the receiver operating characteristic curve (AUC ROC). Real-world implementation on Android and iOS-based attendance applications further validated the model, reaching 88.46% accuracy across 52 tests under 50-400 lux illumination. The findings proved that the proposed hybrid design provides improved accuracy and stability compared with previous approaches.
Human activity recognition using selective kernel network-2D convolutional neural network with ArcFace loss Srinivasaiah, Banushri; Ramegowda, Jagadeesha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp350-360

Abstract

Human activity recognition (HAR) is a widely adopted technique in applications requiring accurate identification of human actions. However, HAR approaches often face challenges in generalizing across complex datasets with multi-view variations, resulting in reduced classification accuracy. Existing classifiers face shortcomings in predicting human activities due to the presence of irrelevant video frames, leading to frequent misclassifications. This research proposes a selective kernel network-2D convolutional neural network with additive angular margin loss for deep face recognition (SKN-2D-CNN with ArcFace loss) to recognize human activity effectively. SKN dynamically adapts the receptive field for learning multi scale spatial features, enhancing the recognition of intricate human activities with varying motion scales. In the embedding space, ArcFace loss introduces an angular margin penalty that improves inter-class separability and intra class compactness for recognition. Together, the proposed method minimizes misclassification in human activity by improving the robustness of feature representation. Feature extraction using visual geometry group 19 (VGG19) captures spatial features like edges, textures and shapes from video frames, enhancing the model’s ability to differentiate between complex human activities. The proposed method achieves high accuracy of 99.16 and 98.75% on the UCF101 and HMDB-51 datasets, respectively, when compared with existing methods such as CNN and bidirectional gated recurrent unit (BiGRU).
Web-based geothermal drilling stuck pipe prediction using decision tree algorithm Muhtadlor, Rosyihan; Rosyid, Nur Rohman; Fauziyyah, Anni Karimatul; Setiawan, Lalu Hendra Permana; Saputra, Irfan; Stasa, Pavel; Benes, Filip; Syafrudin, Muhammad; Alfian, Ganjar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp604-614

Abstract

In geothermal drilling operations, data from rig-mounted sensors play a crucial role in maintaining operational efficiency and preventing drilling failures. However, sensor uncertainties and complex subsurface conditions can lead to stuck pipe incidents, causing significant non-productive time and financial losses. This study proposes web-based drilling monitoring system integrated with machine learning (ML) to predict stuck pipe occurrences in geothermal drilling. Several ML algorithms—decision tree (DT), random forest (RF), naïve Bayes (NB), multilayer perceptron (MLP), and support vector machine (SVM)—were evaluated using geothermal drilling data from an Indonesian geothermal project conducted in 2023. To address class imbalance, the synthetic minority oversampling technique (SMOTE) was applied to the training dataset. Feature selection was performed using the correlation coefficient method, and predictions were generated using a 5 minute sliding window. Among the evaluated models, the DT consistently demonstrated superior performance across multiple prediction horizons (PH), achieving an accuracy of 97.4%, precision of 98.6%, recall of 72.9%, and a ROC-AUC of 0.729 using the top five selected features. The trained model was integrated into web-based monitoring platform that provides visualization and predictive alerts. This system enables early detection and better decision-making, helping improve drilling efficiency, reduce stuck pipe risks, and enhance operational safety.
Review of ChatGPT tools in education systems based on literature Prasad Reddy K. V., Siva; Malepati, Parvathi; Pullamma, Khadarbadar; Mallikarjunachari, Gangapuram; Venkata Rami Reddy, Sareddy; Kumar Vadaga, Anil; Shashank Gudla, Sai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp12-19

Abstract

Artificial intelligence (AI) has rapidly reshaped modern education, with ChatGPT emerging as one of the most influential generative AI tools supporting teaching, learning, and academic administration. This review synthesizes evidence from 65 peer-reviewed studies published since 2022 to evaluate ChatGPT’s educational applications, benefits, constraints, and ethical implications. Findings indicate that ChatGPT enhances personalized learning, academic writing, digital literacy, and instructional efficiency, while offering scalable support for large classrooms. Comparative analyses reveal that ChatGPT demonstrates superior linguistic coherence and reasoning compared to Gemini, Bing Chat/Copilot, and Claude. However, concerns persist regarding hallucinations, academic dishonesty, data privacy, infrastructural disparities, and faculty readiness. The review highlights the need for responsible governance frameworks, AI literacy programs, and equitable institutional policies. Future directions include longitudinal research on learning outcomes, inclusive AI design, cross-cultural adoption patterns, and evolving teacher–student dynamics in AI-augmented environments.
Real-time intelligent virtual assistant based on retrieval augmented generation Arthana, I Ketut Resika; Dewi, Ni Putu Novita Puspa; Saskara, Gede Arna Jude; Pradnyana, I Made Ardwi; Indrayani, Luh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp237-246

Abstract

Improving user experience in accessing information on organizational websites remains a challenge. Users often face complex navigation and multi step searches that slow information retrieval. This study introduces the real time intelligent virtual assistant (RIVA), which integrates large language models (LLMs) with the retrieval-augmented generation (RAG) framework to support real-time interaction with website content. The system was implemented on the Universitas Pendidikan Ganesha (Undiksha) website using a WordPress content management system (CMS) and developed following the design science research (DSR) approach, which includes six stages: problem identification, solution objectives, design and development, demonstration, evaluation, and communication. The retrieval-augmented generation assessment (RAGAS) evaluation indicated that the combined model of text-embedding-ada-002 and semantic chunking yielded the best results, with context precision=0.83, context recall=0.90, response relevancy=0.91, faithfulness=0.83, and answer correctness=0.85. User experience questionnaire (UEQ) testing performed well, particularly in the novelty and stimulation dimensions. These results demonstrate that RIVA can provide users with access to relevant and engaging information. As a result, future research will focus on improving retrieval and developing adaptive semantic chunking for structured and complex data.
Exploring cutting-edge research, applications, and future directions in artificial intelligence across diverse domains Sutikno, Tole
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp1019-1022

Abstract

This issue highlights the most recent advances in artificial intelligence (AI) research, which cover a wide range of applications, methodologies, and emerging technologies. The gathered works highlight AI's transformative potential in a variety of fields, including healthcare, environmental monitoring, energy systems, intelligent transportation, cybersecurity, smart agriculture, and human-computer interactions. The featured studies demonstrate novel applications of deep learning, convolutional neural networks, vision transformers, reinforcement learning, ensemble methods, and explainable AI techniques, with a focus on both performance optimisation and interpretability. The issue also delves into AI integration with IoT, blockchain, big data, and mobile platforms, showcasing scalable and real-time solutions for dynamic, data-intensive settings. Aside from technical accomplishments, the contributions address practical issues such as model generalisation, feature selection, data quality, privacy, and ethical concerns. These works show how AI is improving decision-making, predictive capabilities, and operational efficiency while addressing complex societal and industrial issues. Looking ahead, this issue encourages reflection on AI development trajectories, emphasising the importance of robust, explainable, and adaptive systems that balance computational power and interpretability. This issue aims to inform, inspire, and guide practitioners and researchers in shaping the next generation of intelligent technologies by providing a comprehensive overview of cutting-edge AI research and real-world applications.

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