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Centrality-optimized coalition formation: a genetic algorithm approach with leadership attributes

10.11591/ijai.v15.i1.pp383-398
Anon Sukstrienwong , Sorapak Pukdesree
In graph theory, centrality is often assessed using traditional methods such as closeness centrality, which measures the average shortest path length between nodes in a network. In this study, we primarily focus on developing the proposed approach and demonstrating its effectiveness through initial experimental results. A novel genetic algorithm (GA)–based method named centrality–optimized leadership coalition formation (COLCF) has been designed. It emphasizes actual agent distances according to closeness centrality and leadership attributes in group formation. We detail the COLCF algorithm, present empirical case studies, and provide efficiency comparisons. In accordance with our simulation results, the proposed algorithm is capable of capitalizing on the ideal coalition structure for achieving high closeness centrality when incorporated with leadership attributes. The experimental results demonstrate the algorithm’s robustness and effectiveness in addressing complex coalition formation challenges.
Volume: 15
Issue: 1
Page: 383-398
Publish at: 2026-02-01

Review of ChatGPT tools in education systems based on literature

10.11591/ijai.v15.i1.pp12-19
Siva Prasad Reddy K. V. , Parvathi Malepati , Khadarbadar Pullamma , Gangapuram Mallikarjunachari , Sareddy Venkata Rami Reddy , Anil Kumar Vadaga , Sai Shashank Gudla
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.
Volume: 15
Issue: 1
Page: 12-19
Publish at: 2026-02-01

Real-time intelligent virtual assistant based on retrieval augmented generation

10.11591/ijai.v15.i1.pp237-246
I Ketut Resika Arthana , Ni Putu Novita Puspa Dewi , Gede Arna Jude Saskara , I Made Ardwi Pradnyana , Luh Indrayani
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.
Volume: 15
Issue: 1
Page: 237-246
Publish at: 2026-02-01

Web-based geothermal drilling stuck pipe prediction using decision tree algorithm

10.11591/ijai.v15.i1.pp604-614
Rosyihan Muhtadlor , Nur Rohman Rosyid , Anni Karimatul Fauziyyah , Lalu Hendra Permana Setiawan , Irfan Saputra , Pavel Stasa , Filip Benes , Muhammad Syafrudin , Ganjar Alfian
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.
Volume: 15
Issue: 1
Page: 604-614
Publish at: 2026-02-01

Adaptive deformable feature augmentation and refinement network for scene text detection and recognition

10.11591/ijai.v15.i1.pp831-840
Ratnamala S. Patil , Geeta Hanji , Rakesh Hudud
Scene text recognition (STR) is the task of detecting and identifying text within images captured from natural scenes, a challenging process due to variations in text appearance, orientation, and background complexity. The proposed methodology, adaptive deformable feature augmentation and refinement network (ADFARN), is designed to address these challenges by combining deformable convolutional networks for robust enhanced feature extraction with a novel deep feature refinement (FRE) that leverages refinement for precise text localization. This approach enhances the differentiation between text and background, significantly improving recognition accuracy. The ADFARN methodology includes a comprehensive process of feature extraction, deep feature augmentation module (DFAM), and the generation of score and threshold maps through differentiable binarization. The adaptive nature of the model allows it to handle low resolution and partially occluded text effectively, further increasing its robustness. Additionally, the proposed method aligns visual and textual features seamlessly. Extensive performance evaluation on the common objects in context (COCO)-Text dataset demonstrates that ADFARN outperforms existing state-of-the-art methods in terms of precision, recall, and F1-scores, establishing it as a highly effective solution for STR in real world applications.
Volume: 15
Issue: 1
Page: 831-840
Publish at: 2026-02-01

A survey on leveraging artificial intelligence tools for enhancing advanced mathematical education and problem-solving

10.11591/ijai.v15.i1.pp76-85
Hadeel N. Abosaooda , Syaiba Balqish Ariffin , Osamah Mohammed Alyasiri
Artificial intelligence (AI) has increasingly shaped education, with ChatGPT developed by OpenAI, emerging as a prominent tool due to its ability to generate contextually relevant language and support learning. This survey investigates the integration of ChatGPT into mathematics education, focusing on three dimensions. First, it explores innovative strategies for creating interactive and personalized learning environments that adapt to individual student needs. Second, it evaluates ChatGPT’s specific advantages in mathematics instruction, including providing tailored feedback, assisting with problem-solving, and deepening conceptual understanding. Third, it addresses the challenges of adopting ChatGPT in advanced mathematics education, such as risks of over-reliance, the necessity of balancing AI with traditional pedagogy, and the importance of ongoing professional development for educators. Recent studies highlight ChatGPT’s potential to solve complex mathematical problems, such as those in linear algebra and word problems, while also noting limitations related to accuracy and the preservation of critical thinking skills. The findings demonstrate that ChatGPT can significantly enhance mathematics education by supporting personalized learning and complex problem-solving. Therefore, this study will contribute to the discourse on AI in education by identifying opportunities, challenges, and implications for equity, pedagogy, and the responsible integration of ChatGPT in future classrooms.
Volume: 15
Issue: 1
Page: 76-85
Publish at: 2026-02-01

Human activity recognition using selective kernel network-2D convolutional neural network with ArcFace loss

10.11591/ijai.v15.i1.pp350-360
Banushri Srinivasaiah , Jagadeesha Ramegowda
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).
Volume: 15
Issue: 1
Page: 350-360
Publish at: 2026-02-01

A novel BERT-long short-term memory hybrid model for effective credit card fraud detection

10.11591/ijai.v15.i1.pp788-797
Oussama Ndama , Safae Ndama , Ismail Bensassi , El Mokhtar En-Naimi
In the rapidly evolving landscape of financial transactions, the detection of fraudulent activities remains a critical challenge for financial institutions worldwide. This study introduces a novel bidirectional encoder representation from transformers (BERT)–long short-term memory (LSTM) hybrid model that integrates both textual and numerical data to enhance credit card fraud detection. Leveraging BERT for deep contextual embeddings and LSTM for sequence analysis, the model provides a comprehensive approach that surpasses traditional fraud detection systems primarily based on numerical analysis. On the validation set, the model achieved a recall of 100% and an accuracy of 99.11%, highlighting strong effectiveness in identifying fraudulent transactions under class imbalance. Through rigorous evaluation, the model demonstrated exceptional accuracy and reliability, promising improvements in fraud detection and mitigation. This paper details the development and validation of the hybrid model, emphasizing its use of mixed data types to capture complex patterns in transaction data. The results indicate a new frontier in fraud detection by combining natural language processing (NLP) and sequential data analysis to create a robust solution for real-world applications, supporting the security and integrity of financial systems globally.
Volume: 15
Issue: 1
Page: 788-797
Publish at: 2026-02-01

AI-powered hub optimization: a reinforcement learning and graph-based approach to scalable blockchain networks

10.11591/ijai.v15.i1.pp536-546
Kassem Danach , Hassan Rkein , Alaaeddine Ramadan , Hassan Harb , Bassam Hamdar
Blockchain networks face persistent scalability challenges, including network congestion, high latency, and transaction costs. To address these limitations, this study proposes an AI-driven hub location optimization framework that integrates reinforcement learning (RL), mixed integer linear programming (MILP), and graph neural networks (GNNs). The RL-based hub selection dynamically identifies optimal supernode placement, while MILP ensures cost-efficient transaction routing, and GNNs predict flow patterns for proactive congestion management. Experimental results on Ethereum and Bitcoin datasets demonstrate significant improvements, including a 58.6% reduction in transaction latency, 28.9% gas fee savings, and 41.5% congestion reduction. Beyond performance gains, statistical tests confirm the significance of these improvements, and ablation studies highlight the complementary role of each component.
Volume: 15
Issue: 1
Page: 536-546
Publish at: 2026-02-01

TAHRF: enhancing personalized tourism recommendations with dynamic adaptation

10.11591/ijai.v15.i1.pp374-382
Mohamed Badouch , Mehdi Boutaounte
The rapid growth of online tourism data intensifies information overload, while conventional recommender systems struggle with sparsity, cold-start issues, and single-criteria ratings. This paper presents the trust-aware hybrid recommendation framework (TAHRF), which integrates user-item trust propagation, multi-criteria ratings, and dynamic preference adaptation. TAHRF employs Euclidean-Jaccard trust metrics, item connectivity, and rating consistency, combined with a feedback-driven weighting mechanism. Experiments on TripAdvisor datasets show superior performance: mean absolute error (MAE) reduced to 0.98 (restaurants) and 0.71 (hotels), outperforming multi-criteria tensor-based collaborative filtering (MC-TeCF) baselines. TAHRF also achieves higher precision@5, with coverage maintained under extreme sparsity. Ablation studies confirm the critical role of trust propagation, multi-criteria analysis, and adaptive weighting. TAHRF advances personalized, transparent, and adaptive tourism recommendations.
Volume: 15
Issue: 1
Page: 374-382
Publish at: 2026-02-01

Exploring cutting-edge research, applications, and future directions in artificial intelligence across diverse domains

10.11591/ijai.v15.i1.pp1019-1022
Tole Sutikno
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.
Volume: 15
Issue: 1
Page: 1019-1022
Publish at: 2026-02-01

Malware detection using convolutional neural network-di strategy polar fox optimization algorithm

10.11591/ijai.v15.i1.pp140-153
Parvathi Sathenahalli Jayaprakash , Yogeesh Ambalagere Chandrashekaraiah
Malware attacks have escalated significantly with an increase of internet users and connected devices. With the rise of various types of malwares released by the hackers, constructing new competitive methods are necessary to identify the advanced malware. However, conventional malware detection struggles to identify new and evolving malware variants accurately because of its dependence on handcrafted features and static-signature based methods. To address this problem, this research proposes convolutional neural network (CNN) based di strategy polar fox optimization algorithm (DSPFOA) for malware detection to fine-tune the CNN parameters effectively which later assists to overcome the limitations of CNN. The model integrates the sine chaotic mapping and Cauchy operator mutation as DSPFOA prevents the model from local optima issue, and extends search space solution, also enhance convergence. This ensures that the CNN learns highly discriminative features which makes the system more accurate and robust in detecting both known and evolving malware variants. The CNN DSPFOA achieves a high accuracy of 99.65 and 99.76% by utilizing BIG2015 and Malimg dataset respectively compared to existing methods like masked self-supervised model with swin transformer (MalSort).
Volume: 15
Issue: 1
Page: 140-153
Publish at: 2026-02-01

Enhancing medical language models with big data technologies

10.11591/ijai.v15.i1.pp289-299
Ayoub Allali , Ibtihal Abouchabaka , Najat Rafalia
In this study, we present an end-to-end, big-data–driven framework for continuously enriching and fine-tuning large language models (LLMs) with the latest professional and scientific medical knowledge. Streaming updates from premier sources such as The New England Journal of Medicine (NEJM) are ingested via an Apache Kafka cluster for low-latency delivery and durably archived in a three-node Apache Hadoop (Hadoop distributed file system (HDFS)) system. Each new article is preprocessed into high dimensional embeddings and indexed in a Milvus vector database to enable sub-second semantic retrieval over millions of records. At query or batch time, our retrieval-augmented generation (RAG) module retrieves the top-k relevant embeddings from Milvus and injects them into prompts for DeepSeek-R1, GPT-4o-mini, and Llama 3, models which are hosted, fine tuned, and served via Ollama on an NVIDIA GeForce RTX 3050 Ti GPU for efficient inference and continual learning. The enriched outputs are seamlessly delivered to end users through a Telegram bot programmed in Python using the Telebot library, linking the RAG-enhanced LLMs to an intuitive chat interface. Our Kafka, HDFS, Milvus, RAG, LLM, or Telegram bot pipeline demonstrably improves factual accuracy and topical currency of AI-generated medical insights across clinical decision support, patient engagement and education, drug discovery and development, virtual health assistants, and mental health support, laying the groundwork for truly intelligent, responsive, and data-driven healthcare solutions.
Volume: 15
Issue: 1
Page: 289-299
Publish at: 2026-02-01

Pneumonia classification from chest X-rays using significant feature selection and machine learning

10.11591/ijai.v15.i1.pp592-603
Yugandhar Chodagam , Manjunatha Hiremath
The chest X-ray images of normal lungs differ only subtly from those of lungs with pneumonia, making image-based diagnosis highly challenging. To address this issue, we developed a machine learning (ML)-based, lightweight, end-to-end Python package that processes chest X-ray images, implements robust feature selection methods, and classifies the images using various algorithms. While many studies have focused on improving classification accuracy using newer methods, few have addressed the interpretability of the extracted features or the growing computational demands of complex models. We used four publicly available datasets and extracted first-order, textural, and transform-based radiomic features to test our package. Features were selected using the Shapley additive explanations (SHAP) combined with recursive feature elimination (RFE) and stability selection algorithms. Our final solution contains a method that extracts a finite set of features identified by stability selection and feeds them as inputs into classical ML algorithms. Our model achieved 98% accuracy on the primary dataset, and 97%±1, 96%±2, and 94%±2% accuracy on the other three datasets. Our approach is fast, self-contained, and requires only an ideal set of features, making it suitable for resource-constrained clinical environments.
Volume: 15
Issue: 1
Page: 592-603
Publish at: 2026-02-01

Portable system for real-time traffic volume and speed estimation using YOLOv10

10.11591/ijai.v15.i1.pp300-309
Ida Bagus Sradha Nanda , Masrono Yugihartiman , Eko Primadi Hendri , I Made Suartika
Accurate traffic data is essential for effective transportation planning and policymaking. However, in many regions, especially those lacking intelligent infrastructure, data collection remains dependent on manual methods that are labor-intensive, time-consuming, and susceptible to human error. While advanced systems such as closed-circuit television (CCTV) and area traffic control systems (ATCS) offer automation, their high cost and infrastructure requirements limit widespread adoption. This study proposes a portable, low-cost, and real-time traffic monitoring system based on the YOLOv10 object detection algorithm. The system operates using only a smartphone-grade camera (1080 p, 60 fps) and a standard laptop, eliminating the need for expensive installations. It detects, classifies, and counts vehicles as they pass through a predefined region of interest (ROI), and also estimates their speed based on time–distance measurements. Field evaluations using five one-hour urban traffic videos showed excellent agreement with manual counts, achieving a mean absolute percentage error (MAPE) of just 0.30%. Speed estimation trials conducted on sample clips also demonstrated consistent and plausible results. These findings highlight the system’s potential as a scalable and accurate alternative for traffic monitoring in infrastructure-limited environments.
Volume: 15
Issue: 1
Page: 300-309
Publish at: 2026-02-01
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