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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

Intelligent plant disease detection using twin attention optimal convolutional neural network

10.11591/ijai.v15.i1.pp756-765
Prameetha Pai , Namitha S. J. , Sowmya T. , Amutha S. , Nisarga Gondi
Farming is one of the most important ways for people in India to make a living. Rice is a staple food, and when farmers successfully harvest rice crops, pests often attack them, which costs agriculture a lot of money. There are now a lot of new AI-based ways to help with this problem in rice plants. But those ways don’t work very well because they take a long time and make mistakes when sorting things. This article talks about a new hybrid deep learning (DL) method for finding leaf diseases in rice plants. This process has four main steps: pre-processing, segmentation, feature extraction, and classification. A hybrid DL-based twin attention convolutional neural network (CNN) model classifies segmented images into healthy and unhealthy leaves. But this method has the problem of overfitting. An optimization method based on chaotic slime mould (CSM) solves this problem. The proposed method is compared with bidirectional long short-term memory (Bi-LSTM), recurrent neural network (RNN), deep neural network (DNN), and deep belief network (DBN). The suggested method has an overall accuracy of 99.56%, an F-measure of 99.21%, a sensitivity of 99.16%, a specificity of 98.56%, a precision of 99.26%, a mean absolute error (MAE) of 0.004, a mean squared error (MSE) of 0.004, and a root mean square error (RMSE) of 0.06.
Volume: 15
Issue: 1
Page: 756-765
Publish at: 2026-02-01

Efficient YOLO-based models for real-time ceramic crack detection

10.11591/ijai.v15.i1.pp852-860
Benchalak Maungmeesri , Sasithorn Khonthon , Dechrit Maneetham , Padma Nyoman Crisnapati
The following research work systematically compares four variants of you only look once (YOLO), namely, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 proposed recently, considering the key properties required to perform ceramic surface crack detection tasks with high computational efficiency, real-time inference speed, and low memory usage. A total of 300 images of ceramic surfaces were collected with manually labeled cracks and divided into training, validation, and testing sets in portions of 263, 22, and 15 images, respectively. Each of the four YOLO variants was trained for 50 and 100 epochs, and each was evaluated regarding mean average precision (mAP), inference time, model size, and computational complexity in giga floating point operations per second (GFLOPs). YOLOv9 produced the highest accuracy with mAP values as high as 0.752-0.79 but the highest cost in terms of increased computational complexity. However, among these methods, YOLOv8 can produce the fastest inference (~2-2.3 ms) with a small memory footprint (~6 MB) with an acceptable accuracy of ~0.65-0.67. The results showed that YOLOv8 is the most feasible to deploy in resource constrained industrial automation environments. By offering this comparative study, the research attempts to provide hints for the selection of appropriate YOLO-based models by practitioners in quality control applications related to ceramic manufacturing.
Volume: 15
Issue: 1
Page: 852-860
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

An AI-driven framework for efficient and accurate calibration of electricity meters using extreme gradient boosting

10.11591/ijai.v15.i1.pp154-163
Rosalina Rosalina , Nunik Afriliana
Calibration testing plays a vital role in electricity meter manufacturing to guarantee measurement accuracy and compliance with industry standards. In practice, however, conventional calibration methods are often hindered by lengthy test cycles and the high cost of expanding test bench capacity. This study proposes a data-driven approach to address these limitations by applying machine learning techniques to optimize calibration testing. An extreme gradient boosting (XGBoost) regression model, enhanced through systematic hyperparameter tuning and feature engineering, was developed to predict calibration outcomes using data obtained from existing production test benches. When evaluated under real manufacturing line conditions, the proposed method shortened calibration runtime by about 55% compared with manual procedures relying on power supply units (PSU) and standard meter calculations, while maintaining reliable measurement accuracy. The framework also achieved lower root mean square error (RMSE), demonstrating improved predictive performance. In addition to reporting these results, the study describes the preprocessing pipeline, model selection process, and optimization strategy, providing a practical and replicable framework for integrating artificial intelligence (AI) into industrial calibration processes.
Volume: 15
Issue: 1
Page: 154-163
Publish at: 2026-02-01

Deep learning-based spam detection for WhatsApp chatbot fallback reduction

10.11591/ijai.v15.i1.pp909-918
Satrio Sadewo , Amalia Zahra
Chatbots on WhatsApp are widely used for customer service, but their effectiveness is often undermined by fallback responses when user input cannot be understood. A major cause of these fallbacks is unsolicited spam, which disrupts interactions and reduces service quality. This study develops and evaluates a spam detection system aimed at reducing fallback rates and enhancing user experience. A comparative analysis was conducted between traditional machine learning models (support vector machine (SVM) and decision tree (DT)) and advanced deep learning architectures, including long short-term memory (LSTM) variants (vanilla, bidirectional, stacked, convolutional neural network (CNN)-LSTM, and encoder-decoder) and transformer-based models (bidirectional encoder representations from transformers (BERT)-base, DistilBERT, and cross-lingual language model robustly optimized BERT pretraining approach (XLM-ROBERTa)). Using 170,000 messages sampled from 18 million interactions collected between July 2022 and December 2023, the models were assessed with standard evaluation metrics. Results show that CNN-LSTM and DistilBERT achieved the most robust performance. CNN-LSTM attained a precision of 0.92, recall of 0.91, F1-score of 0.91, and accuracy of 0.94, while DistilBERT achieved precision of 0.92, recall of 0.89, F1-score of 0.90, and accuracy of 0.93. These findings highlight their superior ability to capture contextual patterns in spam messages. Implementing such models is expected to significantly lower fallback rates, thereby improving chatbot reliability and user satisfaction.
Volume: 15
Issue: 1
Page: 909-918
Publish at: 2026-02-01

Air quality prediction using boosting-based machine learning models for sustainable environment

10.11591/ijai.v15.i1.pp515-523
Ahmad Fauzi , Maharina Maharina , Jamaludin Indra , Ayu Ratna Juwita , Agustia Hananto , Euis Nurlaelasari
High levels of air pollution are extremely harmful to humans and the environment. They increase the risk of respiratory infections and lung cancer, especially among vulnerable populations. Therefore, developing effective pollution control measures is crucial for mitigating these negative impacts. We need to implement effective methods to predict and manage air quality for the sake of public health and a healthier environment. In recent years, machine learning (ML) methods have been increasingly utilized in air quality prediction due to their ability to analyze datasets and identify complex patterns. However, the reliability and accuracy of air quality prediction models remain a challenge. This study proposes a boosting-based ML model for predicting air quality. We implemented three stages in the proposed method. In the first stage, we conducted data preprocessing and analysis to eliminate noise, remove redundant data, and encode categorical features. In the second stage, we predicted air quality categories by leveraging 25 ML models, dividing them into three distinct categories. The results show that the extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and adaptive boosting (AdaBoost) models outperform the others in air quality prediction, achieving an accuracy of 99%. Finally, we compared these three models using 10-fold cross validation to ensure they generalize well in last stage.
Volume: 15
Issue: 1
Page: 515-523
Publish at: 2026-02-01

Brain tumor segmentation and classification using artificial hummingbird optimization algorithm

10.11591/ijai.v15.i1.pp429-442
Radhakrishnan Karthikeyan , Arappaleeswaran Muruganandham
The time and medical personnel experience are the only factors that determine whether brain tumors can be manually identified from numerous magnetic resonance imaging (MRI) pictures in medical practice. Many frameworks based on brain tumors are diagnosed using both deep learning and machine learning. This study proposes a Wasserstein deep convolutional generative adversarial network (WDCGAN) optimized using the artificial hummingbird optimization algorithm (AHBOA) for brain tumor segmentation and classification (SCBT). First, the BraTS dataset is used to gather the input data. Then it is pre-processed consuming adaptive self guided filtering (ASGF) and the result is segmented using fuzzy possibilistic C-ordered mean clustering (FPCOMC). After that, features are extracted using the dual tree complex discrete wavelet transform (DT-CDWT). The characteristics of feature extracted are fed to WDCGAN for effectively categorize the various parameters. Then the proposed MATLAB is used to implement the technique, and the performance measurements like F1-score, accuracy, error rate, precision, sensitivity, mean square error, receiver operating characteristic (ROC), and computational time are analyzed. The WDCGAN-AHBOA-SCBT method significantly improves precision in SCBT by integrating adaptive optimization strategies, resulting in 32.18, 32.75, and 32.90% higher precision in contrast to current techniques. This demonstrates that the approach is more accurate and effective, making it a reliable tool for medical diagnosis.
Volume: 15
Issue: 1
Page: 429-442
Publish at: 2026-02-01

Neuro-DANet: dual attention deep neural network long short term memory for autism spectrum disorder detection

10.11591/ijai.v15.i1.pp810-823
Sujatha Hanumantharayappa , Manjula Rudragouda Bharamagoudra
Autism spectrum disorder (ASD) is neurological illness affects ability of individuals to communicate and interact socially, and it is diagnosed in any time. Early detection of ASD is especially significant due to its subtle characteristics and high costs associated with the detection process. Traditional deep learning (DL) models struggle to capture intricate spatiotemporal dependencies in functional magnetic resonance imaging (fMRI) data, resulting in minimized detection performance and poor generalization. To address these drawbacks, the proposed Neuro-DANet combines a dual-attention deep neural network (DA-DNN) with long short term memory (LSTM) to efficiently learn spatial and temporal features from fMRI scans. The continuous wavelet transform (CWT) is used to extract multi-scale features and the principal component analysis (PCA) is utilized to dimensionality reduction, which enhances robustness and efficacy. The dual self-attention mechanism improves the interpretability of the model by focusing on critical brain regions and time steps that are most relevant to ASD severity. The developed Neuro-DANet obtains the highest accuracy of 98.51% on autism brain imaging data exchange (ABIDE)-I and 98.81% on ABIDE-II datasets when compared with traditional algorithms.
Volume: 15
Issue: 1
Page: 810-823
Publish at: 2026-02-01

Automated data exploration with mutual information in natural language to visualization

10.11591/ijai.v15.i1.pp129-139
Hue Thi-Minh Luong , Vinh-The Nguyen , Van-Viet Nguyen , Kim-Son Nguyen , Huu-Khanh Nguyen
Transcribing natural language to visualization (NL2VIS) has been investigated for years but still suffer from several fundamental limitations (e.g., feature selection). Although large language models (LLMs) are good candidates but they incur computation cost and hard to trace their made decisions. To alleviate this problem, we introduced an alternative information-theoretic framework that utilized mutual information (MI) to quantify the statistical relationship between utterances and database features. In our approach, kernel density estimation (KDE) and neural estimation techniques were utilized to estimate MI, and to optimize a diversity-promoting objective balancing feature relevance and redundancy. We also introduced the information coverage ratio (ICR) to quantify the amount of information content preserved in feature selection decisions. In our experiments, we found that the proposed approach improved information-theoretic metrics, with F1-score of 0.863 and an ICR of 0.891. We observed that these improvements did not come at the cost of traditional benchmarks: validity reached 88.9%, legality 85.2%, and chart-type accuracy 87.6%. Moreover, significance tests (p < 0.001) and large effect sizes (Cohen’s d > 0.8) further supported that these improvements were meaningful for feature selection. Thus, this study provides a mathematical framework for applications requiring analytical validity that extends beyond NL2VIS to other machine learning contexts.
Volume: 15
Issue: 1
Page: 129-139
Publish at: 2026-02-01

Artificial intelligence in orthodontics: modeling decision support systems for treatment planning

10.11591/ijai.v15.i1.pp97-105
Sowmya Lakshmi Belur Subramanya , Advaith Vijaya Mohan , Achala Varsha Vishlavath Premalatha , Manchikanti Varunsai
Orthodontic treatment planning involves complex clinical decision-making that can benefit from artificial intelligence (AI). This study evaluates machine learning and deep learning models—including random forest, AdaBoost, gradient boosting, and artificial neural networks (ANNs)—for predicting orthodontic treatment strategies using a dataset of 612 anonymized patient records with 66 clinically validated features across four categories (extraction, non-extraction, functional appliance, and orthopedic case). Preprocessing included imputation, normalization, and the synthetic minority oversampling technique (SMOTE) for class imbalance, while performance was assessed via 10-fold cross-validation. Results showed that ANNs achieved the highest balanced accuracy (0.83), F1-score (0.84), and receiver operating characteristic area under the curve (ROC-AUC) (0.90), outperforming ensemble and baseline models. Shapley additive explanations (SHAP) analysis confirmed clinically meaningful predictors such as vertical face proportions and mandibular plane angle, enhancing interpretability. Although promising, the study is limited by its single-institution dataset and lack of external validation. Future research should incorporate multicenter, multimodal datasets and interpretable-by-design frameworks to enable clinically trusted AI decision-support systems in orthodontics.
Volume: 15
Issue: 1
Page: 97-105
Publish at: 2026-02-01

Design of Antasena: an AI-powered maritime surveillance and anomaly detection system for security decision support

10.11591/ijai.v15.i1.pp269-288
Arif Badrudin , Siswo Hadi Sumantri , Rudy Agus Gemilang Gultom , I Nengah Putra Apriyanto , Umi Laili Yuhana , Fitria Dwi Ratnasari
Indonesia’s vast maritime territory faces serious challenges from illegal fishing, smuggling, and habitat destruction. To address these, the Indonesian Navy (TNI-AL) developed Antasena, an artificial intelligence (AI)-powered smart dashboard integrating automatic identification system (AIS) data, satellite imagery, and conservation metrics. Antasena leverages advanced anomaly detection algorithms, achieving 95.3% accuracy, 94.7% precision, 94.2% recall, and a 96.8% receiver operating characteristic-area under the curve (ROC-AUC) score in identifying vessel anomalies, including unauthorized fishing and smuggling activities. Using the analyze, design, develop, implement, and evaluate (ADDIE) framework, the system supports real-time maritime surveillance and biodiversity monitoring in conservation zones. The main contributions of this study include the development of a user-centric AI-based dashboard for maritime anomaly detection, the integration of multi-source data with machine learning models, and validation through operational field tests with maritime authorities. Antasena offers a scalable and effective solution to strengthen maritime security and protect Indonesia’s marine resources.
Volume: 15
Issue: 1
Page: 269-288
Publish at: 2026-02-01

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

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

Deep feature-based multi-class Alzheimer’s disease classification with statistical performance evaluation

10.11591/ijai.v15.i1.pp695-706
Maysaloon Abed Qasim , Marwa Mawfaq Mohamedsheet Al-Hatab , Lubab H. Albak
This study evaluated the performance of multiple machine learning classifiers for the classification of Alzheimer’s disease (AD) stages using deep features extracted from a pre-trained SqueezeNet model. Magnetic resonance imaging (MRI) scans were processed through SqueezeNet to generate high-dimensional feature vectors, which were then used as achieved an accuracy of 94.78% input to six classifiers: k-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), neural network (NN), naive Bayes (NB), and logistic regression (LR). Models were assessed using a 70/30% training-testing split and 5-, 10-, and 20-fold stratified cross validation. Principal component analysis (PCA) was applied to retain 99% of variance. On the original dataset consisting of 6,400 images, KNN has achieved 97.48% accuracy and 0.998 area under the curve (AUC), and when a larger dataset of 44,000 images was used it achieved an accuracy and of 94.78% and an AUC of 0.987, demonstrating the system’s robustness across scales. Statistical tests, including paired t-tests and Wilcoxon signed-rank tests, confirmed that KNN has significantly leveraged from PCA. These outcomes demonstrate that combining deep feature extraction with PCA improved the reliability and efficiency of the classifier for AD stage prediction.
Volume: 15
Issue: 1
Page: 695-706
Publish at: 2026-02-01
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