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31,377 Article Results

E-TEXTLOC: efficient text localization and extraction in real-world video scenes

10.11591/ijai.v15.i4.pp3537-3545
Dayananda Kodala Jayaram , Puttegowda Devegowda
Localization of an accurate text is quite a complicated issue, especially when attempting to extract from a complex video. It is mainly due to the limitation of resources, dynamic background, and text variability. There is various artificial intelligence based methods adopting machine learning for addressing such issues encounter lower positional accuracy and incur maximized computational cost. Therefore, the proposed system introduces efficient text localization and extraction for comprehensive positional accuracy in complex videos (E-TEXTLOC). Different from conventional approaches, E-TEXTLOC facilitates sampling of video frames while MobileNetV2 is deployed towards faster localization of text. The outcome of recognized text is further refined by a verifier module, which provides a self-supervised response. Assessed on the YouTube video dataset, the proposed model accomplishes 98.2% accuracy with 29.6 ms towards generating analytical outcomes. It means the proposed model accomplishes 6-10% accuracy enhancement with a 40-50% reduction of speed in contrast to the existing system. The implications of the proposed study can be stated towards surveillance system, autonomous vehicles, and assistive devices that works in real-time.
Volume: 15
Issue: 4
Page: 3537-3545
Publish at: 2026-08-01

Predictive model based on machine learning to identify sleep-related health problems

10.11591/ijai.v15.i4.pp3888-3902
Laberiano Andrade-Arenas , Inoc Rubio Paucar , Margarita Giraldo Retuerto , Cesar Yactayo-Arias
Sleep quality has become a growing public health issue worldwide, mainly due to a lack of awareness about its long-term consequences. Despite existing strategies to address this problem, there remains a need for more effective approaches. In this study, an early detection model for sleep disorders was implemented using the extreme gradient boosting (XGBoost) algorithm, following the knowledge discovery in databases (KDD) methodology, which includes the phases of selection, preprocessing, transformation, data mining, and interpretation. A dataset extracted from the Kaggle platform in CSV format was used, consisting of 374 records. With an overall accuracy of 91.5%, a recall of 100% for the insomnia class, and a precision of 100% for sleep apnea, the proposed model demonstrated exceptional performance. It also received an area under the curve (AUC) of 0.909 and an average F1-score of 0.913. With a mean accuracy of 91%, a 95% confidence interval (0.89–0.94), and a p-value of 0.0012, cross-validation confirmed its robustness and showed a statistically significant change from the baseline model. The error rates remained within clinically acceptable ranges, confirming its applicability as a diagnostic support tool. Overall, the results demonstrate the effectiveness of the model in identifying patterns related to sleep disorders.
Volume: 15
Issue: 4
Page: 3888-3902
Publish at: 2026-08-01

YOLO-based deep learning for tooth detection, segmentation, and numbering in panoramic radiographs

10.11591/ijai.v15.i4.pp3591-3602
Sri Oktamuliani , Luqyana Mahdiyah , Haritsul Haq , Nesa Perdana Putri , Wulandani Liza Putri
Panoramic dental radiographs are essential for diagnosis and treatment planning, but are difficult to interpret due to overlapping anatomical structures and limited image quality. Automating tooth detection, segmentation, and numbering can enhance clinical efficiency and reduce observer variability. This study developed a you only look once version 8 (YOLOv8)-based deep learning model for automatic detection and segmentation of teeth in panoramic radiographs. A dataset of 302 images containing 9,009 annotated teeth was divided into 70% training, 20% validation, and 10% testing sets. Teeth were labeled using the Federation Dentaire Internationale (FDI) two-digit numbering system with bounding boxes and segmentation masks. Model performance was assessed using precision, recall, F1-score, and mean average precision (mAP) at intersection over union (IoU) thresholds of 0.5 and 0.5–0.95. The model achieved bounding box precision, recall, and F1-score of 0.9075, 0.9352, and 0.9212, respectively, and segmentation scores of 0.9078, 0.9344, and 0.9209. Bounding box mAP@0.5 reached 0.9510 and mask mAP@0.5 was 0.9504, while stricter thresholds lowered performance to 0.7763 and 0.6896. Class-wise analysis showed high accuracy overall but reduced performance on posterior teeth due to anatomical overlap and peripheral image quality. YOLOv8 enables fast, accurate, and robust tooth detection and segmentation, supporting real-time computer-aided diagnosis in orthodontics, prosthodontics, and forensic dentistry.
Volume: 15
Issue: 4
Page: 3591-3602
Publish at: 2026-08-01

Forecasting income inequality in Vietnam’s regions using machine learning

10.11591/ijai.v15.i4.pp3464-3475
T. Thai-Phuong , L. Nguyen-Son
This study applies artificial neural networks (ANN) and support vector regression (SVR) to forecast the Gini coefficient across Vietnam’s seven socio-economic regions using limited data from 2014–2022 (N = 63). Despite the small dataset, robust techniques including repeated cross-validation, temporal backtesting, regularization, and bootstrapping were employed to mitigate overfitting. The results show a modest continued decline in income inequality through 2025, with faster improvements in urban regions (Red River Delta and Southeast) than in rural and highland areas. ANN outperformed SVR with an average cross-validation coefficient of determination (R²) of 0.93. The novelty lies in its granular regional forecasting and region-specific policy recommendations beyond national-level analyses. Key drivers—per capita income and simple housing—support targeted interventions such as housing credits in the Mekong River Delta and agricultural investments in the Central Highlands. The findings provide an evidence-based foundation for regionally tailored strategies to reduce income disparities in Vietnam.
Volume: 15
Issue: 4
Page: 3464-3475
Publish at: 2026-08-01

Data-driven analysis of growth factors in oyster mushroom cultivation: a case study from Indonesia’s market

10.11591/ijai.v15.i4.pp3568-3580
Yosef Budiman , Gilang Adi Prasetyo , Asma’ Khoirunnisa’ , Hanifah Mar’atush Shalihah , Muhamad Riyan Maulana , Yanuar Agung Fadlullah , Sugiri Sugiri
The oyster mushroom is one of the potential agricultural products that can be developed as an alternative to other agricultural products, to maintain Indonesia's economic condition. However, the production of oyster mushrooms remains low and falls short of the minimum amount of market demand. This study employs a machine learning (ML)–based approach to identify the key parameters influencing oyster mushroom production rates. Recursive feature elimination (RFE) was applied to reduce the initial 19 features to nine, enabling faster processing while maintaining high predictive accuracy. The results showed that agricultural features showed a high contribution rather than environmental, economic, and demographic features. Furthermore, these parameters were related to the train-test analysis to visualize the statistical analysis shown by the best method, adaptive boosting (AdaBoost), with coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE) values of 0.997575, 0.009841, and 0.085884, respectively. Related research relevant to the research findings was analyzed to validate that agricultural product features affect the decline of oyster mushroom production. Other supported research conducted by integrating real-time analysis and twin digital models, which can enhance substrate quality.
Volume: 15
Issue: 4
Page: 3568-3580
Publish at: 2026-08-01

Android-based tomato leaf disease classification using a lightweight MobileNetV2 convolutional neural network

10.11591/ijai.v15.i4.pp3318-3325
Andi Riansyah , Irfan Eka Mahdy , Mochamad Abdul Basir , Noorminshah A. Iahad
Early screening of tomato leaf diseases is important because foliar symptoms can reduce plant vigor and delay appropriate crop management. This study develops an Android-based tomato leaf disease classification system using MobileNetV2 as a lightweight convolutional neural network (CNN) architecture. The contribution of this work is the integration of model training, independent testing, and on-device Android deployment that supports camera and gallery inputs without relying on server-side computation. The dataset consisted of 1,200 balanced tomato leaf images from five classes: bacterial spot, late blight, target spot, tomato yellow leaf curl virus, and healthy leaf. Images were resized, normalized, augmented for training, and divided into training, validation, and independent testing subsets. The model obtained 94.12% training accuracy, 93.00% validation accuracy, and 89.00% independent test accuracy. The confusion matrix showed that tomato yellow leaf curl virus was classified without error, whereas bacterial spot, late blight, target spot, and healthy leaves produced several misclassifications because of similar lesion and discoloration patterns. The results show that MobileNetV2 is suitable for lightweight mobile disease screening, although larger field datasets, cross-validation, model comparison, and explainability analysis are still needed for broader deployment.
Volume: 15
Issue: 4
Page: 3318-3325
Publish at: 2026-08-01

Hybrid deep learning model for the task scheduling in cloud computing

10.11591/ijai.v15.i4.pp3683-3691
Kavita Rani , Om Prakash Sangwan , Ritu Garg
Task scheduling plays a crucial role in optimizing performance, reducing costs, and enhancing system reliability by efficiently allocating resources to workloads. Traditional task scheduling methods lack the ability to efficiently manage workloads and resource distribution, leading to potential inefficiencies in performance and energy consumption. To address these limitations, advanced techniques leveraging deep learning and reinforcement learning are explored. This study proposes a deep learning-based model for task scheduling in cloud computing. The model employs a convolutional neural network (CNN) to predict the optimal machines for task allocation. Additionally, Q-learning is integrated with CNN to facilitate load shifting between machines, ensuring efficient utilization of resources. The dataset used in this work consists of task attributes, such as execution time, resource requirements, which were loaded from a CSV file. Comparative analysis with existing models shows that the proposed approach achieves approximately 94% accuracy and consumes less energy than other models, demonstrating its effectiveness in cloud task scheduling.
Volume: 15
Issue: 4
Page: 3683-3691
Publish at: 2026-08-01

Immuno-bioinformatics analysis of progressive alignment and logic learning machine-derived viral conserved sequences

10.11591/ijai.v15.i4.pp3189-3201
Felza Ridho , Mohammad Isa Irawan , Nurul Hidayat , Awik Puji Dyah Nurhayati
This study proposes a method to identify conserved sequence positions in viral data, with the primary goal of facilitating their translation into proteins. The approach aims to support the early detection of sub-sequences that hold promise as vaccine candidates. The method involves five key steps. First, mutation analysis using the Kimura model: analyzed mutations in the viral data using the Kimura model to provide insights into sequence variations. Second, alignment method selection to align sequences effectively, the progressive alignment approach with the neighbor-joining algorithm was chosen. Third, hybrid algorithm for identifying unchanged sequences: a hybrid algorithm that combines progressive alignment and a logic learning machine (LLM) was employed to identify unchanged sequences. Fourth, determining conserved sequences: based on the longest unchanged sequence, conserved regions within the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data were identified. Fifth, immuno-bioinformatics analysis for vaccine candidate peptides: using immuno-bioinformatics, protein sequences were analyzed to identify potential vaccine candidates based on B cell epitopes. Experimental validation confirmed the algorithm’s ability to identify candidate proteins and generate vaccine-worthy peptides from conserved protein sequences. The streamlined approach focuses on conserved protein sequences, ensuring efficient and targeted identification of vaccine candidates. By leveraging these conserved regions, contributions can be made to the expedited development of vaccines.
Volume: 15
Issue: 4
Page: 3189-3201
Publish at: 2026-08-01

Efficient deep learning for automated corneal ulcer severity classification from fluorescein images

10.11591/ijai.v15.i4.pp3603-3613
Rodiah Rodiah , Indah Sinthya Permata Sari , Matrissya Hermita , Sarifuddin Madenda , Diana Tri Susetianingtias
Corneal ulcers can cause permanent vision loss if not diagnosed and managed promptly, particularly in settings with limited access to ophthalmology services. This study aims to develop an automated deep learning approach for classifying corneal ulcer severity from fluorescein slit-lamp images. An EfficientNetV2-S–based model is employed, incorporating corneal area masking to suppress non-relevant regions and class distribution–based augmentation to address data imbalance. To improve evaluation reliability, a leakage-aware data splitting strategy is applied before and after augmentation. Experimental results show that the proposed approach achieves a maximum validation accuracy of 95.93% under non-leakage conditions for the category classification scenario, while maintaining high training efficiency. These results demonstrate that the proposed method provides a robust and efficient solution for automated corneal ulcer severity assessment and has the potential to support clinical decision-making in ophthalmic practice.
Volume: 15
Issue: 4
Page: 3603-3613
Publish at: 2026-08-01

Performance of majority voting transfer learning deep learning monkeypox disease detection

10.11591/ijai.v15.i4.pp3782-3791
Nur Nafiiyah , Muhammad Nurul Huda
Global health concerns have been raised by the advent of monkeypox following the COVID-19 epidemic, highlighting the need for reliable automated systems to support early skin disease screening. This study proposes a monkeypox skin disease classification framework using a majority voting ensemble based on transfer learning. The ensemble combines predictions from multiple pretrained convolutional neural networks (CNNs) to improve classification robustness. A publicly accessible dataset comprising four classes: monkeypox, chickenpox, measles, and normal skin was used for the experiments. The results indicate that while a single ResNet50 model achieved the highest overall accuracy (99.15%), the majority voting approach yielded higher precision than several individual models, demonstrating improved reliability in distinguishing monkeypox cases. These findings suggest that ensemble-based majority voting can enhance the robustness of monkeypox skin disease classification and may support computer-aided screening systems.
Volume: 15
Issue: 4
Page: 3782-3791
Publish at: 2026-08-01

An empirical comparison of clustering approaches for recency, frequency, and monetary customer segmentation

10.11591/ijai.v15.i4.pp3402-3410
Upekkha Lau , Meditya Wasesa
This study evaluates effectiveness of three clustering techniques—k-means, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN)—applied to the recency-frequency-monetary (RFM) model for customer segmentation in the retail sector. Using sales transaction data from a distributor of computer accessories and printing products. The results show that k-means achieved the best clustering validation scores and effectively identified high-value customers, hierarchical clustering generated less meaningful groupings than k-means, and DBSCAN misclassified key customers as noise. These findings highlight k-means as the most suitable technique for RFM-based segmentation in this retail business context. The study offers practical insights for retail and distribution businesses aiming to adopt data-driven customer strategies and suggests future research to enhance segmentation robustness and refine the RFM framework.
Volume: 15
Issue: 4
Page: 3402-3410
Publish at: 2026-08-01

The role of big data in precision medicine and healthcare monitoring using the MapReduce framework

10.11591/ijai.v15.i4.pp3852-3864
Meenakshi Sankarasubramanian , Meena Chavan , Govindan Manoharan Karthik , Jhansi Pandiri , Arumalla Nagaraju , Idimadakala Madhavilatha
Data analytics has become a cornerstone of precision medicine by enabling doctors and scientists to extract meaningful insights from vast, complex data sets. Most healthcare data are high-dimensional data that not only require longer computational time but also affect the accuracy of analysis. In order to overcome these issues, the MapReduce based big data healthcare monitoring framework is proposed. The proposed work comprises preprocessing, the MapReduce framework, and data classification. The preprocessing can be done using improved min-max normalization, and the big data can be handled using the improved support vector machine (SVM)-recursive feature elimination (RFE) method. Finally, the classification can be done using a deep Q-network (DQN). The performance of the proposed method is analyzed in terms of accuracy, precision, F-measure, and Matthew's correlation coefficient (MCC).
Volume: 15
Issue: 4
Page: 3852-3864
Publish at: 2026-08-01

A transfer learning-based approach for automatic monument detection

10.11591/ijai.v15.i4.pp3326-3341
Santosh Giri , Jebish Purbey , Sunil Adhikari , Paarit Pokharel , Babu R. Dawadi , Bipun Man Pati , Atsushi Ito , Sushant Chalise , Sanjivan Satyal
Architectural heritage connects us to the cultural achievements of past civilizations. Patan Durbar Square in Nepal is home to many such structures, yet identifying them remains a challenge for tourists. This paper presents an automated monument recognition system built on the backbone of convolutional neural networks (CNNs). A dataset of 1832 images of 9 important monuments from Patan was created, and build a detection system with MobileNetV2, a light-weight CNN, to detect monuments in Patan Durbar Square with a near-perfect F1 score of 98.94%. The approach utilizes transfer learning to adapt the model to local architectural styles. A detailed ablation study is performed to determine the optimal network design and augmentation strategies. Class-wise performance is further analyzed to verify robustness against visual occlusion and similarity. Finally, the model is deployed as a mobile application using Flutter and the FastAPI framework. This work demonstrates the viability of lightweight CNNs for real-time cultural heritage preservation.
Volume: 15
Issue: 4
Page: 3326-3341
Publish at: 2026-08-01

Analysis of normalization technique on multi objective preference analysis method

10.11591/ijai.v15.i4.pp3827-3835
Fristi Riandari , Gabriel Ardi Hutagalung , Ferry Fachrizal
Normalization is a critical step in multi-criteria decision analysis (MCDA) because it influences ranking consistency and decision reliability. This study evaluates the effects of four normalization techniques linear max, linear max-min, linear sum, and semi-linear vector, within the multi objective preference analysis (MOPA) framework using a tourism development case involving 18 alternatives and 8 decision criteria with both cost and benefit attributes. The techniques were compared based on ranking behavior, discriminative capability, and robustness using statistical and non-parametric validation. The results show that linear max-min normalization provides the strongest discriminative performance and the most significant statistical results, while semi-linear vector demonstrates high ranking stability and balanced sensitivity. In contrast, linear sum and linear max exhibit lower discriminative capability under the evaluated conditions. Kendall's tau and robustness analyses further confirm that normalization choice significantly affects ranking consistency and decision reliability. These findings provide practical guidance for selecting appropriate normalization techniques and support the development of more reliable MCDA-based decision-making models for complex applications, including sustainable tourism planning.
Volume: 15
Issue: 4
Page: 3827-3835
Publish at: 2026-08-01

Intravenous immunoglobulin resistance prediction in Kawasaki disease using oversampled transformer embeddings

10.11591/ijai.v15.i4.pp3944-3954
Namitha Thattarassery Nanappan , Raghavendra Srinivasaiah , Vinith Rejathalal
Kawasaki disease (KD) is a leading cause of acquired heart disease in children under five. Although intravenous immunoglobulin (IVIG) treatment is usually effective, 10–20% of cases are resistant and at higher risk for coronary complications. Early prediction of IVIG resistance is critical but difficult due to the rarity of KD and imbalanced clinical data. To address this, we propose a novel technique called sentence transformer embeddings with synthetic minority over-sampling technique (SMOTE) oversampling (STESO), which leverages the complementary strengths of transformer-based representation learning and synthetic oversampling. Pretrained models such as paraphrase-MiniLM-L3-v2 are used to convert tabular clinical data into dense text-based embeddings, capturing deeper semantic relationships across features. By coupling these rich embeddings with SMOTE, we balance class distributions directly in the semantic space, enabling traditional machine learning (ML) models to more effectively detect minority (resistant) cases. This synergy yielded substantial improvements in sensitivity and F1-score, with random forest (RF) combined with STESO (RF-STESO) achieving the highest overall performance. Among the models evaluated, our proposed model attained best result as accuracy of 0.85, a sensitivity of 0.81, a specificity of 0.89, and an F1-score of 0.85. Our results underscore that the joint use of transformer embeddings and oversampling is more effective than either approach in isolation, offering a promising pathway for rare disease prediction tasks such as IVIG resistance prediction in KD.
Volume: 15
Issue: 4
Page: 3944-3954
Publish at: 2026-08-01
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