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31,458 Article Results

A deep learning-based system for coral reef image segmentation using YOLOv8 with EfficientNet-B0 in Indonesian waters

10.11591/ijeecs.v43.i2.pp628-639
Raden Sutiadi , Sparisoma Viridi , Giyanto Giyanto , Andarta F. Khoir , Rizkie S. Utama , Elsa D. Aulia , Tri A. Hadi , Ludi Parwadani Aji
Manual coral point count with excel extension (CPCe) analysis requires approximately 4–6 hours to process one 50-image station, limiting the scale of coral reef monitoring. This study presents an artificial intelligence-based workflow using YOLOv8 with an EfficientNet-B0 backbone to automate benthic cover estimation. A total of 8,147 underwater images from 151 transects across 39 Indonesian coral reef stations were annotated into eleven benthic categories for model training, while evaluation was conducted using the 2021 Derawan Islands dataset. During validation, YOLOv8 achieved an average mAP@0.5 of 0.58, recall of 0.79, and an average absolute percentage cover error of 9.8% compared with CPCe. The model processed each image in 3.13 seconds, equivalent to 156.47 seconds per 50-image station, representing a 92–138× speedup over manual CPCe analysis. These results show that the proposed workflow can support scalable and near-real-time coral reef monitoring across Indonesia.
Volume: 43
Issue: 2
Page: 628-639
Publish at: 2026-08-01

Hybridization of hybrid-ARIMA-EM and XGBoost for enhanced price predictive modeling

10.11591/ijai.v15.i4.pp3131-3143
Isam Ahmed M. Yaqoob , Khairul Azhar Kasmiran , Teh Noranis Mohd Aris , Nor Azura Husin , Mohd Yunus Sharum
Managing finance entails the art and science of distributing available and potential funds among various competing needs. Government expenditures fund programs that provide a wide range of services to different population segments. As a result, the demand for enhanced and additional services often surpasses the government's financial capacity. Firstly, the price forecasting procedures for the extreme gradient boosting (XGBoost), gated recurrent unit (GRU), and hybrid-ARIMA-EM models will be summarized. Secondly, the accuracy of the models will be assessed on two real datasets collected from Kaggle (Crude_Oil_Price and KL_apartment). This study then proposes combining the hybrid-ARIMA-EM model with XGBoost to enhance the price forecasting performance in terms of time series analysis. Experimental results show that the suggested combination outperforms other selected models in price forecasting accuracy.
Volume: 15
Issue: 4
Page: 3131-3143
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

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

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

WVisionBERT-VL: a multimodal model architecture for toxicity classification on social media platforms using large language models

10.11591/ijai.v15.i4.pp3365-3375
Witta Listiya Ningrum , Achmad Benny Mutiara , Diana Ikasari
The increasing prevalence of toxic content on social media, conveyed through text, images, and videos, poses significant challenges for automated content moderation systems. Although prior studies have reported promising results in unimodal and bimodal settings, they often fail to capture implicit and contextual toxicity emerging from interactions across multiple modalities, particularly in non-English environments. This paper proposes WVisionBERT-VL, an end-to-end multimodal framework for toxicity detection that integrates text, image, and video modalities within a unified architecture. The proposed model incorporates modality-specific encoders, bidirectional multi-head cross attention (BMHCA) for cross-modal synchronization, and an adaptive fusion gate to dynamically balance modality contributions. A balanced multimodal dataset is constructed from social media platforms, including X, Instagram, and TikTok, and refined using a model-based labeling strategy with limited human-in-the-loop validation. Experimental results on a custom Indonesian dataset demonstrate strong in-domain performance, achieving an accuracy of 94.12%, a macro-F1 of 0.9407, and a receiver operating characteristic - area under the curve (ROC-AUC) of 0.9721, with robustness further validated through five-fold cross-validation. Cross-dataset evaluation highlights challenges related to domain shift, underscoring the need for future research on robust and domain-adaptive multimodal toxicity detection.
Volume: 15
Issue: 4
Page: 3365-3375
Publish at: 2026-08-01

Detection of illegal drug activities in Indonesia via social media X

10.11591/ijai.v15.i4.pp3376-3388
Adi Hanif Sedar , Lukman Yudokusumo , Indra Budi , Amanah Ramadiah , Aris Budi Santoso , Prabu Kresna Putra
In today's digital world, social media platforms such as X (formerly Twitter) are widely used for communication but also for illegal activities, such as selling illicit drugs. This study examines how posts from X related to illegal drug sales in Indonesia can be analyzed using machine learning techniques, with a focus on incorporating features related to personally identifiable information (PII) and web URLs. The main questions are how to use classification methods to detect these posts and which drugs are most often mentioned. A total of 11,777 posts from X were collected using web scraping. After cleaning and processing the data, five machine learning models were used. The support vector machine (SVM) model showed the best results with a weighted F1-score of 97.08%. The findings show that abortion pills are the most frequently mentioned drugs, followed by sexual enhancement and anesthetic drugs. This study also used visual tools to show the most common hashtags, posts distribution over time, and types of drugs discussed. This study underlines the importance of monitoring social media to protect public health and highlights how incorporating PII features can improve the detection of illegal activities. Future research should look at posts in other languages and from other platforms, considering PII features for enhanced analysis.
Volume: 15
Issue: 4
Page: 3376-3388
Publish at: 2026-08-01

Deep learning intrusion detection for software-defined networking using synthetic minority oversampling

10.11591/ijai.v15.i4.pp3703-3711
Prajwalasimha Sindugatta Nagaraja , Navya Rajashekara , Pushpa Bangalore Ramesh , Druva Kumar Siddaraju , Santhosh Kumar Ramachandragowda , Trupti Shripad Tagare
This article proposes an advanced method for network intrusion detection using a combination of recurrent neural networks (RNNs), specifically long short-term memory (LSTM), gated recurrent units (GRU), and bidirectional long short-term memory (BiLSTM) models, enhanced by synthetic minority oversampling technique (SMOTE) to address class imbalance in datasets like network security laboratory–knowledge discovery in databases (NSL-KDD). The method aims to accurately classify network traffic by learning temporal patterns of both normal and malicious activities. SMOTE is employed to balance the dataset, ensuring that underrepresented attack types receive adequate model attention, thereby improving model robustness. The proposed models (LSTM, GRU, and BiLSTM) are trained and evaluated on the NSL-KDD dataset, with hyperparameter tuning performed through RandomizedSearchCV for optimal performance. The results show a significant improvement in accuracy, precision, recall, and F1-score, with BiLSTM demonstrating the highest performance, achieving near-perfect classification results (99.5% accuracy). This method not only mitigates the issue of class imbalance but also leverages the power of RNNs for sequence modeling, offering a promising solution for effective intrusion detection in modern networks.
Volume: 15
Issue: 4
Page: 3703-3711
Publish at: 2026-08-01

A bibliometric analysis of artificial intelligence applications in geospatial and geographic intelligence

10.11591/ijai.v15.i4.pp3036-3052
Chatarina Muryani , Singgih Prihadi , Gentur Adi Tjahjono , Albertus Erico Jerry Krisna Nugroho , Fitria Dewi Kartika
This study presents a comprehensive bibliometric analysis of the application of artificial intelligence in geospatial research and geographic intelligence. Using the Scopus database and visualization tools such as VOSviewer and Microsoft Excel, this study systematically maps the scientific literature across the five analytical dimensions: publication trends, citation structure, co-citation patterns, bibliographic merging, and keyword co-occurrence. The findings indicate a significant increase in global research interest, especially after 2020, with major contributions from the United States, China, and the United Kingdom. The results also reveal thematic clusters ranging from remote sensing and disaster response to smart city planning and spatial prediction. Through science mapping and performance analysis, the study highlights the intellectual structure and conceptual evolution of the field. The study contributes to academic understanding by identifying research gaps, emerging themes, and collaboration patterns, which can guide future interdisciplinary research in the artificial intelligence and geospatial domain.
Volume: 15
Issue: 4
Page: 3036-3052
Publish at: 2026-08-01

SHAP-enhanced ensemble learning for yield prediction: insights from CY-Bench data on Indian wheat

10.11591/ijai.v15.i4.pp3411-3420
Soma Gupta , Dayal Kumar Behera , Satarupa Mohanty , Subhra Swetanisha , Ritik Mallik , Namita Panda
Predicting crop yield accurately is essential for providing food security and improving agricultural practices. This study examines the use of ensemble machine learning models combined with Shapley additive explanations (SHAP) feature selection to improve wheat yield prediction in India. The study uses CY-Bench data, incorporating normalized difference vegetation index (NDVI), meteorological data, and soil moisture data for yield prediction. Various ensemble techniques, including voting, stacking, and boosting are evaluated. Boost m1 ensemble model consistently outperforms other models in the prediction. Additionally, the integration of SHAP-based feature selection with the best ensemble model significantly improves the model accuracy and interpretability by identifying the most influential features affecting yield. The results show the effectiveness of ensemble boosting model, in capturing the complex relationships within agricultural data particularly when combined with feature selection. This method improves the transparency and actionability of machine learning models for agronomists, policymakers, and farmers.
Volume: 15
Issue: 4
Page: 3411-3420
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

Consciousness in artificial intelligence systems: an ontological approach

10.11591/ijai.v15.i4.pp3009-3025
Andreas Yumarma , Wan Sen
The question of whether artificial intelligence (AI) systems can attain consciousness remains central ontological challenge within interdisciplinary discourse spanning AI, philosophy of mind, and cognitive neuroscience. This study investigates the metaphysical status of AI systems by interrogating the explanatory clarity of their existence and the potential emergence of subjectivity from non-biological matter. Employing an integrative methodology that combines a comprehensive literature review of scholarly texts and recent empirical findings with philosophical critical analysis, the research explores the conditions under which AI systems might be considered conscious entities. Findings suggest that the expanding societal integration of AI necessitates a re-evaluation of consciousness and cognition beyond anthropocentric parameters. The study posits a novel interpretive framework for addressing the ontological problem of AI, one that implicates future legal, moral, and interactive structures surrounding conscious non-biological agents. This conceptual repositioning invites a deeper inquiry into the criteria for recognizing, engaging with, and regulating advanced AI systems. By grounding design principles in ontological clarity, the framework offers guidance for constructing AI systems capable of reflexive processing, minimal phenomenality, and ethically aligned behavior that bridges conceptual analysis with implementation pathways.
Volume: 15
Issue: 4
Page: 3009-3025
Publish at: 2026-08-01

Adaptive binary capsule network for complex image recognition

10.11591/ijai.v15.i4.pp3745-3760
Mavis Serwaa Yeboah , Patrick Kwabena Mensah , Adebayo Felix Adekoya , Mighty Abra Ayidzoe
Glaucoma and cataracts are leading causes of blindness worldwide, emphasizing the need for early detection. Convolutional neural networks (CNNs) have shown promise in detecting ocular diseases, but require extensive training datasets. However, medical datasets are scarce, limited, and imbalanced, prompting the use of time-consuming data augmentation techniques. To address these limitations, a computationally efficient and robust capsule network (CapsNet) model was proposed. The model features a novel adaptive contrast spatial filtering (ACSF) algorithm and incorporates a local binary pattern (LBP) algorithm to enhance robustness. This study achieved good recognition accuracy, with scores of 96.90% on the combined dataset, 96.45% on the cataract-only dataset, and 96.78% on the glaucoma-only dataset. The model's performance is comparable to state-of-the-art models, demonstrating its potential to support ophthalmologists in diagnosing cataracts and glaucoma-related eye issues.
Volume: 15
Issue: 4
Page: 3745-3760
Publish at: 2026-08-01

Deep learning-based prognostic modeling of brain tumors

10.11591/ijai.v15.i4.pp3792-3804
Laiali Almazaydeh , Arar Al Tawil
Headline accuracy is not enough to move a deep-learning brain tumor magnetic resonance imaging (MRI) classifier into the clinic. A model also needs repeated-split validation, trustworthy probabilities, a statistical sanity check, and a small enough footprint for hospital hardware. This study cover all four in one framework. Five convolutional neural network (CNN) backbones (visual geometry group (VGG)-16, residual network (ResNet)-50V2, MobileNetV2, EfficientNetB0, and dense convolutional network (DenseNet)-121) are trained on 4,600 public brain MRI scans (2,513 tumor, 2,087 healthy) under a shared two-phase transfer-learning recipe. The usual single split is replaced by stratified 5-fold cross-validation (CV), with paired McNemar and DeLong tests and 1,000 bootstrap resamples. Calibration is judged by expected calibration error (ECE), Brier, reliability diagrams, and temperature scaling. VGG16 wins mean accuracy (98.72 ± 0.42%); ResNet50V2 has the tightest area under the curve (AUC) (0.9986 ± 0.0006); the two are statistically tied. MobileNetV2 is the best-calibrated model out of the box (ECE = 0.0021) with only 2.59 M parameters, making it the most deployable. A four-level confidence-based risk score turns calibrated probabilities into triage tags. This study also flag a measured 3.23% format-duplicate leakage in the public dataset and presents the framework as a radiologist co-pilot, not a prognostic model.
Volume: 15
Issue: 4
Page: 3792-3804
Publish at: 2026-08-01
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