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31,377 Article Results

Computed tomography imaging radiomics: a novel approach to early-stage non-small cell lung cancer prediction

10.11591/ijai.v15.i3.pp2471-2483
Raviteja Balekai , Mallikarjun S. Holi
Radiomics shows promise as non-invasive method for enhancing clinical staging of non-small cell lung cancer (NSCLC) by using quantitative information from computed tomography (CT) scans. This study presents radiomics-based machine learning (ML) approach for staging NSCLC patients into clinical stages I, II, and III based on shape, intensity, and texture features. CT images of 369 NSCLC patients are collected from the cancer imaging archive (TCIA), and extracted 107 radiomic features following image biomarker standardization initiative (IBSI) protocol. The analysis of the sources of variability due to different imaging protocols, using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), showed that these effects were resolved through ComBat harmonization. Recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) are used for feature selection. Five ML algorithms: logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) were used, with an 80:20 train-test split and 10-fold cross-validation. The classifier is assessed using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic (AUROC) curve. The RFE and RF classifier combination performed the best with AUROC of 0.9307 and accuracy of 0.8114. This study illustrates the use of radiomics models in non-invasive classification of NSCLC stages and it is role in clinical decision making.
Volume: 15
Issue: 3
Page: 2471-2483
Publish at: 2026-06-01

Optimization of transfer learning for facial emotion classification on the FER-2013 dataset

10.11591/ijece.v16i3.pp1213-1226
Nida Muhliya Barkah , Shofwatul ‘Uyun
Facial expressions play a key role in non-verbal communication by naturally reflecting human emotions. Facial emotion recognition (FER) using computer vision has gained attention with advances in deep learning. However, deep learning models require large datasets to perform well, posing a challenge for FER tasks with limited data. Transfer learning is a promising approach to address this issue, but a standardized method for FER is yet to be established. This study optimizes three transfer learning models ResNet-50, Inception V3, and Xception on the FER-2013 dataset. Experiments include testing input image sizes, hyperparameter tuning, data augmentation, layer addition, and training methods. Results show each model requires different input sizes for best accuracy. Hyperparameter tuning improves accuracy by 6.35%, 4.69%, and 1.04% for ResNet-50, Inception V3, and Xception, respectively. Augmenting only the disgust class yields better accuracy than augmenting all classes. The freeze fine-tuning method is less effective than fine-tuning alone on datasets with thousands of samples but outperforms the freeze layer method. The best accuracies achieved are 64.89% (ResNet-50), 65.83% (Xception), and 66.40% (Inception V3). These findings provide insights into freeze fine-tuning limitations and guidance for optimizing transfer learning in FER with limited data.
Volume: 16
Issue: 3
Page: 1213-1226
Publish at: 2026-06-01

A novel deep learning-based pollinator species classification using the multimodal species-image regularization framework

10.11591/ijai.v15.i3.pp2685-2697
Pooja Hadimane , Ashoka Kukkuvada
Pollinators, such as bees, butterflies, and other insects, are essential to maintaining biodiversity and ensuring agricultural productivity, with over 80% of flowering plants and 75% of global food crops relying on them for successful reproduction. However, pollinator populations are facing significant declines due to environmental changes, habitat destruction, and climate change, posing substantial risks to ecosystems and global food security. This paper introduces the multimodal species-image regularization (MSIR) framework for automating the classification of pollinator species using both binary classification (pollinator vs. non-pollinator) and multiclass classification (bee genera). The framework integrates multimodal data, including visual images of pollinators and species details such as genus, family, and environmental factors, to improve accuracy and scalability. The system leverages the multimodal contrastive learning framework (MCLF) to align both image and species-detail features into a unified embedding space, enabling mor effective classification. Additionally, the framework applies image-species prototype regularization (ISPR) and species-detail prototype regularization (SDPR) to further enhance the classification accuracy by regularizing the tunable weights based on prototype alignment. The proposed deep learning (DL) model is evaluated against traditional machine learning (ML) methods, such as random forest (RF), and demonstrates superior performance on key metrics, including accuracy, precision, recall, and F1-score.
Volume: 15
Issue: 3
Page: 2685-2697
Publish at: 2026-06-01

Multi-agent autonomous GeoAI framework for scalable and self-improving geospatial intelligence

10.11591/ijai.v15.i3.pp2201-2215
Kim-Son Nguyen , The-Vinh Nguyen , Van-Viet Nguyen , Thi-Minh-Hue Luong , Huu-Khanh Nguyen , Duc-Binh Nguyen
Large language models (LLMs) have recently expanded the scope of automation across many application domains. In geographic information systems (GIS), however, many tasks still require specialized expertise and remain difficult for non-expert users. Recent studies have explored LLM-based geospatial analysis under a single-agent paradigm, but these early systems remain limited by weak coordination, limited error recovery, and dependence on proprietary artifacts. This study proposes multi-agent autonomous geospatial artificial intelligence (MA-GeoAI), a multi-agent architecture in which the planner, coder, validator, debugger, and knowledge agents collaborate through the LangGraph framework. The framework was evaluated on three case studies: population exposure assessment, mobility pattern analysis, and county-level mortality modeling. Unlike general-purpose multi-agent LLM frameworks, MA-GeoAI embeds spatial semantics, coordinate reference system (CRS) consistency checks, geometry validation, and operation-aware coordination directly into the control loop. Across repeated runs, all evaluated systems completed the controlled artifact contract; therefore, the analysis focuses on auditability, runtime, fallback behavior, and reproducibility rather than binary task-completion superiority.
Volume: 15
Issue: 3
Page: 2201-2215
Publish at: 2026-06-01

Class imbalance resolution in IoT networks using advanced elk herd optimization with SMOTE and iteratively fine-tuned deep BiLSTM

10.11591/ijai.v15.i3.pp2920-2934
Srikanth Mudiyanur Sriramappa , Ananda Babu Jayachandra , Vasantha Kumar Mahadevachar , Ashwini Kailas , T. G. Keerthan Kumar
The "internet of things (IoT)" mentions to a system in which multiple network protocols are used to link together disparate devices that are always sharing information with one another. The hope is that this study will add to the existing body of knowledge by suggesting ways to make intrusion detection systems (IDS) more effective. Training datasets for the proposed model were collected from telemetry of network (ToN)-IoT network traffic. After cleaning and normalizing the datasets, the synthetic minority over sampling technique (SMOTE) is used to balance the datasets that are imbalanced. Optimal sampling rates are critical for resolving class imbalance, as SMOTE's efficiency is dependent on it for instances involving minority classes. Improving classification accuracy through finding appropriate sample rates for input datasets is the goal of this paper's introduction of advanced elk herd optimization (AEHO) with SMOTE. Finally, a deep bidirectional long short-term memory (deep BiLSTM) model based on deep learning is used to classify attacks. The fine-tuning technique is used during testing to update the high limits and when combined with the data balancing mechanism and AEHO-based-SMOTE, the results greatly enhance the classification techniques. Deep BiLSTM performs better than 90% in every category: classification, recall, precision, and F1-score.
Volume: 15
Issue: 3
Page: 2920-2934
Publish at: 2026-06-01

Designing and evaluating a community-based digital dictionary system for the Balinese language: An IT innovation adoption study

10.11591/ijece.v16i3.pp1369-1381
Cokorda Pramartha , Madek Jeani Purnama , Ida Bagus Gede Sarasvananda , I Wayan Arka , Ni Luh Watiniasih
Regional and vulnerable languages increasingly depend on digital tools to remain visible and usable in everyday life, yet many dictionary initiatives are described mainly in terms of content or interface features rather than evaluated as information-system innovations. This paper presents an exploratory design science study of a community-based Balinese digital dictionary that supports bidirectional Balinese-Indonesian lookup, Latin and Balinese Unicode script, speech-level information, part-of-speech tagging, related-word search, and role-based contribution workflows. The platform is implemented as a web-based system with a three-tier architecture and relational database. To evaluate adoption readiness, 40 users completed representative tasks and then responded to an adapted Moore and Benbasat IT innovation adoption instrument covering seven constructs. The results show high ease of use, relative advantage, and compatibility, indicating strong functional value and fit with user routines. Image and visibility are moderate, while result demonstrability and visibility show lower reliability and are therefore interpreted as exploratory indicators. The study contributes both a documented digital-dictionary artefact for Balinese language support and a reusable evaluation approach for other early-stage community-facing information and communication technology (ICT) systems. The findings suggest that wider uptake depends not only on technical quality, but also on institutional visibility, outreach, and continued content enrichment.
Volume: 16
Issue: 3
Page: 1369-1381
Publish at: 2026-06-01

Combination of binary particle swarm optimization and random forest for stroke disease prediction

10.11591/ijai.v15.i3.pp2290-2299
Sutikno Sutikno , Rismiyati Rismiyati , Khadijah Khadijah , Abdul Karim
Stroke is a leading cause of death and disability worldwide, making early risk prediction critical for prevention. Machine learning methods such as random forest (RF) have shown strong predictive performance, but accuracy can be further improved through effective feature selection. This research proposes an integrated model that combines binary particle swarm optimization (BPSO) for feature selection with RF for stroke risk classification. Experiments were conducted on two public datasets: the stroke prediction dataset (SPD) and the brain stroke dataset (BSD). Data preprocessing included handling missing values, normalization, and the synthetic minority oversampling technique (SMOTE) to mitigate the minority and majority classes. BPSO was employed to select the most informative features, followed by RF for classification. The BPSO-RF model delivered superior accuracies of 96.13% on the SPD and 96.07% on the BSD, outperforming competing classifiers and feature selection techniques. Important features such as gender, age, work type, residence type, average glucose level, body mass index (BMI), and smoking status were consistently identified as key predictors. These results indicate that integrating swarm intelligence with ensemble learning can effectively improve stroke risk prediction and support clinical decision-making.
Volume: 15
Issue: 3
Page: 2290-2299
Publish at: 2026-06-01

Hyperparameter optimization of deep residual recurrent fusion models for facial emotion recognition

10.11591/ijai.v15.i3.pp2581-2594
Muhammad Munsarif , Ku Ruhana Ku-Mahamud
Deep learning facial emotion recognition (FER) is widely applied in healthcare, education, and human–computer interaction. However, many deep learning models suffer from suboptimal hyperparameter configurations that reduce accuracy and stability. This study proposes three deep residual recurrent fusion models that integrates residual blocks with recurrent neural networks (bidirectional long short-term memory (BiLSTM), long short-term memory (LSTM), and gated recurrent unit (GRU)) to capture both spatial and temporal features. A systematic hyperparameter optimization strategy was applied, tuning kernel size, filter size, recurrent units, batch size, learning rate, dropout, and weight decay to balance generalization and computational efficiency. The models were evaluated on four benchmark datasets: FER2013, FERPlus, RAF-DB, and CK+. The results show that optimized configurations achieved outstanding accuracy, reaching 99.85% on FER2013, 99.99% on FERPlus, and 100% on RAF-DB and CK+. These findings demonstrate that careful hyperparameter tuning significantly enhances feature extraction, mitigates vanishing gradient and overfitting issues, and improves generalization across diverse datasets. The proposed framework highlights the importance of optimization in advancing robust FER systems for real-world applications.
Volume: 15
Issue: 3
Page: 2581-2594
Publish at: 2026-06-01

A transfer hybrid deep learning approach for advanced intrusion detection in IoT-based smart home security

10.11591/ijai.v15.i3.pp2750-2760
Mouad Choukhairi , Ouail Choukhairi , Youssef Fakhri , Ali Choukri
As smart home environments increasingly rely on interconnected internet of things (IoT) devices, they face growing cyber threats originating both externally from malicious actors and internally from compromised or malfunctioning IoT devices. These threats, including unauthorized access, distributed denial of service (DDoS) attacks, and data exfiltration, pose significant risks to the security and privacy of smart home inhabitants. This paper introduces an advanced intrusion detection system (IDS) specifically designed to enhance the security of IoT-based smart home networks. Leveraging a hybrid deep learning approach combining convolutional neural networks (CNN) and long short-term memory (LSTM) models, complemented by transfer learning (TL) and hyper-parameter optimization techniques, our proposed IDS efficiently identifies both external and intra-network threats. Using the IoTID20 dataset, which simulates realistic attack scenarios, the IDS was trained and evaluated to detect abnormal behavior effectively within smart home networks. CNN layers extract spatial features from network traffic, while LSTM layers capture temporal dependencies, enabling robust detection against a range of cyber-threats. Evaluation results demonstrate the IDS’s high detection accuracy and exceptional F1-scores, validating its effectiveness in safeguarding IoT-based smart homes from evolving threats.
Volume: 15
Issue: 3
Page: 2750-2760
Publish at: 2026-06-01

AI-enabled energy-aware routing approach for future-wireless sensor networks

10.11591/ijece.v16i3.pp1543-1561
Shamsher Singh , Mandeep Kumar
Next-generation wireless sensor networks (WSNs) demand intelligent, energy-aware communication mechanisms capable of sustaining long-term operation in environments with varying conditions and strict resource limitations. Traditional routing protocols often fail to optimize energy consumption under varying network densities, heterogeneous traffic patterns, and environmental uncertainties. This research proposes an AI-enabled energy-efficient routing protocol (AI-EERP) designed to enhance network lifetime, stability, and data delivery performance in next-generation WSNs. The protocol integrates machine learning–based node selection, adaptive clustering, and predictive residual-energy estimation to make optimized routing decisions in real time. Using AI-driven models, AI-EERP dynamically adjusts routing paths based on energy patterns, link quality, and network topology changes. The simulation outcomes clearly indicate that the proposed approach achieves notable gains in energy efficiency, packet delivery reliability, and network lifetime when compared with traditional routing protocols, including LEACH, PEGASIS, and HEED. The proposed approach establishes a robust and scalable framework for future intelligent WSN deployments across applications including smart cities, precision agriculture, environment-focused applications and automated industrial operations.
Volume: 16
Issue: 3
Page: 1543-1561
Publish at: 2026-06-01

A hybrid deep learning approach for BoT-IoT intrusion detection

10.11591/ijai.v15.i3.pp2192-2200
Khalid Altarawneh , Ghayth AlMahadin , Ibrahim Altarawni
Internet of things (IoT) devices enhance quality of life and industrial operations but pose significant security risks, necessitating intelligent intrusion detection systems (IDS) to combat evolving cyber threats. This paper proposes a novel IDS framework integrating bio-inspired heuristic feature selection, a generative adversarial network (GAN)-based data augmentation, and an ensemble classifier combining ResNet, AlexNet, and MobileNet. The methodology, tested on the botnet (BoT)-IoT dataset, follows four stages: preprocessing, feature augmentation, feature selection, and ensemble classification. Evaluated on benchmarks including CIC-IDS-2018, NSL-KDD, and UNSW-NB15, the model achieved accuracies of 98.2%, 99.1%, 97.6%, and 98.4%, respectively, with consistently high precision, recall, and F1-scores, demonstrating robust detection of diverse cyberattacks. Beyond accuracy, the framework optimizes processing time for large-scale IoT data, addressing scalability challenges in real-time threat mitigation. By synergizing feature optimization, synthetic data generation, and deep learning architectures, the solution enhances detection rates while minimizing computational overhead. Comparative analysis highlights its superior performance over existing methods, positioning it as a vital tool for securing IoT ecosystems against unauthorized access and malicious activities. The results underscore its potential to fortify IoT network security, balancing efficiency, adaptability, and computational feasibility for practical deployment in resource-constrained environments.
Volume: 15
Issue: 3
Page: 2192-2200
Publish at: 2026-06-01

Double direction optimization: a new metaheuristic that performs exploitation and exploration simultaneously

10.11591/ijai.v15.i3.pp2874-2884
Purba Daru Kusuma , Helmy Widyantara
This research constructs a novel method called double direction optimization (DDO). DDO is constructed based on swarm intelligence (SI) approach and it does not use any metaphor. As its name suggests, it employs a novel algorithm by performing exploitation and exploration simultaneously which is transformed into two sequential searches. In the 1st search, the motion toward the highest quality agent is combined with the motion toward a randomly taken higher quality agent. In the 2nd search, the motion toward the finest entity is combined with the motion relative to a randomly taken agent. In this work, the efficacy of the DDO is assessed using three use cases: 23 functions, four engineering problems, and an economic emission dispatch (EED) problem. In this assessment, there are five metaheuristics that become the benchmark: crayfish optimization algorithm (COA), hiking optimization (HO), osprey optimization algorithm (OOA), carpet weaver optimization (CWO), and dollmaker optimization algorithm (DOA). The result indicates the supremacy of DDO in high dimension functions and competitiveness of DDO in fixed dimension multimodal functions, four engineering problems, and the EED problem.
Volume: 15
Issue: 3
Page: 2874-2884
Publish at: 2026-06-01

Exploring artificial intelligence in vocational learning: teachers’ perspective from Indonesia

10.11591/ijai.v15.i3.pp2009-2023
Yuliansah Yuliansah , Mar’atus Sholikah , Sutirman Sutirman
The rapid development of artificial intelligence (AI) in education has expanded its potential applications. However, empirical evidence from vocational education in developing countries remains limited, particularly regarding differences between certified and uncertified vocational high school (VHS) teachers’ perspectives. This study investigates VHS teachers’ perceptions of AI use in the learning process by explicitly comparing certified and uncertified teachers in Indonesia. Using a quantitative approach combined with data-mining techniques applied to open-ended survey responses, data were collected from 65 VHS teachers in the Special Region of Yogyakarta (DIY). Group differences were examined using a non parametric Mann-Whitney U test. The findings indicate that both certified and uncertified teachers demonstrate consistently positive perceptions of AI in instructional planning, implementation, and assessment, with no statistically significant differences between the two groups. Importantly, this result suggests that openness toward AI integration is not determined by certification status but reflects broader pedagogical orientations among vocational teachers. Teachers perceive AI primarily as a pedagogical partner rather than a substitute for professional educators. The study underscores the need of structured AI-focused professional development and policy support through adequate infrastructure and targeted training to enhance the effectiveness of AI adoption in improving the quality of vocational education in Indonesia.
Volume: 15
Issue: 3
Page: 2009-2023
Publish at: 2026-06-01

Performance comparison of deep learning models for concrete crack detection on mobile devices

10.11591/ijai.v15.i3.pp2811-2825
Sarapee Chunkaew , Somporn Ruang-On , Prawit Nuengmatcha , Kritaphat Songsri-in
Concrete crack detection is essential for structural maintenance, yet traditional manual inspection methods are time-consuming and require specialized expertise. While deep learning offers promising solutions, existing models often demand high computational resources unsuitable for mobile deployment. This research evaluates three convolutional neural network (CNN) architectures, namely mobile network (MobileNet), visual geometry group-16 (VGG-16), and residual network-50 (ResNet-50), to identify an optimal model for practical mobile-based crack detection. A dataset of 1,634 images was collected from online databases and field documentation, categorized into 10 classes across three severity levels: i) severe cracks requiring urgent repair (30%); ii) cracks requiring monitoring (40%); and iii) minor cracks (30%). The models were trained using standardized parameters with 224×224-pixel RGB input, rectified linear unit (ReLU) activation, and softmax classification. Systematic parameter optimization was conducted across epochs, learning rate, dropout rate, and optimizer selection, with stochastic gradient descent (SGD) identified as the optimal optimizer. Experimental results demonstrate that MobileNet achieves the best performance with 80% accuracy and a compact model size of 13.1 megabytes. This study concludes that MobileNet provides an optimal balance between detection accuracy and computational efficiency, enabling practical field deployment for automated concrete crack detection, with expert verification recommended for critical structural assessments.
Volume: 15
Issue: 3
Page: 2811-2825
Publish at: 2026-06-01

Adaptive proportional integral control using neural networks for secondary frequency regulation in microgrids

10.11591/ijai.v15.i3.pp2229-2237
Belkasem Imodane , Mohamed Benydir , Sana Mouslim , Abdellah El Idrissi , Mohamed Ajaamoum , Brahim Bouachrine
Microgrids with high renewable energy integration face a challenge in maintaining frequency stability due to the reduced inertia of inverter-based generation and the intermittent nature of these sources. Although primary frequency regulation using virtual synchronous generator (VSG) strategies can provide fast support, it cannot fully bring the system frequency back to its nominal value. This limitation highlights the importance of secondary frequency regulation, which is implemented using proportional integral (PI) controllers. However, fixed parameter PI regulators often fail to adapt effectively to varying loads and fluctuating renewable generation. This paper proposes an adaptive secondary control strategy for microgrids that combines offline optimization with real time learning. Grey wolf optimization (GWO) is first applied offline to determine the optimal PI gains for multiple disturbance scenarios. These datasets are then used to train an artificial neural network (ANN), which updates the PI parameters in real time to achieve adaptive performance. The proposed control is implemented in a hybrid microgrid with a diesel generator, a permanent magnet synchronous generator (PMSG) wind turbine for primary support and a fuel cell for secondary regulation. Simulation results show that the adaptive PI controller improves frequency recovery and reduces steady-state error compared to conventional fixed gain PI.
Volume: 15
Issue: 3
Page: 2229-2237
Publish at: 2026-06-01
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