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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 1,974 Documents
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Performance comparison of deep learning models for concrete crack detection on mobile devices Sarapee Chunkaew; Somporn Ruang-On; Prawit Nuengmatcha; Kritaphat Songsri-in
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2811-2825

Abstract

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.
Double direction optimization: a new metaheuristic that performs exploitation and exploration simultaneously Purba Daru Kusuma; Helmy Widyantara
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2874-2884

Abstract

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.
Exploring artificial intelligence in vocational learning: teachers’ perspective from Indonesia Yuliansah Yuliansah; Mar’atus Sholikah; Sutirman Sutirman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2009-2023

Abstract

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.
A hybrid deep learning approach for BoT-IoT intrusion detection Khalid Altarawneh; Ghayth AlMahadin; Ibrahim Altarawni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2192-2200

Abstract

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.
Adaptive proportional integral control using neural networks for secondary frequency regulation in microgrids Belkasem Imodane; Mohamed Benydir; Sana Mouslim; Abdellah El Idrissi; Mohamed Ajaamoum; Brahim Bouachrine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2229-2237

Abstract

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.
RGB-D salient object detection with local feature and semantic segmentation Zhang Wang; Kim On Chin; Rayner Alfred; Junyi Chai; Rundong Zhang; Soo See Chai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2774-2785

Abstract

Red, green, blue–depth (RGB-D) salient object detection (SOD) focuses on identifying visually prominent objects by simulating human visual perception. While existing RGB-D SOD methods have demonstrated results, there remain challenges in effectively leveraging extrinsic cues and enhancing feature representation. To address these limitations, novel RGB-D SOD model with local feature extraction and semantic segmentation (LFSS) is introduced, which is built on an encoder-decoder architecture. The encoder preprocesses the input images by merging RGB and depth data through a channel and spatial attention (CSA) module. A local feature extraction module further refines this fusion. The decoder consists of three key modules: i) the multi-feature extraction (MFE) module enhances base features through diverse convolutional operations; ii) the semantic segmentation enhancement (SSE) module optimizes features via spatial pyramid pooling and atrous convolution; and iii) the local/global agreement and edge detection (LGE) module that enables multi-level feature interaction and edge detection. These modules work sequentially to enhance and extract salient objects. LFSS is evaluated on six standard RGB-D SOD datasets (NJU2K, NLPR, STERE, LFSD, SSD, SIP) by four metrics, outperforming the comparison models with up to 1.2% F-measure improvement. LFSS is found to be a versatile model, offering valuable applications in engineering.
Class imbalance resolution in IoT networks using advanced elk herd optimization with SMOTE and iteratively fine-tuned deep BiLSTM Srikanth Mudiyanur Sriramappa; Ananda Babu Jayachandra; Vasantha Kumar Mahadevachar; Ashwini Kailas; T. G. Keerthan Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2920-2934

Abstract

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.
A transfer hybrid deep learning approach for advanced intrusion detection in IoT-based smart home security Mouad Choukhairi; Ouail Choukhairi; Youssef Fakhri; Ali Choukri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2750-2760

Abstract

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.
Hyperparameter optimization of deep residual recurrent fusion models for facial emotion recognition Muhammad Munsarif; Ku Ruhana Ku-Mahamud
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2581-2594

Abstract

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.
A novel deep learning-based pollinator species classification using the multimodal species-image regularization framework Pooja Hadimane; Ashoka Kukkuvada
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2685-2697

Abstract

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.

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