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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 2,057 Documents
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Efficient deep learning for automated corneal ulcer severity classification from fluorescein images Rodiah Rodiah; Indah Sinthya Permata Sari; Matrissya Hermita; Sarifuddin Madenda; Diana Tri Susetianingtias
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3603-3613

Abstract

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.
GWO-optimized sparse Bayesian least squares regression for direction-of-arrival estimation in MIMO networks Anne Gowda Aleri Byregowda; Babu Nallur Venkateshappa; Anughna Narayanaswamy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3431-3440

Abstract

Accurate direction-of-arrival (DOA) estimation is a critical requirement for massive multiple-input multiple-output (MIMO) systems operating in fifth-generation (5G) and beyond (5G/B5G) wireless environments. Although sparse Bayesian learning (SBL)–based techniques have demonstrated improved robustness by exploiting signal sparsity, their performance is often limited by fixed hyperparameter selection, sensitivity to noise, and suboptimal residual error minimization. To address these challenges, this paper proposes an optimized sparse Bayesian least squares regression (SBLSR) framework in which grey wolf optimization (GWO) is employed to adaptively optimize Bayesian hyperparameters and regression coefficients. The proposed approach jointly enforces sparsity and minimizes estimation error, enabling robust DOA estimation under dynamic noise conditions and varying network density. Extensive simulations conducted in a massive MIMO environment demonstrate that the optimized SBLSR consistently outperforms conventional SBLSR and state-of-the-art benchmark techniques in terms of root mean square error (RMSE), closely approaching the Cramér–Rao lower bound (CRLB) across a wide range of signal-to-noise ratios, sensor configurations, and Monte Carlo trials. The findings validate that the suggested optimized SBLSR framework offers a noise-resilient solution for high-precision DOA estimation in practical massive MIMO and MIMO radar systems.
Immuno-bioinformatics analysis of progressive alignment and logic learning machine-derived viral conserved sequences Felza Ridho; Mohammad Isa Irawan; Nurul Hidayat; Awik Puji Dyah Nurhayati
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3189-3201

Abstract

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.
Emotion recognition of electroencephalogram using hybrid convolutional neural networks and vision transformer Esmeralda Contessa Djamal; Revan Vio Endriansyah; Daswara Djajasasmita
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3934-3943

Abstract

Electroencephalogram (EEG) signal-based emotion classification faces challenges in spatio-temporal complexity and inter-channel redundancy. This paper proposes a 2D convolutional neural network (CNN) and vision transformer (ViT) approach. Relevant emotions require signal extraction in the 4-40 Hz frequency band using the discrete wavelet transform (DWT). This study uses the SEED dataset from 15 subjects, with 62 channels reduced to 12. Every 5-second segment is decomposed using DWT into four frequency bands, which are then mapped to a 2D spatial representation for CNNs and ViTs. Experiment results show that the DWT-2D CNN-ViT achieves the best accuracy of 87% with a final loss of 0.087. In the meantime, the CNN-only model (85%), the ViT-only model (77%), and the DWT-only model (47%). Testing with several optimizers also shows that AdamW provides the highest performance with the fastest training time of 12.34 minutes. These results demonstrate that this integration can produce more efficient and stable learning. These findings indicate that the combination of DWT and the CNN-ViT hybrid architecture is effective for accurate, stable EEG-based emotion recognition and is potentially applicable to real-time emotion-monitoring systems.
A review of brain signal analysis in food perception studies Harish S. Sinai Velingkar; Roopa R. Kulkarni; Prashant Patavardhan; Abdul Haq Nalband
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3967-3980

Abstract

Food perception is more than just taste. It is a multisensory experience shaped by smell, texture, and even how the food looks. The human brain works to make sense of all these signals, influencing everything from cravings to food choices. This review looks at more than 40 studies that used electroencephalography (EEG) and other brainwave analysis tools to understand how to process food related stimuli. In order to identify patterns associated with attention, mood, and appetite, these studies looked at brain responses using techniques including event related potentials (ERPs) and spectral analysis. Current research shows that human desire for food, level of focus on it, and the feelings it evokes are all correlated with certain brain signals. However, majority of the studies investigated individual senses in isolation and focused on basic taste attributes such as sweet, salty, or bitter. The complexity of eating in real life, where several senses interact, is missed by this. Furthermore, a lot of research is lab based, short term, and does not represent real world situations. Future research must adopt practical, multimodal methods in order to fully understand how humans perceive food. Such studies could help improve food design, predict consumer behavior, and even support interventions for eating disorders.
Analyzing academic acceptance of artificial intelligence using extended technology acceptance model Nanang Suryadi; Abdurrahman Hakim; Adelia Shabrina Prameka; Wildan Syafitri; Muhammad Irfan Islami
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3090-3102

Abstract

This study aims to analyze the acceptance of artificial intelligence (AI) technology among Indonesian academics using extended technology acceptance model (TAM). The analysis involved general extended technology acceptance model for e-learning (GETAMEL) independent variables, which include subjective norm (SN), experience (EXP), enjoyment (ENJOY), computer anxiety (CA), and self-efficacy (SE), as well as mediator variables: perceived usefulness (PU) and perceived ease of use (PEOU). This study uses technology innovations (TI) as moderator variable and behavioral intention (BI) as a dependent variable. The analysis reveals that SN has a significant influence over PU but not PEOU. ENJOY and SE have a significant positive influence over both PU and PEOU, while EXP and CA don’t have a significant influence over both variables. PU and PEOU have a significant positive influence over BI. TI strengthens the relation between PU and BI, but weakens the relation between PEOU and BI. This finding provides an important outlook for developing a strategy to increase the acceptance of AI technology in the academic environment through an approach that takes into account the factors of pleasure, self-confidence, and TI.
Long short-term memory based activity detection using skeleton joints data: a systematic review Bakkala Santha Kumar; R. Shankar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3026-3035

Abstract

In today's security and surveillance applications, recognizing abnormal activity is critical component of identifying possible hazardous or unusual human behaviors. There is need for new technologies that can detect abnormal human behaviors precisely. The present review investigates various aspects of detection process, focusing on skeleton joints input data. It then explores adoption of deep learning (DL) architectures, such as long short-term memory (LSTMs) and transformers, to improve accuracy and robustness of recognition models. The review explores synergistic integration of LSTMs and transformers to improve recognition of unusual activity. By integrating LSTMs' processing capabilities and attention mechanisms of transformers, enhanced models can accurately identify intricate patterns of activity. Despite the advancements that have been made in the field, the challenges that remain are still related to recognition of unusual activities. These include lack of scalability for large datasets, need for models that can recognize complex behaviors across diverse applications, and need to ensure that detection is performed in a low-latency manner. The paper explores future directions of developing LSTM-based models that can recognize unusual activity using skeleton joint data in a cloud-based environment. The review emphasizes the potential of such solutions that can take advantage of the processing power of graphics processing units (GPUs) and tensor processing units (TPUs) and enable real-time recognition of activity in large datasets.
An optimized deep learning framework for brain tumor classification using magnetic resonance imaging Komal Kumar Napa; Rajkumar Govindarajan; Senthil Murugan Janakiraman; Jayanthi Arumugam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3722-3731

Abstract

Accurate and interpretable classification of brain tumors in magnetic resonance imaging (MRI) scans plays a crucial role in early diagnosis and effective treatment planning. This study introduces a deep learning (DL) framework based on a customized YOLOv5m architecture integrated with a bidirectional feature pyramid network (BiFPN) for multi-class brain tumor classification. The integration of BiFPN enhances multi-scale feature fusion, improving detection across varied tumor types, while YOLOv5m ensures real-time inference capabilities. To mitigate class imbalance, a class-weighted cross-entropy loss is adopted. The model is evaluated on multiple performance metrics, achieving a test accuracy of 88.86%, precision of 88.70%, recall of 88.20%, and F1-score of 88.25%. It also reports a mean average precision (mAP@0.5) of 94.36%, with high class-wise average precision (AP) for glioma, meningioma, pituitary, and no-tumor categories. Computational time for training (12484.81 seconds) and testing (146.82 seconds) confirms the model’s feasibility for real-time clinical deployment. To support interpretability, gradient-weighted class activation mapping (Grad-CAM) is integrated for visualizing class-discriminative regions, helping clinicians understand the model’s predictions. A gradio-based user interface is also developed, enabling intuitive interaction with the system.
Hybrid optimization of dual-port converter for electric vehicles Vidhya Kuruvilla; Immanuel Selvakumar; Pandiyan Venkatesh Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3389-3401

Abstract

Vehicle-to-grid (V2G) technology, which cuts peak loads, levels load, and modulates voltages but generates power system instability, accelerated by the growing popularity of electric-powered vehicles. This research suggests a unique three-level full-bridge non-isolated buck-boost bidirectional direct current (DC)-DC converter that integrates solar (photovoltaic (PV)) systems, vehicle batteries, and the power grid to charge plug-in electric vehicles (EVs). This converter is combined with hybrid Tasmanian-hawk optimization (HTHO). By enabling EV batteries to be charged concurrently from PV systems and the grid, the converter improves charging flexibility and efficiency. The exploration and exploitation phases of HTHO, a new method that combines elements of the Harris hawks and Tasmanian devil algorithms for optimization. Using a 69-node test system, the suggested methodology, which is implemented in MATLAB/Simulink, evaluates power quality improvements in controlled and bidirectional charging operations. At a total harmonic distortion (THD) value of 1.768%, which indicates minimal harmonic distortion in the signal and exceeds conventional optimization techniques, the suggested converter, which integrates with HTHO, enhances charging flexibility and efficiency and helps to preserve overall power system stability. The research strengthens EV charging infrastructure through the seamless integration of natural renewable resources, multi-method optimization, and efficient grid operation strategies.
YOLOv11 optimization for tiny object in crowded scenes Husna Sarirah Husin; Howard Chong Yun Hao; Pan Yuan Fei; Mohsen Marjani; Suriana Ismail
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3452-3463

Abstract

Small object detection in crowded urban and aerial scenes remains a critical challenge due to limited pixel information and information loss in deep neural networks. This study introduces a novel optimization framework for YOLOv11, specifically engineered for tiny-scale targets by integrating convolutional block attention modules (CBAM), k-means anchor clustering, and an enhanced feature pyramid network (FPN). Evaluated on the TinyPerson and COCO-mini datasets, the YOLOv11-optimized model achieves significant performance breakthroughs, delivering a +7.3% gain in mean average precision (mAP) and a +10.5% increase in recall over the baseline. Notably, the model achieved a recall of 0.072 on the TinyPerson dataset, with double sensitivity of standard YOLOv11. With a high-speed inference rate of 27.3 FPS, this research demonstrates that strategic architectural refinements can drastically improve small object detection reliability without compromising real-time viability on edge devices.

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