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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
 192   193   194   195   196 
Effects of sparse datasets on time interval-aware self-attention sequential recommendation models Weishan Ooi; Lee-Yeng Ong; Meng-Chew Leow
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.pp2761-2773

Abstract

Recommendation models serve as crucial filters in managing information, yet they face a few crucial challenges, such as capturing user-item interaction behaviors in sparse datasets. Data sparsity refers to an issue where there is a lack of interactions or missing values in the recommendation dataset. A sparse dataset with a massive number of missing values and interactions leads to more dynamic user behaviors, which suffers a poor recommendation quality. The self-attention mechanism from Transformer can alleviate the effects of data sparsity in datasets by assigning weights to items of interaction behaviors. This allows the model to capture the user dependencies in complex user behavior, which is beneficial for sparse datasets with patterns that are not immediately apparent. This approach has shown its capability to handle large and sparse datasets, as seen in time interval-aware self-attention sequential recommendation model (TiSASRec). It utilized the self-attention mechanism, considering the timestamp and absolute positions of items to estimate the higher attention weights to show the importance of recent items. Thus, this study aims to investigate the effects of sparse datasets by comparing the performance of TiSASRec model with self-attention based sequential recommendation model (SASRec), which excludes time interval-awareness.
High-gain antenna arrays for millimetre-wave energy harvesting: architectures, challenges, and future directions Shalini Mirle Gajendra; Naveen Kalenahalli Bhoganna
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.pp2896-2906

Abstract

The rapid expansion of fifth-generation (5G)/sixth-generation (6G) networks and internet of things (IoT) ecosystems has intensified the need for self sustaining power solutions to support billions of wireless devices. Millimetre-wave (mmWave) energy harvesting (EH) emerges as a viable alternative to traditional battery-powered systems, leveraging ambient radio frequency (RF) signals to provide continuous energy for IoT, smart sensor networks, and next-generation wireless applications. However, several challenges hinder its widespread adoption, including high path loss, low RF to-direct current (DC) conversion efficiency, and the trade-off between high gain and wide bandwidth. This paper presents a comprehensive review of high-gain mmWave antenna arrays, exploring state-of-the-art advancements in beamforming techniques, phased arrays, metasurface-enhanced rectennas, and multi-band EH architectures. We analyse existing methodologies, identifying key research gaps such as scalability constraints, material limitations, and real-world deployment challenges. Additionally, we highlight emerging trends, including artificial intelligence (AI)-driven adaptive beamforming, intelligent metasurfaces, and cost-effective fabrication techniques, which can significantly improve mmWave RF EH efficiency. By addressing these gaps, this study provides insights into future research directions for developing high-performance, scalable, and commercially viable mmWave EH solutions. The findings pave the way for the practical deployment of battery-free IoT devices, smart city infrastructures, and energy-autonomous wireless communication networks in the 6G era.
Parkinsonian gait classification in older adults using time–frequency spectrograms and 2D convolutional neural network Kazi Ashikur Rahman; Ezreen Farina Shair; Nur Zawani Saharuddin; Muhammad Hazwan Adlin Jumaris
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.pp2698-2708

Abstract

Gait disorders in adults aged 50 years and above are a common concern and are often linked to reduced mobility, a higher risk of falls, and a lower quality of life. This study presents a deep learning-based approach to detect gait disorders using vertical ground reaction force (vGRF) signals. The data were collected from older adults, including individuals with Parkinson’s disease (PD) and healthy controls, using force-sensitive resistor sensors. The raw signals were first processed using band-pass filtering and wavelet denoising to remove noise and unwanted variations. After that, the signals were converted into time–frequency representations using the continuous wavelet transform (CWT). These representations were then used as input to a convolutional neural network (CNN) for classification. The model achieved a validation accuracy of 93.48%, with precision, recall, and F1-score all above 92% for both groups. The results show that combining CWT with CNN provides a reliable and efficient way to detect gait disorders. This approach can support clinical evaluation by offering a practical and scalable method for analyzing gait patterns in older adults.
Review of advancements in AI-assisted lung sound analysis for respiratory illness diagnosis in noisy environments Reshma Sreejith; R. Kanesaraj Ramasamy; Wan-Noorshahida Mohd-Isa; Junaidi Abdullah
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.pp2863-2873

Abstract

For several centuries, research has been carried out to address respiratory ailments, which are among the most detrimental to human health. The advent of the stethoscope in the 19th century has facilitated the identification of respiratory sounds. This innovation represents a significant advancement in the identification and diagnosis of numerous respiratory ailments. In Malaysia, public hospitals have traditionally employed stethoscopes in their emergency departments. However, the precision of readings obtained through this method is susceptible to interference from ambient noise, uneven terrain, and suboptimal acoustic performance, particularly during medical transportation. Consequently, this can result in erroneous diagnoses and inappropriate treatment. Potential remedies for addressing the challenges associated with assessing respiratory sounds during medical transportation include advancements in stethoscope technology, novel auditory techniques, and reduced levels of background noise within the transportation environment. The present investigation concerns the effects of developing a new machine learning (ML) algorithm for the assessment of lung sound in conditions of high ambient noise. The objective is to devise a ML algorithm that can categorize acute respiratory illnesses based on their level of urgency in the presence of ambient noise.
Improved boosting-based machine learning algorithms for network intrusion detection in wireless sensor network Said Ouhmi; Housni Khalid; Mbarek Marwan; Hassan Selkhi; Abdelkarim Ait Temghart
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.pp2278-2289

Abstract

Intrusion detection is essential for protecting wireless sensor networks (WSNs) from evolving cyberattacks. This paper proposes an enhanced boosting-based framework that integrates generative adversarial networks (GANs) to address data imbalance, and Harris hawk optimization (HHO) for efficient feature selection. Six boosting algorithms, including adaptive boosting (AdaBoost), gradient boosting (GB), extreme gradient boosting (XGBoost), light gradient‑boosting machine (LightGBM), categorical boosting (CatBoost), and histogram-based GB, were evaluated to determine the most effective configuration. The proposed system achieves an accuracy of 99.18% with a detection time of 12.7 ms on a dataset for intrusion detection systems in WSN (WSN-DS dataset), significantly outperforming the existing boosting-based intrusion detection models. By combining data balancing and feature optimization, the framework enhances both accuracy and resource efficiency, providing a scalable and robust approach for real time threat detection in resource-constrained environments. The results confirm the potential of hybrid boosting methods coupled with advanced data generation and optimization strategies to strengthen the resilience of modern WSNs against emerging network attacks.
Machine learning-enabled joint antenna selection and precoding Monica Nilesh Kalbande; Kanala Sai Madhuri; M. Venkateswara Rao; Saradha Rani Sabbavarapu; Rajyalakshmi Uppada; Lakshmi Durga Rajamahendravarapu
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.pp2369-2376

Abstract

Joint antenna selection (AS) and precoding design is essential for improving spectral efficiency and energy efficiency in multi-antenna wireless communication systems. However, conventional optimization-based solutions rely on exhaustive search and iterative processing, leading to high computational complexity that limits real-time applicability. This work proposes a machine learning-enabled framework that shifts the computational burden from online operation to offline training. Optimal AS and precoding decisions are first generated offline using model-based optimization under diverse channel conditions. A supervised machine learning model is then trained to learn the relationship between channel state information (CSI) and optimal transmission configurations. During online operation, the trained model enables fast and efficient AS with significantly reduced processing time. Numerical results demonstrate that the proposed approach achieves near-optimal system performance while substantially lowering computational complexity, making it well suited for real-time and next-generation wireless communication systems.
Hybrid automated road crack segmentation using morphological operations and boundary tracing Ida Ayu Ari Angreni; Diyanti Diyanti; Vega Valentine
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.pp2181-2191

Abstract

Cracks in the road surface are one of the early indicators of structural damage that has an impact on safety and infrastructure maintenance costs. Accurate early detection is a challenge in complex visual conditions such as uneven lighting and varied asphalt textures. This study proposes an efficient and fully automated hybrid segmentation method to detect cracks in road surface imagery. This method consists of several main stages: image enhancement using contrast limited adaptive histogram equalization (CLAHE), initial segmentation through a combination of Otsu's thresholding, adaptive Gaussian thresholding, and Canny edge detection, followed by mask enhancement with morphological operations (closing, opening, and erosion). The DeepCrack dataset is used as a source of test data. The evaluation results showed high performance with detection accuracy reaching 95.82%. These findings show that the proposed method is not only precise and sensitive, but also adaptive to visual variation without the need for manual training or parameters. A major novelty lies in the integration of three classic segmentation methods in one morphology pipeline that is computationally lightweight yet competitive, making it potential for real-world applications of automated inspection systems.
Intestinal disorders categorization in endoscopic images using deep learning architectures Esha Saxena; Suraiya Parveen; Mohd. Abdul Ahad; Meenakshi Yadav; Mohammad Anas
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.pp2347-2356

Abstract

Gastroenterology is revolutionized by advancements in artificial intelligence (AI). As the gastrointestinal (GI) tract is consulted, globally 40% of the world's and 18% of the Indian population are affected. AI is a reliable sword for diagnosing issues related to the GI tract. The learning capabilities of deep learning (DL) techniques make it widely helpful in medical investigations. The variety of data available in the medical sector generates the need for an appropriate model for every problem domain. The purpose of this research is to explore the significance of medical image pre-processing and the implementation of pre-trained DL models on endoscopic images for the diagnosis of disease. Convolutional neural network (CNN)-based architectures have robust diagnostic potential for medical images. It can assist physicians as a tool for disease analysis, screening and help in investigating further needs. The paper also provides a comparative performance framework showing CNN architectures and preprocessing techniques for endoscopic images to highlight the key points important for investigating GI tract related diseases. The endoscopic images were trained over VGG-16, ResNet-50 and DenseNet-121, DL models. The result suggests that VGG-16 and ResNet-50 gave promising results with an accuracy maximum of 87.50%.
Enhancing diabetes prediction: integrating machine learning with explainable artificial intelligence Shaik Abdul Jaffar; Shadab Siddiqui
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.pp2431-2448

Abstract

Early detection of diabetes is critical in preventing disease progression and improving patient outcomes. This work combines Explainable Artificial Intelligence (XAI) and machine learning to enhance understanding and prediction of diabetes using the Pima Indian Diabetes Dataset. The machine learning models used in this study are Random Forest, Logistic regression and Gradient Boosting, which resulted in the best accuracy of 93.2%. Some of the pre-processing steps taken were handling of missing data, normalization, feature scaling and Synthetic Minority Over-sampling Technique (SMOTE) for handling class imbalance. Use of SHAP and LIME XAI methods has proven that glucose, BMI and insulin are the most crucial features when it comes to prediction. These techniques further enhance the trust of the clinicians and stakeholders by improving the understanding of how the features contribute to individual predictions, which enhances the model prediction as a whole. The findings prove that there is indeed a marked improvement in the understanding of the machine learning models and their predictions with no compromise on performance. This study highlights the benefits of using XAI in machine learning so that there is accuracy and ease of interpretation with immense power within the developed models. 
A hybrid machine learning model for optimized mixed-crop recommendation Ahmed Mohammed Gimba; Pradeep Kumar Mishra
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.pp2314-2324

Abstract

Farmers today encounter more challenges when selecting appropriate variety of crops depending on their farm soil nutrients and climate. This research will assist farmers in choosing suitable mixed-crops depending on the individual farms soil and climate conditions in Andhra Pradesh, India. Using the dataset sourced from Indian Institute of Soil Science (IISS), Bhopal with 2,552 entries. Previous studies focused on only single-crop recommendations. This work proposes a novel hybrid mixed-crop recommendation system (CRS) that incorporates several machine learning (ML) techniques comprise of random forest-ExtraTrees (RF-ExtraTrees), decision tree-C4.5 (DT-C4.5), extreme gradient boosting-gradient boosting (XGBoost-GBoost), quadratic discriminant analysis-linear discriminant analysis (QDA-LDA), and support vector machine-stochastic gradient descent (SVM-SGD) were utilized to recommend mixed-crops. To enhance the reliability of the training process, 20% of the dataset was held in reserve for validation to analyze model performance. The result of the proposed work shows that all the hybrid ML models applied were viable, and RF ExtraTrees has achieved 95.91% best accuracy, 95.08% precision, and 95.91% recall, when contrasted to the other ML models.

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