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30,411 Article Results

Variance-k-means++: A deterministic centroid initialization method based on variance for enhanced clustering stability

10.11591/ijece.v16i3.pp1434-1448
Widodo Widodo , Jiel Vayyad Ramadhan , Muhammad Ficky Duskarnaen , Via Tuhamah Fauziastuti , Chelsea Zaomi Pondayu , Mada Rekadarma Septianda
K-means++ is developed to improve the performance of k-means when choosing a starting centroid. However, both algorithms in clustering still select an initial centroid randomly. Randomly selecting initial centroids has the potential to produce unstable clusters. This paper proposes a deterministic centroid initialization method called variance-k-means++, which utilizes statistical properties—mean and variance—to generate pseudo-centroids and derive initial centroids. The method aims to improve clustering stability and reduce the number of iterations. For the initial study, we used low-dimensional data to conduct the experiment series. Then, we employed two baseline methods for benchmarking, k-means and k-means++. The results show that variance-k-means++ outperformed the baseline method on average. Evaluating in Davies-Bouldin Index (DBI) and convergence analysis, we obtained DBI values at 0.756 and 0,771 for vertical and horizontal variance k-means++ with Iris dataset. At the same time, baseline methods have 0.802 and 0.830 for k-means++ and k-means, respectively. In convergence analysis, the results are 5.158 for vertical and 5.474 for horizontal, while baseline methods are 9.000 and 8.842. The primary contribution of this study lies in its achievement of minimizing the number of iterations while enhancing cluster stability.
Volume: 16
Issue: 3
Page: 1434-1448
Publish at: 2026-06-01

A survey of retrieval algorithms in ad and content recommendation systems

10.11591/ijece.v16i3.pp1518-1530
Yu Zhao , Fang Liu , Yuan Yuan , Yifan Dang
This paper presents a survey of retrieval algorithms used in advertising recommendation and organic content recommendation systems. Modern digital platforms rely on retrieval-based models to efficiently match users with relevant advertisements or personalized content. This survey reviews key techniques including inverted index methods, collaborative filtering, content-based filtering, hybrid recommendation models, and the two-tower neural network architecture widely used in large-scale recommendation systems. The paper compares the objectives, data utilization strategies, and evaluation metrics of ad targeting and organic retrieval systems. Practical challenges such as cold-start problems, data quality, scalability, and privacy considerations are also discussed. This survey further highlights the growing connection between industrial recommendation pipelines and emerging retrieval mechanisms used in large language model (LLM) systems. This survey provides insights into the design principles of modern retrieval systems and outlines future research directions at the intersection of recommendation systems and LLM.
Volume: 16
Issue: 3
Page: 1518-1530
Publish at: 2026-06-01

Sepsis detection using biomarkers and machine learning

10.11591/ijece.v16i3.pp1286-1297
Tuan Anh Vu , Dang Hoai Bac , Minh Tuan Nguyen
Life-threatening dysfunction of organs, known as sepsis, is caused by an imbalanced response of host to infection. In this work, an efficient algorithm is proposed to address vital biomarkers for identification of sepsis using immune-related differential expression genes. A total of 16 gene datasets are processed for the extraction of a gene intersection between different gene datasets and the immune-related gene group, which improve the generalization of the final detection algorithm due to diversity of the input data. A novel gene selection method using sequential forward gene selection, machine learning, and ranked genes based on their importance calculated by a random forest model. A subset of 36 potential immune-related genes, which are identified as the biomarkers from 560 input genes, show an efficiency of the proposed gene selection algorithm. The biomarkers are validated the performance using various machine learning and deep learning related to sepsis diagnosis. The highest statistical performance is shown for the random forest model using the biomarkers as the input with an accuracy of 96.83%, sensitivity of 98.86%, specificity of 86.70%, and AUC of 98.67%. The proposed detection algorithm includes a random forest model and 36 biomarkers, which is simple, effective, and reliable for the applications in clinic environments.
Volume: 16
Issue: 3
Page: 1286-1297
Publish at: 2026-06-01

Analytic algebraic Riccati solution for a robust control system: application to 2-DOF arm robot

10.11591/ijece.v16i3.pp1159-1174
Menad Meriem , Ahmed Foitih Zoubir , Mokhtari Abdellah
An analytic solution to the Riccati algebraic equation has been investigated by employing eigenvalue–eigenvector techniques combined with the Gram–Schmidt orthogonality process. An analytic solution to the Riccati algebraic equation has been investigated by employing eigenvalue–eigenvector techniques combined with the Gram–Schmidt orthogonalization process. The applied method is used to improve robust control of second and third-order state-dependent systems by handling nonlinearities. An H∞ controller is designed in this context via backstepping technique to enhance robustness and reduce computational effort. The effectiveness of this method has been demonstrated on a two-degree-of-freedom (2-DOF) robotic manipulator arm. Simulation results validate the performance of the controller, showing improved tracking accuracy, disturbance rejection, and overall system stability, thereby confirming the efficiency and applicability of the combined analytic Riccati algebraic equation and H∞ backstepping approach for nonlinear robotic systems.
Volume: 16
Issue: 3
Page: 1159-1174
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

Smart water distribution for smart cities based on Internet of Things

10.11591/ijece.v16i3.pp1655-1668
Amal Douli , Khelifa Benahmed , Belkacem Draoui
Against an unprecedented water crisis in our country, balancing water supply and demand is necessary for a secure and sustainable water supply. This challenge requires systems capable of delivering the necessary quantities while conserving resources. Numerous research initiatives focus on addressing water distribution challenges with the help of smart water systems to optimize network operations and minimize water demand. Based on these advancements, this paper proposes a new smart water distribution system for southwest of Algeria. The system integrates the Internet of Things (IoT), information and communication technologies, and smart technologies to address critical attributes for enhancing efficiency. To achieve the efficient management of two-way flows (both water and data) based on water demand and its availability, two innovative architectures have been proposed, using various measurements of water quantity and quality parameters. Algorithms to automate and optimize water distribution are also proposed. According to obtained results, performance has improved, with an accuracy rate of over 98%. These results establish the suggested system as a strong option for intelligent and sustainable water resource management by demonstrating its efficacy and durability.
Volume: 16
Issue: 3
Page: 1655-1668
Publish at: 2026-06-01

GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection

10.11591/ijece.v16i3.pp1307-1318
Saiful Islam , Md. Nasim Akhtar , M. Mahadi Hassan , A. N. M. Rezaul Karim , Israt Binteh Habib
Early and accurate identification of rice leaf diseases is essential for sustainable crop management; however, many existing convolutional neural networks (CNNs) based solutions struggle with class imbalance and limited robustness when applied to real-field data. In this work, a generative adversarial network (GAN) augmented vision transformer (ViT) framework is introduced to overcome these limitations. A deep size representative samples for underrepresented disease categories, resulting in a more balanced training dataset and achieving a Fréchet inception distance (FID) score of 18.6. The balanced dataset is then used to train a vision transformer model that leverages self-attention to capture global contextual features of rice leaf images. Experimental evaluation across ten disease classes shows that the proposed approach attains an overall classification accuracy of 96.5%, exceeding the performance of several established CNN architectures. Additionally, the model demonstrates strong generalization capability on an external field dataset, achieving 94.8% accuracy. To validate real-world applicability, the trained model is deployed on a Jetson Nano edge device, where it delivers efficient inference performance suitable for practical agricultural applications. The findings indicate that combining GAN-based data augmentation with transformer-based learning provides a reliable and scalable solution for rice leaf disease detection.
Volume: 16
Issue: 3
Page: 1307-1318
Publish at: 2026-06-01

Using the technology theory to adoption virtual reality among university students

10.11591/ijece.v16i3.pp1485-1492
Ghaliya AlFarsi , Raghad M. Tawafak , Roy Mathew , Sohail Iqbal Malik , Abir AlSideiri
Virtual reality is a technology field that has become an integral part in most areas of life. Before the 20th century, virtual reality consisted primarily of artificial illusions. Students encounter early obstacles in learning and the current virtual reality (VR) learning mechanism. The research is based on previous studies by filling in the blank by observing the problems that students were facing. The second main point of this research was unified theory using model of technology acceptance and use. This paper focuses on the adoption of a virtual reality learning model in order to improve student academic performance. The results of this paper prove that hypotheses have a positive impact on the factors to use the proposed model.
Volume: 16
Issue: 3
Page: 1485-1492
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

Tuning feature selection to enhance machine learning predictions of bandgap and efficiency in chalcogenide perovskites

10.11591/ijece.v16i3.pp1508-1517
Osphanie Mentari Primadianti , Ryan Nur Iman , Muhammad Zimamul Adli , Agung Muhamad Toha , Agung Surya Wibowo
Solar cell technology has advanced rapidly in efficiency and material innovation. As a renewable energy source, solar cells help mitigate the global energy crisis. Perovskite-based solar cells have recently achieved efficiencies above 25%, surpassing conventional silicon cells. Among emerging materials, chalcogenide perovskites show great promise due to their superior stability compared to halide perovskites. However, they remain in the exploration stage, making accurate predictions of their electrical properties, especially bandgap, essential for assessing potential in solar cell applications. This study predicts bandgap values using computational methods, emphasizing efficiency and cost reduction compared to experimental approaches. Key features derived from collected data include oxidation state, electronegativity, coordination number, ionic radius, and density. Several machine learning (ML) algorithms: AdaBoost Regressor, gradient boosting regressor, support vector regressor, CatBoost Regressor, and k-neighbor regressor, were implemented using Python. The research process involved data collection, preprocessing (feature scaling, fusion, reduction, and selection), model training and testing with 5-fold cross-validation, and hyperparameter optimization to achieve optimal results. Among the tested models, CatBoost Regressor yielded the best performance, achieving a coefficient of determination (R2) of 69.34%, a mean absolute error (MAE) of 23.1%, and root-mean-square error (RMSE) of 29.49%, demonstrating its effectiveness in predicting chalcogenide perovskite bandgaps.
Volume: 16
Issue: 3
Page: 1508-1517
Publish at: 2026-06-01

Prostate magnetic resonance imaging/transrectal ultrasound registration using vision transformer and convolutional neural network

10.11591/ijece.v16i3.pp1188-1198
Hanae Mahmoudi , Hiba Ramadan , Jamal Riffi , Hamid Tairi
Multimodal registration of 3D medical images (3D-MReg) plays a key role in several medical applications and remains a very challenging task as it deals with multimodal images and volumetric objects at the same time. Recently, convolutional neural networks (CNNs) based approaches have been proposed to solve 3D-MReg. However, these techniques cannot preserve the global spatial context required for accurate affine registration since they rely on convolution and regional clustering operations. To solve these problems, we propose a supervised approach that combines both CNN and the vision transformer (ViT) to predict a dense displacement field (DDF). In a first step, our method investigates the power of ViT to capture global voxels dependencies for initial rigid alignment. Then we exploit the force of CNNs to focus on local details within pre-aligned concatenated input 3D moving and fixed images and estimate DDF, which is then applied to the moving labels. Our method has been validated in a prostate magnetic resonance imaging/transrectal ultrasound (MRI/TRUS) dataset and achieved promising results compared to previous work based on only CNNs.
Volume: 16
Issue: 3
Page: 1188-1198
Publish at: 2026-06-01

Hybrid convolutional neural network–transformer models for liver tumor segmentation: a comprehensive review

10.11591/ijece.v16i3.pp1382-1398
Ibrahim Mohamed Attiya , Mostafa Thabet , Mostafa R. Kaseb
Liver cancer is a major cause of cancer deaths worldwide, and early and accurate segmentation of liver tumors is a critical step in cancer diagnosis and treatment. However, existing image segmentation techniques have difficulty handling the variability of liver tumors on different image modalities. The emergence of deep learning (DL) and the development of convolutional neural networks (CNNs) have revolutionized image segmentation techniques. However, CNNs have limitations in handling long-range dependencies, which is a critical requirement for tumor segmentation. To overcome these limitations, researchers have proposed hybrid deep learning architectures, which combine CNNs and attention mechanisms or transformers, to integrate local and global information for image segmentation. In this paper, we provide a comprehensive and analytical review of over 50 state-of-the-art deep learning architectures for liver and tumor segmentation. In addition, we provide an extensive evaluation of 38 hybrid and advanced architectures for liver tumor segmentation and a comprehensive discussion of hybrid CNN-transformer architectures. We propose a novel multi-dimensional taxonomy and evaluate the state-of-the-art architectures on various dimensions, including architectural innovation, segmentation accuracy, computational efficiency, and clinical applicability using benchmark datasets such as LiTS and 3DIRCADb. In our critical evaluation of the state-of-the-art architectures, we identify some of the limitations and challenges of existing research and propose a unified evaluation framework and future research directions on self-supervised learning, explainable artificial intelligence (XAI), federated learning, and lightweight architectures.
Volume: 16
Issue: 3
Page: 1382-1398
Publish at: 2026-06-01

Wind speed prediction and energy estimation using the SARIMA method in Banyumas Regency

10.11591/ijece.v16i3.pp1425-1433
Abdul Hakim Prima Yuniarto , Devi Astri Nawangnugraeni , Rafif Aldo Admaja , Hardeka Muhammad Arsyad
Electricity consumption in Banyumas Regency shows a significant upward trend, indicating growing energy needs across various sectors. Dependence on fossil fuels poses challenges, including environmental pollution, limited resources, and price fluctuations. As a strategic solution, developing new and renewable energy, especially wind energy, is crucial to achieving energy independence and environmental sustainability. This study aims to analyze and predict wind speed in Banyumas Regency and calculate the potential electricity production that residential-scale wind turbines can generate. The method used is the seasonal auto regressive integrated moving average (SARIMA). This study applies it within a machine learning framework, using a grid search for hyperparameter tuning, to accurately predict wind speed from historical NASA POWER data. The results show that the SARIMA (1, 0, 0)×(0, 1, 1, 52) model is the optimal model with the best prediction accuracy, as evidenced by the root mean squared error (RMSE) value of 0.516 m/s and the mean absolute error (MAE) of 0.441 m/s. Based on the model, the predicted average wind speed for the next three months is 3.41 m/s, potentially generating an average daily electricity output of 1.44 kWh. These results indicate that Banyumas Regency has promising potential for the development of small-scale wind power plants to support household energy needs or public street lighting.
Volume: 16
Issue: 3
Page: 1425-1433
Publish at: 2026-06-01

Exploring the relationship of learning engagement, learning interaction, and learning outcomes in gamified massive open online courses

10.11591/ijece.v16i3.pp1329-1338
Azizul Mohd Yusoff , Sazilah Salam , Siti Nurul Mahfuzah Mohamad , Bambang Pudjoatmodjo
This study investigates the interplay between learning engagement, interaction, and outcomes within the context of gamified massive open online courses (G-MOOCs). By synthesizing literature on MOOCs, gamification, and user engagement, the research identifies significant correlations among these variables. Utilizing a structural equation model partial least squares (SEM-PLS) approach, the study analyzes data from a survey of Bachelor of Computer Science students at a technical and vocational education and training (TVET) public university. Results indicate that both learning engagement and interaction significantly influence learning outcomes, with optimal results achieved when both factors are high. These findings highlight the potential of gamification to enhance educational experiences and suggest directions for future research in gamified learning environments.
Volume: 16
Issue: 3
Page: 1329-1338
Publish at: 2026-06-01

Artificial intelligence-based battery management systems in electric vehicles: models, optimization, and future directions

10.11591/ijece.v16i3.pp1645-1654
Hassan Kassem , Tariq Bishtawi
The electric vehicle (EV) depends on the capabilities and durability of the main element of the car — the battery. Conventional battery management systems (BMS) can generally be challenged with regards to state estimation and lifespan forecasting in the face of complicated real-world scenarios. To address these limitations, this study examines how artificial intelligence (AI) has the potential to transform BMS operations. We introduce an in-depth discussion of AI-controlled BMS by examining the state-of-the-art models of precise state-of-charge and state-of-health estimation. The paper also goes into details of how machine learning and deep learning methods can optimize charging strategy, improve thermal management, and predictive diagnostics. The comparison between the data-driven solutions and the traditional methods is going to reveal that there is a high safety, efficiency, and battery life improvement. Lastly, we map the way ahead, taking into consideration issues such as edge computing, explainable AI, and the way of making the BMS a truly self-optimizing system, essential to the next generation of electric cars.
Volume: 16
Issue: 3
Page: 1645-1654
Publish at: 2026-06-01
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