Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

30,468 Article Results

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

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

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

An internet of things-telemedicine platform empowered by 5G mobile networks for Tunisian Rural places

10.11591/ijece.v16i3.pp1261-1271
Ibrahim Monia , Dadi Mohamed Bechir , Rhaimi Belgacem Chibani
With the advent of Internet of Things (IoT) technologies, offering new possibilities for remote healthcare delivery, the medicine sector has undergone significant advancements in recent years. New tools are used, and diagnostics have become more accurate. We suggest creating a platform that can be extended for several applications. This platform has been realized to attest and demonstrate how IoT technology offers devices that could be integrated to provide novel services like remote consultations. Our proposed platform contains novel functionalities such as real-time video calls, instantaneous messaging, live notifications, vital signs monitoring, and electronic health record access. This is accomplished with enhanced qualities of remote healthcare services. Added to this, healthcare access equity will be guaranteed. The paper emphasizes the potential of Laravel 11 as a framework offering powerful features for creating modern and high-performance applications. We have integrated Laravel Reverb, a powerful real-time communication package, to provide seamless real-time communication with users. With our application, notifications and interactions are dynamically created. This allows instant updates to delivery and engages the user experience. The database was designed based on the latest version of MySQL 8, coupled with the advanced capabilities of PHP 8.2. This combination provides unparalleled performance, scalability and reliability. Added to that, IoT’s technology usage helps to improve healthcare access and delivery, especially in underserved areas. Human and machine cooperation is a main factor of the 5th industry level. This is widely respected by our platform. This offers great help, especially for those isolated and underserved areas, as we hope.
Volume: 16
Issue: 3
Page: 1261-1271
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

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

Integrating BERT fine-tuning and genetic algorithm for superior depression detection in social media

10.11591/ijece.v16i3.pp1474-1484
Abd Allah Aouragh , Mohamed Bahaj , Fouad Toufik
Early detection of depression is crucial for minimizing its adverse effects on mental and physical health. Recent advancements in natural language processing facilitate the large-scale analysis of social media texts to identify depressive tendencies. Our study introduces a novel approach by integrating a genetic algorithm for hyperparameter tuning, optimizing the classification performance beyond conventional methods. We provide a comprehensive comparison of vectorization techniques, including term frequency-inverse document frequency (TF-IDF), Word2Vec, and a fine-tuned bidirectional encoder representation from transformers (BERT) model specifically adapted to our dataset. Using a dataset of 7,731 entries, we implemented standard pre-processing steps such as stop word removal and lemmatization before vectorizing the text. Five machine learning algorithms—decision tree, logistic regression, random forest, gradient boosting, and support vector machine—were evaluated, with hyperparameter tuning performed using a genetic algorithm. The highest accuracy (95.99%) and F1-score (95.91%) were achieved with the combination of fine-tuned BERT, support vector machine, and genetic algorithm optimization. This study demonstrates the advantages of integrating BERT fine-tuning with genetic optimization, outperforming traditional TF-IDF and Word2Vec approaches in depression detection.
Volume: 16
Issue: 3
Page: 1474-1484
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

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

Enhancing sEMG finger gesture recognition using optimized 1D-convolutional neural network

10.11591/ijece.v16i3.pp1576-1587
Daniel Sutopo Pamungkas , Sumantri K. Risandriya
Robust and precise finger gesture recognition using surface electromyography (sEMG) is essential for developing intuitive prosthetic control systems. However, sEMG signals are inherently stochastic and non-stationary, posing significant challenges for high-accuracy classification in fine-grained movements. This study proposes an optimized 1D convolutional neural network (1D-CNN) framework for classifying 20 distinct fine-grained finger gestures using raw sEMG data from an 8-channel wearable Myo Armband sensor. Unlike traditional methods that rely on manual feature engineering, the proposed 1D-CNN performs end-to-end learning to automatically extract temporal features. The research specifically investigates the impact of temporal windowing strategies, ranging from 400 to 750 ms, on model performance. Experimental results demonstrate that the optimized 1D-CNN achieves a peak test accuracy of 94.4% with a 550 ms window size, demonstrating the model’s robustness across complex gesture classes and significantly outperforming the baseline principal component analysis- support vector machine (PCA-SVM) method which only attained 73.0% accuracy. While the model achieved perfect classification (100%) for index, middle, and little finger movements, a performance drop was observed in thumb recognition (50%) due to muscular crosstalk from deeper anatomical layers. These findings indicate that the integration of optimized windowing and 1D-CNN architectures provides a highly reliable solution for complex large-scale gesture recognition, offering a robust foundation for the next generation of multi-functional prosthetic hands.
Volume: 16
Issue: 3
Page: 1576-1587
Publish at: 2026-06-01

Transforming electric vehicle charging through solar integration and high-frequency magnetic induction for seamless wireless power transfer

10.11591/ijece.v16i3.pp1118-1131
Selvan Chinnaiyan , Prabhakar Manickam , Madhu Chandra G. , Aarthi V. , Narendra Babu C. R.
The rapid adoption of electric vehicles (EVs) is constrained by limited charging infrastructure, prolonged charging duration, grid dependency, and inefficiencies in conventional wireless charging systems. To address these challenges, this paper proposes a solar-integrated high-frequency inductive wireless charging framework that enables efficient, contactless, and partially dynamic EV charging. The proposed system combines a photovoltaic (PV) energy harvesting subsystem with maximum power point tracking (MPPT), a high-frequency resonant inductive coupling mechanism using a series–series (SS) topology, and an intelligent solar inductive synergy optimization algorithm (SISOA) for adaptive power and energy storage management. The theoretical foundation of the system is based on Faraday’s law of electromagnetic induction and resonant magnetic coupling to enhance mutual inductance and power transfer efficiency. Simulation studies conducted in MATLAB/Simulink demonstrate that the proposed approach achieves a mutual inductance of 82.5, an output voltage of 500 V, and an output power of 4,800 W, while reducing overall power losses to 21.18% and improving system efficiency to 94.5%. The results further reveal that vehicle speed and the number of receiver coils significantly influence charging effectiveness and state-of-charge performance.
Volume: 16
Issue: 3
Page: 1118-1131
Publish at: 2026-06-01

Bearing fault classification using decision trees and neural networks

10.11591/ijece.v16i3.pp1466-1473
Raid Houssem Eddine Sellaoui , Brahim Boulebtateche , Salah Bensaoula
In this study, we test three machine learning methodologies − binary tree, k-nearest neighbors (k-NN), and neural networks (NN) − using a range of hyperparameters. These methods are applied to a dataset consisting of extracted time series characteristics (root mean square (RMS), skewness, and kurtosis from vibration signals of various bearings subjected to different fault conditions from the intelligent maintenance systems (IMS) dataset. We evaluate how effectively these methods classify the condition of the bearings using the provided dataset. We observe the top two methods, artificial neural network (ANN) 99.29% and binary tree 98.84%. With a difference of 0.45%, the binary tree is preferred over the complex ANN due to its ease of interpretation, transparency, and minimal computation requirements. Its integration as code in embedded controllers or electronic control units (ECUs) is more efficient, which makes them faster for real-time processing and safety-critical electric vehicle (EV) systems.
Volume: 16
Issue: 3
Page: 1466-1473
Publish at: 2026-06-01

Hardware-aware comparative study of lightweight convolutional neural networks for Raspberry Pi-based autonomous driving

10.11591/ijece.v16i3.pp1493-1507
Hyung In Kim , Youngmin Park
Deploying deep learning models for autonomous driving on resource-constrained edge devices, such as the Raspberry Pi, presents significant challenges due to strict limitations on inference latency and memory capacity. To address these constraints, this study conducts a comprehensive comparative evaluation of lightweight convolutional neural networks (CNNs) optimized for dual-output regression of steering angle and driving speed. We benchmark a task-specific end-to-end baseline (NVIDIA CNN) against representative classification-oriented architectures—including MobileNet, ShuffleNet, EfficientNet, GhostNet, and SqueezeNet—all reformulated for this regression task. Experiments were conducted on a physical Raspberry Pi-based autonomous RC car platform to assess prediction accuracy, inference speed, and real-world closed-loop driving stability using quantitative metrics such as the normalized jerk ratio. Experimental results demonstrate a clear trade-off: while GhostNetV1 0.5x achieved the highest regression accuracy with a Total R2 score of 95.8% and MobileNetV1 recorded a competitive MAE of 1.95, they failed to provide stable control due to severe high-frequency steering jitter. Conversely, the NVIDIA CNN proved to be the most practical solution for general edge deployment, achieving the lowest inference latency of 61.1 ms (16.4 FPS) and a minimal memory footprint of 2.78 MB, ensuring stable autonomous navigation (1.50xjerk ratio). Furthermore, ShuffleNetV2 0.5x emerged as the superior architecture for trajectory precision, recording the lowest weighted MAE of 1.60. These findings underscore that theoretical accuracy does not guarantee real-world drivability on embedded systems, providing practical guidelines for hardware-aware model selection in edge-based autonomous driving.
Volume: 16
Issue: 3
Page: 1493-1507
Publish at: 2026-06-01

AI-driven log reduction and storage optimization for security operations

10.11591/ijece.v16i3.pp1417-1424
Nutthakorn Chalaemwongwan
In this study, we present an AI-driven framework that integrates semantic log reduction with compliance-aware storage optimization, specifically designed for security operations center (SOC) and managed security service provider (MSSP) environments. Traditional approaches such as uniform compression, keyword filtering, and static tiering often either miss critical anomalies or preserve redundant noise, leading to excessive storage use, slower search performance, and analyst fatigue. The proposed framework addresses these challenges by combining three components: semantic reduction of repetitive entries, anomaly-focused retention supported by self-supervised models, and adaptive tiering aligned with regulatory requirements. Evaluations on HDFS, BGL, CICIDS2017, and Suricata datasets achieved 70%–80% log reduction, 55%–65% storage savings, recall rates above 95%, and a one-third reduction in query latency. These results demonstrate that pre-index reduction, together with anomaly- and compliance-aware retention, offers a scalable and regulator-ready solution for operational security environments.
Volume: 16
Issue: 3
Page: 1417-1424
Publish at: 2026-06-01
1 2 3
...
2031 2032
Show 14 of 2032

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration