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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Enhancing multi-class text classification in biomedical literature by integrating sequential and contextual learning with BERT and LSTM Ndama, Oussama; Bensassi, Ismail; Ndama, Safae; En-Naimi, El Mokhtar
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4202-4212

Abstract

Classification of sentences in biomedical abstracts into predefined categories is essential for enhancing readability and facilitating information retrieval in scientific literature. We propose a novel hybrid model that integrates bidirectional encoder representations from transformers (BERT) for contextual learning, long short-term memory (LSTM) for sequential processing, and sentence order information to classify sentences from biomedical abstracts. Utilizing the PubMed 200k randomized controlled trial (RCT) dataset, our model achieved an overall accuracy of 88.42%, demonstrating strong performance in identifying methods and results sections while maintaining balanced precision, recall, and F1-scores across all categories. This hybrid approach effectively captures both contextual and sequential patterns of biomedical text, offering a robust solution for improving the segmentation of scientific abstracts. The model's design promotes stability and generalization, making it an effective tool for automatic text classification and information retrieval in biomedical research. These results underscore the model's efficacy in handling overlapping categories and its significant contribution to advancing biomedical text analysis.
Multilevel and multisource data fusion approach for network intrusion detection system using machine learning techniques Somashekar, Harshitha; Halebidu Basavaraju, Pramod
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3938-3948

Abstract

To enhance the performance of network intrusion detection systems (NIDS), this paper proposes a novel multilevel and multisource data fusion approach, applied to NSL-KDD and UNSW-NB15 datasets. The proposed approach includes three various levels of operations, which are feature level fusion, dimensionality reduction, and prediction level fusion. In the first stage features of NSL-KDD and UNSW-NB15 both datasets are fused by applying the inner join joint operation by selecting common features like protocol, service and label. Once the data sets are fused in the first level, linear discriminant analysis is applied for 12 feature columns which is reduced to a single feature column leading to dimensionality reduction at the second level. Finally, in the third level, the prediction level fusion technique is applied to two neural network models, where one neural network model has a single input node, two hidden nodes, and two output nodes, and another model having a single input node, three hidden nodes, and two output nodes. The outputs obtained from these two models are then fused using a prediction fusion technique. The proposed approach achieves a classification accuracy of 97.5%.
Enhancing ultrasound image quality using deep structure of residual network Sapitri, Ade Iriani; Nurmaini, Siti; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Firdaus, Firdaus; Islami, Anggun; Tutuko, Bambang; Arum, Akhiar Wista
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3779-3794

Abstract

Ultrasonography, a medical imaging technique, is often affected by various types of noise and low brightness, which can result in low image quality. These drawbacks can significantly impede accurate interpretation and hinder effective medical diagnoses. Therefore, improving image quality is an essential aspect of the field of ultrasound systems. This study aims to enhance the quality of ultrasound images using deep learning (DL). The experiment is conducted using a custom dataset consisting of 2,175 infant heart ultrasound images collected from Indonesian hospitals, and the model is subsequently generalized using other datasets. We propose enhanced deep residual network combined convolutional neural networks (EDR-CNNs) to improve the image quality. After the enhancement process, our model achieved peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM) scores of 38.35 and 0.92 respectively, outperforming other methods. The benchmarking with other ultrasound medical images indicates that our proposed model produces good performance, as evidenced by higher PSNR, lower SSIM, a decrease in mean square error (MSE), and a lower contrast improvement index (CII). In conclusion, this study encapsulates the forthcoming trends in advancing low-illumination image enhancement, along with exploring the prevailing challenges and potential directions for further research.
Privacy and confidentiality in internet of things: a literature review Kandil, Hiba; Benaboud, Hafssa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4249-4258

Abstract

The internet of things (IoT) is a scalable network of interconnected smart devices that aims to improve quality of life, business growth, and efficiency across multiple sectors. Since the IoT is an expanding network, a large amount of data is generated, collected, and exchanged. However, most of this data is personal data that contains private or sensitive information, which makes it a target for several cyber threats due to poor encryption, weak authentication mechanisms, and insecure communications. Therefore, ensuring the privacy and confidentiality of sensitive information remains a critical challenge. This paper presents a comprehensive literature review focusing on privacy and confidentiality issues within the IoT ecosystem. It categorizes existing research into privacy-preserving techniques, authentication and trust mechanisms, and machine learning-based solutions. Beginning by detailing the review methodology employed to gather and analyze relevant research. The review then explores recent research work related to privacy concerns and authentication and trust mechanisms, emphasizing various approaches and solutions developed to address these challenges. The paper further delves into machine learning-based solutions that offer innovative methods for enhancing privacy and confidentiality.
Review of implantable-based wireless body area network metrics issues Majdoubah, Rawan Al; Eljaafreh, Yousef
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4004-4021

Abstract

Recent developments in wireless communications, low-power integrated circuits, and biological physiological sensors have led to a new generation of wireless sensor networks. Body area networks are an interdisciplinary field that allows for real-time updates of medical records via the internet and continuous, affordable health monitoring. Several intelligent physiological sensors can be easily integrated into a flexible wireless body area network for implanted use, supporting early disease detection or computer-assisted rehabilitation. This field relies on the feasibility of small, easily implanted biosensors that do not impede daily activities. The body's implanted sensors record various physiological changes to monitor the patient's status no matter where they are. Nonetheless, because they handle health data, these networks ought to use benchmarking criteria to ensure high levels of service quality. Network routing protocols, wireless technologies, quality of service, privacy and security, energy efficiency, and performance are among the challenges being focused on to better satisfy its expectations. This review aims to comprehensively compare implantable wireless body area network metrics issues, seeking to generate a consistent and understandable overview. This study also attempts to address the gaps and provides a current assessment of the metrics concerning a wireless body area network used in healthcare services.
Two-step majority voting of convolutional neural networks for brain tumor classification Santoso, Irwan Budi; Utama, Shoffin Nahwa; Supriyono, Supriyono
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4087-4098

Abstract

Brain tumor type classification is essential for determining further examinations. Convolutional neural network (CNN) model with magnetic resonance imaging (MRI) image input can improve brain tumor classification performance. However, due to the highly variable shape, size, and location of brain tumors, increasing the performance of tumor classification requires consideration of the results of several different CNN models. Therefore, we proposed a two-step majority voting (MV) on the results of several CNN models for tumor classification. The CNN models included InceptionV3, Xception, DensNet201, EfficientNetB3, and ResNet50; each was customized at the classification layer. The initial step of the method is transfer-learning for each CNN model. The next step is to carry out two steps of MV, namely MV on the three CNN model classification results at different training epochs and MV on the results of the first step. The performance evaluation of the proposed method used the Nickparvar dataset, which included MRI images of glioma, pituitary, no tumor, and meningioma. The test results showed that the proposed method obtained an accuracy of 99.69% with a precision and sensitivity average of 99.67% and a specificity of 99.90%. With these results, the proposed method is better than several other methods.
High-speed field-programmable gate array implementation for mmWave orthogonal frequency-division multiplexing transmitters: design and evaluation Puntsri, Kidsanapong; Bunsri, Bussakorn; Suthisopapan, Puripong
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3813-3823

Abstract

This paper presents a field-programmable gate array (FPGA)-based implementation of an orthogonal frequency-division multiplexing (OFDM) transmitter signal processing chain optimized for high-speed millimeter wave (mmWave) communication systems. The design prioritizes real-time processing efficiency and flexibility. A high-throughput 2048-point inverse fast Fourier transform (IFFT) module, realized using a Radix-2 algorithm, forms the core of the design, showcasing efficient hardware resource utilization. The implementation further includes cyclic prefix (CP) insertion and configurable support for various quadrature amplitude modulation (QAM) modulation orders and pilot arrangements. The design is implemented in VHSIC Hardware Description Language (VHDL) using Vivado 2020 and evaluated on the Zynq UltraScale+ RFSoC ZCU111 evaluation kit. The processing pipeline employs eight parallel lanes for concurrent data computation. Experimental results demonstrate a mean squared error (MSE) of only 0.00013 between the FPGA-generated waveform and its MATLAB-simulated counterpart. Additionally, post-implementation resource utilization analysis shows efficient usage of FPGA resources. These findings validate the efficacy and real-time capability of the proposed FPGA-based OFDM transmitter leverages parallelism and high-speed architecture to efficiently process massive data streams, making it suitable for a wide range of mmWave OFDM applications. In contrast to recent works that focus on lower-order IFFT modules, this paper employs a high-throughput IFFT computation, showcasing efficient hardware resource utilization for high-speed mmWave applications.
Enhancing mobile agent protection using a hybrid security framework combining pretty good protocol and code obfuscation Zraqou, Jamal; Alkhadour, Wesam; Baklizi, Mahmoud; Omar, Khalil; Fakhouri, Hussam
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3913-3927

Abstract

The security of mobile agents, which are autonomous software entities capable of migrating between computers to execute tasks, remains a critical concern in modern information technology. Cybersecurity has been a central component of this technological revolution and continues to be one of the most essential requirements for any software or platform. Despite advances in security measures, protecting mobile agents, particularly those carrying sensitive data, while they transmit over networks remains challenging. This research proposes a novel hybrid security technique, abbreviated as pretty good privacy and code obfuscation framework (PGF), which combines pretty good privacy (PGP) with code obfuscation. PGF is designed specifically to protect mobile agents, focusing on systems like Aglets. The technique aims to safeguard the integrity and confidentiality of the agent's data during transmission. Based on the mobile agent Aglets and the PGF technique, the proposed model enhances security by introducing additional protection layers during agent creation and transmission using PGP and code obfuscation. The comparative analysis demonstrated that PGF outperformed other algorithms in terms of time efficiency and security, effectively handling large data sizes through its hybrid cryptographic approach, which combines asymmetric and symmetric encryption. The model was implemented using the Aglets framework in Java development kit (JDK) and NetBeans and showed high reliability and practicality. However, its current design is tailored to Aglets, and future work could focus on adapting the model to other platforms and optimizing its resource efficiency for constrained environments.
Instance segmentation for PCB defect detection with Detectron2 Chaithanya, Aravalli Sainath; Devi, Lavadya Nirmala; Srividya, Putty
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4172-4180

Abstract

Printed circuit boards (PCBs) are essential in modern electronics, where even minor defects can lead to failures. Traditional inspection methods struggle with complex PCB designs, necessitating automated deep learning techniques. Object detection models like Faster R-CNN and YOLO rely on bounding boxes for defect localization but face overlap issues, limiting precise defect isolation. This paper presents a segmentation-based PCB defect detection model using Detectron2’s Mask R-CNN. By leveraging instance segmentation, the model enables pixel-level defect localization and classification, addressing challenges such as shape variations, complex structures, and occlusions. Trained on a dataset of 690 COCO-annotated images, the model underwent rigorous experimentation and parameter tuning. Evaluation metrics, including loss functions and mean average precision (mAP), assessed performance. Results showed a steady decline in loss values and high precision for defects like mouse bites and missing holes. However, performance was lower for complex defects like spurs and spurious copper. This study highlights the effectiveness of instance segmentation in PCB defect detection, contributing to improved quality control and manufacturing automation.
Sensitivity factors based computationally efficient approach for evaluation and enhancement of available transfer capability Sureban, Manjula S.; Ankaliki, Shekhappa G.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3556-3565

Abstract

Available transfer capability (ATC) is an indication of the capability of the transmission system to efficiently increase power transmission for further commercial trading between two areas or two points. ATC plays an important role in operating power systems economically, reliably, and securely. As the deregulation in the power system can cause overload in the transmission system, ATC evaluation and enhancement are required for secure and reliable operation. The advancements in power generation techniques and switching from centralized generation to distributed generation (DG) with more emphasis on renewable sources have resulted in various approaches to enhance ATC. In this work, a computationally efficient sensitivity-based methodology for evaluating and improving ATC with the presence of renewable generation is proposed. The developed approach is implemented on the IEEE 30 bus system and the outcome is compared with the existing methods in the literature.

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