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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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Pyramidal microwave absorbers: leveraging ceramic materials for improved electromagnetic interference shielding Rosli, Nur Shafikah; Abdullah, Hasnain; Kasim, Linda Mohd; Abdullah, Samihah; Taib, Mohd Nasir; Kasim, Shafaq Mardhiyana Mohamat; Noor, Norhayati Mohd; Ahmad, Azizah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp435-447

Abstract

This study presents the development and optimization of pyramidal microwave absorbers designed for efficient electromagnetic interference (EMI) reduction in anechoic chambers. Based on prior research, this work transitions from conventional flat cement-carbon absorbers to a novel pyramidal design, incorporating silicon carbide (SiC) as ceramic materials. Introducing ceramic materials into the cement-carbon composite aims to enhance absorption across a broader frequency range while maintaining structural integrity. The study evaluates five sets of pyramidal absorbers with varying SiC content within the 1–12 GHz frequency range. Reflectivity performance was assessed using the naval research laboratory (NRL) Arch free space method at a 0° incidence angle. Among the tested absorbers, the set containing 10% SiC demonstrated superior performance, achieving minimum and maximum reflectivity values of -26.6215 and -55.2752 dB, respectively, particularly in the C-band. The findings highlight the significant impact of material composition and porosity on the absorber's effectiveness, providing valuable insights for the future design of high-performance EMI absorbers.
Enhancing single image dehazing with self-supervised convolutional neural network and dark channel prior integration Hari, Unnikrishnan; Bajulunisha, Alla Bukshu; Pandey, Pramod; Rexi, Joseph Arul Michiline; Sujatha, Velusamy; Raj, Thankappan Saju; Velmurugan, Athiyoor Kannan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp520-528

Abstract

The removal of noise from images holds great significance as clear and denoised images are vital for various applications. Recent research efforts have been concentrated on the dehazing of single images. While conventional methods and deep learning approaches have been employed for daytime images, learning-based techniques have shown impressive dehazing results, albeit often with increased complexity. This has led to the persistence of prior-based methods, despite their slightly lower performance. To address this issue, we propose a novel deep learning-based dehazing method utilizing a self-supervised convolutional neural network (CNN). This approach incorporates both the input hazy image and the dark channel prior. By leveraging an encoder, the combined information of the dark channel prior and haze image is encoded into a condensed latent representation. Subsequently, a decoder is employed to reconstruct the clean image using these latent features. Our experimental results demonstrate that our proposed algorithm significantly enhances image quality, as indicated by improved peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values. We perform both quantitative and qualitative comparisons with recently published techniques, showcasing the efficacy of our approach.
A unique YOLO-based gated attention deep convolution network-Lichtenberg optimization algorithm model for a precise breast cancer segmentation and classification Rathinam, Vinoth; Rajendran, Sasireka; Krishnasamy, Valarmathi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1670-1685

Abstract

A novel you only look once (YOLO)-based gated attention deep convolution network (GADCN) classification algorithm is developed and utilized in this present study for the detection of breast cancer. In this framework, contrast enhancement-based histogram equalization is applied initially to produce the normalized breast image with reduced noise artifacts. Then, the breast region is accurately segmented from the preprocessed images with low complexity and segmentation error using the YOLO-based attention network model. To diagnose breast cancer with better accuracy, the GADCN model is used to predict the exact class of image (i.e., benign or malignant). During classification, the activation function is optimally computed with the use of the Lichtenberg optimization algorithm (LOA). It aids in achieving improved classification performance with little complexity in training and assessment. The significance of the present study includes the use of a unique, YOLO-based GADCN-LOA model that helps in the prediction of breast cancer with higher accuracy. It was observed that the model exhibited 99% accuracy for the datasets utilized. In addition, the selected model outperforms well with sensitivity, specificity, precision, and F1-score. Hence the proposed model could be exploited for the diagnosis of breast cancer at an early stage to enable preventive care.
A comprehensive analysis of different models: skin cancer detection Thorat, Amruta; Jadhav, Chaya
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2404-2415

Abstract

Due to fast-growing worldwide air pollution and ozone layer destruction, an alarming number of people are found to have skin cancer, more than any other kind of cancer combined. It is known to be one of the deadliest malignancies; if not identified and cured in its early stages, it is likely to spread to other body parts. Early detection is critical and helps prevent cancer from spreading. This allows for early decisions on diagnostic and treatment options. Early diagnosis and discovery, combined with the right treatment, can save lives. In this paper, we have done a detailed survey on various techniques and models developed for skin cancer detection and also discussed different security-related issues. This work thoroughly explores the several types of models utilized to identify cancer in the skin.
Novel technique to deblurring and blur detection techniques for enhanced visual clarity of ancient images Pawar, Poonam; Ainapure, Bharati
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2314-2324

Abstract

Digital image quality often degrades due to various factors such as noise and blur. Many images are affected by these issues, reducing their clarity and accuracy. This degradation is especially problematic for ancient images, significantly hampers the ability to analyze historical documents and artworks. This paper presents a novel approach to both blur detection and deblur ancient images, enhancing their clarity and readability. This research introduces a technique that combines wavelet transform and convolutional neural networks (CNNs) for effective blur identification and deblurring, specifically aimed at restoring blurred ancient images, regardless of the type of blur degradation. This novel approach demonstrated an average accuracy of 98.3% in blur detection on ancient image datasets. The performance of deblurring algorithms is typically evaluated using metrics such as peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index (SSIM) which quantify fidelity and quality of the deblurred images. In the deblurring, this approach produced PSNR values of 55.5 to 68.3 dB, MSE values of 2.99 to 11.1, and an SSIM of 0.9 across different types of blurs. These results show significant promise for the restoration of ancient images, providing researchers, historians, and archaeologists with valuable tool for conservation cultural heritage.
Advanced stress detection with optimized feature selection and hybrid neural networks Patil, Sangita Ajit; Paithane, Ajay Namdeorao
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1647-1655

Abstract

Stress impacts both mental and physical health, potentially leading to serious conditions like cardiovascular diseases and mental disorders. Early detection of stress is crucial for reducing these risks. This study aims to improve stress detection by analyzing physiological signals, specifically electroencephalography (EEG) and electrocardiogram (ECG). EEG is affordable, while ECG provides detailed insights into cardiovascular health. Feature selection is a major challenge in analyzing these signals. To address this, the research introduces a novel method that combines the Archimedes optimization algorithm (AoA) with the analytical hierarchical process (AHP) to enhance accuracy in both single and multimodal systems. The proposed multimodal system employs a parallel-structured convolutional neural network (CNN) with a deep architecture to extract spatial features and uses a long short-term memory (LSTM) network to capture temporal dynamics. Experimental results show significant improvements: ECG stress detection accuracy rises from 88.6% to 91.79%, EEG accuracy increases from 95% to 96.6%, and multimodal stress detection accuracy reaches 98.6%. These results highlight the effectiveness of the AoA-AHP-based feature selection technique in boosting stress detection accuracy, contributing to improved mental health management and overall well-being.
Improved convolutional neural network-based bearing fault diagnosis using multi-phase motor current signals Huu, Hai Dang; Bui, Ngoc-My; Hoang, Van-Phuc; Bui Quy, Thang; Hoang Thi, Yen
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1656-1669

Abstract

Diagnosing bearing faults of the induction motor is crucial for the maintenance of rotating electrical machines. Numerous methods have been developed and published for monitoring and classifying these faults using sensor data such as vibration, audio, and current signals. Ideally, the current phases are balanced; however, faults disrupt this symmetry, causing each phase to reveal unique diagnostic details. Consequently, studies that rely on a single phase of the current signal may not capture all fault-related characteristics. Research on motor bearing fault diagnosis using two current phases typically extracts features from each phase separately, applying machine learning to classify the faults. Currently, no approach has been proposed to extract features from both phases simultaneously. Furthermore, the proposed solutions have only been published with noise-free data. To address these challenges, this paper introduces an enhanced solution that improves the accuracy of motor bearing fault classification based on an improved convolutional neural network that processes current signals from two phases simultaneously. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches, particularly in scenarios where the sample signals are noise-adding signals. Fault classification accuracy of the proposed improved convolutional neural network (MI-CNN) about 95.12% with noise-adding signals at the signal-to- noise ratio of 20 dB.
Tackling the anomaly detection challenge in large-scale wireless sensor networks Zhukabayeva, Tamara; Adamova, Aigul; Zholshiyeva, Lazzat; Mardenov, Yerik; Karabayev, Nurdaulet; Baumuratova, Dilaram
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2479-2490

Abstract

One of the areas of ensuring the security of a wireless sensor network (WSN) is anomaly detection, which identifies deviations from normal behavior. In our paper, we investigate the optimal anomaly detection algorithms in a WSN. We highlight the problems in anomaly detection, and we also propose a new methodology using machine learning. The effectiveness of the k-nearest neighbor (kNN) and Z Score methods is evaluated on the data obtained from WSN devices in real time. According to the experimental study, the Z Score methodology showed a 98.9% level of accuracy, which was much superior to the kNN 43.7% method. In order to ensure accurate anomaly detection, it is crucial to have access to high-quality data when conducting a study. Our research enhances the field of WSN security by offering a novel approach for detecting anomalies. We compare the performance of two methods and provide evidence of the superior effectiveness of the Z Score method. Our future research will focus on exploring and comparing several approaches to identify the most effective anomaly detection method, with the ultimate goal of enhancing the security of WSN.
Maximum expansion with contiguity constraints scheduling algorithm: enhancing uplink transmission in long-term evolution vehicular environments Ismail, Shafinaz; Maidin, Shajahan; Ali, Darmawaty Mohd; Rahim, Mohd Kamarulnizam Abdul
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1709-1719

Abstract

Uplink scheduling has become increasingly important due to increased activities like uploading videos, photos, and file sharing. Many users share or stream live videos and engage with social networks, significantly increasing uplink data traffic volume. Single carrier frequency division multiple access (SC-FDMA) is favored for its power efficiency and high data rates, benefiting user equipment (UE) battery life. However, maintaining the contiguity of resource blocks (RBs) poses challenges in uplink scheduling. The maximum expansion with contiguity constraints (MECC) algorithm has been introduced to address this challenge. MECC prioritizes contiguity, fairness, and throughput for users at the cell edge. The algorithm operates in two phases: initially allocating RBs proportionally and assigning RBs with the highest metrics while ensuring contiguity. Performance evaluation of MECC, conducted under conditions simulating vehicular movement at 30 km/h, demonstrates its superiority over other algorithms. MECC provides high fairness and throughput for both real-time (RT) and non-real-time (NRT) traffic, making it the preferred scheduler for ensuring quality of service (QoS) for both traffic types. Its focus on contiguity, fairness, throughput, and spectral efficiency establishes MECC as a valuable tool for optimizing uplink transmission in mobile networks, addressing the evolving needs of users in today's digital landscape.
An adaptive audio wave steganography using simulated annealing algorithm Obeidat, Atef Ahmed; Bawaneh, Mohmmed Jazi; Shqair, Sawsan Yousef Abu; Al-Omari, Hamdi A.; Al-shalabi, Emad Fawzi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2237-2253

Abstract

The science of information security has increased in importance to encounter the espionage and information theft. This research proposes a new steganography framework that utilizes simulated annealing (SA) as an artificial intelligence algorithm to support the process of hiding a binary secret message file within an audio wave file. The best path for embedding the secret data inside the audio file is determined through SA that searches for the preferred path according to the content of the host audio file and secret message to be hidden. The least significant bit (LSB) technique was employed to hide message bytes, in which each audio-chosen byte will hold one bit from a secret message byte. The hiding process constructs the stego audio file and extraction key that will be required in an extraction process. The authorized user requires an extraction key and a decryption key to retrieve the hidden message. On the other hand, the attacker requires knowledge of the aforementioned keys and working algorithms that were employed in the hidden process. Robustness against data extraction, detection, imperceptibility (phonological hearing), security, peak signal to noise ratio (PSNR), mean square error (MSE) and capacity as security performance measures were used to evaluate the system. The maximum size of the data to be hidden may reach 12.5% of the data size of the host audio file, in which the average value of MSE and PSNR are (0.0041, 74.73), respectively.

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