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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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An approach for predicting brain tumor with machine learning techniques Shashank, PSRB; Anand, L.; Pitchai, R.
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.pp4332-4340

Abstract

The medical industry relies heavily on image processing for tumor diagnosis. Medical imaging is an ever-evolving and intricate field. Brain tumor (BT) is extremely frequent and may cause death. A BT develops when brain cells divide and grow out of control. The prognosis for people with BT can be greatly improved and the survival rate can be increased if the tumor is detected early. A single individual's brain magnetic resonance imaging (MRI) scan comprises of multiple slices through the 3D anatomical perspective. As a result, extracting tumor from MRI scans is a difficult and time-consuming laborious task. Because of the risks associated with biopsies, an MRI-based automated BT categorization is a safer alternative. The scientific profession has worked tirelessly from the beginning of the millennium to develop an automatic BT segmentation and classification system. Therefore, there is a large body of work in the field dedicated to the study of BT research through machine learning (ML) techniques. The review paper summaries the publicly accessible benchmark datasets typically used and compares various processing approaches, feature extraction (FE), segmentation, and classification algorithms for BT. The report also emphasizes the challenges of BT detection. Our hope is that this survey will provide researchers, clinicians, and other interested parties will gain an in-depth understanding of BT segmentation and classification using ML.
Shearlet-based texture analysis and deep learning for osteoporosis classification in lumbar vertebrae Ramakrishna, Poorvitha Hullukere; Muddaraju, Chandrakala Beturpalya; Jayaramu, Bhanushree Kothathi; Narasimhamurthy, Shobha
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.pp4318-4331

Abstract

Osteoporosis is a bone disorder characterized by reduced bone density and increased fracture risk. It challenges society's health, remarkably among the elderly population. This research proposed an innovative method by combining Shearlet-transform (ST) spectral analysis with a deep learning neural network (DLNN) and a convolutional neural network (CNN), for osteoporosis classification in lumbar vertebrae (LV) L1-L4 of spine X-ray images. The ST enables precise extraction of texture features from images by capturing significant information regarding trabecular bone micro-architecture and bone mineral density (BMD) variations revealing in osteoporosis regions. These extracted features serve as input to a DLNN for automated classification of osteoporotic and non-osteoporotic vertebrae. Similarly, without extracting any features from ST image is directly used as an input to the CNN to classify the images. The experimental results highlight the framework's effectiveness, achieving 96% accuracy in osteoporosis image classification using CNN. Early and precise detection of osteoporosis, particularly in the lumbar vertebrae, is vital for effective treatment and fracture prevention. This study particularly emphasizes the potential and effectiveness of integrating image spectral analysis technique with NN, to improving diagnostic accuracy and clinical decision-making in osteoporosis management.
Comparative of prediction algorithms for energy consumption by electric vehicle chargers for demand side management Abida, Ayoub; Majdoul, Redouane; Zegrari, Mourad
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.pp4192-4201

Abstract

This study focuses on demand side management (DSM), specifically managing electric vehicle (EV) charging consumption. Power distributors must consider numerous factors, such as the number of EVs, charging station availability, time of day, and EV user behavior, to accurately predict EV charging demand. We utilized machine learning algorithms and statistical modeling to predict the energy required by EV users for a specific charger and compared algorithms like K-Nearest Neighbors, XGBoost, random forest regressor, and ridge regressor. To contribute to the existing literature, which lacks studies on future energy prediction for a specific period, we conducted predictions for the next year 2024 on the energy consumption of electric vehicles for an electric vehicle charging point in a Moroccan city. These predictions can be generalized to other chargers as well. Our results showed that K-nearest neighbors (KNN) outperformed other algorithms in accuracy. This study provides valuable insights for distribution operators to manage energy resources efficiently and contributes to the DSM field by highlighting the effectiveness of KNN in predicting EV charging demand.
Ensemble of convolutional neural network and DeepResNet for multimodal biometric authentication system Kailas, Ashwini; Girimallaih, Madhusudan; Madigahalli, Mallegowda; Mahadevachar, Vasantha Kumara; Somashekarappa, Pranothi Kadirehally
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.pp4279-4295

Abstract

Multimodal biometrics technology has garnered attention recently for its ability to address inherent limitations found in single biometric modalities and to enhance overall recognition rates. A typical biometric recognition system comprises sensing, feature extraction, and matching modules. The system’s robustness heavily relies on its capability to effectively extract pertinent information from individual biometric traits. This study introduces a novel feature extraction technique tailored for a multimodal biometric system utilizing electrocardiogram (ECG) and iris traits. The ECG helps to incorporate the liveliness related information and Iris helps to produce the unique pattern for each individual. Therefore, this work presents a multimodal authentication system where data pre-processing is performed on image and ECG data where noise removal and quality enhancement tasks are performed. Later, feature extraction is carried out for ECG signals by estimating the Heart rate variability feature analysis in time and frequency domain. Finally, the ensemble of convolution neural network (CNN) and DeepResNet models are used to perform the classification. The overall accuracy is reported as 0.8900, 0.8400, 0.7900, 0.8932, 0.87, and 0.97 by using convolutional neural network-long short-term memory (CNN-LSTM), support vector machine (SVM), random forest (RF), CNN, decision tree (DT), and proposed MBANet approach respectively.
Integration of strain gauge sensor in biceps muscle movement detection using LabView Kristyawati, Desy; Soerowirdjo, Busono; Christina, Erma Triawati; Harahap, Robby Kurniawan
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.pp3696-3706

Abstract

Muscle injuries caused by sports can have a serious impact on sportsmen, to avoid injuries during sports can be prevented by detecting the wrong movement using a strain gauge sensor attached to the muscle which in this study is devoted to the biceps muscle. The strain gauge will detect muscle movement, and the output generated at the strain gauge will be converted into the form of voltage and current which will be used to be processed using machine learning to get data patterns so that they can be grouped into data patterns of wrong movements and correct movements. The strain gauge movement pattern here is simulated using LabView by using a gauge resistance of 120 Ω, strain configuration Quarter Bridge 1, gauge factor 2.05, Vex is the excitation voltage given to the Wheatstone bridge is 5 V and the initial voltage -180.08 µV, the strain gauge output pattern is obtained in the form of Excel and with this data can be converted into voltage and current.
Indonesian speech emotion recognition: feature extraction and neural network approaches Afifah, Izza Nur; Santoso, Tri Budi; Dutono, Titon
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.pp3769-3778

Abstract

This study explored the challenges of emotion recognition in Indonesian speech using deep learning techniques, addressing the complex nuances of emotional expression in spoken language that posed significant difficulties for automatic recognition systems. The research focused on the application of feature extraction methods and the implementation of convolutional neural networks (CNN) and a hybrid convolutional neural networks-long short-term memory (CNN-LSTM) model to identify emotional states from speech data. By analyzing key features of speech signals, including mel frequency cepstral coefficient (MFCC), zero crossing rate (ZCR), root mean square energy (RMSE), pitch, and spectral centroid, the study evaluated the models’ ability to capture both spatial and temporal patterns in the data. Testing was conducted using an Indonesian dataset comprising 200 samples. The CNN model, utilizing four features (MFCC, ZCR, RMSE, and pitch), and the CNN-LSTM model, which used three features (MFCC, ZCR, and RMSE), both achieved an emotion classification accuracy of approximately 88%. The result showed that the CNN-LSTM model achieved comparable performance with a simpler feature set compared to the CNN model. This highlighted the significance of choosing the appropriate techniques in feature extraction and classification to enhance the accuracy of identifying emotions from speech data while also managing computational complexity.
Numerical modelling of photocurrent for CuInxGa1-xSe2-based bifacial photovoltaic cell Bouchekouf, Seloua; Guentri, Hocine; Hassinet, Liamena; Merzougui, Amina; Kebaili, Farida
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.pp3649-3659

Abstract

Research on thin-film solar cells based on CuInSe2 has demonstrated the potential of this compound for photovoltaic conversion. The introduction of gallium as a substitute for indium has led to the creation of the CuInxGa1-xSe2 (CIGS) structure, which could serve as one of the foundational materials for high-performance solar cells. This paper focuses on modelling the bifacial back surface field (BSF) solar cell. We took the CdS/CIGS thin-film structure as an application example to optimize, through simulation, the physical-electronic and geometric parameters of the various layers of the cell. Our study has led us to interesting results that clearly show that the performance of the cell is precisely controlled by the space charge region associated with the CIGS absorber layer, which is promising for research in photovoltaics due to its high absorption coefficient and the ability to vary its bandgap, allowing for increased conversion efficiency. The high-doped P+ layer (Wbsf) enhances the total photocurrent of the bifacial.
Detecting sensor faults in wireless sensor networks for precision agriculture using long short-term memory Aitamar, Yassine; Abbadi, Jamal El
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.pp3803-3812

Abstract

The reliable acquisition of soil data from wireless sensor networks (WSNs) deployed in farmlands is critical for optimizing precision agriculture (PA) practices. However, sensor faults can significantly degrade data quality, hindering PA techniques. Our work proposes a novel long short-term memory (LSTM) network-based method for fault detection in WSNs for PA applications. Unlike traditional methods, our approach utilizes a lightweight, transfer learning-based LSTM architecture specifically designed to address the challenge of limited labeled training data availability in agricultural settings. The model effectively captures temporal dependencies within sensor data sequences, enabling accurate predictions of normal sensor behavior and identification of anomalies indicative of faults. Experimental validation confirms the effectiveness of our method in diverse real-world WSN deployments, ensuring data integrity and enhancing network reliability. This study paves the way for improved decision-making and optimized PA practices.
Chaotic red-tailed hawk algorithm to optimize parameter power system stabilizer Aribowo, Widi; Abualigah, Laith; Oliva, Diego; Aljohani, Abeer; Sabo, Aliyu
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.pp3536-3545

Abstract

This article introduces a recently created adaptation of the red-tailed hawk (RTH) algorithm. The proposed approach is a modified version of the original RTH algorithm, incorporating chaotic elements to enhance its integrity and performance. The RTH algorithm emulates the hunting behavior of the red-tailed hawk. This article demonstrates the adjustment of the power system stabilizer using the suggested technique in a case study involving a single-machine system. The suggested method was validated by benchmarking against known functions and evaluating its performance on a single-machine system in terms of transient responsiveness. The essay employs the original RTH algorithm as a means of comparison. The simulation results demonstrate that the proposed technique exhibits promising performance.
Cyber-fraud detection methodology by using machine learning algorithms Abu-Khadrah, Ahmed; Al-Washmi, Sahar; Mohd Ali, Ali; Jarrah, Muath
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.pp3949-3956

Abstract

Cybercrime covers a wide array of illegal online activities such as hacking and identity theft, while cyber fraud specifically involves deceptive practices like phishing and fraudulent financial transactions. The rise in technology and digital communication has exacerbated cyber fraud. Although prevention technologies are advancing, fraudsters continually adapt, making effective detection methods essential for identifying and addressing fraud when prevention fails. The proposed model aims to reduce online fraud through new detection algorithms. It utilizes statistical and machine learning techniques, including logistic regression, random forest, and naïve Bayes, to identify non-transactional fraud behaviors. By analyzing a meticulously collected and fine-tuned dataset, the study enhances detection capabilities beyond traditional transaction-focused approaches. The algorithms monitor user interactions and device characteristics to create profiles of normal behaviors and detect deviations indicative of fraud. The evaluation of proposed model showed 100% accuracy. A unified model incorporating all decision-making processes was used, leading to a voting phase and accuracy assessment. This approach consolidates multiple algorithms into a single framework, proving highly effective for comprehensive fraud detection. The research demonstrates the value of integrating machine learning techniques with real-world data to advance fraud detection and emphasizes the importance of continual adaptation to address evolving cyber threats.

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