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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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Machine learning-based clothing recommendation system for women: case study of Lady's confecciones Maestre-Matos, Leydis; Manjarres-Rivera, Manuel; Robles-Algarín, Carlos; Navarro-Meneses, Jose
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4616-4626

Abstract

This paper presents a clothing recommendation system for women based on their body type, aiming to facilitate the purchasing process on the online sales channel of the company Lady's Confecciones located in the city of Santa Marta, Colombia. For this process, a user interface was designed to function in two ways: using a prediction model that takes as inputs a photograph of the user and their height, and a manual mode that receives the measurements of bust, hip and waist. The prediction model implemented the OpenCV library and the skinned multi-person linear (SMPL) model to process images and predict body shape and pose. Five body types were considered: triangle, apple, rectangle, hourglass and inverted triangle, differentiated by bust, waist and hip measurements, according to the conditions provided by the company. The system was able to predict the body measurements of the female participants with a maximum Pearson correlation coefficient of 0.97. For predicting body type, the best results were obtained for the rectangle body shape, with an accuracy of 92.31%.
Statistical analysis of range of motion and surface electromyography data for a knee rehabilitation device Sengchuai, Kiattisak; Sittiruk, Thantip; Booranawong, Apidet; Jindapetch, Nattha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp268-278

Abstract

This work introduces a statistical analysis of knee range of motion (ROM) and surface electromyography (EMG) data gathered from a knee extension rehabilitation device. Real-time ROM and EMG signals of rehabilitation users are measured using a single angle sensor and a two-channel EMG device (for the vastus lateralis and vastus medialis muscles). These signals are collected by the NI-myRIO embedded device in accordance with the designed rehabilitation program. The main contribution and novelty of this study is that real-time signals are automatically processed and transformed into statistical data for use by users and medical experts. A solution for extracting raw signals is proposed, in which several statistical functions such as range, mean, standard deviation, skewness, percentiles, interquartile range, and total knee holding times above the threshold level, are implemented and applied. The proposed solution is tested using data acquired from healthy people, which includes gender, age, body size, knee side, exercise behavior, and surgical experience. Results indicated that real-time signals and related statistical data on the knee’s performance can be efficiently monitored. With this solution, rehabilitation users can practice and learn about their knee performance, while medical experts can evaluate the data and design the best rehabilitation program for users.
Hybrid convolutional neural network-long short-term memory combined model for arrhythmia classification Badiger, Raghavendra; Manickam, Prabhakar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4204-4213

Abstract

The automated examination of electrocardiogram (ECG) signals holds significant importance within the medical field for managing various critical cardiac conditions. Identifying cardiomyopathy and arrhythmias is presently recognized as a challenging endeavor. While machine learning techniques have garnered substantial attention for categorizing these patterns, a predominant focus has been on the classification of arrhythmias. However, existing studies have overlooked instances where arrhythmia leads to cardiomyopathy, a specific cardiac disease scenario. In our research, we introduce an innovative method aimed at distinguishing between cardiomyopathy and cardiomyopathy accompanied by arrhythmia by employing a convolutional neural network (CNN-based) model. This novel approach fills the gap in existing literature by addressing the critical need to classify cases where arrhythmia induces cardiomyopathy, thereby presenting a potential advancement in accurately identifying and managing complex cardiac conditions. The proposed model uses convolution-based CNN model for feature extraction and combines these features with temporal features. Further, a CNN combined long short-term memory (CNN-LSTM) model is presented for classification where CNN models help to obtain the spatial information and LSTM helps to retain the temporal information resulting in improved classification accuracy. the experimental analysis is carried out into two phases where we have classified the rhythms and arrhythmias.
Framework for multiple person identification using YOLOv8 detector: a transfer learning approach Jayaram, Dileep; Vedagiri, Supriya; Ramachandra, Manjunath
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2790-2802

Abstract

Video surveillance extensively uses person detection and tracking technology based on video. The majority of person detection and classification techniques currently in use encounter challenges in video sequences brought on by occlusion, ambient lighting, and variations in human facial position. This paper proposed an effective person identification and classification system based on deep learning, which comprises a you only look once at version 8 (YOLOv8) detection and classification model, to classify human faces in video sequences accurately. This work proposes a new staff-detection and classification (S-DEC) dataset for comprehensive performance evaluation. visual tracker benchmark (VTB) standard database is used for performance comparison with the proposed S-DEC dataset. The proposed technique achieved 98.67% precision accuracy. For the S-DEC dataset, the system gave 94.67% accuracy in identifying facial images from a video sequence of 38 people addressing the pose variation occlusion challenge. Earlier methods used to provide approximately 85% to 90% results taking more execution time. Many existing techniques were successful in detecting people only-identification of the detected person has been done in limited papers. The proposed method uses the cross-stage partial connections (CSPDarknet53) model, integrated with YOLOv8, to achieve faster results. The proposed framework took 35 minutes to train a deep learning model. A testing time of 2 minutes ensured that the proposed framework outplayed other existing methodologies and successfully identified extra information about the detected person.
High-performance speed control for three-phase induction motor based on reverse direction algorithm and artificial neural network Al-Khawaldeh, Mustafa A.; Ghaeb, Jasim A.; Salah, Samer Z.; Alrawajfeh, Mohammad S.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6237-6247

Abstract

This research proposes two approaches for determining the required frequency and modulation index for a pulse-width-modulation (PWM) system in a variable frequency drive (VFD) to control the speed of the three-phase induction motor. The first approach which is the reverse direction algorithm (RDA), uses a set of equations to calculate the necessary frequency and voltage for maintaining a constant motor speed under varying load conditions. The second one involves training a neural network (NN) on data collected by the RDA, which can then be used to continuously adjust the motor speed in real time to adapt to changing load torque requirements. Simulation and laboratory models for the three-phase induction motor are built and the proposed RDA-NN controller is examined. Results have proved that the proposed controller is effective in providing a stable and responsive motor speed control system.
A simplified classification computational model of opinion mining using deep learning Dembala, Rajeshwari; Thammaiah, Ananthapadmanabha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2043-2054

Abstract

Opinion and attempts to develop an automated system to determine people's viewpoints towards various units such as events, topics, products, services, organizations, individuals, and issues. Opinion analysis from the natural text can be regarded as a text and sequence classification problem which poses high feature space due to the involvement of dynamic information that needs to be addressed precisely. This paper introduces effective modelling of human opinion analysis from social media data subjected to complex and dynamic content. Firstly, a customized preprocessing operation based on natural language processing mechanisms as an effective data treatment process towards building quality-aware input data. On the other hand, a suitable deep learning technique, bidirectional long short term-memory (Bi-LSTM), is implemented for the opinion classification, followed by a data modelling process where truncating and padding is performed manually to achieve better data generalization in the training phase. The design and development of the model are carried on the MATLAB tool. The performance analysis has shown that the proposed system offers a significant advantage in terms of classification accuracy and less training time due to a reduction in the feature space by the data treatment operation.
Pedestrian flow prediction in commercial avenue Benhadou, Marwane; Gonnouni, Amina El; Lyhyaoui, Abdelouahid
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5848-5857

Abstract

Mobility plans are one of the most important management tools for city development and an important factor for society and economic growth, where pedestrians are the end goal of any mobility plan. Human behavior is generally unpredictable, and many attempts have been interested at pedestrians' mobility in urban environments, both microscopic and macroscopic (flow, density, and speed) levels. The objective of pedestrian traffic flow prediction is to predict the number of pedestrians at the next moment. Assisting operators and city managers in making decisions in urban environments such as emergency support systems, and quality-of-service evaluation. This study aims to model and predict bi-directional pedestrian flow in a commercial avenue, based on two essential stages, data collection through video recording over two months (pedestrian flow) and data analysis using machine learning algorithms that provide a lower error and a higher accuracy rate. Two metrics were selected as basic measures to evaluate the model performances, root mean square error (RMSE) and coefficient of determination R2. Artificial neural network (ANN) gives a little better performance and fitness.
Improvements the direct torque control performance for an induction machine using fuzzy logic controller Gouaamar, Radouan; Bri, Seddik; Mekrini, Zineb
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1-11

Abstract

This article examines a solution to the major problems of induction machine control in order to achieve superior dynamic performance. Conventional direct torque control and indirect control with flux orientation have some drawbacks, such as current harmonics, torque ripples, flux ripples, and rise time. In this article, we propose a comparative analysis between previous approaches and the one using fuzzy logic. Results from the simulation show that the direct torque control method using fuzzy logic is more effective in providing a precise and fast response without overshooting, and it eliminates torque and flux fluctuations at low switching frequencies. The demonstrated improvements in dynamic performance contribute to increased operational efficiency and reliability in industrial applications.
Prediction of novel malware using hybrid convolution neural network and long short-term memory approach Pachhala, Nagababu; Jothilakshmi, Subbaiyan; Battula, Bhanu Prakash
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4508-4517

Abstract

The rapid evolution of network communication technologies has led to the emergence of new forms of malware and cybercrimes, posing significant threats to user safety, network infrastructure integrity, and data privacy. Despite efforts to develop advanced algorithms for detecting malicious activity, constructing models that are both accurate and reliable remains a challenge, especially in handling vast and dynamically shifting data patterns. The prevalent bag-of-words (BOW) method, while widely used, falls short in capturing crucial spatial and sequence information vital for detecting malware patterns. To address this challenge, the work presented in this paper proposes hybrid convolution neural network-long short-term memory network (CNN-LSTM) combination models, leveraging CNN's spatial information extraction and LSTM's temporal modeling capabilities. Focused on predicting the infiltration of malicious software into personal computers, the proposed hybrid CNN-LSTM model considers factors such as location, firmware version, operating system, and anti-virus software. The proposed models undergo training and evaluation using Microsoft's malware dataset, demonstrating superior performance compared to traditional CNN and LSTM models. The CNN-LSTM model achieves an impressive accuracy of 95% on the Microsoft malware dataset, highlighting its effectiveness in malware detection.
Overbounding Ifree errors based on bayesian Gaussian mixture model for ground-based augmentation system Banu, Sheher; Shanavas, Hameem
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2834-2842

Abstract

A dual-frequency measurement is employed in conjunction with an innovative Ifree filtering technique for mitigating the primary sources of Ifree influence on ground-based augmentation systems (GBAS) to safeguard the reliability of GBAS. The protective level achieved through the conventional Gaussian overbounding approach that are considered as much conventional technique. This adherence to tradition results in decreased reliability and a higher likelihood of false alarms. In contrast, the utilization of the Ifree algorithm contributes to reducing errors associated with dual-frequency measurements. This paper proposes the overbounding process according to Bayesian Gaussian mixture model (GMM) for maintaining Ifree-based GBAS range error. The Bayesian GMM is utilized for single-frequency model errors to examine the ambiguity estimations. The Monte Carlo (MC) simulation is established for defining estimated GMM assurance level accuracy which is attained through the general estimation method. Then, the last Bayesian GMM which is utilized for overbounding Ifree error distribution is investigated. According to the property of convolution invariance, the vertical protection in position field is determined without presenting difficult numerical calculations.

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