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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 111 Documents
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Search results for , issue "Vol 14, No 6: December 2024" : 111 Documents clear
A max-max parametric demand response scheduling algorithm for optimizing smart home environment Saroha, Poonam; Singh, Gopal
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.pp6478-6485

Abstract

The majority of the power distribution problems are addressed in this study by outlining a scheduling and allocation system that is based on rules. The smart home environment incorporates the suggested model as the intermediate layer. In a smart home, managing overload and power failure is the primary goal of the suggested max-max-based demand response scheduling method. The proposed model is an extension of the demand response measure while considering the load and failure rate analysis. This model is applied in a real-time environment that processes the historical information of power usage in the environment. This model captures the information available by the resources, centralized database information, and the current request parameters. The control and configuration unit are defined to process the load, history, and demand of the users. In this model, effective resource allocation and scheduling are provided. The proposed model is compared against conventional first come-first serve (FCFS), shortest job first (SJF), longest job first (LJF), demand response, and fuzzy-based demand response methods. The comparative evaluation is done on average delay, failure rate, and task-switching parameters. The analysis results obtained against these parameters confirm that the presented max-max-based parametric demand response scheduling and resource allocation method enhanced the reliability and effectiveness of the smart home environment.
Development of a digital twin of a network of heating systems for smart cities on the example of the city of Almaty Tyulepberdinova, Gulnur; Kunelbayev, Murat; Shiryayeva, Olga; Sakypbekova, Meruyert; Sarsenbayev, Nurlan; Bayandina, Gulmira
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.pp6656-6674

Abstract

In this paper, a digital twin of the network of heating systems for smart cities is developed using the example of the city of Almaty. The study used machine learning algorithms to estimate future thermal energy consumption and develop thermodynamic formulas. This work offers a thorough and in-depth analysis of thermal energy consumption. In addition, the paper identifies the relationship between thermal energy consumption and ambient temperature, and wind uncertainty in certain urban areas using machine learning methods to predict thermal energy consumption. Using both training and regression models, this interdependence is revealed. The obtained forecasts provide useful information for studying the structure of heat consumption in Almaty and reducing heat losses by reducing overheating in the zones of heating networks. In addition, the study analyzes high-resolution spatial data collected from 385 homes and 62 heat transfer circuits located throughout the city during the heating season. The study examines the degree of relationship between the ambient temperature and the amount of heat energy used in the areas of Astana. A minor impact of wind speed is also estimated. These discoveries allow us to use machine learning algorithms to find the location of hot spots and inefficient zones with high losses.
Network reconfiguration for improving performance system in ULP Sungguminasa considering nonlinear loads Faraby, Muhira Dzar; Rahman, Yuli Asmi; Sofyan, Sofyan; Thaha, Sarma; Lukman, Musfirah Putri; Amaliah, Asma; Mustika, Mustika; Sirad, Mochammad Apriyadi Hadi; Sonita, Anisya
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.pp6066-6075

Abstract

Network reconfiguration is a very economical technique that can improve electrical system performance. The development of semiconductor electrical equipment technology to meet human needs and work, known as nonlinear loads, has had a negative impact in the form of the spread of harmonic distortion which can accelerate the aging process and even damage equipment. In this paper, the effect of the optimization results of network reconfiguration techniques on the Sungguminasa 165-bus Executive Unit Service or Unit Layanan Pelanggan (ULP) electrical system is contaminated with harmonic distortion due to the use of nonlinear loads. This technique was optimized using the particle swarm optimization (PSO) method with a multi objective function in the form of minimizing %THDv and total losses with several limitations. Simulation results from the optimization process of several study cases are shown by activating the five tie switches from the network reconfiguration process on the Sungguminasa 165-bus ULP system which is able to improve power quality by reducing the average %THDv by 3.89% and total losses by 8.19%.
Assessing risk factors for heart disease using machine learning methods Maxutova, Natalya; Tussupov, Jamalbek; Kedelbayeva, Kamilya; Tynykulova, Assemgul; Balabayeva, Zulfiya; Yersultanova, Zauresh; Khamitova, Zhainagul; Zhunussova, Kamila
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.pp6734-6742

Abstract

This paper examines various machine learning methods for assessing risk factors for cardiovascular diseases. To build predictive models, two approaches were used: the extreme gradient boosting (XGBoost) algorithm and a convolutional neural network (CNN). The focus is on analyzing the performance of each model in classification and regression tasks, as well as their ability to identify key biomarkers and risk factors such as cholesterol, ferritin, homocysteine and aspartate aminotransferase (AST) levels. XGBoost parameters have been optimized for working with tabular data, demonstrating high accuracy in risk prediction. The CNN model, despite the initial reduction in error on the training set, showed signs of overfitting when analyzing validation data. Performance evaluation using the metrics of mean squared error (MSE), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC) revealed significant differences between the models. The study results confirm the effectiveness of XGBoost in analyzing tabular data and summarizing risk factor knowledge, while the CNN model needs further optimization to handle sparse data. The work demonstrates the importance of choosing the right model architecture and training parameters to ensure reliable diagnosis of cardiovascular diseases.
Optical coherence tomography angiography image classification and analysis of diabetic retinopathy, using Wasserstein generative adversarial network augmentation Hatode, Pranali Pradeep; Edinburgh, Maniroja
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.pp7046-7056

Abstract

Deep learning algorithms effectively work with, a significant amount of data. trained on small datasets tend to have poor generalization. Data augmentation techniques can be used to make better use of existing training data, improving the applicability of deep learning methods. However, traditional data augmentation methods often produce limited additional credible data. The deep learning approach's performance can be enhanced by generating new data by employing generative adversarial networks (GANs). Although GANs have been extensively used to improve the performance of convolutional neural networks (CNNs), there has been relatively less research on data augmentation methods specifically for GAN training. This study focuses on using a Wasserstein GAN (WGAN) architecture for generating synthetic optical coherence tomography angiography (OCTA) images of diabetic retinopathy to aid in the detection of different types of diabetic retinopathy diseases, including proliferative diabetic retinopathy (PDR), Severe non-PDR (NPDR), Moderate NPDR, and Mild NPDR. WGAN, provides the generator with a more informative learning signal, making training more stable, particularly in high-dimensional spaces. The trained WGAN model is saved in .h5 file format (HDF), converted to portable network graphics (PNG) image format, and then classified into different categories of diabetic retinopathy using a ResNet50 model with various fine-tuning methods. The proposed model has demonstrated better results than the previous study. 99.95% accuracy is exhibited.
Performance enhancement of brushless direct current motor under different novel optimization techniques Ashok, Babu; Kumar, Mahesh
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.pp6225-6236

Abstract

This research paper presents a novel attempt of speed control for brushless direct current (BLDC) motor in low power/servo motor applications. The performance is measured based on the swiftness for the recovery of desired speed amidst in disturbances, sensitive to supply/motor load fluctuations. The proportional integral (PI) controller is competent only for linear time invariant systems. The state of art technology is, PI controller is used with metaheuristic optimization algorithms viz. Honeybee mating optimization (HBO), artificial immune system (AIS), and frog leaping guided algorithm (FLG), for fine tuning of gain coefficients. Earlier literature survey shows power quality and time domain specifications for separate applications. An innovative approach for the assessment of performance indicators like maximum overshoot (M_p), settling time (t_s), power factor (PF) and total harmonic distortion (THD) simultaneously in the optimized PI controller is suggested. By avoiding local optima trapping, this method gives better dynamic performance for various test conditions. MATLAB/Simulink 2021a software is utilized in the examination of performance in various load and speed scenarios, subsequently validated with hardware where cost effective Arduino controller replaced programmable interface controllers (PIC) microcontroller.
Developing a mathematical model for predicting ultimate tensile strength to identify optimal machining parameters Thilagham, Kancheepuram Thirumal; Ladha, Lekshmy Premachandran; Tiwary, Anand Prakash; Haribhau, Munde Kashinath; Dudhajirao, Darade Pradipkumar; Kumar, Shailseh Ranjan
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.pp7116-7125

Abstract

Identifying the ultimate tensile strength (UTS) for friction stir welded joints between AA6082-T6 and AA2014-T87 is crucial for ensuring material compatibility, optimizing welding parameters, and assessing mechanical performance. This information helps engineers design safer, more reliable structures and optimize the welding process, improving the utilization of these aluminum alloys in high-performance applications. Traditional methods for identifying UTS face challenges such as material variability, precise experimental setup, the influence of welding parameters, and are time-consuming and costly. This research aims to develop a mathematical model capable of identifying the UTS based on given inputs, specifically optimal tilt angle, travel speed, and rotational speed. The developed model is further utilized to identify the optimal machining parameters. Processing this manually or through trial and error is time-consuming and complex, highlighting the need to incorporate optimization techniques to determine the optimal parameters efficiently. This research involves several optimization techniques, among which the evolved wild horse optimization (EWHO) performs better, achieving a mean square error of 0.45. This is superior performance compared to other optimization techniques and employed prediction models. This approach saves time, reduces complexity, and enhances precision compared to manual or trial-and-error methods, ultimately improving the efficiency and reliability of material processing.
A deep learning-based surveillance system for enhancing public safety through internet of things and digital technology using Raspberry Pi Sanapannavar, Shreedevi Kareppa; Lakshmanagowda, Chayadevi Mysore; Sundararajan, Geetha
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.pp7198-7210

Abstract

In public spaces, individuals encounter challenges due to the prevalence of malicious activities like theft and kidnapping. As the internet of things (IoT) and digital technology continue to expand rapidly, efforts to create safe environments are becoming increasingly sophisticated. To address these security concerns, a proposed solution involves the utilization of video-capturing technology with the help of a Raspberry Pi web camera. Videos of the surroundings are recorded, a digital signature algorithm is applied to protect the videos, and they are then transmitted to authorized individuals who use them for forensic analysis. This process allows for the identification and investigation of any suspicious or criminal activities. The captured video data is compared with a standard dataset using a deep learning process. By analyzing the content of the videos and identifying the potential threat objects, we can allow for prompt intervention or further investigation by relevant authorities.
An analysis of diverse computational models for predicting student achievement on e-learning platforms using machine learning Tirumanadham, Naga Satya Koti Mani Kumar; Sekhar, Thaiyalnayaki; Muthal, Sriram
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.pp7013-7021

Abstract

Coronavirus disease 2019 (COVID-19) has led many colleges and students to use online learning. In educational databases with so much data, evaluating student development is difficult. E-learning is essential for egalitarian education since it uses technology and contemporary learning techniques. This review research found three ways for predicting online course performance: i) To choose the best features to raise student performance; ii) The most effective algorithms for transforming unbalanced data into balanced data; and iii) The best machine learning algorithms to predict online course performance. This study also offered insights into using hybrid techniques and optimization algorithms to educational data sets to improve student performance prediction. The utilization of data from independent e-learning products to enhance education today requires data processing to ensure quality. In addition to these techniques, our abstract highlights the effectiveness of hybrid feature selection methods like L2 regularization (Ridge) and recursive feature elimination (RFE) and ensemble learning models like random forest, gradient boosting, and AdaBoost. These approaches considerably improve prediction accuracy and tackle huge and sophisticated educational dataset challenges. Our work uses advanced machine-learning approaches to optimize e-learning settings and boost academic achievements in the shifting online education landscape caused by the COVID-19 pandemic.
Improved impressed current cathodic protection systems by incorporating a pulse-feeding technique integrated with internet of things capabilities Adzman, Mohd Rafi; Sallehuddin, Aiman Arif Mohd; Shamsudin, Shaiful Rizam; Husin, Nusaybah Mohd; Haniff, Nur Syakirah Mohammed; Gunasilan, Mahalaksmi; Idris, Muhd Hafizi; Amirruddin, Melaty
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.pp7254-7265

Abstract

This paper introduces an innovative improvement to impressed current cathodic protection (ICCP) systems by integrating a pulse-feeding technique designed to address metal protection degradation during off-potential periods, a common issue in conventional systems. The proposed system enhances the overall effectiveness and reliability of ICCP, providing consistent corrosion protection for critical metal structures. A notable advantage of this method is its simplicity, utilizing a cost-effective microcontroller for pulse feeding. This approach simplifies integration processes and enhances cost-effectiveness, making it an attractive solution for improving cathodic protection system performance without substantial additional costs. The method addresses conventional ICCP weaknesses by applying a high-frequency pulse current during off-potential periods. This reduces excessive negative charge buildup on metal surfaces during interruptions, boosting the system’s effectiveness and stability. Research laboratory experiments were conducted using pulse width modulation (PWM) on an ATmega328P microcontroller to demonstrate the method’s effectiveness. Additionally, an IoT-monitored ICCP system was developed using an ESP32 microcontroller and the Blynk application. Results highlight the superiority of a 50 kHz pulse feeding frequency in preventing corrosion compared to lower frequencies. Overall, this advancement significantly enhances ICCP systems, providing improved corrosion protection and durability in harsh environments.

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