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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 COVID-19 forecasting through deep learning techniques and fine-tuning López, Alba Puelles; Martínez-Béjar, Rodrigo; Kusrini, Kusrini; Setyanto, Arief; Agastya, I Made Artha
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.pp934-943

Abstract

In this study, a comprehensive analysis of classical linear regression forecasting models and deep learning techniques for predicting coronavirus disease of 2019 (COVID-19) pandemic data was presented. Among the deep learning models, the long short-term memory (LSTM) neural network demonstrated superior performance, delivering accurate predictions with minimal errors. The neural network effectively addressed overfitting and underfitting issues through rigorous tuning. However, the diversity of countries and dataset attributes posed challenges in achieving universally optimal predictions. The current study explored the application of the LSTM in predicting healthcare resource demand and optimizing hospital management to provide potential solutions for overcrowding and cost reduction. The results showed the importance of leveraging advanced deep learning techniques for improved COVID-19 forecasting and extending the application of the models to address broader healthcare challenges beyond the pandemic. To further enhance the model performance, future work needed to incorporate additional attributes, such as vaccination rates and immune percentages.
Energy savings by adapting consumer behavior in grid-connected photovoltaic systems with battery storage Andam, Meriem; El Alami, Jamila; Louartassi, Younes; Zine, Rabie
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.pp3688-3702

Abstract

Today, the world faces many energy challenges that make the use of clean energy an obligation and an emergency. These challenges require changes in a wide range of sectors, including transport, industry and residential areas. At present, energy production is responsible for a large amount of greenhouse gas emissions, the main cause of climate change with its various dangerous effects on human life. Therefore, a change towards non-carbon energy is becoming a necessity. Certainly, this evolution has been underway for years and the renewable energy market has developed in a surprisingly efficient way. However, the current situation has reached an alarming state and requires the active participation of all stakeholders, especially consumers. Therefore, the main objective of this paper is to illustrate how consumer behavior can significantly influence and contribute to the optimization of renewable energy systems, especially photovoltaic systems. The paper emphasizes the beneficial integration of batteries and storage systems to achieve energy savings. The results show that with some adjustments in daily behavior, an overall energy saving of 62% can be achieved compared to the normal consumption scenario: the energy obtained from the grid and then the electricity bill is reduced.
Simaksaja: visual novel game with TyranoBuilder software for Islamic moderation in elementary schools Ibda, Hamidulloh; Aniqoh, Aniqoh; Muntakhib, Ahmad; Mar’atussolichah, Mar’atussolichah; Fadhilah, Trifka Dila; Rakhmawati, Nurma Febri
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.pp2955-2964

Abstract

The study aims to determine the development, feasibility, and effectiveness of the Simaksaja game. The research was conducted due to the lack of development of digital games based on religious moderation in elementary schools. This research and development refer to the analysis, design, development, implementation, and evaluation (ADDIE) type of Dick, Carey, and Carey model. Data were collected through student and teacher needs analysis, media expert and material expert validation tests, effectiveness tests, structured observation, in-depth interviews, and document study. The research subjects were 46 teachers and 46 students for the needs analysis and effectiveness test in 2 schools in the District and City of Magelang. The research findings stated that the game features character-driven material, the introduction of the characters Ning and Gus, and quizzes. Based on the feasibility test, the game expert scored 72%, the moderation material expert 88%, and the Aswaja Annahdliyah material expert 72%, and it was feasible to use. This game is effective based on the effectiveness test in two elementary schools, scoring 80% from the educators’ response and 87% from the students’ response. The novelty of the research is that the TyranoBuilder game contains religious moderation characters, Aswaja Annahdliyah, Pancasila student profile, and Rahmatan Lil Alamin student profile.
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.
A 3D reconstruction-based method using unmanned aerial vehicles for the representation and analysis of road sections Benmhahe, Brahim; Alami Chentoufi, Jihane; Basmassi, Mohamed Amine
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.pp1552-1564

Abstract

Due to the fast growth of cities worldwide, roads are increasing daily, and pavement maintenance has become very heavy and costly. Despite all efforts made under the pavement management system to keep the road surface in good shape, several road sections need to be in better condition, which presents a danger for drivers and pedestrians. This paper proposes a novel pavement 3D reconstruction and segmentation approach using the structure from motion technique, unmanned aerial vehicle, and digital camera. The method consists of the 3D modeling of the road by using images taken from different perspectives and the structure from motion technique. In this method, points cloud is sampled and cleaned using statistical outlier removal and noise filters. After that, duplicated and isolated points are eliminated to retain only significant data. The normal road plane is estimated using the principal component analysis technique and the remaining points. This plan presents a root mean square less than 0.85 cm. Finally, distances from those points to the normal plane are calculated and clustered to segment the road into distressed and non-distressed areas. The proposed approach presents a similarity rate to the survey measurement passed 95%. It has demonstrated promising results and has the potential for further improvement by optimizing various steps.
An internet of things-based healthcare system performing on a prediction approach based on random forest regression Shaban, Fahad Ahmed; Golshannavaz, Sajjad
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.pp5755-5764

Abstract

To predict physiological indicators, such as heart rate, blood pressure, and body heat sensors, this study develops an internet of things (IoT)-based healthcare approach performing on random forest regression models and mean square error (MSE). Machine learning approaches such as random forest design is trained to predict factors like age, heart rate, and recorded physiological measures using a dataset generated by sensors with Raspberry Pi. The precision and dependability of the models are assessed by contrasting the predictions with the physiological degrees produced by sensors. IoT-enabled models and sensors are useful for a variety of healthcare monitoring tasks, such as early anomaly detection and quick assistance for medical interventions. It is seen that the proposed model could provide appropriate predictions that are in line with common datasets demonstrated by the results. Moreover, there is strong agreement between the sensor readings and the predicted values for the considered parameters showcasing the outperformance of the proposed healthcare system.
Remote sensing in the analysis between forest cover and COVID-19 cases in Colombia Henao-Céspedes, Vladimir; Garcés-Gómez, Yeison Alberto
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.pp732-740

Abstract

This article explores the relationship between forest cover and coronavirus disease 2019 (COVID-19) cases in Colombia using remote sensing techniques and data analysis. The study focuses on the CORINE land cover methodology's five main land cover categories: artificial territory, agricultural territories, forests and semi-natural areas, humid areas, and water surfaces. The research methodology involves several phases of the unified method of analytical solutions for data mining (ASUM-DM). Data on COVID-19 cases and forest cover are collected from the Colombian National Institute of Health and Advanced Land Observation Satellite (ALOS PALSAR), respectively. Land cover data is processed using QGIS software. The results indicate an inverse relationship between forest cover and COVID-19 cases, as evidenced by Pearson's index ρ of -0.439 (p-value <0.012). In addition, a negative correlation is observed between case density and forests and semi-natural areas, one of the land cover categories. The findings of this study suggest that higher forest cover is associated with lower numbers of COVID-19 cases in Colombia. The results could potentially inform government organizations and policymakers in implementing strategies and policies for forest conservation and the inclusion of green areas in densely populated urban areas.
Active balancing system in battery management system for Lithium-ion battery Ness, Stephanie; Boujoudar, Younes; Aljarbouh, Ayman; Elyssaoui, Lahcen; Azeroual, Mohamed; Zahra Bassine, Fatima; Rele, Mayur
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.pp3640-3648

Abstract

The existing battery management systems perform many functions, such as simply monitoring the battery's voltage, current, and temperature for the most basic and compensating energy imbalances between battery cells for the most advanced systems. In this last example, the function balancing helps protect the battery from obtaining a better lifespan. However, these systems with such functions remain complex because they involve techniques specific to power electronics and energy conversion. The number of components, implementation complexity, and cost increased. The work presented in this paper fits directly into this context. The main objective is to provide a solution to the problem of battery management and careful pack cell balancing. The proposed system aims to balance the battery pack cells based on the intermediate state of charge by charging or discharging the imbalanced cell. The implementation of the proposed control strategy was for a battery pack composed of five cells under MATLAB/Simulink.
Embedded machine learning-based road conditions and driving behavior monitoring Mosleh, Bayan; Hamdan, Joud; Sababha, Belal H.; Alqudah, Yazan A.
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.pp2571-2582

Abstract

Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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.

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