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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 83 Documents
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Search results for , issue "Vol 15, No 3: June 2025" : 83 Documents clear
Routing mechanism ensuring congestion free communication in wireless sensor networks enabled by internet of things for applications in smart healthcare kiran, Kasi Venkata; Rao, T. Srinivasa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2874-2887

Abstract

Recently, the architecture of internet of things (IoT) has been applied towards gathering physical, biological, and dynamic signs of the patients within consumer-oriented electronic-health or health services. In these healthcare systems, various therapeutic sensors are placed on patients to monitor vital signs. However, the process of collecting data in IoT-enabled wireless sensor networks (WSNs) often faces congestion issues, resulting in packet loss, reduced reliability, and decreased throughput. To tackle this challenge, this proposed paper recommends a distributed congestion control algorithm tailored specifically representing IoT-enabled WSNs used in healthcare contexts. The suggested approach improves congestion by employing a priority-based data routing strategy and introduces the precedence queue- based scheduling method to improve reliability. Then the effectiveness of this congestion control process is analyzed statistically, and its performance is verified across extensive simulations and real-life experiments. This solution shows potential for applications like early warning systems for identifying peculiar heart rates, blood pressure, electromyography (EMG), and electrocardiogram (ECG) in hospital or home care settings, thus advancing the current diagnosis capabilities.
Artificial intelligence for automatic moderation of textual content in online chats and social networks Liaskovska, Solomiia; Bacarra, Rex; Martyn, Yevhen; Baidych, Volodymyr; Alsayaydeh, Jamil
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3396-3409

Abstract

The article explores fundamental techniques for converting text into numerical data for machine learning algorithms. It meticulously examines various methods, including word vector representation via neural networks like Word2Vec, and explains the principles behind linear models such as logistic regression and support vector machines. Convolutional neural networks (CNN) and long short-term memory (LSTM) methods are also discussed, covering their components, mechanisms, and training processes. The research extends to developing and testing software for spam detection, hate speech identification, and recognizing offensive language. Using two datasets—one for labeled text messages and another for Twitter posts—the study analyzes data to address challenges like imbalanced data. A comparative analysis among linear models, deep neural networks, and single-layer models, using pre-trained bidirectional encoder representations from transformers (BERT) network, reveals promising results. The convolutional neural network stands out with a remarkable accuracy of 0.95. The study also adapts neural network architectures for hate speech and offensive language classification.
Analysis of geothermal technology development in the Colombian energy transition to 2050 using system dynamics Carreño, Diego Alberto; Dyner, Isaac; Sanint, Enrique Ángel; Aristizábal, Andrés Julián
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2523-2533

Abstract

This research analyzes the current and future prospects of geothermal energy in Colombia using a system dynamics model. The study focuses on evaluating geothermal potential linked to hydrothermal systems, surface manifestations like geysers, and areas near volcanoes. The model, projecting up to 2050, offers a comprehensive assessment of national geothermal potential, considering technical, economic, regulatory, and social factors that influence its integration into the energy matrix. Key findings highlight the need for adjustments to the existing regulatory framework, which currently lacks sufficient incentives for geothermal project development. Additionally, the study underscores the importance of implementing stronger government policies and incentives to promote this renewable energy source. Proper social and environmental management, with active involvement of local communities, is also identified as crucial for project success. The system dynamics approach effectively models the complex interrelationships between variables shaping the future of geothermal energy in Colombia. The developed model serves as a novel tool for technological foresight in this strategic field, identifying obstacles and opportunities to unlock Colombia's significant geothermal potential and providing a systemic perspective on this critical issue for the national energy transition.
Indoor navigation for mobile robots based on deep reinforcement learning with convolutional neural network Dang, Khoa Nguyen; Thi, Van Tran; Thang, Nguyen Van
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2748-2757

Abstract

The mobile robot is an intelligent device that can achieve many tasks in life. For autonomous, navigation based on the line on the ground is often used because it helps the robot to move along a predefined path, simplifies the path planning, and reduces the computational load. This paper presents a method for navigating the four-wheel mobile robot to track a line based on a deep Q-network as a control algorithm to desire the action of the mobile robot and a camera as a feedback sensor to detect the line. The control algorithm uses a convolution neural network (CNN) to generate the mobile robot action, defined as an agent of deep Q-network. CNN uses images from the camera to define the state of the deep Q network. The simulations are performed based on Gazebo software which includes a 3D environment, mobile robot model, line, and Python programming. The results demonstrate the high-performance tracking of mobile robots with complex line trajectories, achieving errors of less than 100 px, which is compared with the traditional vision method (VNS), the MSE of the proposal method is 0.0264 lower than VNS with 0.0406. Showcases proved convincingly that effectiveness suggested a control approach.
Evaluating the feasibility of a photovoltaic-wind-diesel-battery hybrid microgrid for sustainable off-grid electrification in Dakhla, Morocco Fennane, Sara; Kacimi, Houda; Mabchour, Hamza; ALtalqi, Fatehi; Echchelh, Adil
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2655-2668

Abstract

Hybrid renewable energy systems (HRES) present a promising solution for improving energy reliability and reducing costs in remote, off-grid areas. This study explores the feasibility of implementing an HRES in Dakhla, Morocco, where conventional electrical infrastructure is lacking. By integrating photovoltaic (PV) panels, wind turbines, diesel generators, and battery storage, this study aims to optimize energy resource management while balancing technical performance and economic viability. Using real- world data on energy consumption, climatic conditions, and installation constraints, advanced simulation tools such as HOMER were employed to evaluate both technical and economic parameters. The objective was to minimize the cost of energy (COE) while ensuring reliability, availability, and a high renewable fraction. The results, compared with optimization algorithms like genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA), revealed the PV-wind-diesel-battery configuration as the most cost-effective solution. This configuration resulted in a net present cost (NPC) of $829,380, a COE of $0.160/kWh, and minimal CO2 emissions of 54.9 kg/year. The findings highlight the viability of this hybrid microgrid as a sustainable off-grid electrification solution and emphasize the role of renewable energy in addressing global energy challenges.
Smart chatbot for surveys by convolutional networks speech recognition Jimenez-Moreno, Robinson; Baquero, Javier Eduardo Martínez; Umaña, Luis Alfredo Rodriguez
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3410-3417

Abstract

This paper details the development of an innovative voice chatbot interface specifically designed for evaluating user options using a Likert scale by color. The core of this interface is designing a convolutional neural network architecture, which has been trained with MEL spectrogram inputs from seven possible words for each answer. These spectrograms are crucial in capturing the audio features necessary for effective voice recognition and establishing the interactions that occur between the chatbot and the user, allowing the convolutional network to learn and distinguish between different types of user responses accurately. During the training phase, the convolutional neural network achieved an accuracy rate of 91.4%, indicating its robust performance in processing and interpreting voice commands. The interface was tested in a controlled environment, with a group of ten users and a survey of 5 questions, where it achieved a perfect detection accuracy of 100%. The results demonstrate the system's capacity for natural user interaction by voice and employing a free text to speech (TTS) algorithm for the chatbot voice.
A novel convolutional neural network architecture for Alzheimer’s disease classification using magnetic resonance imaging data Abuowaida, Suhaila; Mustafa, Zaid; Aburomman, Ahmad; Alshdaifat, Nawaf; Iqtait, Musab
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3519-3526

Abstract

Accurate categorization of Alzheimer’s disease is crucial for medical diagnosis and the development of therapeutic strategies. Deep learning models have shown significant potential in this endeavor; however, they often encounter difficulties due to the intricate and varied characteristics of Alzheimer’s disease. To address this difficulty, we suggest a new and innovative architecture for Alzheimer’s disease classification using magnetic resonance data. This design is named Res-BRNet and combines deep residual and boundary-based convolutional neural networks (CNNs). Res-BRNet utilizes a methodical fusion of boundary-focused procedures within adapted spatial and residual blocks. The spatial blocks retrieve information relating to uniformity, diversity, and boundaries of Alzheimer’s disease, although the residual blocks successfully capture texture differences at both local and global levels. We conducted a performance assessment of Res-BRNet. The Res-BRNet surpassed conventional CNN models, with outstanding levels of accuracy (99.22%). The findings indicate that Res-BRNet has promise as a tool for classifying Alzheimer’s disease, with the ability to enhance the precision and effectiveness of clinical diagnosis and treatment planning
Optimized wireless power transfer for moving electric vehicles by real-time modification of frequency and estimation of coupling coefficient Yamaguchi, Kazuya; Terada, Haruto; Okamura, Ryusei; Iida, Kenichi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2706-2712

Abstract

In order to prevent global warming, electric vehicles are increasingly recommended than gasoline-powered vehicles that have been widely used in the past. However, problems peculiar to electric vehicles exists, and their widespread utilize is not progressing in Japan and other developed countries. This study performed wireless power transfer assuming that electric vehicles are stationary on a road at some distance from an AC power supply. Frequency of a power supply has significant influence on efficiency of wireless power transfer, and it is important to adjust this value on any situation. Therefore, an experiment was conducted based on the optimal frequency expression derived in the past to confirm the correctness of the expression, finally it achieved 60% transport efficiency. Moreover, since the expression includes coupling coefficient between transmission and receiving inductors, its value must be estimated accurately. In this study, an experiment was conducted to estimate value of coupling coefficient using current and voltage values measured from outside circuits, and it was compared with a theoretical expression obtained from laws on electromagnetics.
Robust deep learning approach for accurate detection of brain tumor and analysis Pallavi, Lanke; Ramya, Thati; Charan, Singupurapu Sai; Amith, Sirigadha; Kumar, Thodupunuri Akshay
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3226-3237

Abstract

Usually, one of the foremost predominant and intricate therapeutic conditions. As broadly perceived, brain tumors are among the foremost significantly harmful circumstances that can radically abbreviate a person’s life expectancy. Various methods are lacking for observing the assortment of tumor sizes, shapes, and areas. When merged with strategies of profound learning, generative adversarial networks (GANs) are competent of catching the measurements, areas, and structures of tumors. Profound learning frameworks will move forward upon the shortage of datasets. It can moreover progress photographs with determination. Classifying and partitioning brain tumors productively is significant. GANs are used in conjunction with an overarching learning handle. A profound learning design called NeuroNet19, could be an intercross of visual geometry group (VGG19) and inverted pyramid pooling module (IPPM) which is utilized to recognize brain tumors. It is clear that, NeuroNet19 employments the foremost exact technique in comparison to all models (DenseNet121, MobileNet, ResNet50, VGG16). The exactness examination gave a Cohen Kappa coefficient of 99% and a F1-score of 99.2%
Enhancing training performance for small models using data-centric approaches El-Khoribi, Reda A.; Emary, Eid; Hassan, Amr Essam
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2951-2964

Abstract

In this work, we propose a new system to improve the performance of classification models by applying data-centric principles. The system optimizes datasets by removing poor-quality samples and generating high-quality synthetic data. We tested the system on various classification models and datasets, measuring its performance with accuracy, precision, recall, and F1-score. The results showed significant improvements in classification performance, highlighting the effectiveness of this data-centric approach. While the scalability to large-scale datasets is still an open question, it offers great potential for future research. This approach could be valuable in critical areas like healthcare, finance, and autonomous systems, where high-quality data is crucial. Future work could explore advanced data augmentation, adapting the system for different data types like text and time-series, and extending it to semi-supervised and unsupervised learning. Our findings emphasize the importance of data quality in achieving better model performance, often overlooked in favor of model architecture. By advancing data-centric artificial intelligence (AI), this work offers a practical framework for researchers and practitioners to optimize datasets and improve machine learning systems.

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