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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 5: October 2024" : 111 Documents clear
New droop-based control of parallel voltage source inverters in isolated microgrid Sanni, Timilehin F.; Awelewa, Ayokunle A.; Adoghe, Anthony U.; Balogun, Adeola; Somefun, Tobi
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.pp4856-4868

Abstract

Microgrids, featuring distributed generators like solar energy and hybrid energy storage systems, represent a significant step in addressing challenges related to the greenhouse effect and outdated transmission infrastructures. The operation and control of islanded microgrids, particularly in terms of grid voltage and frequency, rely on the synchronization of multiple parallel inverters connected to the distributed generators. However, to determine the necessary grid parameters for effective control, the presence of circulating currents from unbalanced grid voltages arises as a challenge. This situation necessitates the development of a new approach to achieve phase angle locking for grid synchronization, with the aim of maintaining the voltage within acceptable limits in islanded microgrids. This objective is realized through the creation of a microgrid network model, design of an adaptive filter, utilizing the double second-order generalized integrator–phase-locked loop (DSOGI-PLL), for dynamic voltage transformation. The design is evaluated by simulation using MATLAB/Simulink. The primary goal is to investigate the DSOGI-PLL-based droop control and compare its performance with the conventional synchronous reference frame–phase-locked loop (SRF-PLL) control approach. Notably, the DSOGI-PLL successfully eliminates the ripples in phase angle estimation, consequently enhancing the quality of voltage output in the microgrid.
A framework for 3D radiotherapy dose prediction using the deep learning approach Hien, Lam Thanh; Toan, Ha Manh; Toan, Do Nang
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.pp5524-5533

Abstract

Cancer is known as a dangerous disease to humans with a very high death rate. There are a lot of cancer treatment methods that have been studied and applied in the world. One of the main methods is using radiation beams to kill cancer cells. This method, also known as radiotherapy, requires experts having a high level of skill and experience. Our work focuses on the 3D dose prediction problem in radiotherapy by proposing a framework aiming to create a medical intelligent system for this problem. To do that, we created a convolutional neural network based on ResNet and U-Net to generate the predicted radiation dose. To improve the quality of the training phase, we also applied some data processing techniques based on the characteristics of the 3D computed tomography (CT) data. The experiment used the dataset from patients who were cancer-treated with radiotherapy in the OpenKBP competition. The results achieved good evaluating metrics, the first is by the Dose-score and the second is by the dose-volume histogram (DVH) score. From the training result, we built the medical system supporting 3D dose prediction and visualizing the result as slices in heatmap form.
Analyzing schools admission performance achievement using hierarchical clustering Fahrudin, Tora; Asror, Ibnu; Wibowo, Yanuar Firdaus Arie
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.pp5566-5584

Abstract

In this study, an implementation of hierarchical clustering methods was conducted in schools’ admission data. We aim to demonstrate that the hierarchical clustering method can be used to help analyze the membership changes of each cluster based on its achievement number of new students from different months period observations. This method can be used by decision-makers to make a strategy for each school which has decreasing achievement from the previous period. In this paper, we employ the hierarchical clustering method to cluster admission performance achievement from fifty Telkom Schools. Instead of clustering admission in one period directly, this paper tried to analyze the movement of clustering membership from one period to another. We observed the movement membership of the group from three categories period, such as monthly, quarterly, and semesterly. The experimental results demonstrate that the monthly scenario was the best clustering result. The monthly scenario achieves the best score for all metrics such as the Dunn index, Silhouette score, Davies-Bouldin index, and Calinski-Harabasz compared to the quarter and semester scenario. There are four schools which are consistent in the first cluster and seven schools which are consistent in the second cluster in all scenarios and all periods.
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.
A novel YOLOv8 architecture for human activity recognition of occluded pedestrians Rajakumar, Shaamili; Azad, Ruhan Bevi
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.pp5244-5252

Abstract

Perception is difficult in video surveillance applications because of the presence of dynamic objects and constant environmental changes. This problem worsens when bad weather, including snow, rain, fog, dark nights, and bright daylight, interferes with the quality of perception. The proposed work aims to enhance the accuracy of camera-based perception for human activity detection in video surveillance during adverse weather conditions. To identify primary human activities, including walking on the road during severe weather, transfer learning from many adverse conditions using real-time images or videos has been proposed as an improvement for you look only once v8 (YOLOv8)-based human activity recognition in poor weather conditions. We collected and sorted training rates into frames from videos depicting human walking activity, their combined forms, and other subgroups, such as running and standing, based on their characteristics. The assessment of the detection efficiency of the previously described images and subgroups led to a comparison of the training weights. The use of real-time activity images for training greatly enhanced the detection performance when comparing the proposed test results to the existing YOLO base weights. Furthermore, a notable improvement in human activity efficiency was obtained by utilizing extra images and feature-related combinations of data techniques.
Target loop antenna prototype with magnetic field reduction method Assamoi, Claude Daniel; Ouattara, Yelakan Berenger; Youan, Bi Tra Jean Claude; Gnamele, N'tcho Assoukpou Jean; Doumbia, Vafi
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.pp5274-5284

Abstract

This paper proposes a new large loop antenna with a reduced magnetic field for a near field communication (NFC) target used in a metallic environment. It also defines a fairly clear method for controlling and, more specifically, reducing the magnetic field associated with loop antennas. This antenna consists of a circular winding, inside which we insert a square winding arranged in the shape of a diamond. The particular structure of this antenna shows that it is possible to dissociate the increase in the induced magnetic field, linked to its large size, from the increase in the number of windings. This is made possible by the application of the physical principle of overlapping magnetic fields, which results in partially destructive interference.
A novel received signal strength indicator method for modeling Massive MIMO beamforming via multi-task deep learning Ramadan, Ibrahim El-Metwally; AbdElHalim, Eman; Saleh, Ahmed Ibrahim; Mostafa, Hossam El-Din Salah
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.pp5285-5296

Abstract

To achieve the best performance in terms of accuracy and complexity of massive multiple-input multiple-output (Massive MIMO) in wireless communication systems, hybrid beamforming (HBF) is a promising technique that provides high data rate multiplexing gains and enhances the spectral efficiency (SE) of the system. In this paper, a novel received signal strength indicator (RSSI) method is proposed to design an HBF for Massive MIMO BF via multitasking deep learning (DL) that minimizes the reliance on the channel state information (CSI) feedback. The trade-off between the enhancement SE of the system and the deep neural networks (DNNs) performance is optimized, and the results reveal that the proposed novel DL techniques achieve predicted spectral efficiencies with accuracy of 99.23% and 95.64% for Deep-HBF and Deep-AFP, respectively. The processing times for Deep-HBF and Deep-AFP are 709.2914 sec and 1425.864 sec, respectively. Notably, Deep-AFP exhibits a higher range of computational complexity compared to Deep-HBF. It is worth mentioning that the proposed techniques utilize the same DNN architecture.
Predicting television programs success using machine learning techniques Fayq, Khalid El; Tkatek, Said; Idouglid, Lahcen
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.pp5502-5512

Abstract

In the ever-evolving media landscape, television (TV) remains a coveted platform, compelling industry players to innovate amid intense competition. This study focuses on leveraging machine learning regression models to precisely predict TV program reach. Our objective is to assess the models' efficacy, revealing a standout performer with a mean absolute percent error of just under 8%. Significantly, we identify features exerting a substantial impact on predictions and explore the potential for model enhancement through expanded datasets. This research extends beyond statistical insights, offering actionable implications for TV channel managers. Empowered by these findings, managers can make informed decisions in program planning and scheduling, optimizing viewer engagement. The temporal analysis of evolving trends over time adds a nuanced layer to our study, aligning it with the dynamic nature of the media landscape. As television retains its dynamic force, our insights contribute not only to academic discourse but also provide practical guidance, enhancing the competitive edge of television channels.
Deep HybridNet with hybrid optimization for enhanced medicinal plant identification and classification Renukaradhya, Sapna; Narayanappa, Sheshappa S.
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.pp5626-5640

Abstract

Herbal leaves, known for their efficacy in treating a range of infectious diseases including cancer, asthma, and heart conditions, are still widely used by medical professionals. Traditionally, villagers have identified these plants visually, but given the similarity in appearance among various species, this method is prone to human error. Accurate identification of these plant species is critical for effective treatment. Hence, the development of an intelligent plant classification system is crucial to reduce the risk of misidentification and enhance treatment accuracy. This paper introduces the deep HybridNet with hybrid optimization module (DeepHybrid-OptNet) a novel deep learning framework for medicinal plant identification and classification. Merging convolutional and recurrent neural network architectures, deep HybridNet excels in extracting complex botanical features through channel-wise feature extraction modules in convolutional neural network (CNN) and feedback loop in recurrent neural network (RNN). The incorporation of a DeepHybrid-OptNet module enhances the model's learning efficiency and accuracy. Empirical results on the Mendley and folio dataset demonstrate the framework's superiority over existing methods in accuracy, precision, and recall making it a valuable asset for botany and herbal medicine research.
Novel proposal for a smart electronic taximeter based on microcontroller systems Hernandez, Cesar; Farfán, Ángel; Giral, Diego
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.pp4996-5007

Abstract

Public transport plays a significant role in the economic development of a country, so the state must guarantee its proper functioning, not only in terms of controlling vehicular traffic and generating adequate roads but also in terms of pricing and customer service. This article proposes a smart electronic taximeter that improves customer service quality and provides greater control for the taxi owner. To achieve this, the smart taximeter has a data entry module (keyboard), a location module (global position system), a time module (date and time), a storage module (memory), a display module (light emitting diode array), an auditory module (speech synthesizer), a communication module (Wi-Fi) and a microcontroller that controls the processes of setup, pricing, billing, and accounting. The results have shown a satisfactory response on the part of the client and the entrepreneur since it allows a higher level of inclusion from the auditory output in Spanish and English, as well as to carry out better financial accounting through the storage of information on the place, date and time, start and end, as well as the duration, distance, fare, surcharges, total cost, and number of each taxi service (ride). Finally, the smart electronic taximeter complies with all Colombian resolution No. 88918 relations of the Ministry of Commerce, Industry and Tourism.

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