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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
Using deep learning to diagnose retinal diseases through medical image analysis Azhibekova, Zhanar; Bekbayeva, Roza; Yussupova, Gulbakhar; Kaibassova, Dinara; Ostretsova, Idiya; Muratbekova, Svetlana; Kakabayev, Anuarbek; Sultanova, Zhanylsyn
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.pp6455-6465

Abstract

The scientific article focuses on the application of deep learning through simple U-Net, attention U-Net, residual U-Net, and residual attention U-Net models for diagnosing retinal diseases based on medical image analysis. The work includes a thorough analysis of each model's ability to detect retinal pathologies, taking into account their unique characteristics such as attention mechanisms and residual connections. The obtained experimental results confirm the high accuracy and reliability of the proposed models, emphasizing their potential as effective tools for automated diagnosis of retinal diseases based on medical images. This approach opens up new prospects for improving diagnostic procedures and increasing the efficiency of medical practice. The authors of the article propose an innovative method that can significantly facilitate the process of identifying retinal diseases, which is critical for early diagnosis and timely treatment. The results of the study support the prospect of using these models in clinical practice, highlighting their ability to accurately analyze medical images and improve the quality of eye health care.
Algae content estimation utilizing optical density and image processing method Kamaluddin, Muhammad Wafiq; Gunawan, Agus Indra; Setiawardhana, Setiawardhana; Dewantara, Bima Sena Bayu; Insivitawati, Era; Asmarany, Anja; Pratama, Ariesa Editya
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.pp6248-6257

Abstract

One of the factors that influence shrimp cultivation is the presence of algae. Precise knowing algae content in the pond is essential for effective management. Most research in the field of algae species carried out by researchers were observing Chlorella Sp. more than the other algae species, with a particular emphasis on substance concentrations. This study proposed non-invasive techniques for quantifying algae abundance, utilizing optical density (OD) and image processing (IP) methods. Three different algae species are frequently found in Indonesia i.e., Chlorella Sp., Thalassiosira Sp., and Skeletonema Sp. are used as sample. Those samples are cultured and prepared in a certain volume with a certain quantity. For experimental and observation purposes, those samples are then diluted into water based on percentage value. The experimental results provided RGB values, which were then used to establish polynomial equations. To verify these equations, two approaches were employed: synthetic image analysis and evaluation using additional data. The mean average error (MAE) was found to be 3.467 for IP method and 3.513 for OD method. It shows that IP method give better result compared to OD method in this study. However, it is very possible that the two methods will complement each other.
Optical laser-generated electricity for powering tilt-meter sensor Nelfyenny, Nelfyenny; Bayuwati, Dwi; Suryadi, Suryadi; Husdi, Irwan Rawal; Mulyanto, Imam; Prasetio, Aditya Dwi; Irawan, Dedi; Widiyatmoko, Bambang; Setiono, Andi
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.pp6140-6147

Abstract

This research investigated the feasibility and efficacy of power over fiber (PoF) transmission systems for geotechnical monitoring applications, addressing challenges associated with traditional power transmission methods. Leveraging fiber optic technology, PoF systems offer advantages such as high reliability, minimal signal loss, and immunity to environmental factors. The study presents a detailed design and implementation of a PoF transmission system, integrating a high-power laser source (HPLS) and photovoltaic technology for efficient power transmission over extended distances. Results demonstrate impressive volt-ampere characteristics and conversion efficiencies, with the optimized system configuration achieving a peak power output of 682 mW. Furthermore, the study evaluated the performance of a surface inclinometer sensor powered by the PoF system, showcasing its effectiveness in monitoring soil movements with remarkable stability and consistent power supply. Future research directions include scalability studies, optimization of system efficiency, and field deployments to broaden the applicability of PoF technology in geotechnical monitoring, ultimately advancing disaster mitigation and infrastructure resilience efforts.
Neutrosophic enhanced convolutional neural network for occupancy detection: structured model development and evaluation Mittal, Ranjeeta; Kumar, Suresh; Chugh, Urvashi
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.pp6619-6627

Abstract

In this study, we introduce an advanced convolutional neural network (CNN) model tailored for house occupancy detection, designed to accommodate the inherent uncertainties and contradictory information often encountered in sensor data. By integrating neutrosophic layers into the CNN architecture, we enable the model to effectively handle indeterminacy, vagueness, and inconsistency present in real-world sensor readings. Our approach employs neutrosophic convolutional, max-pooling, and logic layers, providing a comprehensive framework for feature extraction and decision-making. Through a structured methodology encompassing data preprocessing, model initialization, training, evaluation, and optimization, we demonstrate the efficacy of the proposed model in accurately detecting occupancy status within residential environments. This enhanced CNN model offers improved accuracy, robustness, and interpretability, thereby facilitating its integration into smart home systems and building automation applications, contributing to enhanced efficiency, comfort, and energy savings.
Comparative analysis of deep Siamese models for medical reports text similarity Kurniasari, Dian; Usman, Mustofa; Warsono, Warsono; Lumbanraja, Favorisen Rosyking
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.pp6969-6980

Abstract

Even though medical reports have been digitized, they are generally text data and have not been used optimally. Extracting information from these reports is challenging due to their high volume and unstructured nature. Analyzing the extraction of relevant and high-quality information can be achieved by measuring semantic textual similarity (STS). Consequently, the primary aim of this study is to develop and evaluate the performance of four models: Siamese Manhattan convolution neural network (CNN), Siamese Manhattan long short-term memory (LSTM), Siamese Manhattan hybrid CNN-LSTM, and Siamese Manhattan hybrid LSTM-CNN, in determining STS between sentence pairs in medical reports. Performance comparisons were conducted using Cosine Similarity and word mover's distance (WMD) methods. The results indicate that the Siamese Manhattan hybrid LSTM-CNN model outperforms the other models, with a similarity score of 1 for each sentence pair, signifying identical semantic meaning.
A fusion of cross-shaped window attention block and enhanced 3D U-Net for brain tumor segmentation Polaki, Ramya; Rangarajan, Prasanna Kumar; Pallavi, Gundala; Rajasekhar, Elakkiya; Altalbe, Ali
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.pp7103-7115

Abstract

Brain tumor diagnosis and treatment are primarily reliant on medical imaging, necessitating precise segmentation methodologies for practical clinical solutions. Tumor boundaries are difficult to consistently identify, even with breakthroughs in deep learning. To address this challenge, we propose a novel approach that combines an upgraded 3D U-Net architecture for brain tumor segmentation with cross-shaped window attention (CSWA-U-Net). Current segmentation techniques have limitations, particularly in capturing amorphous tumor shapes and fuzzy boundaries. Our strategy aims to overcome these constraints by combining the complementary capabilities of the expanded 3D U-Net, which is efficient at managing volumetric data and maintaining spatial features, with the cross-shaped window attention, which is well-known for capturing long-range relationships and contextual information. We evaluate our method's efficacy using a variety of performance measures, including specificity, sensitivity, and the Dice score. Our results demonstrate increased performance, with Dice scores of 94.7% for the whole tumor, 93.4% for the enhanced tumor region, and 90.5% for the tumor core. Furthermore, our technique has high sensitivity and specificity, highlighting its potential for improving medical imaging analysis.
Optimal control strategies based on extended Kalman filter in mathematical models of COVID-19 Suhika, Dewi; Saragih, Roberd; Handayani, Dewi; Apri, Mochamad
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.pp6300-6312

Abstract

The Omicron variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is an extremely contagious variant that has garnered global attention due to its potential for rapid spread and its impact on the effectiveness of vaccines and non-pharmacological measures. In this paper, we investigate mathematical models involving vaccinated individuals and control functions to analyze how the spread of coronavirus disease 2019 (COVID-19) infection evolves over time. In the process of constructing a mathematical model for COVID-19, there are many parameters whose values are not yet known with certainty. Therefore, the extended Kalman filter method is used as a tool to estimate these parameters in an effort to better understand the dynamics of the spread and evolution of this disease. This method helps align the mathematical model with existing empirical data, allowing us to make more accurate predictions about the course of the COVID-19 pandemic and plan more precise actions to address the situation. Furthermore, an optimal control design is applied to reduce the number of infected individuals by implementing seven strategies involving a combination of health education, vaccination, and isolation controls. The simulation results we conducted indicate that the use of optimal control strategies can lead to a significant decrease in the number of individuals infected with COVID-19.
Braille code classifications tool based on computer vision for visual impaired Sadak, Hany M.; Khalaf, Ashraf A. M.; Hussein, Aziza I.; Salama, Gerges Mansour
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.pp6992-7000

Abstract

Blind and visually impaired people (VIP) face many challenges in writing as they usually use traditional tools such as Slate and Stylus or expensive typewriters as Perkins Brailler, often causing accessibility and affordability issues. This article introduces a novel portable, cost-effective device that helps VIP how to write by utilizing a deep-learning model to detect a Braille cell. Using deep learning instead of electrical circuits can reduce costs and enable a mobile app to act as a virtual teacher for blind users. The app could suggest sentences for the user to write and check their work, providing an independent learning platform. This feature is difficult to implement when using electronic circuits. A portable device generates Braille character cells using light- emitting diode (LED) arrays instead of Braille holes. A smartphone camera captures the image, which is then processed by a deep learning model to detect the Braille and convert it to English text. This article provides a new dataset for custom-Braille character cells. Moreover, applying a transfer learning technique on the mobile network version 2 (MobileNetv2) model offers a basis for the development of a comprehensive mobile application. The accuracy based on the model reached 97%.
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.
Enhancement of energy and spectral efficiency for mm-wave based 5G communication network Gaikwad, Vishakha; Naik, Ashwini
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.pp6380-6388

Abstract

5G network is an enhanced communication network, designed to converge the requirements of quality of service (QoS) parameters, data and capacity, by means of signals with high QoS and high-speed data rate. Several state-of-art technologies are involved to obtain the requirement of energy efficient communication system with increased number of users, devices, higher data rate with low latency. This paper presents a system which demonstrates the energy and spectral efficiency achieved for various number of nodes in a specific area. This study addresses the improvement in energy and spectral efficiency when the proposed algorithm is used. The proposed algorithm is a combination of swarm based artificial bee colony (ABC) algorithm with neural network. Experimental results have been carried out to observe the performance of QoS parameters such as bit error rate (BER), throughput, power consumption and mean square error (MSE). The maximum energy efficiency achieved is 34% and Spectral efficiency is 36%.

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