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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 1: February 2024" : 111 Documents clear
Sentiment review of coastal assessment using neural network and naïve Bayes Somantri, Oman; Purwaningrum, Santi; Maharrani, Ratih Hafsarah
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.pp681-689

Abstract

An assessment of a place will provide an overview for other people whether the place is feasible to be visited or not. Assessment of coastal places will provide a separate assessment for potential visitors in considering visitation. This article proposes a model using the neural network (NN) and naïve Bayes (NB) methods to classify sentiment toward coastal assessments. The proposed NN and NB models are optimized using information gain (IG) and feature weights, namely particle swarm optimization (PSO) and genetic algorithm (GA) which are carried out to increase the level of classification accuracy. Based on the experimental results, the best level of accuracy for the classification of coastal assessments is 87.11% and is named the NB IG+PSO model. The best model obtained is a model that can be used as a decision support for potential beach visitors in deciding to visit the place.
Breast cancer detection using ensemble of convolutional neural networks Nadkarni, Swati; Noronha, Kevin
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.pp1041-1047

Abstract

Early detection leading to timely treatment in the initial stages of cancer may decrease the breast cancer death rate. We propose deep learning techniques along with image processing for the detection of tumors. The availability of online datasets and advances in graphical processing units (GPU) have promoted the application of deep learning models for the detection of breast cancer. In this paper, deep learning models using convolutional neural network (CNN) have been built to automatically classify mammograms into benign and malignant. Issues like overfitting and dataset imbalance are overcome. Experimentation has been done on two publicly available datasets, namely mammographic image analysis society (MIAS) database and digital database for screening mammography (DDSM). Robustness of the models is accomplished by merging the datasets. In our experimentation, MatConvNet has achieved an accuracy of 94.2% on the merged dataset, performing the best amongst all the CNN models used individually. Hungarian optimization algorithm is employed for selection of individual CNN models to form an ensemble. Ensemble of CNN models led to an improved performance, resulting in an accuracy of 95.7%.
Driver-centered pervasive application for heart rate measurement Abdul Razak, Siti Fatimah; Jun Tong, Yong; Yogarayan, Sumendra; Sayed Ismail, Sharifah Noor Masidayu; Chia Sui, Ong
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.pp1176-1184

Abstract

People spend a significant amount of time daily in the driving seat and some health complexity is possible to happen like heart-related problems, and stroke. Driver’s health conditions may also be attributed to fatigue, drowsiness, or stress levels when driving on the road. Drivers’ health is important to make sure that they are vigilant when they are driving on the road. A driver-centered pervasive application is proposed to monitor a driver’s heart rate while driving. The input will be acquired from the interaction between the driver and embedded sensors at the steering wheel, which is tied to a Bluetooth link with an Android smartphone. The driver can view his historical data easily in tabular or graph form with selected filters using the application since the sensor data are transferred to a real-time database for storage and analysis. The application is coupled with the tool to demonstrate an opportunity as an aftermarket service for vehicles that are not equipped with this technology.
Development of intelligent protection and automation control systems using fuzzy logic elements Ansabekova, Gulbakyt; Sarsikeyev, Yermek; Abdimuratov, Zhubanyshbai; Appakov, Nurbol; Kaliyev, Zhanybek; Umurzakova, Anara; Sarbassova, Nurbanu; Zhumatova, Assel
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.pp556-565

Abstract

In this article, the causes of technological disturbances in electrical systems are considered, and several characteristic disadvantages of the protection and automation of elements of electrical systems are highlighted. The tendency to decrease the reliability of relay protection associated with the transition from analog to digital types of protection is substantiated. Based on the studied examples, the use of fuzzy logic in protections, the expediency of using fuzzy logic elements in protection devices, and the automation of electrical systems to identify types of short circuits are justified. This article analyzes the most common damages and presents the results of modeling an electrical system with transformer coupling, where all types of asymmetric short circuits were initiated. The dynamics of changes in the symmetrical components of short-circuit currents of the forward, reverse, and zero sequences are determined. Rules have been created for the identification of asymmetric types of short circuits. An algorithm of protection and automation operation using fuzzy logic elements has been developed. The proposed algorithm of protection and automation will reduce the time to determine the type of damage and trigger protections.
Fire detection using deep learning methods Bayegizova, Aigulim; Abdikerimova, Gulzira; Kaliyeva, Samal; Shaikhanova, Aigul; Shangytbayeva, Gulmira; Sugurova, Laura; Sugur, Zharkynay; Saimanova, Zagira
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.pp547-555

Abstract

Fire detection is an important task in the field of safety and emergency prevention. In recent years, deep learning methods have shown high efficiency in solving various computer vision problems, including detecting objects in images. In this paper, monitoring wildfires was considered, which allows you to quickly respond to them and prevent their spread using deep learning methods. For the experiment, images from the satellite and images from the FireWatch sensor were taken as initial data. In this work, the deep learning algorithms you only look once (YOLO), convolutional neural network (CNN), and fast recurrent neural network (FastRNN) were considered, which makes it possible to determine the accuracy of a natural fire. As a result of the experiments, an automated fire recognition algorithm using YOLOv4 deep learning methods was created. It is expected that the results of the study will show that deep learning methods can be successfully applied to detect fire in images. This may lead to the development of automated monitoring systems capable of quickly and reliably detecting fire situations, which will help improve safety and reduce the risk of fires.
Effectiveness of filtering methods in enhancing pulmonary carcinoma image quality: a comparative analysis Elavarasu, Moulieswaran; Govindaraju, Kalpana
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.pp358-365

Abstract

In recent years, information technology has vastly improved. The quality of the image has been degraded by noise, which defeats the purpose of the noisy images. The major purpose of this paper is to find out which filters provide a better outcome while preprocessing medical images using computer tomography scans. The purpose of this paper is to remove noise from any images, whether they are real-time datasets or online datasets. To enhance an image for preprocessing, we have compared various filters; these filters are already available, but the major purpose is to identify the best filter. We compared the different parameters to find the best and finally found that the modified bilateral filtering provided a better result. The noise has been removed by using a bilateral filter, and the image clarity has not changed when using this filter. We have discussed the advantages and drawbacks of each approach. The effectiveness of these filters is compared using the peak signal-to-noise ratio, structural similarity index, contrast-to-noise ratio, and mean square error. The proposed algorithm is tested on 5 sample lung images. The results show that the modified bilateral filter produces better results.
Predictive maintenance of rotational machinery using deep learning Ali, Mohamed Iyad; Lai, Nai Shyan; Abdulla, Raed
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.pp1112-1121

Abstract

This paper describes an implementation of a deep learning-based predictive maintenance (PdM) system for industrial rotational machinery, built upon the foundation of a long short-term memory (LSTM) autoencoder and regression analysis. The autoencoder identifies anomalous patterns, while the latter, based on the autoencoder’s output, estimates the machine’s remaining useful life (RUL). Unlike prior PdM systems dependent on labelled historical data, the developed system doesn’t require it as it’s based on an unsupervised deep learning model, enhancing its adaptability. The paper also explores a robust condition monitoring system that collects machine operational data, including vibration and current parameters, and transmits them to a database via a Bluetooth low energy (BLE) network. Additionally, the study demonstrates the integration of this PdM system within a web-based framework, promoting its adoption across various industrial settings. Tests confirm the system's ability to accurately identify faults, highlighting its potential to reduce unexpected downtime and enhance machinery reliability.
Self-steering Yagi-Uda antenna positioning system for television Federis Montañez, John Joshua; Alipante Vargas, James; Francisco Palibino, Mary Grace; Esplana Rebedorial, Rustom Jim; Bio Borilla, Louise Deanna
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.pp435-442

Abstract

The aim of this study is to develop a prototype that automatically improves the position of a Yagi-Uda antenna using a microcontroller and to illustrate its radiation pattern through the use of MATLAB®. This study is intended for students and professors in the electronics engineering field. This served as their educational materials for teaching antenna system principles and theories. Developmental and experimental methods were used to achieve the objectives. The materials and components generally used in this study are a Yagi-Uda antenna, stepper motor, Arduino Uno, L293D motor shield, USB TV stick tuner, slotted optocoupler, ADS1115, coax cable splitter, speaker stand, and timing belt. The statistical tool used in this study was a Z-test to find out if the experiment results were significant. In testing the effectiveness of the automatic antenna system, the TV display in every increment of 1.8° was taken. It was the basis for the effectiveness of the study. At 5% α/2 level (1.96), the computed z value is 1.76, which is less than 1.96. Therefore, there is no significant difference between the picture quality of the TV display at every angle and the desired angle with maximum reception of the signal with the integration of MATLAB®.
Enhancing cryptographic protection, authentication, and authorization in cellular networks: a comprehensive research study Moldamurat, Khuralay; Seitkulov, Yerzhan; Atanov, Sabyrzhan; Bakyt, Makhabbat; Yergaliyeva, Banu
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.pp479-487

Abstract

This research article provides an extensive analysis of novel methods of cryptographic protection as well as advancements in authentication and authorization techniques within cellular networks. The aim is to explore recent literature and identify effective authentication and authorization methods, including high-speed data encryption. The significance of this study lies in the growing need for enhanced data security in scientific research. Therefore, the focus is on identifying suitable authentication and authorization schemes, including blockchain-based approaches for distributed mobile cloud computing. The research methodology includes observation, comparison, and abstraction, allowing for a comprehensive examination of advanced encryption schemes and algorithms. Topics covered in this article include multi-factor authentication, continuous authentication, identity-based cryptography for vehicle-to-vehicle (V2V) communication, secure blockchain-based authentication for fog computing, internet of things (IoT) device mutual authentication, authentication for wireless sensor networks based on blockchain, new secure authentication schemes for standard wireless telecommunications networks, and the security aspects of 4G and 5G cellular networks. Additionally, in the paper a differentiated authentication mechanism for heterogeneous 6G networks blockchain-based is discussed. The findings presented in this article hold practical value for organizations involved in scientific research and information security, particularly in encryption and protection of sensitive data.
Ultraviolet-C lamp control system designed to estimate deactivation of the coronavirus disease Rinanda Saputri, Fahmy; Radithya, Linus Gregorius
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.pp199-205

Abstract

To prevent the transmission of the coronavirus disease (COVID-19), one approach involves the application of disinfectants containing specific chemical compounds. Nonetheless, an overabundance of chemicals may yield adverse effects on both humans and the environment. Therefore, alternative methods are needed to prevent the spread of the virus without endangering humans and the environment. One method that minimizes the use of chemicals is ultraviolet-C (UV-C) light. The method used in this study is to make a UV-C lamp control system based on the internet of things (IoT). Then conduct experiments on the spread of UV-C radiation using a system that has already been built. Based on the research that has been done, a disinfectant system has been successfully designed using two Philips 30 W UV-C lamps with Wemos D1 mini microcontroller and Blynk application. The results of data collection show that the highest ultraviolet-C radiation irradiation on the intended object is 0.017 mW/cm2 with a distance between the two is 1.5 m.

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