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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 3: June 2024" : 111 Documents clear
A novel slotted antenna design for future Terahertz applications Youssef, Amraoui; Halkhams, Imane; El Alami, Rachid; Ouazzani Jamil, Mohammed; Qjidaa, Hassan
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.pp2708-2716

Abstract

A slotted patch antenna operating at 118 GHz is proposed to address challenges in the terahertz (THz) frequency band for wireless communication systems. The antenna design, utilizing a Rogers RO3003 substrate, which has a dielectric constant of ???????? = 3 and tan ???? = 0.001, strategically incorporates slots to enhance key performance parameters. Copper is employed for the ground and radiating patch, and a microstrip feeding method powers the antenna. High frequency structure simulator (HFSS) software is used for design and simulation, revealing resonance at 0.118 THz with a reflection coefficient of -42.41 dB and an impedance bandwidth of 4.42 GHz (115.84–120.26 GHz). At the operating frequency, the antenna exhibits a gain of 7.36 dB, maximum directivity of 7.38 dB, the voltage standing wave ratio (VSWR) of 1.01, and 99.75% radiation efficiency, all within a compact size of 1.5×1.3×0.1 mm³. The suggested antenna outperforms recent counterparts, making it suitable for applications like security screening and wireless communication systems (5G). Future efforts will target bandwidth expansion, gain enhancement, and further size reduction to enhance overall performance.
Feature selection techniques and classification algorithms for student performance classification: a review Alias, Muhamad Aqif Hadi; Hambali, Najidah; Abdul Aziz, Mohd Azri; Taib, Mohd Nasir; Jailani, Rozita
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.pp3230-3243

Abstract

The process of categorizing students’ performance based on input data, encompassing demographic information and final exam results, is recognized as student performance classification. Educational data mining has gained traction in assessing students’ performance. However, this study entails the need to analyze the diverse attributes of students’ information within an educational institution by using data mining techniques. This study thoroughly examines both previous and current methodologies presented by researchers, addressing two main aspects: data preprocessing and classification algorithms applied in student performance classification. Data preprocessing specifically delves into the exploration of feature selection techniques, encompassing three types of feature selection and search methods. These techniques aim to identify the most significant features, eliminate unnecessary ones, and reduce data dimensionality. In addition, classification algorithms play a crucial role in categorizing or predicting student performance. Models such as k-nearest neighbors (KNN), decision tree (DT), artificial neural networks (ANN), and linear models (LR) were scrutinized based on their performance in prior research. Ultimately, this study highlights the potential for further exploration of feature selection techniques like information gain, Chi-square, and sequential selection, particularly when applied to new datasets such as students’ online learning activities, utilizing a variety of classification algorithms.
k-dStHash tree for indexing big spatio-temporal datasets Hooda, Meenakshi; Gill, Sumeet
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.pp2937-2944

Abstract

Today’s era is witness of tremendous ever growing spatial, temporal and spatiotemporal data. The huge spatio-temporal data immensely pushes the need for design and development of novel methods tailored for indexing spatio-temporal data. In this research paper, we propose the design of a novel spatio-temporal data indexing method, named as k-dStHash. We have proposed the algorithm k-dStHashInsertion for inserting spatio-temporal objects and an algorithm k-dStHashSrchPlaceTime has been used to search for the objects at given location and time. It is able to handle datasets with duplicate keys which has been ignored in many research works. Though the algorithm k-dStHashInsertion takes 1.3-1.5 times longer time to insert data in k-dStHash data structure as it needs to find a specific location to organize data efficiently, but when it comes to search for required records it is even more than 90 times faster when analyzed in comparison to brute force method. It is generalized enough to organize any kind of k-dimensional data and time-based data also including object finding, fleet management, clustering, leader identification, nearest neighbor, human/animal tracking, path finding and many more.
Real-time phishing detection using deep learning methods by extensions Minh Linh, Dam; Hung, Ha Duy; Minh Chau, Han; Sy Vu, Quang; Tran, Thanh-Nam
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.pp3021-3035

Abstract

Phishing is an attack method that relies on a user’s insufficient vigilance and understanding of the internet. For example, an attacker creates an online transaction website and tricks users into logging into the fake website to steal their personal information, such as credit card numbers, email addresses, phone numbers, and physical addresses. This paper proposes implementing an extension to prevent phishing for internet users. In particular, this study develops a smart warning feature for the proposed extension using deep learning models. The proposed extension installed in the web browser protects users by checking for, warning about, and preventing untrusted connections. This study evaluated and compared the performance of machine learning models using a malicious uniform resource locator (URL) dataset containing 651,191 data samples. The results of the investigation confirm that the proposed extension using a convolutional neural network (CNN) achieved a high accuracy of 98.4%.
Heat stroke prediction: a perspective from the internet of things and machine learning approach Ke Yin, Lim; Yogarayan, Sumendra; Abdul Razak, Siti Fatimah; Ali Bukar, Umar; Sayeed, Md. Shohel
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.pp3427-3433

Abstract

With the increasing occurrence of heat-related illnesses due to rising temperatures worldwide, there is a need for effective detection and prediction systems to mitigate the risks. Heat stroke, a life-threatening condition occurs when the body’s temperature exceeds 104 degrees Fahrenheit (40 degrees Celsius). It can happen due to prolonged exposure to temperatures. When the body struggles to cool itself down adequately. The internet of things (IoT) and machine learning (ML) are two advancing technologies that have the potential to revolutionize industries and enhance our lives in numerous ways. Currently, monitoring devices are primarily used to diagnose when individuals suffering from heatstroke are at the location. This paper delves into the exploration of utilizing the IoT and ML algorithms to predict heat strokes. It reviews existing studies in this field, focusing on how IoT has been deployed and the application of machine learning techniques. The research aims to define the integration of IoT devices and ML algorithms that has a great potential to detect and predict heat-related illnesses such as heat stroke at an early stage.
Coffee bean graded based on deep net models Balakrishnan Jayakumari, Bipin Nair; Koovamoola Mambilamthoda, Abrav Nanda; Stephen, Shalwin Ambalamoottil; Venkitesan, Pranav; Raghavendra, Venkatesh
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.pp3084-3093

Abstract

Coffee is a widely consumed beverage, and sorting coffee beans is a critical process that ensures high-quality graded coffee products. Coffee beans were graded into nine grades in robusta types. To automate the grading process, a deep learning-based approach was developed using a large dataset of high-resolution images and data augmentation techniques. In contrast to previous studies focusing on robusta type graded into six coffee bean grads, our research extends this framework by employing robusta type into nine grades with an outperformed accuracy. The proposed work uses four deep learning models, namely residual network 34(Resnet34), inception version 3 (Inception v3), efficient network bayesian optimization (EfficientNet-B0), and visual geometry group-16(VGG-16), where trained and evaluated for coffee bean classification into nine grades. The EfficientNet-B0 model exhibited outperformed accuracy, achieving 100% in distinguishing good and bad coffee beans, even in challenging lighting and background conditions.
Bibliometric analysis highlighting the role of women in addressing climate change challenges and achieving sustainable development goals for greener future Rezk, Hegazy; Sayed, Enas Taha
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.pp2480-2490

Abstract

Fossil fuel consumption increased quickly, contributing to climate change that is evident in unusual flooding and draughts, and global warming. Over the past ten years, women's involvement in society has grown dramatically, and they succeeded in playing a noticeable role in reducing climate change. A bibliometric analysis of data from the last ten years has been carried out to examine the role of women in addressing the climate change. The analysis's findings discussed the relevant to the sustainable development goals (SDGs), particularly SDG 7 and SDG 13. The results considered contributions made by women in the various sectors while taking geographic dispersion into account. The bibliometric analysis delves into topics including women's leadership in environmental groups, their involvement in policymaking, their contributions to sustainable development projects, and the influence of gender diversity on attempts to mitigate climate change. This study's results highlight how women have influenced policies and actions related to climate change, point out areas of research deficiency and recommendations on how to increase role of the women in addressing the climate change and achieving sustainability. To achieve more successful results, this initiative aims to highlight the significance of gender equality and encourage inclusivity in climate change decision-making processes.
Detecting vulnerabilities in website using multiscale approaches: based on case study Chowdhury, Mudassor Ahmed; Rahman, Mushfiqur; Rahman, Sifatnur
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.pp2814-2821

Abstract

In the realm of modern web applications, security stands as an utmost priority. To address this critical concern, we've developed a versatile Python script with the primary goal of proactively identifying vulnerabilities and thwarting transient attacks. Leveraging various libraries, this tool comprehensively covers a broad spectrum of threats, including SQL injection (SQLi), cross-site scripting (XSS), cross-site request forgery (CSRF), sensitive data leakage, security misconfiguration, distributed denial-of-service (DDoS) vulnerabilities, and secure socket layer (SSL) or transport layer security (TLS). This Python-based solution prioritizes adaptability, ensuring seamless integration of future updates to effectively combat evolving threats. Utilizing innovative methods such as SQLi and XSS payload injection, the script assesses the susceptibility of input fields. And addressing CSRF vulnerabilities, the script generates and validates tokens, fortifying defenses against unauthorized actions. Employing pattern analysis, it combats sensitive data exposure and security misconfigurations, adeptly identifying elements like credit card numbers, passwords, and headers. Furthermore, the script enhances overall security by scrutinizing SSL/TLS protocols and monitoring port accessibility. It reinforces DDoS detection by actively monitoring traffic patterns, identifying anomalies, and proactively averting disruptions.
A winding design for improving 3-phase induction motor performance Anthony, Zuriman; Bandri, Sepannur; Erhaneli, Erhaneli; Warmi, Yusreni; Zulkarnaini, Zulkarnaini; Dewi Rachman, Arfita Yuana
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.pp2413-2421

Abstract

One of the most popular electric motors used today is the 3-phase induction motor, which has a sturdy design, is less expensive, and is simple to use. Improvements to the materials used in the rotor or stator of induction motors, raising the number of motor phases, and employing a 3-phase induction motor for 1-phase power are just a few of the ways the motor is now being developed to perform better. These studies are all pricey, though. The goal of this study is to determine how to enhance the motor's performance at a reasonable cost. The suggested remedy was to create a 3-phase induction motor winding with a 1-layer design that resembled a symmetrical 6-phase winding. The primary study topics were the motor's rotor speed, mechanical torque, efficiency, and winding current when it was powered by a three-phase power source. The results of the study show that, although consuming less winding current, the 3-phase induction motor with a new winding design outperforms a traditional 3-phase induction motor in terms of rotor speed, mechanical torque, and efficiency.
An online battery–state of charge estimation method using the varying forgetting factor recursive least square - unscented Kalman filter algorithm on electric vehicles Thi Diep, Nguyen; Trung, Nguyen Kien
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.pp2541-2553

Abstract

Accurate and fast estimation of the state of charge is important for the battery management system of electric vehicles. This paper proposes a method to estimate the state of charge of Lithium-ion batteries by the variable forgetting factor recursive least square (VFFRLS) – unscented Kalman filter (UKF) algorithm in real-time without the off-line battery testing data. Since the state observation requires an accurate model, an equivalent circuit model was constructed. Then, the VFFRLS algorithm is used to identify online the battery model parameters based on voltage and current measurements. An advantage of this algorithm is that it requires less initial information and shorter identification time than offline parameter identification. After the model parameters are well identified, the unscented Kalman filter estimates the state of charge and minimizes noise characteristics and uncertainty in the parameter identification process. The VFFRLS algorithm applied in this paper has shown a good result with the model output error of less than 1%, and the identification achieves real-time response. The state of charge obtained by the UKF algorithm has shown satisfactory estimation results with fast convergence speed and small errors. The UKF filter provides the results with a 1.5% error from the reference and converges after 10 cycles.

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