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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 13, No 2: April 2023" : 111 Documents clear
Efficient and linear static approach for finding the memory leak in C Vishruti Desai; Vivaksha Jariwala
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1932-1942

Abstract

Code analysis has discovered that memory leaks are common in the C programming language. In the literature, there exist various approaches for statically analyzing and detecting memory leaks. The complexity and diversity of memory leaks make it difficult to find an approach that is both effective and simple. In embedded systems, costly resources like memory become limited as the system’s size diminishes. As a result, memory must be handled effectively and efficiently too. To obtain precise analysis, we propose a novel approach that works in a phase-wise manner. Instead of examining all possible paths for finding memory leaks, we use a program slicing to check for a potential memory leak. We introduce a source-sink flow graph (SSFG) based on source-sink properties of memory allocation-deallocation within the C code. To achieve simplicity in analysis, we also reduce the complexity of analysis in linear time. In addition, we utilize a constraint solver to improve the effectiveness of our approach. To evaluate the approach, we perform manual scanning on various test cases: link list applications, Juliet test cases, and common vulnerabilities and exposures found in 2021. The results show the efficiency of the proposed approach by preparing the SSFG with linear complexity.
Evidence of personality traits on phishing attack menace among selected university undergraduates in Nigerian Rume Elizabeth Yoro; Fidelis Obukohwo Aghware; Maureen Ifeanyi Akazue; Ayei Egu Ibor; Arnold Adimabua Ojugo
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1943-1953

Abstract

Access ease, mobility, portability, and improved speed have continued to ease the adoption of computing devices; while, consequently proliferating phishing attacks. These, in turn, have created mixed feelings in increased adoption and nosedived users’ trust level of devices. The study recruited 480-students, who were exposed to socially-engineered attack directives. Attacks were designed to retrieve personal data and entice participants to access compromised links. We sought to determine the risks of cybercrimes among the undergraduates in selected Nigerian universities, observe students’ responses and explore their attitudes before/after each attack. Participants were primed to remain vigilant to all forms of scams as we sought to investigate attacks’ influence on gender, students’ status, and age to perceived safety on susceptibility to phishing. Results show that contrary to public beliefs, age, status, and gender were not among the factors associated with scam susceptibility and vulnerability rates of the participants. However, the study reports decreased user trust levels in the adoption of these new, mobile computing devices.
Design and miniaturization of a microsystem to power biomedical implants using grey wolf optimizer-based cuckoo search algorithm Brahim Ouacha; Hamid Bouyghf; Mohammed Nahid; Said Abenna
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1329-1337

Abstract

One of the greatest techniques, inductive coupling is frequently utilized in the biomedical sector for wireless energy transfer to implants. The aim of this article is to develop and analyze the effect of inductor geometrical characteristics, distance between transmitter (TX) and receiver (RX) and also the operating frequency on the wireless power transfer system, using grey wolf optimizer-based cuckoo search (GWO-CS) algorithm. Power transfer efficiency (PTE), power provided to load, and other critical components must all be improved or maximized and miniaturaze the microsystem proposed. The invention, design, and optimization of coils square spirals in a wireless energy transfer system using a resonant inductive link are the emphasis of this paper. The GWO-CS approach is evaluated to existing methods, demonstrated by simulations and to demonstrate the effectiveness of the suggested strategy.
Path tracking control of differential drive mobile robot based on chaotic-billiards optimization algorithm Reham H. Mohammed; Mohamed E. Aboelmorsy; Basem E. Elnaghi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1449-1462

Abstract

Mobile robots are typically depending only on robot kinematics control. However, when high-speed motions and highly loaded transfer are considered, it is necessary to analyze dynamics of the robot to limit tracking error. The goal of this paper is to present a new algorithm, chaotic-billiards optimizer (C-BO) to optimize internal controller parameters of a differential-drive mobile robot (DDMR)-based dynamic model. The C-BO algorithm is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. In addition, a comparison between the performance of C-BO and ant colony optimization (ACO) to determine the optimum controller coefficient that provides superior performance and convergence of the path tracking. The ISE criterion is selected as a fitness function in a simulation-based optimization strategy. For the point of accuracy, the velocity-based dynamic compensation controller was successfully integrated with the motion controller proposed in this study for the robot's kinematics. Control structure of the model was tested using MATLAB/Simulink. The results demonstrate that the suggested C-BO, with steady state error performance of 0.6 percent compared to ACO's 0.8 percent, is the optimum alternative for parameter optimizing the controller for precise path tracking. Also, it offers advantages of quick response, high tracking precision, and outstanding anti-interference capability.
Supreme court dialogue classification using machine learning models Tomin Joseph; Vijayalakshmi Adiyillam
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2350-2355

Abstract

Legal classification models help lawyers identify the relevant documents required for a study. In this study, the focus is on sentence level classification. To be more precise, the work undertaken focuses on a conversation in the supreme court between the justice and other correspondents. In the study, both the naïve Bayes classifier and logistic regression are used to classify conversations at the sentence level. The performance is measured with the help of the area under the curve score. The study found that the model that was trained on a specific case yielded better results than a model that was trained on a larger number of conversations. Case specificity is found to be more crucial in gaining better results from the classifier.
Reliability performance of distribution network by various probability distribution functions Noorfatin Farhanie Mohd Fauzi; Nur Nabihah Rusyda Roslan; Mohd Ikhwan Muhammad Ridzuan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2316-2325

Abstract

Electricity is one of the essential sources for the daily basis activities. The continuous electricity supplied to the customers is one of the main goals for electricity provider. Reliability is one of the main aspects that is focused more on the planning of the power system especially in distribution network. The performance of reliability in the system is evaluated with three main reliability indices: system average interruption frequency index, system average interruption duration index, and customer average interruption duration index. These indices will only give information about the overall condition of the power system without showing the details about the specific of the consumers such as the amount interruptions experienced by customer in the system. In this paper, probability distribution function (PDF) observing the behavior of the components in the system is used in the reliability analysis. Weibull distribution, also known as Weibull family, is one of the most common PDF used in reliability analysis. As Weibull distribution is also related to several distributions, such as exponential and Rayleigh, these types of distributions are applied in the output reliability analysis to observe the performance of reliability in the system. IEEE 9-bus is used as distribution network and carried out using Monte-Carlo simulation.
Reducing torque ripple of induction motor control via direct torque control Qasim Al Azze; Imad Abdul-Rida Hameed
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1379-1386

Abstract

The induction motor is extremely well known and used as an alternating current (AC) machine. Therefore, torque and speed regulations are very essential for this type of machine. This paper presents direct torque control (DTC) based on induction motors (IM). The mathematical model of IM is reported, and the machine is modeled in a synchronous coordinate farm. Classic DTC is applied to IM with two bandwidths of hysteresis controller for electromagnetic torque and stator flux. The system is simulated and investigated via MATLAB/Simulink and the results carry out a high ripple on the torque. There are numerous of improving torque response, one of them is adding a new loop for speed with proportional, integral, and derivative (PID) controllers. IM model with PID based on DTC is simulated through MATLAB. A contrast performance of IM is presented between traditional DTC and DTC with PID. As result, the new DTC with PID carries out improvement in the speed response as well reduces the ripples of torque.
Development of methods for managing energy consumption and energy efficiency in a common system Nigar R. Aslanova; Esmira J. Abdullayeva; Aleksandr V. Beloglazov
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1306-1313

Abstract

The work aims to analyze and examine renewable energy sources (RES) to develop interconnected energy efficiency and energy consumption management system by integrating the software-defined machine-to-machine (M2M) communication. The article’s objectives include analysis of using RES as alternative raw materials for electricity production, the study of intelligent technologies for integrating RES into monitoring and control systems, research of devices and methods for monitoring energy production and consumption, analysis of sensor application for automation of control systems in the energy sector, a study of data transmission and information processing rates. The study results showed that the data transfer rate was delayed by 6 seconds to process 1,000 MB of information. It has been proven that wind energy can be used most efficiently within a 12-hour daily cycle, in contrast to tidal energy and solar energy. It is shown that due to the cyclical nature of obtaining energy from renewable sources, they do not fully provide energy to a large city, on the basis of which it is necessary to additionally use other energy sources. Three different types of power generation facilities were examined and compared. Wind farms were found to have the highest potential for electricity generation, amounting to 1,600-1,700 kW.
An assessment of stingless beehive climate impact using multivariate recurrent neural networks Noor Hafizah Khairul Anuar; Mohd Amri Md Yunus; Muhammad Ariff Baharudin; Sallehuddin Ibrahim; Shafishuhaza Sahlan; Mahdi Faramarzi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2030-2039

Abstract

A healthy bee colony depends on various elements, including a stable habitat, a sufficient source of food, and favorable weather. This paper aims to assess the stingless beehive climate data and examine the precise short-term forecast model for hive weight output. The dataset was extracted from a single hive, for approximately 36-hours, at every seven seconds time stamp. The result represents the correlation analysis between all variables. The evaluation of root-mean-square error (RMSE), as well as the RMSE performance from various types of topologies, are tested on four different forecasting window sizes. The proposed forecast model considers seven of input vectors such as hive weight, an inside temperature, inside humidity, outside temperature, outside humidity, the dewpoint, and bee count. The various network architecture examined for minimal RMSE are long short-term memory (LSTM) and gated recurrent units (GRU). The LSTM1X50 topology was found to be the best fit while analyzing several forecasting windows sizes for the beehive weight forecast. The results obtained indicate a significant unusual symptom occurring in the stingless bee colonies, which allow beekeepers to make decisions with the main objective of improving the colony’s health and propagation.
Human activity recognition with self-attention Yi-Fei Tan; Soon-Chang Poh; Chee-Pun Ooi; Wooi-Haw Tan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2023-2029

Abstract

In this paper, a self-attention based neural network architecture to address human activity recognition is proposed. The dataset used was collected using smartphone. The contribution of this paper is using a multi-layer multi-head self-attention neural network architecture for human activity recognition and compared to two strong baseline architectures, which are convolutional neural network (CNN) and long-short term network (LSTM). The dropout rate, positional encoding and scaling factor are also been investigated to find the best model. The results show that proposed model achieves a test accuracy of 91.75%, which is a comparable result when compared to both the baseline models.

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