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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 6,301 Documents
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Segmentation of optic disc in retinal images for glaucoma diagnosis by saliency level set with enhanced active contour model Sobia Naz; Kabbinale Ananda Radhakrishna Rao
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2801-2811

Abstract

Glaucoma is an ophthalmic disease which is among the chief causes of visual impairment across the globe. The clarity of the optic disc (OD) is crucial for recognizing glaucoma. Since existing methods are unable to successfully integrate multi-view information derived from shape and appearance to precisely explain OD for segmentation, this paper proposes a saliency-based level set with an enhanced active contour method (SL-EACM), a modified locally statistical active contour model, and entropy-based optical disc localization. The significant contributions are that i) the SL-EACM is introduced to address the often noticed problem of intensity inhomogeneity brought on by defects in imaging equipment or fluctuations in lighting; ii) to prevent the integrity of the OD structures from being compromised by pathological alterations and artery blockage, local image probability data is included from a multi-dimensional feature space around the region of interest in the model; and iii) the model incorporates prior shape information into the technique, for enhancing the accuracy in identifying the OD structures from surrounding regions. Public databases such as CHASE_DB, DRIONS-DB, and Drishti-GS are used to evaluate the proposed model. The findings from numerous trials demonstrate that the proposed model outperforms state-of-the-art approaches in terms of qualitative and quantitative outcomes.
Drivers’ drowsiness detection based on an optimized random forest classification and single-channel electroencephalogram Mouad Elmouzoun Elidrissi; Elmaati Essoukaki; Lhoucine Ben Taleb; Azeddine Mouhsen; Mohammed Harmouchi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3398-3406

Abstract

The state of functioning (posture) of a driver at the wheel of a car involves a complex set of psychological, physiological, and physical parameters. This combination induces fatigue, which manifests itself in repeated yawning, stinging eyes, a frozen gaze, a stiff and painful neck, back pain, and other signs. The driver may fight fatigue for a few moments, but it inevitably leads to drowsiness, periods of micro-sleep, and then falling asleep. At the first signs of drowsiness, the risk of an accident becomes immense. In Morocco, drowsiness at the wheel is the cause of 1/3 of fatal accidents on the freeways. Thus, in this paper, a new hybrid data analysis and an efficient machine learning algorithm are designed to detect the drowsiness of drivers who spend most of their time behind the wheel over long distances (older than 35 years). This analysis is based on a single channel of electroencephalogram (EEG) recordings using time, frequency fast Fourier transform (FFT), and power spectral density (PSD) analysis. To distinguish between the two states of alertness and drowsiness, several features were extracted from each domain (time, FFT, and PSD), and subjected to different classifier architectures to conduct a general comparison and achieve the highest detection accuracy (98.5%) and best time consumption (13 milliseconds).
Optimal interdigitated electrode sensor design for biosensors using multi-objective particle-swarm optimization Issa Sabiri; Hamid Bouyghf; Abdelhadi Raihani
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2608-2617

Abstract

Interdigitated electrodes (IDEs) are commonly employed in biological cellular characterization techniques such as electrical cell-substrate impedance sensing (ECIS). Because of its simple production technique and low cost, interdigitated electrode sensor design is critical for practical impedance spectroscopy in the medical and pharmaceutical domains. The equivalent circuit of an IDE was modeled in this paper, it consisted of three primary components: double layer capacitance, Cdl, solution capacitance, CSol, and solution resistance, RSol. One of the challenging optimization challenges is the geometric optimization of the interdigital electrode structure of a sensor. We employ metaheuristic techniques to identify the best answer to problems of this kind. multi-objective optimization of the IDE using multi-objective particle swarm optimization (MOPSO) was achieved to maximize the sensitivity of the electrode and minimize the Cut-off frequency. The optimal geometrical parameters determined during optimization are used to build the electrical equivalent circuit. The amplitude and phase of the impedance versus frequency analysis were calculated using EC-LAB® software, and the corresponding conductivity was determined.
Techniques of deep learning and image processing in plant leaf disease detection: a review Anita S. Kini; Prema K. V. Reddy; Smitha N. Pai
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3029-3040

Abstract

Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated.
Electric vehicles charging station configuration with closed loop control Wisam Mohamed Najem; Omar Sh. Alyozbaky; Shaker M. Khudher
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2428-2439

Abstract

Recently, the demand for electric vehicles (EV) has been on the rise in global markets due to the orientation of national policies to reduce emissions and global warming through the electrification of the transportation sector and the use of clean energy sources. Electric vehicles function on batteries, which must be recharged, either by slow charging at home or by fast charging with a direct current. In the fast-charging process, the batteries can be charged in less than 15 minutes. In this paper, an off-board charger with a three-phase, six-pulse voltage rectifier was designed using the MATLAB/Simulink program. The closed control circuit was simulated, where the simulation results were influenced by changes to the input voltage. When the input voltage was increased or decreased by 5%, this control maintained the value of the current and voltage at the output to be equal to the reference values required to achieve fast charging. The simulation results showed that in the first case where no filter was used, the output voltage and current had a high amount of ripple that exceeded the permissible value. Therefore, a low-pass filter was designed to reduce the ripple factor to a value that was within the permissible limit.
Simulation model of ACO, FLC and PID controller for TCP/AQM wireless networks by using MATLAB/Simulink Buraq Abdulhadi Awad; Manal Kadhim Oudah; Yaser Ali Enaya; Salam Waley Shneen
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2677-2685

Abstract

The current work aims to develop a suitable design for control systems as part of a queue management system using the transmission control protocol/and active queue management (TCP/AQM) protocol to handle the expected congestion in the network. The research also aims to make a comparison between the different control methods, including the traditional proportional integral derivative (PID) and the expert fuzzy logic control (FLC), as well as the optimal ant colony optimization (ACO) that is used according to the performance improvement criteria to reach the best values for parameters the traditional controller (kd, ki, k p), where the addition of the performance indicator time-weighted absolute error (ITAE) was adopted. The use of this method without any other optimization algorithm that can be applied to adjust the parameters of the PID to verify the possibility of improving performance and enhance that with experience and to know the level of improvement for this particular system being the subject of the study. The results showed the superiority of the optimal ACO over both the FLC expert and the conventional PID, as well as the superiority of the FLC expert over the traditional PID.
Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance Chinthakunta Manjunath; Balamurugan Marimuthu; Bikramaditya Ghosh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3549-3560

Abstract

Predicting equities market trends is one of the most challenging tasks for market participants. This study aims to apply machine learning algorithms to aid in accurate Nifty 50 index trend predictions. The paper compares and contrasts four forecasting methods: artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and random forest (RF). In this study, the eight technical indicators are used, and then the deterministic trend layer is used to translate the indications into trend signals. The principal component analysis (PCA) method is then applied to this deterministic trend signal. This study's main influence is using the PCA technique to find the essential components from multiple technical indicators affecting stock prices to reduce data dimensionality and improve model performance. As a result, a PCA-machine learning (ML) hybrid forecasting model was proposed. The experimental findings suggest that the technical factors are signified as trend signals and that the PCA approach combined with ML models outperforms the comparative models in prediction performance. Utilizing the first three principal components (percentage of explained variance=80%), experiments on the Nifty 50 index show that support vector classifer (SVC) with radial basis function (RBF) kernel achieves good accuracy of (0.9968) and F1-score (0.9969), and the RF model achieves an accuracy of (0.9969) and F1-Score (0.9968). In area under the curve (AUC) performance, SVC (RBF and Linear kernels) and RF have AUC scores of 1.
Software calibration for AK8963 magnetometer based on optimal ellipsoidal fitting Aziz El fatimi; Adnane Addaim; Zouhair Guennoun
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2743-2751

Abstract

With the rapid development of mechatronics, systems in package (SiP), in particular the MPU-9250 inertial measurement Unit 9DOF (MPU-6050 6DOF and AK8963 3DOF), are becoming ubiquitous in applications for autonomous navigation purposes. Nevertheless, they suffer from some accuracy problems related to axis misalignment, disturbances, and deviation over time that make them unable to work autonomously for a long time. This paper will present a simple and practical calibration method using a least-squares based ellipsoid fitting method to calibrate and compensate for the error interference of the AK8963 sensor. Towards the end of this paper, a comparison between before and after the calibration is presented to study the software compensation effect and the stability of the magnetic sensor under study.
Optimal protective relaying scheme of distributed generation connected distribution network using particle swarm optimization-gravitational search algorithm technique Arathi Pothakanahalli Bheemasenarao; Shankaralingappa C Byalihal
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2568-2578

Abstract

This paper develops particle swarm optimization integrated with gravitational search algorithm (PSO-GSA) to coordinate the relays in a distribution system with distributed generation (DG) connectivity. This algorithm combines PSO and GSA to improve the performance of the relay protection system. To prevent relay malfunctions following DG penetration, a suitable primary and backup relay is chosen. The PSO-GSA is coded using MATLAB software and tested on an IEEE 4-bus system simulated in Simulink. Results indicate that, when compared to using regular PSO and GSA procedures individually, the PSO-GSA technique reduces the operating time of the relay significantly.
Dysgraphia detection based on convolutional neural networks and child-robot interaction Soukaina Gouraguine; Mustapha Riad; Mohammed Qbadou; Khalifa Mansouri
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2999-3009

Abstract

Dysgraphia is a disorder of expression with the writing of letters, words, and numbers. Dysgraphia is one of the learning disabilities attributed to the educational sector, which has a strong impact on the academic, motor, and emotional aspects of the individual. The purpose of this study is to identify dysgraphia in children by creating an engaging robot-mediated activity, to collect a new dataset of Latin digits written exclusively by children aged 6 to 12 years. An interactive scenario that explains and demonstrates the steps involved in handwriting digits is created using the verbal and non-verbal behaviors of the social humanoid robot Nao. Therefore, we have collected a dataset that contains 11,347 characters written by 174 participants with and without dysgraphia. And through the advent of deep learning technologies and their success in various fields, we have developed an approach based on these methods. The proposed approach was tested on the generated database. We performed a classification with a convolutional neural network (CNN) to identify dysgraphia in children. The results show that the performance of our model is promising, reaching an accuracy of 91%.

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