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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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Wide-band spectrum sensing with convolution neural network using spectral correlation function Rajanna, Anupama; Kulkarni, Srimannarayana; Narasimha Prasad, Sarappadi
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.pp409-417

Abstract

Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB.
An improved rule-based control of battery energy storage for hourly power dispatching of photovoltaic sources Jusoh, Mohd Afifi; Ibrahim, Mohd Zamri; Daud, Muhamad Zalani
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3783-3791

Abstract

This paper presents an improved rule-based control scheme for battery energy storage (BES) system with the goal of minimising the fluctuation output from photovoltaic (PV) sources while ensuring the operational constraints of BES are regulated at the specified ranges for safety purposes. The control scheme is formulated in accordance with the intended operational limitations of the BES, including charge/discharge current limits and state-of-charge (SOC). The simulation studies were carried out using MATLAB/Simulink to evaluate the effectiveness of an improved rule-based control scheme on the 1.2 MW PV system data acquired from a location in Malaysia. Furthermore, a comparative study on the effectiveness of an improved rule-based control scheme compared with the conventional rule-based control scheme has been carried out. The simulation results show that an improved rule-based control scheme can effectively reduce the fluctuations in the output power of the PV sources and dispatch the output to the utility grid on an hourly basis with an efficiency of up to 94.47%. Finally, the comparison results on the effects of the BESS capacity also illustrate that an improved rule-based control scheme is more effective compared to the conventional rule-based control scheme in the previous study.
Machine learning-based lightweight block ciphers for resource-constrained internet of things networks: a review Naik, Mahendra Shridhar; Mallam, Madhavi; Soppinhalli Nataraju, Chaitra
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.pp2896-2907

Abstract

The increasing number of internet of things (IoT) devices, wearable technologies, and embedded systems has experienced a significant increase in recent years. This surge has brought attention to the necessity for cryptographic algorithms that are lightweight and capable of providing security in resource-constrained environments. The primary objective of lightweight block ciphers is to provide encryption capabilities with minimal computational overhead and decreased power consumption. As a result, they are particularly well-suited for use on devices that have limited resources. At the same time, machine learning methodologies have evolved into powerful mechanisms for the purposes of prediction, categorization, and system optimization. This study introduces a challenges and issues involved in integrating machine learning techniques with the development of lightweight block ciphers.
Development of machine learning algorithms in student performance classification based on online learning activities Alias, Muhamad Aqif Hadi; Aziz, Mohd Azri Abdul; Hambali, Najidah; Taib, Mohd Nasir
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.pp7126-7136

Abstract

The field of educational data mining has gained significant traction for its pivotal role in assessing students' academic achievements. However, to ensure the compatibility of algorithms with the selected dataset, it is imperative for a comprehensive analysis of the algorithms to be done. This study delved into the development of machine learning algorithms utilizing students' online learning activities to effectively classify their academic performance. In the data cleaning stage, we employed VarianceThreshold for discarding features that have all zeros. Feature selection and oversampling techniques were integrated into the data preprocessing, using information gain to facilitate efficient feature selection and synthetic minority oversampling technique (SMOTE) to address class imbalance. In the classification phase, three supervised machine learning algorithms: k-nearest neighbors (KNN), multi-layer perceptron (MLP), and logistic regression (LR) were implemented, with 3-fold cross-validation to enhance robustness. Classifiers’ performance underwent refinement through hyperparameter tuning via GridSearchCV. Evaluation metrics, encompassing accuracy, precision, recall, and F1-score, were meticulously measured for each classifier. Notably, the study revealed that both MLP and LR achieved impeccable scores of 100% across all metrics, while KNN exhibited a noticeable performance boost after using hyperparameter tuning.
Plasmonic wave assessment via optomechatronics system for biosensor application Abdullah, Muhammad Rosli; Harun, Noor Hasmiza; Ibrahim, Siti Noorjannah; Abdul Wahab, Azimah; Jamilan, Mohd Azerulazree
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1382-1389

Abstract

Transduction biosensor (mass-based, optical and electrochemical) involves analysis, recognition and amplification in the acquired sample. In this work, the plasmonic-based biosensor was employed without using tags. It is crucial to determine angles of Brewster (Ɵb) and critical (Ɵc) for generating plasmonic resonance (Ɵr). The objective is to verify a cost-effective plasmonic biosensor through Fresnel simulation and experimentation of a developed optomechatronics system. The borosilicate glass, Au and Air layers were simulated with the Winspall 3.02 simulator. The optomechatronics system consists of: 1-optics (650 nm laser, slit, polarizer, photodiode), 2-mechanical (bipolar stepper motors, gears, stages) and 3-electronics (PIC18F4550, liquid crystal display (LCD) and drivers). Later, the software performs angular interrogation by reading the reflected beam from a rotating prism at 0.1125. Experimentation to simulation accuracy indicates that percentage differences for Ɵr and Ɵc are 1% and 0.2%, respectively. In conclusion, excellence verification was successfully achieved between experimentation and simulation. It proved that the low-cost optomechatronics system is capable and reliable to be deployed for the biosensor application.
Empowering E-learning through blockchain: an inclusive and affordable tutoring solution Lgarch, Saadia; Hnida, Meriem; Retbi, Asmaa
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5554-5565

Abstract

This study presents an innovative approach using the Ethereum blockchain to democratize access to tutoring services, advancing educational technology by bridging the affordability gap for learners with limited financial resources. This solution enables low-income learners to access tutoring services without significant expenses by eliminating intermediaries through smart contracts. Learners can directly book tutoring services based on fees and evaluations, ensuring a fair and accessible experience. The findings show that this approach reduces tutoring expenses and improves trust and accountability through transparent transactions and feedback mechanisms. The proposed system demonstrates how blockchain technology can foster a more equitable and efficient educational landscape, offering personalized
Impacts of COVID-19 lockdown period on the Algerian power grid demand Draidi, Abdellah; Assabaa, Mohamed; Bouchahed, Adel; Mehimmedetsi, Boudjemaa
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.pp31-43

Abstract

The coronavirus disease-2019 (COVID-19) spread out at the end of 2019 has sadly caused millions of human losses and hundreds of millions of cases and stressful health situations. As a result, governments forced the worldwide population to stay confined and change their social activities and working behaviors. Under such conditions all economic sectors have been impacted, therefore global electricity consumption pattern has changed consequently. The object of this study is to calculate energy drop for such circumstances to make strategies to face such events in the future. The study we conducted during the period of confinement aims to identify the effects of the Corona epidemic on electricity consumption in Algeria by emphasizing four months (March, April, May, and June) for four years (2018, 2019, 2020, and 2021) by comparing monthly load curves and calculating load deviation for each month.
Efficient implementation of the functional links artificial neural networks with cross-terms for nonlinear active noise control Cong Le, Dinh; Anh Mai, The
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3922-3930

Abstract

This paper proposes an efficient extension of functional links artificial neural networks (EE-FLANN) for the active noise control (ANC) application. The developed EE-FLANN controller can upgrade the model accuracy with the actual system thanks to adding the cross-terms to the trigonometric function. Unlike the method in the generalized FLANN (GFLANN) controller, the EE-FLANN exploits include cross-term symmetry. However, this causes the computational burden to increase remarkably. To reduce this disadvantage, we truncate the cross-terms appropriately based on the simplified strategy. Furthermore, the adaptive algorithm is designed to partially update the filter coefficients appropriately. Specifically, the cross-terms that do not satisfy certain magnitude conditions will be omitted during the update process to reduce costs. Experiments have shown that the proposed EE-FLANN controller can achieve comparable performance to the GFLANN controller but the complexity is reduced by up to 20%.
Smart grid deployment: from a bibliometric analysis to a survey Wetinhoun, Stéphane; Houngue, Pélagie; Roland M. Ahouandjinou, Sèmèvo Arnaud; Degila, Jules
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.pp2436-2448

Abstract

Smart grids are one of the last decades' innovations in electrical energy. They bring relevant advantages compared to the traditional grid and significant interest from the research community. Assessing the field's evolution is essential to propose guidelines for facing new and future smart grid challenges. In addition, knowing the main technologies involved in the deployment of smart grids (SGs) is important to highlight possible shortcomings that can be mitigated by developing new tools. This paper contributes to the research trends mentioned above by focusing on two objectives. First, a bibliometric analysis is presented to give an overview of the current research level about smart grid deployment. Second, a survey of the main technological approaches used for smart grid implementation and their contributions are highlighted. To that effect, we searched the Web of Science (WoS), and the Scopus databases. We obtained 5,663 documents from WoS and 7,215 from Scopus on smart grid implementation or deployment. With the extraction limitation in the Scopus database, 5,872 of the 7,215 documents were extracted using a multi-step process. These two datasets have been analyzed using a bibliometric tool called bibliometrix. The main outputs are presented with some recommendations for future research.
Indirect feedback alignment in deep learning for cognitive agent modeling: enhancing self-confidence analytics in the workplace Yuttachai, Hareebin; Arbaoui, Billel; O-manee, Yusraw
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.pp6699-6710

Abstract

The innovative application of indirect feedback alignment (IFA) in deep learning enhances workplace self-confidence analytics through cognitive agent modeling. IFA addresses the challenge of credit assignment in multi-layer neural networks, offering a more efficient and biologically plausible alternative to traditional backpropagation methods. The paper delves into the integration of IFA in workplace dynamics, focusing on the development of a state-determined system to describe and analyze the dynamics of self-confidence, self-concept, self-esteem, and self-efficacy among employees. Utilizing a combination of endogenous and exogenous factors, the study presents a comprehensive model that captures the complex interplay of these factors in professional settings. The research further conducts experiments to observe and analyze the behavior and pattern formation among real workers in various settings, demonstrating the practical implications of the theoretical model. The findings highlight the potential of IFA in enhancing and accelerating the components of deep learning associated with self-confidence in the workplace, contributing significantly to the fields of neural computation and cognitive psychology. The proposed method was tested in various situations to assess its alignment with the core concepts of workplace self-confidence. Mathematical analysis was employed to explore feasible equilibrium conditions and compatible cases found in the literature.

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