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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 6: December 2024" : 111 Documents clear
Moderating roles of user’s intention to use LINE official account in healthcare context: body mass index Trakulmaykee, Numtip; Choksuchat, Chidchanok; Jetwanna, Korakot Wichitsa-nguan; Inthanuchit, Kochakorn Sukjan
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.pp6807-6816

Abstract

This study aimed to investigate the extended factors based on the technology acceptance model, and the moderating roles of customer behavioral intention factors to use information technology. This research is a questionnaire-based survey with convenience sampling approach where 386 cases were collected from healthcare customers. For statistical analysis, the study used SmartPLS as a tool for regression analysis and descriptive statistics. The findings revealed the influence of social norm on customer behavioral intention to use information technology in the healthcare context as significant factors at 0.001. In addition, the results indicated the small effect of two moderating variables in the proposed model. First, the problematic body mass index (BMI) can be a moderator on the relationship between social norm and customer behavioral intention to use technology at a significant level of 0.05. Second, the technology experience can moderate the relationship between perceived ease of use and customer behavioral intention to use technology at a significant level of 0.05. The proposed model may guide for future exploration, especially information services in healthcare businesses and developers.
Support vector machine method for classifying severity of Alzheimer's based on hippocampus object using magnetic resonance imaging modalities Supriyanti, Retno; Riyanto, Arif Pujo; Ramadhani, Yogi; Aliim, Muhammad Syaiful; Akbar, Muhammad Irham; Widodo, Haris Budi; Alqaaf, Muhammad
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.pp6322-6331

Abstract

Alzheimer's disease is a degenerative brain condition that causes progressive decline in several aspects. Starting from memory, cognitive or thinking abilities, speaking abilities, and behavior. Currently, Alzheimer's diagnosis uses some methods, such as blood tests, scanning with computerized tomography scan (CT scan), or magnetic resonance imaging (MRI). As a reference for determining the level of severity, doctors usually use clinical dementia rating (CDR). CDR is a numerical scale used to measure the severity of dementia symptoms. The doctor will manually compare the patient's condition with those stated on the CDR. This condition will take quite a long time, and sometimes human error will occur. As technology and science develop, doctors can assist in manually detecting Alzheimer's using classification algorithms. Many methods can be used to classify, including the CDR support vector machine (SVM) method. Unfortunately, this method is usually only used to classify two classes. This technology allows the classification process to be carried out automatically and quickly. On the other hand, when using CDR to classify Alzheimer's severity, there are several scales, not just two classes. So, in this research, we modified the use of SVM to classify three levels of severity, namely scale 0 for normal, scale 1 for mild conditions, and scale 2 for moderate conditions. The experiments we carried out provided an accuracy of 90.9%.
Design and enhancement of microstrip patch antenna with frequency selective surface backing for vehicle-to-vehicle communication Troudi, Ikram; Baccouch, Chokri; Belgacem Chibani, Rhaimi
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.pp6445-6454

Abstract

This research investigates the application of frequency selective surface (FSS) reflectors to enhance vehicle-to-vehicle (V2V) communication performance. A compact antenna measuring 32×24×1.6 mm, derived from an FSS 10×10×1.6 mm unit cell, was studied. Integration of FSS technology with the antenna resulted in significant performance improvements. The gain increased from 3.9 to 5.4 dB at 5.9 GHz, representing a 1.5-fold enhancement. Additionally, the bandwidth extended to 230.94 MHz. These advancements demonstrate the efficacy of FSS technology for antenna gain enhancement in V2V systems. The compact design, coupled with improved performance, makes this approach particularly suitable for vehicular applications where space is limited. This study not only showcases the potential of FSS technology in antenna design but also suggests its broader applicability in enhancing V2V communication systems, potentially contributing to the development of more efficient and safer transportation networks.
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.
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.
Performance analysis of wavelet scattering transform-based feature matrix for power system disturbances classification Mansour, Naema M.; Awaad, Ibrahim A.; Abdelsalam, Abdelazeem A.
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.pp6094-6110

Abstract

Recently, the wavelet scattering transform (WST) was introduced as a powerful feature extraction tool for classification processes. It provides good performance in applications involving audio signals, images, medical data, and quadcopters for structural health diagnosis. It is also employed in several electrical engineering applications, such as the classification of induction motor bearing failures, electrical loads, and industrial robot faults. Despite its development, the performance of the wavelet scattering (WS) network constructed in the MATLAB environment to compute WST coefficients has not been highlighted in the literature so far. In this paper, the properties of the WST feature matrix are examined, and the parameters that have a significant impact on coefficient magnitudes and matrix dimensions are defined. With minimal configuration, a WS network could extract low-variance features from real-valued time series for use in machine learning and deep learning applications. The feature matrix, which contains zero, first, and second-level WST coefficients derived from various power system signal configurations, is constructed to be trained using long short-term memory (LSTM) networks. The simulation results demonstrate the efficacy of the proposed classifier with an accuracy approach of 100%. The MATLAB toolbox has been used to create different signals for the WS and LSTM networks. WST has proven to be a powerful tool for power system disturbance classification.
Wideband trans-impedance amplifier with bandwidth tuning for near infra-red spectroscopy bio-medical applications Balasubramanian, Muthukumaran; Balasubramanian, Ramachandran
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.pp6204-6213

Abstract

A wide band trans-impedance amplifier (WBTIA) with tunable bandwidth for near infra-red spectroscopy (NIRS) bio-medical applications is presented in this research article. The first stage of the proposed WBTIA is implemented by a modified inverter-cascode (InvCas) trans-impedance amplifier (TIA) with series and shunt inductive peaking for the bandwidth extension and a common-source amplifier as a second stage for gain boosting. Bandwidth tuning is achieved by a novel tuning mechanism with a fixed capacitor and tunable metal–oxide–semiconductor (MOS) capacitor with a control voltage. The fixed capacitor provides a coarse-grain bandwidth tuning whereas the tunable MOS capacitors are used for fine-grain bandwidth tuning. The WBTIA is designed in 45 nm technology and it achieved a maximum trans-impedance gain (TIG) of 84.91 dBΩ and 354.81 MHz bandwidth. The proposed WBTIA consumes 41.24 µW power from 1 V supply voltage. The input referred current noise at 100 MHz is 169 fA/sqrt (Hz) and the output noise voltage is 69.8 pV/sqrt (Hz).
Homomorphic technique for group data sharing in cloud computing environment Karemallaiah, Jayalakshmi; Revaiah, Prabha
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.pp6612-6618

Abstract

The main aim of this research work is to make it easier for the same group to share and store anonymous data on the cloud securely and effectively. This research work presents verifiable privacy-aware enhanced homomorphic (VPEH) for multiple participants; moreover, the enhanced homomorphic encryption mechanism provides end-to-end encryption and allows the secure computation of data without revealing any data in the cloud. The proposed algorithm uses homomorphic multiplication to compute the hashes product of challenges blocks that make it more efficient, furthermore an additional security model is incorporated to verify the shared data integrity. The VPEH mechanism is evaluated considering parameters such as tag generation, proof generation, and verification; model efficiency is proved by observing the marginal improvisation over the other existing models by varying the number of blocks and number of challenge blocks.
Faculty Assistant Bot-automation of administrative activities using robotic process automation Prasad, Mamidyala Durga; Nandini, Balusu
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.pp6797-6806

Abstract

In this paper, a process workflow for Bot is created using robotic process automation (RPA), associated with artificial intelligence that is used to stream line the administrative tasks and alleviate stress levels of faculty in handling administrative tasks while teaching in higher education. These activities are must for National Academic Audit Council (NAAC) accreditation and All India Survey on Higher Education (AISHE) surveys, which strive to bring quality in teaching higher education by shaping educational policies. Therefore, ensuring the accuracy of this data is paramount to avoid misleading decisions. The Bot automatically gathers student results from the website and saves them into individual files, eliminating the need for human intervention. It is trained to find the related file of student and update his results of upcoming semesters or backlogs. The Bot efficiently manages folders during file saving to enhance retrieval. Additionally, it maintains pertinent student details such as community, caste, and religion, beneficial for educational policy surveys aiming for improved quality. Moreover, it generates and updates reports post each process execution, ensuring data integrity, and can be trained for statistical analysis to predict student outcomes. The UiPath tool is used in the design and testing of the developed Bot
Implementation of artificial intelligence in the prediction of the elastic characteristics of bio-loaded polypropylene with bamboo fibers Laabid, Zineb; Lakhdar, Abdelghani; Mansouri, Khalifa; Siadat, Ali
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.pp6904-6912

Abstract

Artificial intelligence is the current trend in the world, which has taken the opportunity to advance in all its fields, particularly in scientific research. In materials engineering, the results obtained from classic methods such as experimentation, homogenization methods, or finite element methods have become input and validation elements for intelligent models to obtain more effective results in an optimal time frame. In this article, we discuss the use of artificial neural networks to determine the mechanical properties of biocomposites, which are the subject of much research due to the advantages they represent. The properties of these complex materials depend on various parameters, such as the behavior of the constituent materials, the percentage of the mixture, and the manufacturing process. In this work, our goal is to predict how polypropylene behaves elastically when reinforced with 15% various natural fillers. and we will study the impact of bamboo on polypropylene to test and validate our model. By exploiting the results of the Mori-Tanaka model, we were able to generate our dataset, with which we feed our feedforward backpropagation neural network and demonstrate that our biocomposite gained in terms of stiffness, marked by an increase in Young's modulus to 550.3 MPa, with better performance validation and a very good regression coefficient.

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