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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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Hybrid machine learning for stock price prediction in the Moroccan banking sector Itri, Bouzgarne; Mohamed, Youssfi; Omar, Bouattane; Latifa, El Madani; Lahcen, Moumoun; Adil, Oualid
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.pp3197-3207

Abstract

Analyzing historical stock market data using machine-learning techniques is crucial for data scientists and researchers to optimize stock price prediction models. This study uses machine learning regression algorithms and feature selection methods to optimize a simulated stock price prediction model using real historical data from Bank of Africa, a Moroccan bank. The approach compares multiple supervised regression algorithms, such as linear regression, extreme gradient boosting, ordinary least squared, random forest regressor, a linear least-squares L2-regularized, epsilon-support vector regression, and linear support vector regression. Each of these algorithms is associated with different feature selection algorithms to improve the performance of the prediction model. The analysis results revealed that hybridizing algorithms between the highest score percentiles, univariate linear regression, and linear support vector regression perform better according to the root mean squared error and R2-Score measures. This approach overcomes the problems associated with high-dimensional data by reducing the number of features and improving prediction accuracy.
Optimal efficiency on nuclear reactor secondary cooling process using machine learning model Hajar, Ibnu; Kassim, Murizah; Minhat, Mohd Sabri; Azmi, Intan Nabina
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.pp6287-6299

Abstract

This review delves into the quest for optimal efficiency in the secondary cooling process of nuclear reactor water plant coolant systems. Modeling secondary cooling nuclear processes is hardly performed. Thus, Neural networks with traditional statistical methodologies are integrated to innovate a hybrid model to revolutionize nuclear reactor cooling systems' performance, reliability, and safety. A total of 63 indexed papers were reviewed in the nuclear field that analyzed critical research gaps, including the need for uncertainty modeling and resilience against external hazards. Insights into sensor technologies, data analytics, and real-time monitoring underscore the importance of continuous optimization and predictive maintenance were reviewed. A descriptive analysis for a month of sampling data was presented for the parameters of temperature for TT003 and TT004 and pressure for PT002 and PT003 of the secondary process. The confidence level of 95.0% is identified for the temperature and pressure parameters. The lowest standard error was recognized at 0.00032 and 0.01691, respectively. The review culminates with a forward-looking perspective, recognizing the pivotal role of hybrid machine learning models in shaping the future of secondary cooling processes for nuclear reactor water coolant plants to improve the efficiency and sustainability of nuclear reactor systems.
Ataxic person prediction using feature optimized based on machine learning model Seetharama, Pavithra Durganivas; Math, Shrishail
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.pp2100-2109

Abstract

Ataxic gait monitoring and assessment of neurological disorders belong to important areas that are supported by digital signal processing methods and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) techniques. This paper uses spatio-temporal data from Kinect sensor to optimize machine learning model to distinguish between ataxic and normal gait. Existing ML-based methodologies fails to establish feature correlation between different gait parameters; thus, exhibit very poor performance. Further, when data is imbalanced in nature the existing ML-based methodologies induces higher false positive. In addressing the research issues this paper introduces an extreme gradient boost (XGBoost)-based classifier and enhanced feature optimization (EFO) by modifying the standard cross validation (SCV) mechanism. Experiment outcome shows the proposed ataxic person identification model achieves very good result in comparison with existing ML-based and DL-based ataxic person identification methodologies.
The contribution of digitized electroencephalogram in the clinical and therapeutic monitoring of substance uses disorders Mengad, Aziz; Ertel, Merouane; Chakkouch, Meryem; Ouaamr, Ahmed; Elomari, Fatima
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.pp5172-5184

Abstract

The approaches used in clinical and therapeutic monitoring in addictology are multiple and generally based on subjective instruments such as interviews or observations. However, the lack of frankness can be an obstacle, as the patients monitored may not be entirely honest in their responses, and improvement in symptoms does not always mean continued abstinence. This article proposes a new objective method for monitoring the clinical and therapeutic evolution of addicted patients based on the study of electro-physiological changes collected by digitized electroencephalogram (EEG). The study is a case-control study of 30 hospitalized addicts who met the diagnostic and statistical manual of mental disorders (DSM-V) criteria for substance use disorders substance use disorders (SUD) and who underwent a standard digitized EEG at the end of their hospitalization. A control group of 30 healthy individuals was also included. This research shows a dominance of rapid-frequency beta 2 and hypovolted alpha 2 rhythms in cases with a clear sensitivity to activating maneuvers occasioned by hyperpnoea (HPN) and intermittent light stimulation (ISL) giving either a significant slowing of electrogenesis, bi-occipital entrainment, or an oculo-clonic response signifying a need for further care. However, the major challenge in understanding the EEG signal is that it is not always specific to SUD and suggests the need to consider the trans-diagnostic framework.
Assessing electromagnetic field exposure levels in multi-active reconfigurable intelligent surface assisted 5G network Ahmed Salem, Mohammed; Lim, Heng Siong; Chua, Ming Yam; Alaghbari, Khaled Abdulaziz; Zarakovitis, Charilaos; Chien, Su Fong
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.pp4110-4119

Abstract

As 5G mobile networks continue to proliferate in dense urban environments, it becomes increasingly important to understand and mitigate excessive electromagnetic field (EMF) exposure. This study investigates how the downlink EMF exposure levels of 5G millimeter wave (mm-wave) mobile networks are influenced by the integration of multi-active reconfigurable intelligent surfaces (RISs), using a ray-tracing approach. Our research employs a comprehensive two-step methodology: Firstly, we introduce a new RIS-assisted 5G mm-wave network planning technique. This technique leverages a machine learning (ML) approach for the classification of multi-RIS clusters. The primary goal is to optimize coverage while minimizing the number of required RIS deployments. This is achieved by strategically placing RISs based on the ML classification, ultimately aiming to enhance network efficiency. Secondly, we conducted a thorough comparative analysis, evaluating the impact of both passive and active RISs on EMF exposure level throughout a dense urban environment. Passive RIS and active RIS differ in their adaptability to changing network conditions. The result shows that the influence of multi-active RISs on EMF exposure is significant (about 7.5 times higher) compared to passive RISs.
Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques Lestari, Sri; Yulmaini, Yulmaini; Aswin, Aswin; Ma'ruf, Singgih Yulizar; Sulyono, Sulyono; Fikri, Ruki Rizal Nul
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.pp3220-3229

Abstract

Recommendation systems are often implemented in e-commerce and micro, small, and medium enterprises (MSMEs) marketplaces to improve consumer services by providing product recommendations according to their interests. However, it still faces problems, namely sparsity and cold start, thus affecting the quality of recommendations. This research proposes clustering and imputation techniques to overcome this problem. The clustering technique used is k-means, while the missing value imputation method uses average values. The imputation results are then implemented in the k-nearest neighbor (KNN) and naïve Bayes algorithms and evaluated based on performance accuracy. Experimental results show an increase in accuracy of 16.48% in the KNN algorithm from 83.52% to 100%. Meanwhile, the naïve Bayes algorithm increased accuracy by 35.30% from 64.70% to 100%.
Preliminary diagnosis of respiratory diseases: an innovative approach using a web expert system Andrade-Arenas, Laberiano; Molina-Velarde, Pedro; Yactayo-Arias, Cesar
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.pp6600-6611

Abstract

This study addressed the challenge of accurate and timely diagnosis of respiratory diseases such as influenza, asthma, and pneumonia by developing and evaluating a web-based expert system. The objective was to develop and assess both the usability and diagnostic efficiency of a web- based expert system adaptable to mobile devices. A combined methodological approach was used, using the rapid application development (RAD) model to build the system and the user usability system (SUS) to evaluate the usability with the participation of 15 users and 21 simulated cases with a confusion matrix to determine the precision, accuracy, sensitivity, and specificity of the system in diagnosing respiratory diseases. The results showed that the expert system has a considerable capacity to identify and differentiate these diseases, with a precision of 86%, an accuracy of 76%, a sensitivity of 80%, and a specificity of 67%. Furthermore, the usability evaluation using the SUS method yielded an average of 82, indicating a positive perception and good usability by the users. In conclusion, although the results suggest a promising potential to improve the diagnostic process in clinical and community settings, the need for future studies to validate its performance in real clinical settings is recognized.
Machine learning-based electricity theft detection using support vector machines Abro, Safdar Ali; Hua, Lyu Guang; Laghari, Javed Ahmed; Bhayo, Muhammad Akram; Memon, Abdul Aziz
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.pp1240-1250

Abstract

Electricity theft is a serious issue that many nations face, especially in developing areas where non-technical losses can make up a significant percentage of the overall losses sustained by utilities. Electricity theft detection (ETD) is a very challenging task because it frequently introduces irregularities in customer electricity consumption patterns. In recent times, machine learning (ML) techniques have been investigated as a potential solution for ETD. In this research, author propose electricity theft detection based on four kernel functions of support vector machines (SVM). The proposed method analyzes the electricity consumption patterns and then predicts the category of the user. The kernel functions utilized includes polynomial, sigmoid, radial basis function (RBF) and linear kernel function. For experimentation and model training, a dataset of Pakistani utility company is used, which contains the electricity consumption information. The results highlight SVM method works well for accurate ETD. The detection accuracy of the various kernel functions of SVM is 83%, 79%, 80%, and 76% for RBF, polynomial, sigmoid, and linear kernel functions, respectively, demonstrating the effectiveness of the proposed SVM-based method for theft detection. By leveraging these ML-based methods, utility companies can strengthen their ability to detect and prevent electricity theft, leading to improved revenue management and dependability of services.
Fuzzy-proportional-integral-derivative-based controller for stable control of unmanned aerial vehicles with external payloads Tiep, Do Khac; Tien, Nguyen Van
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.pp5094-5106

Abstract

In the paper, a proportional derivative (PD) controller and a fuzzy system tuning gains from proportional integral derivative controller are applied to stabilize an unmanned aerial vehicle (UAV), to control the attitude. Inputs of fuzzy logical controller consist of the speed required for the distance between the current position of quadcopter and the defined reference point and differences between orientation angles and variance in differences. Outputs of fuzzy logical controller consist of the proportional integral derivative coefficients which make pitch, roll, yaw and height values. The fuzzy-PD control algorithm is real-time applied to the quadcopter in MATLAB/Simulink environment. Based on data from experimental studies, although both classical proportional integral derivative controller and fuzzy-PD controller have accomplished to track a defined trajectory with the quadcopter.
An intelligent auto-response short message service categorization model using semantic index Padmaja, Budi; Madhu Bala, Myneni; Rao Patro, Epili Krishna; Chaya Srikruthi, Adiraju; Avinash, Vytla; Sudheshna, Chenumalla
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.pp922-933

Abstract

Short message service (SMS) is one of the quickest and easiest ways used for communication, used by businesses, government organizations, and banks to send short messages to large groups of people. Categorization of SMS under different message types in their inboxes will provide a concise view for receivers. Former studies on the said problem are at the binary level as ham or spam which triggered the masking of specific messages that were useful to the end user but were treated as spam. Further, it is extended with multi labels such as ham, spam, and others which is not sufficient to meet all the necessities of end users. Hence, a multi-class SMS categorization is needed based on the semantics (information) embedded in it. This paper introduces an intelligent auto-response model using a semantic index for categorizing SMS messages into 5 categories: ham, spam, info, transactions, and one time password’s, using the multi-layer perceptron (MLP) algorithm. In this approach, each SMS is classified into one of the predefined categories. This experiment was conducted on the “multi-class SMS dataset” with 7,398 messages, which are differentiated into 5 classes. The accuracy obtained from the experiment was 97%.

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