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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 15, No 1: February 2025" : 111 Documents clear
Optimal turning of a 2-DOF proportional-integral-derivative controller based on a chess algorithm for load frequency control Buranaaudsawakul, Techatat; Ardhan, Kittipong; Audomsi, Sitthisak; Sa-ngiamvibool, Worawat; Dulyala, Rattapon
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp146-155

Abstract

Load frequency control is necessary for power system management. The power system must maintain a frequency range to ensure power supply stability. System faults and demand fluctuations may cause frequencies to change quickly. System stability and integrity suffer. We are optimizing the two-degree-of-freedom (2-DOF) proportional-integral-derivative (PID) controllers chess algorithm. This article addresses electrical load frequency regulation. We employ classical control theory and current adjustment. It aims for electrical system efficiency and dependability. It checks for errors using integral absolute error (IAE), integral squared error (ISE), integral of time multiply absolute error (ITAE), and integral time squared error (ITSE). Particle swarm algorithm (PSO) compares performance. The IAE of 0.03364, nearly identical to it, shows that chess trumps other algorithms in many scenarios. The chess algorithm's ISE was 0.00035, like PSO's 0.03363. The ISE was 0.00036, indicating PSO's error-reduction capabilities. For the chess algorithm, PSO is 0.07929, and ITAE is 0.07647. This indicates the PSO responds faster to system breakdowns and load changes. Finally, the chess algorithm's ITSE is 0.00072, below the PSO 0.00076. The chess algorithm is better at managing long-term load frequency.
Q-learning based forecasting early landslide detection in internet of thing wireless sensor network Jeyakumar, Devasahayam Joseph; Shanmathi, Boominathan; Smitha, Parappurathu Bahulayan; Chowdary, Shalini; Panneerselvam, Thamizharasan; Srinath, Rajagopalan; Mariselvam, Muthuraj; Murali, Mohanan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp425-434

Abstract

The issue of climate modification and human actions terminates in a chain of hazardous developments, comprehensive of landslides. The traditional approaches of observing the environmental attributes that is actually obtaining rainfall data from places can be cruel and suppressing supervising necessitated for careful infliction. Thus, landslide forecasting and early notice is a significant application via wireless sensor networks (WSN) to reduce loss of life and property. Because of the heavy preparation of sensors in landslide prostrate regions, clustering is a resourceful method to minimize unnecessary transmission. In this article we introduce Q-learning based forecasting early landslide detection (Q-LFD) in internet of things (IoT) WSN. The Q-LFD mechanism utilizes a dingo optimization algorithm (DOA) to choose the best cluster head (CH). Furthermore, the Q-learning algorithm forecast the landslide by soil water capacity, soil layer, soil temperature, Seismic vibrations, and rainfall. Experimental results illustrate the Q-LFD mechanism raises the landslide detection accuracy. In addition, it minimizes the false positive, false negative ratio.
Refining thyroid function evaluation: a comparative study of preprocessing methods in diffuse reflectance spectroscopy Shalini, Wincent Anto Win; Rajalakshmi, Thulasi; Suryakala, Selvanayagam Vasanthadev
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp303-310

Abstract

Thyroid dysfunction, comprising conditions such as hyperthyroidism and hypothyroidism, represents a substantial global health challenge, necessitating timely and precise diagnosis for effective therapeutic intervention and patient welfare. Conventional diagnostic modalities often involve invasive procedures, that could cause discomfort and inconvenience for individuals. The non-invasive techniques like diffuse reflectance spectroscopy (DRS) can offer a promising alternative. This study underscores the critical role of preprocessing methods in enhancing the accuracy of thyroid hormone functionality through a non-invasive approach. In the proposed study the spectral data acquired from the DRS setup are subjected to different preprocessing techniques to improve the efficacy of the prediction model. Thirty individuals with thyroid dysfunction were included in the study, and preprocessing methods such as baseline correction, multiplicative scatter correction (MSC), and standard normal variate (SNV), were systematically evaluated. The study highlights that SNV preprocessing outperformed other methods with a root mean square error (RMSE) of 0.005 and an R² of 0.99. In contrast, MSC resulted in an RMSE of 0.87 and an R² of 0.86, while baseline correction showed a RMSE of 0.84 and an unusual R² of 1.09, indicating potential issues. SNV proved to be the most effective technique.
HSPICE simulation and analysis of current reused operational transconductance amplifiers for biomedical applications Chary, Udari Gnaneshwara; Kishore, Kakarla Hari
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp196-207

Abstract

The proposed work focuses on the design of a current-reused biomedical amplifier; it is a microwatt-level electrocardiogram (ECG) analog circuit design that addresses low power consumption and noise efficiency. As implantable devices require unobtrusiveness and longevity, the current reuse technique in this circuit effectively enhances power and noise efficiencies. Using 90 nm technology enables efficient circuit implementation, yielding promising simulation results. At 100 Hz, the noise performance reaches 62.095 nV/√Hz, while the power consumption is only 8.3797 µW. These advancements are pivotal for next-generation implantable devices, ensuring reliable operation and reducing frequent battery replacements, improving patient convenience. Moreover, the high noise efficiency ensures that ECG signals are captured with high fidelity, crucial for accurate monitoring and diagnosis. This research addresses the challenges in implantable ECG analog circuit design and sets a benchmark for future developments. The techniques employed can be adapted for other bio signal monitoring devices, broadening the impact on healthcare technology. Ultimately, this advancement contributes to more efficient, reliable, and long-lasting medical devices, enhancing patient monitoring and healthcare on a broader scale.
Artificial neural networks classification of s-band absorption performance in eco-friendly microwave absorbers Ahmad, Azizah; Taib, Mohd Nasir; Abdullah, Hasnain; Ismail, Nurlaila; Yassin, Ahmad Ihsan Mohd; Mohd Kasim, Linda; Mohamad Noor, Norhayati
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1007-1014

Abstract

Microwave absorbers are essential for applications such as radar stealth and electromagnetic compatibility. Nevertheless, traditional materials encounter obstacles related to cost and sustainability, which has led to the exploration of new options such as materials derived from agricultural waste. This study focuses on the classification challenge of evaluating the absorption performance of eco-friendly microwave absorbers in the S-band (2 to 4 GHz) frequency. Three multilayer perceptron (MLP) algorithms, namely levenberg marquardt (LM), resilient backpropagation (RBP) and scale conjugate gradient (SCG) are assessed for classification accuracy. The dataset consists of 135 absorption performance values of microwave absorbers that were taken from experimental measurements using the naval research laboratory (NRL) arch free. The MLP algorithms will be divided into three divisions, which are training, validation and testing, evaluating important criteria such as accuracy, precision, sensitivity and specificity. The performance of three types of algorithms will be compared using two basic inputs: the absorption values and the single slot sizes. The RBP algorithm achieved 100% accuracy, and a lower mean squared error (MSE) of 0.02500 compared to the LM and SCG. This study provides valuable insights for designing better microwave absorbers and highlights the commercial potential of agricultural waste materials in such applications.
Evaluating machine learning models for predictive analytics of liver disease detection using healthcare big data Khaled, Osama Mohareb; Elsherif, Ahmed Zakareia; Salama, Ahmed; Herajy, Mostafa; Elsedimy, Elsayed
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1162-1174

Abstract

Liver diseases rank among the most prevalent health issues globally, causing significant morbidity and mortality. Early detection of liver diseases allows for timely intervention, which can prevent the progression of such diseases to more severe stages such as cirrhosis or liver cancer. To this end, many machine learning models have been previously developed to early predict liver diseases among potential patients. However, each model has its accuracy and performance limitations. In this paper, we present a comprehensive comparison of three different machine learning models that can be employed to enhance the prediction and management of liver diseases. We utilize a big data set of 32,000 records to evaluate the performance of each model. First, we implement a preprocessing technique to rectify missing or corrupt data in liver disease datasets, ensuring data integrity. Afterwards, we compare the performance of three machine models: k-nearest neighbors (KNN), gaussian naive Bayes (Gaussian NB) and random forest (RF). We concluded that the RF algorithm demonstrates superior performance in our evaluation, excelling in both predictive accuracy and the ability to classify patients accurately regarding the presence of liver disease. Our results show that RF outperforms other models based on several performance metrics including accuracy: 97.3%, precision: 97%, recall: 96%, and f1-score: 95%.
Arabic fake news detection using hybrid contextual features Turki, Hussain Mohammed; Daoud, Essam Al; Samara, Ghassan; Alazaidah, Raed; Qasem, Mais Haj; Aljaidi, Mohammad; Abuowaida, Suhaila; Alshdaifat, Nawaf
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp836-845

Abstract

Technology has advanced and social media users have grown dramatically in the last decade. Because social media makes information easily accessible, some people or organizations distribute false news for political or commercial gain. This news may influence elections and attitudes. Even though English fake news is widely detected and limited, Arabic fake news is hard to recognize owing to a lack of study and data collection. Wara Arabic bidirectional encoder representations from transformers (WaraBERT), a hybrid feature extraction approach, combines word level tokenization with two Arabic bidirectional encoder representations from transformers (AraBERT) variants to provide varied features. The study also discusses eliminating stopwords, punctuations, and tanween markings from Arabic data. This study employed two datasets. The first, Arabic fake news dataset (AFND), has 606,912 records. Second dataset Arabic news (AraNews) has 123,219 entries. WaraBERT-V1 obtained 93.83% AFND accuracy using the bidirectional long short-term memory (BiLSTM) model. However, the WaraBERT-V2 technique obtained 81.25%, improving detection accuracy above previous researchers for the AraNews dataset. These findings show that WaraBERT outperforms word list techniques (WLT), term frequency-inverse document frequency (TF-IDF), and AraBERT on both datasets.
Discrete optimization model for multi-product multi-supplier vehicle routing problem with relaxed time window Firdaus, Muliawan; Mawengkang, Herman; Tulus, Tulus; Sawaluddin, Sawaluddin
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp592-603

Abstract

This study examines the complicated logistics optimization issue known as the vehicle routing problem for multi-product and multi-suppliers(VRP-MPMS), which deals with the effective routing of a fleet of vehicles to convey numerous items from multiple suppliers to a set of consumers. In this problem, products from various suppliers need to be delivered to different customers while considering vehicle capacity constraints, time windows, and minimizing transportation costs. We propose a hybrid approach that combines a generalized reduced gradient method to identify feasible regions with a feasible neighborhood search to achieve optimal or near-optimal solutions. The aim of the exact method is to get the region of feasible solution. Then we explore the region using feasible neighborhood search, to get an integer feasible optimal (suboptimal) solution. Computational experiments demonstrate that our model and method effectively reduce transportation costs while satisfying vehicle capacity constraints and relaxed time windows. Our findings provide a viable solution for improving logistics operations in real-world scenarios.
A new enterprise architecture-based approach for smart city value co-creation Houari, Meryeme El; Haloui, M'barek El; Ettaki, Badia
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp767-782

Abstract

In an era marked by vertiginous technological advancements and urban complexities, digital transformation has emerged to enable cities to offer smart services. This transformation optimizes interaction and collaboration between smart city actors, instead of working in isolated silos. There is a real need for a federative approach that can be aligned with urban vision and goals to support smart city implementation. In this context, enterprise architecture (EA) is emerging as a pivotal force reshaping smart city and supporting its development and transformation. However, the successful implementation of smart city depends also on the collaborative effort to co-create value among city's stakeholders. The present study develops a new approach based on enterprise architecture within the smart city ecosystem. Through methodical delineation our approach seeks to enhance value co-creation, improve smarter service design, and support community engagement.
Bitcoin volatility forecasting: a comparative analysis of conventional econometric models with deep learning models Tripathy, Nrusingha; Mishra, Debahuti; Hota, Sarbeswara; Mishra, Sashikala; Das, Gobinda Chandra; Dalai, Sasanka Sekhar; Nayak, Subrat Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp614-623

Abstract

The behavior of the Bitcoin market is dynamic and erratic, impacted by a range of elements including news developments and investor mood. One well-known aspect of bitcoin is its extreme volatility. This study uses both conventional econometric techniques and deep learning algorithms to anticipate the volatility of Bitcoin returns. The research is based on historical Bitcoin price data spanning October 2014 to February 2022, which was obtained using the Yahoo Finance API. In this work, we contrast the efficacy of generalized autoregressive conditional heteroskedasticity (GARCH) and threshold ARCH (TARCH) models with long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and multivariate Bi-LSTM models. Model effectiveness is evaluated by means of root mean squared error (RMSE) and root mean squared percentage error (RMSPE) scores. The multivariate Bi-LSTM model emerges as mostly effective, achieving an RMSE score of 0.0425 and an RMSPE score of 0.1106. This comparative scrutiny contributes to understanding the dynamics of Bitcoin volatility prediction, offering insights that can inform investment strategies and risk management practices in this quickly changing environment of finance.

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