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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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Robust individual pig tracking Jaoukaew, Aggaluck; Suwansantisuk, Watcharapan; Kumhom, Pinit
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.pp279-293

Abstract

The locations of pigs in the group housing enable activity monitoring and improve animal welfare. Vision-based methods for tracking individual pigs are noninvasive but have low tracking accuracy owing to long-term pig occlusion. In this study, we developed a vision-based method that accurately tracked individual pigs in group housing. We prepared and labeled datasets taken from an actual pig farm, trained a faster region-based convolutional neural network to recognize pigs’ bodies and heads, and tracked individual pigs across video frames. To quantify the tracking performance, we compared the proposed method with the global optimization (GO) method with the cost function and the simple online and real-time tracking (SORT) method on four additional test datasets that we prepared, labeled, and made publicly available. The predictive model detects pigs’ bodies accurately, with F1-scores of 0.75 to 1.00, on the four test datasets. The proposed method achieves the largest multi-object tracking accuracy (MOTA) values at 0.75, 0.98, and 1.00 for three test datasets. In the remaining dataset, the proposed method has the second-highest MOTA of 0.73. The proposed tracking method is robust to long-term occlusion, outperforms the competitive baselines in most datasets, and has practical utility in helping to track individual pigs accurately.
Holdout based blending approaches for improved satellite image classification Kumar Musali, Suresh; Janthakal, Rajeshwari; Rajasekhar, Nuvvusetty
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.pp3127-3136

Abstract

An essential component of remote sensing, image analysis, and pattern recognition is image categorization. The classification of land use using remotely sensed data creates a map-like representation as the final form of the investigation. With its ability to effectively categorize satellite images, machine learning (ML) algorithms have gained significant traction in a number of fields, including land-use planning, disaster response, and natural resource management. Ensemble learning is also a widely used technique for enhancing the precision of satellite image categorization, which combines multiple models to get more precise predictions. Holdout is an ensemble technique, where multiple ML algorithms are used for training on the same dataset. The primary goal of this study is to create a holdout model for classifying satellite images. Initially, this study explores the usage of ML algorithms namely support vector machines (SVM), k-nearest neighbor (KNN), decision trees (DT), gradient boosting classifier (GBC), histogram-based GBC (HGBC), random forest classifier (RF), bagging classifier (BC), XGBoost classifier for classifying satellite images. Later, GBC, HGBC, RF, BC, and XGBoost are combined to build a stacking model. The bagging ensemble model outperforms all other methods and reaches an accuracy of 88.90%. Finally, blending models with holdout approach were developed and achieved accuracy of 93.70%, 94.14%, and 93.87% which outperformed all previous algorithms.
Analysis of research on the implementation of Blockchain technologies in regional electoral processes Ainur, Jumagaliyeva; Elmira, Abdykerimova; Asset, Turkmenbayev; Gulzhan, Muratova; Amangul, Talgat; Shekerbek, Ainur
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.pp2854-2867

Abstract

Implementation of Blockchain technologies in online voting system is becoming increasingly popular in modern society and has significantly efficiency in governance. This article explores how Blockchain technologies can boost government operations, making them more transparent and effective. It focuses on an in-depth analysis of current research and methods on Blockchain-based electronic voting systems. The aim of this study is investigated and analysis the potential contributions of Blockchain technology to e-voting by drawing insights from global best practices. According to literature review and case studies of Blockchain implementation in government are conducted to identify existing systems and methods of e-voting, identifying their strengths and weaknesses by analyzing European countries and preparing the ground for future alternatives. Additionally, it examined the role of public education in fostering trust and understanding of Blockchain technology and analyzed the legislative landscape in neighboring jurisdictions to solidify Blockchain’s role in decision-making processes. The results of the study provide a comprehensive perspective, and the findings emphasize the relevance of the study, its contribution to understanding the problems and prospects of introducing Blockchain into electoral processes at the regional level.
Load forecasting using fuzzy logic, artificial neural network, and adaptive neuro-fuzzy inference system approaches: application to South-Western Morocco Stitou, Hicham; Atillah, Mohamed amine; Boudaoud, Abdelghani; Aqil, Mounaim
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.pp7067-7079

Abstract

The demand for energy on a global scale is continuously rising due to the expansion of energy infrastructure and the increasing number of new appliances. To address this growing need, an efficient energy management system (EMS) has become indispensable. By implementing EMS, both residential and commercial buildings can significantly improve their energy efficiency and consumption. One crucial aspect of enabling EMS to operate efficiently is load forecasting. The accuracy of load forecasting depends on numerous factors. A reliable load forecast model should consider the region’s weather forecast, as it plays a crucial role in developing an accurate prediction. This study is about the medium-term load forecasting (MTLF) for the Province of Taroudant, Morocco, using historical monthly load and weather data for five years (2018 to 2022). To forecast consumed energy three methods are used namely artificial neural network (ANN), fuzzy logic (FL) and adaptive neuro-fuzzy inference system (ANFIS). This paper selects absolute percentage error (APE), mean absolute percentage error (MAPE), correlation coefficient (R) and root mean square error (RMSE) to compare and evaluate the prediction accuracy of models. It has been observed through results analysis that the ANFIS model produces very accurate forecasting prediction with MAPE of 4.75% while ANN and FL models give respectively MAPE of 7.36% and 8.42%.
A simple feed orthogonal excitation X-band dual circular polarized microstrip patch array antenna Das, Debprosad; Hossain, Md. Farhad; Hossain, Md. Azad
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.pp1604-1615

Abstract

This work represents a microstrip patch array antenna which is designed and analyzed for the application of circular polarization in X band frequency range. The proposed antenna array has a very simple microstrip line feeding mechanism and each patch is energized orthogonally to acquire circular polarization without the need for any phase shifters. The array antenna has a slot line in the ground to electrically couple the signals from the microstrip feed line to feed each patch. The outcome demonstrates that the antenna is capable of radiating both left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP). The designed work has a return loss of -41.88 dB, that is the antenna is perfectly matched. The outcome also demonstrates the antenna’s strong gain and directivity capabilities, which are 12.87 dBi and 13.30 dBi, respectively. The antenna resonates circularly at a frequency of 10 GHz.
Performance evaluation of single-mode fiber optic-based surface plasmon resonance sensor on material and geometrical parameters Tazi, Imam; Riana, Dedi; Syahadi, Mohamad; Muthmainnah, Muthmainnah; Sasmitaninghidayah, Wiwis; Aprilia, Lia; Tresna, Wildan Panji
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.pp5072-5082

Abstract

Surface plasmon resonance (SPR) sensors are proficient at detecting minute changes in refractive index, making them ideal for biomolecule detection. Traditional prism-based SPR sensors encounter miniaturization challenges, encouraging exploration of alternatives like fiber optic-based SPR (FO-SPR) sensors. This study comprehensively investigates the effects of material and geometrical parameters on the performance of single-mode FO-SPR sensors using Maxwell's equation solver software based on the finite-difference time-domain (FDTD) method. The findings highlight the influence of plasmonic thin film materials and thickness on SPR spectrum profiles and sensitivity. Silver (Ag) demonstrates superior performance compared to copper (Cu) and gold (Au) in transmission type, achieving a sensitivity of up to 2×103 nm/RIU, while the sensitivities of Cu and Au are lower. Probe length and core diameter impact spectrum profiles, specifically resonance depth, without affecting sensitivity. Furthermore, variations in core refractive index influence both spectrum profiles and sensitivity. Probe types significantly affect both spectrum profiles and sensitivity, with the reflection type surpassing the transmission type. These results provide suggestions for optimizing FO-SPR sensors in biotechnological applications.
Multifactorial Heath-Jarrow-Morton model using principal component analysis Garcia Gaona, Robinson Alexander; Zapata Quimbayo, Carlos Andres
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.pp566-573

Abstract

In this study, we propose an implementation of the multifactor Heath-Jarrow-Morton (HJM) interest rate model using an approach that integrates principal component analysis (PCA) and Monte Carlo simulation (MCS) techniques. By integrating PCA and MCS with the multifactor HJM model, we successfully capture the principal factors driving the evolution of short-term interest rates in the US market. Additionally, we provide a framework for deriving spot interest rates through parameter calibration and forward rate estimation. For this, we use daily data from the US yield curve from June 2017 to December 2019. The integration of PCA, MCS with multifactor HJM model in this study represents a robust and precise approach to characterizing interest rate dynamics and compared to previous approaches, this method provided greater accuracy and improved understanding of the factors influencing US Treasury Yield interest rates.
Optimizing drone-assisted victim localization and identification in mass-disaster management: a study on feasible flying patterns and technical specifications Azmi, Intan Nabina; Kassim, Murizah; Mohd Yussoff, Yusnani; Md Tahir, Nooritawati
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.pp4097-4109

Abstract

The prompt emphasizes the importance of identifying victims in a disaster area within 48 hours and highlights the potential benefits of using drones in search and rescue missions. However, the use of drones is limited by factors such as battery life, processing speed, and communication range. To address these limitations, the paper presents a detailed research study on the most effective flying pattern for drones during search and rescue missions. The study utilized energy consumption and coverage area as performance metrics and collected precise images that could be analyzed by the forensic team. The research was conducted using OMNET++ and fieldwork at Pulau Sebang, Melaka, in collaboration with search and rescue agencies in Malaysia. The results suggest that the square flying pattern is the most effective, as it provides the highest coverage area with reasonable energy utilization. Both simulation and fieldwork results showed coverage of 100% and 97.96%, respectively, for this pattern. Additionally, the paper provides technical specifications for rescue teams to use when deploying drones during search and rescue missions.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logic controller Haseeb, Mahmoud; Hassan Ibrahim Mansour, Ali; Othman, El-Said A.
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.pp2400-2412

Abstract

The power generated by photovoltaic (PV) systems is influenced by environmental factors. This variability hampers the control and utilization of solar cells' peak output. In this study, a single-stage grid-connected PV system is designed to enhance power quality. Our approach employs fuzzy logic in the direct power control (DPC) of a three-phase voltage source inverter (VSI), enabling seamless integration of the PV connected to the grid. Additionally, a fuzzy logic-based maximum power point tracking (MPPT) controller is adopted, which outperforms traditional methods like incremental conductance (INC) in enhancing solar cell efficiency and minimizing the response time. Moreover, the inverter's real-time active and reactive power is directly managed to achieve a unity power factor (UPF). The system's performance is assessed through MATLAB/Simulink implementation, showing marked improvement over conventional methods, particularly in steady-state and varying weather conditions. For solar irradiances of 500 and 1,000 W/m2, the results show that the proposed method reduces the total harmonic distortion (THD) of the injected current to the grid by approximately 46% and 38% compared to conventional methods, respectively. Furthermore, we compare the simulation results with IEEE standards to evaluate the system's grid compatibility.
Time and wavelength diversity schemes for transdermal optical wireless links Almajdoubah, Rawan; Hasan, Omar
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.pp6423-6432

Abstract

Transdermal optical wireless communication systems have gained significant research and commercial attention in recent years. While using the skin as a transmission medium, the performance of such a system is influenced by the transdermal channel conditions between the transmitter and receiver. Indeed, for transdermal optical channel links, the performance of a linked communication system can be significantly affected by skin- induced attenuation and pointing errors. Extra attention has been given to diversity techniques to anticipate this. This research examines a conventional transdermal optical wireless system by analyzing the outage probability, an important performance parameter that has not been previously evaluated for such systems using the same method. In an examination of outage probability, both skin-induced attenuation and stochastic spatial jitter, also referred to as pointing error effects, are included. The usefulness of this topic is demonstrated using analytical formulas and results that determine the probability of system outage under various skin channel conditions and varying levels of stochastic pointing faults. Simulation model using Monte- Carlo simulation method was conducted using MATLAB to validate our suggestions. The simulation results showed a good agreement with numerical results, which proved the effectiveness of using wavelength and time diversity schemes to enhance transdermal optical wireless based systems.

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