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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 13, No 4: August 2023" : 111 Documents clear
A new contraction based on ℋ-simulation functions in the frame of extended b-metric spaces and application Tariq Qawasmeh; Raed Hatamleh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4212-4221

Abstract

The conceptions of b-metric spaces and metric spaces play a remarkable role in proving many theorems of uniqueness and existence solution of such equations as integral or differential equations. The conception of extended b-metric spaces is considered as a generalization concept of b-metric spaces and metric spaces and this concept was employed to unify some new fixed point results in the literature. On the other hand, a new concept of simulation functions founded in 2020 in the name of ℋ-simulation functions and employed this class of functions to unify some fixed point results in the literature. The main objective of this manuscript, is to establish a new contraction namely, (γ, ϕ, θ) ℋ-contraction, this contraction based on the concepts of extended b-metric spaces and the class of functions (ℋ-simulation functions) and the class of functions Θ-functions and the class of functions Φ-functions, We utilize this new contraction to unify the uniqueness and existence of fixed point results in the literature. In addition, some illustrative examples and an interesting application were established to show the novelty of our work.
Numerical analysis in Ar-H2 coupled-coil inductively coupled thermal plasma with Si feedstock for stable operation Yulianta Siregar; Yasunori Tanaka
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3695-3705

Abstract

In nanopowder synthesis, the starting powder to be evaporated is infused in a plasma torch through the upper coil and the lower coil in the coupled model of inductively coupled thermal plasma (coupled-coil inductively coupled thermal plasma (ICTP)). Mixing these evaporated materials to form the coupled ICTP significantly influences the thermodynamic and transport properties. It is essential to understand these complex interactions between coupled ICTP and feedstock evaporation. This research investigated the thermal interactions between silicon raw material powder (Si) with ICTP in coupled 99%Ar/1%H2 through the numerical model developed for the synthesis of Si nanopowder. The feed rate of the Si raw material was set at 0.05, 0.1, and 0.5 g/min. This implies that the increased Si feed is too heavy to vaporize all the injected feed.
Medical system based on thermal optical system and neural network Yahia Zakria Abd El Gawad; Mohamed I. Youssef; Tarek Mahmoud Nasser
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3796-3804

Abstract

Military personnel in the training or operational phases always need constant medical examination, but the presence of efficient medical care is difficult to implement in real-time for such cases. A wireless system for thermal tracking of soldiers was proposed, as well as tracking their vital signs in real time. Thermal cameras are used with an optical system designed to increase the accuracy of the thermal images captured as the change in the electro-cardiogram, heart rate, and temperature measurements are measured using a specially designed circuit. The results from both the thermal system and the biometric system are combined and sent to a computer for analysis using a model prepared with neural network technology. The proposed system was tested, and a database was created for 127 males and 110 females during training and rest times. The neural network model achieved a response time of 85 seconds until the release of the final analysis, and the accuracy of the proposed tracking system is 96%. The main contribution of this paper is the design of an integrated portable system for rapid, in-field, real-time military medical diagnostics.
Robust face recognition using convolutional neural networks combined with Krawtchouk moments Yassir El Madmoune; Ilham El Ouariachi; Khalid Zenkouar; Azeddine Zahi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4052-4067

Abstract

Face recognition is a challenging task due to the complexity of pose variations, occlusion and the variety of face expressions performed by distinct subjects. Thus, many features have been proposed, however each feature has its own drawbacks. Therefore, in this paper, we propose a robust model called Krawtchouk moments convolutional neural networks (KMCNN) for face recognition. Our model is divided into two main steps. Firstly, we use 2D discrete orthogonal Krawtchouk moments to represent features. Then, we fed it into convolutional neural networks (CNN) for classification. The main goal of the proposed approach is to improve the classification accuracy of noisy grayscale face images. In fact, Krawtchouk moments are less sensitive to noisy effects. Moreover, they can extract pertinent features from an image using only low orders. To investigate the robustness of the proposed approach, two types of noise (salt and pepper and speckle) are added to three datasets (YaleB extended, our database of faces (ORL), and a subset of labeled faces in the wild (LFW)). Experimental results show that KMCNN is flexible and performs significantly better than using just CNN or when we combine it with other discrete moments such as Tchebichef, Hahn, Racah moments in most densities of noises.
A machine learning model for predicting innovation effort of firms Ruchi Rani; Sumit Kumar; Rutuja Rajendra Patil; Sanjeev Kumar Pippal
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4633-4639

Abstract

Classification and regression tree (CART) data mining models have been used in several scientific fields for building efficient and accurate predictive models. Some of the application areas are prediction of disease, targeted marketing, and fraud detection. In this paper we use CART which widely used machine learning technique for predicting research and development (R&D) intensity or innovation effort of firms using several relevant variables like technical opportunity, knowledge spillover and absorptive capacity. We found that accuracy of CART models is superior to the often-used linear parametric models. The results of this study are considered necessary for both financial analysts and practitioners. In the case of financial analysts, it establishes the power of data-driven prototypes to understand the innovation thinking of employees, whereas in the case of policymakers or business entrepreneurs, who can take advantage of evidence-based tools in the decision-making process.
Personal innovativeness and facilitating conditions in shaping the outlooks toward m-banking adoption among generation Y in Malaysia Foo-Wah Lim; Ahmad Fakhrorazi; Ridho Bramulya Ikhsan; Karina Silitonga; Wei-Kit Loke; Nik Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4101-4111

Abstract

The study investigated the determinants of m-banking adoption among generation Y (Gen-Y) in Malaysia. The study underpinned the technology acceptance model as the main guideline or a blueprint for analyzing the research model. The study applied a survey research design and investigated structural equation modeling (SEM) under the partial least-square SEM (PLS-SEM) technique using SmartPLS 2.0. A total 358 m-banking users in Malaysia were exerted as respondents who were randomly selected and then investigated. The results of the analysis conducted with PLS-SEM reveal the existence of usage convenience, facilitating conditions, and personal innovation in information technology that has been remarkably influenced by the outlooks that have adopted m-banking in the country of Malaysia. In addition, the usage convenience that has been felt and facilitated by the conditions found can affect the appraised practicality of m-banking remarkably. Finally, the usage convenience of m-banking that is felt among Gen-Y in Malaysia is influenced by the remarkable activation of personal conditions and innovations exerted in information technology.
Performance of low-cost solar radiation logger Agus Risdiyanto; Ant. Ardath Kristi; Agus Junaedi; Bambang Susanto; Noviadi A. Rachman; Anwar Muqorobin; Harjono P. Santosa; Ahmad Fudholi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3885-3894

Abstract

In solar power systems, irradiance value data are among the most important parameters. Such data can be used in installing photovoltaic (PV) modules, such as determining the exact location, tilt angle, and required area, for optimal power efficiency. In this study, the comprehensive simulation and implementation of a solar radiation meter with a PV cell and temperature sensor are presented. The irradiance measurement value is based on the power reading generated by the small capacity of the PV cell at a specific load converted into a digital value in the microcontroller using the implicit Newton polynomial interpolation (NPI) equation as a low-cost alternative method. The effect of temperature is included in the conversion to obtain precise measurement results. Firstly, the structure and characteristics of the PV cell are discussed. Secondly, the parameters, measuring method, and conversion of the measurement reading data using the NPI equation are presented to assess the results. Finally, the simulation of the solar radiation meter using the PSIM and implementation of the hardware are conducted to validate the concepts and compare their results. The proposed hardware has an average error of 2.72% in the implementation of the measurement test.
Rhizostoma optimization algorithm and its application in different real-world optimization problems Esraa A. Mahareek; Mehmet Akif Cifci; Habiba A. El-Zohni; Abeer S. Desuky
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4317-4338

Abstract

In last decade, numerous meta-heuristic algorithms have been proposed for dealing the complexity and difficulty of numerical optimization problems in the realworld which is growing continuously recently, but only a few algorithms have caught researchers’ attention. In this study, a new swarm-based meta-heuristic algorithm called Rhizostoma optimization algorithm (ROA) is proposed for solving the optimization problems based on simulating the social movement of Rhizostoma octopus (barrel jellyfish) in the ocean. ROA is intended to mitigate the two optimization problems of trapping in local optima and slow convergence. ROA is proposed with three different movement strategies (simulated annealing (SA), fast simulated annealing (FSA), and Levy walk (LW)) and tested with 23 standard mathematical benchmark functions, two classical engineering problems, and various real-world datasets including three widely used datasets to predict the students’ performance. Comparing the ROA algorithm with the latest meta-heuristic optimization algorithms and a recent published research proves that ROA is a very competitive algorithm with a high ability in optimization performance with respect to local optima avoidance, the speed of convergence and the exploration/exploitation balance rate, as it is effectively applicable for performing optimization tasks.
An efficient ultra-wideband digital transceiver for wireless applications on the field-programmable gate array platform Santhosh Kumar Ramachandragowda; Devaraju Ramakrishna; Rajashree Narendra
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4432-4440

Abstract

The ultra-wideband (UWB) technology is a promising short-range communication technology for most wireless applications. The UWB works at higher frequencies and is affected by interferences with the same frequency standards. This manuscript has designed an efficient and low-cost implementation of IEEE 802.15.4a-based UWB-digital transceiver (DTR). The design module contains UWB transmitter (TX), channel, and UWB-receiver (RX) units. Convolutional encoding and modulation units like burst position modulation and binary phase-shift keying modulation are used to construct the UWB-TX. The synchronization and Viterbi decoder units are used to recover the original data bits and are affected by noise in UWB-RX. The UWB-DTR is synthesized using Xilinx ISE® environment with Verilog hardware description language (HDL) and implemented on Artix-7 field-programmable gate array (FPGA). The UWB-DTR utilizes less than 2% (slices and look-up table/LUTs), operates at 268 MHz, and consumes 91 mW of total power on FPGA. The transceiver achieves a 6.86 Mbps data rate, which meets the IEEE 802.15.4a standard. The UWB-DTR module obtains the bit error rate (BER) of 2×10-4 by transmitting 105 data bits. The UWB-DTR module is compared with similar physical layer (PHY) transceivers with improvements in chip area (slices), power, data rate, and BER.  
Efficient machine learning classifier to detect and monitor COVID-19 cases based on internet of things framework Felcia Bel; Sabeen Selvaraj
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4605-4611

Abstract

In this research work, coronavirus disease (COVID-19) has been considered to help mankind survive the present-day pandemic. This research is helpful to monitor the patients newly infected by the virus, and patients who have already recovered from the disease, and also to study the flow of virus from similar health issues. In this paper, an Internet of things (IoT) framework has been developed for the early detection of suspected cases. This framework is used for collecting and uploading symptoms (data) through sensor devices to the physician, data analytics center, cloud, and isolation/health centers. The symptoms of the first wave, second wave, and omicron are used to identify the suspects. Five machine learning algorithms which are considered to be the best in the existing literature have been used to find the best machine learning classifier in this research work. The proposed framework is used for the rapid detection of COVID-19 cases from real-world COVID-19 symptoms to mitigate the spread in society. This model also monitors the affected patient who has undergone treatment and recovered. It also collects data for analysis to perform further improvements in algorithms based on daily updated information from patients to provide better solutions to mankind.

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