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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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Peer-to-peer media streaming with HTML5 Ali Tariq Kalil Al-Khayyat; Sanabil A. Mahmood
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2356-2362

Abstract

The knowledge of web real-time communication (WebRTC) and how its customers and server operations are defined in this study. However, the world wide web consortium (W3C) and the internet engineering task force (IETF) have not yet approved an absolute signaling protocol or a complete application programming interface (API) protocol to implement WebRTC and control communication planning. WebRTC requires some type of signaling mechanism. With Chrome, Firefox, and Opera, the primary objective is to create and implement a WebRTC video call across two clients (peers) in the real world while employing local area network (LAN) and wide area network (WAN). This study also demonstrated the design of the server (as an intermediary) and graphical user interface (GUI). Additionally, a signaling method for the peer-to-peer browser connection on the Node.js platform has been developed and successfully put into use. This paper will provide an understanding of web development and an ability to understand WebRTC technology. It will also discuss how to create WebRTC signaling mechanisms and build video conferencing, as well as how to increase design quality using quality of experience (QoE) techniques.
End-to-end deep auto-encoder for segmenting a moving object with limited training data Abdeldjalil Kebir; Mahmoud Taibi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6045-6057

Abstract

Deep learning-based approaches have been widely used in various applications, including segmentation and classification. However, a large amount of data is required to train such techniques. Indeed, in the surveillance video domain, there are few accessible data due to acquisition and experiment complexity. In this paper, we propose an end-to-end deep auto-encoder system for object segmenting from surveillance videos. Our main purpose is to enhance the process of distinguishing the foreground object when only limited data are available. To this end, we propose two approaches based on transfer learning and multi-depth auto-encoders to avoid over-fitting by combining classical data augmentation and principal component analysis (PCA) techniques to improve the quality of training data. Our approach achieves good results outperforming other popular models, which used the same principle of training with limited data. In addition, a detailed explanation of these techniques and some recommendations are provided. Our methodology constitutes a useful strategy for increasing samples in the deep learning domain and can be applied to improve segmentation accuracy. We believe that our strategy has a considerable interest in various applications such as medical and biological fields, especially in the early stages of experiments where there are few samples.
Digital watermarking by utilizing the properties of self-organization map based on least significant bit and most significant bit Khalid Kadhim Jabbar; Munthir Bahir Tuieb; Salam A. Thajeel
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6545-6558

Abstract

Information security is one of the most important branches concerned with maintaining the confidentiality and reliability of data and the medium for which it is transmitted. Digital watermarking is one of the common techniques in this field and it is developing greatly and rapidly due to the great importance it represents in the field of reliability and security. Most modern watermarking systems, however, use the self-organization map (SOM), which is safer than other algorithms because an unauthorized user cannot see the result of the SOM's training. Our method presents a semi-fragile watermark under spatial domain using least significant bit (LSB) and by relying on most significant bit (MSB) when the taken values equal to (2 or 4 bits) depending on the characteristics of SOM through developing the so-called best matching unit (BMU) which working to determine the best location for concealment. As a result, it shows us the ability of the proposed method to maintain the quality of the host with the ability to retrieve data, whether it is a binary image or a secret message.
New methodology to detect the effects of emotions on different biometrics in real time Yahia Zakria Abd Elgawad; Mohamed I. Youssef; Tarek Mahmoud Nasser
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1358-1366

Abstract

Recently, some problems have appeared among medical workers during the diagnosis of some diseases due to human errors or the lack of sufficient information for the diagnosis. In medical diagnosis, doctors always resort to separating human emotions and their impact on vital parameters. In this paper, a methodology is presented to measure vital parameters more accurately while studying the effect of different human emotions on vital signs. Two designs were implemented based on the microcontroller and National Instruments (NI) myRIO. Measurements of four different vital parameters are measured and recorded in real time. At the same time, the effects of different emotions on those vital parameters are recorded and stored for use in analysis and early diagnosis. The results proved that the proposed methodology can contribute to the prediction and diagnosis of the initial symptoms of some diseases such as the seventh nerve and Parkinson’s disease. The two proposed designs are compared with the reference device (beurer) results. The design using NI myRIO achieved more accurate results and a response time of 1.4 seconds for real-time measurements compared to its counterpart based on microcontrollers, which qualifies it to work in intensive care units.
Electric and magnetic field calculation software in transmission lines Luis Imbachi Guerrero; Fredy Jiménez Rubio; Mario Rodríguez Barrera; Diego Giral-Ramírez
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5697-5706

Abstract

There is an interest in the biological effects of exposure to low-frequency electromagnetic fields issued by transmission lines on animals and humans. The fields generated by the lines are relevant for the design and operation of power systems. The study of the electric and magnetic fields in the transmission networks implemented commercial simulators bases on the finite element method. These commercial simulators are characterized by accuracy and high hardware and software requirements. This work presents CEM-LT, a tool that accurately precisely the electric and magnetic field in the transmission lines, with simple and intuitive handling and low processing times, making it ideal for being implemented together with optimization methods. The electric and magnetic field in the servant area for two case studies is analyzed to evaluate the accuracy and processing times. The level of accuracy is characterized by comparing the results with COMSOL obtaining errors of less than 2.4%. The case study with the highest computational requirement achieved a processing time of 3,027 seconds.
New fast Walsh–Hadamard–Hartley transform algorithm Suha Suliman Mardan; Mounir Taha Hamood
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1533-1540

Abstract

This paper presents an efficient fast Walsh–Hadamard–Hartley transform (FWHT) algorithm that incorporates the computation of the Walsh-Hadamard transform (WHT) with the discrete Hartley transform (DHT) into an orthogonal, unitary single fast transform possesses the block diagonal structure. The proposed algorithm is implemented in an integrated butterfly structure utilizing the sparse matrices factorization approach and the Kronecker (tensor) product technique, which proved a valuable and fast tool for developing and analyzing the proposed algorithm. The proposed approach was distinguished by ease of implementation and reduced computational complexity compared to previous algorithms, which were based on the concatenation of WHT and FHT by saving up to 3N-4 of real multiplication and 7.5N-10 of real addition.
Dual techniques of load shedding and capacitor placement considering load models for optimal distribution system Ali Nasser Hussain; Waleed Khalid Shakir Al-Jubori
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5683-5696

Abstract

Voltage stability represents one of the main issues in electrical power system. Under voltage load shedding (UVLS) has long been regarded as one of the most successful techniques to prevent the voltage collapse. However, the ordinary load shedding schemes do not consider the different load models and decreasing in the ‎economic cost that resulted from load disconnection, so the dual techniques of load shedding with reactive compensation are needed. Usually loads being modeled as constant power, while in fact of load flow the various load models are utilized. An investigation of optimal dual load shedding with reactive compensation for distribution system based on direct backward forward sweep method (DBFSM) load flow along with a comparison among the other load models are presented in this paper. The teaching learning-based optimization (TLBO) algorithm is executed in order to reduce power losses and enhance the voltage profile. This algorithm is tested and applied to IEEE-16 bus distribution test system to find the optimal superior capacitor size and placement while minimizing load shading for the network. Five different load shedding sequences are considered and the optimization performance of load models demonstrated the comparison through MATLAB program.
The effectiveness of methods and algorithms for detecting and isolating factors that negatively affect the growth of crops Moldir Yessenova; Gulzira Abdikerimova; Talgatbek Ayazbaev; Gulden Murzabekova; Aisulu Ismailova; Zhanar Beldeubayeva; Aliya Ainagulova; Ayagoz Mukhanova
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1669-1679

Abstract

This article discusses a large number of textural features and integral transformations for the analysis of texture-type images. It also discusses the description and analysis of the features of applying existing methods for segmenting texture areas in images and determining the advantages and disadvantages of these methods and the problems that arise in the segmentation of texture areas in images. The purpose of the ongoing research is to use methods and determine the effectiveness of methods for the analysis of aerospace images, which are a combination of textural regions of natural origin and artificial objects. Currently, the automation of the processing of aerospace information, in particular images of the earth’s surface, remains an urgent task. The main goal is to develop models and methods for more efficient use of information technologies for the analysis of multispectral texture-type images in the developed algorithms. The article proposes a comprehensive approach to these issues, that is, the consideration of a large number of textural features by integral transformation to eventually create algorithms and programs applicable to solving a wide class of problems in agriculture. 
Comparison study of transfer function and artificial neural network for cash flow analysis at Bank Rakyat Indonesia Anifatul Faricha; Siti Maghfirotul Ulyah; Rika Susanti; Hawwin Mardhiana; Muhammad Achirul Nanda; Ilma Amira Rahmayanti; Christopher Andreas
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6635-6644

Abstract

The cash flow analysis is essential to examine the economic flows in the financial system. In this paper, the financial dataset at Bank Rakyat Indonesia was used, it recorded the sources of cash inflow and outflow during a particular period. The univariate time series model like the autoregressive and integrated moving average is the common approach to build the prediction based on the historical dataset. However, it is not suitable to estimate the multivariate dataset and to predict the extreme cases consisting of nonlinear pairs between independent-dependent variables. In this study, the comparison of using two types of models i.e., transfer function and artificial neural network (ANN) were investigated. The transfer function model includes the coefficient of moving average (MA) and autoregressive (AR), which allows the multivariate analysis. Furthermore, the artificial neural network allows the learning paradigm to achieve optimal prediction. The financial dataset was divided into training (70%) and testing (30%) for two types of models. According to the result, the artificial neural network model provided better prediction with achieved root mean square error (RMSE) of 0.264897 and 0.2951116 for training and testing respectively.
A comparative analysis of chronic obstructive pulmonary disease using machine learning, and deep learning Ramadoss Ramalingam; Vimala Chinnaiyan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp389-399

Abstract

Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms. 

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