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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 6,393 Documents
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Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach Sarah A. Ebiaredoh-Mienye; E. Esenogho; Theo G. Swart
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4392-4402

Abstract

Presently, the use of a credit card has become an integral part of contemporary banking and financial system. Predicting potential credit card defaulters or debtors is a crucial business opportunity for financial institutions. For now, some machine learning methods have been applied to achieve this task. However, with the dynamic and imbalanced nature of credit card default data, it is challenging for classical machine learning algorithms to proffer robust models with optimal performance. Research has shown that the performance of machine learning algorithms can be significantly improved when provided with optimal features. In this paper, we propose an unsupervised feature learning method to improve the performance of various classifiers using a stacked sparse autoencoder (SSAE). The SSAE was optimized to achieve improved performance. The proposed SSAE learned excellent feature representations that were used to train the classifiers. The performance of the proposed approach is compared with an instance where the classifiers were trained using the raw data. Also, a comparison is made with previous scholarly works, and the proposed approach showed superior performance over other methods.
A transfer learning with deep neural network approach for diabetic retinopathy classification Mohammed Al-Smadi; Mahmoud Hammad; Qanita Bani Baker; Sa’ad A. Al-Zboon
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3492-3501

Abstract

Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the blood vessels in the eye. Diabetic retinopathy is the root cause of more than 1% of the blindness worldwide. Early detection of this disease is crucial as it prevents it from progressing to a more severe level. However, the current machine learning-based approaches for detecting the severity level of diabetic retinopathy are either, i) rely on manually extracting features which makes an approach unpractical, or ii) trained on small dataset thus cannot be generalized. In this study, we propose a transfer learning-based approach for detecting the severity level of the diabetic retinopathy with high accuracy. Our model is a deep learning model based on global average pooling (GAP) technique with various pre-trained convolutional neural net- work (CNN) models. The experimental results of our approach, in which our best model achieved 82.4% quadratic weighted kappa (QWK), corroborate the ability of our model to detect the severity level of diabetic retinopathy efficiently.
Enhanced dynamic source routing for verifying trust in mobile ad hoc network for secure routing Salman Ali Syed; Shahzad Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp425-430

Abstract

Secure data transfer in mobile ad hoc network (MANET) against malicious attacks is of immense importance. In this paper, we propose a new enhanced trust model for securing the MANET using trust-based scheme that uses both blind trust and referential trust. In order to do this, the trust relationship function has to be integrated with the dynamic source routing (DSR) protocol for making the protocol more secure. We thoroughly analyze the DSR protocol and generate the performance matrices for the data pertaining to packets sent, packets received, packets loss, and throughput. We also analyze the outcome attained from the improvised trust establishment scheme by using the three algorithm implementations in NS2 simulator for detecting and preventing various types of attacks.
New method for summative evaluation of UML class diagrams based on graph similarities Outair Anas; Tanana Mariam; Lyhyaoui Abdelouahid
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1578-1590

Abstract

This paper deals with the problem of the evaluation of the student's productions during the construction of a UML class diagram from textual speciations, which can be a tedious task for teachers. The main objective is to propose a method of summative and semi-automatic evaluation of the class diagrams produced by the students, in order to provide an educational reaction on the learning process, and to reduce the evaluation work for the teachers. To achieve this objective, we must analyze these productions and study the transformation, matching, similarity measurement and comparison of several UML graphs. From this study, we adopted a method based on the comparison and matching of the components of several UML diagrams. This proposal is applied to evaluate UML class diagrams and focuses on the structural and semantic aspects of the UML graph produced by students compared to several solutions proposed by the teacher.
Mesenteric cyst detection and segmentation by multiple K-means clustering and iterative Gaussian filtering Mohamed Nasor; Walid Obaid
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp4932-4941

Abstract

In this article a fully automated machine-vision technique for the detection and segmentation of mesenteric cysts in computed tomography (CT) images of the abdominal space is presented. The proposed technique involves clustering, filtering, morphological operations and evaluation processes to detect and segment mesenteric cysts in the abdomen regardless of their texture variation and location with respect to other surrounding abdominal organs. The technique is comprised of various processing phases, which include K-means clustering, iterative Gaussian filtering, and an evaluation of the segmented regions using area-normalized histograms and Euclidean distances. The technique was tested using 65 different abdominal CT scan images. The results showed that the technique was able to detect and segment mesenteric cysts and achieved 99.31%, 98.44%, 99.84%, 98.86% and 99.63% for precision, recall, specificity, dice score coefficient and accuracy respectively as quantitative performance measures which indicate very high segmentation accuracy.
Blockchain based voting system for Jordan parliament elections Mohammad Malkawi; Muneer Bani Yassein; Asmaa Bataineh
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4325-4335

Abstract

Covid-19 pandemic has stressed more than any-time before the necessity for conducting election processes in an electronic manner, where voters can cast their votes remotely with complete security, privacy, and trust. The different voting schema in different countries makes it very difficult to utilize a one fits all system. This paper presents a blockchain based voting system (BBVS) applied to the Parliamentary elections system in the country of Jordan. The proposed system is a private and centralized blockchain implemented in a simulated environment. The proposed BBVS system implements a hierarchical voting process, where a voter casts votes at two levels, one for a group, and the second for distinct members within the group. This paper provides a novel blockchain based e-Voting system, which proves to be transparent and yet secure. This paper utilizes synthetic voter benchmarks to measure the performance, accuracy and integrity of the election process. This research introduced and implemented new algorithms and methods to maintain acceptable performance both at the time of creating the blockchain(s) for voters and candidates as well as at the time of casting votes by voters.
Determination of the price for a hydro resource with consideration of operating conditions of hydropower plants using complex criteria of profit maxmization T. V. Myatezh; Y. A. Sekretarev
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp2733-2742

Abstract

In this paper, a universal method has been developed to determine the price of a hydro resource (one cubic meter) for the operational regulation of a hydropower plant (HPP), which is a combination of an optimization method and a method for assessing the marginal utility. The proposed approach is based on the correct representation of differential incremental rate characteristics of water at an HPP and fuel at a thermal power plant (TPP). To know the price of a hydro resource used for electricity generation at a hydropower plant. This gives the possibility to increase the efficiency of management both at a hydropower plant, and in a water utilization system as a whole. Using the examples of Novosibirsk HPP, it is expected to develop an estimation of economic effect from the implementation of the developed criteria, the proposed method of the calculation of a hydro resource price at HPP, and the method of separating fuel costs at CHPP. As a result of the implementation the developed method for the HPP, a price of electricity sold in the flexible energy market will be compared to the price of the electricity produced and sold at CHPP, being equal to approximately 330 rubles/MW h.
Impact of hybrid FACTS devices on the stability of the Kenyan power system Mutegi Mbae; Nnamdi Nwulu
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp12-21

Abstract

Flexible alternating current transmission system (FACTS) devices are deployed for improving power system’s stability either singly or as a combination. This research investigates hybrid FACTS devices and studies their impact on voltage, small-signal and transient stability simultaneously under various system disturbances. The simulations were done using five FACTS devices-static var compensator (SVC), static synchronous compensator (STATCOM), static synchronous series compensators (SSSC), thyristor controlled series compensator (TCSC) and unified power flow controller (UPFC) in MATLAB’s power system analysis toolbox (PSAT). These five devices were grouped into ten pairs and tested on Kenya’s transmission network under specific contingencies: the loss of a major generating machine and/or transmission line. The UPFC-STATCOM pair performed the best in all the three aspects under study. The settling times were 3 seconds and 3.05 seconds respectively for voltage and rotor angle improvement on the loss of a major generator at normal operation. The same pair gave settling times of 2.11 seconds and 3.12 seconds for voltage and rotor angle stability improvement respectively on the loss of a major transmission line at 140% system loading. From the study, two novel techniques were developed: A performance-based ranking system and classification for FACTS devices.
A comprehensive review on hybrid network traffic prediction model Jinmei Shi; Yu Beng Leau; Kun Li; Joe Henry Obit
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1450-1459

Abstract

Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction.
A one decade survey of autonomous mobile robot systems Noor Abdul Khaleq Zghair; Ahmed S. Al-Araji
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp4891-4906

Abstract

Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-to-day activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability.

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