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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 2: April 2023" : 111 Documents clear
On the performance analysis of rainfall prediction using mutual information with artificial neural network Shilpa Hudnurkar; Neela Rayavarapu
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.pp2101-2113

Abstract

Monsoon rainfall prediction over a small geographic region is indeed a challenging task. This paper uses monthly means of climate variables, namely air temperature (AT), sea surface temperature (SST), and sea level pressure (SLP) over the globe, to predict monthly and seasonal summer monsoon rainfall over the state of Maharashtra, India. Mutual information correlates the temperature and pressure from a grid of 10° longitude X 10° latitude with Maharashtra’s monthly rainfall time series. Based on the correlations, selected features over the respective latitude and longitudes are given as inputs to an artificial neural network. It was observed that AT and SLP could predict monthly monsoon rainfall with excellent accuracy. The performance of the test dataset was evaluated through mean absolute error; root mean square error, correlation coefficient, Nash Sutcliffe model efficiency coefficient, and maximum rainfall prediction capability of the network. The individual climate variable model for AT performed better in all evaluation parameters except maximum rainfall capability, where the combined model 2 with AT, SLP and SST as predictors outperformed. The SLP-only model’s performance was comparable to the AT-only model. The combined model 1 with AT and SLP as predictors was found better than the combined model 2.
Comparison of specific segmentation methods used for copy move detection Eman Abdulazeem Ahmed; Malek Alzaqebah; Sana Jawarneh; Jehad Saad Alqurni; Fahad A. Alghamdi; Hayat Alfagham; Lubna Mahmoud Abdel Jawad; Usama A. Badawi; Mutasem K. Alsmadi; Ibrahim Almarashdeh
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.pp2363-2374

Abstract

In this digital age, the widespread use of digital images and the availability of image editors have made the credibility of images controversial. To confirm the credibility of digital images many image forgery detection types are arises, copy-move forgery is consisting of transforming any image by duplicating a part of the image, to add or hide existing objects. Several methods have been proposed in the literature to detect copy-move forgery, these methods use the key point-based and block-based to find the duplicated areas. However, the key point-based and block-based have a drawback of the ability to handle the smooth region. In addition, image segmentation plays a vital role in changing the representation of the image in a meaningful form for analysis. Hence, we execute a comparison study for segmentation based on two clustering algorithms (i.e., k-means and super pixel segmentation with density-based spatial clustering of applications with noise (DBSCAN)), the paper compares methods in term of the accuracy of detecting the forgery regions of digital images. K-means shows better performance compared with DBSCAN and with other techniques in the literature.
Phase shifting transformer to reduce power congestions and to redistribute power in interconnected systems Ananda M. Halasiddappa; Malavalli R. Shivakumar
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.pp1215-1220

Abstract

The increased penetration of wind and solar power, as well as the liberalized electricity market, makes the power system network interconnected and complex. As the power demand is increasing daily, the complexity of operating large power systems is also increasing. Congestion in the transmission network may become more common than previously, making power flow management a problem that becomes increasingly important. Unexpected power flows (also known as loop flows) are becoming a bigger issue in today's linked power networks. These flows have a detrimental impact on the safe functioning of integrated power networks, which hinders their ability to conduct cross-border trade. Phase shifting transformers (PSTs) allow real power flow to be controlled by changing the phase shift across the device. This study deals with two interconnected parallel power system networks and the power flow controlled through a PST in between. The simulation results emphasize the importance of the PST in facilitating the transfer of energy throughout the regional transmission interconnection.
NBLex: emotion prediction in Kannada-English code-switch text using naïve bayes lexicon approach Ramesh Chundi; Vishwanath R. Hulipalled; Jay Bharthish Simha
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.pp2068-2077

Abstract

Emotion analysis is a process of identifying the human emotions derived from the various data sources. Emotions can be expressed either in monolingual text or code-switch text. Emotion prediction can be performed through machine learning (ML), or deep learning (DL), or lexicon-based approach. ML and DL approaches are computationally expensive and require training data. Whereas, the lexicon-based approach does not require any training data and it takes very less time to predict the emotions in comparison with ML and DL. In this paper, we proposed a lexicon-based method called non-binding lower extremity exoskeleton (NBLex) to predict the emotions associated with Kannada-English code-switch text that no one has addressed till now. We applied the One-vs-Rest approach to generate the scores for lexicon and also to predict the emotions from the code-switch text. The accuracy of the proposed model NBLex (87.9%) is better than naïve bayes (NB) (85.8%) and bidirectional long short-term memory neural network (BiLSTM) (84.7%) and for true positive rate (TPR), the NBLex (50.6%) is better than NB (37.0%) and BiLSTM (42.2%). From our approach, it is observed that a simple additive model (lexicon approach) can also be an alternative model to predict the emotions in code-switch text.
Performance assessment and analysis of development and operations based automation tools for source code management Pooja Mittal; Poonam Narang
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.pp1817-1826

Abstract

Development and operations (DevOps), an accretion of automation tools, efficiently reaches the goals of software development, test, release, and delivery in terms of optimization, speed and quality. Diverse set of alternative automation tools exist for different phases of software development, for which DevOps adopts several selection criteria to choose the best tool. This research paper represents the performance evaluation and analysis of automation tools employed in the coding phase of DevOps culture. We have taken most commonly followed source code management tools-BitBucket, GitHub actions, and GitLab into consideration. Current work assesses and analyzes their performance based on DevOps evaluation criteria that too are categorized into different dimensions. For the purpose of performance evaluation, weightage and overall score is assigned to these criteria based on existing renowned literature and industrial case study of TekMentors Pvt Ltd. On the ground of performance outcome, the tool with the highest overall score is realized as the best source code automation tool. This performance analysis or measure will be a great benefit to our young researchers/students to gain an understanding of the modus operandi of DevOps culture, particularly source code automation tools. As a part of future research, other dimensions of selection criteria can also be considered for evaluation purposes.
Attention correlated appearance and motion feature followed temporal learning for activity recognition Manh-Hung Ha; The-Anh Pham; Dao Thi Thanh; Van Luan Tran
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.pp1510-1521

Abstract

Recent advances in deep neural networks have been successfully demonstrated with fairly good accuracy for multi-class activity identification. However, existing methods have limitations in achieving complex spatial-temporal dependencies. In this work, we design two stream fusion attention (2SFA) connected to a temporal bidirectional gated recurrent unit (GRU) one-layer model and classified by prediction voting classifier (PVC) to recognize the action in a video. Particularly in the proposed deep neural network (DNN), we present 2SFA for capturing appearance information from red green blue (RGB) and motion from optical flow, where both streams are correlated by proposed fusion attention (FA) as the input of a temporal network. On the other hand, the temporal network with a bi-directional temporal layer using a GRU single layer is preferred for temporal understanding because it yields practical merits against six topologies of temporal networks in the UCF101 dataset. Meanwhile, the new proposed classifier scheme called PVC employs multiple nearest class mean (NCM) and the SoftMax function to yield multiple features outputted from temporal networks, and then votes their properties for high-performance classifications. The experiments achieve the best average accuracy of 70.8% in HMDB51 and 91.9%, the second best in UCF101 in terms of 2DConvNet for action recognition.
Supervised and unsupervised data mining approaches in loan default prediction Jovanne C. Alejandrino; Jovito Jr. P. Bolacoy; John Vianne B. Murcia
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.pp1837-1847

Abstract

Given the paramount importance of data mining in organizations and the possible contribution of a data-driven customer classification recommender systems for loan-extending financial institutions, the study applied supervised and supervised data mining approaches to derive the best classifier of loan default. A total of 900 instances with determined attributes and class labels were used for the training and cross-validation processes while prediction used 100 new instances without class labels. In the training phase, J48 with confidence factor of 50% attained the highest classification accuracy (76.85%), k-nearest neighbors (k-NN) 3 the highest (78.38%) in IBk variants, naïve Bayes has a classification accuracy of 76.65%, and logistic has 77.31% classification accuracy. k-NN 3 and logistic have the highest classification accuracy, F-measures, and kappa statistics. Implementation of these algorithms to the test set yielded 48 non-defaulters and 52 defaulters for k -NN 3 while 44 non-defaulters and 56 defaulters under logistic. Implications were discussed in the paper.
A new hybrid method for mutual coupling minimization of an antenna array Sara Said; Meryem Grari; Yassmina Guetbach; Abdenacer Es-Salhi; Zoheir Cif Allah; Baghaz Elhadi; Ahmed Faize
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.pp2299-2308

Abstract

In this paper, a simultaneous application of geometric modification on patch elements and electromagnetic band gap (EBG) electromagnetic bandgap structures (hybrid method) has been suggested for 3.5 GHz wireless communication applications, to minimize the mutual coupling between radiating elements of microstrip array antennas. The suggested EBG slotted structure is composed of a one square ring and three squares placed on Rogers RO3010 having 10.2 and h=1.27 mm which presents respectively its dielectric constant and thickness. In this approach, the patch elements are geometrically modified, while also employing EBG structures, formed by four EBG cells, placed between the array elements at a near distance. The modification of the geometry of the antenna and the introduction of EBG reduces the mutual coupling of an array antenna with approximately 33 dB on the one hand and improves the antenna gain by approximately 0.43 dB on the other hand. Initially, slots are introduced in the patch geometry and then four EBG unit cells are inserted between two patches, operating at 3.5 GHz. The antenna array design parameters were optimized.
Channel state information estimation for reconfigurable intelligent surfaces based on received signal analysis Aqiel Almamori; Mohammed Adil Abbas
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.pp1599-1605

Abstract

Recently, reconfigurable intelligent surfaces have an increasing role to enhance the coverage and quality of mobile networks especially when the received signal level is very weak because of obstacles and random fluctuation. This motivates the researchers to add more contributions to the fields of reconfigurable intelligent surfaces (RIS) in wireless communications. A substantial issue in reconfigurable intelligent surfaces is the huge overhead for channel state information estimation which limits the system’s performance, oppressively. In this work, a newly proposed method is to estimate the angle of arrival and path loss at the RIS side and then send short information to the base station rather than huge overhead as in previous research. The estimated channel state information is used to beamform the downlink waveform toward users accurately. The simulation results indicate that the proposed algorithm calculated the angle of arrival of users, admirably especially at a high signal-to-noise ratio. Moreover, a considerable spectral efficiency enhancement is obtained as compared to the traditional methods.
Inductively coupled distributed static compensator for power quality analysis of distribution networks Praveen Kumar Yadav Kundala; Mrutyunjaya Mangaraj; Suresh Kumar Sudabattula
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.pp1387-1399

Abstract

In this research paper, an inductively coupled distributed static compensator (IC-DSTATCOM) for three phase three wire (3P3W) electric power distribution system (EPDS) is proposed. The contraction of power quality (PQ) was marked as a perilous droop mode bump into direct coupled distributed static compensator (DC-DSTATCOM). To regain the PQ, inductive coupling transformer is assisted in conjunction with DC-DSTATCOM. The system equivalent circuit of IC-DSTATCOM is accomplished by take into account of impedance of both transformer and DC-DSTATCOM to reveal the filtering technique. The filtering icos∅ mechanism is performed by following the generalized mathematical approach using MATLAB/Simulink. A case education is reviewed in detail to illustrate the performance of both DC-DSTATCOM and IC-DSTATCOM. The IC-DSTATCOM is amplified healthier as compared to other in terms of harmonics shortening, good power factor, load balancing, and potential regulation. To examine the effectiveness, simulation outputs of the IC-DSTATCOM with different PQ parameter indices are presented by following the benchmark measure of IEEE-2030-7-2017 and IEC-61000-1 system code.

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