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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 112 Documents
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Search results for , issue "Vol 12, No 5: October 2022" : 112 Documents clear
Statistical features learning to predict the crop yield in regional areas Pinaka Pani Ramanahalli; Hemanth Kollegal Siddamallu; Ravi Kumar Yelwala Basavaraju
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5321-5329

Abstract

The plethora of information presented in the form of benchmark dataset plays a significant role in analyzing and understanding the crop yield in certain regions of regional territory. The information may be presented in the form of attributes makes a prediction of crop yield in various regions of machine learning. The information considered for processing involves data cleaning initially followed by binning to reduce the missing data. The information collected is subjected to clustering of data items based on patterns of similarity, The data items that are similar in nature is fed to the system with similarity measure, which involves understanding the distance of data items from its related data item leading to hyper parameters for analyzing of information while calculating the crop yield. The information may be used to ascertain the patterns of data that exhibit similarity with nearest neighbor represented by another attribute. Thus, the research method has yielded an accuracy of 89.62% of classification for predicting the crop yield in agricultural areas of Karnataka region.
Design and optimization of a rectangular microstrip patch antenna for dual-band 2.45 GHz/ 5.8 GHz RFID application Sara El Mattar; Abdennaceur Baghdad
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5114-5122

Abstract

This paper introduces a new rectangular slot antenna structure based on a simple rectangular shape with two symmetrical rectangular slots on the radiated element. The aim of this work is to design an antenna and enhance it to function in the band (2.45 GHz and 5.8 GHz). We formulated the dimensions of the antenna using the transmission line model of the analytical methods and then we optimized these parameters using the CST Microwave Studio simulator. We made changes to two important parameters in our design: the position and width of the slots when the other parameters are kept constant. The resulting antenna provides good adaptation, high gain that achieves 5.96 dBi at 2.45 GHz and 6.491 dBi at 5.8 GHz, good return loss values of -49.859 dB and -34.303 dB for the lower and upper operating frequencies respectively. For radio frequency identification (RFID) implementations, the proposed antenna is ideal, and its main advantage is that it has high gain and is simple to design and fabricate.
Review on effectiveness of deep learning approach in digital forensics Sonali Ekhande; Uttam Patil; Kshama Vishwanath Kulhalli
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5481-5592

Abstract

Cyber forensics is use of scientific methods for definite description of cybercrime activities. It deals with collecting, processing and interpreting digital evidence for cybercrime analysis. Cyber forensic analysis plays very important role in criminal investigations. Although lot of research has been done in cyber forensics, it is still expected to face new challenges in near future. Analysis of digital media specifically photographic images, audio and video recordings are very crucial in forensics This paper specifically focus on digital forensics. There are several methods for digital forensic analysis. Currently deep learning (DL), mainly convolutional neural network (CNN) has proved very promising in classification of digital images and sound analysis techniques. This paper presents a compendious study of recent research and methods in forensic areas based on CNN, with a view to guide the researchers working in this area. We first, defined and explained preliminary models of DL. In the next section, out of several DL models we have focused on CNN and its usage in areas of digital forensic. Finally, conclusion and future work are discussed. The review shows that CNN has proved good in most of the forensic domains and still promise to be better.
Efficient feature descriptor selection for improved Arabic handwritten words recognition Soufiane Hamida; Oussama El Gannour; Bouchaib Cherradi; Hassan Ouajji; Abdelhadi Raihani
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5304-5312

Abstract

Arabic handwritten text recognition has long been a difficult subject, owing to the similarity of its characters and the wide range of writing styles. However, due to the intricacy of Arabic handwriting morphology, solving the challenge of cursive handwriting recognition remains difficult. In this paper, we propose a new efficient based image processing approach that combines three image descriptors for the feature extraction phase. To prepare the training and testing datasets, we applied a series of preprocessing techniques to 100 classes selected from the handwritten Arabic database of the Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT). Then, we trained the k-nearest neighbor’s algorithm (k-NN) algorithm to generate the best model for each feature extraction descriptor. The best k-NN model, according to common performance evaluation metrics, is used to classify Arabic handwritten images according to their classes. Based on the performance evaluation results of the three k-NN generated models, the majority-voting algorithm is used to combine the prediction results. A high recognition rate of up to 99.88% is achieved, far exceeding the state-of-the-art results using the IFN/ENIT dataset. The obtained results highlight the reliability of the proposed system for the recognition of handwritten Arabic words.
New foreground markers for Drosophila cell segmentation using marker-controlled watershed Rim Rahali; Yassine Ben Salem; Noura Dridi; Hassen Dahman
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5055-5062

Abstract

Image segmentation consists of partitioning the image into different objects of interest. For a biological image, the segmentation step is important to understand the biological process. However, it is a challenging task due to the presence of different dimensions for cells, intensity inhomogeneity, and clustered cells. The marker-controlled watershed (MCW) is proposed for segmentation, outperforming the classical watershed. Besides, the choice of markers for this algorithm is important and impacts the results. For this work, two foreground markers are proposed: kernels, constructed with the software Fiji and Obj.MPP markers, constructed with the framework Obj.MPP. The new proposed algorithms are compared to the basic MCW. Furthermore, we prove that Obj.MPP markers are better than kernels. Indeed, the Obj.MPP framework takes into account cell properties such as shape, radiometry, and local contrast. Segmentation results, using new markers and illustrated on real Drosophila dataset, confirm the good performance quality in terms of quantitative and qualitative evaluation.
Automatic COVID-19 lung images classification system based on convolution neural network Lamia Nabil Mahdy; Ahmed Ibrahim Bahgat El Seddawy; Kadry Ali Ezzat
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5573-5579

Abstract

Coronavirus disease (COVID-19) still has disastrous effects on human life around the world. For fight that disease. Examination on the patients who have been sucked in quick and cheap way is necessary. Radiography is most effective step closer to this target. Chest X-ray is readily obtainable and cheap option. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral pneumonia from common viral pneumonia is difficult. In this study, X-ray images of 500, 500, 500, and 500 patients for healthy controls, typical viral pneumonia, bacterial pneumonia and COVID-19, were collected respectively. To our knowledge, this was the first quaternary classification study that also included classical viral pneumonia. To efficiently capture nuances in X-ray images, a new model was created by applying convolution neural network for accurate image classification. Our model outperformed to achieve an overall accuracy, sensitivity, specificity, F1-score, and area under curve (AUC) of 0.98, 0.97, 0.98, 0.97, and 0.99 respectively.
Provenance and social network analysis for recommender systems: a literature review Korab Rrmoku; Besnik Selimi; Lule Ahmedi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5383-5392

Abstract

Recommender systems (RS) and their scientific approach have become very important because they help scientists find suitable publications and approaches, customers find adequate items, tourists find their preferred points of interest, and many more recommendations on domains. This work will present a literature review of approaches and the influence that social network analysis (SNA) and data provenance has on RS. The aim is to analyze differences and similarities using several dimensions, public datasets for assessing their impacts and limitations, evaluations of methods and metrics along with their challenges by identifying the most efficient approaches, the most appropriate assessment data sets, and the most appropriate assessment methods and metrics. Hence, by correlating these three fields, the system will be able to improve the recommendation of certain items, by being able to choose the recommendations that are made from the most trusted nodes/resources within a social network. We have found that content-based filtering techniques, combined with term frequency-inverse document frequency (TF-IDF) features are the most feasible approaches when combined with provenance since our focus is to recommend the most trusted items, where trust, distrust, and ignorance are calculated as weight in terms of the relationship between nodes on a network.
An optimal artificial neural network controller for load frequency control of a four-area interconnected power system Basavarajappa Sokke Rameshappa; Nagaraj Mudakapla Shadaksharappa
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4700-4711

Abstract

In this paper, an optimal artificial neural network (ANN) controller for load frequency control (LFC) of a four-area interconnected power system with non-linearity is presented. A feed forward neural network with multi-layers and Bayesian regularization backpropagation (BRB) training function is used. This controller is designed on the basis of optimal control theory to overcome the problem of load frequency control as load changes in the power system. The system comprised of transfer function models of twothermal units, one nuclear unit and one hydro unit. The controller model is developed by considering generation rate constraint (GRC) of different units as a non-linearity. The typical system parameters obtained from IEEE press power engineering series and EPRI books. The robustness, effectiveness, and performance of the proposed optimal ANN controller for a step load change and random load change in the system is simulated through using MATLAB-Simulink. The time response characteristics are compared with that obtained from the proportional, integral and derivative (PID) controller and non-linear autoregressive-moving average (NARMA-L2) controller. The results show that the algorithm developed for proposed controller has a superiority in accuracy as compared to other two controllers.
Presentation of a fault tolerance algorithm for design of quantum-dot cellular automata circuits Seyed Mehdi Dadgar; Razieh Farazkish; Amir Sahafi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4722-4733

Abstract

A novel algorithm for working out the Kink energy of quantum-dot cellular automata (QCA) circuits and their fault tolerability is introduced. In this algorithm at first with determining the input values on a specified design, the calculation between cells makes use of Kink physical relations will be managed. Therefore, the polarization of any cell and consequently output cell will be set. Then by determining missed cell(s) on the discussed circuit, the polarization of output cell will be obtained and by comparing it with safe state or software simulation, its fault tolerability will be proved. The proposed algorithm was implemented on a novel and advance fault tolerance full adder whose performance has been demonstrated. This algorithm could be implemented on any QCA circuit. Noticeably higher speed of the algorithm than simulation and traditional manual methods, expandability of this algorithm for variable circuits, beyond of four-dot square of QCA circuits, and the investigation of several damaged cells instead just one and special cell are the advantages of algorithmic action.
Wind energy integration in Africa: development, impacts and barriers Touria Haidi; Bouchra Cheddadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4614-4622

Abstract

The African renewable energy initiative (AREI), adopted in 2015 by nearly half of the African countries, planned to install 10 GW of renewable energy by the end of 2020 and 300 GW by 2030, of which 100 GW would be wind. These countries have each adopted their own national energy strategy defining their rate of renewable electricity capacity, particularly wind, in the overall energy mix by 2020 and/or 2030. This article aims to assess the implementation of these strategies by evaluating the up-to-date achievements in regards to wind energy and thus infer the AREI realization rate by the end of 2020. It focuses on the wind energy investments of the major African countries while comparing their effective realization rates with those targeted by their national strategies. This article also covers the impact of wind energy integration and the barriers to its development in Africa. Taking into account the recent study published in 2020 by the Global Wind Energy Council which assessed the wind energy potential in Africa at 59 TW, the obtained results show that the huge wind power potential in Africa is still far from being exploited and that only Morocco, Egypt and South Africa are on the right track.

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