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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 12, No 2: April 2022" : 111 Documents clear
An integrated multiple layer perceptron-genetic algorithm decision support system for photovoltaic power plant site selection Rajkumari Malemnganbi; Benjamin A. Shimray
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1965-1972

Abstract

There is a need for non-renewable energy sources in generation of power for almost every domestic and commercial purposes. This source of energy helps in the development of a country. Because of the increasing usage of the fossil fuels and depletion of these resources, our focus has been shifted towards the renewable sources of energy like solar, water and wind. Therefore, in the present scenario, the usage of renewable sources has been increasing rapidly. Selection of a solar power plant (SPP) requires environmental factor, local terrain, and local weather issues. Thus, a large amount of investment is required for installation. Multi-criteria decision making (MCDM) is a method that identifies one in choosing the best sites among the other proposed options. This paper gives a detailed study of optimal ranking of SPP site using analytical hierarchy process (AHP), multiple layer perceptron (MLP) neural network trained with back propagation (BP) algorithm and genetic algorithm (GA). Three SPP sites of India were considered and various important criteria like local weather, geographical location, and environmental factors are included in our study as SPP site selection is a multi-criteria problem. A precise comparison of these three methods is listed in this paper.
Microstrip band-stop filter based on double negative metamaterial Badr Nasiri; Ahmed Errkik; Jamal Zbitou
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1579-1584

Abstract

In this work, we present a novel miniature band stop filter based on double negative metamaterial, this circuit is designed on a low-cost substrate FR-4 of relative permittivity 4.4 and low tangential losses 0.002. The proposed filter has a compact and miniature size of 15 mm in length and 12mm in width without the 50 Ω feed lines. The resonator was studied and analyzed with a view to achieving a band-stop behavior around its resonant frequency. The band-stop characteristics are obtained by implementing the metamaterial resonator on the final structure. The obtained results show that this microstrip filter achieves fractional bandwidth of 40% at 2 GHz. Furthermore, excellent transmission quality and good attenuation are achieved. This filter is an adequate solution for global system for mobile communications (GSM).
Investigating the residential electricity consumption-income nexus in Morocco: a stochastic impacts by regression on population, affluence, and technology analysis Charifa Haouraji; Badia Mounir; Ilham Mounir; Laila Elmazouzi; Abdelmajid Farchi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1089-1101

Abstract

In a comprehensive LMDI-STIRPAT-ARDL framework, this research investigates the residential electricity consumption (REC)-income nexus in Morocco for the period 1990 to 2018. The logarithmic mean Divisia index (LMDI) results show that economic activity and electricity intensity are the leading drivers of Morocco’s REC, followed by population and residential structure. And then, the LMDI analysis was combined with stochastic impacts by regression on population, affluence, and technology (STIRPAT) analysis and the bounds testing approach to search for a long-run equilibrium relationship. The empirical results show that REC, economic growth, urbanization, and electricity intensity are cointegrated. The results further show that there exists a U-shaped relationship between per capita gross domestic product (GDP) and REC: an increase in per capita GDP reduces REC initially; but, after reaching a turning point (the GDPPC level of 17,145.22 Dh), further increases in per capita GDP increase REC. Regarding urbanization, the results reveal that it has no significant impact on Morocco’s REC. The stability parameters of the short and long-term coefficients of residential electricity demand function are tested. The results of these tests showed a stable pattern. Finally, based on the findings mentioned above, policy implications for guiding the country's development and electricity planning under energy and environmental constraints are given.
Automatic missing value imputation for cleaning phase of diabetic’s readmission prediction model Jesmeen Mohd Zebaral Hoque; Jakir Hossen; Shohel Sayeed; Chy. Mohammed Tawsif K.; Jaya Ganesan; J. Emerson Raja
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2001-2013

Abstract

Recently, the industry of healthcare started generating a large volume of datasets. If hospitals can employ the data, they could easily predict the outcomes and provide better treatments at early stages with low cost. Here, data analytics (DA) was used to make correct decisions through proper analysis and prediction. However, inappropriate data may lead to flawed analysis and thus yield unacceptable conclusions. Hence, transforming the improper data from the entire data set into useful data is essential. Machine learning (ML) technique was used to overcome the issues due to incomplete data. A new architecture, automatic missing value imputation (AMVI) was developed to predict missing values in the dataset, including data sampling and feature selection. Four prediction models (i.e., logistic regression, support vector machine (SVM), AdaBoost, and random forest algorithms) were selected from the well-known classification. The complete AMVI architecture performance was evaluated using a structured data set obtained from the UCI repository. Accuracy of around 90% was achieved. It was also confirmed from cross-validation that the trained ML model is suitable and not over-fitted. This trained model is developed based on the dataset, which is not dependent on a specific environment. It will train and obtain the outperformed model depending on the data available.
Super-capacitor energy storage system to recuperate regenerative braking energy in elevator operation of high buildings An Thi Hoai Thu Anh; Luong Huynh Duc
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1358-1367

Abstract

In operating phases of elevators, accelerating, braking modes occur frequently, so braking energy recuperation of elevators has contributed considerably to decrease the total electric energy consumption for operating elevators in multi-floor buildings. In this paper, the supercapacitor energy storage system is used to recover regenerative braking energy of elevators when they operate down full-load and up no-load, reducing fluctuation of voltage on DC bus as well. Therefore, super-capacitor energy storage system (SCESS) will be parallel with line utility to recuperate regenerative braking energy in braking phase and support energy for acceleration phase. The surplus energy will be stored in the supercapacitors thanks to a DC-DC converter capable of exchanging energy bidirectionally in buck/boost modes, and designing control strategy including two control loops. Inner loop-current loop: controlling charge/discharge process of supercapacitors by current iL complying with operation characteristic of elevator; Outer loop-voltage loop: managing UDC-link at a fixed value. Simulation results with elevator system of the ten-floor building, Hanoi, Vietnam installed SCESS have been verified on MATLAB Simulink, SimPowerSystem with saving energy level about 30%.
A new proactive feature selection model based on the enhanced optimization algorithms to detect DRDoS attacks Riyadh Rahef Nuiaa; Selvakumar Manickam; Ali Hakem Alsaeedi; Esraa Saleh Alomari
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1869-1880

Abstract

Cyberattacks have grown steadily over the last few years. The distributed reflection denial of service (DRDoS) attack has been rising, a new variant of distributed denial of service (DDoS) attack. DRDoS attacks are more difficult to mitigate due to the dynamics and the attack strategy of this type of attack. The number of features influences the performance of the intrusion detection system by investigating the behavior of traffic. Therefore, the feature selection model improves the accuracy of the detection mechanism also reduces the time of detection by reducing the number of features. The proposed model aims to detect DRDoS attacks based on the feature selection model, and this model is called a proactive feature selection model proactive feature selection (PFS). This model uses a nature-inspired optimization algorithm for the feature subset selection. Three machine learning algorithms, i.e., k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were evaluated as the potential classifier for evaluating the selected features. We have used the CICDDoS2019 dataset for evaluation purposes. The performance of each classifier is compared to previous models. The results indicate that the suggested model works better than the current approaches providing a higher detection rate (DR), a low false-positive rate (FPR), and increased accuracy detection (DA). The PFS model shows better accuracy to detect DRDoS attacks with 89.59%.
A hybrid approach for categorizing images based on complex networks and neural networks Ali Ebrahimi; Kamal Mirzaie; Ali Mohamad Latif
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1795-1806

Abstract

There are several methods for categorizing images, the most of which are statistical, geometric, model-based and structural methods. In this paper, a new method for describing images based on complex network models is presented. Each image contains a number of key points that can be identified through standard edge detection algorithms. To understand each image better, we can use these points to create a graph of the image. In order to facilitate the use of graphs, generated graphs are created in the form of a complex network of small-worlds. Complex grid features such as topological and dynamic features can be used to display image-related features. After generating this information, it normalizes them and uses them as suitable features for categorizing images. For this purpose, the generated information is given to the neural network. Based on these features and the use of neural networks, comparisons between new images are performed. The results of the article show that this method has a good performance in identifying similarities and finally categorizing them.
Automatic target detection and localization using ultra-wideband radar Dounia Daghouj; Marwa Abdellaoui; Mohammed Fattah; Said Mazer; Youness Balboul; Moulhime El Bekkali
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1695-1702

Abstract

The pulse ultra-wide band (UWB) radar consists of switching of energy of very short duration in an ultra-broadband emission chain, and the UWB signal emitted is an ultrashort pulse, of the order of nanoseconds, without a carrier. These systems can indicate the presence and distances of a distant object, call a target, and determine its size, shape, speed, and trajectory. In this paper, we present a UWB radar system allowing the detection of the presence of a target and its localization in a road environment based on the principle of correlation of the reflected signal with the reference and the determination of its correlation peak.
Bandwidth density optimization of misaligned optical interconnects Hasan Aldiabat; Nedal Al-ababneh
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1626-1635

Abstract

In this paper, the bandwidth density of misaligned free space optical interconnects (FSOIs) system with and without coding under a fixed bit error rate is considered. In particular, we study the effect of using error correction codes of various codeword lengths on the bandwidth density and misalignment tolerance of the FSOIs system in the presence of higher order modes. Moreover, the paper demonstrates the use of the fill factor of the detector array as a design parameter to optimize the bandwidth density of the communication. The numerical results demonstrate that the bandwidth density improves significantly with coding and the improvement is highly dependent on the used codeword length and code rate. In addition, the results clearly show the optimum fill factor values that achieve the maximum bandwidth density and misalignment tolerance of the system.
Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate Karanrat Thammarak; Prateep Kongkla; Yaowarat Sirisathitkul; Sarun Intakosum
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1849-1858

Abstract

Optical character recognition (OCR) is a technology to digitize a paper-based document to digital form. This research studies the extraction of the characters from a Thai vehicle registration certificate via a Google Cloud Vision API and a Tesseract OCR. The recognition performance of both OCR APIs is also examined. The 84 color image files comprised three image sizes/resolutions and five image characteristics. For suitable image type comparison, the greyscale and binary image are converted from color images. Furthermore, the three pre-processing techniques, sharpening, contrast adjustment, and brightness adjustment, are also applied to enhance the quality of image before applying the two OCR APIs. The recognition performance was evaluated in terms of accuracy and readability. The results showed that the Google Cloud Vision API works well for the Thai vehicle registration certificate with an accuracy of 84.43%, whereas the Tesseract OCR showed an accuracy of 47.02%. The highest accuracy came from the color image with 1024×768 px, 300dpi, and using sharpening and brightness adjustment as pre-processing techniques. In terms of readability, the Google Cloud Vision API has more readability than the Tesseract. The proposed conditions facilitate the possibility of the implementation for Thai vehicle registration certificate recognition system.

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