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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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Misalignment fading effects on the ACC performance of relay-assisted MIMO/FSO systems over atmospheric turbulence channels Huu Ai Duong; Van Loi Nguyen; Khanh Ty Luong
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.pp966-973

Abstract

The continuous development of internet of things (IoT) technology enables many devices to be interconnected through the external environment. Meanwhile, 5G technology provides an enhanced quality of services with high data transmission rates, requiring IoT implementation in the 5G architecture. Free-space optical communication (FSO) is considered a promising technique that can provide high-speed communication links, so FSO is an optimal choice for wireless networks to fulfill the full potential of 5G technology, providing speeds of 100 Gb/s or more. By implementing 5G features in IoT, IoT coverage and performance will be enhanced by using FSO models. Therefore, the paper proposed and investigated the multiple-input and multiple-output/free-space optical communication (MIMO/FSO) model using subcarrier quadrature amplitude modulation (SC-QAM) and relay stations over atmospheric turbulence channels by log-normal and gamma-gamma distribution under different turbulence conditions. The performance is examined based on the average channel capacity (ACC), which is expressed in terms of average spectral efficiency (ASE) parameters while changing the different parameters of the model. The mathematical formulas of ACC for atmospheric turbulence cases are calculated and discussed the influence of turbulence strength, the different number of relay stations, misalignment effects, and different MIMO configurations.
Efficient organization of nodes in wireless sensor networks (clustering location-based LEACH) Mohammed Réda El Ouadi; Abderrahim Hasbi
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.pp1011-1017

Abstract

The rapid development of connected devices and wireless communication has enabled several researchers to study wireless sensor networks and propose methods and algorithms to improve their performance. Wireless sensor networks (WSN) are composed of several sensor nodes deployed to collect and transfer data to base station (BS). Sensor node is considered as the main element in this field, characterized by minimal capacities of storage, energy, and computing. In consequence of the important impact of the energy on network lifetime, several researches are interested to propose different mechanisms to minimize energy consumption. In this work, we propose a new enhancement of low-energy adaptive clustering hierarchy (LEACH) protocol, named clustering location-based LEACH (CLOC-LEACH), which represents a continuity of our previous published work location-based LEACH (LOC-LEACH). The proposed protocol organizes sensor nodes into four regions, using clustering mechanism. In addition, an efficient concept is adopted to choose cluster head. CLOC-LEACH considers the energy as the principal metric to choose cluster heads and uses a gateway node to ensure the inter-cluster communication. The simulation with MATLAB shows that our contribution offers better performance than LEACH and LOC-LEACH, in terms of stability, energy consumption and network lifetime.
Sentiment analysis on film review in Gujarati language using machine learning Parita Shah; Priya Swaminarayan; Maitri Patel
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.pp1030-1039

Abstract

Opinion analysis is by a long shot most basic zone of characteristic language handling. It manages the portrayal of information to choose the motivation behind the wellspring of the content. The reason might be of a type of gratefulness (positive) or study (negative). This paper offers a correlation between the outcomes accomplished by applying the calculation arrangement using various classifiers for instance K-nearest neighbor and multinomial naive Bayes. These techniques are utilized to assess a significant assessment with either a positive remark or negative remark. The gathered information considered on the grounds of the extremity film datasets and an association with the results accessible proof has been created for a careful assessment. This paper investigates the word level count vectorizer and term frequency inverse document frequency (TF-IDF) influence on film sentiment analysis. We concluded that multinomial Naive Bayes (MNB) classier generate more accurate result using TF-IDF vectorizer compared to CountVectorizer, K-nearest-neighbors (KNN) classifier has the same accuracy result in case of TF-IDF and CountVectorizer.
Four dimensional hyperchaotic communication system based on dynamic feedback synchronization technique for image encryption systems Hayder Mazin Makki Alibraheemi; Qais Al-Gayem; Ehab AbdulRazzaq Hussein
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.pp957-965

Abstract

This paper presents the design and simulation of a hyperchaotic communication system based on four dimensions (4D) Lorenz generator. The synchronization technique that used between the master/transmitter and the slave/receiver is based on dynamic feedback modulation technique (DFM). The mismatch error between the master dynamics and slave dynamics are calculated continuously to maintain the sync process. The information signal (binary image) is masked (encrypted) by the hyperchaotic sample x of Lorenz generator. The design and simulation of the overall system are carried out using MATLAB Simulink software. The simulation results prove that the system is suitable for securing the plain-data, in particular the image data with a size of 128×128 pixels within 0.1 second required for encryption, and decryption in the presence of the channel noise. The decryption results for gray and colored images show that the system can accurately decipher the ciphered image, but with low level distortion in the image pixels due to the channel noise. These results make the proposed cryptosystem suitable for real time secure communications.
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%.

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