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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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Hypertext transfer protocol performance analysis in traditional and software defined networks during Slowloris attack Anusha A. Murthy; Prathima Mabel John; Rama Mohan Babu Kasturi Nagappasetty
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4268-4279

Abstract

The extensive use of the internet has resulted in novel technologies and protocol improvisation. Hypertext transfer protocol/1.1 (HTTP/1.1) is widely adapted on the internet. However, HTTP/2 is found to be more efficient over transport control protocol (TCP). The HTTP/2 protocol can withstand the payload overhead when compared to HTTP/1.1 by multiplexing multiple requests. However, both the protocols are highly susceptible to application-level denial of service (DoS) attacks. In this research, a slow-rate DoS attack called Slowloris is detected over Apache2 servers enabled with both versions of HTTP in traditional networks and software defined networks (SDN). Server metrics such as server connection time to the webpage, latency in receiving a response from the server, page load time, response-response gap, and inter-packet arrival time at the server are monitored to analyze attack activity. A Monte Carlo simulation is used to estimate threshold values for server connection time and latency for attack detection. This work is implemented in a lab environment using virtual machines, Ryu controller, zodiac FX OpenFlow switch and Apache2 servers. This study also highlights SDN's security benefits over traditional networks.
Energy saving through load balancing of 3-wire loads Abdulkareem Mokif Obais; Ali Abdulkareem Mukheef
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3857-3875

Abstract

In this paper, static var compensators (SVCs) and many load compensation techniques are reviewed. A continuously and linearly controlled compensating susceptance is devised from a switched capacitor bank and a switched reactor bank. The switched capacitor bank is built of four binary weighted thyristor switched capacitors, while the switched reactor bank is built of three binary weighted thyristor switched reactors. Although few switched capacitors and reactor are used, their binary weighted values beside their control scheme make them respond as a continuously and linearly controlled reactive device in capacitive and inductive modes of operation. A load balancing system is constructed of three identical devised compensating susceptances connected in delta-form. It is designed for balancing an 11 kV 50 Hz distribution station. The proposed system is designed and tested on PSpice which is a computer program equivalent in performance to real hardware design. The simulation results of the proposed system have showed significant treatment of severe imbalance conditions.
Design and implementation of a control system for a steel plate cutting production line using programmable logic controller ‪Basheer NajemAldeen Al shamarti‬‏; Nasir Hussein Selman
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3969-3976

Abstract

Most of the old machines installed in different industries are managed manually by system operators. This action makes their productivity low and not suitable to cover the needs in other sectors. In addition, these machines are not without risks due to the proximity of the operators to them. The installing a new full system instead of the old one on the same site requires very high costs. So, modernizing the same old system by making its operation automatic has become necessary to reduce the economic cost significantly. In this paper, an automatic control system based on programmable logic controller (PLC) for the cutting steel plate machine that had been managed manually in a factory is designed. The system includes an Encoder that is used to specify the length of the steel plate to be cut by sending a signal to the PLC. The system operation is completed by using human machine interface (HMI) unit to monitor and control the system performance by the system operator. A practical test for the developed system offered more productivity (more than 30%), more safety, reduces human efforts and the total daily production cost (less than one third).
Bankruptcy prediction model using cost-sensitive extreme gradient boosting in the context of imbalanced datasets Wirot Yotsawat; Kanyalag Phodong; Thawatchai Promrat; Pakaket Wattuya
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4683-4691

Abstract

In the process of bankruptcy prediction models, a class imbalanced problem has occurred which limits the performance of the models. Most prior research addressed the problem by applying resampling methods such as the synthetic minority oversampling technique (SMOTE). However, resampling methods lead to other issues, e.g., increasing noisy data and training time during the process. To improve the bankruptcy prediction model, we propose cost-sensitive extreme gradient boosting (CS-XGB) to address the class imbalanced problem without requiring any resampling method. The proposed method’s effectiveness is evaluated on six real-world datasets, i.e., the LendingClub, and five Polish companies’ bankruptcy. This research compares the performance of CS-XGB with other ensemble methods, including SMOTE-XGB which applies SMOTE to the training set before the learning process. The experimental results show that i) based on LendingClub, the CS-XGB improves the performance of XGBoost and SMOTE-XGB by more than 50% and 33% on bankruptcy detection rate (BDR) and geometric mean (GM), respectively, and ii) the CS-XGB model outperforms random forest (RF), Bagging, AdaBoost, XGBoost, and SMOTE-XGB in terms of BDR, GM, and the area under a receiver operating characteristic curve (AUC) based on the five Polish datasets. Besides, the CS-XGB model achieves good overall prediction results.
Time-varying sliding mode controller for heat exchanger with dragonfly algorithm Arsit Boonyaprapasorn; Suwat Kuntanapreeda; Parinya Sa Ngiamsunthorn; Tinnakorn Kumsaen; Thunyaseth Sethaput
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3958-3968

Abstract

This article proposes the design of a sliding mode controller with a time-varying sliding surface for the plate heat exchanger. A time-varying sliding mode controller (TVSMC) combines the benefit of the control system’s robustness and convergence rate. Using Lyapunov stability theory, the stability of the designed controller is proved. In addition, the controller parameters of the designed controller are specified optimally via the dragonfly algorithm (DA). The input constraint’s effect is considered in the controller design process by applying the concept of the auxiliary system. The bounded disturbances are applied to investigate the robustness of the proposed techniques. Moreover, the quasi-sliding mode controller (QSMC) is developed as a benchmark to evaluate the convergence behavior of the proposed TVSMC technique. The simulation results demonstrate the proposed TVSMC with the optimal parameters provided by the DA algorithm (TVSMC+DA) can regulate the temperature to the desired level under bounded disturbances. When compared to the QSMC method, the TVSMC+DA performs significantly faster convergence speed and greater reduction in chattering occurrence. The results clearly indicate that the proposed controller can enhance convergence properties while being robust to disturbances.
Design and performance analysis of front and back Pi 6 nm gate with high K dielectric passivated high electron mobility transistor Yadala Gowthami; Bukya Balaji; Karumuri Srinivasa Rao
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3788-3795

Abstract

Advanced high electron mobility transistor (HEMT) with dual front gate, back gate with silicon nitride/aluminum oxide (Si3N4/Al2O3) as passivation layer, has been designed. The dependency on DC characteristics and radio frequency characteristics due to GaN cap layers, multi gate (FG and BG), and high K dielectric material is established. Further compared single gate (SG) passivated HEMT, double gate (DG) passivated HEMT, double gate triple (DGT) tooth passivated HEMT, high K dielectric front Pi gate (FG) and back Pi gate (BG) HEMT. It is observed that there is an increased drain current (Ion) of 5.92 (A/mm), low leakage current (Ioff) 5.54E-13 (A) of transconductance (Gm) of 3.71 (S/mm), drain conductance (Gd) of 1.769 (S/mm), Cutoff frequency (fT) of 743 GHz maximum oscillation frequency (Fmax) 765 GHz, minimum threshold voltage (Vth) of -4.5 V, on resistance (Ron) of 0.40 (Ohms) at Vgs=0 V. These outstanding characteristics and transistor structure of proposed HEMT and materials involved to apply for upcoming generation high-speed GHz frequency applications.
Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm Sivakani Rajayyan; Syed Masood Mohamed Mustafa
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4623-4632

Abstract

Dementia is a brain disease that stays in the seventh position of death rate as per the report of the World Health Organization (WHO). Among the various types of dementia, Alzheimer’s disease has more than 70% of cases of dementia. The objective is to predict dementia disease from the open access series of imaging studies (OASIS) dataset using machine learning techniques. Also, the performance of the machine learning model is analyzed to improve the performance of the model using the cuckoo algorithm. In this paper, feature engineering has been focused and the prediction of dementia has been done using the OASIS dataset with the help of data mining techniques. Feature engineering is followed by prediction using the machine learning model Gaussian naïve Bayes (NB), support vector machine, and linear regression. Also, the best prediction model has been selected and done the validation. The evaluation metrics considered for validating the models are accuracy, precision, recall, and F1-Score and the highest values are 95%, 97%, 95%, and 95%. The Gaussian NB has been given these best results. The accuracy of the machine learning models has been increased by eliminating the factors which affect the performance of the models using the cuckoo algorithm.
Big data analytics and internet of things for personalised healthcare: opportunities and challenges Sitalakshmi Venkatraman; Sazia Parvin; Wathiq Mansoor; Amjad Gawanmeh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4306-4316

Abstract

With the increasing use of technologies and digitally driven healthcare systems worldwide, there will be several opportunities for the use of big data in personalized healthcare. In addition, With the advancements and availability of internet of things (IoT) based point-of-care (POC) technologies, big data analytics and artificial intelligence (AI) can provide useful methods and solutions in monitoring, diagnosis, and self-management of health issues for a better personalized healthcare. In this paper, we identify the current personalized healthcare trends and challenges. Then, propose an architecture to support big data analytics using POC test results of an individual. The proposed architecture can facilitate an integrated and self-managed healthcare as well as remote patient care by adapting three popular machine learning algorithms to leverage the current trends in IoT, big data infrastructures and data analytics for advancing personalized healthcare of the future.
Adaptive sliding mode control for uncertain wheel mobile robot Hoa Van Doan; Nga Thi-Thuy Vu
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3939-3947

Abstract

In this paper a simple adaptive sliding mode controller is proposed for tracking control of the wheel mobile robot (WMR) systems. The WMR are complicated systems with kinematic and dynamic model so the error dynamic model is built to simplify the mathematical model. The sliding mode control then is designed for this error model with the adaptive law to compensate for the mismatched. The proposed control scheme in this work contains only one control loop so it is simple in both implementation and mathematical calculation. Moreover, the requirement of upper bounds of disturbance that is popular in the sliding mode control is cancelled, so it is convenient for real world applications. Finally, the effectiveness of the presented algorithm is verified through mathematical proof and simulations. The comparison with the existing work is also executed to evaluate the correction of the introduced adaptive sliding mode controller. Thoroughly, the settling time, the peak value, the integral square error of the proposed control scheme reduced about 50% in comparison with the compared disturbance observer based sliding mode control.
Realtime face matching and gender prediction based on deep learning Thongchai Surinwarangkoon; Vinh Truong Hoang; Ali Vafaei-Zadeh; Hayder Ibrahim Hendi; Kittikhun Meethongjan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4068-4075

Abstract

Face analysis is an essential topic in computer vision that dealing with human faces for recognition or prediction tasks. The face is one of the easiest ways to distinguish the identity people. Face recognition is a type of personal identification system that employs a person’s personal traits to determine their identity. Human face recognition scheme generally consists of four steps, namely face detection, alignment, representation, and verification. In this paper, we propose to extract information from human face for several tasks based on recent advanced deep learning framework. The proposed approach outperforms the results in the state-of-the-art.

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