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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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Smoothing-aided long-short term memory neural network-based LTE network traffic forecasting Mohamed Khalafalla Hassan; Sharifah Hafizah Sayed Ariffin; Sharifah Kamilah Syed-Yusof; Nurzal Effiyana Ghazali; Mohammed Eltayeb Ahmed Kanona; Mohamed Rava
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6859-6868

Abstract

There is substantial demand for high network traffic due to the emergence of new highly demanding services and applications such as the internet of things (IoT), big data, blockchains, and next-generation networks like 5G and beyond. Therefore, network resource planning and forecasting play a vital role in better resource optimization. Accordingly, forecasting accuracy has become essential for network operation and planning to maintain the minimum quality of service (QoS) for real-time applications. In this paper, a hybrid network- bandwidth slice forecasting model that combines long-short term memory (LSTM) neural network and various local smoothing techniques to enhance the network forecasting model's accuracy was proposed and analyzed. The results show that the proposed hybrid forecasting model can effectively improve the forecasting accuracy with minimal data loss.
A constrained model predictive control for the building thermal management with optimal setting design Noureddine Boutchich; Ayoub Moufid; Najib Bennis
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp134-143

Abstract

Today, the building sector is the most important consumer of energy. The main challenge in building management is to obtain the desired performance taking into account many aspects such as comfort requirements, variation of building physical characteristics, system constraints, and energy management. For this purpose, a predictive control approach applied to the building thermal has been designed to achieve desired performances combined with an energy optimization approach based on intrinsic system parameters. The developed approach is applied with an online identification system for effective predictive control to take into account the reel building characteristics and to choose the optimal tuning parameters. The simulation results show good performances in terms of accuracy and robustness face to internal and external disturbances with respect to system constraints.
Response time optimization for vulnerability management system by combining the benchmarking and scenario planning models Arif Basuki; Andi Adriansyah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp561-570

Abstract

The growth of information and communication technology has made the internet network have many users. On the other side, this increases cybercrime and its risks. One of the main attack targets is network weakness. Therefore, cyber security is required, which first does a network scan to stop the attack. Points of vulnerability on the network can be discovered using scanning techniques. Furthermore, mitigation or recovery measures can be implemented. However, it needs a short response time and high accuracy while scanning to reduce the level of damage caused by cyber-attacks. In this paper, the proposed method improves the performance of a vulnerability management system based on network and port scanning by combining the benchmarking and scenario planning models. On a network scanning to discover open ports on a subnet, Masscan can achieve response times of less than 2 seconds, and on scenario planning for detection on a single host by Nmap can reach less than 4 seconds. It was combining both models obtained an adequate optimization response time. The total response time is less than 6 seconds.
Automatic video censoring system using deep learning Yash Verma; Madhulika Bhatia; Poonam Tanwar; Shaveta Bhatia; Mridula Batra
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6744-6755

Abstract

Due to the extensive use of video-sharing platforms and services, the amount of such all kinds of content on the web has become massive. This abundance of information is a problem controlling the kind of content that may be present in such a video. More than telling if the content is suitable for children and sensitive people or not, figuring it out is also important what parts of it contains such content, for preserving parts that would be discarded in a simple broad analysis. To tackle this problem, a comparison was done for popular image deep learning models: MobileNetV2, Xception model, InceptionV3, VGG16, VGG19, ResNet101 and ResNet50 to seek the one that is most suitable for the required application. Also, a system is developed that would automatically censor inappropriate content such as violent scenes with the help of deep learning. The system uses a transfer learning mechanism using the VGG16 model. The experiments suggested that the model showed excellent performance for the automatic censoring application that could also be used in other similar applications.
Development of a web-based single-phase load monitoring and auditing system Oluwaseun Ibrahim Adebisi; Isaiah Adediji Adejumobi; Simeon Matthew; Azeez Aderibigbe Abdulsalam
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6785-6795

Abstract

In a developing nation like Nigeria, the conventional load monitoring and billing system has proved to be tedious, time-consuming, expensive, and prone to human error over the years. Therefore, this creates the need for an efficient system that can assist the Utility to monitor the energy consumption trend of the customers remotely. This work developed a web-based single-phase load monitoring and auditing system using NodeMCU (ESP8266) microcontroller, PZEM-004T sensor, and liquid crystal display (LCD) module for the hardware unit and Blynk internet of things (IoT) platform for the software unit. The system design was implemented around the ESP8266 microcontroller with relevant design models, and standard power and energy equations programmed into the microcontroller in the Arduino integrated development environment. The developed system was load tested to examine its performance and determine its reading error. The hardware and software units of the system operated satisfactorily when tested. The reading accuracy for current and voltage measured by the device were ±0.2% and ±0.4%, respectively, giving a reading error of ±0.8% for power measurement. The developed system is suitable for residential, commercial, and similar applications where the energy usage trend of some small loads is required for management purposes.
Data augmentation by combining feature selection and color features for image classification Kittikhun Meethongjan; Vinh Truong Hoang; Thongchai Surinwarangkoon
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6172-6177

Abstract

Image classification is an essential task in computer vision with various applications such as bio-medicine, industrial inspection. In some specific cases, a huge training data is required to have a better model. However, it is true that full label data is costly to obtain. Many basic pre-processing methods are applied for generating new images by translation, rotation, flipping, cropping, and adding noise. This could lead to degrade the performance. In this paper, we propose a method for data augmentation based on color features information combining with feature selection. This combination allows improving the classification accuracy. The proposed approach is evaluated on several texture datasets by using local binary patterns features.
Auxetic material in biomedical applications: a systematic review Andrés Diaz Melgarejo; Jose Luis Ramírez; Astrid Rubiano
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5880-5889

Abstract

This study reviews and analyzes the different auxetic materials that have been developed in recent years. The search for research articles was carried out through one of the largest databases such as ScienceDirect, where 845 articles were collected, of which several filters were carried out to have a base of 386 articles. There are a variety of materials depending on their structure, composition, and industrial application, highlighting biomedical applications from tissue engineering, cell proliferation, skeletal muscle regeneration, transportation, bio-prosthesis to biomaterial. The present paper provides an overview of auxetic materials and its applications, providing a guide for designers and manufacturers of devices and accessories in any industry.
Octa-band reconfigurable monopole antenna frequency diversity 5G wireless Ali Kadhum Abd; Jamal Mohammed Rasool
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.pp1606-1617

Abstract

An octa-band frequency-reconfigurable antenna (28×14×1.5 mm3) with a broad tuning range is shown. Antenna mode1 (4.31 GHz) works in one single-band mode and two dual-band in modes 2 and 3 (i.e., 3.91 and 5.9 GHz) as well as one tri-band in mode 4 (i.e., 3.09, 5.65, and 7.92 GHz) based on the switching situation of the antenna. Changing capacitance for frequency reconfigurability is accomplished with the use of lumped components. The antenna’s observed tuning spans from 3.09 GHz to 7.92 GHz. for all the resonant bands, the suggested antenna has a voltage standing waves ratio (VSWR)<1.45 except for one band with a VSWR<1.85. From 70.57% to 97.93%, the suggested structure’s radiation efficiency may be calculated. For a better understanding proposed antenna’s far field and scattering characteristics, we used CST Microwave Studio 2021. We may conclude that our suggested antenna is suitable for today’s wireless applications, which need multiband and multimode small antennas. Using a small stainless-steel wire as a switch, a prototype of the antenna design is built and tested to verify the simulation findings. The suggested reconfigurable antenna’s strong concordance between simulated and measured findings.
Software quality model based on development team characteristics Hamidreza Asfa; Taghi Javdani Gandomani
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp859-871

Abstract

Many factors have a significant impact on producing high-quality software products. Development team members are among the most important factors. Paying attention to the quality from this perspective will be a good innovation in the software development industry. Given that team members play a very important role in software products, this study tries to focus specifically on team characteristics in software product quality and provide a qualitative model based on this. The required data were collected through observations and interviews with project managers and development team members in several companies under study. Then, data were analyzed through hierarchical analysis. According to the results, the use of this model led to the improvement of the software development process so that the team members were satisfied with it. Also, time management was improved, and the customer expressed his satisfaction with the use of this model. Finally, data analysis showed that this model may lead to faster product delivery.
Optimal trajectory tracking control for a wheeled mobile robot using backstepping technique Said Fadlo; Abdelhafid Ait Elmahjoub; Nabila Rabbah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5979-5987

Abstract

This work studies an optimal trajectory tracking of a wheeled mobile robot with the objective of minimizing energy consumption. First, the mathematical model, which takes into account the kinematic model of the mobile robot and the dynamic model of the actuators is presented. Then, a backstepping controller is designed and its parameters are tuned to satisfy several strict criteria such as rapid convergence, matching desired trajectory, and minimizing energy. For that, two cost functions were investigated and the best one has been selected. The significant reduction in energy losses achieved for all the proposed motion scenarios proves the effectiveness of our approach.

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