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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 6,393 Documents
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Performance assessment of VoIP service over different handover mechanisms in UMTS networks Qusay Jalil Kadhim; Ali M. Alsahlany; Ahmed Hassan Hadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4214-4220

Abstract

Many researchers have discussed various topics in universal mobile telecommunication system (UMTS) networks: the process of switching from one cell to another for the subscriber and the impact of the quality of the connection during the transition process, quality of services (QoS), the quality of the uplink and downlink carrier line, the various types of code for the voice transmitted through the Internet, especially the research that discussed voice over internet protocol (VoIP) technology as voice travels from cell to cell in mobile networks, depending on the type of delivery. In this paper, a proposed scenario of a UMTS network was implemented to evaluate the multicellular VoIP movement; the proposed UMTS network was simulated using the OPNET 14.5 simulator. The calculation and analysis of the different parameters of the user while moving from one cell to another with different movement speeds considered, the best mean opinion score (MOS) value (3.19) registered for the scenario (soft handover) comparing with another type of handover (3.00).
Performance analysis of sentiments in Twitter dataset using SVM models Lakshmana Kumar Ramasamy; Seifedine Kadry; Yunyoung Nam; Maytham N. Meqdad
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2275-2284

Abstract

Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models.
Increasing radiation power in half width microstrip leaky wave antenna by using slots technique Muhannad Kaml Abdulhameed; Sarah Rafil Hashim; Noor Kamil Abdalhameed; Ahmed Jamal Abdullah Al-Gburi
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.pp392-398

Abstract

The radiation power in the endfire is decreased while the main beam of half substrate integrated waveguide scan from broadside to endfire in a forward. The design of half-width microstrip leaky-wave antenna (HW-MLWA) has been presented in this work to increase the power radiation near endfire by using the slots technique in the radiation element. This slot leads to a decrease the cross-polarization. The proposed design comprises one element of HW-MLWA with repeated meandered square slots in the radiation element. One aspect of this antenna is generated by using a half substrate integrated waveguide with a full tapered feed line. The proposed antenna was terminated by load of 50 Ω, and feed on the other end of the antenna. Finally, the suggested design is simulated and acceptable results were found. The released gain is increased from 10.6 dBi to 12 dBi at 4.3 GHz. This design is suitable for unmanned aerial vehicle UAVs at C band application.
Copy-move forgery detection using convolutional neural network and K-mean clustering Ava Pourkashani; Asadollah Shahbahrami; Alireza Akoushideh
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2604-2612

Abstract

Copying and pasting a patch of an image to hide or exaggerate something in a digital image is known as a copy-move forgery. Copy-move forgery detection (CMFD) is hard to detect because the copied part image from a scene has similar properties with the other parts of the image in terms of texture, light illumination, and objective. The CMFD is still a challenging issue in some attacks such as rotation, scaling, blurring, and noise. In this paper, an approach using the convolutional neural network (CNN) and k-mean clustering is for CMFD. To identify cloned parts candidates, a patch of an image is extracted using corner detection. Next, similar patches are detected using a pre-trained network inspired by the Siamese network. If two similar patches are not evidence of the CMFD, the post-process is performed using k-means clustering. Experimental analyses are done on MICC-F2000, MICC-F600, and MICC-F8 databases. The results showed that using the proposed algorithm we can receive a 94.13% and 96.98% precision and F1 score, respectively, which are the highest among all state-of-the-art algorithms.
A new method for watermarking color images using virtual hiding and El-Gamal ciphering Noor Kadhim Ayoob; Asraa Abdullah Hussein; Rusul Mohammed Neamah
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5251-5258

Abstract

One of the important issues in the era of computer networks and multimedia technology development is to find ways to maintain the reliability, credibility, copyright and non-duplication of digital content transmitted over the internet. For the purpose of protecting images from illegal usage, a watermark is used. A hidden digital watermark is the process of concealing information on a host to prove that this image is owned by a specific person or organization. In this paper, a new method has been proposed to use an RGB logo to protect color images from unlicensed trading. The method depends on retrieving logo data from specific locations in the host to form a logo when the owner claims the rights to those images. These positions are chosen because their pixels match the logo data. The locations of matching pixels are stored in a table that goes through two stages of treatment to ensure confidentiality: First, table compression, second, encoding positions in the compressed table through El-Gamal algorithm. Because the method depends on the idea of keeping host pixels without change, PSNR will always be infinity. After subjecting the host to five types of attack, the results demonstrate that the method can effectively protect the image and hidden logo is retrieved clearly even after the attacks.
An FPGA-based network system with service-uninterrupted remote functional update Tze Hon Tan; Chia Yee Ooi; Muhammad Nadzir Marsono
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3222-3228

Abstract

The recent emergence of 5G network enables mass wireless sensors deployment for internet-of-things (IoT) applications. In many cases, IoT sensors in monitoring and data collection applications are required to operate continuously and active at all time (24/7) to ensure all data are sampled without loss. Field-programmable gate array (FPGA)-based systems exhibit a balanced processing throughput and datapath flexibility. Specifically, datapath flexibility is acquired from the FPGA-based system architecture that supports dynamic partial reconfiguration feature. However, device functional update can cause interruption to the application servicing, especially in an FPGA-based system. This paper presents a standalone FPGA-based system architecture that allows remote functional update without causing service interruption by adopting a redundancy mechanism in the application datapath. By utilizing dynamic partial reconfiguration, only the updating datapath is temporarily inactive while the rest of the circuitry, including the redundant datapath, remain active. Hence, there is no service interruption and downtime when a remote functional update takes place due to the existence of redundant application datapath, which is critical for network and communication systems. The proposed architecture has a significant impact for application in FPGA-based systems that have little or no tolerance in service interruption.
Optimization of network traffic anomaly detection using machine learning ChoXuan Do; Nguyen Quang Dam; Nguyen Tung Lam
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2360-2370

Abstract

In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyber-attack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms.
Various demand side management techniques and its role in smart grid–the state of art Muthuselvi Gomathinayagam; Saravanan Balasubramanian
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.pp150-156

Abstract

The current lifestyle of humanity relies heavily on energy consumption, thus rendering it an inevitable need. An ever-increasing demand for energy has resulted from the increasing population. Most of this demand is met by the traditional sources that continuously deplete and raise significant environmental issues. The existing power structure of developing nations is aging, unstable, and unfeasible, further prolonging the problem. The existing electricity grid is unstable, vulnerable to blackouts and disruption, has high transmission losses, low quality of power, insufficient electricity supply, and discourages distributed energy sources from being incorporated. Mitigating these problems requires a complete redesign of the system of power distribution. The modernization of the electric grid, i.e., the smart grid, is an emerging combination of different technologies designed to bring about the electrical power grid that is changing dramatically. Demand side management (DSM) allow customers to be more involved in contributors to the power systems to achieve system goals by scheduling their shiftable load. Effective DSM systems require the participation of customers in the system that can be done in a fair system. This paper focuses primarily on techniques of DSM and demand responses (DR), including scheduling approaches and strategies for optimal savings.
Real-time human detection for electricity conservation using pruned-SSD and arduino Ushasukhanya S.; Jothilakshmi S.
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1510-1520

Abstract

Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a Modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame.
Natural language processing based advanced method of unnecessary video detection Nazmun Nessa Moon; Imrus Salehin; Masuma Parvin; Md. Mehedi Hasan; Iftakhar Mohammad Talha; Susanta Chandra Debnath; Fernaz Narin Nur; Mohd. Saifuzzaman
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5411-5419

Abstract

In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare the data set. We use both Naive Bayes and logistic regression classification algorithms in this detection system to determine the best accuracy for our system. In our research, our video MP4 data has converted to plain text data using the python advance library function. This brief study discusses the identification of unauthorized, unsocial, unnecessary, unfinished, and malicious videos when using oral video record data. By analyzing our data sets through this advanced model, we can decide which videos should be accepted or rejected for the further actions.

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