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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 112 Documents
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Search results for , issue "Vol 12, No 4: August 2022" : 112 Documents clear
Improving multimedia data transmission quality in wireless multimedia sensor networks though priority-based data collection Falah Abbood, Mohammed; Falih Kadhim, Mohammed; Raheem Kadhim, Ahmed
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3595-3606

Abstract

Wireless multimedia sensor networks (WMSNs) are special kinds of wireless sensor networks (WSN) that can send multimedia data such as audio and video streams. Sensors used in WMSNs are smart, tiny, and resource constraint sensor nodes (SNs) distributed in a large area. Typically, multimedia data are large in comparison to other data types. As a result, WMSNs have to deal with high volumes of packet transmission, leading to a high rate of packet loss and network congestion. Network congestion can significantly affect the quality of service and usually lead to high energy consumption. Thus, to improve the quality of service (QoS) and transmission performance, it is necessary to deal with network congestion. In the past, different packet prioritizing methods were proposed to deal with this issue. However, improving QoS usually requires high energy to function correctly. Consequently, using rechargeable sensor nodes to reduce energy consumption is an acute solution. In this research, priority-based data collection is considered to cut down on data distortion and improve the QoS of the multimedia sensor network. Additionally, energy harvesting sensor nodes were used to reduce energy consumption due to the high transmission rate. The simulation result shows a noticeable improvement in the performance of our proposed method in comparison to previous methods.
Fault tolerant nine-level inverter topology for solar water pumping applications Narasimha Rao Mucherla; Nagaraj Karthick; Airineni Madhukar Rao
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3485-3493

Abstract

Diminished voltage pressure and occasional-general harmonic distortion are the essential causes for such a ways and extensive usage of multi-level inverters (MLIs) in numerous industrial applications. Nonetheless, unwavering quality is one of the significant worries of MLIs as it utilizes countless switches as contrasted to 2-level inverters. Here, a fault tolerant 9-level inverter setup for the use of photovoltaic (PV) system-water pumping applications is suggested. This fault tolerant 9-level inverter is accomplished by combining a 2-level inverter, a 3-level fault tolerant inverter alongside switches with bidirectional ability. The setup is taken care of with four PV fed sources. The arrangement suggested shows the behavior towards switch fault in at least one inverter legs under open circuit conditions. On account of source failure, it could use the better hotspot for introducing continuous power to the water pumping motor. Meanwhile, the suggested fault-tolerant inverter works as seven-level inverter. The activity related to proposed inverter in the course of various failure modes is mentioned and simulated the usage of MATLAB/Simulink.
Stream-keys generation based on graph labeling for strengthening Vigenere encryption Antonius Cahya Prihandoko; Dafik Dafik; Ika Hesti Agustin
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3960-3969

Abstract

This paper address the cryptographic keys management problem: how to generate the cryptographic keys and apply them to secure encryption. The purpose of this research was to study on utilizing graph labeling for generating stream-keys and implementing the keys for strengthening Vigenere encryption. To achieve this objective, the research was carried out in four stages: developing an algorithm for generating stream-keys, testing the randomness of the constructed keys, implementing the eligible keys in a modified Vigenere encryption and, finally, analyzing the security of the encryption. As the result, most of stream-keys produced by the algorithm are random, and the implementation of the stream keys to the modified Vigenere cipher achieve good security. The contributions of this research are utilizing graph labeling to generate stream-keys and providing different encryption keys for different blocks in a block based cipher with low storage capacity. The novel technical results yielded from this research are the algorithm of developing the source of the stream-keys based on graph labeling, the algorithm of constructing the initial block keys, and the protocol of a modified Vigenere encryption using stream-keys and operated in cipher block chaining mode.
Optimal state estimation techniques for accurate measurements in internet of things enabled microgrids using deep neural networks Rao Padupanambur, Sudhakar; Riyaz Ahmed, Mohammed; Divakar, Bangalore Prabhakar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4288-4301

Abstract

The employment of microgrids in smart cities is not only changing the landscape of power generation, transmission, and distribution but it helps in green alleviation by converting passive consumers into active produces (using renewable energy sources). Real-time monitoring is a crucial factor in the successful adoption of microgrids. Real-time state estimation of a microgrid is possible through internet-of-things (IoT). State estimation can provide the necessary monitoring of grid for many system optimization applications. We will use raw and missing data before we learn from data, the processing must be done. This paper describes various Kalman variants use for preprocessing. In this paper a formulated approach along with algorithms are described for optimal state estimation and forecasting, with weights update using deep neural networks (DNN) is presented to enable accurate measurements at component and system level model analysis in an IoT enabled microgrid. The real load data experiments are carried out on the IEEE 118-bus benchmark system for the power system state estimation and forecasting. This research paves a way for developing a novel DNN based algorithms for a power system under dynamically varying conditions and corresponding time dependencies.
Employing deep learning for lung sounds classification Dhari Satea, Huda; Saleem Elameer, Amer; Hussein Salman, Ahmed; Dhari Sateaa, Shahad
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4345-4351

Abstract

Respiratory diseases indicate severe medical problems. They cause death for more than three million people annually according to the world health organization (WHO). Recently, with corona virus disease 19 (COVID-19) spreading the situation has become extremely serious. Thus, early detection of infected people is very vital in limiting the spread of respiratory diseases and COVID-19. In this paper, we have examined two different models using convolution neural networks. Firstly, we proposed and build a convolution neural network (CNN) model from scratch for classification the lung breath sounds. Secondly, we employed transfer learning using the pre-trained network AlexNet applying on the similar dataset. Our proposed model achieved an accuracy of 0.91 whereas the transfer learning model performing much better with an accuracy of 0.94.
Forest quality assessment based on bird sound recognition using convolutional neural networks Nazrul Effendy; Didi Ruhyadi; Rizky Pratama; Dana Fatadilla Rabba; Ananda Fathunnisa Aulia; Anugrah Yuwan Atmadja
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4235-4242

Abstract

Deforestation in Indonesia is in a status that is quite alarming. From year to year, deforestation is still happening. The decline in fauna and the diminishing biodiversity are greatly affected by deforestation. This paper proposes a bioacoustics-based forest quality assessment tool using Nvidia Jetson Nano and convolutional neural networks (CNN). The device, named GamaDet, is a portable physical product based on the microprocessor and equipped with a microphone to record the sounds of birds in the forest and display the results of their analysis. In addition, a Google Collaboratorybased GamaNet digital product is also proposed. GamaNet requires forest recording audio files to be further analyzed into a forest quality index. Testing the forest recording for 60 seconds at an arboretum forest showed that both products could work well. The GamaDet takes 370 seconds, while the GamaNet takes 70 seconds to process the audio data into a forest quality index and a list of detected birds.
A survey of deepfakes in terms of deep learning and multimedia forensics Wildan Jameel Hadi; Suhad Malallah Kadhem; Ayad Rodhan Abbas
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4408-4414

Abstract

Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfakedetection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a deeper overview of i) the environment of deepfake creation and detection, ii) how deep learning and forensic tools contributed to the detection of deepfakes, and iii) finally how in the future incorporating both deep learning technology and tools for forensics can increase the efficiency of deepfakes detection.
Innovative unmanned aerial vehicle self-backhauling hybrid solution using RF/FSO system for 5G network Mohammad A. Massad; Baha' Adnan Alsaify; Abdallah Y. Alma'aitah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4483-4506

Abstract

The impractical association of a dedicated fiber optic backhauling link with each basestation in future wireless area network (WAN) networks promoted self-backhaulingto become one of the most practical techniques for ultra-dense deployments. Selfbackhauling reduces the number of stations with fiber-optic links, while the remaining stations can communicate with the core network through wireless multi-hopingconnections. Nevertheless, routing through self-backhauling stations is an NP-hardproblem. In this study, we propose a routing scheme based on a semi-distributedself-learning algorithm to reduce the end-to-end latency which achieve better stability against the dynamic nature of the mobile network, such as load variations and linkfailures. The proposed solution offers changing propagation medium between freespace optical (FSO) and radio frequency (RF); this dynamic change between every twohops reduces power consumption, increases throughput, and minimizes latency. Basedon the performed simulation, our proposed algorithm measured better overall bit error rate (BER) compared to both RF-only and free-space optical FSO-only techniquesresulting in increased backhauling capacity and reduced overall route interference.
The prediction of coronavirus disease 2019 outbreak on Bangladesh perspective using machine learning: a comparative study Rahman, Maqsudur; Ahmed, Md. Tofael; Nur, Shafayet; Touhidul Islam, Abu Zafor Muhammad
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4276-4287

Abstract

Coronavirus disease 2019 (COVID-19) has made a huge pandemic situation in many countries of the world including Bangladesh. If the increase rate of this threat can be forecasted, immediate measures can be taken. This study is an effort to forecast the threat of present pandemic situation using machine learning (ML) forecasting models. Forecasting was done in three categories in the next 30 days range. In our study, multiple linear regression performed best among the other algorithms in all categories with R2 score of 99% for first two categories and 94% for the third category. Ridge regression performed great for the first two categories with R2 scores of 99% each but performed poorly for the third category with R2 score of 43%. Lasso regression performed reasonably well with R2 scores of 97%, 99% and 75% for the three categories. We also used Facebook Prophet to predict 30 days beyond our train data which gave us healthy R2 scores of 92% and 83% for the first two categories but performed poorly for the third category with R2 score of 34%. Also, all the models’ performances were evaluated with a 40-day prediction interval in which multiple linear regression outperformed other algorithms.
Fake news detection for Arabic headlines-articles news data using deep learning Hassan Najadat; Mais Tawalbeh; Rasha Awawdeh
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3951-3959

Abstract

Fake news has become increasingly prevalent in recent years. The evolution of social websites has spurred the expansion of fake news causing it to a mixture with truthful information. English fake news detection had the largest share of studies, unlike Arabic fake news detection, which is still very limited. Fake news phenomenon has changed people and social perspectives through revolts in several Arab countries. False news results in the distortion of reality ignite chaos and stir public judgments. This paper provides an Arabic fake news detection approach using different deep learning models including long short-term memory and convolutional neural network based on article-headline pairs to differentiate if a news headline is in fact related or unrelated to the parallel news article. In this paper, a dataset created about the war in Syria and related to the Middle East political issues is utilized. The whole data comprises 422 claims and 3,042 articles. The models yield promising results.

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