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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 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. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 64 Documents
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Search results for , issue "Vol 12, No 2: April 2023" : 64 Documents clear
A cascade multi-level inverter topology with reduced switches and higher efficiency Osama Yaseen Khudair Al-Atbee; Khalid M. Abdulhassan
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4138

Abstract

The use of multi-level inverter (MLI) technologies in high-power, medium-voltage energy regulation has been more popular in recent years. Despite the fact that the multilayer inverter has a lot of benefits, it has certain disadvantages in the layer of higher levels due to the enormous number of semiconductor switches that it employs in its construction. This may result in the inverter being of a huge size and costing a lot of money, as well as a significant rise in losses. As a result, the new MLI is suggested to minimize the number of switches in order to alleviate these challenges. This article describes a cascaded multilevel inverter with lower devices. The suggested cascaded multilevel inverter is intended for use in minimizing total harmonic distortion (THD), as shown in MATLAB/Simulink by the graph. Multilevel inverters benefit from the switching pattern of semiconductor switches, which may be used to improve their overall performance. This approach lowers the switching loss while simultaneously increasing the efficiency. In order to validate the suggested approach, simulations are carried out using the MATLAB/Simulink programming environment.
Text-to-image generation based on AttnDM-GAN and DMAttn-GAN: applications and challenges Razan Bayoumi; Marco Alfonse; Mohamed Roushdy; Abdel-Badeeh M. Salem
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4199

Abstract

The deep fake faces generation using generative adversarial networks (GANs) has reached an incredible level of realism where people can’t differentiate the real from the fake. Text-to-face is a very challenging task compared to other text-to-image syntheses because of the detailed, precise, and complex nature of the human faces in addition to the textual description details. Providing an accurate realistic text-to-image model can be useful for many applications such as criminal identification where the model will be acting as the forensic artist. This paper presents text-to-image generation based on attention dynamic memory (AttnDM-GAN) and dynamic memory attention (DMAttn-GAN) that are applied to different datasets with an analysis that shows the different complexity of different datasets’ categories, the quality of the datasets, and their effect on the results of the resolution and consistency of the generated images.
Model of optimal distribution of network resources with constraints on quality of service indicators Amirsaidov Ulugbek; Qodirov Azamat
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4706

Abstract

Existing algorithms and mathematical models of queuing at the nodes of a telecommunications network are considered in this paper. The necessity of coordinated solutions to problems of distribution of channel and buffer resources of the network is shown. A model for the optimal distribution of channel and buffer resources on network nodes has been developed. The optimization (minimization) criterion is the total average packet delay with constraints on the quality of service (QoS) indicators of heterogeneous flows. The optimization problem is presented as a constrained nonlinear programming problem and solved using the “fmincon” program of the optimization toolbox MATLAB package.
Social crisis detection using Twitter based text mining-a machine learning approach Shoaib Rahman; Nusrat Jahan; Farzana Sadia; Imran Mahmud
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.3957

Abstract

Social-media and blogs are increasingly used for social-communication, an idea and thought publishing platform. Public intentions, wisdom, problems, solutions, mental states are shared in social media. Text is being the best and the most common way to communicate over social networks. All kinds of data shared in social sites like Facebook, Twitter, and Microblogs. People from different pursuance uses these media to publish thoughts and convey messages through text. Consequently, occurrences in social life are rapidly discussed in social blogs in daily manner. This work aims at discovering ongoing social crisis from the Twitter data. Text mining technique and sentiment analysis were applied to detect the current social crisis from the social sites. Twitter data were collected to identify the recent social crisis. Furthermore, the identified crisis was compared to reputed newspapers. A hybrid method used to detect recent social issues resulted nicely. However, our proposed analysis shows identifying rate 89%, 95%, 83%, 53%, and 98% for the top 5 identified crisis accordingly in the date between 27 February and 11 March 2020. The strategy used in this study for the detection of recent social crisis will contribute to the social life and findings of crisis will be eliminated easily.
Evaluation of Bernoulli Naive Bayes model for detection of distributed denial of service attacks Ayodeji Olalekan Salau; Tsehay Admassu Assegie; Adedeji Tomide Akindadelo; Joy Nnenna Eneh
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4020

Abstract

Distributed denial of service is a form of cyber-attack that involves sending several network traffic to a target system such as DHCP, domain name server (DNS), and HTTP server. The attack aims to exhaust computing resources such as memory and the processor of a target system by blocking the legitimate users from getting access to the service provided by the server. Network intrusion prevention ensures the security of a network and protects the server from such attacks. Thus, this paper presents a predicitive model that identifies distributed denial of service attacks (DDSA) using Bernoulli-Naive Bayes. The developed model is evaluated on the publicly available Kaggle dataset. The method is tested with a confusion matrix, receiver operating characteristics (ROC) curve, and accuracy to measure its performance. The experimental results show an 85.99% accuracy in detecting DDSA with the proposed method. Hence, Bernoulli-Naive Bayes-based method was found to be effective and significant for the protection of network servers from malicious attacks.
Reduction of false negatives in multi-class sentiment analysis Chris Aloysius; P. Tamil Selvan
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4682

Abstract

Sentiment analysis classifications are done as positive, negative, as well as neutral ones. The increased usage of social media and its effects on society call for a more thorough, fine-grained explanation than that. In this study, classification is done in five classes-strongly positive, weakly positive, neutral, weakly negative, and strongly negative-in a more precise manner. Instead of using the typical ways of measuring accuracy alone, a novel method to eliminate false negatives (FN) is focused together with a fine-grained categorization. A bigger risk in sentiment analysis is a false negative. FN classification occurs when the context's polarity is identified as True when it is actually false. A complex dataset is used in this research for the experimental study, and the entire dataset is separated into five classes. Each class's FN are assessed using the suggested methodology. Comparing the proposed strategy to other, it was found to achieve about 53% more reduction in FN cases than rule based models and better predictions than compared machine learning models.
Simulation of 3D-space vector modulation for neutral point clamped inverters Palanisamy Ramasamy; Ramkumar Ravindran; Neetu Gupta; Gunjan Sardana; Indumathi Sekar; Venugopala Aparna Marthanda; Selvakumar Kuppusamy
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4502

Abstract

This paper gives an idea to simulation of three-dimensional space vector modulation for neutral point clamped multilevel inverter. Three dimensional-space vector modulation (3D-SVM) algorithm is progressed method of two dimensional-space vector modulation (2D-SVM) algorithm; it leads to reduce the complexity in reference vector identification and switching time calculation, also it includes the various advantages of 2D-SVM like minimized total harmonic distortion, reduced EMI issues. A simple system for the assortment of switching state vectors to track the reference voltage vectors without using any redundant switching vectors. This proposed method tracks the reference vector by identifyinglsubcubes and prisms by using mathematicallconditions. Here the cost of the proposedltechnique is independentlof voltagellevels oflinverter. This paper realizes the accomplishment of 3D-SVM using a neutral point clamped inverter. The simulation results of the proposed method are verified using MATLAB/Simulink.
Nearest-neighbor field algorithm based on patchMatch for myocardial perfusion motion estimation/correction Haider Ali Jasim Alshamary; Ahmad Sulaiman Abdullah; Sadeq Adnan Hbeeb
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4216

Abstract

Deformation correction and recovery of dynamic magnetic resonance images (DMRI) with low complexity algorithms without compromising image quality is a challenging problem. We proposed a motion estimation deformation-correction compressive sensing (DC-CS) scheme to recover dynamic images from its undersampled measurements. We simplify the complex optimization problem into three sub-problems. The contributions of this research are: introducing a global search strategy instead of the DC registration step, guaranteeing a non-explicit motion estimation that avoids any spatial alignment or registration of the images, and lowering the computational cost to the minimum by using PatchMatch (PM). The simulation result shows that the PM algorithm accelerates the recovery time without losing the quality in comparison with the DC algorithm.
Depression detection in social media comments data using machine learning algorithms Vasha, Zannatun Nayem; Sharma, Bidyut; Esha, Israt Jahan; Al Nahian, Jabir; Polin, Johora Akter
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4182

Abstract

Depression is the next level of negative emotions. When a person is in a sad mood or going through a difficult situation and it is not leaving him and giving him pain continuously and he is unable to bear it anymore, that situation is called depression. The last stage of depression occurs in suicide. According to the World Health Organization (WHO), Currently, 4.4% of people in the world are currently suffering from depression. In 2021, fourteen thousand people committed suicide all over the world and the rating of suicide is increasing day by day. So, our study is to find depressed people by their comments, posts, or texts on social media. We collected almost 10,000 data from Facebook posts, comments, and YouTube comments. Data mining and machine learning (ML) algorithms make our work easier and play a big role in easily detecting a person’s emotions. We applied six classifiers to predict depression non-depression and found the best accuracy on a support vector machine (SVM).
An ameliorated Round Robin algorithm in the cloud computing for task scheduling Nermeen Ghazy; Afaf Abdelkader; Mervat S. Zaki; Kamal A. Eldahshan
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4524

Abstract

Cloud computing is an advanced technology that offers types of assistance on requests. Because of the huge measure of requests got from cloud clients, all requests should be managed efficiently. Therefore, the task scheduling is critical in cloud computing. The provision of computational resources in cloud is controlled by a cloud provider. It is necessary to design high-efficiency scheduling algorithms that are compatible with the corresponding computing paradigms. This paper introduces a new task scheduling method for cloud computing called an ameliorated Round Robin algorithm (ARRA). The proposed algorithm develops an optimal time quantum based on the average of task burst time using fixed and dynamic manners. The experimental results showed that the ARRA significantly outperformed other algorithms including improved RR, enhanced RR, dynamic time quantum approach (ARR) and enhanced RR (RAST ERR) in terms of the average waiting time, average turnaround time and response time.

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