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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 65 Documents
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Search results for , issue "Vol 12, No 3: June 2023" : 65 Documents clear
Analysis and description S-box generation for the AES algorithm-a new 3D hyperchaotic system Hayder Kadhim Zghair; Mehdi Ebady Manaa; Safa Saad A. Al-Murieb; Fryal Jassim Abd Al-Razaq
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4824

Abstract

In this paper, a description, and analysis of a novel 3-D dimension hyperchaotic system is implemented. The proposed system oscillation is two-order autonomous and consisted of a nine-term and symmetric oscillation w.r.t x-axis. It is proved analysis by Kaplan-York dimension, waveform analysis, phase portrait, and Lyapunov exponent. This work-study stability and equilibrium point and Routh stability criteria produced that the new system has one unstable point from the type saddle-focus point. One of the characteristics of the proposed system is hyperchaotic since this system has two Lyapunov large than zero. This system is applied to generate a chaotic  (S-box) based in advanced encryption standard (AES) algorithm for text encryption and gives a high level of security. In addition to the description, and analysis S-box. Therefore. the proposed algorithm is satisfied the high randomness of entropy value and passes the National Institute of Standards and Technology (NIST) parameters and another test. Mathematica and MATLAB programs simulated some results.
A solution approach to minimum spanning tree problem under fermatean fuzzy environment Francis Remigius Perpetua Mary; Swaminathan Mohanaselvi; Said Broumi
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4794

Abstract

In classical graph theory, the minimal spanning tree (MST) is a subgraph with no cycles that connects each vertex with minimum edge weights. Calculating minimum spanning tree of a graph has always been a common problem throughout ages. Fuzzy minimum spanning tree (FMST) is able to handle uncertainty existing in edge weights for a fuzzy graph which occurs in real world situations. In this article, we have studied the MST problem of a directed and undirected fuzzy graph whose edge weights are represented by fermatean fuzzy numbers (FFN). We focus on determining an algorithmic approach for solving fermatean fuzzy minimum spanning tree (FFMST) using the modified Prim’s algorithm for an undirected graph and modified optimum branching algorithm for a directed graph under FFN environment. Since the proposed algorithm includes FFN ranking and arithmetic operations, we use FFNs improved scoring function to compare the weights of the edges of the graph. With the help of numerical examples, the solution technique for the proposed FFMST model is described.
A multiband triangular antenna for wireless communication applications Nagnath Biradar; Kishan Singh
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4844

Abstract

Modern technology has made it easier to perform many tasks, including data, voice, video, and short-range device-to-device communication. These characteristics operate at different frequencies. In order to realize compact electronic devices and give users ease of movement, the antenna should operate on various frequency bands. This paper discusses the design of quad band antenna operating from 2.1 to 2.6 GHz, 3.9 to 4.9 GHz, 5.1 to 6.3 GHz, and 7.4 to 11.2 GHz. The realization of multiband is achieved using slots as parasitic elements on the radiator. These slots alter the antenna's regular current flow by generating a local, out-of-phase current channel with the same amplitude. The presented antenna has an overall electrical dimension of 0.22×0.16×0.01 λ3 (λ is determined using the frequency of 2.1 GHz). The 1.6 mm thickness FR4 substrate serves as the development platform for the proposed multiband antenna. The antenna has good reflection coefficient (S11), voltage standing wave ratio (VSWR), radiation characteristics, anda peak gain of around 5.1 dB. The results show design usefulness for wireless applications and are consistent with the measured values.
Fish drying machine with PV system for fisherman to support blue economy I Gusti Made Ngurah Desnanjaya; I Komang Arya Ganda Wiguna; I Made Aditya Nugraha
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4690

Abstract

The abundance of fish catches in Indonesia is excellent potential. Still, if the abundant results cannot be adequately managed and are just wasted, it will eventually lead to bad things. This problem was also found in Seraya village, Karangasem, Bali, where large fish yields and the fish processing process were still constrained by weather and environmental conditions causing the expected results to not be achieved. To overcome this, a photovoltaic (PV) system-based fish dryer was developed that can assist the fish drying process. Utilization of this system is also supported by good solar energy potential. The system can generate 402.78 Wh of electrical energy per day, covering 104.89% of the electrical energy demand of the fish dryer. The results of statistical tests using the Mann-Whitney test for fish weight and unpaired t-test for fish moisture content showed no significant results (p0.05). This value states that there is no difference in the results of drying fish with the PV system and the traditional method. From this, we can conclude that fish drying using a solar power system works similarly to conventional fish drying methods.
Loss reduction of transmission lines using PSO-based optimum performance of UPFC Shaimaa A. Hussein; Dhari Yousif Mahmood; Ali Hussein Numan
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4559

Abstract

Transmission line losses are one of the essential topics and issues in power systems research. Several methods and techniques have been used to reduce these losses, and one of these modern techniques is flexible alternating current transmission systems (FACTS). In this paper, one of the most important types of this technology, the unified power flow controller (UPFC), was used to reduce losses in the Iraqi national grid (ING) 400 kV. This paper presents an efficient method for minimizing losses of transmission lines in the ING system (400 kV) 46-bus approach. A particle swarm optimization (PSO)-based optimum proportional-integral (PI) controller with UPFC was proposed to obtain the optimal location of UPFC and optimum parameters of the PI controller to achieve the objective function of the research. MATLAB coded the algorithm. The Newton-Raphson method was employed to perform load flow analysis. The results showed that the best place for UPFC is buses (14-17) named BGE4 (Baghdad)-AMN4 (Baghdad), and the total active power and reactive power losses decreased from 727.4593 to 579.3874 MW and from 5155.9 to 3971.1 MVAR, respectively and also led to voltage regulation.
Improve steganography system using agents software based on statistical and classification technique Estabraq Hussein Jasim Halboos; Abbas M. Albakry
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4540

Abstract

In digital communications, information security is a paramount necessity. In the hiding algorithm, there are three basic parameters: security, capacity, and imperceptibility. Therefore, there are many ways to design the steganography algorithm, such as least significant bit (LSB), discrete wave transformation (DWT), and discrete cosine transform (DCT). The aim of this paper is to improve agent software design based on a steganography system. It proposed an agent system based on a support vector machine (SVM) classifier to hide a secret message in a certain cover image. The common dataset for steganography uses 80% training and 20% testing to get accurate results. Developing an agent system depends on six statistical parameters such as energy, standard deviation, histogram, variance, mean, and entropy. This resulted in features classified by the SVM classifier to predict the best cover image to be nominated for embedding. Worthy results were obtained in terms of imperceptibility, attack, and cover image prediction by statistical issues.
Framework for selecting the best software quality model for a smart health application based on intelligent approach Ashraf Mousa Saleh; Odai Enaizan
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4945

Abstract

There is difficulty in knowing how to weigh the factors of software quality models so that decision-making can be eased. Furthermore, previous work was limited to undertake evaluation and selection of appropriate software quality model based upon multi-criteria in the context of smart health applications. This paper aims to evaluate and select an appropriate model of software quality based on multi-criteria decision-making (MCDM) by three phases of framework. Firstly, investigation of software quality models and factors that were identified based on ‘fuzzy delphi’. Secondly, identification of quality models that have uniform multi-criteria so that a decision matrix could be established. Uniform multi-criteria were used in the decision matrix as the basis of the models of quality and the multi-criteria. Subsequently, MCDM approach is adopted and the bases used in the employment of the MCDM approach for the eva luation and selection of the software quality model were technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy analytical hierarchy process (FAHP). The results demonstrated that seven quality factors could be considered as the key factors based upon fuzzy delphi, i.e., usability, maintainability, reliability, interoperability, portability, modifiability, and efficiency. Also, reults shows that McCall is the most appropriate model.
COVID-19 classification using CNN-BiLSTM based on chest X-ray images Denis Eka Cahyani; Anjar Dwi Hariadi; Faisal Farris Setyawan; Langlang Gumilar; Samsul Setumin
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4848

Abstract

Cases of the COVID-19 virus continue to spread still needs to be considered even though we have entered the post-pandemic era. Rapid identification of COVID-19 cases is necessary to prevent the virus from spreading further. This study developed a chest X-ray-based (CXR) COVID-19 classification for COVID-19 detection using the convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) combination model and compared the CNN-BiLSTM combination model with CNN models. The CNN models used in this study are the transfer learning models, namely Resnet50, VGG19, InceptionV3, Xception, and AlexNet. This research classifies CXR into three groups: COVID-19, normal, and viral pneumonia. In comparison to other models, the Resnet50-BiLSTM model is the most accurate and hence the best. The accuracy of the Resnet50-BiLSTM model was 98.48%. The model that obtains the next highest accuracy i.e Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, InceptionV3, Xception-BiLSTM, Xception, AlexNet-BiLSTM, and AlexNet. In this study, precision, recall, and F1-measure are also employed to demonstrate that Resnet50-BiLSTM achieves the highest value compared to other approaches. When compared to previous studies, this study enhances classification performance results.
A missing data imputation method based on salp swarm algorithm for diabetes disease Geehan Sabah Hassan; Noora Jamal Ali; Asma Khazaal Abdulsahib; Farah Jasim Mohammed; Hassan Muwafaq Gheni
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4528

Abstract

Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve Bayesian classifier (NBC) have been enhanced as compared to the dataset before applying the proposed method. Moreover, the results indicated that issa was performed better than the statistical imputation techniques such as deleting the samples with missing values, replacing the missing values with zeros, mean, or random values.
A binary classification model of COVID-19 based on convolution neural network Reham Sabah Saeed; Bushra Kadhim Oleiwi Chabor Alwawi
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4832

Abstract

The outbreak of the new coronavirus (COVID-19) had resulted in the creation of a disaster all over the world and it had become a highly acute and severe illness. The prevalence of this disease is increasing rapidly worldwide. The technology of deep learning (DL) became one of the hot topics in the computing context and it is widely implemented in a variety of the medical applications. Those techniques proved to be sufficient tools for the clinicians in automatic COVID-19 diagnosis. In the present study, a DL technology that is based on convolution neural networks (CNN) models had been suggested for the binary COVID-19 classification. In the initial step of the suggested model, COVID-19 data-set of chest X-ray (CXR) images have been obtained then preprocessed. Whereas in the second stage, a new CNN model has been built and trained for diagnosing COVID-19 data-set as (positive) infection or (negative) normal cases. The suggested architecture had a success in classifying COVID-19 with the training model accuracy that had reached 96.57% for the training data-set and 92.29% for validating data-set and could reach the target point with a minimal learning rate for training this model with promising results.

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