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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
 502   503   504   505   506 
Detection of heart pathology using deep learning methods Naizagarayeva, Akgul; Abdikerimova, Gulzira; Shaikhanova, Aigul; Glazyrina, Natalya; Bekmagambetova, Gulmira; Mutovina, Natalya; Yerzhan, Assel; Tanirbergenov, Adilbek
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6673-6680

Abstract

In the directions of modern medicine, a new area of processing and analysis of visual data is actively developing - a radio municipality - a computer technology that allows you to deeply analyze medical images, such as computed tomography (CT), magnetic resonance imaging (MRI), chest radiography (CXR), electrocardiography and electrocardiography. This approach allows us to extract quantitative texture signs from signals and distinguish informative features to describe the heart's pathology, providing a personified approach to diagnosis and treatment. Cardiovascular diseases (SVD) are one of the main causes of death in the world, and early detection is crucial for timely intervention and improvement of results. This experiment aims to increase the accuracy of deep learning algorithms to determine cardiovascular diseases. To achieve the goal, the methods of deep learning were considered used to analyze cardiograms. To solve the tasks set in the work, 50 patients were used who are classified by three indicators, 13 anomalous, 24 nonbeat, and 1 healthy parameter, which is taken from the MIT-BIH Arrhythmia database.
Novel hybrid generative adversarial network for synthesizing image from sketch Murthy, Pavithra Narasimha; Yeliyur Hanumanthaiah, Sharath Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6293-6301

Abstract

In the area of sketch-based image retrieval process, there is a potential difference between retrieving the match images from defined dataset and constructing the synthesized image. The former process is quite easier while the latter process requires more faster, accurate, and intellectual decision making by the processor. After reviewing open-end research problems from existing approaches, the proposed scheme introduces a computational framework of hybrid generative adversarial network (GAN) as a solution to address the identified research problem. The model takes the input of query image which is processed by generator module running 3 different deep learning modes of ResNet, MobileNet, and U-Net. The discriminator module processes the input of real images as well as output from generator. With a novel interactive communication between generator and discriminator, the proposed model offers optimal retrieval performance along with an inclusion of optimizer. The study outcome shows significant performance improvement.
Optimization algorithms for steady state analysis of self excited induction generator Athamnah, Ibrahim; Anagreh, Yaser; Anagreh, Aysha
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6047-6057

Abstract

The current publication is directed to evaluate the steady state performance of three-phase self-excited induction generator (SEIG) utilizing particle swarm optimization (PSO), grey wolf optimization (GWO), wale optimization algorithm (WOA), genetic algorithm (GA), and three MATLAB optimization functions (fminimax, fmincon, fminunc). The behavior of the output voltage and frequency under a vast range of variation in the load, rotational speed and excitation capacitance is examined for each optimizer. A comparison made shows that the most accurate results are obtained with GA followed by GWO. Consequently, GA optimizer can be categorized as the best choice to analyze the generator under various conditions.
Statistical analysis of the key scheduling of the new lightweight block cipher Kapalova, Nursulu; Algazy, Kunbolat; Haumen, Armanbek; Sakan, Kairat
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6817-6826

Abstract

This research paper is aimed at studying the generation of round keys (GRK) of the lightweight block cipher (LBC), which provides an optimal balance between security, performance, and minimal costs in internet of things (IoT). For comparative analysis, the GRK of the well-known PRESENT algorithm was studied. A number of studies have been carried out to assess the cryptographic strength of encryption algorithms, however, less attention has been paid to the assessment of the reliability of GRK algorithms, which can lead to a possible weakening of a cryptosystem. A trusted GRK should issue random and independent round keys regardless of the secret key. The experiments were carried out with secret keys of low and high density, as well as random numbers. The obtained results show that the GRK of the LBC algorithm generates random round keys that successfully pass tests of the National Institute of Standards and Technology (NIST) for randomness.
Optimal placement of battery energy storage system considering penetration of distributed generations Nguyen, Thuan Thanh; Nguyen, Hoai Phong; Duong, Thanh Long
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6068-6078

Abstract

This paper proposes the optimal problem of location and power of the battery-energy-storage-system (BESS) on the distribution system (DS) considering different penetration levels of distributed generations (DGs). The objective is to minimize electricity cost of the DS in a typical day considering the power limit of DG fed to the DS. Growth optimizer (GO) is first applied to search the BESS’s location and power for each interval of the day. The considered problem and GO method are evaluated on the 18-node DS with two penetrations levels of photovoltaic system and wind turbine. The results demonstrate that the optimal BESS placement significantly reduces electricity cost. Furthermore, the optimal BESS location and power also help to reduce the cut capacity of DGs as their power greater than the load demand. The compared results between GO and particle swarm optimization (PSO) method have shown that GO reaches the better performance than PSO in term the optimal solution and the statistical results. Thus, GO is an effective approach for the BESS placement problem.
Improving the efficiency of photovoltaic cells embedded in floating buoys Nazerian, Vahdat; Asadollahi, Hossein; Sutikno, Tole
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp5986-5999

Abstract

Solar cells are used to power floating buoys, which is one of their applications. Floating buoys are devices that are placed on the sea and ocean surfaces to provide various information to the floats. Because these cells are subjected to varying environmental conditions, modeling and simulating photovoltaic cells enables us to install cells with higher efficiency and performance in them. The parameters of the single diode model are examined in this article so that the I-V, P-V diagrams, and characteristics of the cadmium telluride (CdTe) photovoltaic cell designed with three layers (CdTe, CdS, and SnOx) can be extracted using A solar cell capacitance simulator (SCAPS) software, and we obtain the parameters of the single diode model using the ant colony optimization (ACO) algorithm. In this paper, the objective function is root mean square error (RMSE), and the best value obtained after 30 runs is 5.2217×10-5 in 2.46 seconds per iteration, indicating a good agreement between the simulated model and the real model and outperforms many other algorithms that have been developed thus far. The above optimization with 200 iterations, a population of 30, and 84 points was completed on a server with 32 gigabytes of random-access memory (RAM) and 30 processing cores.
Design and development of a fuzzy explainable expert system for a diagnostic robot of COVID-19 Beggar, Omar El; Ramdani, Mohammed; Kissi, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6940-6951

Abstract

Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability.
Design and build an underwater fishing light based on internet of thing Nugraha, I Made Aditya; Ngurah Desnanjaya, I Gusti Made
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp7108-7114

Abstract

The utilization of fishing gear has been growing until now. One of these tools is an underwater fishing light that has been used by fishermen. However, until now this device still works conventionally and only provides one color. Based on these problems and the rapid development of the internet of things (IoT), an underwater fishing light was created and designed that can work with the IoT system and provide various colors of light according to the needs of fishermen. The design of this tool uses several tools and materials, such as generators, power adapters, controller modules, generators, and the Magic Home Pro application. This tool operates at 12 V DC and consumes 144 W of electric power. Utilization of this tool can also indirectly reduce the consumption of electrical and fuel energy on board and support the blue economy. The results of testing the device show that the IoT system can work well, provide light colors according to orders, is waterproof, and provides a fairly good light intensity with an average of 6.89 Lux (0-8 Lux).
Building a recommendation system based on the job offers extracted from the web and the skills of job seekers Mgarbi, Hanae; Chkouri, Mohamed Yassin; Tahiri, Abderrahim
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6964-6971

Abstract

Recruitment, or job search, is increasingly used throughout the world by a large population of users through various channels, such as websites, platforms, and professional networks. Given the large volume of information related to job descriptions and user profiles, it is complicated to appropriately match a user's profile with a job description, and vice versa. The job search approach has drawbacks since the job seeker needs to search a job offers in each recruitment platform, manage their accounts, and apply for the relevant job vacancies, which wastes considerable time and effort. The contribution of this research work is the construction of a recommendation system based on the job offers extracted from the web and on the e-portfolios of job seekers. After the extraction of the data, natural language processing is applied to structured data and is ready for filtering and analysis. The proposed system is a content-based system, it measures the degree of correspondence between the attributes of the e-portfolio with those of each job offer of the same list of competence specialties using the Euclidean distance, the result is classified with a decreasing way to display the most relevant to the least relevant job offers.
A simplified optimization for resource management in cognitive radio network-based internet-of-things over 5G networks Purushothama, Vani Bengaluru; Ramakrishnan, Sundaraguru
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6513-6524

Abstract

With increasing evolution of applications and services in internet-of-things (IoT), there is an increasing concern of offering superior quality of service to its ever-increasing user base. This demand can be fulfilled by harnessing the potential of cognitive radio network (CRN) where better accessibility of services and resources can be achieved. However, existing review of literature shows that there are still open-end issues in this regard and hence, the proposed system offers a solution to address this problem. This paper presents a model which is capable of performing an optimization of resources when CRN is integrated in IoT using five generation (5G) network. The implementation uses analytical modeling to frame up the process of topology construction for IoT and optimizing the resources by introducing a simplified data transmission mechanism in IoT environment. The study outcome shows proposed system to excel better performance with respect to throughput and response time in comparison to existing schemes.

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