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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
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Classification of cervical spine fractures using 8 variants EfficientNet with transfer learning Bayangkari Karno, Adhitio Satyo; Hastomo, Widi; Surawan, Tri; Lamandasa, Serlia Raflesia; Usuli, Sudarto; Kapuy, Holmes Rolandy; Digdoyo, Aji
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.pp7065-7077

Abstract

A part of the nerves that govern the human body are found in the spinal cord, and a fracture of the upper cervical spine (segment C1) can cause major injury, paralysis, and even death. The early detection of a cervical spine fracture in segment C1 is critical to the patient’s life. Imaging the spine using contemporary medical equipment, on the other hand, is time-consuming, costly, private, and often not available in mainstream medicine. To improve diagnosis speed, efficiency, and accuracy, a computer-assisted diagnostics system is necessary. A deep neural network (DNN) model was employed in this study to recognize and categorize pictures of cervical spine fractures in segment C1. We used EfficientNet from version B0 to B7 to detect the location of the fracture and assess whether a fracture in the C1 region of the cervical spine exists. The patient data group with over 350 picture slices developed the most accurate model utilizing the EfficientNet architecture version B6, according to the findings of this experiment. Validation accuracy is 99.4%, whereas training accuracy is 98.25%. In the testing method using test data, the accuracy value is 99.25%, the precision value is 94.3%, the recall value is 98%, and the F1-score value is 96%.
The Bayes model for the protection of human interests Zharova, Anna; Elin, Vladimir; Levashov, Mikhail
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.pp6419-6425

Abstract

This article is aimed at solving a number of issues related to the problems, risks, and threats arising from the profiling of human activity. In this study, the Bayesian method was used, to determine the quantitative and qualitative characteristics of personal data for ensuring the security of this data by dint of reducing the redundancy of data processed by artificial intelligence (AI). A thought experiment to test the possibility of reducing the redundancy of personal data processed by AI allows us to conclude that using the Bayesian method allows to protect human rights to privacy. With this approach, instead of the method associated with the collection and accumulation of the most sensitive categories of personal data, we proposed a method that is associated with obtaining probabilistic estimates of the values of the parameters of these data by conducting statistical studies of the specified personal data without their collection and accumulation. The probabilistic estimates of the parameters of some sensitive personal data obtained in this way can replace their exact values and can be used by AI in the criteria for filtering personal data subjects, including for the purpose of making a decision.
Correlation between capital markets and cryptocurrency: impact of the coronavirus Ariya, Kanyawut; Chanaim, Somsak; Dawod, Ahmad Yahya
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.pp6637-6645

Abstract

The objective of the study is to use daily Thai data analysis to strengthen correlations between Bitcoin and conventional asset measurements. The most popular asset prices and indices include gold, oil, the SET50 index, Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), Dashcoin (DASH), Stellar Lumens (XLM), Binance coin (BNB), and Dogecoin (DOGE). We find a significant correlation between cryptocurrencies and the digital economy using a matrix approach to the Pearson correlation coefficient. With the help of a minimal spanning tree model and random matrix theory, we can determine the shortest route between assets. Yet, as predicted, only a small percentage of the greatest eigenvalues diverge. We are also developing a novel technique to find the SET-50 index. In an investment portfolio during the coronavirus period, alternatives to the gold price and the DOGE may offer possibilities for risk diversification.
A deep learning-based mobile app system for visual identification of tomato plant disease Wang, Aurelius Ryo; Shabrina, Nabila Husna
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.pp6992-7004

Abstract

Tomato is one of many horticulture crops in Indonesia which plays a vital role in supplying public food needs. However, tomato is a very susceptible plant to pests and diseases caused by bacteria and fungus. The infected diseases should be isolated as soon as it was detected. Therefore, developing a reliable and fast system is essential for controlling tomato pests and diseases. The deep learning-based application can help to speed up the identification of tomato disease as it can perform direct identification from the image. In this research, EfficientNetB0 was implemented to perform multi-class tomato plant disease classification. The model was then deployed to an android-based application using machine learning (ML) kit library. The proposed system obtained satisfactory results, reaching an average accuracy of 91.4%.
Detection of lung pathology using the fractal method Abdikerimova, Gulzira; Shekerbek, Ainur; Tulenbayev, Murat; Sultanova, Bakhyt; Beglerova, Svetlana; Dzhaulybaeva, Elvira; Zhumakanova, Kamshat; Rysbekkyzy, Bakhytgul
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.pp6778-6786

Abstract

Currently, the detection of pathology of lung cavities and their digitalization is one of the urgent problems of the healthcare industry in Kazakhstan. In this paper, the method of fractal analysis was considered to solve the task set. Diagnosis of lung pathology based on fractal analysis is an actively developing area of medical research. Conducted experiments on a set of clinical data confirm the effectiveness of the proposed methodology. The results obtained show that fractal analysis can be a useful tool for early detection of lung pathologies. It allows you to detect even minor changes in the structure and texture of lung tissues, which may not be obvious during visual analysis. The article deals with images of pathology of the pulmonary cavity, taken from an open data source. Based on the analysis of fractal objects, they were pre-assembled. Software algorithms for the operation of the information system for screening diagnostics have been developed. Based on the information contained in the fractal image of the lungs, mathematical models have been developed to create a diagnostic rule. A reference set of information features has been created that allows you to create algorithms for diagnosing the lungs: healthy and with pathologies of tuberculosis. 
Integrated application of synergetic approach for enhancing intelligent steam generator control systems Isamiddin, Siddikov; Dilnoza, Umurzakova
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1508-1518

Abstract

This article focuses on the integrated application of the synergetic approach to enhance the quality of intelligent steam generator control systems.By combining various techniques such as model-based control, adaptive control, and artificial intelligence, an efficient and flexible control system can be developed. Model-based control utilizes mathematical models of steam generators to formulate control algorithms and predict system behavior. Adaptive control enables the system to adapt to changing conditions by adjusting control parameters based on real-time measurements. Artificial intelligence techniques, including neural networks and genetic algorithms, facilitate learning, optimization, and data-driven decision-making processes. The objectives of this research are to investigate the benefits of the synergetic approach in steam generator control, including improved steam generation efficiency, optimized energy consumption, enhanced system stability and reliability, and adaptability to varying operating conditions and disturbances. The findings and conclusions of this study are expected to provide valuable insights for engineers, researchers, and professionals involved in the design and implementation of intelligent steam generator control systems. By integrating the synergetic approach, substantial enhancements in control quality can be achieved, leading to optimal operation and maximum efficiency of power plants.
Investigation of duty cycle controlled inductive wireless power transfer converter using series-series compensation for electric vehicle application Bhukya, Bhavsingh; Gotluru, Suresh Babu; Bhukya, Mangu; Bhukya, Ravi Kumar; Dongari, Vaani
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6214-6224

Abstract

This paper presents series-series (SS) compensation topologies that include both primary side duty cycle control (PSDCC) and secondary side duty cycle control (SSDCC) methods. The main challenge for noncontact charging (NCC) for electric vehicles (EVs) batteries, the power transfer capability and efficiency in primary side proved to be unproductive. The investigation considers the primary side control duty cycle control (transmitter and receiver) and the secondary side duty cycle control (transmitter and receiver) in terms of compensation capacitor voltage, coil voltage, load side voltage, current, and power. By adjusting the duty cycle within the range of 0.1 to 0.5, it is possible to control power without significantly decreasing the system's efficiency, by using the SSDCC method. The evaluated parameters, including 1.5 kW output power, 85 kHz resonance frequency, and 120 mm ground clearance, are suitable for three-wheeler auto rickshaws. These findings are verified through MATLAB/Simulink software and compared with experimental results.
Maximum power point tracking and space vector modulation control of quasi-z-source inverter for grid-connected photovoltaic systems Jaoide, Essaid; El Aamri, Faicel; Outazkrit, Mbarek; Radouane, Abdelhadi; Mouhsen, Azeddine
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1424-1436

Abstract

The quasi-Z-source inverter (qZSI) become one of the most promising power electronic converters for photovoltaic (PV) applications, due to its capability to perform a buck-boost conversion of the input voltage in a single stage. The control strategy based maximum power point tracking (MPPT) and proportional integral (PI) controller are well known in grid-connected with traditional configuration but not in qZSI. This paper presents a control strategy for qZSI grid-connected based on the MPPT algorithm and the linear control by PI controllers. This is complemented by the capability to efficiently transfer the harvested power to the grid, ensuring a unity power factor. The proposed control strategy effectively separates the control mechanisms for the direct current (DC) and alternating current (AC) sides by utilizing the two control variables, the shoot-through duty ratio and the modulation index. An adapted space vector modulation technique is then utilized to generate the switching pulse width modulation (PWM) signals, using these two control variables as inputs. The proposed approach was tested and validated under MATLAB/Simulink and PLECS software.
Smart fuzzy incubator for free-range chicken on internet of things Islamiyah, Mufidatul; Arifin, Samsul
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5344-5355

Abstract

This paper presents the design of an artificial incubator for free-range chicken eggs. The incubator is designed to maintain optimal conditions and humidity for embryo development and this incubator is equipped with an egg turning mechanism. This incubator is designed using NodeMCU 8266 hardware which is used as a control that connects to the database server, and this incubator is built using multiplex, exhaust fan, mist maker, motor tuning, DS18B20, DHT-11 and real-time clock (RTC) DS3231. The temperature and humidity conditions in the incubator are maintained within the desired range and the egg changing mechanism works effectively. The results of this research show that this artificial incubator can be a reliable and effective tool for hatching free-range chicken eggs. This incubator is very easy to use and maintain and very affordable for local free-range chicken farming. This future research will focus on determining the conditions under which the incubator will provide the best results in terms of hatching efficiency for free-range chickens.
Secure aware software development life cycle on medical datasets by using firefly optimization and machine learning techniques Obulesu, Ooruchintala; Suneel, Sajja; Jangili, Sudhakar; Ledalla, Sukanya; Swetha, Ballepu; Borra, Subba Reddy
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4195-4203

Abstract

The abstract highlights the critical need for securing sensitive medical data, emphasizing the challenges in the medical domain due to the confidentiality of patient, disease, doctor, and staff information. The proposed study introduces a novel approach using machine learning, specifically integrating the firefly optimization technique with the random forest algorithm, to classify medical data in a secure manner. The significance lies in addressing the security concerns associated with medical datasets, offering a solution that prioritizes confidentiality throughout the software development life cycle (SDLC). The proposed algorithm achieves an impressive accuracy of 96%, showcasing its efficacy in providing a robust and secure framework for the development of applications involving medical data. This research contributes to advancing the field of medical data security, offering a practical solution for safeguarding sensitive information in healthcare applications.

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