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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 63 Documents
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Search results for , issue "Vol 11, No 2: April 2022" : 63 Documents clear
Hyper parameter tuning based gradient boosting algorithm for detection of diabetic retinopathy: an analytical review Parul Datta; Prasenjit Das; Abhishek Kumar
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3559

Abstract

The pipelines of approaches for classifying diabetic retinopathy were examined in this study. The effort entails developing appropriate transformations and estimators that can be used to automate the process of diabetic retinopathy detection. The segmentation of the blood vessels was done using a hybrid algorithm that uses Otsu and median filter to get the region of interest. Further, ten classifiers were investigated in order to develop an automated pipeline for diabetic retinopathy detection. The ten classifiers were reviewed based on earlier work in a similar setting and on an exploration of new ways for identifying diabetic retinopathy. To overcome the challenge of low volume of dataset, data argumentation was done so that a generic classifier can be configured. Extensive hyper parameter tuning was performed, and it was shown that the gradient boosting approach is the most stable technique for detecting diabetic retinopathy. This was validated using a 10K fold cross validation method on many metrics (accuracy, recall, precision, and v-measure score). Hyper-parameter tuning helped in achieving accuracy of 0.96.
Prediction of COVID-19 disease severity using machine learning techniques Alaa H. Ahmed; Mokhaled N. A. Al-Hamadani; Ihab A. Satam
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3272

Abstract

A terrifying spread of COVID-19 (which is also known as severe acute respiratory syndrome coronavirus 2 or SARS-COV-2) led scientists to conduct tremendous efforts to reduce the pandemic effects. COVID-19 has been announced pandemic discovered in 2019 and affected millions of people. Infected people may experience headache, body pain, and sometimes difficulty in breathing. For older people, the symptoms can get worse. Also, it can cause death because of the huge effect on some parts of the human body, particularly for those who have chronic diseases like diabetes. Machine learning algorithms are applied to patients diagnosed with Corona Virus to estimate the severity of the disease depending on their chronic diseases at an early stage. Chronic diseases could raise the severity of COVID-19 and that is what has been proved in this paper. This paper applies different machine learning techniques such as random forest, decision tree, linear regression, binary search, and k-nearest neighbor on Mexican patients’ dataset to find out the impact of lifelong illnesses on increasing the symptoms of the virus in the human body. Besides, the paper demonstrates that in some cases, especially for older people, the virus can cause inevitable death.
Notice of Retraction: Comparative evaluation of SiC/GaN “MOSFET” transistors under different switching conditions Ghanim Thiab Hasan; Ali Hlal Mutlaq; Kamil Jadu Ali
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3445

Abstract

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting beei@iaescore.com.-----------------------------------------------------------------------The aim of this paper is to conduct a mutual comparison of switching energy losses in cascade gallium nitride (GaN) and silicon "super junction" MOSFET” transistor, in both cases designed for a maximum operating voltage of (650 V). For the analysis of switching characteristics of transistors used double pulse test method by using detailed SPICE simulation model. Data on transient on and off processes were generated using the “LTspice” simulation package in a wide range of drain currents with two different gate resistance values of the tested transistors. The total energy losses in the GaN have been simulated during one transistor at (on and off cycle). The obtained results indicate that the superior switching characteristics of GaN devices for a drain current of (30 A) is five to eight times less than the switching characteristics of silicon “MOSFET” transistor when compared to silicon components, especially during operation of transistors with high drain currents.

Page 7 of 7 | Total Record : 63

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