scholarly journals Determination of power engineering equipment’s defects in predictive analytic system using machine learning algorithms

2020 ◽  
Vol 1683 ◽  
pp. 042056
Author(s):  
I A Shcherbatov ◽  
G N Turikov
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Power Engineering

scholarly journals Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1993
Author(s):  
Fernando Pérez-Sanz ◽  
Miriam Riquelme-Pérez ◽  
Enrique Martínez-Barba ◽  
Jesús de la Peña-Moral ◽  
Alejandro Salazar Nicolás ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Learning Algorithms ◽  
Selection Procedure ◽  
Machine Learning Algorithms ◽  
Liver Biopsies ◽  
Convenient Technique ◽  
End Stage ◽  
Macrovesicular Steatosis

Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.

scholarly journals Determination of COVID-19 Patients Using Machine Learning Algorithms

2022 ◽  
Vol 31 (1) ◽  
pp. 207-222
Author(s):  
Marium Malik ◽  
Muhammad Waseem Iqbal ◽  
Syed Khuram Shahzad ◽  
Muhammad Tahir Mushtaq ◽  
Muhammad Raza Naqvi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Mohamed F. Abd El-Aal ◽  
Ali Algarni ◽  
Aisha Fayomi ◽  
RAahayu Abdul Rahman ◽  
Khudir Alrashidi
Keyword(s):  
Machine Learning ◽  
Gross Domestic Product ◽  
Learning Algorithms ◽  
Arima Model ◽  
Machine Learning Algorithms ◽  
Domestic Product ◽  
Gradient Boosting ◽  
Primary Determinant ◽  
Per Capita

This study aims to determine the primary determination of FDI inflow to Egypt using machine learning algorithms and the ARIMA model and get an accurate prediction of FDI inflow to Egypt during the current decade (2020–2030) and approved that the gradient boosting model is the most accurate algorithms. Also, we find stability in economic indicators in Egypt during the current decade using the ARIMA model. The last step approved that the primary determinant of FDI inflow to Egypt is the Human Development Index, followed by population size, gross domestic product per capita, lending rate, and gross domestic product value.

scholarly journals DETERMINATION OF THE INITIAL GUESS FOR THE PROBLEM OF MEMRISTOR MODEL PARAMETERS EXTRACTION USING MACHINE LEARNING ALGORITHMS

2020 ◽  
Author(s):  
Evgeniy Shamin ◽  
Dmitriy Zhevnenko ◽  
Fedor Meshchaninov ◽  
Vladislav Kozhevnikov ◽  
Evgeniy Gornev
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Initial Guess ◽  
Machine Learning Algorithms ◽  
Model Parameters ◽  
Parameters Extraction ◽  
Current Voltage ◽  
Current Voltage Characteristics ◽  
Characteristic Features

The focus of this work is on the algorithm of extraction of parameters of the memristor model from the experimentally obtained current-voltage characteristics. The problem of finding the initial guess for this algorithm based on current-voltage characteristic features is stated and solved by means of machine learning algorithms.

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