scholarly journals Features of the educational program «Nanotechnologies and nanomaterials in energy»: machine learning

2022 ◽  
Vol 2150 (1) ◽  
pp. 012031
Author(s):  
D. D. Babenko ◽  
A. S. Dmitriev ◽  
P.G. Makarov ◽  
I.A. Mikhailova
Keyword(s):  
Machine Learning ◽  
Educational Program ◽  
Research University ◽  
Educational Programs ◽  
Green Energy ◽  
Machine Learning Methods ◽  
Research And Innovation ◽  
The World ◽  
Analysis And Synthesis ◽  
Design Options

Abstract The results of the development of new educational programs in the field of nanotechnology and nanomaterials in the energy sector, which have been developed and are actively used by the National Research University “MPEI”, are presented. Modern nanomaterials and nanotechnological processes in traditional and alternative (“green”) energy require new approaches, including statistical methods for the analysis and synthesis of experimental data and design options. For this reason, without the active use of machine learning methods, it is impossible to train qualified specialists in the field of promising energy problems and their solutions. Through teaching, research, and innovation, «MPEI» exceptional community pursues its mission of service to the nation and the world.

scholarly journals Features of machine learning in the study of the main factors of development of countries of the world

2021 ◽  
Vol 110 ◽  
pp. 02006
Author(s):  
Ludmila Borisova ◽  
Galina Zhukova ◽  
Anna Kuznetsova ◽  
Julie Martin
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Life Expectancy ◽  
Infant Mortality Rate ◽  
Live Births ◽  
Goods And Services ◽  
Machine Learning Methods ◽  
The World

The paper analyzes the socio-economic and demographic indicators of life expectancy in the countries of the world. Methods of regression analysis and machine learning are used. Statistically significant indicators that affect life expectancy around the world have been identified. When analyzing the data using machine learning methods, 13 of the 14 analyzed indicators were statistically significant. Significant indicators, in addition to those selected in the regression analysis, were 3: the under-five infant mortality rate (per 1,000 live births), the Net Barter Terms of Trade Index (2000 = 100), and Imports of goods and services (in % of GDP) (in the regression analysis, only the infant death rate was significant). In addition, it should be noted that there is a significant decrease in the under-five infant mortality rate (per 1,000 live births) for the EU, CIS and South-East Asian countries compared to the border set in the study for all countries: 4.65 vs. 34.9, a decrease in the birth rate from 2.785 to 1.85, a sharp increase in exports of goods and services: from 23.17 to 80.59, a halving in imports of goods and services, a drop in population growth from 2.105 to 0.85. The performed statistical analysis strongly supports the use of machine learning methods in identifying statistically significant relationships between various indicators that characterize the development of countries, if there are gaps in the data.

A Process for Increasing the Samples of Coffee Rust Through Machine Learning Methods

2018 ◽  
Vol 9 (2) ◽  
pp. 32-52
Author(s):  
Jhonn Pablo Rodríguez ◽  
David Camilo Corrales ◽  
Juan Carlos Corrales
Keyword(s):  
Machine Learning ◽  
Agricultural Product ◽  
American Phytopathological Society ◽  
Learning Methods ◽  
Coffee Rust ◽  
Machine Learning Methods ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Coffee Farmers ◽  
Reliable Representation

This article describes how coffee rust has become a serious concern for many coffee farmers and manufacturers. The American Phytopathological Society discusses its importance saying this: “…the most economically important coffee disease in the world…” while “…in monetary value, coffee is the most important agricultural product in international trade…” The early detection has inspired researchers to apply supervised learning algorithms on predicting the disease appearance. However, the main issue of the related works is the small number of samples of the dependent variable: Incidence Percentage of Rust, since the datasets do not have a reliable representation of the disease, which will generate inaccurate predictions in the models. This article provides a process about coffee rust to select appropriate machine learning methods to increase rust samples.

scholarly journals A Hybrid Computational Framework for Intelligent Inter-continent SARS-CoV-2 Sub-strains Characterization and Prediction

2020 ◽  
Author(s):  
Moses Ekpenyong ◽  
Mercy Edoho ◽  
Udoinyang Inyang ◽  
Faith-Michael Uzoka ◽  
Itemobong Ekaidem ◽  
...  
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Genome Diversity ◽  
Genome Sequences ◽  
Computational Framework ◽  
Machine Learning Methods ◽  
Novel Approach ◽  
The World ◽  
Global Initiative ◽  
Cognitive Knowledge

Abstract Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database (https://www.gisaid.org/), between December 2019 and August 20, 2020, a total of 157 human SARS-CoV-2 (complete) genome sequences processed by gender, across 6 continents of the world, were analyzed. We hypothesized that data speaks for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate multiple emergence of SARS-CoV-2 sub-strains and explained the diversity of the SARS-CoV-2. Interestingly, some viral sub-strains progressively transformed into new sub-strain clusters indicating varying amino acid and strong nucleotide association derived from same origin. A novel approach to cognitive knowledge mining from enriched genome datasets and output targets labeling, helped intelligent prediction of emerging or new viral sub-strains.

scholarly journals People's Councils for Ethical Machine Learning

2018 ◽  
Author(s):  
Dan McQuillan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Big Data ◽  
Knowledge Production ◽  
The Core ◽  
Machine Learning Methods ◽  
The Family ◽  
The World ◽  
Social Media Platforms ◽  
Drone Warfare

Machine learning is a form of knowledge production native to the era of big data. It is at the core of social media platforms and everyday interactions. It is also being rapidly adopted for research and discovery across academia, business and government. This paper will explore the way the affordances of machine learning itself, and the forms of social apparatus that it becomes a part of, will potentially erode ethics and draw us in to a drone-like perspective. Unconstrained machine learning enables and delimits our knowledge of the world in particular ways: the abstractions and operations of machine learning produce a ‘view from above’ whose consequences for both ethics and legality parallel the dilemmas of drone warfare. The family of machine learning methods is not somehow inherently bad or dangerous, nor does implementing them signal any intent to cause harm. Nevertheless, the machine learning assemblage produces a targeting gaze whose algorithms obfuscate the legality of its judgements, and whose iterations threaten to create both specific injustices and broader states of exception. Given the urgent need to provide some kind of balance before machine learning becomes embedded everywhere, this paper proposes people’s councils as a way to contest machinic judgements and reassert openness and discourse.

Research of the applicability of machine learning methods for assessment of departments’ performance

2021 ◽  
pp. 41-51
Author(s):  
A. R. Mukanova ◽  
Sh. A. Otsokov
Keyword(s):  
Machine Learning ◽  
Performance Indicators ◽  
Research University ◽  
Educational Organizations ◽  
University Staff ◽  
Machine Learning Methods ◽  
Efqm Model ◽  
University Department ◽  
Python Programming ◽  
Engineering Institute

Currently, a number of educational organizations in Russia and abroad, including the National Research University “Moscow Power Engineering Institute” (MPEI), are introducing the European improvement model EFQM, designed to analyze and improve the educational, scientific and other activities of the departments. In accordance with this model, each university department is assigned a score equal to the sum of points for two groups of criteria: criteria from the group of opportunities and criteria from the group of results. To obtain such assessments, a commission consisting of external experts, EFQM assessors and university staff meets with heads of departments. Based on the results of the discussion of the results of the meetings, the commission determines the score and rating of the departments in accordance with the EFQM model.The purpose of the work presented in the article is to study the possibility of using machine learning to simplify the work of experts in terms of obtaining estimates according to criteria from a group of results.The article proposes a system for evaluating the activities of departments according to criteria from a group of results based on machine learning. A program in the Python programming language has been developed, which evaluates the activities of departments according to these criteria for each department of the MPEI. The program receives the initial data for such assessments from the monitoring system of key performance indicators implemented in MPEI.

scholarly journals Methods of Machine Learning in the Master’s Educational Program

2019 ◽  
Vol 135 ◽  
pp. 03069 ◽  
Author(s):  
Mikhail Krichevsky ◽  
Julia Martynova ◽  
Artur Budagov
Keyword(s):  
Machine Learning ◽  
Educational Program ◽  
Qualitative Description ◽  
Learning Tools ◽  
Learning Methods ◽  
Production Methods ◽  
Software Products ◽  
Machine Learning Methods ◽  
Organization And Management ◽  
Knowledge Intensive

This article considers methods of machine learning, which are introduced into the master’s educational program under the direction of “Organization and management of knowledge-intensive industries”. This direction should be primarily focused on the digitalization of education. Digital economy, which is rapidly becoming part of modern management and production methods, is changing approaches to education and universities, which should graduate people who comply with the requirements of the digital job environment. The study is aimed at demonstrating the capabilities of machine learning methods in case of their introduction into the disciplines of this direction. Thus, we can switch from a qualitative description of most disciplines in this direction to a quantitative interpretation of the results. The task at hand is best solved by such machine learning tools as neural and fuzzy systems that can be used to solve classification, regression and clustering problems. We have analysed the composition of disciplines and have chosen the most important ones in terms of the introduction of machine learning methods into them. The article presents the possibilities of using machine learning methods by the example of a number of practical exercises that are included in the programs of disciplines of this direction. We have identified a number of disciplines of this direction, which need to be supplemented with additional machine learning materials. The article offers the composition of such materials, including theoretical foundations and practical exercises in selected disciplines. The study provides solutions of the most important practical tasks from various disciplines, obtained with the help of Statistica and MatLab software products.

scholarly journals Dank or not? Analyzing and predicting the popularity of memes on Reddit

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Kate Barnes ◽  
Tiernon Riesenmy ◽  
Minh Duc Trinh ◽  
Eli Lleshi ◽  
Nóra Balogh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Communication ◽  
Predictive Power ◽  
Research Interest ◽  
Predictive Analysis ◽  
Machine Learning Methods ◽  
Internet Users ◽  
Machine Learning Model ◽  
The World ◽  
Looking Glass

AbstractInternet memes have become an increasingly pervasive form of contemporary social communication that attracted a lot of research interest recently. In this paper, we analyze the data of 129,326 memes collected from Reddit in the middle of March, 2020, when the most serious coronavirus restrictions were being introduced around the world. This article not only provides a looking glass into the thoughts of Internet users during the COVID-19 pandemic but we also perform a content-based predictive analysis of what makes a meme go viral. Using machine learning methods, we also study what incremental predictive power image related attributes have over textual attributes on meme popularity. We find that the success of a meme can be predicted based on its content alone moderately well, our best performing machine learning model predicts viral memes with AUC=0.68. We also find that both image related and textual attributes have significant incremental predictive power over each other.

scholarly journals World Covid-19 Vaccine Names Classification Using Neural Network Method

2021 ◽  
Vol 1 (1) ◽  
pp. 31
Author(s):  
Kristiawan Nugroho
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Neural Network Method ◽  
The Public ◽  
Machine Learning Methods ◽  
The Neural Network ◽  
The World ◽  
Network Method

The Covid-19 pandemic has occurred for a year on earth. Various attempts have been made to overcome this pandemic, especially in making various types of vaccines developed around the world. The level of vaccine effectiveness in dealing with Covid-19 is one of the questions that is often asked by the public. This research is an attempt to classify the names of vaccines that have been used in various nations by using one of the robust machine learning methods, namely the Neural Network. The results showed that the Neural Network method provides the best accuracy, which is 99.9% higher than the Random Forest and Support Vector Machine(SVM) methods.

scholarly journals A Hybrid Computational Framework for Intelligent Inter-continent SARS-CoV-2 Sub-strains Characterization and Prediction

2020 ◽  
Author(s):  
Moses Ekpenyong ◽  
Mercy Edoho ◽  
Udoinyang Inyang ◽  
Faith-Michael Uzoka ◽  
Itemobong Ekaidem ◽  
...  
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Genome Diversity ◽  
Genome Sequences ◽  
Computational Framework ◽  
Machine Learning Methods ◽  
Novel Approach ◽  
The World ◽  
Global Initiative ◽  
Cognitive Knowledge

Abstract Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database (https://www.gisaid.org/), between December 2019 and August 20, 2020, a total of 157 human SARS-CoV-2 (complete) genome sequences processed by gender, across 6 continents of the world, were analyzed. We hypothesized that data speaks for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate multiple emergence of SARS-CoV-2 sub-strains and explained the diversity of the SARS-CoV-2. Interestingly, some viral sub-strains progressively transformed into new sub-strain clusters indicating varying amino acid and strong nucleotide association derived from same origin. A novel approach to cognitive knowledge mining from enriched genome datasets and output targets labeling, helped intelligent prediction of emerging or new viral sub-strains.

The Problem-Oriented Approach in the Basic Mathematical Courses for Engineering Education

2019 ◽  
pp. 176-187
Author(s):  
Olga Alexandrovna Dotsenko ◽  
Andrey Alexandrovich Zhukov ◽  
Tatiana Dmitrievna Kochetkova ◽  
Elena Gennagyevna Leontyeva
Keyword(s):  
Mathematical Modeling ◽  
Numerical Methods ◽  
Engineering Education ◽  
Research University ◽  
Problem Based Learning ◽  
Learning Management Systems ◽  
Educational Programs ◽  
Information Support ◽  
The World ◽  
Oriented Approach

Problem-based learning takes a well-deserved place in the educational programs of leading universities in the world. Meanwhile it is known that this approach has been well developed for training students of economy and medicine. There are certain difficulties in setting targets as well as in teaching methods in basic technical subjects, in particular in the mathematical courses. The chapter presents an analysis of the peculiar features of problem-based learning in a research university for basic courses of the first two years of study. The discipline “Numerical Methods and Mathematical Modeling” is given as an example of the application of this approach. The main topics are proposed and lesson plans are provided. The information support of the courses is carried out in the learning management systems. The elements of this approach have been put into practice of training course and it was shown that the material was achieved much better.

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