scholarly journals ENCODING OF CATEGORIAL FEATURES IN NEURAL NETWORKS

2021 ◽  
Vol 2021 (1) ◽  
pp. 3-8
Author(s):  
Denis Barkov ◽  
Svetlana Senotova
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Mathematical Model ◽  
Programming Language ◽  
Data Preparation ◽  
Machine Learn ◽  
Python Programming Language ◽  
Coding Method ◽  
Python Programming

The relevance and areas of application of machine learning are investigated, one of the machine learn-ing algorithms - neural networks, as well as one of the data preparation processes before extracting a mathematical model - the coding of categorical features using the target coding method is consid-ered. Implemented a coding algorithm in the Python programming language

scholarly journals Checking the adequacy of polynomial dependencies of a single-phase asynchronous motor without external phase-shifting devices for ship automation systems

2021 ◽  
pp. 71-78
Author(s):  
А.Б. Каракаев ◽  
А.В. Костенко
Keyword(s):  
Mathematical Model ◽  
Programming Language ◽  
Single Phase ◽  
Asynchronous Motor ◽  
Phase Shifting ◽  
Experiment Planning ◽  
Python Programming Language ◽  
Automation Systems ◽  
External Phase ◽  
Python Programming

Статья посвящена сравнению результатов расчётов полиномиальных зависимостей однофазного асинхронного двигателя, полученных с использованием методов планирования эксперимента, с математической моделью построенной с помощью программ на ЭВМ на языке программирования Python. Актуальность работы заключается в том, что задачи аппроксимационного типа не всегда возможно решить, используя ЭВМ, поэтому исследователи часто применяют методы планирования эксперименты для решения таких задач и после уже стоят математическую модель на ЭВМ и проверяют адекватность полученных результатов. В результате в статье авторами предоставляется план проверки адекватности полиномиальных зависимостей однофазного асинхронного электродвигателя без внешних фазосдвигающих устройств для систем судовой автоматики. По итогу выполненной работы авторы приходят к выводу об адекватности построенной математической модели на ЭВМ с использованием языка программирования Python. The article is devoted to the comparison of the results of calculations of polynomial dependencies of a single-phase asynchronous motor, obtained using methods of experiment planning, with a mathematical model built using computer programs in the Python programming language. The relevance of the work lies in the fact that problems of the approximation type are not always possible to solve using a computer, therefore, researchers often use methods of planning experiments to solve such problems and after that they already have a mathematical model on a computer and check the adequacy of the results obtained. As a result, the authors provide a plan for checking the adequacy of the polynomial dependencies of a single-phase asynchronous electric motor without external phase-shifting devices for ship automation systems. As a result of the work performed, the authors come to the conclusion about the adequacy of the constructed mathematical model on a computer using the Python programming language.

Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language

2019 ◽  
Vol 44 (3) ◽  
pp. 348-361 ◽  
Author(s):  
Jiangang Hao ◽  
Tin Kam Ho
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Programming Language ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
The Past ◽  
Machine Learning Methods ◽  
Python Programming Language ◽  
And Behavior ◽  
Python Programming

Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review Scikit-learn, a machine learning package in the Python programming language that is widely used in data science. The Scikit-learn package includes implementations of a comprehensive list of machine learning methods under unified data and modeling procedure conventions, making it a convenient toolkit for educational and behavior statisticians.

scholarly journals Voice Assistant based Telecommunication System (Tele-Bot)

2020 ◽  
Vol 9 (4) ◽  
pp. 1463-1465
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Programming Language ◽  
Voice Recognition ◽  
Telecommunication System ◽  
Python Programming Language ◽  
Artificial Intelligence Technology ◽  
Scripting Language ◽  
High Level ◽  
Python Programming

In this research we are aiming to plan, develop and deploy a model which is based on voice recognition. We trying to inculcate algorithm which are based on machine learning and also using artificial intelligence technology. We are learning the stages of voice recognition technology, depth of its working accuracy, probabilistic use cases, and system friendliness with the help of Python Programming Language. In order to increase the efficiency of system we are going to take response time into consideration which is crucial requirement into current environment. Python is easy to learn, High Level, Power full programming Scripting language. Fully developed voice recognition modules are to be used for development of our research oriented topic

scholarly journals Machine Learning-Based Technique for Image Classification and Identification of Similarity for Commonness of Source

2020 ◽  
Vol 9 (6) ◽  
pp. 544-548
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Edge Detection ◽  
Image Classification ◽  
Programming Language ◽  
Python Programming Language ◽  
Reserve Bank ◽  
Python Programming ◽  
Highlight Extraction ◽  
Reserve Bank Of India

In India Every year RBI (Reserve bank of India) faces the issue of fake currency. Fake Currency has consistently been an issue that has made a lot of chaos in the market. The expanding mechanical progressions have made the opportunities for making progressively fake currency which is circled in the market which decreases the general economy of the nation. There are machines present at banks and other business regions to check the validness of the monetary forms. Be that as it may, a typical man doesn't approach such frameworks and henceforth a requirement for a product to distinguish counterfeit cash emerges, which can be utilized by average folks. This proposed framework utilizes Image Processing to identify whether the currency is real or fake. The framework is structured utilizing Python programming language and OpenCV. It comprises of the means, for example, grayscale detection, edge detection, Highlight Extraction, and so forth which are performed utilizing reasonable strategies. And which will be further implemented in the Framework for Classification and Identification of Similarity for Commonness of Source

Machine learning for the extragalactic astronomy educational manual

2019 ◽  
Vol 15 (S367) ◽  
pp. 461-463
Author(s):  
Maksym Vasylenko ◽  
Daria Dobrycheva
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Programming Language ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Python Programming Language ◽  
Python Programming

AbstractWe evaluated a new approach to the automated morphological classification of large galaxy samples based on the supervised machine learning techniques (Naive Bayes, Random Forest, Support Vector Machine, Logistic Regression, and k-Nearest Neighbours) and Deep Learning using the Python programming language. A representative sample of ∼315000 SDSS DR9 galaxies at z < 0.1 and stellar magnitudes r < 17.7m was considered as a target sample of galaxies with indeterminate morphological types. Classical machine learning methods were used to binary morphologically classification of galaxies into early and late types (96.4% with Support Vector Machine). Deep machine learning methods were used to classify images of galaxies into five visual types (completely rounded, rounded in-between, smooth cigar-shaped, edge-on, and spiral) with the Xception architecture (94% accuracy for four classes and 88% for cigar-like galaxies). These results created a basis for educational manual on the processing of large data sets in the Python programming language, which is intended for students of the Ukrainian universities.

scholarly journals Neural Network Programming in Python

2019 ◽  
Vol 8 (6S4) ◽  
pp. 373-377
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Programming Language ◽  
Function Approximation ◽  
Learning Rule ◽  
Network Programming ◽  
Short Introduction ◽  
Python Programming Language ◽  
Python Programming ◽  
Backpropagation Learning

In this paper a basic introduction to neural networks is made. An emphasis is given on a two layer perceptron used extensively for function approximation. The backpropagation learning rule is than briefly introduced. A short introduction into Python programming language is made and a program for the perceptron design is written and discussed in some detail. The “neurolab” library is used for this purpose.

scholarly journals Python for Automating Machine Learning Tasks

2021 ◽  
Vol 2 (2) ◽  
pp. 77-82
Author(s):  
Tinatin Mshvidobadze
Keyword(s):  
Machine Learning ◽  
Programming Language ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Breach ◽  
Learning Tasks ◽  
Python Programming Language ◽  
Malicious Insiders ◽  
Python Programming ◽  
Email Filtering

Machine learning is used in a variety of computational tasks where designing and programming explicit algorithms with good performance is not easy. Applications include email filtering, recognition of network intruders or malicious insiders working towards a data breach. In this article we will focus on basics of machine learning, tasks and problems and various machine learning algorithms. The article discusses the Python programming language as the best language for automating machine learning tasks.

A Python application to generate digital signals

2020 ◽  
Vol 65 (1) ◽  
pp. 96-104
Author(s):  
Tatian-Cristian Mălin
Keyword(s):  
Numerical Simulation ◽  
Programming Language ◽  
Digital Signals ◽  
Python Programming Language ◽  
Python Programming

We introduce in this paper an application developed in the Python programming language that can be used to generate digital signals with known frequencies and amplitudes. These digital signals, since have known parameters, can be used to create benchmarks for test and numerical simulation.

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