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

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.

About the need to change the content of professional training of a future informatics teacher in artificial intelligence

2020 ◽  
pp. 12-26
Author(s):  
K. V. Rozov
Keyword(s):  
Artificial Intelligence ◽  
Programming Language ◽  
Information Technologies ◽  
Professional Training ◽  
Educational Process ◽  
Federal State ◽  
High Tech ◽  
Current Trends ◽  
Python Programming Language ◽  
Python Programming

The article examines the current state of teacher training in artificial intelligence. The content of disciplines related to artificial intelligence included in the curricula of pedagogical universities of Russia is analyzed. The current trends in the selection of software tools used for developments in artificial intelligence technologies in Russia and abroad are considered. The objective of the article is to substantiate the need to review the content of the training of a future informatics teacher in artificial intelligence, taking into account current trends in the development of artificial intelligence technologies and the requirements of Federal State Educational Standards. From studying expert systems, the Prolog and Lisp languages, the author suggests moving on to studying modern tools for implementing artificial intelligence technologies using the Python programming language. The article provides the content of the discipline “Technology of Artificial Intelligence” proposed by the author, being tested at the Novosibirsk State Pedagogical University. Such a change in the content is intended to ensure the practical orientation of the educational process within the framework of the relevant discipline, to increase the competence of the future informatics teacher in the field of modern high tech information technologies, to promote the development of motivation to learn the current Python programming language, thereby increasing its competitiveness in the labor market.

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 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

PYTHON PROGRAMMING LANGUAGE IN PEDAGOGICAL UNIVERSITY: FROM THE BASES TO ARTIFICIAL INTELLIGENCE

2019 ◽  
pp. 26-33 ◽  
Author(s):  
K. V. Rozov ◽  
A. V. Podsadnikov
Keyword(s):  
Artificial Intelligence ◽  
Programming Languages ◽  
Programming Language ◽  
Professional Training ◽  
Future Teachers ◽  
Python Programming Language ◽  
Artificial Intelligence In Education ◽  
Key Issues ◽  
Organizational Features ◽  
Python Programming

The article actualizes the choice of Python programming language for professional training of future teachers of informatics in pedagogical university. Python, being one of the most relevant programming languages, is being introduced into education, and the future informatics teacher should be ready to effectively use it to teach the basics of programming. In addition, the Python language is one of the leading tools for the implementation of advanced technologies of artificial intelligence, and therefore, owning this programming language teacher can be part of the mechanism of implementation of artificial intelligence in education. The objective of the article is to consider the features of the organization of the course of programming in Python for the training of future teachers of informatics. Methodology. During the development of the author’s course of programming in Python educational literature on programming in this language was analyzed, as well as articles by practicing teachers and researchers describing their experience of teaching Python. Results and conclusion. To ensure quality training of future teachers of informatics at Novosibirsk State Pedagogical University a course of programming in Python was developed and tested in the discipline “Programming”. During the development of the course, typical problems of teaching Python at school and university were taken into account, identified by both the authors of the article and other practicing programming teachers. The structure of the developed course with the description of its constituent sections is given. The article presents the results of a survey of students of 3d and 4th undergraduate courses, who completed the presented course, on the key issues covered in the article. Respondents noted the relevance of learning Python and the effectiveness of certain organizational features of the course. The continuity of the course for further training of future teachers of informatics to the current technologies of artificial intelligence is revealed.

scholarly journals On the Performance of the Python Programming Language for Serial and Parallel Scientific Computations

2005 ◽  
Vol 13 (1) ◽  
pp. 31-56 ◽  
Author(s):  
Xing Cai ◽  
Hans Petter Langtangen ◽  
Halvard Moe
Keyword(s):  
Programming Language ◽  
Numerical Experiments ◽  
Parallel Programs ◽  
Scientific Applications ◽  
Language Implementation ◽  
Parallel Performance ◽  
Python Programming Language ◽  
Programming Techniques ◽  
High Level ◽  
Python Programming

This article addresses the performance of scientific applications that use the Python programming language. First, we investigate several techniques for improving the computational efficiency of serial Python codes. Then, we discuss the basic programming techniques in Python for parallelizing serial scientific applications. It is shown that an efficient implementation of the array-related operations is essential for achieving good parallel performance, as for the serial case. Once the array-related operations are efficiently implemented, probably using a mixed-language implementation, good serial and parallel performance become achievable. This is confirmed by a set of numerical experiments. Python is also shown to be well suited for writing high-level parallel programs.

scholarly journals Object Detection with a Webcam Using the Python Programming Language

2021 ◽  
Vol 2 (2) ◽  
pp. 103-111
Author(s):  
Ali Basrah Pulungan ◽  
Zhafranul Nafis ◽  
Muhammad Anwar ◽  
Hastuti ◽  
Hamdani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Object Recognition ◽  
Object Detection ◽  
Programming Language ◽  
Python Programming Language ◽  
Detection Of Objects ◽  
The Times ◽  
Python Programming ◽  
Shape And Size

Technology developed rapidly along the times, various ways are done to make works easier. One of them is by utilizing artificial intelligence, likes the use of a webcam as a sensor in detecting an object through several stages of image processing. There was research on object recognition previously using three measurement parameters based on color, shape, and size of objects. It is used a webcam as the sensing sensor, and image processing is processed with python programming. In this article, writer described about image processing for the detection of objects used in the research. The results of this device have been tested and are able to detect objects properly based on predetermined color, shape and size. Object detection using a webcam can work properly according to what the author wants.

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 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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