scholarly journals Machine learning: technologies and potential application at mining companies

2020 ◽  
Vol 166 ◽  
pp. 03007
Author(s):  
Snizhana Zelinska
Keyword(s):  
Machine Learning ◽  
Subject Area ◽  
Learning Technologies ◽  
Mining Equipment ◽  
Practical Application ◽  
Mining Operations ◽  
Mining Companies ◽  
Dispatch System ◽  
The Subject ◽  
Selection Of

Implementation of machine learning systems is currently one of the most sought-after spheres of human activities at the interface of information technologies, mathematical analysis and statistics. Machine learning technologies are penetrating into our life through applied software created with the help of artificial intelligence algorithms. It is obvious that machine learning technologies will be developing fast and becoming part of the human information space both in our everyday life and in professional activities. However, building of machine learning systems requires great labour contribution of specialists in the sphere of artificial intelligence and the subject area where this technology is to be applied. The article considers technologies and potential application of machine learning at mining companies. The article describes basic methods of machine learning: unsupervised learning, action learning, semi-supervised machine learning. The criteria are singled out to assess machine learning: operation speed; assessment time; implemented model accuracy; ease of integration; flexible deployment within the subject area; ease of practical application; result visualization. The article describes practical application of machine learning technologies and considers the dispatch system at a mining enterprise (as exemplified by the dispatch system of the mining and transportation complex “Quarry” used to increase efficiency of operating management of enterprise performance; to increase reliability and agility of mining and transportation complex performance records and monitoring. There is also a list of equipment performance data that can be stored in the database and used as a basis for processing by machine learning algorithms and obtaining new knowledge. Application of machine learning technologies in the mining industry is a promising and necessary condition for increasing mining efficiency and ensuring environmental security. Selection of the optimal process flow sheet of mining operations, selection of the optimal complex of stripping and mining equipment, optimal planning of mining operations and mining equipment performance control are some of the tasks where machine learning technologies can be used. However, despite prospectivity of machine learning technologies, this trend still remains understudied and requires further research.

Model of predicting customer behavior based on big data analysis technologies

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Anastasiia Ivanitska ◽  
Dmytro Ivanov ◽  
Ludmila Zubik
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Behavior ◽  
Learning Technologies ◽  
User Preferences ◽  
Data Sets ◽  
Learning Technology ◽  
Behavior Prediction ◽  
Network Information ◽  
Selection Of

The analysis of the available methods and models of formation of recommendations for the potential buyer in network information systems for the purpose of development of effective modules of selection of advertising is executed. The effectiveness of the use of machine learning technologies for the analysis of user preferences based on the processing of data on purchases made by users with a similar profile is substantiated. A model of recommendation formation based on machine learning technology is proposed, its work on test data sets is tested and the adequacy of the RMSE model is assessed. Keywords: behavior prediction; advertising based on similarity; collaborative filtering; matrix factorization; big data; machine learning

THEORETICAL DEFINITIONS FOR AUXILIARY CONCEPTS OF VARIOUS LEVELS OF ABSTRACTION

2020 ◽  
pp. 28-31
Author(s):  
Valentin Karpovich
Keyword(s):  
Theoretical Model ◽  
Subject Area ◽  
Theoretical Concept ◽  
Theoretical Knowledge ◽  
Practical Application ◽  
Levels Of Abstraction ◽  
Theoretical Concepts ◽  
The Subject ◽  
Logical Constraints

Theoretical knowledge may contain various levels of abstraction represented by logical constructions from the observed characteristics of objects from the subject area of the theory. The degree of abstractness can be de-scribed by the complexity of the structures obtained from the initial observational terms. Such auxiliary construc-tions are characterized as explicit or implicitdefinitions of theoretical concepts in terms of observational. One of the techniques for constructing such definitions is the operationalization of abstractions by a system of reduction sentences. In this case a theoretical concept is characterized as “open” and plays a role of logical and methodo-logical constraints for expanding the possible connections of the theoretical model with the help of concepts from the domain of intended practical application.

Algorithm for Automated Generation of a Training Sample for Solving the Problem of Determining Semantic Similarity between a Pair of Keywords using Machine Learning Methods

2021 ◽  
Vol 12 (6) ◽  
pp. 283-294
Author(s):  
K. V. Lunev ◽  
Keyword(s):  
Machine Learning ◽  
Semantic Similarity ◽  
Training Sample ◽  
Subject Area ◽  
Training Dataset ◽  
Training Set ◽  
Automated Generation ◽  
Machine Learning Methods ◽  
Subjective Value ◽  
The Subject

Currently, machine learning is an effective approach to solving many problems of information-analytical systems. To use such approaches, a training set of examples is required. Collecting a training dataset is usually a time-consuming process. Its implementation requires the participation of several experts in the subject area for which the training set is collected. Moreover, for some tasks, including the task of determining the semantic similarity of keyword pairs, it is difficult even to correctly draw up instructions for experts to adequately evaluate the test examples. The reason for such difficulties is that semantic similarity is a subjective value and strongly depends on the scope, context, person, and task. The article presents the results of research on the search for models, algorithms and software tools for the automated formation of objects of the training sample in the problem of determining the semantic similarity of a pair of words. In addition, models built on an automated training sample allow us to solve not only the problem of determining semantic similarity, but also an arbitrary problem of classifying edges of a graph. The methods used in this paper are based on graph theory algorithms.

scholarly journals Possible Solutions to the Problems of Microfinance Organizations with the Application of Intelligent Methods of Machine Learning

2018 ◽  
Vol 12 (2) ◽  
pp. 66-71
Author(s):  
A. V. Zolotaryuk ◽  
I. A. Chechneva
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Subject Area ◽  
Microfinance Institutions ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Economic Information ◽  
The Subject ◽  
Intelligent Methods

The authors consider the problems associated with the activities of microfinance organizations, and directions to eliminate them. The subject of the study is the need to introduce machine learning to solve urgent problems. Machine learning methods are increasingly being implemented to analyze financial and economic information, which reduces and eliminates some of the difficulties. Although currently these methods are not widely used in the field of microfinance institutions (MFIs), there are opportunities for their application. The aim of the work is to determine the prospects for the use of these methods in MFOs. The article describes the subject area of research, associated with MFIs. The authors identify the main groups of problems related to MFOs, consider the possibility of introducing machine learning for data analysis in this area and determine the main directions of the possible use of machine learning for MFIs. The authors concluded that such methods are applicable for assessing the performance of MFIs.

scholarly journals Cultural-Historical Foundations of School Course in Biology

2020 ◽  
Vol 25 (3) ◽  
pp. 5-16
Author(s):  
L.N. Suchorukova ◽  
E.I. Isaev
Keyword(s):  
Middle School ◽  
General Education ◽  
Subject Area ◽  
Educational Process ◽  
Learning Activity ◽  
Theoretical Concepts ◽  
Methodological Basis ◽  
The Subject ◽  
Biological Literacy ◽  
Selection Of

The article analyzes the provisions of the cultural-historical psychology that serve as a theoretical and methodological basis for the practice of general education in biology. International monitoring of educational achievements reveals low biological literacy of Russian schoolchildren. The authors see the main reason in the insufficiently thought-out selection of the subject content. In middle school courses, it is mostly empirical, reduced to the study of the structure and functions of organisms and their diversity. In high school courses, it is theoretical, but theoretical concepts are given in a ready-made form, are not sufficiently interconnected and are often reduced to dictionary definitions, which negatively affects the development of cognitive and personal abilities of students. Currently, general biological education is being reformed, and the concentric construction of the subject content is being replaced with a linear one, which completely eliminates theoretical concepts from the middle school courses. The authors see the solution to the problem in updating the content of the school course in biology. As a methodological basis for the selection of content, a system approach is considered, the provisions of which were implemented by L.S. Vygotsky in the construction of the subject area of the cultural-historical psychology. Vygotsky’s ideas about developmental learning and their further elaboration in the general psychological and psychological-pedagogical theory of activity are suggested as a theoretical basis for the organization of the educational process. Special attention is paid to the theory of learning activity developed in the works of D.B. Elkonin, V.V. Davydov, their disciples and followers. The paper presents the concept of the content for the school course in biology and describes the experience of its implementation.

An evaluation of information sources for current awareness on occupational diseases

1986 ◽  
Vol 12 (5) ◽  
pp. 247-255 ◽  
Author(s):  
Clive Price ◽  
Rosemary A. Burley
Keyword(s):  
Occupational Diseases ◽  
English Language ◽  
Information Sources ◽  
Subject Area ◽  
Primary Sources ◽  
Research Information ◽  
Subject Field ◽  
Secondary Sources ◽  
The Subject ◽  
Selection Of

An evaluative study of a selection of primary and sec ondary information sources of potential use for current aware ness in the field of occupational diseases is presented. This study identifies the more important English language primary sources of occupational diseases research information. Re search studies in the field of occupational diseases, however, are scattered widely in the medical literature. This study com pares the usefulness of a variety of secondary sources as current awareness tools for bringing together this widely scattered information. Several secondary sources are useful but, despite considerable overlap between these sources, no single source provides comprehensive coverage of the subject field. Scanning of a number of primary sources together with several secondary sources is recommended as the best means of keeping abreast of the latest research information in this subject area.

scholarly journals RESEARCH OF ACID TRANSACTION IMPLEMENTATION METHODS FOR DISTRIBUTED DATABASES USING REPLICATION TECHNOLOGY

2021 ◽  
pp. 19-31
Author(s):  
Oksana Mazurova ◽  
Artem Naboka ◽  
Mariya Shirokopetleva
Keyword(s):  
Relational Databases ◽  
System Analysis ◽  
Distributed Databases ◽  
Subject Area ◽  
Relational Model ◽  
Distributed Transactions ◽  
Business Operations ◽  
The Subject ◽  
Effective Use ◽  
Selection Of

Today, databases are an integral part of most modern applications designed to store large amounts of data and to request from many users. To solve business problems in such conditions, databases are scaled, often horizontally on several physical servers using replication technology. At the same time, many business operations require the implementation of transactional compliance with ACID properties. For relational databases that traditionally support ACID transactions, horizontal scaling is not always effective due to the limitations of the relational model itself. Therefore, there is an applied problem of efficient implementation of ACID transactions for horizontally distributed databases. The subject matter of the study is the methods of implementing ACID transactions in distributed databases, created by replication technology. The goal of the work is to increase the efficiency of ACID transaction implementation for horizontally distributed databases. The work is devoted to solving the following tasks: analysis and selection of the most relevant methods of implementation of distributed ACID transactions; planning and experimental research of methods for implementing ACID transactions by using of NoSQL DBMS MongoDB and NewSQL DBMS VoltDB as an example; measurements of metrics of productivity of use of these methods and formation of the recommendation concerning their effective use. The following methods are used: system analysis; relational databases design; methods for evaluating database performance. The following results were obtained: experimental measurements of the execution time of typical distributed transactions for the subject area of e-commerce, as well as measurements of the number of resources required for their execution; revealed trends in the performance of such transactions, formed recommendations for the methods studied. The obtained results allowed to make functions of dependence of the considered metrics on loading parameters. Conclusions: the strengths and weaknesses of the implementation of distributed ACID transactions using MongoDB and VoltDB were identified. Practical recommendations for the effective use of these systems for different types of applications, taking into account the resources consumed and the types of requests.

scholarly journals Impact of the Choice of Cross-Validation Techniques on the Results of Machine Learning-Based Diagnostic Applications

2021 ◽  
Vol 27 (3) ◽  
pp. 189-199
Author(s):  
Ilias Tougui ◽  
Abdelilah Jilbab ◽  
Jamal El Mhamdi
Keyword(s):  
Machine Learning ◽  
Clinical Study ◽  
Cross Validation ◽  
Learning Technologies ◽  
Data Availability ◽  
Support Vector ◽  
Training Set ◽  
The Subject ◽  
Validation Set ◽  
Diagnostic Applications

Objectives: With advances in data availability and computing capabilities, artificial intelligence and machine learning technologies have evolved rapidly in recent years. Researchers have taken advantage of these developments in healthcare informatics and created reliable tools to predict or classify diseases using machine learning-based algorithms. To correctly quantify the performance of those algorithms, the standard approach is to use cross-validation, where the algorithm is trained on a training set, and its performance is measured on a validation set. Both datasets should be subject-independent to simulate the expected behavior of a clinical study. This study compares two cross-validation strategies, the subject-wise and the record-wise techniques; the subject-wise strategy correctly mimics the process of a clinical study, while the record-wise strategy does not.Methods: We started by creating a dataset of smartphone audio recordings of subjects diagnosed with and without Parkinson’s disease. This dataset was then divided into training and holdout sets using subject-wise and the record-wise divisions. The training set was used to measure the performance of two classifiers (support vector machine and random forest) to compare six cross-validation techniques that simulated either the subject-wise process or the record-wise process. The holdout set was used to calculate the true error of the classifiers.Results: The record-wise division and the record-wise cross-validation techniques overestimated the performance of the classifiers and underestimated the classification error.Conclusions: In a diagnostic scenario, the subject-wise technique is the proper way of estimating a model’s performance, and record-wise techniques should be avoided.

scholarly journals Intensive underground mining technologies: Challenges and prospects for the coal mines in Russia

2021 ◽  
pp. 60-69
Keyword(s):  
Coal Mining ◽  
High Performance ◽  
Underground Mining ◽  
Strain Analysis ◽  
Environmental Indicators ◽  
Mining Equipment ◽  
Underground Coal Mining ◽  
Mining Operations ◽  
Mining Companies ◽  
Production Growth

The article presents the key features of underground coal mining that influence the development of mining technologies and mining equipment design. It covers the most important challenges faced by underground coal mining companies and discusses growth areas in mining technology that are aligned with the paradigm of sustainable development. Using the example of underground coal mining operations, it illustrates such concepts as the intelligent mine and the invisible mine, discussing how they can be brought to life. It also shows how underground coal mining companies can improve their productivity, OSH management, and environmental indicators to make their products competitive. As mining operations are becoming more intensive and the average depth of mining is growing, which is accompanied by an increase in both methane emissions and risks associated with rock mechanics processes, it is becoming vital to accurately predict how the rock mass will behave and, by applying stress-strain analysis, to identify hazardous zones and their boundaries. The article discusses several mine layouts and how their parameters are adjusted to ensure intensive mining. Among the factors that hinder growth in coal production, it highlights the underutilisation of high-performance mining equipment. It also contains a list of key principles aimed at fostering production growth in the underground coal mining sector and improving its competitiveness.

Recording Lectures, Streaming, Downloading, Podcasting, Vodcasting, and Webcasting

2008 ◽  
pp. 176-195
Author(s):  
Richard Caladine
Keyword(s):  
Higher Education ◽  
Educational Outcomes ◽  
Teaching And Learning ◽  
Learning Experience ◽  
Subject Area ◽  
Learning Technologies ◽  
Student Interaction ◽  
Fundamental Part ◽  
The Subject ◽  
Efficient Learning

There can be a range of reasons to record lectures or presentations, from the creation of resources to meeting the needs of distant students. Of course recordings are one-way. The information in them flows from the recorded file to students and student interaction with recordings is generally limited to interacting with the controls of the player, that is, they can pause, stop, and replay the recording in part or in its entirety. It can be argued that this interaction adds another level of access to educational presentations. While this low level of interaction can have positive educational outcomes it cannot be equated with interactions between students and teachers. Clearly the person-to-person interactions have the potential for far greater educational outcomes ranging from the answering of questions to the exploration and extension of the subject area. In cases where students are distant from teachers and interact with recorded resources other technologies and techniques are need to provide viable two-way communications channels between them. All learning technologies impose on teaching and learning activities and recordings of presentations are no exception. It is argued that recordings by themselves seldom, if ever, are sufficient for effective and efficient learning in higher education. However, it is suggested that recordings when used in conjunction with other learning technologies and techniques can be a fundamental part of the learning experience.

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