Basic principles of machine learning

2020 ◽  
pp. 43-67
Keyword(s):  
Machine Learning ◽  
Basic Principles

On techniques of expert systems on the example of the Akinator program

2020 ◽  
Vol 25 (2) ◽  
pp. 7-13
Author(s):  
Zhangozha A.R. ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Expert Systems ◽  
Knowledge Base ◽  
Hybrid Systems ◽  
Online Game ◽  
Basic Principles ◽  
The Moment ◽  
Definition Of

On the example of the online game Akinator, the basic principles on which programs of this type are built are considered. Effective technics have been proposed by which artificial intelligence systems can build logical inferences that allow to identify an unknown subject from its description (predicate). To confirm the considered hypotheses, the terminological analysis of definition of the program "Akinator" offered by the author is carried out. Starting from the assumptions given by the author's definition, the article complements their definitions presented by other researchers and analyzes their constituent theses. Finally, some proposals are made for the next steps in improving the program. The Akinator program, at one time, became one of the most famous online games using artificial intelligence. And although this was not directly stated, it was clear to the experts in the field of artificial intelligence that the program uses the techniques of expert systems and is built on inference rules. At the moment, expert systems have lost their positions in comparison with the direction of neural networks in the field of artificial intelligence, however, in the case considered in the article, we are talking about techniques using both directions – hybrid systems. Games for filling semantics interact with the user, expanding their semantic base (knowledge base) and use certain strategies to achieve the best result. The playful form of such semantics filling programs is beneficial for researchers by involving a large number of players. The article examines the techniques used by the Akinator program, and also suggests possible modifications to it in the future. This study, first of all, focuses on how the knowledge base of the Akinator program is built, it consists of incomplete sets, which can be filled and adjusted as a result of further iterations of the program launches. It is important to note our assumption that the order of questions used by the program during the game plays a key role, because it determines its strategy. It was identified that the program is guided by the principles of nonmonotonic logic – the assumptions constructed by the program are not final and can be rejected by it during the game. The three main approaches to acquisite semantics proposed by Jakub Šimko and Mária Bieliková are considered, namely, expert work, crowdsourcing and machine learning. Paying attention to machine learning, the Akinator program using machine learning to build an effective strategy in the game presents a class of hybrid systems that combine the principles of two main areas in artificial intelligence programs – expert systems and neural networks.

scholarly journals Big data, machine learning and artificial intelligence: a neurologist’s guide

2020 ◽  
pp. practneurol-2020-002688
Author(s):  
Stephen D Auger ◽  
Benjamin M Jacobs ◽  
Ruth Dobson ◽  
Charles R Marshall ◽  
Alastair J Noyce
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Clinical Practice ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Basic Principles ◽  
Biological Neural Networks

Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.

scholarly journals Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing

2019 ◽  
Author(s):  
Charles F Rowlands ◽  
Diana Baralle ◽  
Jamie M Ellingford
Keyword(s):  
Machine Learning ◽  
In Silico Analysis ◽  
Mrna Splicing ◽  
Comparative Approach ◽  
Mendelian Disease ◽  
Learning Approaches ◽  
Basic Principles ◽  
Prediction Tools ◽  
Splicing Defects ◽  
Made In

Defects in pre-mRNA splicing are frequently a cause of Mendelian disease. Despite the advent of next-generation sequencing, allowing a deeper insight into a patient’s variant landscape, the ability to characterize variants causing splicing defects has not progressed with the same speed. To address this, recent years have seen a sharp spike in the number of splice prediction tools leveraging machine learning approaches, leaving clinical geneticists with a plethora of choices for in silico analysis. In this Review, some basic principles of machine learning are introduced in the context of genomics and splicing analysis. A critical comparative approach is then used to describe seven recent machine learning-based splice prediction tools, revealing highly diverse approaches and common caveats. We find that, although great progress has been made in producing specific and sensitive tools, there is still much scope for personalized approaches to prediction of variant impact on splicing. Such approaches may increase diagnostic yields and underpin improvements to patient care.

Machine learning approaches for elucidating the biological effects of natural products

2020 ◽  
Author(s):  
Ruihan Zhang ◽  
Xiaoli Li ◽  
Xingjie Zhang ◽  
Huayan Qin ◽  
Weilie Xiao
Keyword(s):  
Machine Learning ◽  
Natural Products ◽  
Biological Effects ◽  
Learning Approaches ◽  
Basic Principles

This review presents the basic principles, protocols and examples of using the machine learning approaches to investigate the bioactivity of natural products.

scholarly journals An overview of machine learning methods for monotherapy drug response prediction

2021 ◽  
Author(s):  
Farzaneh Firoozbakht ◽  
Behnam Yousefi ◽  
Benno Schwikowski
Keyword(s):  
Machine Learning ◽  
Drug Response ◽  
Response Prediction ◽  
Data Types ◽  
Basic Principles ◽  
Drug Responses ◽  
Machine Learning Methods ◽  
Fundamental Understanding ◽  
Biological Problem ◽  
Clinical Drug

Abstract For an increasing number of preclinical samples, both detailed molecular profiles and their responses to various drugs are becoming available. Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses. Here, we provide a uniquely wide and deep systematic review of the rapidly evolving literature on monotherapy drug response prediction, with a systematic characterization and classification that comprises more than 70 ML methods in 13 subclasses, their input and output data types, modes of evaluation, and code and software availability. ML experts are provided with a fundamental understanding of the biological problem, and how ML methods are configured for it. Biologists and biomedical researchers are introduced to the basic principles of applicable ML methods, and their application to the problem of drug response prediction. We also provide systematic overviews of commonly used data sources used for training and evaluation methods.

scholarly journals Vegetation cover analysis of the mountainous part of north-eastern Siberia by means of geoinformation modelling and machine learning (basic principles, approaches, technology and relation to geosystem science)

2021 ◽  
Vol 38 ◽  
pp. 00142
Author(s):  
Moisey Zakharov ◽  
Mikhail Cherosov ◽  
Elena Troeva ◽  
Sebastien Gadal
Keyword(s):  
Machine Learning ◽  
Vegetation Cover ◽  
Slope Aspect ◽  
Learning Approaches ◽  
Eastern Siberia ◽  
Basic Principles ◽  
Vegetation Map ◽  
Mountain Landscape ◽  
North Eastern ◽  
Vegetative Indices

For the first time, the geoinformation modelling and machine learning approaches have been used to study the vegetation cover of the mountainous part of North-Eastern Siberia – the Orulgan medium-altitude mountain landscape province. These technologies allowed us to distinguish a number of mapping units that were used for creation and analysis of 1:100 000 scale vegetation map of the interpreted key area. Based on the studies, we decided upon the basic principles, approaches and technologies that would serve as a methodology basis for the further studies of vegetation cover of the large region. Relief, slope aspect, genetic types of sediments, and moisture conditions were selected as supplementary factors to the vegetative indices for differentiation of both plant communities and vegetation map units.

scholarly journals Closing the Feedback Loop in DevOps Through Autonomous Monitors in Operations

2021 ◽  
Vol 2 (6) ◽  
Author(s):  
Adha Hrusto ◽  
Per Runeson ◽  
Emelie Engström
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Public Transportation ◽  
Feedback Loop ◽  
Qualitative Data ◽  
Design Science ◽  
Basic Principles ◽  
Rule Based ◽  
Tight Connection ◽  
Solution Design

AbstractDevOps represent the tight connection between development and operations. To address challenges that arise on the borderline between development and operations, we conducted a study in collaboration with a Swedish company responsible for ticket management and sales in public transportation. The aim of our study was to explore and describe the existing DevOps environment, as well as to identify how the feedback from operations can be improved, specifically with respect to the alerts sent from system operations. Our study complies with the basic principles of the design science paradigm, such as understanding and improving design solutions in the specific areas of practice. Our diagnosis, based on qualitative data collected through interviews and observations, shows that alert flooding is a challenge in the feedback loop, i.e. too much signals from operations create noise in the feedback loop. Therefore, we design a solution to improve the alert management by optimizing when to raise alerts and accordingly introducing a new element in the feedback loop, a smart filter. Moreover, we implemented a prototype of the proposed solution design and showed that a tighter relation between operations and development can be achieved, using a hybrid method which combines rule-based and unsupervised machine learning for operations data analysis.

Basic principles of TikTok algorithms

2021 ◽  
Vol 5 (2(15)) ◽  
pp. 61-76
Author(s):  
Vasilii Konstantinovich Alekhin ◽  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Learning Algorithms ◽  
User Interaction ◽  
Machine Learning Algorithms ◽  
Behavioral Patterns ◽  
Basic Principles ◽  
The Difference ◽  
Audio Track ◽  
Database Clustering

Social network TikTok has strong competitive differentiator in comparing with other platforms. ByteDance exploits machine learning algorithms to generate a recommendation feed (for you page). The algorithm bases on two main mechanisms. The first mechanism provides content database clustering depending on the type, audio track, video captions, and hashtags. The second mechanism analyzes the user’s behavioral patterns based on their actions in the application. The next step is the formation of user interaction scenarios. The difference between the predicted behavior and the real one is the object of analysis. If it equals zero, then the recommendations feed is formed correctly. The user is watching more and more interesting videos, just scrolling through video after video.

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