scholarly journals Prospects for the use of artificial neural networks for problem solving in clinical transplantation

2021 ◽  
Vol 23 (2) ◽  
pp. 177-182
Author(s):  
R. M. Kurabekova ◽  
A. A. Belchenkov ◽  
O. P. Shevchenko
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Information Technologies ◽  
Learning Technologies ◽  
Human Intelligence ◽  
Solid Organ ◽  
Clinical Transplantation ◽  
Artificial Intelligence Systems

Management of solid organ recipients requires a significant amount of research and observation throughout the recipient’s life. This is associated with accumulation of large amounts of information that requires structuring and subsequent analysis. Information technologies such as machine learning, neural networks and other artificial intelligence tools make it possible to analyze the so-called ‘big data’. Machine learning technologies are based on the concept of a machine that mimics human intelligence and and makes it possible to identify patterns that are inaccessible to traditional methods. There are still few examples of the use of artificial intelligence programs in transplantology. However, their number has increased markedly in recent years. A review of modern literature on the use of artificial intelligence systems in transplantology is presented.

Network Security Policy Automation

2022 ◽  
pp. 83-112
Author(s):  
Myo Zarny ◽  
Meng Xu ◽  
Yi Sun
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Network Security ◽  
Security Policy ◽  
Learning Technologies ◽  
Use Cases ◽  
Hybrid Cloud ◽  
Cloud Environments ◽  
Enterprise Security

Network security policy automation enables enterprise security teams to keep pace with increasingly dynamic changes in on-premises and public/hybrid cloud environments. This chapter discusses the most common use cases for policy automation in the enterprise, and new automation methodologies to address them by taking the reader step-by-step through sample use cases. It also looks into how emerging automation solutions are using big data, artificial intelligence, and machine learning technologies to further accelerate network security policy automation and improve application and network security in the process.

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 Outlier Detection in High Dimensional Data

2021 ◽  
Vol 10 (5) ◽  
pp. 128-130
Author(s):  
Anusha L. ◽  
Nagaraja G S
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Outlier Detection ◽  
Text Processing ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Human Intelligence ◽  
Over Time

Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception. Artificial Intelligence is the broader field that contains several subfields such as machine learning, robotics, and computer vision. Machine Learning is a branch of Artificial Intelligence that allows a machine to learn and improve at a task over time. Deep Learning is a subset of machine learning that makes use of deep artificial neural networks for training. The paper proposed on outlier detection for multivariate high dimensional data for Autoencoder unsupervised model.

scholarly journals The Revolution of Artificial Intelligence and the Significance of Adopting AI in different Industries

2019 ◽  
Vol 8 (4S2) ◽  
pp. 642-644
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Learning Technologies ◽  
Large Sets ◽  
Wide Range ◽  
Data Points ◽  
Machine Interaction ◽  
The Revolution

Artificial Intelligence (AI) is one of the most widely inflated technologies in several industries today. With the emergence of IoT, Big data and Digitalization, many industries produce large sets of data and AI begins to be the prominence for solving the increasing number of complications in this relevance. Artificial Intelligence (AI) and Machine Learning (ML) applications, spectacle substantial guarantee in gaining commercial traction in several businesses as AI brings with a probable of genuine human-to-machine interaction. When machines become intelligent, they can understand needs, connect with data points and arrive at better decisions. Therefore, Artificial Intelligence (AI) and Machine Learning technologies are being quickly adopted in wide range of applications in several industries. In this paper, we epitomize the fundamentals and the significance of adopting of Artificial Intelligence technologies in different industries.

Network Security Policy Automation

2018 ◽  
pp. 232-261
Author(s):  
Myo Zarny ◽  
Meng Xu ◽  
Yi Sun
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Network Security ◽  
Security Policy ◽  
Learning Technologies ◽  
Use Cases ◽  
Hybrid Cloud ◽  
Cloud Environments ◽  
Enterprise Security

Network security policy automation enables enterprise security teams to keep pace with increasingly dynamic changes in on-premises and public/hybrid cloud environments. This chapter discusses the most common use cases for policy automation in the enterprise, and new automation methodologies to address them by taking the reader step-by-step through sample use cases. It also looks into how emerging automation solutions are using big data, artificial intelligence, and machine learning technologies to further accelerate network security policy automation and improve application and network security in the process.

Survey of Applications of Neural Networks and Machine Learning to COVID-19 Predictions

2022 ◽  
pp. 30-57
Author(s):  
Richard S. Segall
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Recurrent Neural Networks ◽  
Big Data Analytics ◽  
Data Sets ◽  
Open Source Data ◽  
Source Data

The purpose of this chapter is to illustrate how artificial intelligence (AI) technologies have been used for COVID-19 detection and analysis. Specifically, the use of neural networks (NN) and machine learning (ML) are described along with which countries are creating these techniques and how these are being used for COVID-19 diagnosis and detection. Illustrations of multi-layer convolutional neural networks (CNN), recurrent neural networks (RNN), and deep neural networks (DNN) are provided to show how these are used for COVID-19 detection and prediction. A summary of big data analytics for COVID-19 and some available COVID-19 open-source data sets and repositories and their characteristics for research and analysis are also provided. An example is also shown for artificial intelligence (AI) and neural network (NN) applications using real-time COVID-19 data.

Machine Learning and Deep Learning Technologies

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Model Building ◽  
Learning Technologies ◽  
Decision Makers ◽  
Human Intervention ◽  
Learning Techniques ◽  
Tools And Techniques

In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Such minimal human intervention can be provided using machine learning, which is the application of advanced deep learning techniques on big data. This paper aims to analyse some of the different machine learning and deep learning algorithms and methods, aswell as the opportunities provided by the AI applications in various decision making domains.

scholarly journals Exploiting Artificial Neural Networks Machine Learning Errors for Attacks on AI Systems

2021 ◽  
pp. 23-32
Author(s):  
Т. В. Гавриленко ◽  
А. В. Гавриленко
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
Current Paradigm ◽  
Artificial Intelligence Systems

В статье приведен обзор различных методов атак и подходов к атакам на системы искусственного интеллекта, построенных на основе искусственных нейронных сетей. Показано, что начиная с 2015 года исследователи в различных странах активно развивают методы атак и подходы к атакам на искусственные нейронные сети, при этом разработанные методы и подходы могут иметь критические последствия при эксплуатации систем искусственного интеллекта. Делается вывод о необходимости развития методологической и теоретической базы искусственных нейронных сетей и невозможности создания доверительных систем искусственного интеллекта в текущей парадигме. The paper provides an overview of methods and approaches to attacks on neural network-based artificial intelligence systems. It is shown that since 2015, global researchers have been intensively developing methods and approaches for attacks on artificial neural networks, while the existing ones may have critical consequences for artificial intelligence systems operations. We come to the conclusion that theory and methodology for artificial neural networks is to be elaborated, since trusted artificial intelligence systems cannot be created in the framework of the current paradigm.

The Postdigital Challenge of Critical Media Literacy

2019 ◽  
Vol 1 (1) ◽  
pp. 26-37 ◽  
Author(s):  
Petar Jandrić
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Media Literacy ◽  
The Political ◽  
Complex Interplay ◽  
Critical Media Literacy ◽  
Data Literacy ◽  
Critical Media

This article situates contemporary critical media literacy into a postdigital context. It examines recent advances in data literacy, with an accent to Big Data literacy and data bias, and expands them with insights from critical algorithm studies and the critical posthumanist perspective to education. The article briefly outlines differences between older software technologies and artificial intelligence (AI), and introduces associated concepts such as machine learning, neural networks, deep learning, and AI bias. Finally, it explores the complex interplay between Big Data and AI and teases out three urgent challenges for postdigital critical media literacy. (1) Critical media literacy needs to reinvent existing theories and practices for the postdigital context. (2) Reinvented theories and practices need to find a new balance between the technological aspects of data and AI literacy with the political aspects of data and AI literacy, and learn how to deal with non-predictability. (3) Critical media literacy needs to embrace the posthumanist challenge; we also need to start thinking what makes AIs literate and develop ways of raising literate thinking machines. In our postdigital age, critical media literacy has a crucial role in conceptualisation, development, and understanding of new forms of intelligence we would like to live with in the future.

scholarly journals From Big Data to Artificial Intelligence: chemoinformatics meets new challenges

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Igor V. Tetko ◽  
Ola Engkvist
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Chemical Industry ◽  
Life Sciences ◽  
Biomedical Data ◽  
Special Session ◽  
New Methods ◽  
New Challenges

Abstract The increasing volume of biomedical data in chemistry and life sciences requires development of new methods and approaches for their analysis. Artificial Intelligence and machine learning, especially neural networks, are increasingly used in the chemical industry, in particular with respect to Big Data. This editorial highlights the main results presented during the special session of the International Conference on Neural Networks organized by “Big Data in Chemistry” project and draws perspectives on the future progress of the field. Graphical Abstract

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