scholarly journals Machine Learning: An Overview and Applications in Pharmacogenetics

Genes ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1511
Author(s):  
Giovanna Cilluffo ◽  
Salvatore Fasola ◽  
Giuliana Ferrante ◽  
Velia Malizia ◽  
Laura Montalbano ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Narrative Review ◽  
The Other ◽  
Underlying Structure ◽  
The Past ◽  
Satisfactory Performance ◽  
Final Goal ◽  
Wide Range ◽  
Anti Cancer

This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the design and the development of algorithms that give computers capability to learn without being explicitly programmed. ML is a sub-field of artificial intelligence, and to date, it has demonstrated satisfactory performance on a wide range of tasks in biomedicine. According to the final goal, ML can be defined as Supervised (SML) or as Unsupervised (UML). SML techniques are applied when prediction is the focus of the research. On the other hand, UML techniques are used when the outcome is not known, and the goal of the research is unveiling the underlying structure of the data. The increasing use of sophisticated ML algorithms will likely be instrumental in improving knowledge in pharmacogenetics.

scholarly journals A Novel Technique to Regenerate Sculpture Using Generative Adversarial Network

2020 ◽  
Author(s):  
S.A.K. Jainulabudeen ◽  
H. Shalma ◽  
S. Gowri Shankar ◽  
D. Anuradha ◽  
K. Soniya
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
The Other ◽  
Generative Adversarial Network ◽  
Novel Technique ◽  
The Past ◽  
Adversarial Network ◽  
The Many

Dancing, music or any format of art has been a prominent thing from the past centuries. The many dynasties ruled the nation for centuries but every king encouraged the art one way or the other. The present day is just a minute part of the finest part of that era of art; the art of any form had been lost in the shadows to redeem the lost art we are going to use the latest technology like machine learning and artificial intelligence. The art lovers of the present age can seek the knowledge of lost art through this modern day technology. The retrieval of this art can only be done if there is a possibility to learn their language which helps in reading the old sculptures or the paintings on the walls of the ancient architecture. Now using the present day technology we are going to recoup that lost art through reading the walls of those structures where the art has been hidden for centuries. So at present we do not allow the art to continue to fall into shadow and extinguish later on, thus in this paper we present a DC-GAN model which has been created to inherit all the artistic skills of our ancestors by training from the key images of art designed as sculptures by our forefathers.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals In defence of machine learning: Debunking the myths of artificial intelligence

2018 ◽  
Vol 14 (4) ◽  
pp. 734-747 ◽  
Author(s):  
Constance de Saint Laurent
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recent Progress ◽  
Life Time ◽  
Clear Understanding ◽  
Social Scientists ◽  
Scientific Publications ◽  
The Past ◽  
Potential Benefits ◽  
The Media

There has been much hype, over the past few years, about the recent progress of artificial intelligence (AI), especially through machine learning. If one is to believe many of the headlines that have proliferated in the media, as well as in an increasing number of scientific publications, it would seem that AI is now capable of creating and learning in ways that are starting to resemble what humans can do. And so that we should start to hope – or fear – that the creation of fully cognisant machine might be something we will witness in our life time. However, much of these beliefs are based on deep misconceptions about what AI can do, and how. In this paper, I start with a brief introduction to the principles of AI, machine learning, and neural networks, primarily intended for psychologists and social scientists, who often have much to contribute to the debates surrounding AI but lack a clear understanding of what it can currently do and how it works. I then debunk four common myths associated with AI: 1) it can create, 2) it can learn, 3) it is neutral and objective, and 4) it can solve ethically and/or culturally sensitive problems. In a third and last section, I argue that these misconceptions represent four main dangers: 1) avoiding debate, 2) naturalising our biases, 3) deresponsibilising creators and users, and 4) missing out some of the potential uses of machine learning. I finally conclude on the potential benefits of using machine learning in research, and thus on the need to defend machine learning without romanticising what it can actually do.

scholarly journals Retrosynthetic Accessibility Score (RAscore) - Rapid Machine Learned Synthesizability Classification from AI Driven Retrosynthetic Planning

2020 ◽  
Author(s):  
Amol Thakkar ◽  
Veronika Chadimova ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
Jean-Louis Reymond
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Virtual Screening ◽  
Generative Models ◽  
Synthetic Route ◽  
Synthetic Accessibility ◽  
Wide Range ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Retrosynthetic Analysis

<p>Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes 4,500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for the pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity. </p>

scholarly journals Machine Learning and Artificial Intelligence in Online Fake Transaction Alerting

2015 ◽  
Vol 3 (2) ◽  
pp. 115-126 ◽  
Author(s):  
Naresh Babu Bynagari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fraud Detection ◽  
Learning Ability ◽  
The Core ◽  
The Past ◽  
Music Selection ◽  
Intelligent Analysis ◽  
Set Up ◽  
Detection Mechanisms

Artificial Intelligence (AI) is one of the most promising and intriguing innovations of modernity. Its potential is virtually unlimited, from smart music selection in personal gadgets to intelligent analysis of big data and real-time fraud detection and aversion. At the core of the AI philosophy lies an assumption that once a computer system is provided with enough data, it can learn based on that input. The more data is provided, the more sophisticated its learning ability becomes. This feature has acquired the name "machine learning" (ML). The opportunities explored with ML are plentiful today, and one of them is an ability to set up an evolving security system learning from the past cyber-fraud experiences and developing more rigorous fraud detection mechanisms. Read on to learn more about ML, the types and magnitude of fraud evidenced in modern banking, e-commerce, and healthcare, and how ML has become an innovative, timely, and efficient fraud prevention technology.

scholarly journals Machine Learning Based Behavioral Modification

2019 ◽  
Vol 8 (6S2) ◽  
pp. 1134-1138
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Analysis ◽  
Time Of Day ◽  
Computational Techniques ◽  
Behavioral Modification ◽  
Optimal Temperature ◽  
The Past ◽  
Different Types ◽  
Artificial Intelligence Technology

To build up a particular profile about a person, the study of examining the comportment is known as Behavior analysis. Initially the Behavior analysis is used in psychology and for suggesting and developing different types the application content for user then it developed in information technology. To make the applications for user's personal needs it becoming a new trends with the use of artificial intelligence (AI). in many applications like innovation to do everything from anticipating buy practices to altering a home's indoor regulator to the inhabitant's optimal temperature for a specific time of day use machine learning and artificial intelligence technology. The technique that is use to advance the rule proficiency that rely upon the past experience is known as machine learning. By utilizing the insights hypothesis it makes the numerical model, and its real work is to infer from the models gave. To take the information clearly from the data the methodology utilizes computational techniques.

scholarly journals Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 62 ◽  
Author(s):  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Frank R. Dunshea ◽  
Sigfredo Fuentes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Food Industry ◽  
Emerging Technologies ◽  
Low Cost ◽  
Consumer Preference ◽  
Beverage Industry ◽  
Wide Range ◽  
Alcoholic Drinks ◽  
Different Levels

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

Review and Applications of Machine Learning and Artificial Intelligence in Engineering

2020 ◽  
pp. 1-12
Author(s):  
Melda Yucel ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Development Process ◽  
Current Trend ◽  
Engineering Applications ◽  
The Past ◽  
Applications Of Machine Learning ◽  
Basic Features

This chapter presents a summary review of development of Artificial Intelligence (AI). Definitions of AI are given with basic features. The development process of AI and machine learning is presented. The developments of applications from the past to today are mentioned and use of AI in different categories is given. Prediction applications using artificial neural network are given for engineering applications. Usage of AI methods to predict optimum results is the current trend and it will be more important in the future.

A View from a Distance

2003 ◽  
Vol 21 (2) ◽  
pp. 367-376 ◽  
Author(s):  
Piotr Górecki
Keyword(s):  
Middle Ages ◽  
Human Activity ◽  
Legal History ◽  
Turning Point ◽  
Social Groups ◽  
The Other ◽  
Routine Activity ◽  
Quarter Century ◽  
The Past ◽  
Wide Range

Susan Reynolds's article is a culmination and a turning point. It builds on several approaches to medieval law and culture, of which two strike me as especially important. One is a study of legal history as a domain of human activity, especially habitual or routine activity, pursued by a wide range of social groups. The other is a search for the meaning and the criteria of the enormous transition during the central Middle Ages, which Christopher Dawson at the dawn of this subject, and Robert Bartlett in its currently definitive moment, have identified as “the making of Europe.” The first subject exists above all thanks to the work of Reynolds herself, while the second is an outcome of a number of quite distinct scholarly trajectories, spanning several generations. Apart from some suggestive and implicit links, those two subjects have, over the past quarter century, been pursued separately. Reynolds's article brings them together.

Regulatory law: some lessons from the past

Legal Studies ◽  
1992 ◽  
Vol 12 (1) ◽  
pp. 1-19 ◽  
Author(s):  
A. I. Ogus
Keyword(s):  
Social Engineering ◽  
The Other ◽  
Regulatory Agencies ◽  
Legal Form ◽  
The Past ◽  
Wide Range ◽  
Single European Market ◽  
Regulatory Law ◽  
The One ◽  
And Control

Regulation as a legal form of social engineering has been subjected to much analysis in the last decade or so. The importance of the topic to contemporary law cannot be overstated: on the one hand, it has been the avowed aim of government to ‘deregulate’ industry; on the other hand, and paradoxically, both the concomitant policy of privatisation and the evolution towards a Single European Market have increased the need for regulation in appropriate areas. The efforts to explore the strengths and weaknesses of different regulatory forms have brought together scholars from a wide range of disciplines. Administrative lawyers have been concerned with how the power of decision-making is allocated between institutions and the general problems of accountability and control of discretion to which this gives rise. Socio-legal researchers have critically examined the practices of regulatory agencies as regards rule formulation and enforcement.

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