Advances in Deep Learning, Artificial Intelligence and Robotics

2021 ◽

Vol 07 (3&4) ◽

pp. 7-14

Author(s):

Devnath Jayaswal ◽

Keyword(s):

Artificial Intelligence ◽

Health Care ◽

Deep Learning ◽

Health Care Sector ◽

Healthcare Sector ◽

Current Scenario ◽

Health Complications ◽

And Robotics ◽

Major Domain

Health Care is one of the major domain sectors of our country. As this domain has many different aspect of implementation, as per the current scenario of Diseases and health complications. This paper will discuss about how, the Artificial Intelligence (A.I.) and robotics can be beneficial and plays a major role on, health care domain with respect to the Efficiently Diagnose, Developing New Medicines, Earlier Detection of Diseases, Advance Treatment Care, A.I-Deep learning For the Critical Decision’s. As this Information will help to give more clarity on what, A.I. & Robotics contributes for the major Diseases Treatment by the advancement of Technology. This can be beneficial for not only Doctors, Patients, or Firm but can also be helpful for citizen people as well. The objective of this paper is to study the role of AI and Robotics in Healthcare Sector and its impact.

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Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human-like learning

Behavioral and Brain Sciences ◽

10.1017/s0140525x17000243 ◽

2017 ◽

Vol 40 ◽

Cited By ~ 3

Author(s):

Pierre-Yves Oudeyer

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Social Learning ◽

Intrinsic Motivation ◽

Scaling Up ◽

Learning Systems ◽

Natural Interaction ◽

Autonomous Choice ◽

Autonomous Development ◽

And Robotics

AbstractAutonomous lifelong development and learning are fundamental capabilities of humans, differentiating them from current deep learning systems. However, other branches of artificial intelligence have designed crucial ingredients towards autonomous learning: curiosity and intrinsic motivation, social learning and natural interaction with peers, and embodiment. These mechanisms guide exploration and autonomous choice of goals, and integrating them with deep learning opens stimulating perspectives.

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EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE USING DEEP LEARNING FOR DETECTING GASTRIC CANCER IN ENDOSCOPIC IMAGES

10.1055/s-0038-1637183 ◽

2018 ◽

Author(s):

T Hirasawa ◽

K Aoyama ◽

J Fujisaki ◽

T Tada

Keyword(s):

Artificial Intelligence ◽

Gastric Cancer ◽

Deep Learning ◽

Endoscopic Images

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Developing an Artificial Intelligence Project in your Radiology Department

Indian Journal of Musculoskeletal Radiology ◽

10.25259/ijmsr_50_2019 ◽

2020 ◽

Vol 2 ◽

pp. 58-61 ◽

Cited By ~ 1

Author(s):

Syed Junaid ◽

Asad Saeed ◽

Zeili Yang ◽

Thomas Micic ◽

Rajesh Botchu

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Health Care ◽

Deep Learning ◽

Learning Algorithms ◽

Radiology Department ◽

Short Article ◽

Computing Power ◽

Road Map ◽

Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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Virtual Screening Meets Deep Learning

Current Computer - Aided Drug Design ◽

10.2174/1573409914666181018141602 ◽

2018 ◽

Vol 15 (1) ◽

pp. 6-28 ◽

Cited By ~ 6

Author(s):

Javier Pérez-Sianes ◽

Horacio Pérez-Sánchez ◽

Fernando Díaz

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Virtual Screening ◽

Great Increase ◽

New Drugs ◽

Learning Approach ◽

Screening Strategies ◽

Computer Aided ◽

Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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