Intelligent Biomedical Engineering Operations by Cloud Computing Technologies

2018 ◽  
pp. 297-317
Author(s):  
Hasan Armutlu
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Biomedical Engineering ◽  
Health Problems ◽  
Vital Importance ◽  
Research Fields ◽  
Computing Technologies ◽  
Alternative Research ◽  
Medical Health ◽  
Application Scope

Cloud computing is an effective way of using hardware- and software-oriented resources at optimum levels. Thanks to this technology, it is possible to share large amounts of resources effectively and accurately among target users. Because it is a rapidly growing technology, one cannot deny that it has remarkable relations with alternative research fields having great potential and application scope. It is clear that artificial intelligence is one of these fields. As associated with both these research fields, the purpose of this chapter is to examine artificial-intelligence-based biomedical engineering works supported/connected with cloud computing. Because it has a vital importance with applications regarding the medical/health problems, biomedical engineering needs support from the most recent technologies and research fields in this manner. So, the chapter provides a view over the intersection of these three research fields as trying to improve awareness among interested readers.

Intelligent Biomedical Engineering Operations by Cloud Computing Technologies

Biotechnology ◽  
2019 ◽  
pp. 576-596
Author(s):  
Hasan Armutlu
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Biomedical Engineering ◽  
Health Problems ◽  
Vital Importance ◽  
Research Fields ◽  
Computing Technologies ◽  
Alternative Research ◽  
Medical Health ◽  
Application Scope

Cloud computing is an effective way of using hardware- and software-oriented resources at optimum levels. Thanks to this technology, it is possible to share large amounts of resources effectively and accurately among target users. Because it is a rapidly growing technology, one cannot deny that it has remarkable relations with alternative research fields having great potential and application scope. It is clear that artificial intelligence is one of these fields. As associated with both these research fields, the purpose of this chapter is to examine artificial-intelligence-based biomedical engineering works supported/connected with cloud computing. Because it has a vital importance with applications regarding the medical/health problems, biomedical engineering needs support from the most recent technologies and research fields in this manner. So, the chapter provides a view over the intersection of these three research fields as trying to improve awareness among interested readers.

Internet of Things and Nature-Inspired Intelligent Techniques for the Future of Biomedical Engineering

2018 ◽  
pp. 263-282
Author(s):  
Gur Emre Guraksin
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Biomedical Engineering ◽  
Scientific Community ◽  
Engineering Applications ◽  
Related Literature ◽  
Research Fields ◽  
The Future ◽  
Autonomous Machines ◽  
General Network

Along with the rise of artificial intelligence (AI), there are many different research fields gaining importance. Because of the growing amount of data and needs for immediate access to information for dealing with the problems, different types of research fields take place within the scientific community. Internet of things (IoT) is one of them, and it enables devices to communicate with each other in order to form a general network of physical, working devices. The objective of this chapter in this manner is to provide a general discussion of using nature-inspired techniques of AI to form the future of biomedical engineering over IoT. Because it is often thought that the medical services of the future will be based on autonomous machines supported with AI and IoT, discussing such a topic by considering biomedical engineering applications will be good for the related literature.

Internet of Things and Nature-Inspired Intelligent Techniques for the Future of Biomedical Engineering

Biotechnology ◽  
2019 ◽  
pp. 543-561
Author(s):  
Gur Emre Guraksin
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Biomedical Engineering ◽  
Scientific Community ◽  
Engineering Applications ◽  
Related Literature ◽  
Research Fields ◽  
The Future ◽  
Autonomous Machines ◽  
General Network

Along with the rise of artificial intelligence (AI), there are many different research fields gaining importance. Because of the growing amount of data and needs for immediate access to information for dealing with the problems, different types of research fields take place within the scientific community. Internet of things (IoT) is one of them, and it enables devices to communicate with each other in order to form a general network of physical, working devices. The objective of this chapter in this manner is to provide a general discussion of using nature-inspired techniques of AI to form the future of biomedical engineering over IoT. Because it is often thought that the medical services of the future will be based on autonomous machines supported with AI and IoT, discussing such a topic by considering biomedical engineering applications will be good for the related literature.

Decomposition of tasks in a group of robots using cloud computing technologies

2017 ◽  
Vol 12 (1) ◽  
pp. 83-88
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Cloud Computing ◽  
Experimental Verification ◽  
Decomposition Algorithms ◽  
Complex Task ◽  
Large Problem ◽  
Working Capacity ◽  
Linear Distribution ◽  
Effective Teams ◽  
Computing Technologies

In this paper, various variants of decomposition of tasks in a group of robots using cloud computing technologies are considered. The specifics of the field of application (teams of robots) and solved problems are taken into account. In the process of decomposition, the solution of one large problem is divided into a solution of a series of smaller, simpler problems. Three ways of decomposition based on linear distribution, swarm interaction and synthesis of solutions are proposed. The results of experimental verification of the developed decomposition algorithms are presented, the working capacity of methods for planning trajectories in the cloud is shown. The resulting solution is a component of the complex task of building effective teams of robots.

Reinforcement Learning for Cloud Computing Digital Library

2014 ◽  
Vol 571-572 ◽  
pp. 105-108
Author(s):  
Lin Xu
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Reinforcement Learning ◽  
Digital Library ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Current Status ◽  
Self Learning ◽  
New Framework

This paper proposes a new framework of combining reinforcement learning with cloud computing digital library. Unified self-learning algorithms, which includes reinforcement learning, artificial intelligence and etc, have led to many essential advances. Given the current status of highly-available models, analysts urgently desire the deployment of write-ahead logging. In this paper we examine how DNS can be applied to the investigation of superblocks, and introduce the reinforcement learning to improve the quality of current cloud computing digital library. The experimental results show that the method works more efficiency.

scholarly journals IMPROVING INTERNET OF THINGS PARKING SYSTEMS

2021 ◽  
Vol 19 (3) ◽  
pp. 163
Author(s):  
Dušan Bogićević
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Information Needs ◽  
Vehicle Classification ◽  
The Internet ◽  
Parking Space ◽  
Automated Systems ◽  
The Internet Of Things

Edge data processing represents the new evolution of the Internet and Cloud computing. Its application to the Internet of Things (IoT) is a step towards faster processing of information from sensors for better performance. In automated systems, we have a large number of sensors, whose information needs to be processed in the shortest possible time and acted upon. The paper describes the possibility of applying Artificial Intelligence on Edge devices using the example of finding a parking space for a vehicle, and directing it based on the segment the vehicle belongs to. Algorithm of Machine Learning is used for vehicle classification, which is based on vehicle dimensions.

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