UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 5 Issue 10
October-2018
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

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Published Paper ID:
JETIR1810920


Registration ID:
190849

Page Number

115-116

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Title

Recognition of virtual environment VE by robot virtual machine RVM.

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Abstract

Abstract: Virtualisation is a simulated, or virtual, computing environment as opposed to a physical environment. Virtualisation often includes computer-generated versions of hardware, operating systems, storage devices and more. Projection of behaviour will enable highlight the process by which the RVM can reflect its recognition of it’s own VE. This not only offers the abilities to measure, infer and deduce environmental indicators, from delicate ecologies and natural resources to urban environments but also identify internet of things on the network space, it’s when virtualisation of workforce can happen. Also, RVM own capability identify IoT is found by collecting, processing, and analyzing the data generated by things on the network space. It is RVM identifying internet of things in network space or Iot find RVM on the same network space. This also calls for security. Thereby the call of RVM is primary when this paper is focussed on the process and outcome of RVM identifying VE. The dialogue between RVM’s should not be interchanged with IoT and IoRT. The IoT is a more encompassing phenomenon, which includes Machine-to-Human communication (M2H), Radio Frequency Identification (RFID), Location-Based Services (LBS), Lab-on-a-Chip (LOC) sensors, Augmented Reality (AR), robotics and vehicle telematics. The RVM creating internet of things understand it’s own environment. Forecasting whether RVM recognizes the environment on and of it’s own is key to inclusion of recognition process in latest trends. Forecasting can be key in developing AI quotient of RVM and mutual friendly agreement of RVM and human. Potential of RVM is one of the major metrics that determines the development in terms of number and capability to produce likeable data while in the process of recognizing VE. Recognition pattern of the process of recognition of RVM depends on it’s run time. Difference in run time indicative is of it’s learning or virtual AI quotient as it’s virtual machine. The success of recognition is with high AI factor as forecasting also aids in giving evidence of participation of robotic virtual machine in farming and intelligent project. The data retrieved can be Microsoft AI and can be arranged according to it’s essential metrics and environment.

Key Words

Virtualisation, projection, forecasting and potential as metrics, data.

Cite This Article

"Recognition of virtual environment VE by robot virtual machine RVM.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 10, page no.115-116, October-2018, Available :http://www.jetir.org/papers/JETIR1810920.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Recognition of virtual environment VE by robot virtual machine RVM.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 10, page no. pp115-116, October-2018, Available at : http://www.jetir.org/papers/JETIR1810920.pdf

Publication Details

Published Paper ID: JETIR1810920
Registration ID: 190849
Published In: Volume 5 | Issue 10 | Year October-2018
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.19588
Page No: 115-116
Country: London, Middlesex, United Kingdom .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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