DATA-DRIVEN: MODELING AND LEARNING

Supriya A. Darunde; Vijay M. Rakhade; Lowlesh N. Yadav

doi:10.59367/9s2pes65

Authors

Supriya A. Darunde Computer Science & Engineering Shri Sai College of Engineering and Technology, Bhadrawati, India
Vijay M. Rakhade Computer Science & Engineering Shri Sai College of Engineering and Technology, Bhadrawati, India
Lowlesh N. Yadav Computer Science & Engineering Shri Sai College of Engineering and Technology, Bhadrawati, India

DOI:

https://doi.org/10.59367/9s2pes65

Keywords:

Scientific disco identical, Big Data analysis, Biological ingredients, Integrated Computational Materials Science Engineering, oil and air industry, Artificial intelligence, Machine learning

Abstract

In the previous, data on which science and engineering are grounded was rare and regularly obtained by researchers proposed to validate a given premise. Each research was able to vintage only identical limited data. Today, data is copious and copiously collected in each research at an identical minor cost. Data-driven modelling and technical disco identical is a variation of pattern on how many glitches, both in science and engineering, are lectured. Some scientific pitches have been with artificial intelligence for some period due to the characteristic difficulty in obtaining laws and comparisons to label some singularities. However, today data-driven tactics are also overflowing pitches like mechanism and ingredients science, where the outdated approach seemed to be extremely reasonable. In this paper, we analyse the tender of data-driven modelling and model learning procedures to dissimilar pitches in science and engineering.

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IJRECE VOL. 7 ISSUE 1 (JANUARY- MARCH 2019) pg. no 275-278.