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Deep learning model for MEMS

EuroSciCon Conference on Nanotechnology & Smart Materials
October 04 -06 ,2018 Amsterdam , Netherlands

Ankit Agarwal

Mobileum, India

ScientificTracks Abstracts: Nano Res Appl

DOI: 10.21767/2471-9838-C6-024

Abstract

Since a decade, deep learning (DL) has been exploited in various fields such as healthcare, automobile, electronics, weather prediction, telecom and many more. DL has the ability to learn the dependence between two sets of data and to generalize on unseen data, whereas major characteristic of DL is to discover intricate structure in large datasets. It has huge potential to be used in materials process and micro-electro-mechanical systems (MEMS). MEMS devices’ experimental and commercial simulator results may not be matching due to unavoidable environmental conditions while experimenting, difference in design and fabricated device, etc. DL model is made using MEMS devices experimental study which may give accurate predictive result compared to simulators. These analytical models prepared using DL may be more accurate, fast and cost effective solution as compared to commercial available MEMS softwares.

Biography

Ankit Agarwal has completed his B Tech from BIET, Jhansi, India and M Tech from IIT, Delhi. He worked as Research Assistant at Trinity College Dublin, Dublin City, Ireland. Currently, he is working as a Senior Data Scientist in Mobileum. His interests are to explore machine learning and deep learning for experimental applications. He has already demonstrated deep learning for telecom and computer vision. He is highly motivated to apply deep learning for MEMS systems.

E-mail: [email protected]

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