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An ECC with Probable Secure and Efficient Approach on Noncommutative Cryptography Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Employing Machine Learning Models to Solve Uniform Random 3-SAT

Authors : Aditya Atkari, Nishant Dhargalkar, Hemali Angne

Published in: Data Communication and Networks

Publisher: Springer Singapore

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Abstract

We have employed a chosen set of machine learning models to solve the 3-CNF-SAT problem. Through f1-scores, we obtain how these algorithms perform at solving the problem as a classification task. The implication of this endeavour is exciting given the property of the NP-complete class problems being polynomial-time reducible to each other.

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previous chapter Improved ITCA Method to Mitigate Network-Layer Attack in MANET
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Literature
1.
2.
D. Selsam, M. Lamm, B. Bunz, P. Liang, D.L. Dill, Learning a SAT Solver from Single-Bit Supervision Submitted on 11 Feb 2018, last revised 5 Jan 2019. Department of Computer Science Stanford University Stanford, CA 94305 and Microsoft Research Redmond, WA 98052. https://​arxiv.​org/​pdf/​1802.​03685.​pdf
Metadata
Title
Employing Machine Learning Models to Solve Uniform Random 3-SAT
Authors
Aditya Atkari
Nishant Dhargalkar
Hemali Angne
Copyright Year
2020
Publisher
Springer Singapore
Book
Data Communication and Networks

Print ISBN: 978-981-15-0131-9

Electronic ISBN: 978-981-15-0132-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-0132-6_17