Convex Least Angle Regression Based LASSO Feature Selection and Swish Activation Function Model for Startup Survival Rate Cover

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Convex Least Angle Regression Based LASSO Feature Selection and Swish Activation Function Model for Startup Survival Rate

Cybernetics and Information Technologies

Volume 23 (2023): Issue 4 (November 2023)

By: Ramakrishna Allu and Venkata Nageswara Rao Padmanabhuni

Open Access

|Nov 2023

Authors

Ramakrishna Allu

Ramakrishna.allu@gmail.com

Department of Computer Science and Engineering, GITAM (Deemed to be university), Visakhapatnam, India

Venkata Nageswara Rao Padmanabhuni

nagesh.uma@hotmail.com

Department of Computer Science and Engineering, GITAM (Deemed to be university), Visakhapatnam, India

DOI: https://doi.org/10.2478/cait-2023-0039 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702

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Language: English

Page range: 110 - 127

Submitted on: Jun 22, 2023

Accepted on: Oct 20, 2023

Published on: Nov 30, 2023

Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Convex Least Angle Regression based Least Absolute Shrinkage and Selection Operator (CLAR-LASSO),

Crunch base dataset,

Swish activation function based Long Short Term Memory (LSTM)

Related subjects:

Computer sciences,

Information technology

© 2023 Ramakrishna Allu, Venkata Nageswara Rao Padmanabhuni, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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