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

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

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,
Startup,
Survival rate,
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.

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