#6255. Predicting the success rate of a start-up using LSTM with a swish activation function

September 2026publication date
Proposal available till 18-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Control and Optimization;
Control and Systems Engineering;
Information Systems;
Computer Networks and Communications;
Signal Processing;
Artificial Intelligence;
Human-Computer Interaction;
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
The researchers emphasised more small-scale start-ups which required improvement in the economy decreased the failure rate by avoiding the valuable resources. The resources were required to be used without wasting them lead to the development of the economy and reduces the rate of unemployment. Therefore, to minimise resource wastage and to avoid the risk of failure the researchers considered certain factors that affected the failure and success of small-scale industries. The present research uses the Crunch Base dataset that predicts the success or failure of a start-up by using the Long Short Term Memory (LSTM). The LSTM unit Recurrent Neural Network (RNN) uses the Swish activation function in Feed Forward Neural (FFN) Network for the classification.
Keywords:
Crunch base dataset; gross domestic product; long short term memory; recurrent neural network; start-up companies

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