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Enhancing Sequential Model Performance with Squared Sigmoid TanH (SST) Activation Under Data Constraints
Activation functions enable neural networks to learn complex representations by introducing non-linearities.
APALU: A Trainable, Adaptive Activation Function for Deep Learning Networks
Addressing these limitations, we introduce a novel trainable activation function, adaptive piecewise approximated activation linear unit (APALU), to enhance the learning performance of deep learning across a broad range of tasks.
From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations
This research aims to explore the impact of Machine Learning (ML) on the evolution and efficacy of Recommendation Systems (RS), particularly in the context of their growing significance in commercial business environments.
Nonnegative Matrix Factorization to understand Spatio-Temporal Traffic Pattern Variations during COVID-19: A Case Study
The outputs of the analysed spatio-temporal traffic pattern variation behaviours will be useful in the fields of traffic management in Intelligent Transportation System and management in various stages of pandemic or unavoidable scenarios in-relation to road traffic.
