no code implementations • 14 Feb 2024 • Barathi Subramanian, Rathinaraja Jeyaraj, Rakhmonov Akhrorjon Akhmadjon Ugli, Jeonghong Kim
Activation functions enable neural networks to learn complex representations by introducing non-linearities.
no code implementations • 13 Feb 2024 • Barathi Subramanian, Rathinaraja Jeyaraj, Rakhmonov Akhrorjon Akhmadjon Ugli, Jeonghong Kim
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.
no code implementations • 12 Feb 2024 • Kapilya Gangadharan, K. Malathi, Anoop Purandaran, Barathi Subramanian, Rathinaraja Jeyaraj
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.
no code implementations • 5 Nov 2021 • Anandkumar Balasubramaniam, Thirunavukarasu Balasubramaniam, Rathinaraja Jeyaraj, Anand Paul, Richi Nayak
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.