Advait Chandorkar, Department of Mechanical Engineering, Indian Institute of Technology Ropar, Rupnagar, India
This paper presents a physics-informed deep learning approach for predicting the replicator equation, allowing accurate forecasting of population dynamics. This methodological innovation allows us to derive governing differential or difference equations for systems that lack explicit mathematical models. We used the SINDy model first introduced by Fasel, Kaiser, Kutz, Brunton, and Brunt 2016a to get the replicator equation, which will significantly advance our understanding of evolutionary biology, economic systems, and social dynamics. By refining predictive models across multiple disciplines, including ecology, social structures, and moral behaviours, our work offers new insights into the complex interplay of variables shaping evolutionary outcomes in dynamic systems
Evolutionary Game Theory, Deep learning, Replicator equation, Non-linear Dynamics.
Aman Goel and Ayushi Mishra, Department of Computing Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
In this project, we present a Gesture Recognition System designed to control computer operations like mouse movement and keyboard typing by hand gestures. We integrated various machine learning technologies such as computer vision and used the OpenCV python library to detect and interpret hand movements with high precision in a real-time environment. We also developed a custom Neural network which we trained on our custom dataset to ensure that the dataset met our expectations, thereby increasing the accuracy of gesture recognition. Users can perform various actions such as navigating through screens, clicking, scrolling, and typing, all of which can be achieved using simple hand gestures. We have created a GUI that is easy to use and can be used by anyone with no prior knowledge.
Screen Control, Gestures, OpenCV, Hand Gestures, Sign Language detection.
