Graph Foundation Models (GFMs) are designed to work with graph data (networks of interconnected entities). They are pre-trained on massive graph datasets to learn the inherent structure and patterns within networks. It is excel at network analysis, i

This proposal aims to leverage deep learning techniques, specifically neural networks, to enhance the accuracy of emission quantification from XCO2 images provided by OCO2/3 satellites.

Bot to generate EDI Specification

AI model to detect emotion in speech.

This module is designed to automatically remove license headers and commented code from source code files. It will support multiple programming languages and can be easily extended to accommodate new ones. The module will be integrated into the data

Explore performance of ML based decoding algorithms for the new generation of quantum LDPC error correction codes

The objective of this research work is to find possible way of detecting finger movements with help of powerful sensors, which would thereafter be used to detect the sign language that was made and then convert it to audio.

The objective is to predict the false positive alerts from the alerts present in QRadar SIEM by using Machine Learning and Data Science.

Classically geometric invariances have been used in machine learning. Some recent works have proposed the same for quantum ML :- https://arxiv.org/pdf/2205.02261.pdf https://arxiv.org/pdf/2210.07980.pdf We will explore some of these and potential