2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CV2023
Localizing and Assessing Node Significance in Default Mode Network using Sub-Community Detection in Mild Cognitive Impairment
Ameiy Acharya, Chakka Sai Pradeep, Neelam Sinha
Our study aims to utilize fMRI to identify the affected brain regions within the Default Mode Network (DMN) in subjects with Mild Cognitive Impairment (MCI), using a novel Node Sig…
cs.LG2023★ 2 cited
Identification of Stochasticity by Matrix-decomposition: Applied on Black Hole Data
Sai Pradeep Chakka, Sunil Kumar Vengalil, Neelam Sinha
Timeseries classification as stochastic (noise-like) or non-stochastic (structured), helps understand the underlying dynamics, in several domains. Here we propose a two-legged matr…
cs.LG2023
Identifying Stochasticity in Time-Series with Autoencoder-Based Content-aware 2D Representation: Application to Black Hole Data
Chakka Sai Pradeep, Neelam Sinha
In this work, we report an autoencoder-based 2D representation to classify a time-series as stochastic or non-stochastic, to understand the underlying physical process. Content-awa…