8 citations · 10 across the 5 of their papers we have counts for
6 papers
Leveraging Physiological Signals to Predict Exam Outcomes with Machine Learning
Lala Yamazaki, Ramchandra Rimal
This study investigates the application of machine learning models to predict exam outcomes using physiological data collected during examination sessions. Physiological stress ind…
Graph Attention Network-Based Detection of Autism Spectrum Disorder
Abigail Kelly, Ramchandra Rimal, Arpan Sainju
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by atypical brain connectivity. One of the crucial steps in addressing ASD is its early detection. Th…
Comparative study of machine learning and deep learning methods on ASD classification
Ramchandra Rimal, Mitchell Brannon, Yingxin Wang +1
The autism dataset is studied to identify the differences between autistic and healthy groups. For this, the resting-state Functional Magnetic Resonance Imaging (rs-fMRI) data of t…
Sparse Popularity Adjusted Stochastic Block Model
Majid Noroozi, Marianna Pensky, Ramchandra Rimal
In the present paper we study a sparse stochastic network enabled with a block structure. The popular Stochastic Block Model (SBM) and the Degree Corrected Block Model (DCBM) addre…
Estimation and Clustering in Popularity Adjusted Stochastic Block Model
Majid Noroozi, Ramchandra Rimal, Marianna Pensky
The paper considers the Popularity Adjusted Block model (PABM) introduced by Sengupta and Chen (2018). We argue that the main appeal of the PABM is the flexibility of the spectral…
Density Deconvolution with Small Berkson Errors
Ramchandra Rimal, Marianna Pensky
The present paper studies density deconvolution in the presence of small Berkson errors, in particular, when the variances of the errors tend to zero as the sample size grows. It i…