Sourabh Singh
Indian Institute of Technology Delhi
6 Papers
4 Citations
Sourabh Singh is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Computer science & Overfitting. The author has an hindex of 3, co-authored 5 publications. Previous affiliations of Sourabh Singh include Indian Institutes of Technology.
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Papers
Predicting Young's modulus of oxide glasses with sparse datasets using machine learning
Suresh Bishnoi,Sourabh Singh,R. Ravinder,Mathieu Bauchy,Nitya Nand Gosvami,Hariprasad Kodamana,N. M. Anoop Krishnan +6 more
TL;DR: In this paper, Gaussian Process Regression (GPR) was used to predict Young's modulus for silicate glasses having a sparse dataset and showed that GPR significantly outperforms NN for the sparse dataset while ensuring no overfitting.
Interpreting the optical properties of oxide glasses with machine learning and shapely additive explanations
Mohd Zaki,Vineeth Venugopal,R. Ravinder,Suresh Bishnoi,Sourabh Singh,Amarnath R. Allu,J. Jayadeva,N. M. Anoop Krishnan +7 more
TL;DR: In this article , a Shapely additive explanation (SHAP) was used to identify the contribution of each of the input components toward the target prediction of the Abbe number and refractive index of oxide glasses.
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An Adaptive, Interacting, Cluster-Based Model For Predicting the Transmission Dynamics of COVID-19
R. Ravinder,Sourabh Singh,Suresh Bishnoi,Amreen Jan,Amit Sharma,Hariprasad Kodamana,N. M. Anoop Krishnan +6 more
TL;DR: A new adaptive, interacting, and cluster-based mathematical model is developed to predict the granular trajectory of COVID-19 and is applied to make detailed predictions for COVID19 incidences at the district and state level in India.
16
An Adaptive, Interacting, Cluster-Based Model Accurately Predicts the Transmission Dynamics of COVID-19
R. Ravinder,Sourabh Singh,Suresh Bishnoi,Amreen Jan,Abhinav Sinha,Amit Sharma,Amit Sharma,Hariprasad Kodamana,N. M. Anoop Krishnan,N. M. Anoop Krishnan +9 more
TL;DR: A new adaptive, interacting, and cluster-based mathematical model is developed to predict the granular trajectory COVID-19 and it is shown that R0 as the basic reproduction number exhibits significant spatial and temporal variation in these countries.