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Shubham Dokania
Researcher at Delhi Technological University
Publications - 9
Citations - 101
Shubham Dokania is an academic researcher from Delhi Technological University. The author has contributed to research in topics: Population & Differential evolution. The author has an hindex of 2, co-authored 7 publications receiving 52 citations. Previous affiliations of Shubham Dokania include Indraprastha Institute of Information Technology.
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Journal ArticleDOI
FlavorDB: a database of flavor molecules.
Neelansh Garg,Apuroop Sethupathy,Apuroop Sethupathy,Rudraksh Tuwani,Rudraksh Tuwani,Rakhi Nk,Shubham Dokania,Shubham Dokania,Arvind Iyer,A. Gupta,Shubhra Agrawal,Navjot Singh,Navjot Singh,Shubham Shukla,Shubham Shukla,Kriti Kathuria,Kriti Kathuria,Rahul Badhwar,Rakesh Kanji,Anupam Jain,Avneet Kaur,Rashmi Nagpal,Ganesh Bagler +22 more
TL;DR: Data-driven studies based on FlavorDB can pave the way for an improved understanding of flavor mechanisms, as well as facilitate exploration of flavor molecules for divergent applications.
Journal ArticleDOI
A hierarchical anatomical classification schema for prediction of phenotypic side effects.
Somin Wadhwa,Aishwarya Gupta,Aishwarya Gupta,Shubham Dokania,Shubham Dokania,Rakesh Kanji,Rakesh Kanji,Ganesh Bagler +7 more
TL;DR: This study presents a ‘hierarchical anatomical classification schema’ which aggregates side effects into organs, sub-systems, and systems and shows that Random Forest model yields best classification accuracy at each level of coarse-graining.
Proceedings ArticleDOI
Opportunistic Self Organizing Migrating Algorithm for Real-Time Dynamic Traveling Salesman Problem
TL;DR: In this paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) is proposed to solve real-time Dynamic Traveling Salesman Problem (DTSP).
Proceedings ArticleDOI
Opportunistic Self Organizing Migrating Algorithm for real-time Dynamic Traveling Salesman Problem
TL;DR: An Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been proposed that introduces a novel strategy to generate perturbations effectively and allows the individual to span across more possible solutions and thus, is able to produce better solutions.
Proceedings ArticleDOI
IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes
TL;DR: A new dataset, IDD-3D, is built, which consists of multimodal data from multiple cameras and LiDAR sensors with 12k annotated driving LiDar frames across various traffic scenarios, and discusses the need for this dataset through statistical comparisons with existing datasets and highlights benchmarks on standard 3D object detection and tracking tasks in complex layouts.