S
Sanjay Ghosh
Researcher at Indian Institute of Technology Roorkee
Publications - 209
Citations - 3999
Sanjay Ghosh is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Bilateral filter & Normalized Difference Vegetation Index. The author has an hindex of 30, co-authored 196 publications receiving 3079 citations. Previous affiliations of Sanjay Ghosh include Indian Institutes of Technology & University of Cambridge.
Papers
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Journal Article
Psychobiological changes in female subjects exposed to tobacco dust.
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Mitochondrial differentiation in kinetoplastid protozoa: a plethora of RNA controls
Samit Adhya,Sudarshana Basu,Suvendra N. Bhattacharyya,Saibal Chatterjee,Gunjan Dhar,Srikanta Goswami,Sanjay Ghosh,Pratik Home,Bidesh Mahata,Gayatri Tripathi +9 more
TL;DR: Multilevel post-transcriptional regulatory mechanisms by which the expression of the nuclear and mitochondrially encoded components of respiratory enzymes is coordinated are revealed, as well as the identities of some general and gene-specific factors controlling mitochondrial differentiation.
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Geospatially based distributed rainfall-runoff modelling for simulation of internal and outlet responses in a semi-forested lower Himalayan watershed
TL;DR: In this paper, a Geographic Information System (GIS)-based distributed rainfall-runoff model for simulating surface flows in small to large watersheds during isolated storm events is presented, which takes into account the amount of interception storage to be filled using a modified Merriam (1960) approach before estimating infiltration by Smith and Parlange (1978) method.
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Sequential Aza‐Claisen Rearrangement and Ring‐Closing Metathesis as a Route to 1‐Benzazepine Derivatives.
Book ChapterDOI
Urban Growth and Land Use Simulation Using SLEUTH Model for Adama City, Ethiopia
Yanit Mekonnen,Sanjay Ghosh +1 more
TL;DR: In this paper, a cellular automaton model known as SLEUTH has been standardize using multi historical digital maps of areas to forecast the future coverage of an urban and land use, the model will use the best fit growth rule parameters by narrowing coefficients throughout calibration mode and passed down to predict future urban growth pattern, generate various probability map and LULC map.