Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Sugata Sen"'
Publikováno v:
Indian Journal of Public Health, Vol 68, Iss 3, Pp 366-373 (2024)
Background: Thalassemia is an inherited blood disorder characterized by abnormal production of hemoglobin. The prevalence of thalassemia in India varies depending on the region and population. The study used a pre- and postcounseling cross-sectional
Externí odkaz:
https://doaj.org/article/30f745f2208e40a48d40e2253c899200
Autor:
Sourav Malakar, Saptarsi Goswami, Bhaswati Ganguli, Amlan Chakrabarti, Sugata Sen Roy, K. Boopathi, A. G. Rangaraj
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 3, Iss 4, Pp 946-965 (2021)
Complex weather conditions—in particular clouds—leads to uncertainty in photovoltaic (PV) systems, which makes solar energy prediction very difficult. Currently, in the renewable energy domain, deep-learning-based sequence models have reported be
Externí odkaz:
https://doaj.org/article/4cecab61e78a4827b7ccdcd42902c09e
Autor:
Moumita Chatterjee, Sugata Sen Roy
Publikováno v:
Statistica, Vol 79, Iss 4, Pp 445-460 (2020)
In this paper, we study the pattern of inclusion and exclusion of players from a team in any team sport. Usually these inclusions and exclusions are related to the player’s performance in the matches previously played. Also the inclusion and exclus
Externí odkaz:
https://doaj.org/article/758aefbdd9434ee1972cc97171507d3c
Autor:
Sourav Malakar, Saptarsi Goswami, Bhaswati Ganguli, Amlan Chakrabarti, Sugata Sen Roy, K. Boopathi, A. G. Rangaraj
Publikováno v:
Energies, Vol 15, Iss 10, p 3568 (2022)
Accurate short-term solar forecasting is challenging due to weather uncertainties associated with cloud movements. Typically, a solar station comprises a single prediction model irrespective of time and cloud condition, which often results in subopti
Externí odkaz:
https://doaj.org/article/1f3092adb519407eaf7735f572eb45e0
Publikováno v:
Frontiers in Physics, Vol 7 (2019)
In our study we report on some of the novel properties of a square lattice filled with white sites, randomly occupied by black sites (with probability p). We consider connections up to the second nearest neighbors, according to the following rule. Ed
Externí odkaz:
https://doaj.org/article/78bebcbe88e74300aef4fd6836960746
Autor:
Debapriya Mondal, Bhaswati Ganguli, Sugata Sen Roy, Babli Halder, Nilanjana Banerjee, Mayukh Banerjee, Maitreya Samanta, Ashok K. Giri, David A. Polya
Publikováno v:
Water, Vol 6, Iss 5, Pp 1100-1117 (2014)
There is a growing discussion about the possibility of arsenic mitigation measures in Bengal and similar areas leading to undesirable substitution of water-borne-pathogen attributable risks pathogens for risks attributable to arsenic, in part because
Externí odkaz:
https://doaj.org/article/296b95f021c549c0aa5e44f871c38de9
Autor:
Sibnarayan Guria, Sugata Sen Roy
Publikováno v:
Journal of Statistical Theory and Applications (JSTA), Vol 15, Iss 4 (2016)
In this paper we study the diagnostics of a multiresponse regression model with a first-order autoregressive error sequence. The deletion technique is used to identify the outliers taking account of the dependence structure of the errors. Besides the
Externí odkaz:
https://doaj.org/article/6e634fbf7434403cb260a5818130de31
Akademický článek
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Publikováno v:
Journal of the Royal Statistical Society Series C: Applied Statistics.
The central idea of this paper is to compare mean responses of several subjects in the presence of censoring and subject-specific variation. We develop a semiparametric mixed model for fitting subject-specific hazard curves to a set of censored failu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::590bc9d48f03615275988f7e6bba40cf
https://doi.org/10.1093/jrsssc/qlad030
https://doi.org/10.1093/jrsssc/qlad030
Autor:
Sugata Sen Roy, Bhaswati Ganguli, K. Boopathi, A. G. Rangaraj, Sourav Malakar, Amlan Chakrabarti, Saptarsi Goswami
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 3, Iss 47, Pp 946-965 (2021)
Machine Learning and Knowledge Extraction; Volume 3; Issue 4; Pages: 946-965
Machine Learning and Knowledge Extraction; Volume 3; Issue 4; Pages: 946-965
Complex weather conditions—in particular clouds—leads to uncertainty in photovoltaic (PV) systems, which makes solar energy prediction very difficult. Currently, in the renewable energy domain, deep-learning-based sequence models have reported be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b92537cd75c8b5138a1c83eaac728ddc
https://www.mdpi.com/2504-4990/3/4/47
https://www.mdpi.com/2504-4990/3/4/47