V
V. Chandramouli
Researcher at Indian Institute of Technology Guwahati
Publications - 29
Citations - 1553
V. Chandramouli is an academic researcher from Indian Institute of Technology Guwahati. The author has contributed to research in topics: Artificial neural network & Insulin. The author has an hindex of 13, co-authored 29 publications receiving 1459 citations. Previous affiliations of V. Chandramouli include Indian Institutes of Technology & Arba Minch University.
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One Week’s Treatment With the Long-Acting Glucagon-Like Peptide 1 Derivative Liraglutide (NN2211) Markedly Improves 24-h Glycemia and α- and β-Cell Function and Reduces Endogenous Glucose Release in Patients with Type 2 Diabetes
Kristine B. Degn,Claus B. Juhl,Jeppe Sturis,Grethe Jakobsen,Birgitte Brock,V. Chandramouli,Joergen Rungby,Bernard R. Landau,Ole Schmitz +8 more
TL;DR: 1 week's treatment with a single daily dose of the GLP-1 derivative liraglutide, operating through several different mechanisms including an ameliorated pancreatic islet cell function in individuals with type 2 diabetes, improves glycemic control throughout 24 h of daily living, i.e., prandial and nocturnal periods.
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Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records
TL;DR: Results suggest that the conceptual revisions can improve estimation of missing precipitation records by defining better weighting parameters and surrogate measures for distances that are used in the inverse distance weighting method.
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Deriving a General Operating Policy for Reservoirs Using Neural Network
H. Raman,V. Chandramouli +1 more
TL;DR: In this paper, a dynamic programming (DP) model was used to improve the operation and efficient management of available water for the Aliyar Dam in Tamil Nadu, India, using a neural network procedure (DPN) and using a multiple linear regression procedure (DPR) model.
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Multireservoir Modeling with Dynamic Programming and Neural Networks
TL;DR: The multireservoir model based on the dynamic programming-neural network algorithm gives improved performance in this study.
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Water quality assessment of an untreated effluent impacted urban stream: the Bharalu tributary of the Brahmaputra River, India.
TL;DR: Elevated levels of total phosphorus, BOD and depleted DO level in the downstream were used to develop an ANN model by taking total phosphorus and BOD as inputs and dissolved oxygen as output, which indicated that an ANN based predictive tool can be utilized for monitoring water quality in the future.