Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Damani, Om P."'
Change detection for aerial imagery involves locating and identifying changes associated with the areas of interest between co-registered bi-temporal or multi-temporal images of a geographical location. Farm ponds are man-made structures belonging to
Externí odkaz:
http://arxiv.org/abs/2302.14554
Deep learning has led to many recent advances in object detection and instance segmentation, among other computer vision tasks. These advancements have led to wide application of deep learning based methods and related methodologies in object detecti
Externí odkaz:
http://arxiv.org/abs/2111.15613
Existing techniques for the cost optimization of water distribution networks either employ meta-heuristics, or try to develop problem-specific optimization techniques. Instead, we exploit recent advances in generic NLP solvers and explore a rich set
Externí odkaz:
http://arxiv.org/abs/2111.11865
Autor:
Venkateswaran, Jayendran, Damani, Om
We present a System Dynamics (SD) model of the Covid-19 pandemic spread in India. The detailed age-structured compartment-based model endogenously captures various disease transmission pathways, expanding significantly from the standard SEIR model. T
Externí odkaz:
http://arxiv.org/abs/2004.08859
Autor:
Chaudhari, Dipak L., Damani, Om
Publikováno v:
EPTCS 187, 2015, pp. 1-13
In this paper, we describe an IDE called CAPS (Calculational Assistant for Programming from Specifications) for the interactive, calculational derivation of imperative programs. In building CAPS, our aim has been to make the IDE accessible to non-exp
Externí odkaz:
http://arxiv.org/abs/1508.03892
Autor:
Damani, Om P.
We design a new co-occurrence based word association measure by incorporating the concept of significant cooccurrence in the popular word association measure Pointwise Mutual Information (PMI). By extensive experiments with a large number of publicly
Externí odkaz:
http://arxiv.org/abs/1307.0596
Lexical co-occurrence is an important cue for detecting word associations. We present a theoretical framework for discovering statistically significant lexical co-occurrences from a given corpus. In contrast with the prevalent practice of giving weig
Externí odkaz:
http://arxiv.org/abs/1008.5287
Akademický článek
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Akademický článek
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Publikováno v:
In Journal of Parallel and Distributed Computing 2003 63(12):1193-1218
