Institution
Indian Institute of Technology Bombay
Education•Mumbai, India•
About: Indian Institute of Technology Bombay is a education organization based out in Mumbai, India. It is known for research contribution in the topics: Population & Thin film. The organization has 16756 authors who have published 33588 publications receiving 570559 citations.
Papers published on a yearly basis
Papers
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TL;DR: An attempt to design and develop an appropriate storage, collection and disposal plan for the Asansol Municipality Corporation of West Bengal State (India) and a GIS optimal routing model is proposed to determine the minimum cost/distance efficient collection paths for transporting the solid wastes to the landfill.
279 citations
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TL;DR: Atmospheric particulate PAH concentrations were measured at two locations in Mumbai (formerly Bombay), India as discussed by the authors, where pyrene and benz(a)anthracene#chrysene were abundant at Saki Naka and Indian Institute of Technology (IIT) sites.
278 citations
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TL;DR: In this article, the effect of jet-to-plate spacing and Reynolds number on the local heat transfer distribution to normally impinging submerged circular air jet on a smooth and flat surface was investigated.
277 citations
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TL;DR: In this paper, the alignment of the inner tracking system of the ALICE Large Ion Collider Experiment (ALICE ITS) with the Millepede global approach has been studied and the results obtained for the ITS alignment using about 10(5) charged tracks from cosmic rays that have been collected during summer 2008.
Abstract: ALICE (A Large Ion Collider Experiment) is the LHC (Large Hadron Collider) experiment devoted to investigating the strongly interacting matter created in nucleus-nucleus collisions at the LHC energies. The ALICE ITS, Inner Tracking System, consists of six cylindrical layers of silicon detectors with three different technologies; in the outward direction: two layers of pixel detectors, two layers each of drift, and strip detectors. The number of parameters to be determined in the spatial alignment of the 2198 sensor modules of the ITS is about 13,000. The target alignment precision is well below 10 mu m in some cases (pixels). The sources of alignment information include survey measurements, and the reconstructed tracks from cosmic rays and from proton-proton collisions. The main track-based alignment method uses the Millepede global approach. An iterative local method was developed and used as well. We present the results obtained for the ITS alignment using about 10(5) charged tracks from cosmic rays that have been collected during summer 2008, with the ALICE solenoidal magnet switched off.
277 citations
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TL;DR: Automatic sarcasm detection is the task of predicting sarcasm in text as mentioned in this paper, which is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm of sentiment-bearing text.
Abstract: Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Beginning with an approach that used speech-based features, automatic sarcasm detection has witnessed great interest from the sentiment analysis community. This article is a compilation of past work in automatic sarcasm detection. We observe three milestones in the research so far: semi-supervised pattern extraction to identify implicit sentiment, use of hashtag-based supervision, and incorporation of context beyond target text. In this article, we describe datasets, approaches, trends, and issues in sarcasm detection. We also discuss representative performance values, describe shared tasks, and provide pointers to future work, as given in prior works. In terms of resources to understand the state-of-the-art, the survey presents several useful illustrations—most prominently, a table that summarizes past papers along different dimensions such as the types of features, annotation techniques, and datasets used.
277 citations
Authors
Showing all 17055 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jovan Milosevic | 152 | 1433 | 106802 |
C. N. R. Rao | 133 | 1646 | 86718 |
Robert R. Edelman | 119 | 605 | 49475 |
Claude Andre Pruneau | 114 | 610 | 45500 |
Sanjeev Kumar | 113 | 1325 | 54386 |
Basanta Kumar Nandi | 112 | 572 | 43331 |
Shaji Kumar | 111 | 1265 | 53237 |
Josep M. Guerrero | 110 | 1197 | 60890 |
R. Varma | 109 | 497 | 41970 |
Vijay P. Singh | 106 | 1699 | 55831 |
Vinayak P. Dravid | 103 | 817 | 43612 |
Swagata Mukherjee | 101 | 1048 | 46234 |
Anil Kumar | 99 | 2124 | 64825 |
Dhiman Chakraborty | 96 | 529 | 44459 |
Michael D. Ward | 95 | 823 | 36892 |