Institution
International Institute of Information Technology, Hyderabad
Education•Hyderabad, India•
About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Authentication & Internet security. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).
Topics: Authentication, Internet security, Wireless sensor network, Machine translation, Deep learning
Papers published on a yearly basis
Papers
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Proceedings Article•
01 Nov 2012TL;DR: This paper proposes a recognition scheme for the Indian script of Devanagari using a Recurrent Neural Network known as Bidirectional LongShort Term Memory (BLSTM) and reports a reduction of more than 20% in word error rate and over 9% reduction in character error rate while comparing with the best available OCR system.
Abstract: In this paper, we propose a recognition scheme for the Indian script of Devanagari. Recognition accuracy of Devanagari script is not yet comparable to its Roman counterparts. This is mainly due to the complexity of the script, writing style etc. Our solution uses a Recurrent Neural Network known as Bidirectional LongShort Term Memory (BLSTM). Our approach does not require word to character segmentation, which is one of the most common reason for high word error rate. We report a reduction of more than 20% in word error rate and over 9% reduction in character error rate while comparing with the best available OCR system.
64 citations
TL;DR: In this article, the authors demonstrate the application of Autotune, a methodology aimed at automatically producing calibrated building energy models using measured data, in two case studies, by deliberately injecting faults into more than 60 parameters.
64 citations
19 Oct 2016
TL;DR: The study indicates that Hindi (i.e., the native language) is preferred over English for expression of negative opinion and swearing, and develops classifiers for opinion detection in these languages.
Abstract: Linguistic research on multilingual societies has indicated that there is usually a preferred language for expression of emotion and sentiment (Dewaele, 2010). Paucity of data has limited such studies to participant interviews and speech transcriptions from small groups of speakers. In this paper, we report a study on 430,000 unique tweets from Indian users, specifically Hindi-English bilinguals, to understand the language of preference, if any, for expressing opinion and sentiment. To this end, we develop classifiers for opinion detection in these languages, and further classifying opinionated tweets into positive, negative and neutral sentiments. Our study indicates that Hindi (i.e., the native language) is preferred over English for expression of negative opinion and swearing. As an aside, we explore some common pragmatic functions of code-switching through sentiment detection.
63 citations
TL;DR: A detailed comparative analysis reveals that the proposed scheme achieves superior security and functionality features, and offers comparable storage, communication and computational costs as compared to other existing competing schemes.
63 citations
Authors
Showing all 2066 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Ravi Shankar | 66 | 672 | 19326 |
| Joakim Nivre | 61 | 295 | 17203 |
| Aravind K. Joshi | 59 | 249 | 16417 |
| Ashok Kumar Das | 56 | 278 | 9166 |
| Malcolm F. White | 55 | 172 | 10762 |
| B. Yegnanarayana | 54 | 340 | 12861 |
| Ram Bilas Pachori | 48 | 182 | 8140 |
| C. V. Jawahar | 45 | 479 | 9582 |
| Saurabh Garg | 40 | 206 | 6738 |
| Himanshu Thapliyal | 36 | 201 | 3992 |
| Monika Sharma | 36 | 238 | 4412 |
| Ponnurangam Kumaraguru | 33 | 269 | 6849 |
| Abhijit Mitra | 33 | 240 | 7795 |
| Ramanathan Sowdhamini | 33 | 256 | 4458 |
| Helmut Schiessel | 32 | 117 | 3527 |