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: Computer science & Authentication. 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: Computer science, Authentication, Deep learning, Artificial neural network, Internet security
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a field study of thermal comfort was conducted in six naturally ventilated hostel buildings of composite climate considering Class-II protocol of field measurement during summer 2011, where objective and subjective measurements were collected.
54 citations
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TL;DR: This letter addresses the issue of determining the number of speakers from multispeaker speech signals collected simultaneously using a pair of spatially separated microphones and suggests that for a given speaker, the relative spacings of the instants of significant excitation of the vocal tract system remain unchanged in the direct components of the speech signals at the two microphones.
Abstract: In this letter, we address the issue of determining the number of speakers from multispeaker speech signals collected simultaneously using a pair of spatially separated microphones. The spatial separation of the microphones results in time delay of arrival of speech signals from a given speaker. The differences in the time delays for different speakers are exploited to determine the number of speakers from the multispeaker signals. The key idea is that for a given speaker, the relative spacings of the instants of significant excitation of the vocal tract system remain unchanged in the direct components of the speech signals at the two microphones. The time delays can be estimated from the cross-correlation of the Hilbert envelopes of the linear prediction residuals of the multispeaker signals collected at the two microphones.
54 citations
01 Jan 2010
TL;DR: The ICON10 tools contest was dedicated to the task of dependency parsing for Indian languages (IL), and three languages namely, Hindi, Telugu and Bangla were explored.
Abstract: The ICON10 tools contest was dedicated to the task of dependency parsing for Indian languages (IL). Three languages namely, Hindi, Telugu and Bangla, were explored. The motivation behind the task was to investigate and solve the challenges in IL parsing by making annotated data available to the larger community.
54 citations
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01 Apr 2020TL;DR: It is observed that Ensemble and Hybrid models with neural networks and SVM are being more adopted for credit scoring, NPA prediction and fraud detection, and lack of comprehensive public datasets continue to be an area of concern for researchers.
Abstract: Credit risk is the risk of financial loss when a borrower fails to meet the financial commitment. While there are many factors that constitute credit risk, due diligence while giving loan (credit scoring), continuous monitoring of customer payments and other behaviour patterns could reduce the probability of accumulating non-performing assets (NPA) and frauds. In the past few years, the quantum of NPAs and frauds have gone up significantly, and therefore it has become imperative that banks and financial institutions use robust mechanisms to predict the performance of loans. The past two decades has seen an immense growth in the area of artificial intelligence, most notably machine learning (ML) with improved access to internet, data, and compute. Whilst there are credit rating agencies and credit scoring companies that provide their analysis of a customer to banks on a fee, the researchers continue to explore various ML techniques to improve the accuracy level of credit risk evaluation. In this survey paper, we performed a systematic literature review on existing research methods and ML techniques for credit risk evaluation. We reviewed a total of 136 papers on credit risk evaluation published between 1993 and March 2019. We studied the implications of hyper parameters on ML techniques being used to evaluate credit risk and, analyzed the limitations of the current studies and research trends. We observed that Ensemble and Hybrid models with neural networks and SVM are being more adopted for credit scoring, NPA prediction and fraud detection. We also realized that lack of comprehensive public datasets continue to be an area of concern for researchers.
54 citations
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TL;DR: The results indicate that the performance of the proposed SFF-based methods for emotional speech is comparable to the results for neutral speech, and is better than the results from many of the standard methods.
54 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 |