Search

Your search keyword '"Sivaselvan, B."' showing total 84 results
Start Over Author "Sivaselvan, B."
84 results on '"Sivaselvan, B."'

1. STATS-BERT: An Enhanced BERT Model with Statistical Masking for Improved Transfer Performance

Author

Mercy Faustina, J., Rajagopal, Abhinand, Kausik, N., Parameshwaran, Pranav, Sivaselvan, B., Hartmanis, Juris, Founding Editor, Goos, Gerhard, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ghosh, Ashish, editor, King, Irwin, editor, Bhattacharyya, Malay, editor, Sankar Ray, Shubhra, editor, and K. Pal, Sankar, editor

Published
2024
Full Text
View/download PDF

7. An Online Supervised Learning Framework for Crime Data

Author

Vidhyut, Shiv, Uppada, Santosh Kumar, SivaSelvan, B., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tripathi, Meenakshi, editor, and Upadhyaya, Sushant, editor

Published
2021
Full Text
View/download PDF

8. Frequent Pattern Mining Approach for Fake News Detection

Author

Pranave, S., Uppada, Santosh Kumar, Vishnu Priya, A., SivaSelvan, B., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tripathi, Meenakshi, editor, and Upadhyaya, Sushant, editor

Published
2021
Full Text
View/download PDF

9. A Study of Deep Learning Methods for Mitotic Cell Detection Towards Breast Cancer Diagnosis

Author

Kaushik, S., Raghavan, S. Vijaya, Sivaselvan, B., Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Singh, Mayank, editor, Gupta, P.K., editor, Tyagi, Vipin, editor, Flusser, Jan, editor, Ören, Tuncer, editor, and Kashyap, Rekha, editor

Published
2019
Full Text
View/download PDF

10. A Novel Rare Itemset Mining Algorithm Based on Recursive Elimination

Author

Kataria, Mohak, Oswald, C., Sivaselvan, B., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hoda, M. N., editor, Chauhan, Naresh, editor, Quadri, S. M. K., editor, and Srivastava, Praveen Ranjan, editor

Published
2019
Full Text
View/download PDF

13. Domain Specific Hierarchical Huffman Encoding

Author

Ilambharathi, K., Manik, G. S. N. V. Venkata, Sadagopan, N., and Sivaselvan, B.

Subjects
Computer Science - Information Theory, Computer Science - Data Structures and Algorithms
Abstract

In this paper, we revisit the classical data compression problem for domain specific texts. It is well-known that classical Huffman algorithm is optimal with respect to prefix encoding and the compression is done at character level. Since many data transfer are domain specific, for example, downloading of lecture notes, web-blogs, etc., it is natural to think of data compression in larger dimensions (i.e. word level rather than character level). Our framework employs a two-level compression scheme in which the first level identifies frequent patterns in the text using classical frequent pattern algorithms. The identified patterns are replaced with special strings and to acheive a better compression ratio the length of a special string is ensured to be shorter than the length of the corresponding pattern. After this transformation, on the resultant text, we employ classical Huffman data compression algorithm. In short, in the first level compression is done at word level and in the second level it is at character level. Interestingly, this two level compression technique for domain specific text outperforms classical Huffman technique. To support our claim, we have presented both theoretical and simulation results for domain specific texts.

Published
2013

14. Frequent Sequence Mining Approach to Video Compression

Author

Karthik, M., Oswald, C., Sivaselvan, B., Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Ganapathi, Geetha, editor, Subramaniam, Arumugam, editor, Graña, Manuel, editor, Balusamy, Suresh, editor, Natarajan, Rajamanickam, editor, and Ramanathan, Periakaruppan, editor

Published
2018
Full Text
View/download PDF

15. Frequent Pattern Mining Guided Tabu Search

Author

Avula, Sandeep, Oswald, C., Sivaselvan, B., Barbosa, Simone Diniz Junqueira, Series Editor, Chen, Phoebe, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, R, Shriram, editor, and Sharma, Mak, editor

Published
2018
Full Text
View/download PDF

16. A Frequent and Rare Itemset Mining Approach to Transaction Clustering

Author

Tummala, Kuladeep, Oswald, C., Sivaselvan, B., Barbosa, Simone Diniz Junqueira, Series Editor, Chen, Phoebe, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, R, Shriram, editor, and Sharma, Mak, editor

Published
2018
Full Text
View/download PDF

17. Hierarchical Clustering Approach to Text Compression

Author

Oswald, C., Akshay Vyas, V., Arun Kumar, K., Vijay Sri, L., Sivaselvan, B., Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Series editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Sa, Pankaj Kumar, editor, Sahoo, Manmath Narayan, editor, Murugappan, M., editor, Wu, Yulei, editor, and Majhi, Banshidhar, editor

Published
2018
Full Text
View/download PDF

18. Clinical Thermography for Breast Cancer Screening: A Systematic Review on Image Acquisition, Segmentation, and Classification.

Author

Kaushik, R., Sivaselvan, B., and Kamakoti, V.

Subjects
*MEDICAL thermography, *EARLY detection of cancer, *BREAST cancer, *ARTIFICIAL intelligence, *INFRARED imaging, *THERMOGRAPHY, *DIGITAL mammography
Abstract

There is a life after breast cancer. The prerequisite is early detection. Breast cancer is curable when detected early, tiny, and has not spread – regular screening aids in early detection. Clinical Thermography and artificial intelligence are potentially a good fit for early breast cancer screening. This survey paper presents a systematic review of artificial intelligence-based breast cancer screening using thermal infrared cameras. Initially, we will present the qualitative analysis of the existing literature regarding the trend and distribution. This review manuscript will then explore the literature about infrared thermal image acquisition and storage techniques. We will then highlight various segmentation techniques used for processing infrared thermal images. This paper presents the experimental results of the traditional image processing and deep learning-based segmentation techniques available in the literature using infrared breast thermal images. We then summarize the works that have used artificial intelligence to segment and classify infrared thermal images. The existing literature shows opportunities to explore the area of explainable artificial intelligence (AI). Explainable AI will make clinical Thermography into assistive technology for the medical community. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

21. A Graph-Based Frequent Sequence Mining Approach to Text Compression

Author

Oswald, C., Ajith Kumar, I., Avinash, J., Sivaselvan, B., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ghosh, Ashish, editor, Pal, Rajarshi, editor, and Prasath, Rajendra, editor

Published
2017
Full Text
View/download PDF

23. An Efficient Text Compression Algorithm - Data Mining Perspective

Author

Oswald, C., Ghosh, Anirban I., Sivaselvan, B., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Prasath, Rajendra, editor, Vuppala, Anil Kumar, editor, and Kathirvalavakumar, T., editor

Published
2015
Full Text
View/download PDF

31. Smart Multimedia Compressor—Intelligent Algorithms for Text and Image Compression.

Author

Oswald, C and Sivaselvan, B

Subjects
*IMAGE compression, *DATA compression, *ALGORITHMS, *DATA mining, *GRAPHICAL user interfaces, *COMPRESSORS
Abstract

A number of text compression algorithms have been proposed in the past decades, which have been very effective and usually operate on conventional character/word-based approaches. A novel data compression perspective of data mining is explored in this research and the paper focuses on novel frequent sequence/pattern mining approach to text compression. This work attempts to make use of longer-range correlations between words in languages for achieving better text compression. We propose a novel and efficient method by making the compression of any word-level text in a universal manner for corpora across domains referred as Universal Huffman Tree-based encoding. The major contribution of this work is in terms of avoidance of code table communication to the decoder. Simulation results over benchmark datasets indicate that Universal Huffman encoding employing frequent sequence mining (achieves [20%, 89%] improvement in compression in reduced time. The paper also contributes a usable interface for Data compression that employs the proposed frequent sequent mining-based data compression algorithm. The interface supports features such as feedback, consistency, usability, navigation, visual appeal, performance and accessibility on par with existing compression softwares. The work results in an intelligent data compression software employing knowledge engineering perspective. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results