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Aditi Sharan

Aditi Sharan

Dr. Aditi Sharan
Aditi Sharan
Associate Professor
Centre/School/Special Centre
School of Computer & Systems Sciences
Room No
L-01
Off. Phone
011-26704682
Email
aditisharan@mail.jnu.ac.in, aditisharan@gmail.com
Personal Webpage
BMineLytics Research Group
Qualifications

Ph.D. , M.Sc. (Computer Science) 

Areas of Interest/Specialization

Text Mining, Opinion Mining & Sentiment Analysis, Information Retrieval, Natural Language Processing

Currently involved in Biomedical text mining: Information retrieval, Text Classification, Text mining for precision medicine, Text mining for Adverse Drug Reactions, Knowledge Graphs and their applications in biomedical text mining.

 

Experience

20 Years  

Best Peer Reviewed Publications

1.“An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain segmentation”, Hanuman Verma, R.K. Agrawal, Aditi Sharan, Applied Soft Computing 46 (2016) 543-557.

2.“A new fuzzy logic based query expansion model for efficient information retrieval using relevance feedback approach”, Jagendra Singh and Aditi Sharan,  Neural Computing And Applications, Vol.  28 Issue 9 , September  2017 , pp 2557-2580,  Springer.

3. Singh, A., & Sharan, A. (2018). Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web. International Journal on Semantic Web and Information Systems (IJSWIS), 14(3), 134-166. (http://doi.org/ 10.4018/IJSWIS.2018070107, SCIE IF:1.5)

4. Singh, A., Sharan, A. Unsupervised genetic programming based linkage rule (UGPLR) Miner for entity linking in semantic web. Evol. Intel. 12, 609–632 (2019). https://doi.org/10.1007/s12065-019-00263-0

5. Kumar, A., Verma, S., & Sharan, A. (2021). ATE-SPD: simultaneous extraction of aspect-term and aspect sentiment polarity using Bi-LSTM-CRF neural network. Journal of Experimental & Theoretical Artificial Intelligence, 33(3), 487-508. https://doi.org/10.1080/0952813X.2020.1764632

6. Kumar, A., Saini, M., & Sharan, A. (2020). Aspect category detection using statistical and semantic association. Computational Intelligence, 36(3), 1161-1182.

Recent Peer Reviewed Journals/Books

1. Ahmad O., Verma S., Azim S., Sharan A. (2022) Hybridizing Sentence Transformer Model with Multi-KNN for Biomedical Documents. In: Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6460-1_11.

2. Malik, N., Sharan, A., & Verma, S. (2021). Semantic Web Solutions for Improvised Search in Healthcare Systems. Semantic Web for Effective Healthcare, 95-113. 

3. Kumar, A., Verma, S., & Sharan, A. (2021). ATE-SPD: simultaneous extraction of aspect-term and aspect sentiment polarity using Bi-LSTM-CRF neural network. Journal of Experimental & Theoretical Artificial Intelligence, 33(3), 487–508. doi:10.1080/0952813X.2020.1764632.

4. A. Kumar, M. Saini, A. Sharan. “Aspect category detection using statistical and semantic association”. In: Computational Intelligence 36.3 (2020), pp. 1161–1182. doi: 10.1111/coin.12327.

5. Sushil Kumar, Avantika Sahu, Aditi Sharan, Information Systems and Management Science (ISMS 2021), Deep learning based architecture for Entity Extraction from Covid related documents, To be published in : Lecture Notes in network and Systems.

6. Shahid Azim, Samridhi Dev and Aditi sharan, Artificial Intelligence for Biomedical Informatics, Book chapter 06, To be published in: Artificial Intelligence Technologies for computational Biology,CRC Press.

 

A warm welcome to the modified and updated website of the Centre for East Asian Studies. The East Asian region has been at the forefront of several path-breaking changes since 1970s beginning with the redefining the development architecture with its State-led development model besides emerging as a major region in the global politics and a key hub of the sophisticated technologies. The Centre is one of the thirteen Centres of the School of International Studies, Jawaharlal Nehru University, New Delhi that provides a holistic understanding of the region.

Initially, established as a Centre for Chinese and Japanese Studies, it subsequently grew to include Korean Studies as well. At present there are eight faculty members in the Centre. Several distinguished faculty who have now retired include the late Prof. Gargi Dutt, Prof. P.A.N. Murthy, Prof. G.P. Deshpande, Dr. Nranarayan Das, Prof. R.R. Krishnan and Prof. K.V. Kesavan. Besides, Dr. Madhu Bhalla served at the Centre in Chinese Studies Programme during 1994-2006. In addition, Ms. Kamlesh Jain and Dr. M. M. Kunju served the Centre as the Documentation Officers in Chinese and Japanese Studies respectively.

The academic curriculum covers both modern and contemporary facets of East Asia as each scholar specializes in an area of his/her interest in the region. The integrated course involves two semesters of classes at the M. Phil programme and a dissertation for the M. Phil and a thesis for Ph. D programme respectively. The central objective is to impart an interdisciplinary knowledge and understanding of history, foreign policy, government and politics, society and culture and political economy of the respective areas. Students can explore new and emerging themes such as East Asian regionalism, the evolving East Asian Community, the rise of China, resurgence of Japan and the prospects for reunification of the Korean peninsula. Additionally, the Centre lays great emphasis on the building of language skills. The background of scholars includes mostly from the social science disciplines; History, Political Science, Economics, Sociology, International Relations and language.

Several students of the centre have been recipients of prestigious research fellowships awarded by Japan Foundation, Mombusho (Ministry of Education, Government of Japan), Saburo Okita Memorial Fellowship, Nippon Foundation, Korea Foundation, Nehru Memorial Fellowship, and Fellowship from the Chinese and Taiwanese Governments. Besides, students from Japan receive fellowship from the Indian Council of Cultural Relations.