Dr. Chirantana Mallick

Faculties: Centre for Data Science Faculty

Assistant Professor

Ph.D.: Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, India

M.E.: Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, India

B.E.: Department of Computer Science and Engineering, Burdwan University, India

Professional Engagements:

Research Topics:

Natural Language Processing (NLP), Bio-Natural Language Processing (Bio-NLP), Machine Learning, Deep Learning, Text mining

Research Focus:

Access Control:

The research goal in NLP is to advance the understanding, generation, and interaction with natural language through computational methods. This involves developing innovative algorithms and models that enhance language understanding, sentiment analysis, machine translation, and information retrieval. The objective is to create more efficient, accurate, and contextually aware NLP systems that can handle diverse languages and tasks.

Biological Natural Language Processing (Bio-NLP):

The research goal in Bio-NLP (Biological Natural Language Processing) is to leverage natural language processing techniques and methodologies to extract, analyze, and understand biological and biomedical information from vast amounts of textual data. This involves developing specialized algorithms and models that can accurately extract relevant information from scientific literature, electronic health records, and biological databases. The objective is to advance the field of bioinformatics by automating the curation and integration of biological knowledge from diverse sources, enabling accelerated biomedical research and drug discovery. Additionally, the research aims to improve clinical decision support systems by extracting actionable insights from patient records and medical literature.

Awards & Achievements:

MHRD Doctoral Scholarship (July 2018 – July 2022), MHRD, Govt. of India.
MHRD Post-graduate Scholarship (July 2016 – June 2018), MHRD, Govt. of India.
Best Paper Award for “Deep learning-based automated feature engineering for rice leaf disease prediction”, Computational Intelligence in Pattern Recognition (CIPR) 2020.
Secured Second position in BE degree, Burdwan University, 2015.

Publications:

Book Chapter:

“Evolutionary Algorithm Based Summarization for Analyzing COVID-19 Medical Reports”, Understanding COVID-19: The Role of Computational Intelligence, 31-58, 2021, DOI : https://doi.org/10.1007/978-3-030-74761-9_2
“Hybridization of Fuzzy Theory and Nature-Inspired Optimization for Medical Report Summarization”, Nature-Inspired Optimization Methodologies in Biomedical and Health Care, 2022

Journal:

“Evolutionary Algorithm based Ensemble Extractive Summarization for Developing Smart Medical System”, Interdisciplinary Sciences: Computational Life Sciences 2021. DOI : https://doi.org/10.1007/s12539-020-00412-5
“Ensemble summarization of bio-medical articles integrating clustering and multi-objective evolutionary algorithms”, Applied Soft Computing, 2021 DOI : https://doi.org/10.1016/j.asoc.2021.107347

Conferences:

“An Unsupervised COVID-19 Report Summarizer for Developing Smart Healthcare System”, Computational Intelligence in Pattern Recognition 2021. ISBN : 978-981-16-25 43-5
“A Game Theoretic Approach on Semantic Segmentation Along with Transfer Learning”, Computational Intelligence in Pattern Recognition, 2021. ISBN : 978-981-16-2543-5
“Deep learning-based automated feature engineering for rice leaf disease prediction”, Computational Intelligence in Pattern Recognition 2020. DOI : https://doi.org/10.1007/978-981-15-2449-3_11
“A graph based approach on extractive summarization”, Emerging Technologies in Data Mining and Information Security 2019. DOI: https://doi.org/10.1007/978-981-13-1498-8_16
“Extractive summarization of a document using lexical chains”, Soft Computing in Data Analytics, 2019. DOI : https://doi.org/10.1007/978-981-13-0514-6_78
“Graph-based text summarization using modified TextRank”, Soft Computing in Data Analytics, 2019. DOI : https://doi.org/10.1007/978-981-13-0514-6_14