Keynote Speaker (1)

 #AI for infectious diseases: #Pneumonia, #TB  and #Covid19

Professor  KC Santosh, PhD
Chair, Department of Computer Science, University of South Dakota (USA)  and  

AI has contributed a lot in healthcare. Infectious disease outbreak is no exception. The talk will provide a walk through about how AI-guided tools help in predicting/detecting infectious diseases, such as Pneumonia, TB, and Covid-19. Infectious disease prediction and unexploited data will be discussed, as predictive analytical tools are limited to education and training (at least for Covid19). It also covers shallow learning (handcrafted features) as well as deep learning mechanism in both image modalities: CT scan and Chest X-ray. Additionally, an obvious question, how big data is big will be discussed by taking two key points into account: data augmentation and transfer learning. 
Bio:  Prof. KC Santosh is the Chair of the Department of Computer Science at the University of South Dakota (USD). Before joining USD, Prof. Santosh worked as a research fellow at the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). He was a postdoctoral research scientist at the LORIA research centre (with industrial partner, ITESOFT (France)). He has demonstrated expertise in artificial intelligence, machine learning, pattern recognition, computer vision, image processing and data mining with applications, such as medical imaging informatics, document imaging, biometrics, forensics, and speech analysis. His research projects are funded by multiple agencies, such as SDCRGP, Department of Education, National Science Foundation, and Asian Office of Aerospace Research and Development. He is the proud recipient of the Cutler Award for Teaching and Research Excellence (USD, 2021), the President’s Research Excellence Award (USD, 2019), and the Ignite Award from the U.S. Department of Health and Human  Services (2014). For more information, follow: http://kc- and (research lab).

Keynote Speaker (2)

  Improved Reinforcement Learning for Intelligent Devices

     Dr. Parthasarathy Subashini,  Professor of Computer Science

                        Center for Machine Learning and Intelligence

                        Avinashilingam University for Women

                        Coimbatore, India

Artificial intelligence (AI) has been penetrated in a big way in the daily lives of every human being. In every aspects of day to day requirements met – be it the product recommendations that triggers on any social networking platforms, or the interactive sessions involved with. It can also be defined as AI refers to any machine that behaves in a way that would be considered smart or intelligent if the same behaviour were to be displayed by manpower. In current scenario, industries were looking into exploring AI, to hold in short to reduce the excess manpower, time taken in accomplishing the task and also provide results that can reflect individual’s requirement. AI, therefore, is of huge interest to researchers as it has the potential to change the reporting trend of scientific advancement. And while AI-powered applications in research are in an emerging stage, the periphery of options which AI can open for researchers will be the booming possibilities.  Reinforcement learning is conventionally defined as Intelligence exhibited by machines and operationally it can be defined as those areas of R&D practiced by computer scientists like Computer vision, Natural Language processing, Robotics (including Human –Robot Interactions), Search and planning, Social media analysis (including crowd sourcing) and Knowledge Representation and Reasoning.

Researchers require the best possible tools to tackle the challenges in such disciplines to address the common requirements. The advancement of the state of the art will continue to improve vision of the unseen. The keynote address will focus upon highlighting the necessity for improved research paradigm and also case study where and how it is done.


Professor Subashini’s research has spanned a large number of disciplines like Image analysis, Pattern recognition, Neural networks, and Computational Intelligence. Concurrently, she contributed to several fields of mathematics, especially Nature inspired computing. She has authored or co-authored 6 Books, 7 Book chapters, 1 Monograph, 165 papers, including IEEE, Springer’s in various international, national journals and conferences. In course of her research and teaching, Professor Subashini has mentored over a hundred of post graduate students and guiding several doctoral students. She has held positions as reviewer, chairpersons for different peer reviewed journals.  Under her supervision, she has ten research projects of worth more than 4.5 crores from various funding agencies like Defence Research and Development Organization, Department of Science and Technology, SERB and University Grants Commission. She has visited many countries like Singapore, Malaysia, Dubai, France, Switzerland, Italy, Canada, Germany, Spain, Czech Republic, Rome, Morocco, United States of America and China for various knowledge sharing events. As a member of IEEE, IEEE Computational Intelligence Society and IEEE Computer Society of India, she extended her contribution as IEEE Chair for Women in Computing under IEEE Computer Society of India Council in the year 2015-2016. She has established active international teams with University of Wyoming, USA and University HASSAN II Casablanca, Morocco by executing MoU with these 2 Universities. She is the Co-ordinator for ‘Centre for Machine Learning and Intelligence’ funded by Department of Science and Technology, Govt. of India. She is also an active Research Consultant for few Institutions.

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