Research Work

My research and research interests -

• Machine Learning & Deep Learning (Applications & Mathematical models in various domains)

• Data Science ( Statistical Analysis)

• Cloud & Distributed Computing

• Databases (SQl/NoSQl/Object Relational DBs)

• Computer Vision

 Journals

• Surbhi Agrawal , Pranavh Vummidi , Ravi Teja Devu , Rithin Ponduri, Review on Different Machine Learning Algorithms for

Disease Prediction, International Journal of Multidisciplinary Research Transactions (IJMRT), Volume (6), Issue 6, ISSN (Print):2663-2381, 2024

• Margarita Safonova, Surbhi Agrawal, Archana Mathur, Suryoday Basak, Kakoli Bora," Quantifying the Classification of Exoplanets: in Search for the Right Habitability Metric",  European Physical Journal, ST special issue: Modeling, Machine Learning and Astronomy, Springer, vol. 230, pp.  2207-2220, 2021 .

• Suryoday Basak, Snehanshu Saha, Archana Mathur, Kakoli Bora, Simran Makhija, Margarita Safonova, Surbhi Agrawal; CEESA Meets Machine Learning: From Earth Similarity to Habitability Classification of Exoplanets, Astronomy and Computing (Elsevier), vol. 30,  pp. 100-135, November 2019.

• Snehanshu Saha, Suryoday Basak, Margarita Safonova, Surbhi Agrawal, Kakoli Bora, Poulami Sarkar and Jayant Murthy: Theoretical Validation of Potential Habitability via Analytical and Boosted Tree Methods: An Optimistic Study on Recently Discovered Exoplanets, Astronomy & Computing (Elsevier), vol. 23, pp .141-150, 2018.

• Kakoli Bora, Snehanshu Saha, Surbhi Agrawal, Margarita Safonova, Swati Routh, Anand Narasimhamurthy, CD-HPF: New Habitability Score Via Data Analytic Modeling, Astronomy and Computing (Elsevier), vol 17, pp. 129-143, 2016 .

• Agrawal Surbhi, Naik Pradeep, Srikanta Murthy, "A Survey On Various Task Scheduling Algorithms Toward Load Balancing In Public Cloud, Journal" , American Journal Of Applied Mathematics, Special Issue: Frontiers in Mathematics and Computing , vol.3, pp. 14-17, 2014.

• Sai Prasanna, Surbhi Agrawal, "Dynamic Job Shop Scheduling with Sequence Dependent Routes using Particle Swarm Optimization" ,International Journal of Scientific Engineering Research, vol. 5, Issue 8, pp. 505 ,  August-2014 .

•  Surbhi Agrawal, Suryoday Basak, Snehanshu Saha, Archana Mathur, Kakoli Bora and Margarita Safonova, "CEESA: A habitability score computation approach validated by machine learning" , 70th IAC, Washington D.C., USA,21-25 October 2019.

• Snehanshu Saha, Archana Mathur, Kakoli Bora, Suryoday Basak and Surbhi Agrawal, "A New Activation Function for Artificial Neural Net Based Habitability Classification" , ICACCI, Bangalore: IEEE 2018, ISBN 978-1-5386-5314-2 (ICCACI’18), PES - Bangalore South Campus, Bangalore, Sept 19-20, 2018.

•  Surbhi Agrawal, Suryoday Basak, Kakoli Bora, Jayant Murthy, "A Comparative Analysis of the CobbDouglas Habitability Score (CDHS) with the Earth Similarity Index (ESI)" ,  ICACCI, Bangalore: IEEE 2018, ISBN 978-1-5386-5314-2, 1775-1780  (ICCACI’18),  PES - Bangalore South Campus, Bangalore, Sept 19-20, 2018.

•  Surbhi Agrawal, Rahul Aedula, Rahul J.S., ”Machine Learning Analysis of Gravitational Waves”, International Conference on Modeling Machine Learning and Astronomy,  PES University, Bangalore, Springer Publication Nov 2019.

•  Aedula, R., Madhukumar, Y., Saha, S., Mathur, A., Bora, K., Agrawal, S. ,  "L1 Norm SVD-Based Ranking Scheme: A Novel Method in Big Data Mining",  Advances in Intelligent Systems and Computing, vol 1167. Springer, Singapore ,2021. 

• Mohammed Viquar, Suryoday Basak, Ariruna Dasgupta, Surbhi Agrawal and Snehanshu Saha, “Machine Learning in Astronomy: A Case Study in Quasar-Star Classification",  Advances in Intelligent Systems and Computing”, Springer, 2018.

•  Surbhi Agrawal, Suryoday Basak, , Snehanshu Saha, Abhijit Jeremiel Theophilus, Kakoli Bora, "Habitability Classification of Exoplanets: A Machine Learning Insight" , 19th National Space Science Symposium (NSSS-2016), held at ISRO, Thiruvananthpuram , 2016.

•  Jyotirmoy Sarkar, Snehanshu Saha, Surbhi Agrawal, "An Efficient use of Principal Component Analysis in Workload Characterization- A Study" ,AASRI Conference On Sport Engineering Computer Science (SECS 2014),AASRI Procedia8(2014) , 68-74, Elsevier Publication, 2014.

• Surbhi Agrawal, Roopa Narayan, Snehanshu Saha, "An Enhancement to Clousim Via Distributed Data Storage",  3rd International Conference on Advances in Computing , Communications Informatics (ICACCI-2014),Sep24-27, Noida, 2014.

Patents

1. A UK Design patent titled “ Big Data Analytics Processing Server Equipment For Financial Transactions” granted on 12th April 2024, having Design number: 6356910. Status: Granted.

2. Dr. Surbhi Agrawal et al., published a patent titled “ENHANCEMENT OF SECURITY ISSUES IN DISTRIBUTED COMPUTING USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING”, Indian Innovation Patent; Application No. 202241028741 on 27th May 2022;

Articles in Newsletters/Magazines

•  Margarita Safonova, Snehanshu Saha, Jayant Murthy, Madhu Kashyap, C. Sivaram, Suryoday Basak, Surbhi Agrawal, Kakoli Bora, Pros and Cons of Classification of Exoplanets: in Search for the Right Habitability Metric, Astrobiology Newsletter, Vol 11, 2018.

• Surbhi Agrawal, Margarita Safonova, Kakoli Bora, Suryoday Basak and Snehanshu Saha, Note on Proxima Centauri b: Theoretical validation of potential habitability via CD-HPF, Astrobiology Newsletter, Vol 10(4), 2017.

Book Chapters

• Agrawal Surbhi, Patil Mallanagouda, Malini M. Patil , Data Augmentation Approaches Using Cycle Consistent Adversarial Networks" ,  published in Springer Publication Book titled as GANs for Data Augmentation in Healthcare, Pages 111-131    

• Mallanagouda Patil, Malini M. Patil, Surbhi Agrawal , "WGAN for Data Augmentation" , Springer Publication Book titled as GANs for Data Augmentation in Healthcare,  Pages 223-241 

• Rahul Aedula, Yashasvi Madhukumar, Snehanshu Saha, Archana Mathur, Kakoli Bora and Surbhi Agrawal, L1 Norm SVD based Ranking Scheme: A Novel Method in Big Data Mining, AISC, Springer, 2018

• Surbhi Agrawal, Kakoli Bora and Swati Routh, "Machine Learning Approaches for Supernovae Classification” book chapter published in Handbook of Research on Applied Cybernetics and Systems Science, 207-219, April-2017, DOI: 10.4018/978-1-5225-2498-4.ch009