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Our team

Dr. Priyadarshini Pattanaik

Lecturer
Dr. Priya Pattanaik worked as a postdoctoral scientist at IMT Atlantique, France, in the Image and Information Processing department of the LaTIM research group, focusing on developing concepts and tools to address one of the great challenges of the Musculoskeletal (MSK) field: understanding and exploiting the link between the shape and function of a joint (2020-2022).

Overview

Dr. Priya Pattanaik is a lecturer at the Berlin School of Business and Innovation in Germany. Her research skills are primarily in quantitative analysis and developing machine learning algorithms with deep neural networks and graphical models for visual computing, including medical image analysis and disease detection. She worked as a postdoctoral scientist at IMT Atlantique, France, in the Image and Information Processing department of the LaTIM research group, focusing on developing concepts and tools to address one of the great challenges of the Musculoskeletal (MSK) field: understanding and exploiting the link between the shape and function of a joint (2020-2022). She also worked as a postdoctoral fellow in collaboration with a range of academic institutions and industrial partners, such as Télécom SudParis, the University of Saclay, a team from the Centre for Mathematical Morphology of Mines ParisTech, and the company TRIBVN (2019).

In March 2019, she successfully defended her doctoral thesis, which focuses on the use of machine learning for classifying microscopic blood smear images to detect malaria early. She has numerous publications in high-impact SCI and Scopus-indexed research journals and conferences.

Areas of expertise

  • Artificial Intelligence / Machine Learning
  • Digital Health
  • Computer Vision
  • Imaging & Visualization
  • Cloud Computing

Publications

  • P. Pattanaik. and D. Nagpal, “Comparison of machine learning algorithms used to catalog Google Appstore”, Journal of Medical Artificial Intelligence, 6, 2023. doi: 10.21037/jmai-23-58
  • Abbass, W., Hussain, R., Abbas, N., Malik, S.A., Javed, M.A., Khan, M.Z., Alsisi, R.H., Noorwali, A. and Pattanaik, P, “Channel Allocation to GAA Users Using Double Deep Recurrent Q-Learning Based on Double Auction Method”, IEEE Access, 2023. DOI: 10.1109/ACCESS.2023.3326432
  • Ayman Noor, P. Pattanaik, Waseem Alromema, Talal H. Noor, M.Z.Khan, “Deep Feature Detection Approach for COVID-19 Classification based on X-ray Images”, International Journal of Advanced Computer Science and Applications (IJACSA) – Volume 14, No 5, 2023, Q3 Journal, Scopus/SCI Indexed. https://dx.doi.org/10.14569/IJACSA.2023.0140514
  • Pattanaik, Priyadarshini. “Automated Segmentation for Knee Joint MRI Images Using Hybrid UNet+ Attention.” In 2022 Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON), pp. 56-61. IEEE, 2022. DOI: 10.1109/TEECCON54414.2022.9854515
  • Nagpal, D., Panda, S.N. and Pattanaik, P., 2022. Machine learning-based attribute value search technique software component retrieval. Computational Intelligence in Software Modelling, 13, p.33.
  • P. Pattanaik, M.Z.Khan, P.K. Patnaik “ ILCAN: A New Vision Attention-Based Late Blight Disease Localization and Classification”, Arabian Journal for Science and Engineering. (2021) SCI Indexed (Published). https://doi.org/10.1007/s13369-021-06201-6
  • Abdulfattah Noorwali, Ahmad Naseem Alvi, Mohammad Zubair Khan, Muhammad Awais Javed, Wadii Boulila, Priyadarshini A Pattanaik,”A Novel QoS-Oriented Intrusion Detection Mechanism for IoT Applications”, Wireless Communications and Mobile Computing. (2021) Q1 Journal, SCI Indexed (Published). https://doi.org/10.1155/2021/9962697
  • Dimple Nagpal, S. N. Panda, M. Malarvel, P.A. Pattanaik, M. Z. Khan, “A Review of Diabetic Retinopathy: datasets, approaches, evaluation metrics and future treads”, Journal of King Saud University – Computer and Information Sciences. (2021) Q1 Journal, SCI Indexed (Published). https://doi.org/10.1016/j.jksuci.2021.06.006
  • P. Pattanaik, Mohit Mittal, Mohammad Zubair Khan ” Unsupervised Deep Learning Cad Scheme For The Detection Of Malaria In Blood Smear Microscopic Images”, IEEE Access, No. 8, pp.94936-94946. (2020) SCI Indexed (Published). DOI: 10.1109/ACCESS.2020.2996022
  • P. Pattanaik, Mohit Mittal, MZ Khan, SN Panda, “Malaria Detection Using Deep Residual Networks with Mobile Microscopy”, Journal of King Saud University – Computer and Information Sciences. (2020) SCOPUS (Published). https://doi.org/10.1016/j.jksuci.2020.07.003
  • Chiranji Lal Chowdhary, Mohit Mittal, P. Pattanaik, Zbigniew Marszalek, “An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm.” Sensors 20, No. 14, pp. 3903, (2020). SCI Indexed (Published). https://doi.org/10.3390/s20143903
  • P. Pattanaik, “A Stacked Denoising Autoencoder Compression Sampling Method for Compressing Microscopic Images”, Smart Healthcare Analytics in IoT Enabled Environment, vol. 178, pp. 191-199, Springer, Cham. (2020) Book Chapter SCOPUS (Published)
  • P. Pattanaik, Zelong Wang, Patrick Horain, ” Deep CNN Frameworks Comparison for Malaria Diagnosis”, The Irish/International Machine Vision and Image processing (IMVIP). (2019) CORE RANK – C. arXiv preprint arXiv:1909.02829. (Published). https://doi.org/10.48550/arXiv.1909.02829
  • P. Pattanaik, Tripti Swarnkar, ” Vision based malaria parasite image analysis : A systematic review”, International Journal of Bioinformatics Research and Applications, Vol.15, No.1, pp.1-32. Inderscience. (2018) SCImago (Published)
  • P. Pattanaik, Tripti Swarnkar, “Comparative Analysis of Morphological Techniques for Malaria Detection”, International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, Vol. 13, No.4, pp. 49-65. (2018) Web of Science, ESCI Indexed, SCOPUS. (Published)
  • P. Pattanaik, Tripti Swarnkar, Debabala Swain, ” Deep filter bridge for malaria identification and classification in microscopic blood smear images”, International Journal of Advanced Intelligence Paradigms (IJAIP). (2018). UGC, SCOPUS (Accepted)
  • P. Pattanaik, ” Unsupervised Deep Neural Scheme for Mobile Phone based Unlabelled Medical Image Classification”, International Journal of Recent Technology and Engineering (IJRTE), Vol. 7, No. 6, pp. 123-127. (2019) SCOPUS (Published)
  • Itishree Mohanty, P. Pattanaik, Tripti Swarnkar, “Automatic Detection of Malaria Parasites Using Unsupervised Techniques”, International Conference in Computational Vision and Bioengineering (ISMAC – CVB), pp. 41-49. Springer, Cham, (2018). Book Chapter SCOPUS (Published)
  • P. Pattanaik, Tripti Swarnkar, Debdoot Sheet, “Object detection technique for malaria parasite in thin blood smear images”,2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2120-2123. (2017) DOI: 10.1109/BIBM.2017.8217986 (Published)
  • Subhalaxmi Panda, P. A. Pattanaik, and Tripti Swarnkar, “A Higher Education Predictive Model Using Data Mining Techniques”, Educational Data Mining Practices in Indian Academia (EduDM). (2017) Scopus (Elsevier) ACM Digital (Published)
  • P.Pattanaik, S. Roy and P.K Pattnaik ” Performance Study Of Some Dynamic Load Balancing Algorithms In Cloud Computing Environment”, 2nd International Conference on Signal Processing & Integrated Networks, pp. 619-624. (2015) DOI: 10.1109/SPIN.2015.7095363 Scopus (Published)
  • P.Pattanaik, “Adaptive and dynamic load optimization strategies for cloud computing environment”, 48th Annual Convention of Operational Research Society of India (ORSI)”. (2015) (Published)
  • P.A.Pattanaik, S. Roy and P.K Pattnaik ” Load Balancing Adaption of Some Evolutionary Algorithms In Cloud Computing Environment”, African Journal of Computing and ICT , Vol 8, No.2, pp. 161-168. (2015) IEEE Nigeria, SCOPUS (Published)
  • P.A.Pattanaik ” An encrypted mechanism for securing cloud data from data mining attack” IOSR Journal Of Computer Engineering, pp. 92-94. (2014) UGC, SCOPUS (Published)
  • P.A.Pattanaik “Kerberos Based Electronic Tender system” Ijera International Journal of Engineering Research & Applications, Vol. 4, No.1, pp.481-484. (2013) UGC, SCOPUS (Published)

Patents

  • Automated Diabetes Machine (ADM), (2021), Australian Patent.
  • System and Method for Providing Emergency Medical Services, (2019), Indian Patent.