R
Rojalina Priyadarshini
Researcher at Global University (GU)
Publications - 60
Citations - 480
Rojalina Priyadarshini is an academic researcher from Global University (GU). The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 10, co-authored 48 publications receiving 261 citations. Previous affiliations of Rojalina Priyadarshini include Pondicherry Engineering College & C. V. Raman College of Engineering, Bhubaneshwar.
Papers
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Journal ArticleDOI
A deep learning based intelligent framework to mitigate DDoS attack in fog environment
TL;DR: A novel Source based DDoS defence mechanism which can be used in fog environment as well as the cloud environment to mitigate DDoS attacks and provides deep learning (DL) based detection method which makes use of the network traffic analysis mechanisms to filter and forward the legitimate packets to the server and can block the infected packets to cause further attacks.
Journal ArticleDOI
Cluster head selection based on Minimum Connected Dominating Set and Bi-Partite inspired methodology for energy conservation in WSNs
TL;DR: The results are encouraging and the proposed MSDS-MI system is found to be more efficient than Connected Dominating sets, Pseudo Dominating Sets, Dynamic Cluster Head Genetic Algorithm, and Distributed Self-Healing Approach.
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DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks
TL;DR: A fog-based deep learning model, DeepFog is processed that collects the data from individuals and predicts the wellness stats using a deep neural network model that can handle heterogeneous and multidimensional data.
Proceedings ArticleDOI
A Novel approach to predict diabetes mellitus using modified Extreme learning machine
TL;DR: The concept of modified extreme learning machine is used to identify the patients of being diabetic or non-diabetic basing on some previously given data which in turn helps the medical people to identify whether someone is affected by diabetes or not.
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FogLearn: Leveraging Fog-Based Machine Learning for Smart System Big Data Analytics
TL;DR: In this paper, the authors have discussed the emergence of fog computing for mining analytics in big data from geospatial and medical health applications and developed fog computing based framework i.e. FogLearn for application of K-means clustering in Ganga River Basin Management and real world feature data for detecting diabetes patients suffering from diabetes mellitus.