Assistant Professor
School of Computer Science and Artificial Intelligence
Advanced Cloud Architectures, Distributed Systems, Cloud Security and Privacy, Machine Learning and AI Integration, Big Data Analytics
Quantum Computing in the Cloud, Cloud-Native Technologies, Cloud Security and Compliance, Multi-Cloud and Hybrid Cloud Architectures, Green Cloud Computing
Ph.D in Computer Science from K L E F Deemed To Be University, Guntur
M.Tech in Computer Science from JNTU University, Hyderabad
B.Tech in Computer Science and Technology from JNTU University, Hyderabad
Assistant Professor at SR University, Warangal, from 2024-07-11 to .
Assistant Professor at Kakatiya Institute of Technology and Science, from 2020-02-28 to 2023-12-30.
Assistant Professor at Vaagdevi College of Engineering, from 2012-06-15 to 2016-12-31.
Ph.D
PG
UG
“A Novel Implementation of Fuzzy Keyword Search over Encrypted Data in Cloud Computing” International Journal of Computer Trends and Technology, Vol. 01, Issue 03, 2011, pp. 275-279.
“Adaptive Fuzzy Search Over Encrypted Data in Cloud Computing” International Journal of Computer Science and Technology, Vol. 03, Issue 04, 2012, pp. 883-886.
“Fuzzy Keyword Search in XML Data” International Journal of Scientific & Engineering Research, Vol. 04, Issue 06, 2013, pp. 679-687.
“Web Graphs For Analyzing Users Interest Based on Social Networking” International Journal of Computer Science And Technology, Vol. 04, Issue 03, 2013, pp. 413-417.
“Basic Prototype Analysis for Different Approaches for Security in Cloud Computing” International Journal of Applied Sciences Engineering and Management, Vol. 04, Issue 04, 2015, pp. 61-65.
“DES Secured k-NN Query over Secure Data in Clouds” Journal of Theoretical and Applied Information Technology, Vol.91, Issue 02, 2016, pp. 384-389.
“Fine-Grained Multi-Access Control via Group Sharing in Distributed Cloud Data” Journal of Theoretical and Applied Information Technology, Vol.95, Issue 14, 2017, pp. 3242-3250.
“Advanced Hybrid Approach to Provide Privacy for Cross-site and XSS Attacks in Cloud Computing” Journal of Advanced Research in Dynamical & Control Systems, Vol. 10, Special Issue 07, 2018, pp. 1315-1321.
“Advanced Energy Efficient Scheduling of Servers in Cloud” Journal of Management Information and Decision Sciences, Volume 24, Issue 5, 2021.
“Network Intrusion Detection System for Internet of Things based on Enhanced Flower Pollination Algorithm and Ensemble Classifier”, Concurrency and Computation: Practice and Experience - Wiley Online Library, Accepted on 05 April 2022, ISSN: 1532-0634 (online), ISSN: 1532-0626 (print), Impact Factor: 1.536. The standard age of Journal - 21 Yrs. (since 2001), https://doi.org/10.1002/cpe.7103.
“A comprehence study of DDoS attack detecting algorithm using GRU-BWFA classifier”, Measurement: Sensors Vol 24, December 2022, ISSN: 2665-9174, Impact Factor: 0.93, https://doi.org/10.1016/j.measen.2022.100570.
“Intrusion Attack Detection Using Firefly Optimization Algorithm and Ensemble Classification Model” Wireless Pers Commun (2023), Vol 132, pages 1899–1916, (2023), Published: 21 August 2023, Impact Factor: 2.017, https://doi.org/10.1007/s11277-023-10687-8.
“Network Intrusion Detection Method Using Stacked BILSTM Elastic Regression Classifier with Aquila Optimizer Algorithm for Internet of Things (IoT)”, International Journal on Recent and Innovation Trends in Computing and Communications, Vol. 11 No. 2S (2023), Published: April 15, 2022, https://doi.org/10.17762/ijritcc.v11i2s.6035.
“Hybridization of Bottlenose Dolphin Optimization and artificial fish swarm algorithm with efficient classifier for detecting the network intrusion in Internet of Things (IoT)”, International Journal of Intelligent Systems and Applications in Engineering, Vol. 12 No. 6s (2024), Published: 30.11.2023, ISSN:2147-6799, pp. 220-232, Impact Factor: 1.74.
“Stacked autoencoder with weighted loss function for intrusion detection in IoT application”, Multimedia Tools and Applications, Published: 13.08.2024, ISSN: 1573-7721, DOI:10.1007/s11042-024-19962-7, http://dx.doi.org/10.1007/s11042-024-19962-7.
The SMART Student Data Management Device is an integrated system that captures, processes, and analyzes data related to student performance, behavior, and engagement. It is designed to facilitate educators, administrators, and parents in monitoring and supporting student progress in real-time. By leveraging advanced technology and data analytics, the system aims to create a more personalized and effective educational experience.
Machine learning programming in the context of immune and physical fitness prediction uses algorithms and statistical models to analyze data and make predictions about an individual’s health. By training models on historical and real-time data, the system can forecast various health outcomes, optimize fitness routines, and potentially predict susceptibility to illnesses or injuries.