Assistant Professor
School of Computer Science and Artificial Intelligence
K L E F Deemed To Be University
9
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 Comprehensive Analysis of Intrusion Detection System in Cloud Computing using Artificial Intelligence Technique, 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN), 2025-01-24, DOI: 10.1109/iciscn64258.2025.10934411
“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.
“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.
“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.
“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.
“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.
A Deep Learning and Optimization Method for Detecting Network Intrusion in IOT, 2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), DOI: 10.1109/icatiece56365.2022.10047140.
“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.
“Advanced Energy Efficient Scheduling of Servers in Cloud” Journal of Management Information and Decision Sciences, Volume 24, Issue 5, 2021.
“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.
Dynamic and secure authentication using IKC for distributed cloud environment, Advances in Intelligent Systems and Computing, 2018 | Book, DOI: 10.1007/978-981-10-7563-6_12, EID: 2-s2.0-85045647867
“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.
“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.
“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.
“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.
“Fuzzy Keyword Search in XML Data” International Journal of Scientific & Engineering Research, Vol. 04, Issue 06, 2013, pp. 679-687.
“Adaptive Fuzzy Search Over Encrypted Data in Cloud Computing” International Journal of Computer Science and Technology, Vol. 03, Issue 04, 2012, pp. 883-886.
“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.
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.
.png)
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.
Artificial Intelligence Horoscope Device is a testament to how modern technology can be harnessed to create products that are not only functional but also deeply engaging on a personal level. It represents the convergence of art, technology, and user-centric design—an intersection that is likely to drive future innovations in the consumer electronics and lifestyle sectors.
The system integrates AI with diabetic patient healthcare data to predict and manage eye health risks. Data from eye exams, blood sugar patterns, physical activity, and diet is collected and analyzed by AI algorithms. The system classifies patients as at-risk or stable, providing personalized recommendations and alerts. Continuous monitoring and updates create a feedback loop for effective eye health management.
