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VENKATA SIVA KUMAR PASUPULETI

VENKATA SIVA KUMAR PASUPULETI

Associate Professor · Computer Science and Engineering
Author
APID
3365
ORCID
0000-0001-9381-0472
Scopus
https://www.scopus.com/authid/detail.uri?authorId=57815372200
Google Scholar
https://scholar.google.com/citations?hl=en&user=lcDmUi8AAAAJ
Location
Hyderabad, Telangana, India
Expertise
Computer Networks, Algorithms, Network Security, Cloud computing, Distributed systems
Areas of interest
Computer Networks, Algorithms, Network Security, Cloud computing, Distributed systems
Member since
September 2023

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Publications

  1. A Comparative Study On Manual And Automated System for Wieless Networks
  2. A Comparative study of different attribute based encryption models for cloud data security.
  3. Code based Secret Sharing Schemes for MANET
  4. Development of a Scalable System for Stealthy P2P Botnet Detection
  5. Different Methodologies on Block chain Consensus Models – A Survey.
  6. Efficient quantum cryptography technique for key distribution
  7. Examination of clients conduct in web based business sites
  8. Fingerprint Image Enhancement for Crime Detection Using Deep Learning
  9. Generalized deep learning model for Classification of Gastric, Colon and Renal Cancer
  10. Global Exponential Stability Result for Complex Valued Hysteretic Neuron Model
  11. Green Cloud Computing and Its Role in Reducing Carbon Footprint.
  12. Group Data Sharing in Cloud Environment using Key Aggregate Mechanism
  13. Input-Output Stability of Neural Network with Hysteresis
  14. Multi-Class Pathology Classification using a Generalized Deep Learning Model
  15. Multi-Level Secret Sharing Scheme for MobileAd-Hoc Networks
  16. Prediction And Providing Medication For Thyroid Disease Using Machine Learning Technique
  17. Problem Solving Optimization using Dynamic Programming Approach
  18. Role and Importance of Cryptography Techniques in Cloud Computing
  19. Secure Auditing and Effective Data Storage in Cloud Environment by using Key Management.
  20. Stability Analysis of Discrete-Time Bidirectional Associative Neural networks with Hysteresis