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Anand, Dama
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Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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A superior secure key spawn using boosted uniqueness encryption for cloud computing in advanced extensive mobile network Chandra, G. Rajesh; Mohan, K. Jagan; Khalaf, Osamah Ibrahim; Gopisetty, Guru Kesava Dasu; Anand, Dama; Algburi, Sameer; Lakshmi, S. Vijaya
SINERGI Vol 28, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.2.019

Abstract

The cloud computing sector, including mobile networks has increased in the present time. Because of advanced features and security related information in cloud. So many methods are available for handling these problems. Cloud security, large number of methods existing for provide security. Among that, so many widespread techniques cast-off to protected data in cloud based on Individuality based encryption. This method specialty is allowing only authorized end users for access legal data and avoid smalevolent attack. Individuality -based encryption method follows up the four stages like Name, Key generation, encryption and decryption. Among these Key generation is most important for generating secure key. It provides unbreakable and non-derivable secure keys to provide strong security. This paper provides a novel approach for providing advanced security called identity-based encryption. This approach uses segment of a bitidentity thread in demandto evade seepage of user’s data identity, if any attacker decodes the key also. Statistical reports show that the proposed algorithm takes less time in the process of decryption and encryption compared to other traditional approaches. One more feature of our novel method is skinning the user’s uniqueness by using parametric curve fitting. It contains a polynomial interpolation function.
High-performance sentiment classification of product reviews using GPU(parallel)-optimized ensembled methods Rao, Annaluri Sreenivasa; Reddy, Yeruva Jaipal; Navya, Guggilam; Gurrapu, Neelima; Jeevan, Jala; Sridhar, M.; Reddy, Desidi Narasimha; Pathuri, Siva Kumar; Anand, Dama
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.010

Abstract

Sentiment analysis is an important approach in natural language processing (NLP) that extracts information from text to infer underlying emotions or views. This technique entails classifying textual information into feelings like "positive," "negative," or "neutral." By evaluating data and labeling, client input may be classified on scales such as "good," "better," "best," or "bad," "worse," resulting in a sentiment classification. With the fast expansion of the World Wide Web, a massive library of user-generated data—opinions, thoughts, and reviews—has evolved, notably for diverse items. E-commerce firms use this data to gather attitudes and views from social media sites like Facebook, Twitter, Amazon, and Flipkart. The GPU-CUDA-ENSEMBLED algorithm is a GPU-accelerated method for sentiment classification, enhancing predictive performance by minimizing variances and biases. It outperforms existing algorithms like SLIQ and MMDBM, demonstrating GPU mining's efficiency. The proposed algorithm utilizes GPU-accelerated sentiment analysis to accurately predict smartphone ratings, providing valuable insights for businesses to maximize customer feedback potential.
Co-Authors Algburi, Sameer Chandra, G. Rajesh Gopisetty, Guru Kesava Dasu Gurrapu, Neelima Jeevan, Jala Khalaf, Osamah Ibrahim Lakshmi, S. Vijaya Mohan, K. Jagan Navya, Guggilam Pathuri, Siva Kumar Rao, Annaluri Sreenivasa Reddy, Desidi Narasimha Reddy, Yeruva Jaipal Sridhar, M.