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Your search keyword '"Rekha, K. Sashi"' showing total 20 results
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20 results on '"Rekha, K. Sashi"'

2. Analysis the Accuracy of Rice Grains Quality Using Neural Networks

Author

Menaka, S., Rekha, K. Sashi, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Shakya, Subarna, editor, Balas, Valentina Emilia, editor, Kamolphiwong, Sinchai, editor, and Du, Ke-Lin, editor

Published
2022
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6. Comparative analysis of attribute based encryption algorithm with RSA algorithm for reduced storage cost of public verification key.

Author

Prasanna, S. Bindu and Rekha, K. Sashi

Subjects
*COMPARATIVE studies, *STORAGE, *COST, *INFORMATION sharing, *CONFIDENCE intervals
Abstract

Public verification key storage cost is reduced for secure outsourced computation using Novel Attribute Based Encryption (ABE) algorithm. Novel ABE and Rivest Shamir Adleman (RSA) algorithm with sample size=20 was evaluated 10 times, with confidence interval of 95% and at 80% pre-test power to decrease the storage cost of public key. Novel ABE encoding is based on the attributes which are used to reduce the size of storage cost of the public key for flexible data sharing. ABE appears to have reduced storage cost of 1098 bytes compared to RSA storage cost of 6281 bytes. The Novel ABE algorithm appears to have a slight increase in significant value of Pis equal to 0.394 and P is greater than 0.05. ABE shows a decreased storage cost of public key compared to RSA algorithm. [ABSTRACT FROM AUTHOR]

Published
2023
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19. Comparative Analysis of Identifying Accuracy of Online Misinformation of Covid-19 Using SVM Algorithm with Decision Tree Classification.

Author

Pravallika, N. and Rekha, K. Sashi

Subjects
*DECISION trees, *SUPPORT vector machines, *ALGORITHMS, *COVID-19, *MISINFORMATION, *KERNEL functions
Abstract

Aim: To improve the accuracy percentage of predicting misinformation about COVID-19 using SVM algorithm. Materials and methods: Support Vector Machine (SVM) with sample size = 20 and Decision Tree classification with sample size = 20 was iterated at different times for predicting the accuracy percentage of misinformation about COVID19. The Novel Poly kernel function used in SVM maps the dataset into higher dimensional space which helps to improve accuracy percentage. Results and Discussion: SVM has significantly better accuracy (94.48%) compared to Decision Tree accuracy (93%). There was a statistical significance between SVM and the Decision Tree (p=0.000) (p<0.05 Independent Sample T-test). Conclusion: SVM with Novel Poly kernel helps in predicting with more accuracy the percentage of misinformation about COVID-19. [ABSTRACT FROM AUTHOR]

Published
2021
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