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7 results on '"Christalin Nelson. S"'

1. Tuning I/O Subsystem: A Key Component in RDBMS Performance Tuning

Author

Hitesh Kumar SHARMA, Christalin Nelson. S, and Sanjeev Kumar SINGH

Subjects
Tuning, I/O, RDBMS, Technology (General), T1-995, Computer software, QA76.75-76.765
Abstract

In a computer system, the fastest storage component is the CPU cache, followed by the system memory. I/O to disk is thousands of times slower than an access to memory. This fact is the key for why you try to make effective use of memory whenever possible and defer I/Os whenever you can. The majority of the user response time is actually spent waiting for a disk I/O to occur. By making good use of caches in memory and reducing I/O overhead, you can optimize performance. The goal is to retrieve data from memory whenever you can and to use the CPU for other activities whenever you have to wait for I/Os. This paper examines ways to optimize the performance of the system by taking advantage of caching and effective use of the system's CPUs.

Published
2016

3. Location and Energy Based Hierarchical Dynamic Key Management Protocol for Wireless Sensor Networks

Author

Christalin Nelson, S., Dhiviya Rose, J., Barbosa, Simone Diniz Junqueira, Series Editor, Chen, Phoebe, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Bhattacharyya, Pushpak, editor, Sastry, Hanumat G., editor, Marriboyina, Venkatadri, editor, and Sharma, Rashmi, editor

Published
2018
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4. Diagnosis of COVID-19 patients by adapting hyper parametertuned deep belief network using hosted cuckoo optimization algorithm

Author

Veerraju Gampala, Karunya Rathan, Christalin Nelson S, Francis H Shajin, and P. Rajesh

Subjects
Deep Learning, Biophysics, COVID-19, Humans, Medicine (miscellaneous), Bayes Theorem, Neural Networks, Computer, General Medicine, Algorithms
Abstract

COVID-19 is an infection caused by recently discovered corona virus. The symptoms of COVID-19 are fever, cough and dumpiness of breathing. A quick and accurate identification is essential for an efficient fight against COVID-19. A machine learning technique is initiated for categorizing the chest x-ray images into two cases: COVID-19 positive case or negative case. In this manuscript, the categorization of COVID-19 can be determined by hyper parameter tuned deep belief network using hosted cuckoo optimization algorithm. At first, the input chest x-ray images are pre-processed for removing noises. In this manuscript, the deep belief network method is enhanced by hosted cuckoo optimization approach for getting optimum hyper tuning parameters. By this, exact categorization of COVID-19 is attained effectively. The proposed methodology is stimulated at MATLAB. The proposed approach attains 28.3% and 23.5% higher accuracy for Normal and 32.3% and 31.5% higher accuracy for COVID-19, 19.3% and 28.5% higher precision for Normal and 45.3% and 28.5% higher precision for COVID-19, 20.3% and 21.5% higher F-score for Normal and 40.3% and 21.5% higher F-score for COVID-19. The proposed methodology is analyzed using two existing methodologies, as Convolutional Neural Network with Social Mimic Optimization (CNN-SMO) and Support Vector Machine classifier using Bayesian Optimization algorithm (SVM-BOA).

Published
2022

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