Machine Learning & PBFT Blockchain Methodology on AWS for Proteomics Analytics

Challa, Sravanthi (2023) Machine Learning & PBFT Blockchain Methodology on AWS for Proteomics Analytics. Masters thesis, Dublin, National College of Ireland.

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Abstract

Proteomics research, in particularly on PROTEIN IDENTIFICATION, an area that is expanding very Fastly and has the potential to completely change how the world think about biology and medicine. In order to help proteomics analytics to reach new high level, this research project looks into the very helpful technology combination of Machine Learning (ML), Practical Byzantine Fault Tolerance (PBFT) Blockchain, and Amazon Web Services (AWS). This research focuses on how we use machine learning (ML) algorithms like KNN (K-Nearest Neighbors), Neural Networks, Decision Trees, Random Forest, Logistic Regression, Random Forest or Logistic Regression can interpret complex patterns in proteomics datasets so that it can improve the accuracy of protein identification and quantification. Also, this study includes about how to take in the PBFT Blockchain into the proteomics data management system to work on AWS cloud to obtain latency. Through confidentiality and Encryption, this integration works to strengthen data integrity, security, and traceability throughout the proteomics data process, resulting in increased dependability and credibility in research findings. An interaction between PBFT Blockchain’s strong security features and ML-driven precision in protein analytics is the main expected results, which gives us more precise protein identification and quantification while guaranteeing unmatched levels of data integrity and security in proteomics research.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Gupta, Shaguna UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	26 Mar 2025 14:25
Last Modified:	26 Mar 2025 14:25
URI:	https://norma.ncirl.ie/id/eprint/7335

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