Block chain driven of organ donation and transplantation

Abstract

One of the most talked-about aspects of social media is the dissemination of information about natural and man-
made catastrophes and other news stories. Important responsibilities in this domain include the automated detection
of critical needs and the rapid dissemination of relevant information via shared postings and messages. The primary
objective of this study is to find a way to use social media as a foundation for disaster management and emergency
response. Using text analysis methods, this procedure aims to enhance emergency response processes and filter
information using automatically obtained data to aid relief operations. More specifically, we extracted real-time
content related to emergency events using social media datasets and applied state-of-the-art Machine Learning
(ML), Deep Learning (DL), and Natural Language Processing (NLP) based on supervised and unsupervised
learning. This allowed for a quick response during a critical situation. The procedure also makes advantage of the
blockchain architecture to eliminate the need for a central authority and to verify the authenticity of the identified
events. In order to prevent the spread of false information about an event on social media, the primary goal of
implementing an integrated system is to increase system security and transparency. Subjects Included in the Index:
Deep Learning, Natural Language Processing, Blockchain, Machine Learning, and Event Detection.