International Journal of Nonlinear Analysis and Applications
Supervision calamity of public opinion actions based on field programmable gate array and machine learning
Document Type : Research Paper
Authors
- A Srinivasa Rao 1
- Brahmadesam Viswanathan Krishna 2
- D Saravanan 3
- D Beulah David 4
- O Rama Devi 5
- Anju Asokan 6
- D Stalin David 3
1 Department of CSE, Dhanekula Institute of Engineering & Technology, Vijayawada, India.
2 Department of CSE, Rajalakshmi Engineering College, Chennai, India.
3 Department of CSE, IFET Colleg of Engineering, Villupuram, India.
4 Department of CSE, Jeppiaar Engineering College, Chennai, India.
5 Department of CSE, Lakireddy Bali Reddy College of Engineering, Mylavaram, India.
6 Department of ECE, Sri Krishna College of Technology, Coimbatore, India.
Abstract
The community's rise, public opinion network's popularity, and emergency personnel's advancement have changed drastically. Individuals can now go online from any place and through communications systems to share their opinions and attitudes more efficiently and more often. As illustrated by the preceeding network approach, public sentiment on neural networks and IoT is critical for the public sector, the public interest, and a special event in an emergency (IoT). In terms of data security and anonymity, the proposed program is not safe and has environmental problems. Network public opinion's approach is based on FPGAs and machine education. FPGAs (Field Programmable Gate Array) Instant perspectives, possible future themes, knowledge exchange, excellent content and Team variance are used to build machine learning. In this popular sentiment network, several disasters have seriously threatened the security of our community. Public views on disaster networks in all type of internet media, such as internet news, blogs and webpages, are inextricably connected with society. This plays into unprecedented stress the ability of the government to deal with crises and their consequences.
Keywords
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Volume 12, Issue 2
November 2021Pages 1187-1198
- Receive Date: 08 March 2021
- Revise Date: 16 May 2021
- Accept Date: 22 June 2021