Intelligent Smart Waste Management Using Regression Analysis: An Empirical Study

Title

Intelligent Smart Waste Management Using Regression Analysis: An Empirical Study

Creator

Rath A.; Das Gupta A.; Rohilla V.; Balyan A.; Mann S.

Description

The term deep learning is seen as an important part of artificial intelligence that allows the system to understand and make decisions without special human intervention. In-depth learning uses a variety of statistical models and programs that allow different computational properties to reach the highest point. It is estimated that the market development of artificial intelligence and technology for deep learning will amount to USD 500 billion by 2026. The use of advanced technology, such as neural networks, enables better image recognition and the use of automated processes for deep operations. The main purpose of the study is to understand the critical determinants of Deep Learning in Creating a better City through Intelligent Smart Waste Management, the major determinants cover: System usability scale, Implementation of RFID sensors and Optimizing route selection. The proposed work is that implementation of advanced tools like deep learning methodologies and machine learning tools can support in managing the waste in a smart way, this will enable in creating better cities, enhance the environment and support sustainable living. Smart cities today need to use tools like deep learning and other artificial intelligence to effectively manage waste. Smart vessels are mainly controlled and implemented, which makes it easier for users to open vessels, it is also suitable for storing solid and dry waste, but provides information on the total degree of filling, can share data and information with central waste management service, you can collect waste quickly and avoid flooding. To achieve this, governments, administrators and communities are introducing sensors that transmit data and information to the waste management company in real-time and take appropriate action. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Source

Communications in Computer and Information Science, Vol-1591 CCIS, pp. 138-148.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Analysis of variance; Deep learning; Regression analysis; Smart waste management

Coverage

Rath A., School of Commerce, Finance & Accountancy, CHRIST (Deemed to be University), Lavasa, Pune, India; Das Gupta A., Department of Geography, WBES, Chandernagore Government College affiliated to the University of Burdwan, West Bengal, Hooghly, India; Rohilla V., Department of Computer Science, Maharaja Surajmal Institute of Technology, New Delhi, India; Balyan A., ECE Department, Maharaja Surajmal Institute of Technology, New Delhi, India; Mann S., Department of IT, Maharaja Surajmal Institute of Technology, New Delhi, India

Rights

Restricted Access

Relation

ISSN: 18650929; ISBN: 978-303107011-2

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-031-07012-9_12

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131925224&doi=10.1007%2f978-3-031-07012-9_12&partnerID=40&md5=45d3c353a7d6c0ebdf6d1daa50728d9a