使用深度学习和物联网的智能废物管理

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Communication Networks and Distributed Systems Pub Date : 2019-05-15 DOI:10.30534/IJNS/2019/10832019
Jobin Joseph
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引用次数: 4

摘要

人口的增长和城市化的发展给国家的环境安全敲响了警钟。不正确的处理和废物处理将对这些发展中的城市造成巨大的威胁。因此,需要妥善收集废物,并对废物进行分类和妥善处置。印度目前的垃圾处理系统是由未分类和无组织的垃圾收集,然后在不同的站点进行分类[7]。这种由手工劳动力进行的分离会给废物分类者带来与健康有关的问题,而且由于数量庞大,效率较低,耗时且不完全可行。本文提出了一个解决方案,可以识别和分类的废物,并将其组织到特定的垃圾箱(可回收的,有机的和有害的废物),而不需要任何人工。该系统使用深度学习算法对废物进行识别和分类;分类后的再循环废物和有机废物可作更好的用途。这一过程将有助于环境变得更有价值和生态安全,帮助我们创造丰富的绿色生态系统和充满希望的美好未来。
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Smart Waste Management Using Deep Learning with IoT
The emerging rise in the population and urbanisation alarms the nation for its environment safety. The incorrect handling and waste disposal will cause immense threat to these growing cities. Therefore, it has raised the need for proper waste collection and classification of wastes with proper disposal. The current waste disposal system in India consists of unclassified and unorganized wastes collected and then segregated at different stations[7]. This segregation done by manual labour forces can bring health related issues to the waste sorters and also being less efficient, time consuming and not completely feasible due to their large amount. This paper proposes a solution that can identify and classify the waste and organize it into the particular waste bin (recyclable, organic and harmful wastes) without any human hand. The system uses deep learning algorithms to identify and classify the wastes into particular category; the categorized recycled and organic wastes can be used for future better purposes. This process will help the environment in making more valuable and ecologically safe and help us to make rich green ecosystem and a promising better future.
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来源期刊
CiteScore
2.50
自引率
46.20%
发文量
57
期刊介绍: IJCNDS aims to improve the state-of-the-art of worldwide research in communication networks and distributed systems and to address the various methodologies, tools, techniques, algorithms and results. It is not limited to networking issues in telecommunications; network problems in other application domains such as biological networks, social networks, and chemical networks will also be considered. This feature helps in promoting interdisciplinary research in these areas.
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