In this paper, we present a fully automated computer vision application for littering quantification based on images taken from the streets and sidewalks. We have employed a deep learning based framework to localize and classify different types of wastes. Since there was no waste dataset available,...
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The country started pmiting waste imports roughly since 2016 and completely banned 24 types of sopd waste in January 2018. It's easy to understand: You can't expect China to deal with the world's garbage forever. But to stop the imports is not enough for China to become sustainable. Now...
In addition, the present invention can classify the collected waste batteries according to type so that an additional classification work is omitted, and accordingly can greatly improve workability and convenience due to the collection of waste batteries. patents-wipo The invention relates to a machin...
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The other important aspect of BERT is that it can be adapted to many types of NLP tasks very easily. In the paper, we demonstrate state-of-the-art results on sentence-level (e.g., SST-2), sentence-pair-level (e.g., MultiNLI), word-level (e.g., NER), and span-level (e.g....
There are different types of text classifiers, the one I will be addressing here is a machine learning one! Training To make the classifier understand where documents should go you must first train it. By training you manually set up two or more classes (e.g. spam and legitimate) and ...
The filtering categories databases periodically receive updates from the Cisco update server. The URL categories database includes many different factors and sources of data internal to Cisco and from the Internet. One of the factors occasionally considered, heavily modified from ...
1. A method for classifying a message comprising: receiving the message; identifying in the message a distinguishing property; generating a signature using the distinguishing property; and comparing the signature to a database of signatures generated by previously classified messages. 2. A method for...
Factors such as low wages, precarious occupational relationships, unhealthy work, long working hours, and exploitation of the workforce are expected to be found in different types of work activities in the interior of the Brazilian northeast, something that contributes to a greater chance of ...