IOT COVID-19 DETECTION
ABSTRACT:-
There are a number of Online selling product websites in today’s world. The quality of the product is defined from the review which they get from previous customers. The customer checks the review section before purchasing the product. If any product has a bad review, the customer thinks a thousand times before purchasing it. But there is a possibility to get a fake review from customers although the quality of product is best. Fake reviews are done to decrease the purchasing possibilities of a product. But it is difficult to read a thousand reviews about a good product for customers to read and decide whether to buy or not the product. According to a survey of 2,005 U.S. shoppers by Platform, a software system for managing online reviews and customer interaction, 93 of shoppers say online reviews affect their buying decision. This means that for any high-volume buying and selling business, having a strong system for ranking user reviews is essential, as reviews are a type of social proof. To do this accurately on a large scale (taking into account the savings in time and value), it makes sense to use automation. to reduce these problems, we find the solution that are the techniques of machine learning.
In this paper, we design a system which is able to detect fake reviews before the purchasing of a product. We aim to explain all the customer reviews of a product and compare the products based on reviews that can be done in one place. In this system , we give the weightage according to the steps of the detection process. Each step has different weightage based on the accuracy of the algorithm. We use 3 algorithms which are Naive bayes, SVM and Random forest to detect the fake review. This paper offers numerous original techniques to do these tasks and Our experimental results using reviews of a number of products shifted online demonstrate the efficiency of the techniques.
PROPOSED SYSTEM:-
The main objective of the work presented in this document is to design and implement a complete tracking system consisting of a mobile mini-server as the central unit on a Raspberry Pi to locate the soldier’s location, status and health information Communication from a minimum. Low enforcement unit attached to the soldier’s arm. In addition, the system offers an emergency button which, after pressing, prompts for relief if a soldier is with a collection of test cases was applied and the results obtained with our example have shown promising precision and efficiency as soon as such a system is applied. Our system provides one handheld device for each salesperson and one handheld device for the manager. Opportunity to worry about the gang using an online site and Android app.There is a humanitarian aspect to this technique in that it reduces the consequences of the many problems in battle, including the lack of soldiers and also the problem of health compliance.
- User Register and Login: User have to register and login to check Covid-19 affected or not by using body temperature, cough and pulse sensor.
- Temperature and pulse analysis: User body temperature and pulse fetching through temperature sensor and pulse sensor to identify covid-19 with machine learning models such as naive bayes, SVM, Random forest, Decision tree.
- Cough Analysis: User cough sound get recorded and applying synthetic minority oversampling technique (SMOTE) and cough analyzer will identify using machine learning models
- Predicting Covid-19: With the help of machine learning model classifying the result covid-19 or not.
- Classification Model Generation: Cough and covid-19 dataset added to train machine learning models for classification.
HARDWARE AND SOFTWARE REQUIREMENTS
HARDWARE:
- Processor: Intel Core i3 or more.
- RAM: 4GB or more.
- Hard disk: 250 GB or more.
- Pulse sensor
- Temperature Sensor
- Raspberry pi
SOFTWARE:
- Operating System : Windows 10, 7, 8.
- Spyder, Jupyter notebook, Flask, Raspberry pi