Face Mask DetectorLast Updated on May 3, 2021
This project is based on OpenCV, Convolutional Neural Network, Caffe-based face Detector, Keras Models, TensorFlow, and on python language.
Our face mask detector didn't use any morphed masked images dataset. The model is accurate, and since we used the simple Sequential Model architecture, it’s also computationally efficient and thus making it easier to deploy the model to embedded systems (Raspberry Pi, Google Coral, etc.).
This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines.
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Password CheckerLast Updated on May 3, 2021
This can be the most secure way for you to check if your password has ever been hacked. This is a password checker which checks whether this password has been used before or not. and if it has been used then the number of times it has been found. It makes it easy for you to understand that your password is strong enough to keep or is it too light. Its working is pretty simple, in my terminal i write the python file with my code checkmypass.py followed by the password to check if its ever been hacked , its gonna check as many passwords as we list in the terminal. I have used passwords API (pawned password) and SHAH1 (algorithm) to hash the given password into some complex output which is hard to hack also only the first five characters of hash version of password has been used for super privacy so that the real one is safe. The concept of k-anonymity is used it provides privacy protection by guaranteeing that each record relates to at least k individuals even if the released records are directly linked (or matched) to external information. I have added this on my Github repository.
THIS CAN BE REALLY EFFECTIVE FOR SOME PERSONEL USE.
Air Quality Analysis And Prediction Of Italian CityLast Updated on May 3, 2021
- The value of CO in mg/m^3 reference value with respect to the available data. Please assume if you need, but do specify the same.
- The value pf CO in mg/m^3 for the next 3 3 weeks on hourly averaged concentration
Data Set Information
located on the field in a significantly polluted area, at road level,within an Italian city. Data were recorded from March 2004 to February 2005 (one year)representing the longest freely available recordings of on field deployed air quality chemical sensor devices responses. Ground Truth hourly averaged concentrations for CO, Non Metanic Hydrocarbons, Benzene, Total Nitrogen Oxides (NOx) and Nitrogen Dioxide (NO2) and were provided by a co-located reference certified analyzer. Evidences of cross-sensitivities as well as both concept and sensor drifts are present as described in De Vito et al., Sens. And Act. B, Vol. 129,2,2008 (citation required) eventually affecting sensors concentration
0 Date (DD/MM/YYYY)
1 Time (HH.MM.SS)
2 True hourly averaged concentration CO in mg/m^3 (reference analyzer)
3 PT08.S1 (tin oxide) hourly averaged sensor response (nominally CO targeted)
4 True hourly averaged overall Non Metanic HydroCarbons concentration in microg/m^3 (reference analyzer)
5 True hourly averaged Benzene concentration in microg/m^3 (reference analyzer)
6 PT08.S2 (titania) hourly averaged sensor response (nominally NMHC targeted)
7 True hourly averaged NOx concentration in ppb (reference analyzer)
8 PT08.S3 (tungsten oxide) hourly averaged sensor response (nominally NOx targeted)
9 True hourly averaged NO2 concentration in microg/m^3 (reference analyzer)
10 PT08.S4 (tungsten oxide) hourly averaged sensor response (nominally NO2 targeted)
11 PT08.S5 (indium oxide) hourly averaged sensor response (nominally O3 targeted)
12 Temperature in Â°C
13 Relative Humidity (%)
14 AH Absolute Humidity.
Foreign Direct Investment (Fdi)Last Updated on May 3, 2021
I have used "TABLEAU" as my data visualization tool in the project. I have taken the dataset of foreign direct investment and perform various operations to produce interactive dashboard with the help of the functions provided by the tableau. I think we should have some knowledge of a business intelligence tool like tableau , PowerBI because to build these amazing dashboard we can't use programming language . It will be very complex to draw such visualization with the help of programming languages. That's why I have taken this project so that I can improve my Knowledge in these BI tools. Here I have tried to understand the trend of investments through the years with the help of some unique and different graphs and charts. I have concluded some of the observations like the sectors which are getting the heigest investment and which sectors are getting very minimum investments as well as the growth levels of the different sectors. I have also visualized that how the magnitude of investment is changing across the years. I have also tried to scale the future trend of investment for the upcoming years. I can't explain my all observation here because it will go too lengthy I will mention my project link below But If I want to conclude my project in a line , what I have done is in this project I have covered all the aspects of investments over the years and produced a interactive and eye-catching dashboard.
Vaccine PredictionLast Updated on May 3, 2021
Can you predict whether people got H1N1 and seasonal flu vaccines using information they shared about their backgrounds, opinions, and health behaviors?
In this challenge, we will take a look at vaccination, a key public health measure used to fight infectious diseases. Vaccines provide immunization for individuals, and enough immunization in a community can further reduce the spread of diseases through "herd immunity."
As of the launch of this competition, vaccines for the COVID-19 virus are still under development and not yet available. The competition will instead revisit the public health response to a different recent major respiratory disease pandemic. Beginning in spring 2009, a pandemic caused by the H1N1 influenza virus, colloquially named "swine flu," swept across the world. Researchers estimate that in the first year, it was responsible for between 151,000 to 575,000 deaths globally.
A vaccine for the H1N1 flu virus became publicly available in October 2009. In late 2009 and early 2010, the United States conducted the National 2009 H1N1 Flu Survey. This phone survey asked respondents whether they had received the H1N1 and seasonal flu vaccines, in conjunction with questions about themselves. These additional questions covered their social, economic, and demographic background, opinions on risks of illness and vaccine effectiveness, and behaviors towards mitigating transmission. A better understanding of how these characteristics are associated with personal vaccination patterns can provide guidance for future public health efforts.
I have created two model, one for H1N1 and another for Seasonal Vaccine.
Image ProcessingLast Updated on May 3, 2021
What is image processing ?
The aim of pre-processing is an improvement of the image data that suppresses unwilling distortions or enhances some image features important for further processing, although geometric transformations of images (e.g. rotation, scaling, translation) are classified among pre-processing methods here since similar.
Preprocessing refers to all the transformations on the raw data before it is fed to the machine learning or deep learning algorithm. For instance, training a convolutional neural network on raw images will probably lead to bad classification performances.
convolutional neural network
A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data. ... A neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain.
CNNs are used for image classification and recognition because of its high accuracy. The CNN follows a hierarchical model which works on building a network, like a funnel, and finally gives out a fully-connected layer where all the neurons are connected to each other and the output is processed.
Problem description: Case study , We have a dataset has 3 subfolders inside it are single prediction contains only 2 images to test the model and prediction so that we know our CNN model is working , test set with 2000 images (1000 of dogs and 1000 of cats) where we will evaluate our model , training set contains 8000 images 4000 of cats and 4000 of dogs as we are going to train our CNN model on these images of dogs and cats . so basically our CNN model is going to predict whether the image given is of a a cat or a dog. By generating random number on google then choosing the image . Eg: cat
Prediction for CAT
PREDICTION FOR DOG