Web Forum – Covid Action Platform

Last Updated on May 3, 2021

About

The dramatic spread of COVID-19 has disrupted lives, livelihoods, communities and businesses.

 

People need to know necessary actions, precautions, daily report and queries about covid.

  In response to this emergency , we developed covid action platform web forum page.

 

This project is aimed at developing online form for the users group discussion covid

 

This is web based tool .Any user can post the doubts topics and reply for the other user doubts.

 

Nowadays ,People facing a lot of fake news and the means at which they communicate with one another to deliberate on a solution has always been very difficult .

 

That’s why there is need for the provision of an efficient and easy way users can actually relate each other so the harness the strength in teaming up while solving problem .The covid action platform forum provides a platform.

More Details: WEB FORUM – COVID ACTION PLATFORM

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Smart Health Monitoring App

Last Updated on May 3, 2021

About

The proposed solution will be an online mobile based application. This app will contain information regarding pre and post maternal session. The app will help a pregnant lady to know about pregnancy milestone and when to worry and when to not. According to this app, user needs to register by entering name, age, mobile number and preferred language. The app will be user friendly making it multi-lingual and audio-video guide to help people who have impaired hearing or sight keeping in mind women who reside in rural areas and one deprived of primary education. The app will encompass two sections pre-natal and post- natal.

           In case of emergency i.e. when the water breaks (indication) there will be a provision to send emergency message (notification) that will be sent to FCM (Firebase Cloud Messaging), it then at first tries to access the GPS settings in cell, in case the GPS isn’t on, Geolocation API will be used. Using Wi-Fi nodes that mobile device can detect, Internet, Google’s datasets, nearby towers, a precise location is generated and sent via Geocoding to FCM, that in turn generates push notifications, and the tokens will be sent to registered user’s, hospitals, nearby doctors, etc. and necessary actions will be implemented, so that timely            help will be provided

More Details: Smart Health Monitoring App

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A Review On Weather Forecasting Techniques Using Machine Learning

Last Updated on May 3, 2021

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Weather depicts the atmospheric conditions of a particular place at a particular time. The basic weather elements comprise of temperature, wind, pressure, cloudiness and humidity. Every day, the Meteorological Department prepares weather maps for the upcoming day with the help of the data obtained from various weather stations around the world. Weather forecasts help in taking measures in advance in case of the probability of bad weather and in planning your day ahead.

 

Different instruments are used to measure various weather elements like, a thermometer is used to measure the temperature, whereas, a barometer is used to measure pressure. Similarly, a wind vane is used to find the direction of wind and a rain gauge is used to measure the amount of rainfall. Thus, with the help of the data collected through these instruments we get the weather forecast in the form of weather charts.

 

In order to decrease so much manual labour, these weather forecasting techniques are now getting replaced with machine learning models that can predict future weather quite accurately with the help of previously collected data. In this report, we are discussing some of the weather forecasting techniques that are most-likely to be used in order to get accurate weather predictions result. Herein we are comparing the results of the various models, just to get the best results.

 

Keywords: Weather Forecasting, ARIMA, Holt Linear, Holt Winter, Stationarity, Dickey- Fuller


More Details: A REVIEW ON WEATHER FORECASTING TECHNIQUES USING MACHINE LEARNING

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Hotel Management System Using Python

Last Updated on May 3, 2021

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This Project is done using python 3.x which depicts a front end interface of the hotel management system which is done using GUI interface and has the menu, where the user has a list of choices to select the food he wants and this interface has the food rating section where the user has to give the rating in which the food he took, and this interface is done using basic components of GUI. The GUI I used here is Tkinter, and by using List boxes, buttons, the text box is deployed in this interface, which is user-friendly. This interface is done because the situation of covid is increasing tremendously, to reduce the people frequently going outside for food, this interface has been developed. In this interface, we can also set background color and

font color. Here we can also set the background dimension and in this application, we can also change font sizes and also with rows and columns. This interface asks the user to enter his name, mobile number, email id and also asks whether a user prefers a choice of veg or nonveg and also gives a chance to give the food specification whether he needs the food spicy, salty, and some other and the user can choose whether he needs to pay cash, or online payment either which he can also give food rating and he can also select coupons and apply in this interface.

More Details: HOTEL MANAGEMENT SYSTEM USING PYTHON

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Human Computer Interaction Using Iris,Head And Eye Detection

Last Updated on May 3, 2021

About

HCI stands for the human computer interaction which means the interaction between the humans and the computer.

We need to improve it because then only it would improve the user interaction and usability. A richer design would encourage users and a poor design would keep the users at bay.

We also need to design for different categories of people having different age,color,gender etc. We need to make them accessible to older people.

It is our moral responsibility to make it accessible to disabled people.

So this project tracks our head ,eye and iris to detect the eye movement by using the viola Jones algorithm.But this algorithm does not work with our masks on as it calculated the facial features to calculate the distance.

It uses the eucledian distance to calculate the distance between the previous frame and the next frame and actually plots a graph.

It also uses the formula theta equals tan inverse of b/a to calculate the deviation.

Here we are using ANN algorithm because ANN can work with incomplete data. Here we are using constructive or generative neural networks which means it starts capturing our individual images at the beginning to create our individual patterns and track the eye.

Here we actually build the neural network and train it to predict

Finally we convert it to mouse direction and clicks and double clicks on icons and the virtual keyboard.

As a contributing or moral individuals it is our duty to make devices compatible with all age groups and differently abled persons.

More Details: Human Computer Interaction using iris,head and eye detection

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Web Base Application Heart Failure Prediction System

Last Updated on May 3, 2021

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In this situation, approximately 17 million people kill globally per year in the whole world because of cardiovascular disease, and they mainly exhibit myocardial-exhibit myocardial infarction and heart failure. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.

In this heart prediction problem statement, we are trying to predict whether the patient's heart muscle pumps blood properly or not using Logistic Regression. In this project, a dataset is downloaded from the UCI repository and this dataset is real. this dataset is collected from one of the most famous hospitals is in the United Kingdom (UK) in 2015 and there are 299 patient records and 12 features(attribute) and one label. Based on that 12 features, we will predict whether the patient's heart working properly or not.

In this problem statement, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning & classifiers to both predict the patient’s survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone.

For model building we use various library packages like Pandas, Scikit learns (sklearn), matplotlib, Seaborn, Tensorflow, Keras, etc., then we will use data description, Data description involves carrying out initial analysis on the data to understand more about the data, its source, volume, attributes, and relationships. Once these details are documented, any shortcomings if noted should be informed to relevant personnel. after that, we use the data cleaning method for cleaning the dataset to check if there are any missing values or not and we split the dataset into training & testing purposes with 70%, 30% criteria. Then the next step is Model Building, The process of model building is also known as training the model using data and features from our dataset. A combination of data (features) and Machine Learning algorithms together give us a model that tries to generalize on the training data and give necessary results in the form of insights and/or predictions. Generally, various algorithms are used to try out multiple modeling approaches on the same data to solve the same problem to get the best model that performs and gives outputs that are the closest to the business success criteria. Key things to keep track of here are the models created, model parameters being used, and their results. And the last step is to analyze the result in this step we check our model score or accuracy by using Confusion Matrix and Model Score. For this model, we got 80% accuracy. In the future, we try to improve that accuracy. For model deployment, we use the python flask and based on that we build the web-based application.


More Details: Web Base Application Heart Failure Prediction System

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