Covid Prediction Using Chest X-Ray

Last Updated on May 3, 2021

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In this use case, I have worked on how to predict Covid19 using a Chest X-Ray of the patient. We all know the techniques used these days are through the blood which takes a comparatively longer time of doctor, and also it is expensive, whereas X-Ray is quite cheap and then feeding the image to our model, it will automatically predict Covid. I hace used CNN and VGG16 model for this usecase.


Find my blog here: https://www.cluzters.ai/Usecase/1161/covid-prediction-using-chest-x-ray

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Enterprise Ai

Last Updated on May 3, 2021

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Enterprise AI is about enhancing the customer satisfaction index and to ensure customer stickiness to your organization. By infusing emerging technologies like artificial intelligence to engage and retain the set of customers. Using AI algorithm, we should address the use case. The business operation

processes like determining the customer sentiments from various different media like - Social media, Audio Calls, Video Calls, Images, Emails & Chats, interact with customers to provide quick and effortless

solutions, analyze and learn from buying behaviour to generate next best offer, ascertain customer retention and ensure lesser churn, derive AI-based Customer Segmentation, manage customer

touchpoints, evaluate customer feedback and engage with the customers. We provide a membership card to all the customers who purchase stocks in the store. By scanning the QR code the customer can fill the

feedback. Through the user can easily complete the feedback (Bad, Good, Very good) after purchasing. We are providing three categories (Bronze, Gold and Platinum) for our customers to categorize their

purchasing list to calculate the purchasing efficiency based on their quality ,they purchase. The customer who gives feedback as very good, they come under platinum category, best offers are provided to

them (free purchase for Rs.1000). Notifications will be sent to customers through the messages about the new products available along with its price. Best offers are also provided on festival occasions. We classify the feedback using classification algorithms like random forest to get the positive and negative feedbacks.

Negative feedback will be collected and rectified soon. Through this approach, the shopkeeper is able to get clear feedback about his shop easily.



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Javascript Form Validation Project

Last Updated on May 3, 2021

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I have made JavaScript Form Validation using HTML/CSS/JavaScript. JavaScript form validation is a technical process where a web-form checks if the information provided by a user is correct. This form can be used for updating your information like name,city,etc. , and then after click on submit button the information which you have updated will be shown.

In the JavaScript Form Validation Project, when the user provides all the data and submits the form, usually by hitting the button, the information is sent to the server and validated. The response of the validator is sent back to the user's computer and it's visualised as either a confirmation message.

JavaScript Form Validation is an important feature of good user experience; by catching invalid data, the user can fix it straight away.


Working of JavaScript Form Validation Project:-

In this Project, when you enter data, the browser and/or the web server will check to see that data is in the correct format and within the constraint set by the application.If the information is correctly formatted, the application allows the data to be submitted and saved in a database.

This project is done by using validation attributes on form elements .These are:-

1 . required :- Specifies whether a form field needs to be filled in before the form can be submitted.

2. type :- Specifies whether the data needs to be a number, an emaill address, or some other specific preset type.

3. pattern :- Specifies a regular expression that defines a pattern the entered data needs to follow.

In this Project, after giving your personal information like your name, your address,etc, and then if you click on submit button , then the information which you have filled will be shown .

If you want to update some information ,then you can also do by clicking on update button.The updated information will be shown.

If you want to delete some record ,then you can also delete it by clicking on delete button.


Duration of Project:- 10 months

My role in this project is of Developer

Skills used :- HTML/CSS/JavaScript


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Multi Label Question Classification For Agricultural Domain

Last Updated on May 3, 2021

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                 MULTI LABEL QUESTION CLASSIFICATION


Basically, Multi-Label means, the questions asked by a client are of what type? 

Here our ML model should be able to segregate the questions into descriptive type question or one-word type question or both and then answer it effectively.

We have classified the data into three major types:

1) Definition Type Question 2) Descriptive Type Questions 3) Factoid Type Questions 

KEYWORD IDENTIFIER Keywords are used to identify the question type.

The input query is scanned and the primary keywords such as who, when etc. are identified.

These keywords help in finding out the expected answer type. But words like how and which do not give a clear idea about the question type.

To get a clear idea about the question type and obtain the relevant answer, additional keywords are required.

These are known as secondary keywords. The secondary keywords provide additional information about the question type which further helps in extracting the answer from the document.

 For example

a)Which sector is the backbone of the Indian economy? Which is the best season for a particular crop?

 Here the “which” keyword acts as the primary key. This helps us understand the question type as which and the expected answer for such type of question is a factoid type of answer which precisely answers the type of “sector”


Methodology


Data preprocessing techniques such as data redundancy, stop words, data cleaning, puntuactions, etc are done first.

Using the Naive Bayes Classifier &Python's Scikit-learn package we were able to upload the Model database containing 50 questions with their answers.

Naive Bayes and SVM is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. 

Training and testing of data are done and then the model predicts the appropriate answer.

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Machine Learning (Heart Disease Prediction Model)

Last Updated on May 3, 2021

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This is web based API model which predicts the probability of having a heart disease

Here I had a dataset of few patients where I had information like CRF, Hypothrodism, HT,DM.

I have splitted the data so that I can train , and then test our prediction by finding out accuracy using various Python Algorithms.

The library used here are numpy , matplotlib, pandas, sklearn and pickle of Python.

I preprocessed the data and performed various splitting options.

I observed various plots using library matplotlib.

I have used numpy and pandas to to read the data and observe various statistical things.

I have used various algorithms like:

Random forest ( model file in github as modelRF.py)

Decision tree ( modelDT.py).

SVM (modelSVM.py)

ANN (modelANN.py)

Naive Bayes (modelNB.py)

In each algorithm I fitted my training data, saved model to the disk , loaded the model using Pickle library and then finally compared the result .

All the accuracy was found out for each algorithm and all of them showed accuracy greater than 85%.

All this model building was done in model.py files , modelNB (naive bayes) modelSVM (support vector machine) etc . according to the algorithm

After finding accuracy from every algorithm.

I finally built a model using library flask , request,jsonify,render_template ,keras and loaded the model using pickle .

The final features of the model was predicted and finally created as app.py.

As the model runs on local host we also added various html tags and styling using CSS to make it more presentable.

The code is shared freely on Github platform.

Link added below

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Task-Manager Backend Rest-Api(Node.Js)

Last Updated on May 3, 2021

About

Technology Used:

  • Node.js
  • Express js
  • MongoDB


Library Used:

  • jwt (json web token)
  • bcrypt
  • validator
  • sharp
  • multer


General Description:


  • In this project user can create its own tasks.
  • User can manage their tasks according to their preferences.
  • User can edit or delete the particular task and also user can track the status of task (i.e completed or pending).


Usage:


  • In order to use application you should register in an application. You can make it by calling Sign Up API.
  • The password is stored in Encrypted format in database.
  • In Login API we generate an access token using jwt.
  • In order to call create,update,delete API'S we have to pass an access token in header section of the request.
  • If we don't pass an access token then user will got a message 'Please Authenticate'.


Database Structure:


Task:

description : String,

completed :Boolean,

owner : ObjectId,

timestamps :true


User:

name : String,

email :String,

password :String

age : Number,

tokens:[{

token:type:String

}],

avtar : Buffer


API'S:


User:

URL TYPE Description

  • /users/login POST login
  • /users/ POST SignUp
  • /users/me GET Profile
  • /users/logout POST logout
  • /users/logoutall POST logout from all devices
  • /users/me DELETE delete user
  • /users/me PUT Updating user
  • /upload POST Uploading avtar
  • /users/me/avtar DELETE delete user avtar



Task:

URL TYPE Description

  • /task POST Create Task
  • /task GET Getting Task
  • /task/:id PUT Updating Task
  • /task/:id DELETE Deleting Task
  • /users/logoutall POST logout from all devices
  • /users/me DELETE delete user


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