Pneumonia Prediction Using chest x-ray Image Machine Learning

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Chest x-ray: An x-ray exam will allow your doctor to see your lungs, heart and blood vessels to help determine if you have pneumonia. When interpreting the x-ray, the radiologist will look for white spots in the lungs (called infiltrates) that identify an infection. Build an algorithm to automatically identify whether a patient is suffering from pneumonia or not by looking at chest X-ray images. The algorithm had to be extremely accurate because lives of people is at stake. This is a Flask web app designed to analyze a chest x-ray and predict whether a person has TB/pneumonia or not.

Models : 

The model is based on a  convolutional neural network that has been trained on a dataset of 800 images from two sources

The model has an overall accuracy of 83% and an F1 score of 80%.

A negative prediction means that the chest X-ray is most likely normal while the contrary is implied by a positive prediction

Environment and tools

  1. flask
  2. tensorflow

Runtime Python Version  : python-3.8.2

Datasets Link

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Credit Card Fraud Detection Machine Learning Project

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Frauds in mastercard transactions are common today as most folks are using the mastercard payment methods more frequently. this is often thanks to the advancement of Technology and increase in online transaction leading to frauds causing huge loss . Therefore, there's need for effective methods to scale back the loss. additionally , fraudsters find ways to steal the mastercard information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack then on. This paper aims in using the multiple algorithms of Machine learning like support vector machine (SVM), k-nearest neighbor (Knn) and artificial neural network (ANN) in predicting the occurrence of the fraud. Further, we conduct a differentiation of the accomplished supervised machine learning and deep learning techniques to differentiate between fraud and non-fraud transactions.

link of dataset=https://www.kaggle.com/mlg-ulb/creditcardfraud

The datasets contains credit card transactions over a two day collection period in September 2013 by European cardholders. There are a total of 284,807 transactions, of which 492 (0.172%) are fraudulent.

The dataset contains numerical variables that are the result of a principal components analysis (PCA) transformation. This transformation was applied by the original authors to maintain confidentiality of sensitive information. Additionally the dataset contains Time and Amount, which were not transformed by PCA. The Time variable contains the seconds elapsed between each transaction and the first transaction in the dataset. The Amount variable is the transaction amount, this feature can be used for example-dependant cost-senstive learning. The Class variable is the response variable and indicates whether the transaction was fraudulant.

The dataset was collected and analysed during a research collaboration of Worldline and the Machine Learning Group of Université Libre de Bruxelles (ULB) on big data mining and fraud detection.

Models

  • Applied various classification techniques like :-
  • Logistic Regression Light
  • GBM K Nearest Neighbors (KNN ) Classification
  • Trees Random Forest
  • SVM XGBoost Classifier

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.6.10, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.6.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

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  6. You can download the product after the purchase by a direct link on this page.

 

Hypo Thyroid Disease prediction Machine Learning Project

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Hypo Thyroid Disease prediction Machine Learning Project

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Hypothyroid diseases (underactive thyroid) is a condition in which the body doesn't produce enough of important thyroid hormones. The condition may lead to various symptoms at late ages. More information about the disease is available at https://www.mayoclinic.org/diseases-conditions/hypothyroidism/symptoms-causes/syc-20350284 .

The Data

The data was from: http://archive.ics.uci.edu/ml/datasets/thyroid+disease. I used "allhypo.data" for the analysis. "allhypo.names" contains the column names of the data. Include the info about primary data processing in the Jupyter notebook list below.

set of algorithms performed to carry out the analysis of the "thyroid-disease" database published in the UCI page
URL data source
data: https://archive.ics.uci.edu/ml/machine-learning-databases/thyroid-disease/sick-euthyroid.data
names: https://archive.ics.uci.edu/ml/machine-learning-databases/thyroid-disease/sick-euthyroid.names

Algorithms

  • Naıve Bayes
  • KNN
  • ANN
  • Random Forest
  • SVM
  • FSF
  • PCA
  • LCA

Related sources

Ionita, Irina. (2016). Prediction of Thyroid Disease Using Data Mining Techniques. BRAIN. Broad Research in Artificial Intelligence and Neuroscience. Vol.7. pp.115-124.
URL: https://www.researchgate.net/publication/321145710_Prediction_of_Thyroid_Disease_Using_Data_Mining_Techniques

Ammulu K., Venugopal. (2017). Thyroid Data Prediction using Data Classification Algorithm. IJIRST –International Journal for Innovative Research in Science & Technology. Vol.4. Issue 2. July 2017. ISSN (online): 2349-6010
URL: http://www.ijirst.org/articles/IJIRSTV4I2054.pdf

Geetha K., Santosh S. Eficient Thyroid Disease Classification Using Differential Evolution with SVM. Journal of Theoretical and Applied Information Technology. Vol.88. No.3. E-ISSN: 1817-3195
URL: http://www.jatit.org/volumes/Vol88No3/4Vol88No3.pdf

Banu, Gulmohamed. (2016). Predicting Thyroid Disease using Linear Discriminant Analysis (LDA) Data Mining Technique. Communications on Applied Electronics. 4. 4-6. 10.5120/cae2016651990. URL: https://www.caeaccess.org/research/volume4/number1/banu-2016-cae-651990.pdf

Lou H, Wang L, Duan D, Yang C,Mammadov M (2018) RDE: A novel approach to improve the classification performance and expressivity of KDB. PLoS ONE 13(7): e0199822. URL: https://doi.org/10.1371/journal.pone.0199822

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  5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
  6. You can download the product after the purchase by a direct link on this page.

Android Tetris Game Project with Source Code

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This Tetris game was created with the Android Studio environment, written in Java.

Tetris is a puzzle game in which geometric shapes called "tetrominoes" fall down onto a playing field, and the player has to arrange them to form gapless lines. Pajitnov took inspiration from pentomino, a classic puzzle game consisting of all the different shapes that can be made by combining five squares -- 12 in total -- with the goal of arranging them in a wooden box like a jigsaw puzzle.
To simplify things, he knocked that down to four squares, thus reducing the number of shapes from 12 to seven. He called the game Tetris, combining the Greek numeral "tetra" (meaning four) and tennis, his favorite sport.
Pajitnov himself was immediately hooked. "I couldn't stop myself from playing this prototype version, because it was very addictive to put the shapes together," he said on the phone from Seattle, where he now lives.

Software Requirement

  1. Android Studio
  2. Latest Version
  3. Internet Connection
  4. Java

Download link

 

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Android Ludo game project with Source code

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You can find a simple android ludo game developed in android studio. This is just sample project to illustrate canvas in android with threading for game development.

This is 2D game developed for a simple stat of game development in android studio. Please go through code and you can understand yourself. If you still face any issues then do post issues and I will try to solve it.

Ludo game

Ludo written in java, java is very comfortable language for game development but Unity game is perfect game engine to implement high level supported games like pubg, subway surfers, Free fire and more. Mostly used for develop video games, web plugin.

Android Ludo  Game Source Code

Here codes are not explained because spaces are not enough, and online visitors also don’t like the code explanation. They expect only source code which is original on ludo. The full coding is java and xml. If you have good knowledge java then you able to easily make money online. We post android article, projects, applications and more on company website. Get the android applications codes, ideas for that,  to download source code & working from Android Studio IDE.

 

Source code Download Link

 Apk File Download zip

 

GST Billing Project in Python Django with Source Code

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GST invoice is a bill or receipt of items sent or services that a seller or service provider offers to a customer. It specifically lists out the services/products, along with the total amount due. One can check a GST invoice to determine said product or service prices before CGST and SGST are levied on them.

What is a GST bill? As a GST registered dealer, you are required to provide GST Invoices, also known as bills to your clients. An invoice or a bill is a list of goods sent or services provided, along with the amount due for payment. You can create GST compliant invoices FREE of cost using ClearTax Billing Software.

Simplest GST Billing Project in Python Django .

Features :

  • Easily create invoices
  • Manage inventory
  • Keep books and track balances
  • Print Bill.
  • Automatic Tax Calculation.
  • Automatic Gst Calculation.

Installation Steps: 

  • cmd -  virtualenv -p python3 venv
  • cmd-  source venv/bin/activate
  • cmd-  pip install -r requirements.txt
  • cmd  - python manage.py migrate
  • cmd  -python manage.py runserver

Download Link

 

Beauty Parlour Management System using PHP and MySQL

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100 + PHP Projects with Source Code 

Beauty Parlour Management System is essentially a web-based application that's inbuilt Php with CSS, Ajax and js (javascript). And for the backend of the system, SQL server has been used (i.e. Database) in order that it'll be easy to retrieve later. Also, the most aim of the system is to assist the user to book the appointment within the salon for online.
The system is essentially for the users where they will book a meeting in salon/ salon with login. The users of the system include the customers where they can register initially with the minimum details and will be allowed to make an appointment. Moreover, the system has two panels i.e. Admin and User. The user can make a meeting within the parlour and therefore the admin of the system approves it. Besides, the user also can modify this scheme consistent with their requirements. The user can extract a zipper file containing the ASCII text file and may import into Sublime Text 3 for application development.
Besides, they will also choose the service which they're trying to urge within their specific date and time within the system. All these activities of creating appointments like choosing service also as date and time are going to be recorded within the database for all the events. This project integrates a login panel for a more secure system. Moreover, the system also provides contact details in order that the user has no difficulty in searching the parlour. Besides, the user can visit parlour at a selected date and time because the system already records the appointment made by them.

Brief overview of the technology:

HTML: HTML is used to create and save web document. E.g. Notepad/Notepad++

  1. CSS : (Cascading Style Sheets) Create attractive Layout
  2. JavaScript: it is a programming language, commonly use with web browsers.
  3. Bootstrap: responsive designing.
  4. jQuery

Back end: PHP, MySQL

  1. PHP: Hypertext Preprocessor (PHP) is a technology that allows software developers to create dynamically generated web pages, in HTML, XML, or other document types, as per client request. PHP is open source software.
  2. MySQL: MySql is a database, widely used for accessing querying, updating, and managing data in databases.

Software Requirement(any one)

  1. WAMP Server
  2. XAMPP Server
  3. MAMP Server
  4. LAMP Server

Installation Steps

1. Download zip file and Unzip file on your local server.
2. Put this file inside "c:/wamp/www/" .
3. Database Configuration
Open phpmyadmin
Create Database named salon_management.sql.
Import database matrimony.sql from downloaded folder(inside database)
4. Open Your browser put inside "http://localhost/Folder Name/"

User Login

  1. id-ram@gmail.com
  2. Password-ram12345

Staff Id Password

  1. id-dilu@gmail.com
  2. Password-dilu1234

Admin Id password

  1. id-admin
  2. password-admin

 

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Hospital Management System Java JSP Mysql

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This is a hospital management user interface for managing, monitoring and controlling the system in a Hospital. This application is developed in java, which mainly focuses on basic operations in a hospital like adding new patient information, and updating new information, assigning the doctor for patient. It features a familiar and well thought-out, an attractive online user interface, combined with strong searching Insertion and reporting capabilities. The Backend of the project is designed with Java, MySQL for database connectivity and front end using HTML, CSS, and Bootstrap. This was my Java mini-project for third semester UG.

Modules and their requirements :-

User Module :

  1. Login
  2. Registration
  3. View Menu
  4. Book Appointment.

Admin Module :

  1. Login
  2. View Menu
  4. View All User
  5. View All Doctors
  6. View All Appointment

Doctor Module :

  1. Login
  2. Registration
  3. View Appointment.
  4. Profile .

 

Software Requirements

Technology used:

  1. Front end – JSP
  2. Backend – MYSQL

Software:

  1.  IDE - Netbeans 8.2
  2.  Database support - MySQL 5.7
  3.  Operating system – Windows 8 and above
  4.  Server deployment - Glassfish server

Technology:

  • HTML is integrated in JSP. It provides a means to structure text based information in a document. It allows users to produce web pages that include text, graphics and
    hyperlinks.
  •  Javascript is a scripting language which supports the development of both client and server applications. It is preferred at client side to write programs that can be
    executed by a web browser within the context of a web page.
  •  CSS(Cascading Style Sheets) is a style sheet language used for describing the presentation of a document written in a markup language. Although most often used to set the visual style of web pages and user interfaces written in HTML and XHTML, the language can be applied to any XML document,
  •  SQL is the language used to manipulate relational databases. It is tied closely with the relational model. It is issued for the purpose of data definition and data
    manipulation.
  • Java Server pages is a simple yet powerful technology for creating and maintaining dynamic-content web pages. It is based on the Java programming language. It can be thought of as an extension to servlet because it provides more functionality than servlet A JSP page consists of HTML tags and JSP tags. The jsp pages are easier to maintain than servlet because we can separate designing and
    development.

We require a JDBC connection between the front end and back end components to write

to the database and fetch required data.

Download Link 

 

Covid-19 Hospital Management python django Project with Source Code

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Covid-19 Hospital, Patient Management, Beds Management, Python Django Project with Source Code . Covid-19 Hospital patient management , system will full and advanced features. Hospital Management System is a web application for the hospital which manages doctors and patients. In this project, we use Python Django  and SQLite database. A responsive web-app with aesthetic and accessible UI for managing COVID patients of a certain hospital built using Django and Bootstrap framework

Features: Covid-19 Hospital Management python django

 

  1. Clean aesthetic User Interface, which changes dynamically as per the status of patient changes
  2. In bed availability grid the red color indicates that bed is occupied else available
  3. It is one of the two pages available for public to view
  4. Here you can add patient for storing it to the db.
  5. Patient's relative contact details are also taken to check if the relative has contacted COVID virus
  6. Bed numbers which are available are only shown
  7. Information filled here, will make changes in dashboard dynamically
  8. Since there are relatively more COVID patients than any other viruses/diseases, a checklist for COVID symptoms only is present
  9. Here you can search patients wrt to name, bed no. doctor assigned and status
  10. You can also find update button to update the patient details.
  11. This is where the actual updates for individual patients are done
  12. Seat Management.
  13. Equipment Management.
  14. Oxygen Management.
  15. Doctor Management.
  16. Daily Patient Routine Checkup .
  17. Hospital capacity, including information on ICU capacity and available ventilators
  18. Staffing levels, including any shortages
  19. How many patients are coming into the hospital with confirmed or suspected COVID-19 cases
  20. Many other relevant details that public health officials need to properly coordinate COVID responses

Technical Specification:

  1. Frontend
  2. HTML5
  3. CSS3
  4. JQuery
  5. Bootstrap4
  6. Backend
  7. Django framework
  8. Database
  9. SQLite

Installation

  • Setup virtual environment
  • Exceute pip install -r requirements.txt.
  • run python manage.py runserver.

Download Link 

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Live Face Mask Detection Project in Machine Learning

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Face Mask Detection web applicaion built with Flask, Keras-TensorFlow, OpenCV. It can be used to detect face masks both in images and in real-time video.

The goal is to create a masks detection system, able to recognize face masks both in images, both in real-time video, drawing bounding box around faces. In order to do so, I finetuned MobilenetV2 pretrained on Imagenet, in conjunction with the OpenCV face detection algorithm: that allows me to turn a classifier model into an object detection system. Live Face Mask Detection Project in Machine Learning.

Technologies

  • Keras/Tensorflow
  • OpenCV
  • Flask
  • MobilenetV2

Installation:

You have to install the required packages, you can do it:

  • via pip pip install -r requirements.txt
  • or via conda conda env create -f environment.yml

Once you installed all the required packages you can type in the command line from the root folder:

python app.py

and click on the link that the you will see on the prompt.

Datasets

The dataset used for training the model is available here.