Extreme Laboratory Management System

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ExtremeLab is an integrated website and mobile application to manage the medical laboratories system, as it fulfills all the needs of medical laboratories from all their professional aspects, as it allows the users of the system to enter the results of medical reports and facilitates communication and cooperation between laboratory employees and extracts all the required reports from the laboratory and also allow patients to follow up their medical reports, lab branches, and submit a home visit request

Admin panel Key Features:

  1. Managing Tests.
  2. Managing Cultures & Antibiotics.
  3. Managing Tests & Cultures price list.
  4. Doctors management.
  5. Patient management.
  6. Generating patient receipt.
  7. Full management of patient test report.
  8. Printing patient receipt & test report.
  9. Print barcodes
  10. Managing patient home visit request.
  11. Displaying home visit schedule per day.
  12. Notification system (Message & Home visit).
  13. Creating contracts with discounts.
  14. Multi-user with different roles.
  15. Multi-branch laboratory.
  16. Internal chat between lab employees.
  17. Monitoring online users.
  18. Accounting module includes (Expenses, Income, Profits).
  19. Laboratory configuration settings.
  20. Ability to backup database.
  21. Supports Multi language
  22. Supports RTL

Patient panel & Mobile Application Key Features:

  1. Patient profile management.
  2. Ability to view patient test reports & receipts.
  3. Sending patient home visit request.
  4. Viewing laboratory branches.
  5. Notifying patient with Patient code & Results (Email, SMS).
  6. Supports Multi language
  7. Supports RTL

Technologies Used

  1. Laravel framework version 7
  2. HTML , CSS , Javascript , JQUERY (Website)
  3. React Native ( Mobile )
  4. Google Maps
  5. Twilio SMS Gateway

For Live Demo & Enquiry  :

Call/WhatsApp : +916263056779

Email : official@projectworlds.in

Script Come With :

  • Free Installation support
  • Free technical support
  • Future product updates
  • Quality checked by PROJECTWORLDS
  • Lowest price guarantee
  • 3 months support included

Online Taxi Booking Python Django with Real time Map

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Buy Now ₹6501

An online taxi booking Python Django project is a web-based application that allows users to book a taxi ride online through a user-friendly interface. The application integrates with real-time maps to show the location of available taxis and their estimated arrival times.

The project involves designing a database using Django's built-in ORM to store user and ride data. The application's frontend is created using HTML, CSS, and JavaScript to provide an interactive user interface for booking rides, tracking ride progress, and paying for rides.

The Python code interacts with the database to retrieve user data, such as name, email, and payment information, and with real-time map APIs to display the location of available taxis and their estimated arrival times. It uses algorithms to match user requests with available taxis, manage ride progress, and calculate fare amounts.

Some key features of an online taxi booking Python Django project may include:

  1. User registration and login
  2. Ride booking and tracking
  3. Real-time map integration
  4. Payment processing and receipt generation
  5. Ride history and user reviews
  6. Driver and vehicle management
  7. Ride cancellation and refund management
  8. Mobile app integration

Overall, an online taxi booking Python Django project provides a convenient and user-friendly way for users to book and track taxi rides, while providing drivers with a simple way to manage their bookings and pickups. The real-time map integration helps to minimize wait times and improve the overall user experience.

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. Django: Project has been developed over the Django 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 3.7.0, 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 manage.py runserver

Online Time Table Generator PHP MYSQL

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A timetable generator PHP MySQL project is a web-based application that generates a schedule of classes for a school or university based on the available courses, teachers, and classrooms. The application helps to reduce the time and effort required to manually create a timetable and minimize scheduling conflicts between classes, teachers, and classrooms.

The project typically involves designing a database using MySQL to store the necessary information such as course details, teacher information, classroom availability, and scheduling time slots. The application's frontend is created using HTML, CSS, and JavaScript to create an interactive user interface for managing the information.

The PHP code interacts with the MySQL database to retrieve the necessary data to generate the timetable. It uses algorithms to schedule classes and manage conflicts, and displays the final timetable in a user-friendly format, typically an HTML table.

Some key features of a timetable generator PHP MySQL project may include:

  1. Ability to add, edit, and delete courses, teachers, and classrooms
  2. Ability to define scheduling time slots and the duration of each class
  3. Ability to automatically schedule classes and manage conflicts such as double-booked classrooms or teachers
  4. Ability to generate different views of the timetable, such as weekly or monthly views
  5. Ability to print the timetable or export it to other formats such as PDF or Excel

Overall, a timetable generator PHP MySQL project helps to automate the process of creating and managing schedules for educational institutions, thereby saving time and effort while improving efficiency and reducing scheduling conflicts.

Users Roles :

  1. Admin
  2. Teacher/Consultant/Faculty
  3. Student

Admin : The page require user id and password to start the application.

Login is a process by which individual access to a computer system is controlled by identifying and authenticating the user through the cardinalities presented by the user.

Admin can add or delete the category, subcategory etc.

Teacher : Staff can register by admin.

The staff have to login to get more information about the time schedule Dashboard.

Student: Student can register the account by clicking on new register.

He/she can add the account for the various Courses.

The student have to login to get more information about the time schedule.

Brief overview of the technology

Front end: HTML, CSS, JavaScript

  1. HTML: HTML is used to create and save web document. E.g. Notepad/Notepad++
  2. CSS : (Cascading Style Sheets) Create attractive Layout
  3. Bootstrap : responsive design mobile freindly site
  4. JavaScript: it is a programming language, commonly use with web browsers.

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
  5. Xamp PHP 5.5 download link -  Click Here

How to Run

Requirements

  1. Download and Install any local web server such as XAMPP/WAMP.
  2. Download the provided source code zip file. (download button is located below)

Installation/Setup ( Note : Watch Above Demo Video to  Underatand )

  1. Open your XAMPP/WAMP's Control Panel and start the Apache and MySQL.
  2. Extract the downloaded source code zip file.
  3. If you are using XAMPP, copy the extracted source code folder and paste it into the XAMPP's "htdocs" directory. And If you are using WAMP, paste it into the "www" directory.
  4. Browse the PHPMyAdmin in a browser. i.e. http://localhost/phpmyadmin
  5. Create a new database naming Database Name.
  6. Import the provided SQL file. The file is known as timetable.sql located inside the db folder.
  7. Browse the Online Clothi Store in a browser. i.e. http://localhost/Project Folder Name/ .

Download Link

Crop Recommendation using Random Forest flask web app

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Buy Now ₹1501

Buy Now Project Report ₹1001

The Crop Recommendation Flask Web App is a web application that recommends the best crop to grow based on soil and climate conditions. The project involves building a machine learning model that can predict the crop yield based on several parameters such as soil pH, temperature, rainfall, humidity, and crop type. The machine learning model is then integrated into a Flask web application to provide farmers with a simple and easy-to-use tool for crop selection.

Here's a general overview of the project:

  1. Data collection: Collect soil and climate data from reliable sources such as the National Soil Information System and the National Oceanic and Atmospheric Administration (NOAA).
  2. Data preprocessing: Clean and prepare the data for use in the machine learning model.
  3. Feature selection: Select the most important features that can affect the crop yield, such as soil pH, temperature, rainfall, humidity, and crop type.
  4. Model training: Train a machine learning model using the preprocessed data and the selected features.
  5. Model evaluation: Evaluate the performance of the machine learning model to ensure it can accurately predict the crop yield.
  6. Flask app development: Develop a Flask web application that allows users to input soil and climate parameters and get a recommendation for the best crop to grow.
  7. Deployment: Deploy the web application to a server so that it can be accessed by users.

Overall, the Crop Recommendation Flask Web App project can be a valuable tool for farmers to increase their crop yield and improve their farming practices.

 Algorithm :

  1. *Random Forest Classifier* is used for development of model.
  2. Only three algorithms are used to predict the output. They are *Logistic Regression*, *XGBoost* and *Random Forest*.\
    1. Accuracy of the model using Logistic Regression is 95%.
    2. Accuracy of the model using Random Forest Classifier is 99%.
    3. Accuracy of the model using XGBoost Classifier is 99%.

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

Rainfall Prediction using LogisticRegression Flask Web App

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Buy Now ₹1501

The Rainfall Prediction using LogisticRegression Flask Web App project is a web application that predicts the amount of rainfall based on historical weather data. The project involves building a logistic regression model that can predict the amount of rainfall based on several weather parameters, and integrating this model into a Flask web application.

Here's a general overview of the project:

  1. Data collection: Collect historical weather data from reliable sources such as the National Oceanic and Atmospheric Administration (NOAA) or India Meteorological Department (IMD).
  2. Data preprocessing: Clean and prepare the weather data for use in the logistic regression model.
  3. Feature selection: Select the most important features that can affect the rainfall prediction, such as temperature, humidity, wind speed, and cloud cover.
  4. Model training: Train a logistic regression model using the preprocessed weather data and the selected features.
  5. Model evaluation: Evaluate the performance of the logistic regression model to ensure it can accurately predict the amount of rainfall.
  6. Flask app development: Develop a Flask web application that allows users to input weather parameters and get a prediction of the amount of rainfall.
  7. Deployment: Deploy the web application to a server so that it can be accessed by users.

Overall, the Rainfall Prediction using LogisticRegression Flask Web App project can be a valuable tool for farmers and other industries that rely on weather predictions.

Explanation of the logistic regression model:

Logistic regression is a statistical model that is used for binary classification problems, where the goal is to predict whether an observation belongs to a particular class or not. The logistic regression model uses a logistic function to map the input features to a probability output. The logistic function is a sigmoid function that outputs a value between 0 and 1, which can be interpreted as the probability of the observation belonging to the positive class.

Here are the key components of the logistic regression model:

  1. Input features: The input features are the variables that are used to predict the outcome. In the case of rainfall prediction, the input features could include temperature, humidity, wind speed, and cloud cover.
  2. Weights: Each input feature is assigned a weight, which reflects the strength of the relationship between that feature and the outcome variable. The weights are learned during the training process and are used to make predictions.
  3. Bias term: The logistic regression model also includes a bias term, which is added to the weighted sum of the input features to produce the final prediction.
  4. Logistic function: The logistic function is a sigmoid function that is used to map the input features to a probability output. The logistic function has a characteristic S-shaped curve and outputs a value between 0 and 1.
  5. Decision boundary: The decision boundary is the threshold value that is used to determine whether an observation belongs to the positive class or the negative class. The decision boundary is typically set to 0.5, meaning that any observation with a predicted probability greater than 0.5 is classified as belonging to the positive class, while any observation with a predicted probability less than 0.5 is classified as belonging to the negative class.
  6. Training: During training, the logistic regression model is fed a set of labeled data and adjusts its weights to minimize the difference between the predicted output and the actual output. This process is typically done using an optimization algorithm such as gradient descent.

Overall, the logistic regression model is a simple and interpretable model that can be used for binary classification tasks, such as rainfall prediction.

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

 

Plant Disease Prediction using CNN Flask Web App

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Buy Now Source Code ₹1501

Buy Now Project Report ₹1001

The plant disease prediction Flask project is a web application that utilizes machine learning algorithms to predict whether a plant is healthy or diseased based on an image of the plant. The project involves building a machine learning model that can classify plant images as healthy or diseased and integrating this model into a Flask web application.

The project generally consists of the following steps:

  1. Data collection: Collect images of healthy plants and plants with different types of diseases.
  2. Data preprocessing: Clean and prepare the image data for use in the machine learning model.
  3. Model training: Train a machine learning model using the preprocessed image data.
  4. Model evaluation: Evaluate the performance of the machine learning model to ensure it can accurately classify plant images.
  5. Flask app development: Develop a Flask web application that allows users to upload images of plants and get a prediction of whether the plant is healthy or diseased.
  6. Deployment: Deploy the web application to a server so that it can be accessed by users.

Overall, the plant disease prediction Flask project is an innovative solution to the problem of identifying plant diseases and can be a valuable tool for farmers and researchers.

Overview of the CNN algorithm:

Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that are well-suited for image classification tasks. The key idea behind CNNs is to learn a set of filters that can be used to extract meaningful features from the input image. These filters are learned automatically during the training process.

Here are the main steps involved in a CNN algorithm:

  1. Convolution: The input image is convolved with a set of learnable filters. The filters are applied to small patches of the image and slide across the entire image to produce a set of feature maps.
  2. ReLU Activation: The feature maps are passed through a Rectified Linear Unit (ReLU) activation function, which applies a non-linear transformation to the output of each convolutional layer.
  3. Pooling: The feature maps are downsampled using a pooling operation, which reduces the spatial dimensionality of the feature maps while retaining the most important features.
  4. Fully Connected Layers: The output of the convolutional and pooling layers is flattened and passed through one or more fully connected layers, which compute the final classification scores.
  5. Softmax Activation: The final layer uses a softmax activation function to produce a probability distribution over the possible classes.
  6. Training: During training, the CNN is fed a set of labeled images and adjusts the weights of its filters to minimize the difference between the predicted output and the actual output.
  7. Evaluation: After training, the CNN is evaluated on a separate set of images to measure its performance. This involves computing metrics such as accuracy, precision, recall, and F1 score.

Overall, CNNs have achieved state-of-the-art performance on a wide range of image classification tasks, including plant disease prediction.

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