Image-Based Bird Species Identification Using Machine Learning

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This project contains code and resources for building a web application that utilizes Convolutional Neural Networks (CNN) to predict bird images. The application allows users to upload an image of a bird, and the trained CNN model will predict the species of the bird.

Introduction

With the advancement of deep learning techniques, building image classifiers has become more accessible than ever. This project demonstrates how to leverage CNNs to create a web application for predicting bird species from images.

Features

  1. Upload bird images to get predictions on their species.
  2. User-friendly interface for easy interaction.
  3. Utilizes a CNN model trained on bird image datasets for accurate predictions.

Model Training

The CNN model used for predicting bird species is trained on a bird image dataset. If you wish to retrain the model or use a different dataset, you can modify the training script (train.py) and replace the dataset accordingly.

To train the model:

python train.py

Dataset

This version of the dataset adds 10 new species to the previous version. In addition using a dataset analysis tool I was able to clean the dataset so that there are no duplicate or near-duplicate images in the dataset. This ensures no leakage between the train, test and validation datasets. Also so defective low information images were also removed. So now you are using a clean dataset.
Data set of 525 bird species. 84635 training images, 2625 test images(5 images per species) and 2625 validation images(5 images per species. This is a very high quality dataset where there is only one bird in each image and the bird typically takes up at least 50% of the pixels in the image. As a result even a moderately complex model will achieve training and test accuracies in the mid 90% range. Note: all images are original and not created by augmentation
All images are 224 X 224 X 3 color images in jpg format. Data set includes a train set, test set and validation set. Each set contains 525 sub directories, one for each bird species. The data structure is convenient if you use the Keras ImageDataGenerator.flow_from_directory to create the train, test and valid data generators. The data set also include a file birds.csv. This cvs file contains 5 columns. The filepaths column contains the relative file path to an image file. The labels column contains the bird species class name associated with the image file. The scientific label column contains the latin scientific name for the image. The data set column denotes which dataset (train, test or valid) the filepath resides in. The class_id column contains the class index value associated with the image file's class.
NOTE: The test and validation images in the data set were hand selected to be the "best" images so your model will probably get the highest accuracy score using those data sets versus creating your own test and validation sets. However the latter case is more accurate in terms of model performance on unseen images.

Link :- https://www.kaggle.com/datasets/gpiosenka/100-bird-species

 

Fruits Freshness Classification using Deep Learning Python Project

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Fruits Freshness Classification using Deep Learning Python Project  is a web application, implemented with Python (Flask framework), which uses a convolutional neural network on the back-end to perform fruit classification. The system is able to distinguish 6 classes of fruits: fresh/rotten apples, fresh/rotten oranges and fresh/rotten bananas. The user is able to interact with the app by uploading images or by showing the fruits to the web-camera. The app uses Web Speech API to make the experience more interactive and fun.

Warning! As there's no fallback class like "a non-fruit object", please don't take it personally when the model classifies the photo of you as a rotten banana 😅 (this also applies to any object, that doesn't belong to the mentioned classes).

Dependencies

For this project, the following tools were used:

  1. Tensorflow 2 for building and training the model;
  2. Numpy for working with arrays;
  3. Matplotlib for visualizing the data;
  4. Flask for implementing the server side;
  5. HTML5, CSS3, JavaScript (with Web Speech API and particles.js) on the front-end.

Dataset for training

The dataset used for training and evaluating the model: Fruits fresh and rotten for classification by Sriram Reddy Kalluri. The obtained model has achieved 99% accuracy on the test set.

Network implementation

The network itself was implemented using transfer learning. The MobileNet V2 model developed at Google was used as a base model for feature extraction from our data. A custom classification layer was added on top and trained separately.

Installation

To install and run locally in a production mode:


cmd-1 - pip install -r requirements.txt --user
cmd-2 - python app.py

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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.

Computer Parts Classification using CNN Web App Project

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In today's digital era, the demand for automated systems capable of recognizing and categorizing objects in images has surged dramatically. One area where such systems can prove invaluable is in the classification of computer parts. From CPUs and GPUs to RAM modules and motherboards, accurately identifying these components is crucial for various applications, including inventory management, e-commerce, and technical support.

This project aims to leverage the power of Convolutional Neural Networks (CNNs), a type of deep learning algorithm well-suited for image classification tasks, to develop a system capable of accurately classifying computer parts. Additionally, the system will be deployed within a web application using the Flask framework, allowing users to easily interact with it through a user-friendly interface.

By combining advanced computer vision techniques with web development technologies, this project not only addresses the challenge of computer parts classification but also demonstrates the practical application of artificial intelligence in real-world scenarios. This project report will detail the methodology employed, the results obtained, and conclusions drawn from the development and deployment of the

CNN-based computer parts classification system in Flask.

We Have use image scraper with chrome driver to scrap the images for training, to use image scraper-

  1. Run the chrome driver in same directry 2.Then open img_scrape.ipynb file
  2. Mension the no of images you required 4.Change the output directry according to your preference
  3. Done , It will automaticly save the images for you

CNN Model building for computer part classification

 

I have used 6 classes of computer parts like- CPU,Monitor,Mouse,Keybord,SSD,Webcam

I used 100 epochs,To increase the accuracy you can increase the epochs

Technology Overview:

  1. Convolutional Neural Networks (CNNs):
    • CNNs are a type of deep learning algorithm specifically designed for image recognition and classification tasks.
    • They consist of multiple layers, including convolutional layers, pooling layers, and fully connected layers, allowing them to automatically learn features from input images.
    • CNNs have shown remarkable performance in various computer vision tasks and are widely used in image classification, object detection, and image segmentation.
  2. Flask Framework:
    • Flask is a lightweight and extensible web framework for Python, ideal for building web applications and APIs.
    • It provides tools and libraries for routing requests, handling HTTP responses, and managing application states.
    • Flask follows the WSGI (Web Server Gateway Interface) specification, making it compatible with various web servers and deployment environments.
  3. TensorFlow:
    • TensorFlow is an open-source machine learning framework developed by Google for building and training neural network models.
    • It offers high-level APIs for building and training models quickly, as well as low-level APIs for advanced customization and optimization.
    • TensorFlow includes tools for distributed training, model serving, and deployment across different platforms and devices.
  4. Data Preprocessing Techniques:
    • Data preprocessing plays a crucial role in preparing the input data for training neural network models.
    • Techniques such as resizing images to a uniform size, normalizing pixel values, and augmenting data with transformations like rotation and flipping help improve model performance and generalization.
  5. HTML/CSS/JavaScript:
    • Front-end technologies like HTML, CSS, and JavaScript are used to create the user interface of the web application.
    • HTML provides the structure of the web page, CSS styles the elements, and JavaScript adds interactivity and dynamic behavior to the application.
  6. Model Deployment:
    • Once the CNN model is trained and evaluated, it needs to be deployed in a production environment for real-world usage.
    • Flask provides a convenient way to deploy machine learning models by integrating them into web applications as RESTful APIs.
    • The trained model can be loaded and executed within the Flask application, allowing users to interact with it through HTTP requests.

Installation

Use the package manager pip to install the requirements.txt file package.

  • cmd-1 - pip install -r requirements.txt --user
  • cmd -3  python app.app

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  3. We offer Paid Customization installation Support
  4.  If you have any questions please contact  Support Section
  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.

Ai generated Fake Face and Real Face detection using Deepfake web app project

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This is a Ai generated Fake Face and Real Face detection using Deepfake Machine Learning project  built with convolutional neural networks. The classifier was trained on a data set comprised of 1400 images (700 of each class) and tested on 600 images (300 per class). The classifier achieved an accuracy of **83.2%**. You can find more performance metrics and information about this project in
the repository. To use this web application just drag and drop a face image to be classified by the model. While you think about that, have a 🍪 and refresh the page once or twice to classify a few built-in faces embedded into the app. The classifier will return the result with the associated probability that a specific face image belongs to either the ```Real``` or ```Fake``` class. The model's architecture summary is also presented below

In recent years, advancements in artificial intelligence (AI) have led to the emergence of sophisticated techniques for generating fake images and videos, commonly known as deepfakes. These manipulations, facilitated by deep learning algorithms, have raised significant concerns regarding their potential to spread misinformation, manipulate public opinion, and infringe upon individuals' privacy and security.

Detecting deepfake content has become a crucial challenge in combating the proliferation of misleading information and protecting digital integrity. This project focuses on the development of a deep learning-based system for the detection of AI-generated fake faces, with the ultimate goal of distinguishing them from real faces.

The proliferation of deepfake technology has profound implications across various domains, including journalism, politics, entertainment, and cybersecurity. Misuse of deepfake content can lead to reputational damage, identity theft, and even exacerbate societal tensions. Therefore, developing robust techniques to identify deepfakes is imperative to mitigate these risks and safeguard the integrity of digital content.

This project aims to contribute to the ongoing efforts in deepfake detection by leveraging machine learning algorithms and computer vision techniques. By analyzing subtle discrepancies between real and fake faces, the proposed system seeks to provide a reliable means of identifying manipulated content and enhancing trust in digital media.

Technologies Used:

  1. Deep Learning Frameworks:
    • TensorFlow or PyTorch: Widely-used frameworks for building and training deep learning models, including convolutional neural networks (CNNs) for image classification and detection tasks.
  2. Computer Vision Libraries:
    • OpenCV: A popular library for computer vision tasks such as image preprocessing, feature extraction, and object detection.
    • scikit-image: Provides a collection of algorithms for image processing and manipulation, which can be useful for data preprocessing and augmentation.
  3. Machine Learning Tools:
    • scikit-learn: Offers a range of machine learning algorithms and tools for data preprocessing, model evaluation, and metrics calculation.
    • XGBoost or LightGBM: Gradient boosting libraries that can be used for classification tasks, especially if ensemble methods are desired.
  4. Streamlit and Web Development:
    • Streamlit: The primary framework for building interactive web applications with Python, allowing for the seamless integration of machine learning models with user-friendly interfaces.
    • Flask or FastAPI: Lightweight web frameworks that can be used for building backend APIs to support the Streamlit application.
  5. Image Manipulation and Visualization:
    • Matplotlib or Seaborn: Libraries for creating visualizations and plots to display model outputs, evaluation metrics, and detection results.
    • Pillow: Python Imaging Library for opening, manipulating, and saving many different image file formats.
  6. Other Utilities:
    • NumPy and Pandas: Fundamental libraries for numerical computing and data manipulation, which are essential for handling image data and preprocessing.
    • tqdm: Provides a progress bar for tracking the progress of data loading, model training, and inference tasks.

 

Installation

Use the package manager pip to install the requirements.txt file package.

  • cmd-1 - pip install -r requirements.txt --user
  • cmd-2   cd app
  • cmd -3  python -m streamlit run.app

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Read Before Purchase  :

  1. One Time Free Installation Support.
  2. Terms and Conditions on this page: https://products.projectworlds/terms
  3. We offer Paid Customization installation Support
  4.  If you have any questions please contact  Support Section
  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.

Online Loan management system project in Python Django

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A loan management application built with Django, SQLite3, JavaScript, HTML, CSS, and Bootstrap 5. The system enables customers to request loans after registration, with admin approval. Customers can make monthly loan payments, and both customers and admins have dedicated dashboards.

Features

 

  1. User Registration: Customers must register before requesting loans.

  2. Loan Requests: Customers can request loans, pending admin approval.

  3. Admin Approval: Admins can approve or reject customer loan requests.

  4. Loan Payments: Customers can make monthly loan payments.

  5. Dashboards: Both customers and admins have access to dedicated dashboards.

Usage

 

  1. Register as a customer to request loans.
  2. Admins approve or reject loan requests in the admin dashboard.
  3. Customers can make monthly loan payments through the application.
  4. Both customers and admins have dashboards for efficient management.

Django Installation Steps :-

  1. Install Python 3.8
  2. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  3. Finally run cmd - python manage.py runserver

 

A simple Caterpillar game built in python mini project with source code

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an overview of the development and implementation of a simple Caterpillar game in Python. The game is designed to be a fun and interactive way for users to play a classic snake-like game where they control a caterpillar and navigate it around the screen to eat food and grow in length.

Objectives

The main objectives of the project are as follows:

  1. Develop a simple and easy-to-understand game using Python.
  2. Implement basic game mechanics such as user input, movement, collision detection, and score tracking.
  3. Create an interactive graphical user interface (GUI) using the Tkinter library.
  4. Enhance user experience by adding sound effects and visual feedback.

Technologies Used

  1. Python programming language
  2. Tkinter library for GUI development
  3. Pygame library for sound effects
  4. IDE (Integrated Development Environment) such as Visual Studio Code or PyCharm

Game Description

The Caterpillar game consists of the following elements:

  1. Caterpillar: The main character controlled by the player.
  2. Food: Appearing randomly on the screen for the caterpillar to eat and grow.
  3. Obstacles: Static objects that the caterpillar must avoid colliding with.
  4. Score: Keeping track of the player's progress.
  5. Game Over: Triggered when the caterpillar collides with an obstacle or itself.

## 🌟 How to run
Running the script is really simple! Just open a terminal in the folder where your script is located and run the following command:

```command
python Caterpillar.py

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Online Hotel Reservation System Project in Python Django

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The Hotel Reservation System developed using Django is a web-based application designed to streamline the process of booking hotel rooms for guests. It provides a user-friendly interface for both guests and hotel administrators to manage room reservations efficiently.

In the bustling hospitality industry, efficient management of hotel reservations is paramount for ensuring guest satisfaction and maximizing revenue. The Hotel Reservation System developed using Django addresses this need by providing a comprehensive solution for streamlining the booking process.

In today's digital era, guests increasingly prefer the convenience of booking accommodations online. Thus, our system aims to meet this demand by offering a user-friendly web-based platform where guests can effortlessly browse available rooms, make reservations, and manage their bookings from the comfort of their homes or on the go.

For hotel administrators, the system offers robust tools for managing room inventory, tracking reservations, and optimizing occupancy rates. With intuitive features and real-time updates, hotel staff can efficiently handle bookings, ensuring a smooth and seamless experience for both guests and staff alike.

By leveraging the power of Django, a high-level Python web framework known for its scalability, security, and rapid development capabilities, our Hotel Reservation System delivers a reliable and feature-rich solution tailored to the needs of modern hotels and their guests.

Throughout this project report, we will delve into the system's architecture, functionalities, implementation details, and future enhancements, providing a comprehensive overview of how the Hotel Reservation System revolutionizes the way hotels manage bookings and enhance guest experiences.

A web application to manage a hotel. It contains the following features.

  1. Search rooms with check in , check out, room type.
  2. View all rooms, categorywise
  3. Add room to the wishlist
  4. Booking rooms
  5. Sign up, Sign in, profile update.
  6. Booking history.

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. SQLite : SQLite database has been used as database for the project
  7. 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 3.8, 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.

Project on Django Installation Steps :-

  1. Install Python 3.8
  2. Install Django version 3.1
  3. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  4. Finally run cmd - python manage.py runserver

Online Pizza Ordering System in Python Django

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Welcome to our Online Pizza Ordering System, where you can satisfy your pizza cravings with just a few clicks! This system is designed to streamline the pizza ordering process, offering a user-friendly interface for customers and efficient order management for administrators.

Features:

Pizza Menu:

  • Explore our diverse pizza menu featuring a variety of mouth-watering options. Each pizza comes with a detailed description and price, making it easy for users to choose their favorite flavors.

Order Placement:

  • With a simple click, users can place their pizza orders directly from the menu. The system records the selected pizza, generates a unique order ID, and timestamps the order placement.

Order Status Tracking:

  • Keep tabs on your pizza order with our real-time order status tracking. Users can visit the "Order Status" section, enter their order ID, and view essential information such as the pizza details, order timestamp, and whether the order is completed or pending.

Admin Panel:

  •  The system empowers administrators with a dedicated admin panel to efficiently manage incoming orders. Admins can mark orders as completed and maintain an organized view of both completed and pending orders.

How to Use:

Explore the Menu:

  •  Visit the "Pizza Menu" section to explore our delightful pizza options.
  •  Each pizza is accompanied by its name, description, and price.

Place Your Order:

  •  Click on the "Order" link next to your preferred pizza to initiate the order placement process.
  • Your order is assigned a unique ID, and the system records the timestamp.

Track Your Order:

  • Navigate to the "Order Status" section.
  •  Enter your order ID to retrieve real-time updates on your pizza order, including its current status.

Admin Functions:

  • Admins can access the admin panel to view and manage all incoming orders.
  •  Mark orders as "Completed" once they are ready for delivery or pickup.

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. SQLite : SQLite database has been used as database for the project
  7. 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 2.7, 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.

Project on Django Installation Steps :-

  1. Install Python 3.7 Or Higher
  2. Install Django version 2.2.0
  3. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  4. Finally run cmd - python manage.py runserver

Online Hostel Visitor Management System in php mysql

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The Hostel Visitor Management System is a comprehensive online solution designed to efficiently track and manage visitors entering and exiting a hostel. Developed using PHP, MySQL, HTML, CSS, Bootstrap, and JavaScript, this system provides an intuitive and user-friendly interface for hostel administrators to oversee visitor activity and generate insightful reports.

Key Features:

  1. Visitor Entry Form:

  •    - The system facilitates the recording of visitor details such as name, contact number, and purpose of the visit through a dedicated entry form.
  •    - Hostel staff can easily input this information into the system, ensuring a seamless and organized record of incoming visitors.

  1. Visitor Exit Form:

  •    - For accurate tracking, the system includes a visitor exit form, enabling staff to record the departure time of each visitor.
  •    - The exit form ensures that administrators have a complete log of visitor activity, helping maintain security and accountability.

  1. Admin Dashboard:

  •    - Hostel administrators can securely log in to the system using personalized credentials, accessing a centralized dashboard for managing visitor data.
  •    - The dashboard provides an overview of daily visitor statistics, including the total number of visitors and their entry and exit times.

  1. Generate Reports:

  •    - The system empowers administrators to generate detailed reports by selecting specific date ranges.
  •    - Customizable date parameters allow for a focused analysis of visitor activity, aiding in trend identification and overall hostel security assessment.

  1. Responsive Design:

  •    - Utilizing Bootstrap and responsive design principles, the system ensures a consistent and user-friendly experience across various devices, including desktops, tablets, and mobile phones.

Security Measures:

  •    - The system incorporates best practices for data security, protecting sensitive visitor information and login credentials.
  •    - Implementation of prepared statements in the PHP backend helps prevent SQL injection, enhancing the overall robustness of the system.

Scalability and Customization:

  •    - Designed with scalability in mind, the system can be expanded and customized to meet the unique requirements of different hostels.
  •    - Additional features, such as notifications, visitor categorization, and integration with access control systems, can be incorporated based on specific needs.

In conclusion, the Hostel Visitor Management System provides a reliable and scalable solution for hostel administrators to streamline visitor management processes. With its user-friendly interface and robust features, the system contributes to enhanced security, organization, and efficiency within hostel premises.

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) : 

  • WAMP Server
  • XAMPP Server
  • MAMP Server
  • LAMP Server

( Make Sure PHP Version 5.5 to 5.6 )

Xamp PHP 5.6 download link -  Click Here

Xamp PHP 5.5 download link -  Click Here

Installation Steps

1. Download zip file and Unzip file on your local server.
2. Put this file inside "c:/xampp/htdocs/" .
3. Database Configuration
Open phpmyadmin
Create Database named hostel_vistor.
Import database hostel_vistor.sql from downloaded folder(inside database)
4. Open Your browser put inside "http://localhost/Hostel-Visitor-Management-System-php-mysql/"