My Projects

 IoT Home Device Management System - (React App) The internship has Four sections Frontend, Backend, Flutter app, hardware. This application was based on Postgres, Node.js, React, and Flutter. The Backend and hardware part was done by or mentor Aditya Patel, the React part was done by Me and one of my Project Partners Bhavya Gosai, and the Flutter part was done by our other two project members Aman Desai and Jash Tewani. The React Application or so-called Web App in the Internship, the app is built in react js. the App is also completely Responsive.  The Web App include the Landing Page, Sign IN / SignUP page , Features Page, Contact Page and the Dashboard. the Dashboard have further sections Devices section, User Setting's section and Setting's section. The Landing page includes the Navbar, Hero section, Features section, Pricing section, clients section, Footer. The Contact us page includes the contact details of the company ans well as the contact form where the user can po...

 University Event Management Using React, Nodejs & MongoDB The project is made using React as the Frontend library, Nodejs as a server-side language, and MongoDB as the database. The react application is Divided into components In the Hierarchy where the root or the main component is the App.js and the branches start under it. The Hierarchy of the React application is given in the Bellow Figure. In react, the Web Page or the Application is divided into components and these components are nested inside one another to form a single page. In react we write the code in the .js file but it renders HTML content onto the Browser, this is done with the JSX also known as "JavaScript XML", the JSX is similar to HTML with a slight difference in the syntax such as the class in HTML is className in JSX as the class is a reserved word in the JS. unlike HTML where we use the anchor tag to jump on the other page, we still can use the anchor tag to jump on other pages but there is anothe...

  Sign Language Detection Using open-cv python Haar Cascade classifiers are an effective way for object detection. This method was proposed by Paul Viola and Michael Jones in their paper Rapid Object Detection using a Boosted Cascade of Simple Features . The algorithm implemented in OpenCV can also be used to detect other things, as long as you have the right classifiers. My OpenCV distribution came with classifiers for eyes, upper body, hands, frontal face and profile face. Haar Cascade is a machine learning-based approach where a lot of positive and negative images are used to train the classifier. Frequently used keywords haar Positive Images:  These images contain the images which we want our classifier to identify. Negative Images:  Images of everything else, which do not contain the object we want to detect. Features  Initially, the algorithm needs a lot of positive images (images of hand) and negative images (images without hand) to train the classifier. Then ...