Passionate AI and Machine Learning Enthusiast | Versatile Developer with a Flair for Data Science

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Dev Sanghvi  • Peerlist

This project involved designing a prototype for an Alumni Management System that can be used by universities to manage and engage with their alumni. The system is built with a focus on user experience, featuring a clean and intuitive interface. It allows universities to maintain alumni records, facilitate networking among alumni, and provide platforms for alumni to stay connected with the university.

Alumni Management System

This is a sample project to make a Scrapper for products from Amazon.

Amazon Scrapper

During this internship, developed a deep learning model to detect intrusions within the Internet of Medical Things (IoMT). The project involved optimizing a Random Forest Classifier, achieving high accuracy in detecting potential security threats. Various hyperparameter configurations were explored, including adjustments to the number of trees, criterion, and maximum depth.

Deep Learning-based IoT Security Model

This project focuses on creating a GUI-based machine learning model that facilitates interactive learning. The model allows users to experiment with different machine learning techniques through an intuitive graphical interface, making it accessible to non-experts. The project is designed to enhance the understanding of machine learning concepts through hands-on interaction.

GUI-Based Machine Learning Model for Interact

•	The system helps to maintain the records of the patient and allows the doctors to retrieve the patient’s previous prescriptions and details and has an inventory for medicines wherein a trigger is created to order stocks when a medicine’s quantity goes below 20% it can automatically order stock from suppliers. •	Technology Used: Oracle SQL Express Edition.

Hospital Management System

Stock.Pi is a market sentiment analysis tool designed for stock market prediction and analysis. The project includes a chatbot that assists users in making informed decisions regarding their investments by analyzing market sentiment. The tool utilizes advanced web scraping, sentiment analysis, and data processing techniques to provide real-time insights into the stock market.

Stock.Pi

The Text Detector project is focused on the development of a Convolutional Neural Network (CNN) model for recognizing text in 16-segment displays, commonly used in industrial applications. This project integrates OpenCV for image and video processing, EasyOCR for text recognition, and a Flask-based UI for visualization. The system is designed to be highly accurate and efficient, allowing for real-time text extraction from images and videos.

Text Detector

This project involves the development and implementation of a Vision Transformer (ViT) model for image regression tasks. The ViT architecture leverages transformer models, which have been traditionally used in natural language processing, to handle image data efficiently. The project’s focus is on enhancing image regression accuracy and exploring the benefits of transformer-based models over traditional convolutional neural networks (CNNs). The project includes extensive work in deep learning, data processing, and the integration of the model with backend systems for further application development.