Objective - Explore deep learning's role in oral cancer detection using ResNet50 and VGG16 models, achieving 94% and 95% accuracies.

Methodology - Train models with diverse oral cancer images, utilizing CNNs to extract features and learn discriminative representations from histopathological slides, oral cavity images, and clinical photographs.

Evaluation - Employ cross-validation and independent test sets, quantifying model performance through metrics like sensitivity, specificity, error rate, probability, and accuracy.

Comparative Analysis - Compare ResNet50 and VGG16 with state-of-the-art methods, demonstrating remarkable accuracies of 94% and 95%, respectively, for oral cancer detection.

Integration - Link ResNet50 to the front end using HTML, CSS, JavaScript, and Flask, enhancing accessibility and usability of the oral cancer detection system.

Objective - Develop a gas leakage detection system for homes and industries, using Atmel 89c51 Microcontroller to detect LPG/CNG leaks, ensuring safety through SMS alerts and automatic gas valve shutdown.

Implementation - Successfully realized the system, integrating GSM module for SMS alerts and emergency measures to turn off gas supply.

Microcontroller - Employed Atmel 89c51 Microcontroller for efficient control, providing a cost-effective solution with low development costs for wider accessibility.

Scope - Extensive applications in home automation, with potential for added features like automatic gas booking and fire safety. Adaptable for industrial use in environments with harmful and flammable gases.

Cost and Accessibility - Developed at a significantly low cost, ensuring affordability. The device's practicality is immense, contributing to home and industrial safety.

Effortless Task Management - Web app streamlines task handling, leveraging SQLAlchemy for robust database operations, facilitating seamless task creation, reading, updating, and deletion.

Database Integration - Utilizes SQLAlchemy for efficient database operations, ensuring reliability and performance in managing task-related data seamlessly within the web application.

CRUD Functionality - Enables users to effortlessly Create, Read, Update, and Delete tasks, enhancing task management efficiency through intuitive and user-friendly functionalities.

Dynamic User Interface - Employs Jinja2 templating to create a dynamic, responsive interface, providing users with a visually pleasing and adaptable environment for efficient task management.

Optimization - Enhances task handling processes, ensuring an optimized user experience. The integration of robust database operations and dynamic interfaces contributes to effective and streamlined task management.

TextOp Web App - Utilizes HTML, CSS, Bootstrap, JavaScript, and React to create an intuitive text manipulation tool with user-centric design.

User-Centric Design - Prioritizes user experience, ensuring responsiveness, accessibility, and high-performance processing for seamless text manipulation.

Technological Stack - Crafted with HTML for structure, CSS for styling, Bootstrap for design consistency, JavaScript for interactivity, and React for dynamic user interfaces.

Intuitive Functionality - Empowers users with easy text manipulation features, enhancing usability and ensuring a satisfying experience with the application.

Responsive and High-Performance - Designed to be responsive across devices, providing a consistent experience. High-performance processing ensures swift and efficient text manipulation operations.