Rashi Agarwal
Hello! I am Rashi, a prefinal year student at University of Petroleum and Energy Studies (UPES) . My curiosity fueled me to explore different domains, such as Cloud Computing, DevOps and Machine Learning. Research provides me an avenue to work with the state of art methods and constantly challenge myself. More recently I have been exploring my research interests in Cloud Computing and Machine Learning domains.
Skills
Projects
Self-Driving Car Simulation
This project aims to provides users with driverless car simulation, Webcam enabled authentication page to enable only verified users to access the simulation with the use of Artificial Intelligence algorithms and data structures.
MediCURE- An application to predict disease and suggest suitable treatment
This project proposes a web application for the users to identify the disease they have been infected with based on the symptoms they have been suffering. It also includes the capability of recommending home remedies as a treatment for the disease that the user may be infected with. The application is deployed on AWS Cloud using services: EC2, WAF.
Built Serverless Rest-API and deployed on AWS Cloud
Built a serverless REST API using Javascript and Node.js. It can perform basic CRUD operations on Dynamo-DB table, locally tested using Postman and finally deployed on AWS Cloud using services like: DynamoDB, Lambda, API Gateway.
RLang Compiler deployed on Docker
Designed a web application using Flask for a custom-made language (RLang) compiler which is capable of converting custom language that can perform string and arithmetic operations following BODMAS rule, to python. The application is deployed on Docker. Also been awarded a copyright by Government of India for this project.
Publications
Using Deep Learning Approach for Land-Use and Land-Cover Classification based on Satellite images
IEEE 2nd ASIANCON 2022
This research is fundamental to survey the degree to which social, monetary, and natural factors influence urbanization. This will likewise assist with urban planning. As laborious process of handcrafted feature extraction has not helped obtain high accuracies, this paper proposes use of Deep Learning approach that explores different Image Recognition Models using various ML classifiers on remote sensing images.
Covid-19 Analysis using Deep Learning Methods and Computed Tomography Scans
2021 IEEE Bombay Section Signature Conference (IBSSC)
This paper implements different pre-trained CNN feature extraction models using various Machine Learning (ML) classifiers on chest CT scans to analyze Covid-19 infected patients. The implementation of pre-trained models and classifiers reduce the time taken for manual detection of disease and helps doctors to prevent the life of a patient.