Developed a web application using Firebase Functions, OpenAI, and Firebase Hosting to enable real-time image generation from text prompts. Notably, this project achieved the first-ever integration of the OpenAI image generation API within the Firebase environment using Javascript. It utilizes Firebase Functions to provide serverless APIs, Firebase Hosting for the client-side interface, and OpenAI to transform user-supplied text prompts and image size preferences into visually stunning images in real-time.

The PM Dashboard is a web application that helps teams work together on projects. It's like a digital control center for managing tasks and schedules. People can use it to create projects, update them, and keep track of who's responsible for what. It also has a calendar feature for scheduling important events. Test: Email: test@test.com, Password: test123

Use machine learning to predict diabetes and embed the model on an interactive web page. The Diabetes Prediction Analysis is a project that uses advanced technology to forecast whether someone is at risk of developing diabetes. This prediction model is embedded in a user-friendly website that you can interact with. The motivation behind this project was to apply what I know in machine learning, data analysis, software development, and web design to address a real-world business challenge.

Developed a realistic disaster recovery environment for a robot using AI in robotics. The project involved creating a dynamic simulation representing a collapsed building with obstacles such as fire, debris, and a survivor. The robot, equipped with sensors and AI algorithms, navigates through the environment, alerts first responders, and completes the goal of finding the survivor.

A collaborative technology blog, maintained by tech professionals. This blog showcases in-depth insights into a wide range of tech topics, from embedded software development to the dynamic field of machine learning. With an average daily readership of over 400 unique visitors. It reflects a dedication to staying informed about the latest trends and a commitment to sharing knowledge and expertise with a diverse and engaged audience. This project not only highlights the collective expertise but also the ability to engage, inform, and inspire within the tech community.

This project implements a spatial data analysis to identify "hot zones" and "hot cells" in a dataset of taxi pickup points using Apache Spark and Scala. It involves Hot Zone Analysis (identifying zones with highest pickups via spatial range joins and aggregation) and Hot Cell Analysis (calculating Z-Scores for statistically significant clusters). SQL queries and UDFs are used for data manipulation.