As a Master's student in the Internet of Things program at Florida International University, I have gained valuable experience in machine learning, deep learning, and cloud services. I am now seeking full-time opportunities to apply these skills in the practical world. In my free time, I enjoy engaging in various outdoor activities such as playing badminton, going on adventures, and exploring new places.
I am also a member of the Eco Club, and we built an electric car to participate in the Shell Eco-marathon America 2023. I also volunteered as an event organizer for our college beach festival and enjoyed organizing events.
In the IoT industry, security poses a significant challenge. This project focuses on the development of a system that detects and classifies botnet attacks using machine learning techniques.
Skills: Python, Jupiter Notebook ML algorithms(KNN, MNB, Decision Trees, Random Forest)
Led an IoT-driven supply chain optimization initiative at Gala Groceries, resolving the challenge of perishable inventory management. Reduced storage costs by 15% while ensuring consistent fresh produce supply and enhancing customer satisfaction.
Skills: Python, Data Analysis, Data Modeling, Data Visualization, Development, evaluation and Machine Learning
Implemented a Smart Home utilizing IoT technology, enabling seamless control of household appliances through mobile devices. This innovative solution addresses household challenges such as convenience, energy efficiency, accessibility, and time-saving.
Tools Used: Arduino, Node MCU, sensors, and Mqtt dash app
Developed a MATLAB-based multimodal image fusion system using SWT and PCA, catalyzing efficient combination and enhancement of visual information from multiple sources.
Skills: MATLAB, Image Processing, Stationary Wavelet Transform(SWT) and Principal Component Analysis (PCA)
This project explores the relationship between power usage and temperature in IoT devices. It identifies key power-saving methods and investigates how temperature fluctuations affect energy efficiency in these devices, contributing to more sustainable IoT solutions.
Tools Used: Raspberrypi0, 1,2, DHT22, INA219 Sensors,Python, Wi-fi and Things Board.
Solving the challenge of loan approval prediction involves not only determining whether an application is accepted or rejected but also understanding the underlying reasons. This project aims ensuring transparency and interpretability in the decision-making process.
Skills: Python, Google Colab, Logistic Regression, Random Forest, XGB Classifier, Decision tree XAI techniques(LIME and SHAP Algorithms)
