# STUDENT PROJECTS

Following projects have been made as a part of my academic journey in groups of 2-5 members.

## MATHEMATICAL MODEL PYTHON PACKAGE

Course Work: Programming for Data Science

(M.S. in Applied Mathematics)

Created a python module to develop and analyze mathematical models using Pandas, Matplotlib, NumPy, and Math libraries.

Implemented the Lotka-Volterra and Chemostat model, with the scope of adding more models in the future.

## BRAIN CT HEMORRHAGE SEGMENTATION AND CLASSIFICATION USING NEURAL NETWORKS

Course Work: Machine Learning and Statistical Theory

(M.S. in Applied Mathematics)

Trained a CNN in order to classify the location of hemorrhage in CT Scans with an accuracy of 68% on the testing set.

Utilized U-Net, a pre-trained model to train a segmentation network with an accuracy of 92% on the testing set.

## DYNAMICAL SYSTEMS FOR GROWTH OF POPULATION OF WHALE

Course Work: Mathematical Methods and Modeling

(M.S. in Applied Mathematics)

Developed a dynamic system to predict the population growth of whales.

The logistic Model was also deployed on the same database to optimize the model.

The model was studied and compared by changing parameter values.

## PULSE DIAGNOSIS MATHEMATICAL MODEL USING MACHINE LEARNING

Course Work: Final Thesis

(B.Tech Biomedical Engineer)

Decision Tree Learning, SVM Kernel Method, and Artificial Neural Network were used to quantify the relationship between arterial pulse signal features and the presence of disease in a subject.

Processed raw signals, developed filters to reduce noise in the collected signals.

Due to the nature of the data, a nonlinear decision function was chosen over a linear decision function.

On the validation set, accuracy was 73% for ANN, 55% for SVM, and 45% for decision tree.

## DEEP LEARNING FOR DIABETIC RETINOPATHY

Course Work: Enigma (Project Competition Event) in Technoczar (Technical Festival)

(B.Tech. in Biomedical Engineering)

Deployed a CNN classifier on an image database from Kaggle to determine the presence of Diabetic Retinopathy.

Implemented a data augmentation method and created a generator pipeline in the Keras module, in order to overcome the small magnitude of training data.

On the validation set, accuracy was 70%