Danvanth Darshan Poovanalingam

Recent graduate (2023) from SRM Institute of Science and Technology, holding a Bachelor's degree in Electronics and Computer Engineering. Possesses substantial experience in developing and implementing Python-based projects, with a particular emphasis on Machine Learning and Deep Learning algorithms. Demonstrates proficiency in utilizing various software tools for artificial intelligence applications. Characterized by a strong enthusiasm for the field and a proven ability to rapidly acquire and apply new technological skills. Dedicated and adaptable professional with a proactive attitude and the ability to learn quickly. Strong work ethic and effective communication skills. Eager to contribute to a dynamic team and support organizational goals.

Completed my Bachelor's degree First Class with Distinction

Participation in SRM Innovation Fest 2022

Represented Ghana for SRM Multi United Nations 2021

• Data Analytics Using Deep Learning (June'22- July'22)
• Corise Python of Data Science Certification (March'2023)
• Coursera Programming for Everybody (May'2020)
• GAIP Masters in Data Science (*)
• Coursera Business Analysis and Process Management (May'2021)

Identification of Crops Using Residual Neural Network in MATLAB:

This project mainly focuses on Identification of crops of different types in a field using CNN (Convolutional Neural Network) using the pre-trained model ResNET-50. The image dataset is used from IMAGENET datasets. Using SVM (Support Vector Machine ) and PCA (Principal Compound Analysis) the features are extracted and using CNN the crop images are predicted. I used MATLAB software to build this model.

Hand Sign Detection using Python:

Using simple libraries such as Numpy, Tkinter and OpenCV a basic hand sign detection model is built. It used graphical plot to identify the signs made by hand.

Reconstruction of Visual Scene Images Using fMRI:

This is the final project of the internship a Data Analytics using Deep Learning. Here CNN is used to extract the data from the fMRI image and GAN (Generative Adversarial Network) is used to reconstuct the image using the extracted features from fMRI images and compared with the corresponding image to identify the accuracy.