This Project explores a distributed method for common factor attack to RSA Moduli. In a memory resource constrained environment, the computational resources may not be sufficient enough to create the product tree required for batchwise-gcd of the entire dataset. As the size of the dataset increases the size of the product tree increases exponentially. Hence,we show a parallel processing method with theoretical proof to show that satisfactory level of accuracy can be achieved using this method.

Instructional Videos have given an open challenge to all current methods of education system. They have high potential of acceptability among all kind of learners. Instructional Videos have become a label for many recent course initiatives from higher education institution. We believe that Instructional Videos are going to be next generation textbooks. The Goal of this Project is to automatically identify the locations where the topic has changed in an instructional video. We specifically want to explore techniques that use acoustic (i.e. spoken) features.

Material Understanding in 3D Models using Kinect: Captured stereo RGBD images using Microsoft Kinect v2. Used InfiniTAM Reconstruction framework to build 3D Reconstruction model using captured color & depth images. Integrated a CNN based semantic segmentation library with InfiniTAM Model to understand the material in the reconstructed 3D model.

The Radio Cab Space is Heating up. Understanding the Cab demand and supply could definitely trigger the efficiency of the radio cab services. Getting the data of cab rides and using various ML models we can predict the best in favor of cab service providers. In this work, I present a few different models to predict the number of cab pickups that will occur at a specific time and location; these predictions could definitely boost the efficiency of cab service providers. I implemented decision tree regression model yielding co-efficient of determination close to unity which is significant improvement over other regression models.

With advancements in digital communication & security online shopping has garnered a significant pace since last decade. Most shopping websites allows buyers to share their feedback (reviews) about their purchased product. Since there are lots of reviews & they are mostly unstructured text – it is difficult to employ any data mining techniques to get interesting (non-trivial, implicit, previously unknown and potentially useful) information. It is also difficult to build an opinion mining application to compare various product models to make a purchase decision. These comparisons should be based on the features or aspects of the products. In this work, we explored Non negative Matrix Factorization based techniques to extract aspects from the raw reviews.

Programmed and debugged the fully functional command line and GUI based simulator for SIC Assembler. The Simplified Instructional Computer (also abbreviated SIC) is a hypothetical computer system that is used to learn systems programming.