ACADEMICS

I passed ICSE 2013 with a aggregate percentage of 87.2%

I passed ISC 2015 with a aggregate percentage of 93.75%

I did my B.E. in Computer Science and Engineering from IIEST,Shibpur with a CGPA of 7.83.

I am currently undergoing my MTech in Computer Science and Engineering from IIT,KGP with a CGPA of 8.48.

PROJECTS:

1. TOPIC IDENTIFICATION USING UNSUPERVISED SYLLABIC SEGMENTATION APPROACH IN SPEECH DOCUMENTS(UNDER PROFESSOR K.S. RAO):

This project involves a syllable-based approach to unsupervised pattern discovery from speech.We first segment speech into syllable like units.The syllable tokens are then described using a set of features and clustered into a finite number of syllable classes.Finally,recurring syllable sequences or individual classes are treated as word candidates.We would then classify the speech documents into topicwise clusters.

2. DIGIT RECOGNITION USING MACHINE LEARNING AND DEEP LEARNING ALGORITHMS:

We collected the MNSIT dataset from Kaggle and used machine learning and deep learning techniques and then analysed the results.We also used the handwritten digit dataset from scikit- learn and applied machine learning techniques on it to analyse the results.

3. DOCUMENT SUMMARIZATION(UNDER PROFESSOR PAWAN GOYAL):

We had large amounts of data present on various range of topics.We used the Lexrank(Texrank) algorithm and the Degree Summarization algorithm to find out the summary corresponding to every topic.Then we compared this summary to the Gold standard summary to find out the precision,Recall and F-Score.

4. BUILDING A BASIC CALCULATOR FOR ANDROID SYSTEMS:

We build a Basic Calculator for android system using the python package Kivy.

5. PREDICT SURVIVAL IN THE TITANIC DISASTER USING MACHINE LEARNING ALGORITHMS:

We collected the dataset for this project from Kaggle In this project we analyse what sorts of people were likely to survive. in the titanic disaster.We apply the tools of machine learning to predict which passengers survived the tragedy.

6. CLASSIFICATION TASK OF BREAST CANCER DATASET USING MACHINE LEARNING ALGORITHMS:

We analysed a breast cancer dataset collected from UCI Machine learning repository and analysed the dataset to perform a classification task of whether breast cancer would return back after being treated using various machine learning algorithms.

7. SENTIMENT ANALYSIS OF MOVIE REVIEWS(UNDER PROFESSOR SUSANTA CHAKROBORTY):

We collected movie reviews from a movie review dataset of multiple movies.We then performed sentiment analysis on these reviews to detect the positive and negative sentiments using word matching appproach,naive bayes for text processing approach and a formula based approach and compared the results.Then we finally found out the summary of the reviews using graph based summarization.

8. STUDIES ON VARIOUS CRYPTOGRAPHIC TECHNIQUES AND THEIR CRYPTANALYSIS(UNDER PROFESSOR MALAY KULE):

We studied vaious basic cryptographic techniques and then we analysed the One time pad algorithm in details.We also implemented the modified one time algorithms.Then we studied the evolutionary algorithm known as Genetic algorithm and used this algorithm to perform cryptanalysis of One time pad using Genetic algorithm.