SHOBHA KUMARI

admf3h@r.postjobfree.com

m

977-***-****, 914-***-****

NIT MANIPUR, imphal, India

SKILLS

C C++

JAVA(Beginner)

HTML&CSS Excel

LANGUAGES

HINDI

Full Professional Proficiency

ENGLISH

Full Professional Proficiency

ORIYA

Full Professional Proficiency

INTERESTS

AI& Machine Learning

Data Structure

Operating System

OOPs

Software Engineering

EDUCATION

BACHELOR OF TECHNOLOGY(B.TECH), CSE(2016-2020)

National Institute of Technology, Manipur

07/2016 - 06/2020, 7.0/10

B.tech

XII(Senior Secondary) (2015)

Jawahar Navodaya Vidyalaya, Sheikhpura

07/2013 - 06/2015, 70%

Science

X( Secondary)(2013)

Jawahar Navodaya Vidyalaya, Sheikhpura

07/2012 - 06/2013, 9.8/10

ORGANIZATIONS

Fresher Meet 2017, NIT MANIPUR

Coordinator

Event coordinator of TECH MEET 2019, NIT MANIPUR

Coordinator in SPORTS MEET 2019, NIT MANIPUR

CERTIFICATES

Winter Training on " Machine Learning using Python"( 2nd Dec to 13 Dec 2018)

(12/2018 - 12/2018)

Machine Learning Training Certificate (Microsoft)

PERSONAL PROJECTS

Summer internship(Stock predictions based on numrai) (05/2019 - 06/2019) In this work we present an Neural Network approach to predict stock market. The design of Neural Network model with the basic features and parameters. We have used the activation functions along with the crossvalidation sets. We finally used the dataset from the Numeri. Numerai is a data science competition in which number of users upload there result for the stock prediction. In this competition the dataset which is provided by the Numerai it is unlabelled. We have achieved the accuracy of 83% on the dataset.

Teeth classification using CNN(Convolutional Neural Network) (4th year project)

(03/2020 - 05/2020)

An Orthopantomogram (OPD) generally known as panoramic radiography By using this method, the data has been pre-processed in order to remove all the null values and noises present in the data. Built model that includes four convolutional layers, one max pooling layers, one dropout layer and two dense layers has been used in this model. The model mainly consists of two parts viz Convolutional base and Classifier. The Convolutional base is mainly used to extract features. The extracted features are used to train the Classifier. The sample dataset has been segmented and processed to remove unwanted noise in the images. The images are segmented manually into four classes namely, Incisor, Canine, Molar and Pre-Molar and processed the datasets into a size of 16 KB and 224x224 dimensions. The datasets are divided into the ratio of 80:20. 80% of the images are used for training the model and the remaining 20% is used for model testing. The training samples are further divided into training datasets and validation datasets into the ratio of 80:20. Processing of the images enhances the characteristics of the image and helps in increasing the accuracy of the used model.

Courses

Courses

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