Anwesha Chakraborty

Anwesha Chakraborty

@anweshachakraborty17

Love to Learn & do Coding being a Computer Science Engineer.

Computer Science Engineering Student Kolkata, India
11
Followers
9
Following
23
Public Repos
0
Private Repos

Language Breakdown

Lines of code distribution across 19 owned repositories

225K Total LOC
HTML
79,685 lines
35.4%
N/A
C++
31,058 lines
13.8%
N/A
Python
23,035 lines
10.2%
N/A
CSS
22,440 lines
10.0%
N/A
Hack
21,949 lines
9.7%
N/A
Other
47,157 lines
20.9%
N/A
T

T-Shaped Developer

T-shaped

Deep in HTML with broad versatility

HTML
C++
Python
CSS
Hack

Collaboration Network

Global Impact visualization

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Anwesha Chakraborty
0 active collaborators

Repos

23

PRs

0

Growth

+18%

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Coding Streak

Contribution activity over the past year

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Top Repositories

Iris-Recognition-Using-MATLAB

Iris recognition is a reliable and accurate biometric identification system for user authentication. It is used for capturing an image of an individual’s eye. The performance of iris recognition systems is measured using segmentation. Segmentation is used to localize the correct iris region in the particular portion of an eye and it should be done accurately and correctly for removing the eyelids, eyelashes, reflection, and pupil noises present in the iris region. In our paper, we are using Daughman’s Algorithm segmentation method for Iris Recognition.

13 3
MATLAB
Student-Performence-Tracker-DesignL

Student Performance Tracker is a web-based application developed using HTML, CSS, JavaScript, PHP and MySQL Database. This application provides an easy way to student in searching the details of their academic attendance and marks percentage details the with graph and meritlist. Students can search their academic performance and details of student’s attendance and marks percentages are added by admin.

3 3
Hack
Python_Bootcamp

Complete Python Programming Bootcamp 2020

1 0
Python
Java-Programming

Problem Solving Using OOP based JAVA Programming

1 0
Java
Crime-Data-Analysis-using-Data-Mining

AIR Crime Analyzer is a Crime Data Analysis Project using Data Mining Technics. Crime analyses is one among the important application of knowledge mining. Crime Analyzer is a law enforcement function that involves systematic analysis for identifying and analyzing patterns and trends in crime and disorder. Data processing contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications It can help the analysts to spot crimes faster and help to form faster decisions. The main objective of crime analysis is to seek out the meaningful information from great deal of knowledge and disseminates this information to officers and investigators within the field to help in their efforts to apprehend criminals and suppress criminal activity. In this project, K-means Clustering is employed for crime data analysis.

0 0
HTML
Durbin-Book-Finder

A Book Finding or Searching Web Application, developing its Fontend using REACT js along with Google Books API.

0 0
JavaScript
Book-Finder_JavaScript

A Book finding or searching web-application using HTML, CSS, JavaScript, Bootstrap and Most importantly REACT js for Font-End.

0 0
JavaScript
Digital-Image-Processing
0 0
Tic-Tac-Toe-Game-Using-JQuery

This is a single-player Tic Tac Toe game panel. In this game, the user will play with the computer. It is designed with simple logic to make the player win easily against the computer.

0 0
JavaScript
Real-Time-Object-Detection-Using-TensorFlow

Object detection is a computer vision technique in which a software system can detect, locate, and trace the object from a given image or video. The special attribute about object detection is that it identifies the class of object (person, table, chair, etc.) and their location-specific coordinates in the given image. Tensorflow is an open-source library for numerical computation and large-scale machine learning that ease Google Brain TensorFlow, the process of acquiring data, training models, serving predictions, and refining future results. Tensorflow bundles together Machine Learning and Deep Learning models and algorithms and it allow developers to create a graph of computations to perform. Each node in the graph represents a mathematical operation and each connection represents data. Hence, instead of dealing with low-details like figuring out proper ways to hitch the output of one function to the input of another, the developer can focus on the overall logic of the application. Creating accurate Machine Learning Models that are capable of identifying and localizing multiple objects in a single image remained a core challenge in computer vision. But, with recent advancements in Deep Learning, Object Detection applications are easier to develop than ever before. TensorFlow’s Object Detection API is an open-source framework built on top of TensorFlow that makes it easy to construct, train, and deploy object detection models.

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Open Source Impact

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