alien_invasion_game

billsplit-backend

billsplit-frontend

calculator-progate_project

Web Development based 'Calculator' Project for Progate Week of Learning.

clarence-bot

A multipurpose discord bot with admin, fun and information commands

competitive_submissions

All of my accepted submissions on various sites.

fullstackopen_assignments

Assignment Submissions for fullstackopen.com

javafx_task_management_dashboard_ui

A JavaFX Task Management Dashboard UI with basic functionality.

juit-placement-portal-backend

Backend for the minor project of 6th Semester.

linkedin-automation

logs_project

A Django web application deployed on Heroku

msh

MSH is a simple C shell implementation with few builtin commands.

muditmahajan21

one-reader-for-all

A single website you can go to avoid work — read Reddit, Hacker News, Stack Exchange and more in one place.

openocrcorrect

An end to end Interactive Interface for correcting mistakes in OCR output.

plant-disease-detection-web-application

Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.

price-comparison

A price Comparison API as part of Durinpay Assignment.

qt_chat

qt_text_editor

QT Text Editor

qtcalculator

A GUI Calculator developed with QT Framework for C++

react-portfolio-template

Modern React Portfolio Template (FREE)

starter-express-api

starter-react-app

statsbot

Discord Bot to gather data from public APIs using node.js and discord.js!

striver-sde-sheet

Striver SDE Sheet Solutions

tcp_server-client_program

A simple stream socket TCP server-client implementation in C.

thrain

Secure Text Transfer Using Diffie-Hellman Key Exchange Based On Cloud