umarbrowser

anyZipcrack-dictionary

This python script will crack any zip file using given dictionary file

Attendify

The attendance management system called Attendify is an Android application designed to streamline and simplify the process of managing attendance for a single classroom. The app provides an intuitive user interface for both students and Teacher to facilitate attendance tracking, enabling more accurate and efficient record-keeping.

BrainImagingPipelines

BIPs

dockerImageTvb

Simple Dockerfile for The Virtual Brain(TVB) proposal, under INCF for GSoC2018

green-connect-2023-gdsc-solution-challenge

Green Connect is a mobile app that helps smallholder farmers in developing countries access markets, training, and financing to increase their agricultural productivity and income can be linked to several United Nations Sustainable Development Goals (SDGs).

Neuropackage

Simple Package(Example) for packaging Tvb to Neurodebian

python-guide

Python best practices guidebook, written for Humans.

python_project_to_debian_package

Is is the example for my tutorial

TextEditor

TextEditor Developed using QT Creator C++

umarbrowser/TextEditor

TextEditor Developed using QT Creator C++

umarbrowser/allensdk-feedstock

A conda-smithy repository for allensdk.

umarbrowser/flasky

Companion code to my O'Reilly book "Flask Web Development", second edition.

umarbrowser/-HACKTOBERFEST2K20

Add all sorts of application for hacktoberfest.

umarbrowser/7-pwa-features-demo

Seven web-platform features you didn't know existed.

umarbrowser/ApkStudio

Cross-platform IDE for Reverse-engineering Android applications

umarbrowser/cd0037-API-Development-and-Documentation-exercises

Repository associated with CD0037 - API Development and Documentation. Formerly ND0044 v2 - C2

umarbrowser/cd0039-Identity-and-Access-Management

umarbrowser/full-blockchain-solidity-course-py

Ultimate Solidity, Blockchain, and Smart Contract - Beginner to Expert Full Course | Python Edition

umarbrowser/fyyur

umarbrowser/GitHubGraduation-2022

Join the GitHub Graduation Yearbook and "walk the stage" on June 11.

umarbrowser/hacktoberfest

Participate in Hacktoberfest by contributing to any Open Source project on GitHub! Here is a starter project for first time contributors. #hacktoberfest

umarbrowser/hacktoberfest-3

Hacktoberfest | Simple website for contributors of #hacktoberfest

umarbrowser/hacktoberfest-4

EASY PULL REQUEST! Don't forget to star if it helped!

umarbrowser/Hacktoberfest2020-1

This Repo is created by Abhishek Doifode for the beginners to promote open source programming (hacktoberfest), you just need to add any type of code in any programing lang you want, just commit your name at the end.

umarbrowser/hacktoberfest2020-2

🌱 Contribute your favorite 📚 Data Structure implementation, 🕸 Algorithms, and 🎲 Projects. 😊 | Very Active Repository, Star and Share with your friends |

umarbrowser/hacktoberfest2020-3

Submit Just 4 PRs to earn SWAGS and Tshirts🔥

umarbrowser/Jetpack-Compose-Android-Examples

Learn Jetpack Compose for Android by Examples. Learn how to use Jetpack Compose for Android App Development. Android’s modern toolkit for building native UI.

umarbrowser/Keiko-Corp

HTML challenge for Hacktoberfest 2020

umarbrowser/other-projects

My final project on ALCWithgoogle

umarbrowser/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.

umarbrowser/programmedpatterns

Practice exercises in pattern recognition, ideal for beginner and intermediate programmers.

umarbrowser/react-admin-template

Simple React admin template - Hooks, Redux, Bootstrap :white_check_mark: :metal:

umarbrowser/Soil-quality

umarbrowser/soloproject-tier1-ecalendar

Chingu Solo Project - Tier 1 - eCalendar

umarbrowser/state-LGA-NG

A dropdown of state and LGA in Nigeria. A simple javaScript plugin that shows the dropdown of LGA based on a Selected State

umarbrowser/token-list

The community maintained Solana token registry

umarbrowser/vagrantTvb

Simple vagrantfile script for virtual brain under INCF, GSoC 2018 prove

umarbrowser/VANET-In-Blockchain

A blockchain DApp based implementation of Vehicular Ad-hoc Netwoks to provide a decentralized approach to the prevalent VANET solution, making things faster, reliable and less prone to security breaches and cyber attacks.

umarbrowser/web-general-stater

My starter file for web development