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android-maps-directions

android-mvp-architecture

This repository contains a detailed sample app that implements MVP architecture using Dagger2, GreenDao, RxJava2, FastAndroidNetworking and PlaceholderView

android-mvp-realtime-chat

Android MVP Realtime Chat using RxJava and Socket.IO

autobahn-java

WebSocket & WAMP in Java for Android and Java 8

beginner-s-project-php

login, signup, database connection, logout and etc

cgpa-calculator-for-4-subs

cgpa_garbage

It is a Calculator for university students.

cgpacalculator

cgpacalculatorwiki

cgpagarbage-web

compare-between-languages

Implement searching on the given data in C++/Java, Shell Script, and Python/JavaScript. Choose any three programming languages, however, ShellScript is mandatory. Write programs to read the given file and implement the search by disease, total death by year, etc

cricscoregae

App Engine backend for obtaining cricket scores

delyo-food-delivery-android

An Food Delivery concept UI.

dichokro-web

disher

MVVM with compose (the meal db api)

h5totflite-converter

H5 to TFLite Converter.

keras-android-xor

How to run a Keras model on Android using Tensorflow API.

malaria-detection-with-ml-kit

Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is detected by trained microscopists who analyze microscopic blood smear images. Modern deep learning techniques may be used to do this analysis automatically. The need for the trained personnel can be greatly reduced with the development of an automatic accurate and efficient model. In this article, we propose an entirely automated Convolutional Neural Network (CNN) based model for the diagnosis of malaria from the microscopic blood smear images. A variety of techniques including knowledge distillation, data augmentation, Autoencoder, feature extraction by a CNN model and classified by Support Vector Machine (SVM) or K-Nearest Neighbors (KNN) are performed under three training procedures named general training, distillation training and autoencoder training to optimize and improve the model accuracy and inference performance. Our deep learning-based model can detect malarial parasites from microscopic images with an accuracy of 99.23% while requiring just over 4600 floating point operations. For practical validation of model efficiency, we have deployed the miniaturized model in different mobile phones and a server-backed web application. Data gathered from these environments show that the model can be used to perform inference under 1 s per sample in both offline (mobile only) and online (web application) mode, thus engendering confidence that such models may be deployed for efficient practical inferential systems.

manual-traffic-signal-system

In this project, a single device which can control a cross-road traffic. A traffic police can control a cross-road using this.

notice-board

nsuofferlistcrisis

try to solve TBA problem in NSU

pdf-viewer

A Lightweight PDF Viewer Android library which only occupies around 125kb while most of the Pdf viewer occupies up to 16MB space.

pothik-app

sarkerrabi

Config files for my GitHub profile.

sarkerrabi.github.io

sensormanager

Android Sensor Manager Library

simple-blog-android-application

socket.io-android-sample

Example with node.js server with Socket.io library and Java client application to send and receive webSocket messages

spacetablayout

Not actively maintained, but PR are welcomed

straiberry-charts

An awesome Chart library for android