Android machine learning libraries
An android library that uses technologies like artificial Intelligence, machine learning, and deep learning to make developers understand the content that they are displaying in their app.
The target is to set up a high quality and real-time image process and machine learning library which is implemented in pure java. The framework can run application on java desktop and android platform.
Android chatbot using: Dialogflow (Api.ai) for machine learning & NLP, AWS DynamoDB NoSQL Database, DynamoDB Object Mapper, AWS Cognito Identity. Android Architecture Components: Room Persistence Library, ViewModel, Live Data. MVVM Architecture Pattern
Android TensorFlow Lite Machine Learning Example.This is an example project for integrating TensorFlow Lite into Android application This project include an example for object detection for an image taken from camera using TensorFlow Lite library.
Android News Application With Weka Machine Learning Library
Used Weka library to benchmark Machine learning algorithms (SVM, LR, NBC, KNN) on Android
Desktop class machine learning library for Android & Android Wear
JCoil is an android application that through the Coil library, downloads images from urls and also labels them through Machine Learning (Firebase MLKit). 🖼🔬📥
Using JavaML library with Android to do simple machine learning.
A small machine learning library for Java/Android (weka inspired)
ListenMyBook is Android App that give user different experience when reading book using power of Machine Learning. Every user who using this app will have to take picture of book page and can listen what content of the book. ListenMyBook also give possibility to convert book page content into any language and listen the voice easily.
Sample app for Android's user, which demonstrate usage of our ActivitRecognition library (activity recognition based on machine learning and Tensorflow).
Basic Machine Learning Application integrated with GOOGLE VISION | FACE DETECTION LIBRARY
A QR and Barcode scanner for android mobile devices using Firebase Machine Learning Toolkit, Material design, MVVM architecture, Coroutines and Jetpack Libraries.
Android simple realtime machine learning labeler from camera using firebase ml-kit and fotoapparat's camera and frameprocessor library
An Android application for engineering thesis. It include open-source library SMILING with Machine Learning algorithms, to show the concept drift detection from stream module.
A simple code scanner app for Android built with latest CameraX Jetpack library and Google's on device Machine Learning SDK ML-KIt.
The Main idea of this application is to connect to open source data sites eg. (data.gov , data.worldbank.org,...) and visualize its data using MPAndroidChart Library https://github.com/PhilJay/MPAndroidChart and implement Machine Learning algorithms on it (further developlment).
FYP - Final Year Project - B.Eng in Mechatronics Engineering: Calorie-counting/diet-management Android application using ML image classifier and Bluetooth kitchen scale. This app uses the TensorFlow machine learning library, trained with the MobileNet image classification model, to detect food types. With the food classified, nutritional info is retrieved from the USDA nutritional info database, and the user can use a Bluetooth kitchen scale to record the weight value. This is used to calculate the calories and macronutrients of the food item or overall meal. Can be used with text-input for search and weight values also. Currently integrated with the "Skale 2" Bluetooth scale, but further support being added. Work in progress.
Android library for augmented reality effects using machine learning and computer vision on face from camera in real time.
Driver drowsiness is the most critical cause of road accidents so detection of drowsiness play a vital role in preventing road accidents. We are developing an android app that will alert drivers before an accident occurs. This will reduce the number of road accidents on a road. Drowsiness is a natural phenomenon that happens in human body due to different factors. Machine learning was applied to predict drowsiness and improve drowsiness prediction using facial recognition technology and eye-blink recognition technology. In this app, front camera will take a picture of drowsy driver then this picture will be taken as input. In processing the detected image, we are using OpenCV Library. OpenCV Library uses Haar Cascade Classifier for detection images such as eyes and face. Eyes and face will be the target in this system. This application will be implemented on Android Operating System. Drowsiness detection system will send alert to the driver when the driver feels asleep while driving a car, this can avoid accidents. Driver which is the user in this application, if they close their eyes within one second, the sensor which is the front camera in the smartphone will catch and process this event and then trigger the system to give voice alert to the user. Moreover, if the driver is willing to turn on back camera then it will detect the lane detection violation and will calculate the distance from the vehicle ahead of it. If the distance is too close, then it will generate an alarm. It will also generate an alarm if there is a violation of the lane on the road.
An android app built using compose that makes use of various machine learning kits , this repo shows you examples of how to implement various MLKit features, you can also use our libraries directly in your project
Android application for detecting objects || Using machine learning libraries || camera usage || gallery || image taking from emulator
Building "Face Detection" Android App in Android Studio, using Java programming language and "Machine Learning" implementation using the "ML Kit" - "Face Detection" library,
Building the "Language Translator" Application in Android Studio, using Java language and Machine Learning implementation through ML-Kit Translation library
Building an "Image Labeling ML Kit" Android App in Android Studio IDE, using Java programming language and "Machine Learning" implementation with the "Firebase ML-Kit" - "Image Labeling" library.
A demo of all the Machine Learning operations that are possible to do on-device in an android phone with the help of ML-Kit library.
