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irmansyah's Projects

android-kotlin-mvp-architecture icon android-kotlin-mvp-architecture

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

android-mvp-architecture icon android-mvp-architecture

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

awesome-flutter icon awesome-flutter

An awesome list that curates the best Flutter libraries, tools, tutorials, articles and more.

axion-technologies-hnh icon axion-technologies-hnh

Hotter’n Hell Hundred is the largest one hundred mile cycling events in the United States and the world. Held in Wichita Falls, TX, this event brings thousands of registrants within the city limits, resulting in a spike in revenue throughout local businesses, restaurants, and hotels, and overall for the city of Wichita Falls. There are several sub-events within the Hotter’n Hell Hundred, such as different races, dining events, consumer shows and a medical symposium. The proposed Hotter’n Hell Hundred mobile application serves as the ultimate source of information for the events, enabling users to quickly register for events and explore local places. In addition, the mobile application allows users to navigate through race during the event, tracking their speed, nearby rest stops, alternate routes, and finish line, ensuring the best possible experience during their stay in Wichita Falls.

fasthub icon fasthub

FastHub the ultimate GitHub client for Android.

flexygrid icon flexygrid

this is repo is pinch flexible grid for recyclerview

horizonnet icon horizonnet

Pytorch implementation of HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation.

human-activity-recognition-with-neural-network-using-gyroscopic-and-accelerometer-variables icon human-activity-recognition-with-neural-network-using-gyroscopic-and-accelerometer-variables

The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.

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