toquoccuong,github

Java library for integrating the map into software and services with state-of-the-art online and offline map matching that can be used stand-alone and in the cloud.

better-modelling-of-aircraft-taxi-movements

It is a fact that the aviation industry is expected to develop remarkably in near future. Consequently, delays and environmental problems are caused, due to aircraft congestion in the airports. Thus, it is necessary for the airports to use the existing infrastructure efficiently and generate some changes in the system and the way airports are operated. For this reason, the prediction of aircraft taxi time is substantial in order to help airports understand what is necessary to change so as to optimise their efficiency and reduce the aircraft taxi time. This project concerns Manchester’s airport and data about the aircrafts’ features and external factors were given in order to predict taxi time. This machine learning project was following the CRISP-DM process for data mining. All the processes were handled on Python, as it provides Pandas library which creates a useful data frame that provides an easy way to handle and modify the data. Moreover, the scikit-learn library was used for the machine learning was used for the machine learning procedure, by providing all the algorithm that are necessary for this problem. The machine learning algorithms that were applied are Linear regression, Polynomial Regression, Random Forests and Multilayer Perceptrons. The examination of algorithms that were applied showed that the most suitable for the project is Polynomial regression, because it provides the most precise and accurate prediction of taxi time with accuracy equal to 79.94%. Furthermore, it was noted the importance of each variable as two datasets were applied (one has two extra variables) and the variables were ranked regarding the variable selection technique that was used

big-data-benchmark-for-big-bench

Big Bench Workload Development

bigdl

BigDL: Distributed Deep Learning Library for Apache Spark

bingoo

BinGoo! A Linux bash based Bing and Google Dorking Tool

cdr-stats

Call Analytics Solution for Freeswitch, Asterisk, Kamailio and other VoIP Switches

cdrf

CDRF (Call Detail Record Forecasting): A multitask learning architecture using deep learning networks for mobile traffic forecasting

datasploit

An #OSINT Framework to perform various recon techniques on Companies, People, Phone Number, Bitcoin Addresses, etc., aggregate all the raw data, and give data in multiple formats.

dbscan-on-spark

An implementation of DBSCAN runing on top of Apache Spark

deep-learning-papers-reading-roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

deepspeech

A TensorFlow implementation of Baidu's DeepSpeech architecture

deepspeech.pytorch

Speech Recognition using DeepSpeech2.

distributed-graph-analytics

Distributed Graph Analytics (DGA) is a compendium of graph analytics written for Bulk-Synchronous-Parallel (BSP) processing frameworks such as Giraph and GraphX. The analytics included are High Betweenness Set Extraction, Weakly Connected Components, Page Rank, Leaf Compression, and Louvain Modularity.

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