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A long-polling Comet transport can be created by dynamically creating script elements, and setting their source to the location of the Comet server, which then sends back JavaScript (or JSONP) with some event as its payload. Each time the script request is completed, the browser opens a new one, just as in the XHR long polling case.
MOA is an open-source framework software that allows to build and run experiments of machine learning or data mining on evolving data streams. It includes a set of learners and stream generators that can be used from the graphical user interface (GUI), the command-line, and the Java API.
Software Creator Initial release Software license [a] Open source Platform Written in Interface OpenMP support OpenCL support CUDA support ROCm support [1] Automatic differentiation [2] Has pretrained models Recurrent nets Convolutional nets RBM/DBNs Parallel execution (multi node) Actively developed BigDL: Jason Dai (Intel) 2016 Apache 2.0 ...
Open-Source Deep-Learning Software for Java and Scala on Hadoop and Spark. Direct Web Remoting: library that enables Java on the server and JavaScript in a browser to interact and call each other as simply as possible. EclipseLink: Java persistence solution addressing relational, XML, and database web services. EHCache
LIBSVM – C++ support vector machine libraries; mlpack – open-source library for machine learning, exploits C++ language features to provide maximum performance and flexibility while providing a simple and consistent application programming interface (API) Mondrian – data analysis tool using interactive statistical graphics with a link to R
SystemDS: scalable machine learning; Tapestry: component-based Java web framework; Apache Tcl Committee Tcl integration for Apache httpd; Rivet: Server-side Tcl programming system combining ease of use and power; Websh: Websh is a rapid development environment for building powerful, fast, and reliable web applications in Tcl
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Machine learning can be used to combat spam, scams, and phishing. It can scrutinize the contents of spam and phishing attacks to attempt to identify malicious elements. [15] Some models built via machine learning algorithms have over 90% accuracy in distinguishing between spam and legitimate emails. [16]