Showing 7 open source projects for "ui framework"

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  • 1
    iGesture is a Java-based gesture recognition framework providing access to multiple gesture recognition algorithms and different input devices. The iGesture Workbench supports the definition and evaluation of customised gesture sets and algorithms.
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  • 2
    RubyMoss is a Web - Based Framework written with Ruby on Rails, intended to support all analysis and data processing for Mossbauer Spectroscopy. Written under BSD License.
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  • 3
    LSG is a Windows application framework designed for visual data integration and based on Microsoft .NET 2.0 and CAB. An admin utility manages LSG apps, modules, events, and services, and a client integrates the modules and provides functionality hooks.
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  • 4
    Framework for storing and editing Forestry-related XML data, with a specialised graphical interface for each object type. Plug-in API for adding functionality. Coded in Java, uses WebStart for distribution, swing GUI, ~100k lines of code.
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  • 5
    This project provides a framework for modeling attribute-based objects in applications. (Think BeOS on an abstract level.) Uses Qt for UI and STL-like classes, and SQLite for the default backend.
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  • 6
    Entity Modeled Components (EMC) are visual and interactive components that are auto-generated for a given entity class. Tables, Lists, XML Data Stores, Data Entry Forms, Graph Charts are few of the planned components.
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  • 7
    ExoPlanet

    ExoPlanet

    GUI based toolkit for running common Machine Learning algorithms.

    ExoPlanet provides a graphical interface for the construction, evaluation and application of a Machine Learning model in predictive analysis. With the back-end built using the numpy and scikit-learn libraries, as a toolkit, ExoPlanet couples fast and well tested algorithms, a UI designed over the Qt4 framework, and graphs rendered using Matplotlib to provide the user with a rich interface, rapid analytics and interactive visuals. ExoPlanet is designed to have a minimal learning curve, allowing researchers to focus on the applicative aspect of Machine Learning rather than their implementation details. It provides algorithms for unsupervised and supervised learning, which may be done with continuous or discrete labels. ...
    Downloads: 0 This Week
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