Showing 357 open source projects for "artificial intelligence java source code"

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  • 1
    ART - Artificial Reasoning Toolkit
    Java library devoted to handle Genetic Algorithms and Classifier Systems. It has been engineered to be used into agent based simulation models and to search bounded optimal solutions in wide solution spaces. It runs on distributed clusters.
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  • 2

    AraRooter

    Find Arabic Root Word

    Using Machine Learning, AraRooter finds the three-lettered root of any Arabic lemma with around 84% accuracy.
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  • 3
    Spec is a voice control based on the libraries of Sphinx-4.
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  • 4
    UnBFuzzy is an API for fuzzy-logic and learning algorithms for fuzzy systems.
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  • 5

    Cinefile

    A category-based approach to exploring film data.

    Cinefile is a prototype of a category-based method of database exploration. It allows the user to identify abstract categories of films by providing examples of category members, learns to classify films as belonging or not belonging to those categories, and provides a graphical interface for exploring and comparing categories. Cinefile is designed to work with data retrieved from the Internet Movie Database (imdb.com). This data is used for classification and is the subject of the...
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  • 6
    Monk Computer Vision

    Monk Computer Vision

    A low code unified framework for computer vision and deep learning

    Monk is an open source low code programming environment to reduce the cognitive load faced by entry level programmers while catering to the needs of Expert Deep Learning engineers. There are three libraries in this opensource set. - Monk Classiciation- https://monkai.org. A Unified wrapper over major deep learning frameworks. Our core focus area is at the intersection of Computer Vision and Deep Learning algorithms. - Monk Object Detection - https://github.com/Tessellate-Imaging...
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  • 7

    Supertagger

    Software for assigning supertags.

    Supertagging is a process of statistical lexical disambiguation, preprocessing step to parsing, which assigns LTAG tree categories to the lexical items present in the input sentence. Thus, if the input sentence is in the form of a dependency tree, the task of the supertagger is to assign the most probable TAG family to each node and edge in the dependency tree.
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