Semantic Modeling
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Java Modeling Language (JML) v.1.0
The Java Modeling Language (JML) is a behavioral interface specification language that can be used to specify the behavior of Java modules (as in design by contract -- DBC).
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JPhysChem modeling tool v.1.0
JPhysChem - a mathematical modeling toolbox designed to provide an abstract layer and a comprehensive graphical front-end for general model building and specific modeling and data processing in the field of mass spectrometry and physical chemistry.
Object-Role Modeling v.1.0
The Object-Role Modeling (ORM) standard version 2, associated schemas and generation tools, and a reference implementation in the form of the Natural Object-Role Modeling Architect for Visual Studio (NORMA) product.
PySlice, parametric modeling controller v.1.6.4
Pyslice provides utility functions for parametric modeling.
S3DB - simple sloppy semantic database v.b3.5
S3DB - Simple Sloppy Semantic Database - is a PHP / mySQL,postgreSQL implementation of a local database infrastructure with a semantic architecture.
Semantic Graph Mediawiki Extension v.0.9.0
A mediawiki extension supplying a set of parser functions to generate and display graphviz, freemind and hypergraph graphs from the content of a semantic mediawiki, and parsing an uploaded freemind file.
Semantic Mapping Testing v.1.0
Testing tool for Semantic Bridges.
Semantic MediaWiki v.1.7.1
Semantic MediaWiki is a module to the MediaWiki software (powering Wikipedia), which extends the Wiki with ideas from the Semantic Web.
MeshMagic 3D Modeling Software Free v.1.10
With MeshMagic 3D, viewing and editing stl files has never been easier. Import STL files created with CAD and drafting software. You can then use MeshMagic to rotate and rescale the existing meshes.
AutoSummary Semantic Analysis Engine v.rc
AutoSummary uses Natural Language Processing to generate a contextually-relevant synopsis of plain text.
Java Neural Modeling Framework new GUI v.1.0
Program to performing the complete cycle of neural networks analysis: preparing data, choosing neural network (CasCor, MP, LogRegression, PNN), learning of network, monitoring learning state, ROC-analysis, optimization of network parameters using GA.