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LeanData is a Demand Management solution that supports all go-to-market strategies such as account-based sales development, geo-based territories, and more. LeanData features a visual, intuitive workflow native to Salesforce that enables users to view their entire lead flow in one interface. LeanData allows users to access the drag-and-drop feature to route their leads. LeanData also features an algorithms match that uses multiple fields in Salesforce.
PREESM provides a complete framework for fast prototyping. It generates a simulation and an optimized multiprocessor code from graph descriptions of algorithm and architecture. Please visit the project homepage at http://preesm.sourceforge.net.
ProViz is a program visualization framework for Java. It is built as a plugin to the Eclipse IDE. (We are getting close to publishing resources and making this project distributable. Stay tuned!) A video demo: http://www.youtube.com/watch?v=BsoK1n-OM
Supply chain managers, executives, and businesses seeking AI-powered solutions to optimize planning, operations, and decision-making across the supply
Logility is a market-leading provider of AI-first supply chain management solutions engineered to help organizations build sustainable digital supply chains that improve people’s lives and the world we live in. The company’s approach is designed to reimagine supply chain planning by shifting away from traditional “what happened” processes to an AI-driven strategy that combines the power of humans and machines to predict and be ready for what’s coming. Logility’s fully integrated, end-to-end platform helps clients know faster, turn uncertainty into opportunity, and transform the supply chain from a cost center to an engine for growth.
G.A.V. (Graph Algorithm Visualizer) is a tool that visualizes algorithms from graph theory. A step-by-step visualization from each different algorithm allows the user to understand the particular algorithm very easily.
gaKnn(Genetic Algorithm Optimized K Nearest Neighbor Classification framework) is a frameowork for KNN optimization with a genetic algorithm. The genetic algothm used for this is JGAP (http://jgap.sourceforge.net/).