We are pleased to announce a partnership with Fujitsu Laboratories, the R&D arm of Fujitsu Ltd.
Fujitsu Laboratories, the R&D arm of Fujitsu Ltd., developed a new graph AI technology that uses a novel tensor decomposition method called “Deep Tensor”. Deep Tensor leverages new tensor processing techniques and a deep neural network to automatically extract relevant features of graph data.
Fujitsu Laboratories has collaborated with customers across industry verticals to demonstrate that the Deep Tensor decomposition method results in high accuracy analytics for various types of graph data.
Additionally, Fujitsu Laboratories has integrated deep tensor technology with knowledge graph to provide additional explanation and basis behind AI-generated findings.
Applications of Deep Tensorinclude understanding diseases and cures based on genetic mutations, identifying fraudulent activities from banking transactions and extracting insights from social media data. An example in the area of network security where breaches at corporations have resulted in billions of dollars of losses and erosion of company reputation and prevention of these attacks and securing corporate networks from intruders is a business-critical priority for CEOs and CIOs. By storing the underlying network topology in a graph database and applying Deep Tensor companies are able stop brute force or denial of service attacks even when the underlying network patterns, which can be represented as graphs, are changing dynamically.
Larus continually grow and evolve. Fujitsu Partnership is part of our path to new growth. We expand and enhance our offer for our customers, thanks to a graph-based approach to artificial intelligence and by empowering AI technology with related context.
With Deep Tensor by Fujitsu we help organizations enhance the efficiency of their business operations by extracting actionable insights from connected data.
For the additional information on the joint offerings please contact us at email@example.com.
Please read the article available on Neo4j blog ⏩ https://bit.ly/2UO9eTC