Data Science

Cognitive Computing for Big Data – Coates

Machine Learning, Cognitive Computing, and Artificial Intelligence are heavily overloaded terms, and often treated as if they could be a "silver bullet" remedy to any problem in Big Data. Review what's feasible and what is less so in our Cognitive Computing for Big Data presentation. [ PDF | Video ]

Introduction to Machine Intelligence – Coates 

Scianta is happy to release this free training material that covers the basics for applying machine intelligence and cognitive computing concepts to big data sources. You'll learn quickly about data handling, anomaly detection, transaction behavior analysis, and impact analysis, without bogging down in the details of implementation.

  1. Intro to Machine Intelligence [ PDF | Video ]
  2. Data Handling part 1 [ PDF | Video ]
  3. Data Handling part 2 [ PDF | Video ]
  4. Anomaly Detection [ PDF | Video ]
  5. Transactional Behavior [ PDF | Video ]
  6. Impact Analysis [ PDF | Video ]

Principles of Cognitive Computing – Cox

White Paper

We're pleased to announce the release of Volume 1: Principles of Cognitive Computing by Earl Cox, Chief Scientist, Scianta Analytics. The first chapter of Dr. Cox's forthcoming book on cognitive computing, this is an excellent deep dive into the science underlying Scianta Analytics Conceptual Search and Semantic Reasoning engines.  While the subject matter is quite technical, Dr. Cox delivers it in the approachable, easy to understand style that has made him one of the most important authors and thought leaders in machine learning, fuzzy logic and cognitive computing. [ PDF ]

 

Behavior Based Anomaly Detection in Healthcare – Cox

White Paper

Conservative estimates of provider fraud (and abuse) - that is, fraud committed by doctors and other care givers - range between 10% and 12% of the roughly $650 billion spent annually on healthcare in the United States. Given the enormous amounts of money involved in the American healthcare industry, the shallowness of the regulatory oversight, the complexity of today's medical service protocols, and the relative ease with which abusive behaviors can be disguised or buried in the high transaction volumes processed by most insurers, it is easy to understand how abusive and ultimately fraudulent behavior can arise. Managed healthcare fraud is further complicated by the fragmentary nature of the patterns themselves - claims are dispersed across time and across many different insurance companies often renamed and defined according to different medical protocols so that no single insurer or oversight agency has a complete picture of a provider's activities. [ PDF ]

 

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