overviewadaptersapplicationscore toolsetscognitive operating systemsanomaly detection

core toolsets

Core Toolsets developed by third parties can be integrated seamlessly into the FOUNDATION Platform for building applications utilizing the Cognitive Operating System and Adaptors.

PyTorch is an open source machine learning library based on the Torch library that facilitates building deep learning projects used for applications such as computer vision and natural language processing. It is built to be flexible and modular for research, with the stability and support needed for production deployment.

http://pytorch.org

Tensor flow is an open source software library developed by Google. It is for numerical computation using data flow graphics. Tensor Flow was originally developed by researchers and engineers working on Google Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep learning research.

https://www.tensorflow.org/

Keras is a model-level library that provides high-level building blocks for developing deep learning models. Keras has two backend implementations available for TensorFlow and Theano.

https://keras.io/

OpenCV is an open source Computer Vision library adopted around the world with a community of more than forty-seven thousand users. Designed for computational efficiency and with a strong focus on real-time applications in the field of Computer Vision, it can be used for advanced robotics.

https://opencv.org/

NVIDIA VisionWorks toolkit is a software development package for Computer Vision and Image Processing.

https://developer.nvidia.com/embedded/visionworks

Apache Spark is an open source cluster-computing framework. Spark is a fast and general engine for large-scale data processing.

https://spark.apache.org/

Scikit-learn is a machine learning library that features simple and efficient tools for predictive data analysis  with various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN.

https://scikit-learn.org/

Video SDK

The video SDK consists of APIs for building FOUNDATION Video from the in-house detector and tracker for constructing video meta data from video sources.

Scada SDK

The SCADA SDK consists of APIs for building FOUNDATION SCADA from the in-house time series analysis, normalization, standardization, and entropy code.

Network SDK

The NETWORK SDK consists of APIs for building FOUNDATION Cyber from in-house normalization, standardization, and Statistic Centric Features for building network meta data from network traffic.

Machine Learning SDK

The MLE SDK consists of APIs for building the Patented Behavior Analytics and Patented Cognitive Analytics technology

Image SDK

The Image SDK consists of APIs for building FOUNDATION Image  for constructing image meta data from image sources.