Abstract: Streaming data channels are configured to inject time series-based data sets into a private cloud platform at which complex event processing engines implementing a recurrent neural network using a Nonlinear Autoregressive Moving-Average (NARMA) model identifies patterns in the data sets to continuously determine future/predictive data points for each data set. The dynamic future/predictive data points/values serve as inputs to machine learning engines, which implement multi variate regression analysis to derive a dynamic variable value for a mechanism.

Abstract: Systems, computer program products, and methods are described herein for implementing hybrid cloud computation engines using infrastructure optimization. The present invention may be configured to identify, from a group of available infrastructures, an infrastructure to perform a computing request, where the group of available infrastructures includes an enterprise infrastructure and one or more cloud provider infrastructures. The present invention may be further configured to provide, to the identified infrastructure and via a blockchain distributed ledger, the computing request and input data for the computing request, where the computing request includes a request to generate, using a recurrent neural network, based on data in the blockchain distributed ledger, and based on the input data, output data. The present invention may be further configured to receive, from the identified infrastructure and via the blockchain distributed ledger, the output data.

Abstract: Systems, computer program products, and methods are described herein for implementing hybrid cloud computation engines using infrastructure optimization. The present invention may be configured to identify, from a group of available infrastructures, an infrastructure to perform a computing request, where the group of available infrastructures includes an enterprise infrastructure and one or more cloud provider infrastructures. The present invention may be further configured to provide, to the identified infrastructure and via a blockchain distributed ledger, the computing request and input data for the computing request, where the computing request includes a request to generate, using a recurrent neural network, based on data in the blockchain distributed ledger, and based on the input data, output data. The present invention may be further configured to receive, from the identified infrastructure and via the blockchain distributed ledger, the output data.

Abstract: Embodiments of the present invention provide a system for an intelligent and adaptive real time valuation engine. The described system receives a plurality of input data from data pipelines, the input data including a request for an available transaction value given certain parameters. The system provides the plurality of input data into a long short term memory neural network engine for the identification of dependencies between data characteristics and transaction values, providing a continuous stream of predicted transaction for multiple nodes of the neural network engine. The system then assigns weighting to the outputs of the neural network engine, and determines the available transaction values based on these weighted outputs.

Abstract: Methods and systems for a crypto-machine learning enabled blockchain based profile pricer are described herein. In one example, computer-readable instructions are stored in memory, and one or more processors execute the instructions to determine requested information for a user that can be displayed on a user interface in real-time or near real-time of the user's request. In addition, the provided data can be customized for the user based on a user profile stored in the memory as well as based on third party data stored in a database of related information. The data is communicated to the user using blockchains. At least some advantages of such an arrangement are providing requested data to a user, for display on a user interface, in a transparent, secure, and timely manner.

Abstract: A crypto-machine learning enabled blockchain based profile pricer processes computer-readable instructions to determine requested information for a user that can be displayed on a user interface in real-time or near real-time of the user's request. In addition, the crypto-machine learning enabled blockchain based profile pricer processes provided data to be customized for the user based on a user profile stored in the memory as well as based on third party data stored in a database of related information. The pricer communicates the data to the user using blockchains via a smart contract using a private key and initiates a data transfer based on an input received from the user.

Abstract: A crypto-machine learning enabled blockchain based profile pricer processes computer-readable instructions to determine requested information for a user that can be displayed on a user interface in real-time or near real-time of the user's request. In addition, the crypto-machine learning enabled blockchain based profile pricer processes provided data to be customized for the user based on a user profile stored in the memory as well as based on third party data stored in a database of related information. The pricer communicates the data to the user using blockchains via a smart contract using a private key and initiates a data transfer based on an input received from the user.

Abstract: Methods and systems for a crypto-machine learning enabled blockchain based profile pricer are described herein. In one example, computer-readable instructions are stored in memory, and one or more processors execute the instructions to determine requested information for a user that can be displayed on a user interface in real-time or near real-time of the user's request. In addition, the provided data can be customized for the user based on a user profile stored in the memory as well as based on third party data stored in a database of related information. The data is communicated to the user using blockchains. At least some advantages of such an arrangement are providing requested data to a user, for display on a user interface, in a transparent, secure, and timely manner.

Abstract: Methods and systems for a crypto-machine learning enabled blockchain based profile pricer are described herein. In one example, computer-readable instructions are stored in memory, and one or more processors execute the instructions to determine requested information for a user that can be displayed on a user interface in real-time or near real-time of the user's request. In addition, the provided data can be customized for the user based on a user profile stored in the memory as well as based on third party data stored in a database of related information. The data is communicated to the user using blockchains. At least some advantages of such an arrangement are providing requested data to a user, for display on a user interface, in a transparent, secure, and timely manner.