Dell Products Inc.

Arnab Chowdhury

Ramakanth Kanagovi

Sumant Sahoo

A system, method, and computer-readable medium are provided for a hardware component failure prediction system that can incorporate a time-series dimension as an input while also addressing issues related to a class imbalance problem associated with failure data. Embodiments utilize a double-stacked long short-term memory (DS-LSTM) deep neural network with a first layer of the DS-LSTM passing hidden cell states learned from a sequence of multi-dimensional parameter time steps to a second layer of the DS-LSTM that is configured to capture a next sequential prediction output. Output from the second layer is combined with a set of categorical variables to an input layer of a fully-connected dense neural network layer. Information generated by the dense neural network provides prediction of whether a hardware component will fail in a given future time interval.

Real-time predictive maintenance of hardware components using a stacked deep learning architecture on time-variant parameters combined with a dense neural network supplied with exogeneous static outputs

A method, system and computer readable medium for performing a purchase prediction operation. The purchase prediction operation includes: selecting a target purchaser, the purchase prediction operation providing a purchase prediction for the target purchaser; capturing a product term associated with a most recent purchase period of the target purchaser; performing a sequential recommendation operation, the sequential recommendation operation providing a sequence recommendation score; and, generating a purchase pattern prediction for the target user based upon the sequential recommendation score.

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Bengaluru, India

Arnab Chowdhury