Intel Corporation

Kalpana A Algotar

Rita Chattopadhyay

Addicam V Sanjay

Shao-Wen Yang

Michael C Wu

Ravindra V Narkhede

Ali Ashrafi

Hoang Tran Van

Guang-He Lee

Daniel Gutwein

Amith Harsha

John Pilkin

A disclosed example method to predict an injury for a target player on a target date includes determining a first probability of injury of the target player based on probabilities of injuries of second players having similarities with the target player; determining a second probability of injury of the target player based on injuries of the target player; determining a third probability of injury of the target player based on the first probability of injury of the target player and the second probability of injury of the target player; and generating, by executing an instruction with the processor, a report of a predicted probability of injury of the target player for the target date based on the third probability of injury of the target player.

Methods and apparatus to predict sports injuries

Systems, apparatuses and methods may provide for technology that assigns confidence levels to data bins containing similarity data and length of stay data, wherein the similarity data and the length of stay data correspond to a plurality of previous admissions. Additionally, the confidence levels may be weighted based on a distribution metric that assigns higher weights to denser regions. A length of stay of a target admission may be predicted based on the weighted confidence levels.

Automatic prediction of patient length of stay and detection of medical center readmission diagnoses

A set of samples are returned by radio frequency identifier (RFID) reader corresponding to the readings of signals emitted from a particular RFID tag, each sample including a respective set of features identifying values of the attributes of the signals as detected. At least some of the features are provided as inputs to a random forest of decision trees, each providing a prediction that the particular RFID tag is located in one of a plurality of defined zones in a particular environment. From outputs of the plurality of decision trees based on the set of samples, it can be determined that the particular RFID tag is located in a particular one of the plurality of zones at a first instance in time.

RFID location detection

In various embodiments, an RFID Antenna/Tag Location Configuration device (RLC) may facilitate placement of one or more RFID antennas in a physical space. The RLC may collect RFID data from tags determine which of the RFID antennas need to be relocated. The RLC may determine, based on collected RFID data, whether each antenna is a dominant antenna and/or has a substantial read rate. If an antenna is not dominant and/or does not exhibit a substantial read-rate, the RLC ma indicate that the antenna should be relocated. The RLC may also be configured to filter collected RFID data prior to using the data for determination of antennas. The RLC may also determine, using the RFID antennas, a physical location of RFID tags in the physical space using detected signal strength for RFID tags. Additional embodiments may be described and/or claimed.

RFID antenna re-location and/or RFID location

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Chandler, AZ, United States of America

Kalpana A Algotar