Systems for predicting communication settlement times across disparate networks store a first tier of a machine learning architecture comprising multiple machine learning models and an aggregation layer; store a second tier comprising rule sets for predicting settlement times; receive multiple data feeds corresponding to multiple communication data types; generate feature inputs based on the data feeds; input the feature inputs into the respective models to generate respective outputs; generate, using the aggregation layer, a third feature input based on the outputs; determine, based on the third feature input, a first rule set for predicting settlement times; receive a communication; predict a settlement time based on the first rule set; determine an aggregated communication load at a first time based on the settlement time; determine a performance availability requirement based on the load; determine a recommendation based on the performance availability requirement; and generate the recommendation based on the settlement time.

A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

An object recognition processing apparatus includes: an object fusion processing unit 6 for fusing detection information signals of objects existing around a vehicle, which are detected by a plurality of object detection units to output object fusion signals; a same object fusion result combination candidate selection processing unit for selecting, for a combination of object fusion signals, candidates of the object fusion signals to be combined; a same object fusion result combination determination processing unit for determining whether or not a combination of the selected object fusion signals is a combination of object fusion signals of the same object; and a same object fusion combination integration processing unit for recognizing the objects existing around the vehicle on the basis of a result of the determination.

An image processing apparatus includes a subject area detector and a subject area determinator. The subject area detector is configured to perform subject detection processing to detect a subject area from an input image. The subject area determinator is configured to determine a final subject area by majority decision processing that is based on the subject areas detected in the subject detection processing performed a plurality of times.

A vehicle receives sensor data from at least one of its sensors as it approaches an intersection and determines whether a traffic flow control device for the intersection is detected. When detected, a detected type, a detected state, or both of the traffic flow control device is determined. Using a type of the intersection, at least one of an existing type or an existing state of the traffic flow control device is determined, where the traffic flow control device is undetected or the detected type, the detected state, or both are determined with a detection confidence less than a defined level of detection confidence. The traffic flow control device is tagged with a label including its location and existing type, the existing state, or both within at least one control system for the vehicle. The vehicle is operated within vehicle transportation network using a control system that incorporates the label.

The present invention relates to a method for categorizing a moving object within a scene In particular, the present invention relates to a method specifically taking into account the three dimensional data within a scene for determining the location of and type of objects present within the scene. The method involves determining a first probability level for the type of object based on its size and shape, determining a second probability level for the type of object based on its relative speed, and defining the type of the object based on a combination of the first and the second probability level. The invention also relates to a corresponding system and a computer program product.

According to an embodiment, an information processing apparatus includes an attribute determiner and a setter. Each of acquired first sets indicates a combination of observation information indicating a result of observation of an area surrounding a moving body and position information. The attribute determiner is configured to determine, based on the observation information, an attribute of each of areas into which the area surrounding the moving body is divided, and to generate second sets, each indicating a combination of attribute information indicating the attribute of each area and the position information. The setter is configured to set, based on the second sets, reliability of the attribute of the area of a target second set, from correlation between the attribute of the area of the target second set and the attribute of corresponding areas each indicating the area corresponding to the target area in the areas of the other second sets.

A system performs operations including receiving pairs of sensor signals indicative of imaging of an environment, each pair of sensor signals including (i) a first sensor signal received from a first sensor and including first spatial coordinates and a first signal characteristic and (ii) a second sensor signal including second spatial coordinates and a second signal characteristic. The operations include identifying valid pairs of sensor signals, including determining that an address including the first coordinates of the first sensor signal of a given pair and the second coordinates of the second sensor signal of the given pair corresponds to an admissible address in a set of admissible addresses stored in a data storage. The operations include identifying, from among the valid pairs of sensor signals, pairs of sensor signals that satisfy a threshold, including determining that a value based on a combination of the first signal characteristic of a given pair and the second signal characteristic of the given pair satisfies a threshold value. The operations include generating a representation of a target in the environment based on the identified pairs of sensor signals that satisfy the threshold.

According to an embodiment, an information processing apparatus includes a memory having computer executable components stored therein; and a processing circuit communicatively coupled to the memory. The processing circuit acquires a plurality of pieces of observation information of surroundings of a moving body, generates a plurality of pieces of attribute information of the surroundings of the moving body on the basis of the plurality of pieces of observation information, and sets a reliability of the attribute information of the surroundings of the moving body on the basis of correlation of the plurality of pieces of attribute information.

Embodiments discussed herein facilitate building and/or employing model(s) for determining tumor prognoses based on a combination of radiomic features and pathomic features. One example embodiment can perform actions comprising: providing, to a first machine learning model, at least one of: one or more intra-tumoral radiomic features associated with a tumor or one or more peri-tumoral radiomic features associated with a peri-tumoral region around the tumor; receiving a first predicted prognosis associated with the tumor from the first machine learning model; providing, to a second machine learning model, one or more pathomic features associated with the tumor; receiving a second predicted prognosis associated with the tumor from the second machine learning model; and generating a combined prognosis associated with the tumor based on the first predicted prognosis and the second predicted prognosis.