The present application includes a method and system for real-time predictive scheduling. The system receives information from at least one workload input and at least one personnel input, calculating an initial schedule based on the information and on analytics rules in a scheduling analytics engine. The system then allocates incoming workloads to customer service representatives according to the initial schedule, while monitoring adherence to the initial schedule by calculating deviation from schedule adherence. If the deviation from schedule adherence exceeds an acceptable deviation from schedule adherence within the analytics rules, the system calculates an updated schedule.

Machine learning systems for predictive targeting and optimizing engagement are described herein. In various embodiments, the system includes 1) training a first machine learning computer model to generate machine predicted outcomes; (2) determining weights based on the machine predicted outcomes; (3) generating a second machine learning computer model based on the weights; and (4) generating machine learned predictions for candidates.

The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

Systems and methods of inferential user matching include inferring an interest in matching between the first user and the second user based at least in part on a first profile of a first user and a second profile of a second user. Based at least in part on the inferred interest in matching, the systems and methods match the first user and the second user for a service, and transmit (i) a first representation of the first user to a portable device of the second user, and (ii) a second representation of the second user to a portable device of the first user.

Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.

A method for creating a patient-specific surgical plan includes receiving one or more pre-operative images of a patient having one or more infirmities affecting one or more anatomical joints. three-dimensional anatomical model of the one or more anatomical joints is created based on the one or more pre-operative images. One or more transfer functions and the three-dimensional anatomical model are used to identify a patient-specific implantation geometry that corrects the one or more infirmities. The transfer functions model performance of the one or more anatomical joints as a function of anatomical geometry and anatomical implantation features. surgical plan comprising the patient-specific implantation geometry may then be displayed.

A system, a method and a non-transitory computer readable storage medium for base calling are described. The base calling method includes processing through a neural network first image data comprising images of clusters and their surrounding background captured by a sequencing system for one or more sequencing cycles of a sequencing run. The base calling method further includes producing a base call for one or more of the clusters of the one or more sequencing cycles of the sequencing run.

A non-transitory computer-readable recording medium storing a risk analysis program for an artificial intelligence (AI) system, the analysis program being a program for causing a computer to execute processing, the processing including: acquiring a plurality of pieces of relational information that include at least two attributes among an attribute of a type of an object person, an attribute of a type of processing, and an attribute of a type of data, wherein the relational information is determined on a basis of a configuration of the AI system; determining a priority of the plurality of pieces of relational information on a basis of the attribute of the type of the object person; and outputting one or a plurality of check items selected on a basis of the determined priority from among a plurality of check items associated with each attribute as a checklist for the AI system.

Systems and methods for scoring and visualizing the effects of variants in biological sequences. Variants may include substitutions, insertions and deletions. The method comprises encoding biological sequences as vector sequences and then operating a neural network in the forward-propagation mode and possibly in the back-propagation mode to compute variant scores. Variant scores are determined by normalizing the gradients. Variant scores may be used to select a subset of variants, which are then used to produce modified vector sequences which are analyzed by the neural network operating in forward-propagation mode, to determine improved variant scores. The variant scores may be visualized using black and white, greyscale or colored elements that are arranged in blocks with dimensions corresponding to different possible symbols and the length of the sequence. These blocks are aligned with the biological sequence, which is illustrated by a symbol sequence arranged in a line.

An ultrasound imaging apparatus and a control method thereof. The ultrasound imaging apparatus may include: a display; a communication unit; and a processor configured to be operatively connected to the display and the communication unit. The processor may obtain a first ultrasound image of a subject and a result of an analysis of the first ultrasound image. The processor may also control the display to display a user interface, which allows selection of an operating mode of the ultrasound imaging apparatus, based on the result of the analysis.