An artificial intelligence assisted medical diagnosis method for a sepsis is proposed. A database reading step is performed to read a sepsis database and at least one database to be tested of a storing unit. The sepsis database includes a plurality of sepsis data, and the at least one database to be tested includes a plurality of data to be tested. A data table creating step is performed to create a sepsis data table according to the sepsis data, and create a data table to be tested according to the data to be tested. A model training step is performed to train the sepsis data table according to a K-fold cross-validation and a machine learning algorithm to generate a sepsis diagnosis model. A sepsis predicting step is performed to input the data table to be tested into the sepsis diagnosis model to calculate a sepsis prediction result.

A three-dimensional (3D) printer includes a nozzle and a camera configured to capture a real image or a real video of a liquid metal while the liquid metal is positioned at least partially within the nozzle. The 3D printer also includes a computing system configured to perform operations. The operations include generating a model of the liquid metal positioned at least partially within the nozzle. The operations also include generating a simulated image or a simulated video of the liquid metal positioned at least partially within the nozzle based at least partially upon the model. The operations also include generating a labeled dataset that comprises the simulated image or the simulated video and a first set of parameters. The operations also include reconstructing the liquid metal in the real image or the real video based at least partially upon the labeled dataset.

Aspects of the disclosure relate to using ultrasonic or other types of signals to detect a driver in a vehicle. A computing platform may receive ultrasonic sensing data associated with mobile devices in the vehicle from a signal transmitter. Unique identifiers of the mobile devices may be determined. Based on the ultrasonic sensing data and the unique identifier, a relative distance from the signal transmitter to each mobile device in the vehicle may be determined. The computing platform may use a machine learning classifier to determine that a particular occupant is a driver in the vehicle based on the relative distance.

A document management system analyzes document clauses using document clause clusters. The document management system uses measures of similarity between document clauses from different documents to assign clauses to clause clusters. Clause clusters may be used to perform various analyses, such as to assign clauses a classification corresponding to a relevant clause cluster. The document management system provides analyses performed using document clause clusters for user review, such as to approve clause clusters, classify clause clusters, modify clause clusters, or some combination thereof.

Systems and methods for adaptive verification may include a memory and a processor. The memory may be configured to store a plurality of animation templates. The processor may be configured to perform a first challenge process to request a first user image from a first predetermined distance, receive the first user image, request a second user image from a second predetermined distance, receive the second user image, transmit the first user image and the second user image for a verification process, the verification process including identification of one or more user attributes, receive a third user image associated with the one or more user attributes identified during the verification, and display the third user image including an adaptation, wherein the adaptation is generated for at least one of the plurality of animation templates, the adaptation illustrating the one or more user attributes.

Methods, apparatus, systems, and articles of manufacture are disclosed. An example apparatus comprises at least one memory, instructions, and processor circuitry to execute the instructions. The processor circuitry executes the instructions to provide a neural network a plurality of raw bytes for malware classification. The processor circuitry executes the instructions to generate a visualization of features extracted from the plurality of raw bytes. The processor circuitry executes the instructions to generate a heatmap for the plurality of raw bytes based on gradient activations of the neural networks. The processor circuitry executes the instructions to perform a dimensionality reduction based on features of the plurality of raw bytes identified in the heatmap.

Systems, computer-implemented methods, and computer program products that facilitate temporalizing and/or spatializing a machine learning and/or artificial intelligence network are provided. In various embodiments, a processor can combine output data from different layers of an artificial neural network trained on static image data. In various embodiments, the processor can employ the artificial neural network to infer an outcome from an image instance in a sequence of images based on combined output data from the different layers of the artificial neural network.

Systems and methods for site-based calibration of object detection values, such as for surveillance video cameras, are described. Video data from a video image sensor may be processed using an object detector to determine object data and a confidence score for a detected object. The object data and confidence score may be post-processed to apply calibration values based on the camera location to one or more parameters used for determining detection events. Event notifications may be sent for detection events. The calibration values may be determined from a calibration period where a verification object detector is used to verify the object detections and failure analysis is applied to determine calibration values for the camera location.

Techniques regarding inferring parameters of one or more mechanistic models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a machine learning component that can identify a causal relationship in a mechanistic model via a machine learning architecture that employs a parameter space of the mechanistic model as a latent space of a variational autoencoder.

Described herein are systems and methods for determining if a vehicle is parked. In one example, a system includes a processor, a sensor system, and a memory. Both the sensor system and the memory are in communication with the processor. The memory includes a parking determination module having instructions that, when executed by the processor, cause the processor to determine, using a random forest model, when the vehicle is parked based on vehicle estimated features, vehicle learned features, and vehicle taillight features of the vehicle that are based on sensor data from the sensor system.