A system and method are provided that use context awareness with device-dependent training to improve precision while reducing classification latency and the need for additional computing, such as by relying on cloud-based processing. Moreover, the following can leverage signal analysis with multiple sensors and secondary validation in a multi-modal approach to track passive events that would otherwise be difficult to identify using classical methods. In at least one implementation, the system and method described herein can leverage low power sensors and integrate already available human behavior in modular algorithms isolating specific context to reduce user interact time and training to a minimum.

Method and system of deploying a machine learning neural network (MLNN). The method comprises receiving a set of input features associated with data representative of a patient medical state at input layers of a trained MLNN, the trained MLNN comprising an output layer interconnected to the input layers via intermediate layers configured in accordance with an initial matrix of weights, a subset of the input features being activated responsive to a data sufficiency threshold reached in conjunction with deactivating, from the intermediate layers, a remainder of the input layers, the trained MLNN produced in accordance with adjusting the initial matrix of weights in diminishment of false positives in providing, at the output layer, a patient state diagnosis, and generating, at the output layer, a medical state diagnosis in accordance with the diminishment of false positives.

Systems and methods for malware detection using multiple neural networks are provided. According to one embodiment, for each training sample, a supervised learning process is performed, including: (i) generating multiple code blocks of assembly language instructions by disassembling machine language instructions contained within the training sample; (ii) extracting dynamic features corresponding to each of the code blocks by executing each of the code blocks within a virtual environment; (iii) feeding each code block into a first neural network and the corresponding dynamic features into a second neural network; (iv) updating weights and biases of the neural networks based on whether the training sample was malware or benign; and (v) after processing a predetermined or configurable number of the training samples, the neural networks criticize each other and unify their respective weights and biases by exchanging their respective weights and biases and adjusting their respective weights and biases accordingly.

A computer system and method for extending parallelized asynchronous reinforcement learning for training a neural network is described in various embodiments, through coordinated operation of plurality of hardware processors or threads such that each functions as a worker agent that is configured to simultaneously interact with a target computing environment for local gradient computation based on a loss determination and to update global network parameters based at least on local gradient computation to train the neural network through modifications of weighted interconnections between interconnected computing units as gradient computation is conducted across a plurality of iterations of a target computing environment, the loss determination including at least a policy loss term (actor), a value loss term (critic), and an auxiliary control loss. Variations are described further where the neural network is adapted to include terminal state prediction and action guidance.

An apparatus and a method for predicting traffic information are provided. The apparatus includes a storage to store a model for correcting traffic information for each traffic state in a road section, and a controller that determines the traffic state in the road section to be predicted based on K-means clustering algorithm, obtains a correcting value by using a model for correcting traffic information corresponding to the traffic state in the road section to be predicted, and corrects traffic information based on the obtained correcting value to predict real-time traffic information with higher accuracy.

A collaborative platform applies various machine learning techniques to correlate potential purchasers with high-value articles of property that may be of interest. Attributes, characteristics, preferences, and the like of a potential purchaser are scored against attributes and features of articles. The platform learns from interaction by the agents and the potential purchasers to become more attuned to the desires and lifestyle of purchasers and to gain more and more pertinent information from the listing agents regarding high-value articles, to ultimately to arrive at a better match between a high value article for sale and a likely purchaser.

A decision-making method for an agent action and a related device are provided and are used in the field of communication technologies. The method includes: a first agent processes first state information obtained from an environment through a first model, to obtain a first cooperation message; the first agent sends the first cooperation message to at least one second agent; the first agent receives second cooperation message sent by the at least one second agent; the first agent processes the first cooperation message and the second cooperation message through a second model, to obtain a first cooperation action performed by the first agent, where the second cooperation message is sent by the at least one second agent.

Methods, systems, and computer program products for automatically detecting outliers in federated data are provided herein. A computer-implemented method includes obtaining local outlier-related data from multiple client systems within a federated learning environment; detecting one or more federated learning environment-level outliers from at least a portion of the multiple client systems by processing at least a portion of the obtained local outlier-related data using one or more artificial intelligence models; determining at least one calibration parameter for detecting federated learning environment-level outliers based at least in part on the one or more detected federated learning environment-level outliers; and outputting the at least one determined calibration parameter to at least a portion of the multiple client systems within the federated learning environment.

Systems, methods, apparatuses, and computer-readable media for categorized text determination and organization are described. In one embodiment, an apparatus may include a processor and a memory storing instructions which when executed by the processor cause the processor to determine a plurality of contextual text elements in at least one text source, combine the plurality of contextual text elements, classify events associated with at least a portion of the plurality of contextual text elements, and determine text elements related to at least a portion of the contextual text elements. Other embodiments are described.

Embodiments can provide a computer implemented method for simulating transaction data using a reinforcement learning model including an intelligent agent, a policy engine, and an environment, the method including: providing standard customer transaction data representing a group of customers having similar transaction characteristics as a goal; and performing a plurality of iterations to simulate the standard customer transaction data, wherein the plurality of iterations is performed until a degree of similarity of simulated customer transaction data relative to the standard customer transaction data is higher than a first predefined threshold. In each iteration, the method includes: conducting, by the intelligent agent, an action including a plurality of simulated transactions; comparing, by the environment, the action with the goal; providing by the environment, a feedback associated with the action based on a degree of similarity relative to the goal; and adjusting, by the policy engine, a policy based on the feedback.