Systems and methods for determining infectious disease states are provided. An ensemble classifier is obtained using a training dataset including labels and attribute values for a plurality of genes including at least 20 genes selected from one or more of Table 1, Table 2, Table 8, and Table 9. For each of a plurality of random seeds, initial classifiers with pseudo-randomly assigned hyperparameters are binned and downsampled using evaluation scores obtained from one or more iterations of K-fold cross-validation. The ensemble classifier is formed from initial classifiers with the best score for each random seed. Infectious disease states are determined for a test subject by inputting attribute values for the plurality of genes to a trained ensemble classifier. Compositions and kits for determining infectious disease states, including amplification primers for the plurality of genes, are further provided.

A method and system is disclosed for creating a machine learning model that is reconfigurable. A fixed parameter model is created to include fixed feature values obtained during a training process for the machine learning model. The fixed parameter model may include a fixed base classifier used by the machine learning model to classify objects detected by an ultra-sonic system within a vicinity of a vehicle. A configurable parameter model may be created to include feature values that are different from the fixed feature values, the configurable parameter model including a modified base classifier. A vehicle controller may receive and update the fixed parameter model with the configurable parameter model. The machine learning model may be updated to use the configurable parameter model to classify the objects detected by the ultra-sonic system.

Methods, computer program products, and/or systems are provided that perform the following operations: setting a memory buffer having contiguous memory blocks; obtaining a decision tree comprising nodes including split nodes and leaf nodes, wherein each of the split nodes includes at least two child nodes that are ordered according to a likelihood of accessing a child node after each of the split nodes; mapping the nodes onto respective blocks of the memory blocks, each of the memory blocks storing attributes of a corresponding one of the nodes, wherein each of the split nodes and any child nodes of each split node are mapped onto successive blocks, wherein ordered child nodes of a same one of the split nodes are mapped onto successive blocks; executing the nodes by processing the attributes of the nodes as accessed from the memory according to an order of the memory blocks in the memory buffer.

There is provided a system and method for traffic signal control of a traffic network with a learned model. The method including: receiving sensor readings from the traffic network, the sensor readings including positions and speeds of vehicles approaching each intersection; using a learned dynamics model that takes the sensor readings as input, predicting a plurality of possibilities for position and velocity of the vehicles approaching each intersection in a future timestep; determining a action for the one or more intersections by performing a tree search on the plurality of possibilities and selecting the possibility with a highest action value; and outputting the action to the traffic network for implementation as a traffic control action at the one or more intersections.

According to embodiments of the present disclosure, a method, an apparatus, a device, and a computer-readable storage medium for determining a category of an entity are provided. The method includes: based on a suffix of the entity, obtaining a suffix feature associated with the suffix; determining one or more candidate categories of the entity based on a name of the entity; and determining a set of categories of the entity based on the one or more candidate categories and the suffix feature.

An AI-based process monitoring system access a plurality of data sources having different data formats to collect and analyze KPI data and shortlist KPIs that are to be used for determining the impact of automation of an automated process or sub-process. Information regarding an automated process is received and KPIs associated with the process and sub-processes of the process are identified. The identified KPIs are put through an approval process and the approved KPIs are presented to a user for selection. The user-selected KPIs are evaluated based on classification, ranking and sentiments associated therewith. The evaluations are again presented to the user along with a set of questionnaires wherein each of the questions has a dynamically controlled weight associated therewith. Based at least on the weights and user responses, a subset of the evaluated KPIs are shortlisted for use in evaluating the impact of process automation.

A learning device includes: a learning unit configured to read out feature amounts of learning data from a data memory and derive a branch condition for a node of a decision tree based on the feature amounts, to perform learning of the decision tree; and a discriminator configured to perform determining, in accordance with the branch condition, a node to which learning data is to be branched from the node corresponding to the branch condition. The learning unit is configured to, in parallel with processing of the discriminator reading out learning data at a specific node from the data memory via a first port of the data memory and performing the determining, read out, from the data memory via a second port, learning data at a node on which the discriminator is configured to perform determining subsequent to the specific node and derive the branch.

The herein disclosed technology provides methods and systems that utilize machine learning solutions to identify web-based channels that are sources pirated copyright material, such as by using a machine learning classifier that is trained on historical copyright piracy data and channel features that may be determined and analyzed for each of a collection of channels without analyzing video or audio content of the channel.

A system is configured for determining a technology stack in a software application to perform a work project. The system receives and evaluates the work based on its characteristics. A plurality of technology stacks is generated by implementing different combinations of technology stack components. The technology stack components include application servers and webservers. Each of the technology stacks is simulated performing the work project. Based on the simulation results of each technology stack, a performance of each technology stack is evaluated. The system identifies a first technology stack performing at a level higher than a performance threshold and at a highest performance level among the plurality of technology stacks. The system deploys the first technology stack in the software application to perform the work project.

A system includes a server associated with a resource utilization, a database storing historical data including resource utilization values over a first time period, and a processor. The processor identifies, from the historical data, a maximum resource utilization value and determines a duration of time for which the resource utilization exceeds a percentage of the maximum. The processor predicts, based on the historical data, a maximum predicted resource utilization value over a second time period, later than the first. The processor also determines, based on the historical data, an upper bound of a resource utilization confidence interval. The processor generates, based on the maximum value over the first time period, the duration of time, the maximum predicted value over the second time period, and the upper bound, a recommendation to consolidate the server with a second server and/or to release computational resources. The processor transmits the recommendation to an administrator.