Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Methods of predicting a fault in a diagnostic laboratory system include providing one or more sensors; generating data using the one or more sensors; inputting the data into an artificial intelligence algorithm, the artificial intelligence algorithm configured to predict at least one fault in the diagnostic laboratory system in response to the data; and predicting at least one fault in the diagnostic laboratory system using the artificial intelligence algorithm. Other methods, systems, and apparatus are also disclosed.

Abstract: A method and system are provided to infer and use machine learning algorithms that is applied to financial data and also to determine: (i) compliance with the applied algorithms, and (ii) whether any exceptions to and/or variations from the algorithms are within an organization's norms. A method includes: receiving financial data; applying a set of dynamic or interdependent algorithms to the received financial data to obtain a set of outcomes; and using a machine learning classifier to classify the outcomes as: (i) algorithm compliant, (ii) potentially algorithm non-compliant, or (iii) algorithm non-compliant. Additionally, the method includes generating controllership persona based actionable triggers, which on acceptance corrects the anomalies in financial data, enabling the method and system to perform as a comprehensive, contextual, consistent & machine learning powered, persona based real time controllership engine leading to reduced misstatements in reported financial data.

Abstract: Enterprise applications need to store and evaluate permissions on per User, per Entity and per Action basis for hundreds of Users and thousands of permissions. Most of the times this data takes up to 5 database tables to store the Role Based Access Control (RBAC) permissions. Selecting permissions for user from database consumes time while any User attempts to perform any Action. Sometimes the time taken to check permission is more than time taken to perform the required Action. Thus the current approaches for RBAC are inefficient in all—computation TIME, runtime MEMORY and database STORAGE. Binary arithmetic is known for being vast in scalability, smallest in memory and fastest in speed. This paper describes a new method which uses binary data structure and binary arithmetic to accurately check User permissions. We also claim that this method is the most scalable and fastest possible for Role Based Access Control.

Abstract: Enterprise applications need to store and evaluate permissions on per User, per Entity and per Action basis for hundreds of Users and thousands of permissions. Most of the times this data takes up to 5 database tables to store the Role Based Access Control (RBAC) permissions. Selecting permissions for user from database consumes time while any User attempts to perform any Action. Sometimes the time taken to check permission is more than time taken to perform the required Action. Thus the current approaches for RBAC are inefficient in all—computation TIME, runtime MEMORY and database STORAGE. Binary arithmetic is known for being vast in scalability, smallest in memory and fastest in speed. This paper describes a new method which uses binary data structure and binary arithmetic to accurately check User permissions. We also claim that this method is the most scalable and fastest possible for Role Based Access Control.

Abstract: The disclosure describes a method for automating the transformation data for business applications. The method involves a transformation engine using a data map between a set of physical entities corresponding to business objects in a source database instance and a corresponding set of physical entities in a target database instance. The data transformation engine using the data map generates automatically scripts to extract data from the source database instance to a set of intermediate tables, transform the data in the set of intermediate tables, and load the transformed data to the target database instance. The data map may be generated based on one or more of foreign key information and constraint information. The method also considers the dependencies of various business objects on each other and the sequence in which the business objects should be transformed.