Abstract: Methods, apparatus, and processor-readable storage media for generating test cases for software testing using machine learning techniques are provided herein. An example computer-implemented method includes obtaining user input data associated with at least one software application; identifying one or more predetermined types of information, including one or more of topic-related information and entity-related information, from at least a portion of the user input data using at least a first set of one or more machine learning techniques; generating one or more test cases for testing at least a portion of the at least one software application by processing at least a portion of the identified information using at least a second set of one or more machine learning techniques; and performing one or more automated actions based at least in part on at least one of the one or more generated test cases.

Abstract: Systems, computer-implemented methods, and/or computer program products are provided that facilitate integrating sustainability solutions into an organization's existing business model using artificial intelligence. A computer-implemented method comprises extracting, by a system comprising a processor, one or more objective functions of an enterprise system from defined business model data for the enterprise system, the one or more objective functions defining relationships between one or more processes of the enterprise system and one or more business objectives of the enterprise system.

Abstract: An apparatus comprises a processing device configured to determine specifications for an information technology asset to be developed, and to identify, utilizing at least one machine learning model, whether at least one of the specifications for the information technology asset is defect-prone, wherein a given specification is identified as defect-prone responsive to at least one output of the at least one machine learning model indicating that the given specification has at least a threshold likelihood of resulting in one or more defects during development of the information technology asset. The processing device is also configured to establish a mapping between the one or more identified defect-prone specifications for the information technology asset and one or more components of the information technology asset, and to modify one or more development processes for the information technology asset based at least in part on the established mapping.

Abstract: Methods, systems, and computer program products for generating context-aware process-based model determinations for greenhouse gas emissions from agricultural fields are provided herein.

Abstract: Methods, systems, and computer program products for enhancing spatial and temporal resolution of greenhouse gas emission estimates for agricultural fields using cohort analysis techniques are provided herein.

Abstract: A method, computer system, and a computer program product for improving environmental impact estimations is provided. The present invention may include obtaining data pertaining to an agricultural area. The present invention may include deriving one or more features from the data pertaining to the agricultural area. The present invention may include identifying one or more stubble burning areas within the agricultural areas based on the one or more derived features. The present invention may include determining an environmental impact for each of the one or more stubble burning areas.

Abstract: Methods, systems, and computer program products for generating GHG emissions estimations associated with logistics contexts using machine learning techniques are provided herein.

Abstract: Methods, systems, and computer program products for generating context-aware process-based model determinations for greenhouse gas emissions from agricultural fields are provided herein.

Abstract: Methods, systems, and computer program products for enhancing spatial and temporal resolution of greenhouse gas emission estimates for agricultural fields using cohort analysis techniques are provided herein.

Abstract: A device may include a secure processor and a secure memory coupled to the secure processor. The secure memory may be inaccessible to other device systems. The secure processor may store some keys and/or entropy values in the secure memory and other keys and/or entropy values outside the secure memory. The keys and/or entropy values stored outside the secure memory may be encrypted using information stored inside the secure memory.

Abstract: A device may include a secure processor and a secure memory coupled to the secure processor. The secure memory may be inaccessible to other device systems. The secure processor may store some keys and/or entropy values in the secure memory and other keys and/or entropy values outside the secure memory. The keys and/or entropy values stored outside the secure memory may be encrypted using information stored inside the secure memory.

Abstract: A device may include a secure processor and a secure memory coupled to the secure processor. The secure memory may be inaccessible to other device systems. The secure processor may store some keys and/or entropy values in the secure memory and other keys and/or entropy values outside the secure memory. The keys and/or entropy values stored outside the secure memory may be encrypted using information stored inside the secure memory.

Abstract: A device may include a secure processor and a secure memory coupled to the secure processor. The secure memory may be inaccessible to other device systems. The secure processor may store some keys and/or entropy values in the secure memory and other keys and/or entropy values outside the secure memory. The keys and/or entropy values stored outside the secure memory may be encrypted using information stored inside the secure memory.