Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining target application container configuration data to identify one or more target container base image referenced in the target application container configuration: subjecting script data associated to the one or more target container base image to text based processing for evaluation of security risk associated to the one or more container base image, the script data obtained from at least one candidate hosting computing environment; and selecting a hosting computing environment from the at least one computing environment for hosting the target application container, the selecting in dependence on the text based processing.

Abstract: A method, a computer system, and a computer program product for a multi-dimension artificial intelligence (AI) agriculture advisor is provided. Embodiments of the present invention may include creating a user profile. Embodiments of the present invention may include preparing and transforming the external data. Embodiments of the present invention may include conducting a hypothesis on the transformed data. Embodiments of the present invention may include validating the transformed data. Embodiments of the present invention may include training an artificial intelligence (AI) model based on the transformed data. Embodiments of the present invention may include validating and retraining the artificial intelligence (AI) model. Embodiments of the present invention may include matching the user data with the artificial intelligence (AI) model. Embodiments of the present invention may include ranking results based on the matching the user data with the artificial intelligence (AI) model.

Abstract: A system can evaluate multiple candidate scripts. The system receives a problem statement and a sample solution script. The system selects an additional script based on the sample solution script, and compiles a list of candidates including the sample and additional scripts. Then, for each of the candidates, the system simulates execution of the script and scores performance of the script. The system then presents results of the execution.

Abstract: Guiding agribusiness producer prescriptive decisions is provided. A first risk coefficient and a first profit coefficient corresponding to selling a commodity via a traditional market and a second risk coefficient and a second profit coefficient corresponding to selling the commodity via a futures market are calculated. A minimized level of risk is calculated based on the first and second risk coefficient and information in a profile received from a producer of the commodity. A maximized level of profit is calculated based on the first and second profit coefficient and information in the profile received from the producer of the commodity. A recommendation is sent to a dashboard with a justification including calculations of the minimized level of risk and the maximized level of profit, a first percentage of the commodity to sell via the futures market and a second percentage of the commodity to sell via the traditional market.

Abstract: A method, a computer system, and a computer program product for a multi-dimension artificial intelligence (AI) agriculture advisor is provided. Embodiments of the present invention may include creating a user profile. Embodiments of the present invention may include preparing and transforming the external data. Embodiments of the present invention may include conducting a hypothesis on the transformed data. Embodiments of the present invention may include validating the transformed data. Embodiments of the present invention may include training an artificial intelligence (AI) model based on the transformed data. Embodiments of the present invention may include validating and retraining the artificial intelligence (AI) model. Embodiments of the present invention may include matching the user data with the artificial intelligence (AI) model. Embodiments of the present invention may include ranking results based on the matching the user data with the artificial intelligence (AI) model.

Abstract: A method, a computer system, and a computer program product for an artificial intelligence (AI) based agribusiness logistics advisor is provided. Embodiments of the present invention may include receiving a first user data. Embodiments of the present invention may include collecting a second user data and external data. Embodiments of the present invention may include preparing and transforming the second user data and the external data. Embodiments of the present invention may include conducting a hypothesis on the transformed data. Embodiments of the present invention may include validating the transformed data. Embodiments of the present invention may include training an artificial intelligence (AI) model based on the transformed data. Embodiments of the present invention may include matching the first user data with the artificial intelligence (AI) model. Embodiments of the present invention may include ranking results based on the matching the first user data with the artificial intelligence (AI) model.

Abstract: A method, computer system, and a computer program product for verifying the consistency of a current medical product is provided. The present invention may include generating a quantity associated with each of one or more active principles in the current medical product based on a plurality of current medical product data and a plurality of information from the data domains. The present invention may then include comparing the generated quantity associated with each of the one or more active principles in the current medical product with one or more constraints associated with a feasible solution associated with the current medical product. The present invention may further include determining a level of counterfeit risk based on the compared quantity associated with each of the one or more active principles in the current medical product with the one or more constraints from the feasible solution for the current medical product.

Abstract: Predicting perishable food stock quantity for replenishment. A search strategy is created for searching at least unstructured data along multiple dimensions based on the user input. A search of a network of computers is performed according to the search strategy. A machine learning model associated with a dimension is invoked, for each of the multiple dimensions. The machine learning model outputs a replenishment quantity along each of the multiple dimensions. The replenishment quantities of the multiple dimensions are merged to provide a predicted suggestion.