Abstract: A method utilizes Artificial Intelligence (AI) system to broadcast a real-time event to a particular broadcast audience. The AI system receives a profile of a particular broadcast audience for a real-time event, and concurrent real-time Audio/Video (/V) streams for a particular portion of the real-time event. The AI system ranks the concurrent real-time A/V streams for the particular portion of the real-time event based on the types of event activities depicted in each of the concurrent real-time A/V streams and the profile of the particular broadcast audience for the real-time event; selects a particular real-time A/V stream, from the concurrent real-time A/V streams, for the particular broadcast audience based on the ranking; and directs a performance of a broadcast action related to the selected particular real-time A/V stream.

Abstract: An embodiment includes calculating an efficacy value of a context assigned to an asset, the efficacy value being based at least in part on a percentage of assets assigned to the context. The embodiment also includes calculating, responsive to the calculated efficacy value being within a predetermined range, a normalized relevance value of a first tag associated with the asset. The embodiment also includes generating relative relevance value for the first tag and a reason code associated with a basis for the relative relevance value of the first tag based at least in part on the normalized relevance value of the first tag. The embodiment also includes initiating, responsive to generating the relative relevance value and the reason code for the first tag, a database command on a training dataset stored in the database that includes the first tag.

Abstract: In an approach to anonymous inventory tracking, one or more computer processors receive a first image of one or more products from a user. The one or more computer processors remove data associated with an identity of the user from the first image. The one or more computer processors determine that a storage location associated with the one or more products in the first image is detected. The one or more computer processors determine an inventory count of the one or more products in the first image. The one or more computer processors submit the first image and the inventory count of the one or more products in the first image to a blockchain ledger in association with the storage location. The one or more computer processors take an action based on the inventory count.

Abstract: In an approach to estimating product inventory count, one or more computer processors receive one or more images of one or more products residing on a product storage location from an image capturing device. Based on the received images, one or more computer processors determine a count of the one or more products. One or more computer processors determine a confidence in the count of the one or more products. In response to determining the confidence is below a threshold, one or more computer processors calculate a recommended position of the image capturing device to produce an improved image of the one or more products. One or more computer processors transmit instructions to the image capturing device to move to the recommended position. One or more computer processors determine whether the image capturing device is in the recommended position.

Abstract: Provided is a method for deploying multiple solar power units to a location. The method comprises determining a location to which a solar power unit is deploying. The method further comprises determining that a second solar power unit is deploying to a same location as the solar power unit. A redeployment plan for the second solar power unit is generated. The redeployment plan is provided to the second solar power unit. The method further comprising deploying the solar power unit to the location.

Abstract: Provided is a method for deploying multiple solar power units to a location. The method comprises determining a location to which a solar power unit is deploying. The method further comprises determining that a second solar power unit is deploying to a same location as the solar power unit. A redeployment plan for the second solar power unit is generated. The redeployment plan is provided to the second solar power unit. The method further comprising deploying the solar power unit to the location.

Abstract: Provided is a method for relocating a solar power unit in response to a redeployment event. A first location of a deployed solar power unit may be determined. A processor may detect a redeployment event for the solar power unit at the first location. In response to the redeployment event, the processor may determine a new location for the solar power unit. The method may further comprise relocating the solar power unit to the new location.

Abstract: Provided is a method for relocating a solar power unit in response to a redeployment event. A first location of a deployed solar power unit may be determined. A processor may detect a redeployment event for the solar power unit at the first location. In response to the redeployment event, the processor may determine a new location for the solar power unit. The method may further comprise relocating the solar power unit to the new location.

Abstract: An approach is provided for using a machine learning model to reduce food waste. Estimation models for food items are created by using a machine learning model. The estimation models have prediction functions specifying timelines during which the food items are not spoiled and are in a condition for consumption. Prediction function solutions are generated for a food item inventory to indicate menu items for respective time periods. Each menu item includes food item(s) which are in condition for consumption based on the estimation models. Menu recommendations corresponding to the time periods are generated. Each menu includes menu item(s) indicated by the prediction function solutions. The menu recommendations reduce a waste of the food items through spoilage by increasing a likelihood of consumption of the food items within the respective timelines.

Abstract: A method and system for enhancing a facial image of a user in real time by digital generation of a portion of a facial image using artificial intelligence (AI) during a video conference with a plurality of participants. The method and system including receiving a digital image of a first portion of a user's face in real time from a camera viewing the first portion of the user's face. The camera is unable to view the second portion of the user's face. The method and system includes improving resolution and/or digitally completing the second portion of the user's facial image, using an AI system. The improving resolution including, receiving the digital image at the AI system which includes a Generative Adversarial Network (GAN). The method and system includes generating, in real time, a complete enhanced digital facial image of the user's face using the GAN.