Abstract: An embodiment includes identifying a tree type of vegetation depicted in an image. The embodiment segments that portion of the image using edge-detection processing resulting in a contour line that defines a tree perimeter. The embodiment detects that the tree is within a buffer distance from a power line. The embodiment determines the tree's species by comparing the contour line to candidate contour lines of different tree species and calculates a diameter of the tree's crown using the contour line. The embodiment estimates the tree's height using the species and the diameter of the crown. The embodiment calculates a risk value for the tree based on a risk of contact between the power line and the tree and issues a work order to maintain the tree to prevent contact with the power line.

Abstract: Mechanisms are provided to automatically generate a machine learning (ML) computer model. The mechanisms automatically generate a plurality of aggregated dataset groups, each having original dataset(s) grouped together based on a degree of correlation between characteristics of each of the original datasets. The mechanisms automatically generate, for each aggregated dataset group, a plurality of ML computer model instances, each being a ML computer model configured with a different combination of thresholds and hyperparameters than other ML computer model instances. The plurality of ML computer model instances are executed to generate performance metric information for each ML computer model instance. The performance metric information is analyzed to select a set of ML computer model instances for the aggregated dataset. The mechanisms select one or more ML computer model instances from across all of the sets of ML computer model instances as a candidate for deployment to a decision support computing system.

Abstract: A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users.

Abstract: A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users.

Abstract: A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users.

Abstract: A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users.

Abstract: A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users.

Abstract: A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users.

Abstract: Estimating monthly heating oil consumption of a building that uses heating oil and non-oil source of energy, may include separating by applying statistical models, yearly consumption of oil data associated with the building into base load oil consumption and space heating oil consumption. The separating may also include determining monthly base load oil consumption associated with the building. Monthly space heating consumption of oil may be estimated by applying a heating degree day density function to the space heating oil consumption. The monthly space heating consumption may be aggregated with the monthly base load oil consumption to estimate the monthly heating oil consumption.