Abstract: A system and method for generating a predictive model include accessing data from a dynamic dataset and a static dataset, generating a unique individual profile for each of a plurality of subjects within a population, assigning a class attribute to each subject, and based on the unique individual profile and the class attribute for each subject, developing a classification model based on the unique individual profile and the class attribute for each of the plurality of subject, and generating an individual score for each of the plurality of subjects using the classification model. The unique individual profile is generated from the data in the dynamic and the static datasets.

Abstract: Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level.

Abstract: Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level.

Abstract: Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level.

Abstract: Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level.

Abstract: Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level.

Abstract: A system and method for generating a predictive model include accessing data from a dynamic dataset and a static dataset, generating a unique individual profile for each of a plurality of subjects within a population, assigning a class attribute to each subject, and based on the unique individual profile and the class attribute for each subject, developing a classification model based on the unique individual profile and the class attribute for each of the plurality of subject, and generating an individual score for each of the plurality of subjects using the classification model. The unique individual profile is generated from the data in the dynamic and the static datasets.

Abstract: A system, method, and computer program product for evaluating a set of data records to identify critical records in said set which lie very close to a class boundary and are sensitive to small changes in attribute values, such that the small changes may result in the switching of classes. Additionally, a system, method, and computer program product is presented that uses said critical records to improve performance of a classifier.