Abstract: A system for issue prediction based on multidimensional data analysis includes a model generator that receives a resolved data item relating to a service issue. The resolved data item includes different attributes corresponding to multiple data dimensions and adjusts a population of attributes based on a statistical data model and a deep learning data model operating independent of each other. The statistical data model operates on the attributes for providing a predictive feature and the deep learning data model operates on the attributes for providing a predictive label based on performance metrics related to the data dimensions. The predictive feature and the predictive label collectively define training data. The model generator also trains a classification model based on the training data for predicting a potential issue related to an unresolved data item. The trained data model provides a trigger based on the potential issue being related to the performance metrics.

Abstract: Systems and methods for assessing damage in a damaged object are disclosed. The method comprises receiving visual data of the damaged object by a computing system. The visual data is converted into at least one Multi-Dimensional (MD) representation of the damaged object. The method further comprises identifying a first set of characteristic points in the at least one MD representation of the damaged object. The first set of characteristic points includes at least one subset of characteristic points, and each of the at least one subset of characteristic points substantially corresponds to a portion of the damaged object. The method furthermore comprises determining at least one first set of contour maps of the portion of the damaged object using the at least one MD representation. Using the first set of characteristic points and the at least one first set of contour maps, the damage in the damaged object is assessed.