Abstract: The present invention relates to a system for video processing, wherein the system (10) comprises: an input unit (11), a processing unit (12) and an output unit (13). The input unit (11) inputs a video which includes one or more events defining a boundary of a respective scene within the video. The processing unit (12) processes the video to identify the event and insert a cue point at the boundary. The output unit (13) outputs the processed video. A method (20) for video processing is also disclosed.

Abstract: A computer-implemented method for hyper-parameter tuning for machine learning algorithms using pattern recognition and reduced search space approach comprising the steps of obtaining outputs from the machine learning models based on a limited number of parameter combination that is obtained using Latin Hypercube sampling; estimating errors for each actual data and predicted data, assuming the data is not there but other data is using pattern recognition technology; determining parameter combination that gives maximum error in prediction using pattern recognition technology; adding the data where the most error will likely occur to an actual dataset in order to increase the accuracy in subsequent prediction; predicting the parameter combination that yields the best accuracy using pattern recognition technology; determining reduced search space for each parameter for subsequent hyper-parameter tuning; and repeating previous steps from step until the highest accuracy is achieved.

Abstract: A computer-implemented method for predicting output values in a multidimensional dataset comprising the steps of arranging a multidimensional dataset in a hierarchical order to a two-dimensional order; computing randomness of different permutations of variables; reordering the hierarchical order based on the randomness; computing contribution of each variable to an output; interpolating or extrapolating contribution values of each variable via mapping technique; and determining a predictive value for any given input by summing up the impact of each variable determined previously.