Abstract: A system for optimizing the selection of transcription engines is provided. The system includes an alphanumeric preprocessor, an audio analysis preprocessor, a categorical preprocessor, and a continuous variable preprocessor which is configured to receive outputs from the alphanumeric, the audio analysis, and the categorical preprocessors and to generate data used by a modeling module to create a list of ranked transcription engines. The transcription engines are used to generate transcriptions of media data sets.

Abstract: A system for optimizing selection of transcription engines using a combination of selected machine learning models. The system includes a plurality of preprocessors that generate a plurality of features from a media data set. The system further includes a deep learning neural network model, a gradient boosted machine model and a random forest model used in generating a ranked list of transcription engines. A transcription engine is selected from the ranked list of transcription engines to generate a transcript for the media dataset.

Abstract: A method of predicting disk drive failure at a central processing facility using an evolving drive failure prediction algorithm (DFPA) is disclosed. A set of quality metric values are transmitted from each of a plurality of remote disk drives to the central processing facility. The DFPA is executed at the central processing facility in response to the quality metric values to detect an impending failure of at least one of the remote disk drives. The DFPA is evolved at the central processing facility in response to a reference data base of quality metric values and a corresponding failure indicator. The processes is repeated so as to improve the accuracy of the DFPA over time.

Abstract: A method of binning disk drives by evaluating quality metrics prior to a final quality audit is disclosed. A number of disk drives are assembled, and a plurality of quality metrics are generated for each disk drive representing a plurality of operating characteristics. The quality metrics for each disk drive are evaluated for binning the disk drives into a plurality of lots including a first lot and a second lot. A final quality audit (FQA) is performed by executing a number of write and read operations for a plurality of the disk drives in each lot and classifying each disk drive as passing or failing the FQA. If the number of disk drives that fail the FQA out of the first lot falls below a first threshold, the first lot is classified as acceptable for a first tier customer. If the number of disk drives that fail the FQA out of the second lot falls below a second threshold, the second lot is classified as acceptable for a second tier customer.

Abstract: A subset of quality metrics as input to a disk drive failure prediction algorithm (DFPA) may be selected using a genetic algorithm. The DFPA is executed for an initially selected generation of subset quality metrics using quality metric values stored in a reference data base. At least one DFPA setting is adjusted and the DFPA executed again for the selected subset. After training the DFPA, the best DFPA setting is saved for the selected subset. A fitness score is generated for the selected subset, representing an accuracy of the DFPA relative to failure indicators stored in the reference data base. At least one genetic operator is applied in response to the fitness scores to generate a new generation of subsets. The process is repeated until a best subset of quality metrics and corresponding DFPA setting are found.