Abstract: Techniques for classifying, using a deep learning model, histopathological whole slide images (WSIs) as comprising images of cancerous or non-cancerous tissue and/or as comprising images of cancerous tissue having a genetic mutation or not having a genetic mutation are described herein. The techniques include at least one processor configured to instantiate a container-based processing architecture to train and/or use the deep learning model to process and classify at least one WSI. In some embodiments, a treatment may be selected and administered based on a classification result obtained from the deep learning model.

Abstract: A controller and method of classifying a user into one of a plurality of user classes. One or more voice samples are received from the user, from which a frequency spectrum is generated. One or more values defining respective features of the frequency spectrum are extracted from the frequency spectrum. Each of the respective features are defined by values of frequency, amplitude, and/or position in the spectrum. One or more of the respective features are resonant frequencies in the voice of the user. A user profile of the user is generated and comprises the extracted one or more values. The user profile is supplied to a machine learning algorithm that is trained to classify users as belonging to one of the plurality of user classes based on the one or more values in their respective user profile.

Abstract: A controller and method of classifying a user into one of a plurality of user classes. One or more voice samples are received from the user, from which a frequency spectrum is generated. One or more values defining respective features of the frequency spectrum are extracted from the frequency spectrum. Each of the respective features are defined by values of frequency, amplitude, and/or position in the spectrum. One or more of the respective features are resonant frequencies in the voice of the user. A user profile of the user is generated and comprises the extracted one or more values. The user profile is supplied to a machine learning algorithm that is trained to classify users as belonging to one of the plurality of user classes based on the one or more values in their respective user profile.

Abstract: Systems and methods for signal analysis using orbits of a chaotic system are provided. For example, a multiresolution analysis may be constructed and cupolets may be used to approximate arbitrary signals and compress images. Cupolets may be phase transformed to produce compact cupolets that are well-suited for producing sharp changes in signals, or to produce compact cupolets that are more oscillatory and have less or no sharp global maximum amplitudes. Alternatively, cupolets may be phase transformed to allow for optimal or near optimal adjustment to fit a signal.

Abstract: Systems and methods for signal analysis using orbits of a chaotic system are provided. For example, a multiresolution analysis may be constructed and cupolets may be used to approximate arbitrary signals and compress images. Cupolets may be phase transformed to produce compact cupolets that are well-suited for producing sharp changes in signals, or to produce compact cupolets that are more oscillatory and have less or no sharp global maximum amplitudes. Alternatively, cupolets may be phase transformed to allow for optimal or near optimal adjustment to fit a signal.