Abstract: This disclosure relates generally to multi-class multi-label classification and more particularly to contradiction avoided learning for multi-class multi-label classification. Conventional classification methods do not consider contradictory outcomes in multi-label classification tasks wherein contradictory outcomes have significant negative impact in the classification problem solution. The present disclosure provides a contradiction avoided learning multi-class multi-label classification. The disclosed method utilizes a binary contradiction matrix constructed using domain knowledge. Based on the binary contradiction matrix the training dataset is divided into two parts, one comprising contradictions and the second without contradictions. The classification model is trained using the divided datasets using a contradiction loss and a binary cross entropy loss to avoid contradictions during learning of the classification model.

Abstract: The present invention relates to the field of evaluating clinical diagnostic models. Conventional metrics does not consider context dependent clinical principles and is unable to capture critically important features that ought to be present in a diagnostic model. Thus, present disclosure provides a method and system for evaluating clinical efficacy of multi-label multi-class computational diagnostic models. Diagnosis for a given dataset of diagnostic samples is obtained from the diagnostic model which is then classified as wrong, missed, over or right diagnosis, based on which a first penalty is calculated. A second penalty is calculated for each diagnostic sample using a contradiction matrix. The first and second penalties are summed up to compute a pre-score for each diagnostic sample. Finally, the diagnostic model is evaluated using a metric that is based on sum of pre-scores, and scores from a perfect and a null multi-label multi-class computational diagnostic model.

Abstract: Methods and apparatuses are described in which unstructured computer text is analyzed for generation of customized digital documents. A server tokenizes and encodes historical user interactions and historical digital documents into multidimensional vectors. The server trains an interaction classification model using the multidimensional vectors as input to generate a classification for an input user interaction, and trains a language generation model using the multidimensional vectors as input to generate a customized digital document based upon an input user interaction. The server receives a new user interaction and encodes the new user interaction into a new multidimensional vector. The server executes the trained interaction classification model using the new vector as input to generate a digital document classification. The server executes the trained language generation model using the new vector and the classification as input to generate a customized digital document.

Abstract: An AI platform to enable one or more users to design and create AI enabled applications is provided. The AI platform comprises a data module configured to condition data received from a plurality of data sources to generate a corresponding data pipeline; wherein the data module comprises a plurality of reusable data components. The AI platform further comprises an intelligent processing module configured to process a plurality of datasets received on the data pipeline and generate a corresponding artificial intelligence (AI) pipeline; wherein the intelligent processing module comprises a plurality of reusuable data processing components.

Abstract: Embodiments of the present disclosure provide a system and a method of controlling a robot for autonomous navigation. The method includes receiving a set of point values defining LIDAR data from a LIDAR sensor scanning a 2D omnidirectional plane, receiving a sensor value from an ultrasonic sensor having a 3D field of view excluding the plane, and resolving an observable field of view for the LIDAR sensor, where the observable field of view includes a blind spot of the LIDAR sensor, and modifying the LIDAR data using the sensor value based on the object being located in the blind spot indicated by the sensor value less than one or more point values corresponding to a portion of the plane extending along the observable field of view, where the modified LIDAR data indicates the object being detected by the LIDAR sensor despite the object located outside the 2D field of view.

Abstract: Methods and apparatuses are described in which unstructured computer text is analyzed for generation of customized digital documents. A server tokenizes and encodes historical user interactions and historical digital documents into multidimensional vectors. The server trains an interaction classification model using the multidimensional vectors as input to generate a classification for an input user interaction, and trains a language generation model using the multidimensional vectors as input to generate a customized digital document based upon an input user interaction. The server receives a new user interaction and encodes the new user interaction into a new multidimensional vector. The server executes the trained interaction classification model using the new vector as input to generate a digital document classification. The server executes the trained language generation model using the new vector and the classification as input to generate a customized digital document.

Abstract: An AI platform to enable one or more users to design and create AI enabled applications is provided. The AI platform comprises a data module configured to condition data received from a plurality of data sources to generate a corresponding data pipeline; wherein the data module comprises a plurality of reusable data components. The AI platform further comprises an intelligent processing module configured to process a plurality of datasets received on the data pipeline and generate a corresponding artificial intelligence (AI) pipeline; wherein the intelligent processing module comprises a plurality of reusuable data processing components.