Abstract: A unified model for a neural network can be used to predict a particular value, such as a customer value. In various instances, customer value may have particular sub-components. Taking advantage of this fact, a specific learning architecture can be used to predict not just customer value (e.g. a final objective) but also the sub-components of customer value. This allows improved accuracy and reduced error in various embodiments.

Abstract: A unified model for a neural network can be used to predict a particular value, such as a customer value. In various instances, customer value may have particular sub-components. Taking advantage of this fact, a specific learning architecture can be used to predict not just customer value (e.g. a final objective) but also the sub-components of customer value. This allows improved accuracy and reduced error in various embodiments.

Abstract: A unified model for a neural network can be used to predict a particular value, such as a customer value. In various instances, customer value may have particular sub-components. Taking advantage of this fact, a specific learning architecture can be used to predict not just customer value (e.g. a final objective) but also the sub-components of customer value. This allows improved accuracy and reduced error in various embodiments.

Abstract: A method and a system for counting pigs in a pig house. The system includes a computing device and an imaging device. The computing device has a processor and a storage device storing computer executable code. The computer executable code is configured to: receive images captured from one end to the other end of the house; detect keypoints in the images using a neural network; identify pig skeletons by associating several related keypoints; track the skeletons in the images to obtain trajectories; divide each image into an activated zone and a deactivated zone; designate a spatial value of 0 for the skeletons in the activated zone and a spatial value of 1 for the skeletons in the deactivated zone; summating first order difference of the spatial values for each trajectory to obtain a trajectory count; and add the trajectory counts to obtain pig count.

Abstract: While artificial neural networks can be used to predict particular values in certain contexts, convolutional neural networks are not typically used in these contexts—instead they may be employed for image recognition. However, raw transactional data may be structured to take advantage of convolutional neural network (CNN) techniques by arranging the data such that correlations are increased between nearby other data. In arranging data in this manner, the structured CNN (SCNN) can operate efficiently without having to make use of engineered data features, the generation and maintenance of which can be a time-consuming process.

Abstract: A method and a system for counting pigs in a pig house. The system includes a computing device and an imaging device. The computing device has a processor and a storage device storing computer executable code. The computer executable code is configured to: receive images captured from one end to the other end of the house; detect keypoints in the images using a neural network; identify pig skeletons by associating several related keypoints; track the skeletons in the images to obtain trajectories; divide each image into an activated zone and a deactivated zone; designate a spatial value of 0 for the skeletons in the activated zone and a spatial value of 1 for the skeletons in the deactivated zone; summating first order difference of the spatial values for each trajectory to obtain a trajectory count; and add the trajectory counts to obtain pig count.

Abstract: An automatic classification method for distinguishing between indolent and clinically significant carcinoma using multiparametric MRI (mp-MRI) imaging is provided. By utilizing a convolutional neural network (CNN), which automatically extracts deep features, the hierarchical classification framework avoids deficiencies in current schemes in the art such as the need to provide handcrafted features predefined by a domain expert and the precise delineation of lesion boundaries by a human or computerized algorithm. This hierarchical classification framework is trained using previously acquired mp-MRI data with known cancer classification characteristics and the framework is applied to mp-MRI images of new patients to provide identification and computerized cancer classification results of a suspicious lesion.

Abstract: An automatic classification method for distinguishing between indolent and clinically significant carcinoma using multiparametric MRI (mp-MRI) imaging is provided. By utilizing a convolutional neural network (CNN), which automatically extracts deep features, the hierarchical classification framework avoids deficiencies in current schemes in the art such as the need to provide handcrafted features predefined by a domain expert and the precise delineation of lesion boundaries by a human or computerized algorithm. This hierarchical classification framework is trained using previously acquired mp-MRI data with known cancer classification characteristics and the framework is applied to mp-MRI images of new patients to provide identification and computerized cancer classification results of a suspicious lesion.

Abstract: While artificial neural networks can be used to predict particular values in certain contexts, convolutional neural networks are not typically used in these contexts—instead they may be employed for image recognition. However, raw transactional data may be structured to take advantage of convolutional neural network (CNN) techniques by arranging the data such that correlations are increased between nearby other data. In arranging data in this manner, the structured CNN (SCNN) can operate efficiently without having to make use of engineered data features, the generation and maintenance of which can be a time-consuming process.

Abstract: A densely connected neural network can be used to predict values using a trained model. This unique architecture allows for more accurate prediction by allowing better data visibility across different layers of the network in various embodiments. Outputs from every previous layer in the neural network can be forwarded to every subsequent layer. Input selection operations may be performed to reduce and/or combine increased numbers of inputs that may arrive at downstream neurons. The architecture may be broadly applied in a large number of different modeling contexts.

Abstract: A unified model for a neural network can be used to predict a particular value, such as a customer value. In various instances, customer value may have particular sub-components. Taking advantage of this fact, a specific learning architecture can be used to predict not just customer value (e.g. a final objective) but also the sub-components of customer value. This allows improved accuracy and reduced error in various embodiments.