Simultaneous Detection and Localization of Pathological Abnormal Deformities using a Novel Class of Convolutional Neural Network – Deep models have become a popular alternative to conventional approaches for the study of pathological brain diseases. However, they do not adequately capture the dynamics of neuronal interactions that play a crucial role in the development of Alzheimer’s disease. In this paper, we propose a novel model that integrates both the functional and the non-functional interactions of the brain. Our method achieves state of the art performance on the ICDAR dataset, and can be deployed on the real-world dataset using a convolutional neural network (CNN). Experimental results indicate the superior performance of our approach.

A general framework to solve unsupervised machine learning problems based on sparse classification can be proposed. The problem of solving a supervised classification problem is typically characterized by finding a high-dimensional, sparse matrix for a data point. In the classification problem, two strategies are used to perform classification: first, (a) a new low-dimensional sparse matrix is proposed, and (b) it was found. The new sparse matrix is first obtained by learning the low-dimensional matrix from the data point, and then (c) it is used to approximate the new low-dimensional matrix to the data point matrix. In the supervised setting, we show how this approach can be used in a generic classifier-based classification algorithm, where a new low-dimensional matrix is estimated and then combined with the new matrix. Using these two methods, we can find a low-dimensional sparse matrix for a data point on which the classification error of our algorithms is negative.

A new type of kernel based on functional views

# Simultaneous Detection and Localization of Pathological Abnormal Deformities using a Novel Class of Convolutional Neural Network

A Deep Learning Approach for Precipitation Nowcasting: State of the ArtA general framework to solve unsupervised machine learning problems based on sparse classification can be proposed. The problem of solving a supervised classification problem is typically characterized by finding a high-dimensional, sparse matrix for a data point. In the classification problem, two strategies are used to perform classification: first, (a) a new low-dimensional sparse matrix is proposed, and (b) it was found. The new sparse matrix is first obtained by learning the low-dimensional matrix from the data point, and then (c) it is used to approximate the new low-dimensional matrix to the data point matrix. In the supervised setting, we show how this approach can be used in a generic classifier-based classification algorithm, where a new low-dimensional matrix is estimated and then combined with the new matrix. Using these two methods, we can find a low-dimensional sparse matrix for a data point on which the classification error of our algorithms is negative.