With the advancement in modern imaging techniques like CT scan, MRI, PET scan etc., a
vast amount of data is generated every day in the field of healthcare. Big data contains …

Recently, circuit analysis and optimization featuring neural-network models have been
proposed, reducing the computational time during optimization while keeping the accuracy …

This paper presents a transrectal dual-modality near-infrared (NIR) diffuse optical
tomography technique coupled with ultrasonography that provides an integrated method for …

In this thesis, we present new methods for solving nonlinear optimization problems. These
problems are difficult to solve because the nonlinear constraints form feasible regions that …

Deep learning has promised us great outcomes when enough data are fed to it. Deep
learning is a branch of artificial intelligence which employs artificial neural networks to learn …

Although the backpropagation (BP) scheme is widely used as a learning algorithm for
multilayered neural networks, the learning speed of the BP algorithm to obtain acceptable …

The data security issue in federated learning is critical. While federated learning allows
clients to collaboratively participate in the global model training without sharing private data …

We present a neural network application to the diagnosis of vocal and voice disorders, these
disorders should be diagnosed in the early stage and normally cause changes in the voice …

The back-propagation algorithm calculates the weight changes of an artificial neural
network, and a two-term algorithm with a dynamically optimal learning rate and a momentum …

In this paper we present the results of a comparison among six different weight initialization
methods with two training algorithms and six databases. The comparison is performed by …