The goal of this project is to apply multilayer feedforward neural networks to phishing email detection and evaluate the effectiveness of this approach. We design the feature set, process the phishing dataset, and implement the neural network (NN) systems. We then use cross validation to evaluate the performance of NNs with different numbers of hidden units and activation functions. We also com...

Nearest neighbor (NN) search in high dimensional feature space is widely used for similarity retrieval of multimedia information. However, recent research results in the database literature reveal that a curious problem happens in high dimensional space. Since high dimensional space has high degree of freedom, points could be so scattered that every distance between them might yield no signific...

This paper investigates an application of Neural Networks (NNs) to the decentralized guaranteed H∞ performance for a class of large-scale uncertain nonlinear systems. In order to guarantee the adequate H∞ performance level for the nonlinear systems, nonlinear linear matrix inequality (NLMI) condition is derived. The linear matrix inequality (LMI) approach instead of the NLMI is used to construc...

Let q = 3l+2 be a prime power. Maximal designed distances of imprimitive Hermitian dual containing q2-ary narrow-sense (NS) BCH codes of length n = (q 6−1) 3 and n = 3(q 2 −1)(q2 +q+1) are determined. For each given n, non-narrow-sense (NNS) BCH codes which achieve such maximal designed distances are presented, and a series of NS and NNS BCH codes are constructed and their parameters are comput...

Back-Propagation (BP), Extended Kalman Filter (EKF) and Particle Swarm Optimization (PSO) are three of the most widely used algorithms for training feed forward Neural Networks (NNs). This paper presents an accurate DGPS land vehicle navigation system using multi-layered NNs based on the BP, EKF and PSO learning algorithms. The network setup is developed based on mathematical models to avoid ex...

We discuss alternative norms to train Neural Networks (NNs). We focus on the so called Multilayer Perceptrons (MLPs). To achieve this we rely on a Genetic Algorithm called an Eclectic GA (EGA). By using the EGA we avoid the drawbacks of the standard training algorithm in this sort of NNs: the backpropagation algorithm. We define four measures of distance: a) The mean exponential error (MEE), b)...

This critical review examines whether oral stimulation via NTrainer patterned orocutaneous therapy is more effective than a pacifier at establishing the non-nutritive suck (NNS) in order to accelerate the transition time to oral feeding in preterm infants. Overall, research suggests that both forms of oral stimulation establish the NNS and have a positive effect on feeding outcomes. However, it...

This paper reviews the current status and challenges of Neural Networks (NNs) based machine learning approaches for modern power grid stability control including their design and implementation methodologies. NNs are widely accepted as Artificial Intelligence (AI) approaches offering an alternative way to control complex and ill-defined problems. In this paper various application of NNs for pow...

Neural networks (NNs) have been successfully applied to solve a variety of application problems including classification and function approximation. They are especially useful as function approximators because they do not require prior knowledge of the input data distribution and they have been shown to be universal approximators. In many applications, it is desirable to extract knowledge that ...

A recent “third wave” of Neural Network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, this new NN research is often referred to as deep learning. Stemming from this tide of NN work, a number of research...