Neural network

intelligence allows for an easier way to execute tasks, while still remaining efficient. Neural networks are a vast aspect of the topic of artificial intelligence. Artificial neural networks are composed of processing elements that process information and categorize them by subject and elements. Neural networks are used in a variety of aspects, even those as capital punishment cases and the legal system. Neural Networks (ANNs) can also be used to identify relationships between aspects that are not commonly

Regarding the various service quality models discussed in section 2.3.1 to 2.3.9 the gap model considers the perceptions and expectations of the customers, the gap being the difference between expectations and perceptions (P-E). The model resulted in five dimensions that are generic in nature. However, other models such as ‘Technical and Functional Quality Model’, ‘Perception-only Model’ and ‘Evaluated Performance and Normed Quality Model’ consider only the perceptions of the customers. Technical

making problem, having wide practical application in various fields. Extreme Learning Machine (ELM) pro- posed by Huang et al.[1], is an effective machine learning technique for real valued classification. ELMis a single hidden layer feedfo5 rward network in which the weights between input and hidden layer are initialized randomly. ELM uses analytical approach to compute weights between hidden and output layer [2], which makes it faster compared to other gradient based classifiers ([3, 4]). Various

The below figure 3.1 illustrates the loss and accuracy for every epoch succeeded by the last assessment of the trained model. Figure 3.1: Multi Layer Perceptron Neural Network epochs 3.2 Convolutional Neural Network Using Keras As mentioned in the previous chapter, the Convolutional Neural Network(CNN) is very similar to Neural Network(NN) as they also constitute learnable weights and bias plus. In it, every neuron gets some input, implements a scalar product and follows it with nonlinearity optionally[50]

3. Neural Networks This technique follows the same notion of how human brain works. It consists of collection of highly interconnected processing elements that convert a set of given inputs to a set of desired outputs. The widely used neural network for intrusion detection is Multilayer Perceptions (MLP).Neural Network based intrusion detection systems are intended to classify the normal and intrusion patterns and the type of the attack. For this purpose, the network is trained on various types

1. INTRODUCTION 1.1 BACKGROUND Friction stir welding ( FSW) and Artificial neural networks (ANN) are two well known approaches in their respective field. FSW is a well known method in material sciences and ANN in computer science. The aim is to combine these two approaches to get some important results. Friction stir welding (FSW) is a novel method of joining materials in solid state. It was invented by the Welding Institute (TWI) in 1991. It was initially applied for joining aluminium and later

     The neural network model attempts to explain that which is known about

traders are developed using Keras, a high level neural network toolkit wrapped up by Tensorflow. The data is generated from Bristol Stock Exchange developed by Cliff[21] which is an Automated market. This generated data is then trained and tested using deep learning neural network traders. This research also gives a comprehensive knowledge of Neural Networks especially Multi Layer Perceptron Neural Network, LSTM (Long Short-Term Memory) Recurrent Neural Network, and BSE. Moreover, it also

A growing expansion of wireless systems and applications operating at centimetre-and millimetre-wave frequencies motivates the development of effective and affordably-priced technologies for manufacturing cm- and mm-wave components like antennas, filters, directional couplers, etc. Advances in technology associated with signal processing, R.F. components and batteries have stimulated more innovative applications in wireless that has in turn stimulate for extensive research to find new solutions to

classification of patients, whether diabetes is present or not.can make diabetes to be classified as tested positive or tested negative. In this research work, backpropagation neural network has been used to classify patient that are tested positive as binary 1 and patient that are tested negative as binary 0.The use of trained neural network gave recognition rate of 81% on test and 80% on the training as compare to previous research work on diabetes using ADAP which gives 76% recognition rate. 1.0