Neural Networks and Their Applications

A neural network is a collection of neurons that are interconnected and interactive through signal processing operations. The traditional term "neural network" refers to a biological neural network, i.e., a network of biological neurons. The modern meaning of this term also includes artificial neural networks, built of artificial neurons or nodes. Machine learning includes adaptive mechanisms that allow computers to learn from experience, learn by example and by analogy. Learning opportunities can improve the performance of an intelligent system over time. One of the most popular approaches to machine learning is artificial neural networks. An artificial neural network consists of several very simple and interconnected processors, called neurons, which are based on modeling biological neurons in the brain. Neurons are connected by calculated connections that pass signals from one neuron to another. Each connection has a numerical weight associated with it. Weights are the basis of long-term memory in artificial neural networks. They express strength or importance for each neuron input. An artificial neural network "learns" through repeated adjustments of these weights.

As a machine learning algorithm, neural network has been widely used in various research projects to solve various critical problems. The concept of neural networks is inspired from the human brain. The paper will explain the actual concept of Neural Networks such that a non-skilled person can understand basic concept and also make use of this algorithm to solve various tedious and complex problems. The paper demonstrates the designing and implementation of fully design Neural Network along with the codes. It gives various architectures of ANN also the advantages, disadvantages & applications.

An artificial neural network is a system based on the operation of biological neural networks, in other words, is an emulation of biological neural system.

Artificial neural networks (ANNs) were originally developed as mathematical models of the information processing capabilities of biological brains (McCulloch and Pitts, 1988; Rosenblatt, 1963; Rumelhart et al., 1986). Although it is now clear that ANNs bear little resemblance to real biological neurons, they enjoy continuing popularity as pattern classifiers. The basic structure of an ANN is a network of small processing units, or nodes, joined to each other by weighted connections. In terms of the original biological model, the nodes represent neurons, and the connection weights represent the strength of the synapses between the neurons. The network is activated by providing an input to some or all of the nodes, and this activation then spreads throughout the network along the weighted connections. The electrical activity of biological neurons typically follows a series of sharp 'spikes', and the activation of an ANN node was originally intended to model the average firing rate of these spikes.

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection toward visual content analysis, and medical image registration for its pre-processing and post processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging.

Journal of the Society of Dyers and Colourists, 1998

—An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. This paper gives overview of Artificial Neural Network, working & training of ANN. It also explain the application and advantages of ANN.

Lecturer 14-15-Artificial Neuron Networks 2 Artificial neural networks  Artificial neural network (ANN)  Inspired by biological neural systems, i.e., human brains  ANN is a network composed of a number of artificial neurons  Neuron  Has an input/output (I/O) characteristic  Implements a local computation  The output of a unit is determined by  Its I/O characteristic  Its interconnections to other units  Possibly external inputs 2/16/2023 2 Artificial neural networks  ANN can be seen as a parallel distributed information processing structure  ANN has the ability to learn, recall, and generalize from training data by assigning and adjusting the interconnection weights  The overall function is determined by  The network topology  The individual neuron characteristic  The learning/training strategy  The training data 3 Applications of ANNs  Image processing and computer vision  E.g., image matching, preprocessing, segmentation and analysis, computer vision, image compression, stereo vision, and processing and understanding of time-varying images  Signal processing  E.g., seismic signal analysis and morphology  Pattern recognition  E.g., feature extraction, radar signal classification and analysis, speech recognition and understanding, fingerprint identification, character recognition, face recognition, and handwriting analysis  Medicine  E.g., electrocardiographic signal analysis and understanding, diagnosis of various diseases, and medical image processing