Artificial neural networks (ANNs), usually simply called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains.An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.
Components of the Book:
- Chapter1
Promoter prediction in E. coli based on SIDD profiles and Artificial Neural Networks
- Chapter 2
The use of artificial neural networks to study fatty acids in neuropsychiatric disorders
- Chapter 3
Evaluation of a pressure head and pressure zones in water distribution systems by artificial neural networks
- Chapter 4
Forecasting Maximum Seasonal Temperature Using Artificial Neural Networks “Tehran Case Study”
- Chapter 5
Investigation of discharge coefficient of trapezoidal labyrinth weirs using artificial neural networks and support vector machines
- Chapter 6
Estimation of absolute permeability using artificial neural networks (multilayer perceptrons) based on well logs and laboratory data from Silurian and Ordovician deposits in SE Poland
- Chapter 7
Estimation of vapor pressures, compressed liquid, and supercritical densities for sulfur dioxide using artificial neural networks
- Chapter 8
Removal of Cu(II) using three low-cost adsorbents and prediction of adsorption using artificial neural networks
- Chapter 9
Modelling shares choice to enter in a portfolio using artificial neural networks (ANN)
- Chapter 10
Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks: an application to a controlled clinical trial on renal carcinoma
- Chapter 11
Corneal power evaluation after myopic corneal refractive surgery using artificial neural networks
- Chapter 12
Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks
- Chapter 13
Prediction of conversion of laparoscopic cholecystectomy to open surgery with artificial neural networks
- Chapter 14
Indian stock market prediction using artificial neural networks on tick data
- Chapter 15
Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer's patients from controls in the Nun Study
Readership:
Students, academics, teachers and other people attending or interested in Artificial Neural Networks.
Charles Bland
Charles Bland, Department Natural Sciences and Environmental Health, Mississippi Valley State University, Itta Bena, Mississippi, USA
Lucio Tonello
Lucio Tonello, MRI Unit, MRC Clinical Sciences Centre, Imaging Sciences Department, Imperial College London, Hammersmith Hospital, London, UK
Elia Biganzoli
Elia Biganzoli, Unit of Medical Statistics, Biometry and Bioinformatics, Fondazione IRCSS Istituto Nazionale dei Tumori, Milan, Italy
David Snowdon
David Snowdon, Sanders Brown Center on Aging and Department of Neurology, University of Kentucky, Lexington, Kentucky, USA
Piero Antuono
Piero Antuono, Department of Neurology, Medical College of Wisconsin, Milwaukee, USA
Michele Lanza
Michele Lanza, Centro Grandi Apparecchiature, Seconda Università di Napoli, Naples, Italy
and more...