Neural Networks: History and Applications


Doug Alexander (Editor)

Series: Computer Science, Technology and Applications
BISAC: COM014000

With respect to the ever-increasing developments in artificial intelligence and artificial neural network applications in different scopes such as medicine, industry, biology, history, military industries, recognition science, space, machine learning and etc., Neural Networks: History and Applications first discusses a comprehensive investigation of artificial neural networks.

Next, the authors focus on studies carried out with the artificial neural network approach on the emotion recognition from 2D facial expressions between 2009 and 2019. The major objective of this study is to review, identify, evaluate and analyze the performance of artificial neural network models in emotion recognition applications.

This compilation also proposes a simple nonlinear approach for dipole mode index prediction where past values of dipole mode index were used as inputs, and future values were predicted by artificial neural networks. The study was also conducted for seasonal dipole mode index prediction because the dipole mode index is more prominent in the Sep-Oct-Nov season.

A subsequent study focuses on how mammography has a high false negative and false positive rate. As such, computer-aided diagnosis systems have been commercialized to help in micro-calcification detection and malignancy differentiation. Yet, little has been explored in differentiating breast cancers with artificial neural networks, one example of computer-aided diagnosis systems. The authors aim to bridge this gap in research.

The penultimate chapter reviews the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. Then, the accuracy of each plasticity rule with respect to its temporal encoding precision is examined, and the maximum number of input patterns it can memorize using the precise timings of individual spikes as an indicator of storage capacity in different control and recognition tasks is explored.

In closing, a case study is presented centered on an intelligent decision support system that is built on a neural network model based on the Encog machine learning framework to predict cryptocurrency close prices.
(Imprint: Nova)



Table of Contents


Chapter 1. Artificial Neural Networks, Concept, Application and Types
(M. Khishe and Gh. R. Parvizi, Department of Electronic Engineering, Imam Khomeini University of Naval Science, Nowshahr, Iran, and others)

Chapter 2. Emotion Recognition from Facial Expressions Using Artificial Neural Networks: A Review
(Sibel Senan, Zeynep Orman and Fulya Akcan, Department of Computer Engineering, Istanbul University-Cerrahpasa, Istanbul, Turkey)

Chapter 3. Dipole Mode Index Prediction with Artificial Neural Networks
(Kalpesh R. Patil and Masaaki Iiyama, Post-Doctorate Research Scholar, Academic Center for Computing and Media Studies, Kyoto University, Kyoto, Japan, and others)

Chapter 4. Efficacy of Artificial Neural Networks in Differentiating Breast Cancers in Digital Mammography
(Sundaran Kada, PhD, Fuk-hay Tang, PhD, Faculty of Health and Social Sciences, Western Norway University of Applied Sciences, Bergen, Norway, and others)

Chapter 5. Supervised Adjustment of Synaptic Plasticity in Spiking Neural Networks
(Saeed Solouki, Control and Intelligent Processing Center of Excellence, Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran)

Chapter 6. A Review on Intelligent Decision Support Systems and a Case Study: Prediction of Cryptocurrency Prices with Neural Networks
(Zeynep Orman, Emel Arslan, Burcu Ozbay and Emad Elmasri, Department of Computer Engineering, Istanbul University-Cerrahpasa, Istanbul, Turkey)


Additional information