/pdf/using-neural-networks-and-gis-to-forecast-land-use-changes-a-47spf9ee4x.pdf

Using neural networks and GIS to forecast land use changes: a Land Transformation Model

A comparison of three different approaches to tourist arrival forecasting

Differentiating between good credits and bad credits using neuro-fuzzy systems

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are ‘the norm’ and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.

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Machine learning for detecting gene-gene interactions: a review.

We parameterized neural net‐based models for the Detroit and Twin Cities metropolitan areas in the US and attempted to test whether they were transferable across both metropolitan areas. Three different types of models were developed. First, we trained and tested the neural nets within each region and compared them against observed change. Second, we used the training weights from one area and applied them to the other. Third, we selected a small subset (∼1%) of the Twin Cities area where a lot of urban change occurred. Four model performance metrics are reported: (1) Kappa; (2) the scale which correct and paired omission/commission errors exceed 50%; (3) landscape pattern metrics; and (4) percentage of cells in agreement between model simulations. We found that the neural net model in most cases performed well on pattern but not location using Kappa. The model performed well only in one case where the neural net weights from one area were used to simulate the other. We suggest that landscape metrics are ...

/pdf/calibrating-a-neural-network-based-urban-change-model-for-52y2oxzvve.pdf

Calibrating a neural network‐based urban change model for two metropolitan areas of the Upper Midwest of the United States

Foundations. Motivation. Neural-Network Fundamentals. Single Neuron Computations. Network Computations. Network Simulation. Foundations Summary. Suggested Readings. Bibliography. Paradigms. The Backpropagation Network. The Counterpropagation Network. Adaptive Resonance Theory. The Multidirectional Associative Memory. The Hopfield Memory. Network-Learning Summary. Suggested Readings. Bibliography. Application Design. Developing a Data Representation. Pattern Representation Methods. Exemplar Analysis. Training and Performance Evaluation. A Practical Example. Application-Design Summary. Suggested Readings. Bibliography. Associative Memories. Associative-Memory Definitions. Character Recognition. State-Space Search. Image Interpolation. Diagnostic Aids. Associative-Memory Summary. Suggested Readings. Bibliography. Business and Financial Applications. Financial Modeling. Market Prediction. Bond Rating. Predicting Commodity Futures. Financial-Applications Summary. Suggested Readings. Bibliography. Pattern Classification. NETtalk. Radar-Signature Classifier. Prostate-Cancer Detection. Pattern-Classification Summary. Suggested Readings. Bibliography. Image Processing. Image-Processing Networks. Gender Recognition from Facial Images. Imagery Feature Discovery. Aircraft Tracking in Video Imagery. Image-Processing Summary. Suggested Readings. Bibliography. Process Control and Robotics. Control Theory. Cart/Pole Balancer. Bipedal-Locomotion Control. Robotic Manipulator Control. Control-Application Summary. Suggested Readings. Bibliography. Fuzzy Neural Systems. Fuzzy Logic. Implementation of a Fuzzy Network. Fuzzy Neural Inference. Fuzzy Control of BPN Learning. Fuzzy Neural-System Summary. Suggested Readings. Bibliography. Answers to Selected Exercises. Index. 0201539217T04062001

David M. Skapura

Papers

Building neural networks