Dr. John Abbot has been a scientific researcher for over 30-years both in academia and industrial settings. Currently, Dr. Abbot is a Professorial Research Fellow at Central Queensland University in Noosa, Australia. The majority of his projects are related to environmental issues including his current project for forecasting medium and long-term monthly rainfall in Australia. Dr.
In the mining industry, most of the underground injuries and fatalities are due to rock falls (i.e. fall of hanging wall/roof). CSIR has developed a device that assists any miner in making an objective decision when determining the integrity of the hanging wall. A trained neural network model is embedded into the device. The device then records the sound emitted when a hanging wall is tapped. The sound is then preprocessed before being input into a trained neural network model and the trained model classifies the hanging wall as either intact or detached.
Gary Howorth has been working in the oil and gas industry for 25-years and has a Degree in Electronic Engineering specializing in digital control theory and a MBA. Mr. Howorth has worked for several key corporations in the industry including BP, Arthur Andersen (Petroleum Services Group), PA Consulting and currently PFC Energy performing quantitative analysis on new and obscure modeling.
Fraser Cook is the Principal Engineer for Artificial Intelligence (AI) at Qualitrol-DMS in the United Kingdom. Qualitrol-DMS supply condition monitoring systems and services for the electrical power transmission and distribution industry. Qualitrol-DMS is the world leader in Partial Discharge (PD) monitoring for Gas Insulated Switchgear (GIS). Mr. Cook studied Artificial Intelligence while receiving his Masters degree in Cognitive Science from Division of Informatics, University of Edinburgh.
Using NeuroSolutions, this study searched through a database of WWW businesses and classified them as “interesting” and “not interesting” for determining future business ventures. With generalized models, the new data sets were classified at 84.75% on average correctly.
This paper reports on oil classification with fluorescence spectroscopy. Using NeuroSolutions, the objective is to classify the oil fluorescence spectra based on a laboratory dataset of fluorescence spectra of several oil classes (sludge, crude and heavy oil). The classification was carried out using the following three methods: channel relationship method (CRM), artificial neural networks (ANNs), and support vector machines (SVMs).
Using remote sensing satellite imagery, multi-spectral sensor data is used for applications to map forest types or changes in a forest or to extract statistical information from the images together with available maps. In the case of mapping of forest types and changes, the information is essential for forestry companies in order to keep their databases up-to-date for planning and forest management. In addition, the imagery has been used to do wall-to-wall mapping of land use and vegetation from space as well as measuring the depth in the sea water, mapping along the coastlines and measuring the vegetation at the bottom of the sea. The primary benefit of using NeuroSolutions over other methods is not having to set up a mathematical model for the classification and regression applications. Common classification methods used in remote sensing is based on the assumption of normal distributed classes. However, in Mr. Rosengren's applications, the classes often have multimodal distributions. Using NeuroSolutions rather than other classification and regression methods, often save as much as 25% of the time and cost of the project!
Using NeuroSolutions, Mr. Roebber developed a snow density system that is currently employed by the National Weather Service to assist in the prediction of snow fall depths. Mr Roebber's system uses several different types of neural network architectures including the Principal Component Analysis (PCA) and Multilayer Perceptron (One and Two Hidden Layer) to complete his ensemble of 10 artificial neural networks. The ensemble correctly diagnoses 60.4% of the snow event cases examined, which is a substantial improvement over the 41.7% correct using the sample climatology, 45% correct using the 10-to-1 ratio (see table), and 51.7% correct using the National Weather Service "new snowfall to estimated meltwater conversion" table. The Heidke skill score measures the fraction of correct forecasts after eliminating those forecasts which would be correct due purely to random chance. The ensemble technique attains Heidke skill scores of 0.34 - 0.42, which is an increase of 75% - 183% over the next most skillful approach!
Corporate accounting statements provide financial markets, and tax services with valuable data on the economic health of companies, although financial indices are only focused on a very limited part of the activity within the company. Useful tools in the field of processing extended financial and accounting data are the methods of Artificial Intelligence, aiming the efficient delivery of financial information to tax services, investors, and financial markets where lucrative portfolios can be created.
Mr. Murphy has integrated NeuroSolutions neural networks into several areas of professional sports including forecasting risk of injury, player performance and classifying match strategies. He uses approximately 20 to 30 different breadboards (including a mixture of classification and function approximation networks) such as: Multilayer Perceptron (MLP), Radial Basis Function (RBF), Modular Networks and Principal Component Analysis (PCA) networks for pre-processing. For predicting risk of injury the network occupies approximately 25 inputs including training loads over a 3-week period, wellness (i.e. fatigue, sleep quality & stress), pain & comfort ratings (i.e. foot, ankle, calf, groin, etc.) and player conditioning.