Evor L. Hines

Evor L. Hines joined the School of Engineering at Warwick in 1984. He was promoted to Reader in 2005 and to a personal chair in 2009. He obtained his DSc (Warwick) in 2007 and is a Fellow of both the Institute of Engineering and Technology and the Higher Education Academy, in addition to being a Chartered Engineer. His main research interest is concerned with intelligent systems and their applications. Most of the work has focused on artificial neural networks, genetic algorithms, fuzzy logic, neurofuzzy systems and genetic programming. Typical application areas include, inter alia, intelligent sensors such as the electronic nose, medicine, non-destructive testing, computer vision, and telecommunications. He has co-authored in excess of 230 articles and supervised over 30 research students in addition to currently leading the Information and Communication Technologies Research Group in the School of Engineering.

Publications

Intelligent Systems for Machine Olfaction: Tools and Methodologies
Intelligent Systems for Machine Olfaction: Tools and Methodologies
Evor L. Hines, Mark S. Leeson. © 2011. 354 pages.
Intelligent systems are systems that, given some data, are able to learn from that data. This makes it possible for complex systems to be modeled and/or for performance to be...
Applied Signal and Image Processing: Multidisciplinary Advancements
Applied Signal and Image Processing: Multidisciplinary Advancements
Rami Qahwaji, Roger Green, Evor L. Hines. © 2011. 414 pages.
Image and signal processing techniques are receiving increasing interest because of their numerous real-world applications. Data is now available in different forms, different...
Evolutionary Algorithms for Multisensor Data Fusion
Evolutionary Algorithms for Multisensor Data Fusion
Jianhua Yang, Evor L. Hines, John E. Sloper, D. D. Iliescu, Mark S. Leeson. © 2011. 16 pages.
The aim of Multisensor Data Fusion (MDF) is to eliminate redundant, noisy or irrelevant information and thus find an optimal subset from an array of high dimensionality. An...
Detection of Diseases and Volatile Discrimination of Plants: An Electronic Nose and Self-Organizing Maps Approach
Detection of Diseases and Volatile Discrimination of Plants: An Electronic Nose and Self-Organizing Maps Approach
Reza Ghaffari, Fu Zhang, D. D. Iliescu, Evor L. Hines, Mark S. Leeson, Richard Napier. © 2011. 17 pages.
The diagnosis of plant diseases is an important part of commercial greenhouse crop production and can enable continued disease and pest control. A plant subject to infection...
Tomato Plant Health Monitoring: An Electronic Nose Approach
Tomato Plant Health Monitoring: An Electronic Nose Approach
Fu Zhang, D. D. Iliescu, Evor L. Hines, Mark S. Leeson. © 2011. 18 pages.
Electric noses (e-noses), taking their inspiration from the human olfactory system, have been extensively used in food quality control and human disease monitoring. This chapter...
Enhancing the Classification of Eye Bacteria Using Bagging to Multilayer Perceptron and Decision Tree
Enhancing the Classification of Eye Bacteria Using Bagging to Multilayer Perceptron and Decision Tree
Xu-Qin Li, Evor L. Hines, Mark S. Leeson, D. D. Iliescu. © 2011. 17 pages.
Eye bacteria are vital to the diagnosis of eye disease, which makes the classification of such bacteria necessary and important. This chapter aims to classify different kinds of...
A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal Dispersion Coefficient in Rivers
A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal Dispersion Coefficient in Rivers
Jianhua Yang, Evor L. Hines, Ian Guymer, Daciana D. Iliescu, Mark S. Leeson, Gregory P. King, XuQuin Li. © 2009. 17 pages.
In this chapter a novel method, the Genetic Neural Mathematical Method (GNMM), for the prediction of longitudinal dispersion coefficient is presented. This hybrid method utilizes...