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Artificial Intelligence Techniques

This article explores the diverse applications of neural networks in fields such as biomedical analysis and engineering. It discusses various use cases including data analysis for cardiovascular risk factors, EEG alertness classification, ECG pattern recognition, and EMG disease detection. Significant studies highlight the effectiveness and accuracy of neural networks in these areas, showing promising results like 99% accuracy in training phases and over 95% in EEG classifications. It also delves into other innovative applications such as storm wave prediction and control of underwater vehicles.

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Artificial Intelligence Techniques

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  1. Artificial Intelligence Techniques Applications of Neural Networks

  2. Example Applications • Analysis of data • Classifying in EEG • Pattern recognition in ECG • EMG disease detection.

  3. Gueli N et al (2005) The influence of lifestyle on cardiovascularrisk factors analysis using a neural network Archives of Gerontology and Geriatrics 40 157–172 • To produce a model of risk facts in heart disease. • MLP used • The accuracy was relatively good for chlorestremia and triglyceremdia: • Training phase around 99% • Testing phase around 93% • Not so good for HDL

  4. Subasi A (in press) Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients Expert Systems with Applications xx (2004) 1–11 • Electroencephalography (EEG) • Recordings of electrical activity from the brain. • Classifying operation • Awake • Drowsy • Sleep

  5. MLP • 15-23-3 • Hidden layer – log-tanh function • Output layer – log-sigmoid function • Input is normalise to be within the range 0 to 1.

  6. Accuracy • 95%+/-3% alert • 93%+/-4% drowsy • 92+/-5% sleep • Feature were extracted and form the input to the network, from wavelets.

  7. Karsten Sternickel (2002) Automatic pattern recognition in ECG time series Computer Methods and Programs in Biomedicine 68 109–115 • ECG – electrocardiographs – electrical signals from the heart. • Wavelets again. • Classification of patterns • Patterns were spotted

  8. Abel et al (1996) Neural network analysis of the EMG interference pattern Med. Eng. Phys. Vol. 18, No. 1. pp. 12-l 7 • EMG – Electromyography – muscle activity. • Interference patterns are signals produce from various parts of a muscle- hard to see features. • Applied neural network to EMG interference patterns.

  9. Classifying • Nerve disease • Muscle disease • Controls • Applied various different ways of presenting the pattern to the ANN. • Good for less serve cases, serve cases can often be see by the clinician.

  10. Example Applications • Wave prediction • Controlling a vehicle • Image processing • Condition monitoring

  11. Wave prediction • Raoa S, Mandal S(2005) Hindcasting of storm waves using neural networksOcean Engineering 32 (2005) 667–684 • MLP used to predict storm waves. • 2:2:2 network • Good correlation between ANN model and another model

  12. van de Ven P, Flanagan C, Toal D (in press) Neural network control of underwater vehiclesEngineering Applications of Artificial Intelligence • Semiautomous vehicle • Control using ANN • ANN replaces a mathematical model of the system.

  13. Kuo RJ, et al (in press) Part family formation through fuzzy ART2 neural networkDecision Support Systems • Image recognition of part • Deals with the shifting of the part

  14. Silva et al (2000) THE ADAPTABILITY OF A TOOL WEAR MONITORING SYSTEM UNDER CHANGING CUTTING CONDITIONS Mechanical Systems and Signal Processing (2000) 14(2), 287-298 • Modelling tool wear • Combines ANN with other AI (Expert systems) • Self organising Maps (SOM) and ART2 investigated • SOM better for extracting the required information.

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