Download Applications and Innovations in Intelligent Systems XVI: by Ted Scully, Kenneth N. Brown (auth.), Tony Allen BA, MSc, PDF

By Ted Scully, Kenneth N. Brown (auth.), Tony Allen BA, MSc, PhD, Richard Ellis BSc, MSc, Miltos Petridis DipEng, MBA, PhD, MBCS, AMBA (eds.)

The papers during this quantity are the refereed software papers provided at AI-2008, the Twenty-eighth SGAI foreign convention on leading edge innovations and purposes of synthetic Intelligence, held in Cambridge in December 2008.

They current new and leading edge purposes of man-made intelligence throughout a variety of domain names; divided into sections on desktop studying, internet applied sciences, clever structures and AI in overall healthiness Care. the quantity additionally comprises the textual content of brief papers offered as posters on the conference.

This is the 16th quantity within the program and thoughts sequence, which deals an annual photo of the newest functions of synthetic intelligence throughout undefined, trade, schooling and govt.

The Technical movement papers are released as a significant other quantity lower than the identify learn and improvement in clever structures XXV.

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Read or Download Applications and Innovations in Intelligent Systems XVI: Proceedings of AI-2008, the Twenty-eighth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence PDF

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Extra info for Applications and Innovations in Intelligent Systems XVI: Proceedings of AI-2008, the Twenty-eighth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence

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A simplified data flow model can be seen in Figure 1. Therefore, it is clearly worthwile to consider using AI techniques in the light of precision farming. 1 Research Target With this contribution we aim at finding suitable methods to visualize agricultural data with a high degree of precision and generality. We present different data sets which shall be visualized. We present experimental results on real and recent agricultural data. Our work helps in visualizing and understanding the available data, which is an important step in data mining.

For example, in Figure 9 the group of eight I-layer neurons, four ON-neurons and four OFF-neurons, corresponds to one A-layer neuron. The thresholds T i and T j are selected randomly from the range [0, bmax], where bmax is maximal brightness of the image pixels. The i-th neuron of the A-layer is active (ai = 1) only if outputs of all the neurons of its I-layer group are equal to 1 and is nonactive (ai = 0) in the opposite case. ON- and OFF-neurons of the I-layer in the Computer Vision System for Manufacturing of Micro Workpieces 29 structure of the LIRA neural classifier correspond to positive and negative points in the structure of the PCNC neural classifier.

3; the number of training cycles is 30; the number of ON-neurons in the I-layer neuron group that corresponds to one A-layer neuron is 3, the number of OFF-neurons is 5; 8 distortions for training including ±1 pixel horizontal, vertical and bias translations and 4 distortions for recognition including ±1 pixel horizontal and vertical translations. Paired t-test [16] for mean recognition rate was used to evaluate significance of difference in recognition rates for CWT and STFT with null hypothesis of no difference in recognition rates and alternative of mean recognition rate for CWT being higher than the one for STFT.

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