Use of regression analysis and electronic computers in the prediction of coronary artery disease.
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Neural networks are used as models of cognitive systems. On the basis of results from brain research, the processing of knowledge and information can be conceived as occurring through parallel interaction of multiple but simple and uniform processor elements in a network structure. In these 'neural networks' knowledge is stored in a distributed way throughout the network and subjected to parallel processing. According to Hebb's concept of synaptic plasticity, the process of learning is based on the strengthening of the links between the processing elements. The implementation of network models in electronic data processing systems allows for simulation of cognitive phenomena such as learning and forgetting or abilities such as recognition of acoustical or optical patterns, in a more efficient way than conventional computer programming. Neural network modelling can help to understand the abilities and disorders of the brain.
The analytical method for calculation of electron dose distributions described in part I was experimentally verified using a 42-MeV betatron. For this, a comparison was made between the calculated and the measured dose distributions obtained with a homogeneous or inhomogeneous water or polystyrol phantom with plane or with uneven surfaces, the inhomogeneities consisting in cavities and cork intercalations. Thus, the accuracy of the algorithm and of the consideration for inhomogeneities and irregular surfaces is demonstrated, and limitations of the method's applicability are shown. Some examples of calculated dose distributions in coplanar irradiation techniques using solely electron beams or else a combined treatment with photons and electrons are exhibiting the particular bearing of electronic data processing on the treatment planning in depth therapy with electrons in the energy range above 20 MeV.
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