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deficient. Applications of Artificial Neural Networks for Nonlinear Data is a collection of innovative research on the contemporary nature;
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Artificial neural network research is one of the new directions for new generation computers. Current research suggests that open box;
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Artificial neural network research is one of the new directions for new generation computers. Current research suggests that open box;
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processes. Artificial neural networks are a set of statistical modeling tools that are capable of processing nonlinear data with strong accuracy;
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processes. Artificial neural networks are a set of statistical modeling tools that are capable of processing nonlinear data with strong accuracy;
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Deep Neural Networks and Applications makes the readers aware about the various artificial neural networks (ANN) and the topologies related;
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Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different;
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Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and;
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, medical image fusion based on pulse coupled neural networks, artificial neural networks for intrusion detection, recognition of Arabic sign;
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course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers;
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Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that;
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constructing confidence and prediction intervals Ideal as a textbook for MBA and graduate-level courses in applied neural network modeling, artificial;
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of pharamceutical microemulsions; massive-training artificial neural networks for supervised enhancement/suppression of lesions/patterns in medical images;
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Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks;
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Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks;
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The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple;
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The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple;
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This book presents a broad overview on the current various design modules and applications of artificial neural systems. Artificial neural;
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Artificial neural networks provide a powerful tool to help doctors analyse, model and make sense of complex clinical data across a broad;
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Artificial neural networks provide a powerful tool to help doctors analyse, model and make sense of complex clinical data across a broad;
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literature regarding the use of artificial neural networks for the modelling and optimization of food drying processes. The applications;
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analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks;
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In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering;
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The potential value of artificial neural networks (ANN) as a predictor of malignancy has begun to receive increased recognition. Research;
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with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial;
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of neural network paradigms, however, the newest of these have not appeared in book form-until now. Recent Advances in Artificial Neural Networks;
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Artificial Neural Networks for Renewable Energy Systems and Real-World Applications presents current trends for the solution of complex;
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