Titlebook: Artificial Neural Networks; Hugh Cartwright Book 2015Latest edition Springer Science+Business Media New York 2015 ANN.Artificial Intellige

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Artificial Neural Network for Charge Prediction in Metabolite Identification by Mass Spectrometry,ir internal structure. Interpretation of experimental CID spectra always involves some form of in silico spectra of potential candidate molecules. Knowledge of how charge is distributed among fragments is an important part of CID simulations that generate in silico spectra from the chemical structur

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Prediction of Bioactive Peptides Using Artificial Neural Networks,tivity by computational means can help us to understand their mechanism of action and deliver powerful drug-screening methodologies. In this chapter, we describe how to apply artificial neural networks to predict antimicrobial peptide activity.

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AutoWeka: Toward an Automated Data Mining Software for QSAR and QSPR Studies,d through a chemical intuition-driven trial-and-error process or via data-driven predictive modeling. The latter is based on the concept of quantitative structure-activity/property relationship (QSAR/QSPR) when applied in modeling the biological activity and chemical properties, respectively, of com

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Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In

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Modulation of Grasping Force in Prosthetic Hands Using Neural Network-Based Predictive Control, with the electromechanical aspects of prosthetic devices present great challenges for precise control of this type of device. Model-based controllers may overcome this issue. Moreover, given the complexity of these kinds of electromechanical systems, neural network-based modeling arises as a good f

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Application of Artificial Neural Networks in Computer-Aided Diagnosis,in the interpretation of medical images, either to help with lesion detection or to help determine if the lesion is benign or malignant. Artificial neural networks (ANNs) are usually employed to formulate the statistical models for computer analysis. Receiver operating characteristic curves are used

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Developing a Multimodal Biometric Authentication System Using Soft Computing Methods,vantages, mainly in embedded system applications. Hard and soft multi-biometric, combined with hard and soft computing methods, can be applied to improve the personal authentication process and to generalize the applicability..This chapter describes the embedded implementation of a multi-biometric (