AIMC Topic: Neural Networks, Computer

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Using neural networks to understand the information that guides behavior: a case study in visual navigation.

Methods in molecular biology (Clifton, N.J.)
To behave in a robust and adaptive way, animals must extract task-relevant sensory information efficiently. One way to understand how they achieve this is to explore regularities within the information animals perceive during natural behavior. In thi...

Developing a multimodal biometric authentication system using soft computing methods.

Methods in molecular biology (Clifton, N.J.)
Robust personal authentication is becoming ever more important in computer-based applications. Among a variety of methods, biometric offers several advantages, mainly in embedded system applications. Hard and soft multi-biometric, combined with hard ...

Modulation of grasping force in prosthetic hands using neural network-based predictive control.

Methods in molecular biology (Clifton, N.J.)
This chapter describes the implementation of a neural network-based predictive control system for driving a prosthetic hand. Nonlinearities associated with the electromechanical aspects of prosthetic devices present great challenges for precise contr...

GENN: a GEneral Neural Network for learning tabulated data with examples from protein structure prediction.

Methods in molecular biology (Clifton, N.J.)
We present a GEneral Neural Network (GENN) for learning trends from existing data and making predictions of unknown information. The main novelty of GENN is in its generality, simplicity of use, and its specific handling of windowed input/output. Its...

Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR).

Methods in molecular biology (Clifton, N.J.)
This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to t...

AutoWeka: toward an automated data mining software for QSAR and QSPR studies.

Methods in molecular biology (Clifton, N.J.)
UNLABELLED: In biology and chemistry, a key goal is to discover novel compounds affording potent biological activity or chemical properties. This could be achieved through a chemical intuition-driven trial-and-error process or via data-driven predict...

Prediction of bioactive peptides using artificial neural networks.

Methods in molecular biology (Clifton, N.J.)
Peptides are molecules of varying complexity, with different functions in the organism and with remarkable therapeutic interest. Predicting peptide activity by computational means can help us to understand their mechanism of action and deliver powerf...

Use of artificial neural networks in the QSAR prediction of physicochemical properties and toxicities for REACH legislation.

Methods in molecular biology (Clifton, N.J.)
With the introduction of the REACH legislation in the European Union, there is a requirement for property and toxicity data on chemicals produced in or imported into the EU at levels of 1 tonne/year or more. This has meant an increase in the in silic...

A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents.

Methods in molecular biology (Clifton, N.J.)
Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibi...

Predicting bacterial community assemblages using an artificial neural network approach.

Methods in molecular biology (Clifton, N.J.)
Microbial communities are found in nearly all environments and play a critical role in defining ecosystem service. Understanding the relationship between these microbial communities and their environment is essential for prediction of community struc...