Expert Systems in Chemistry Research

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A01=Markus C. Hemmer
articial
artificial intelligence methods
Author_Markus C. Hemmer
Category=UYQE
Certainty Factors
chemical data interpretation
Chemical Shifts
chemoinformatics applications
Correlation Coefcient
Data Set
Data Sets
descriptor
engine
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
expert system design for laboratory research
Expert Systems
fuzzy
genetic algorithm optimization
inference
Inference Engine
Infrared Spectrum
Knowledge Base
Kohonen Network
Kohonen Neural Networks
laboratory automation
LIMS
logic
Lter Coefcients
molecular
Molecular Descriptors
Molecular Input Line Entry Specication
networks
neural
nonlinear statistical analysis
Partial Atomic Charge
QSAR
QSAR Model
RDF
RDF Descriptor
shells
Smoothing Parameter
Software Agent
Substructure Search
Synthesis Planning
User Input

Product details

  • ISBN 9781420053234
  • Weight: 722g
  • Dimensions: 156 x 234mm
  • Publication Date: 13 Dec 2007
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Expert systems allow scientists to access, manage, and apply data and specialized knowledge from various disciplines to their own research. Expert Systems in Chemistry Research explains the general scientific basis and computational principles behind expert systems and demonstrates how they can improve the efficiency of scientific workflows and support decision-making processes.

Focused initially on clarifying the fundamental concepts, limits, and drawbacks of using computer software to approach human decision making, the author also underscores the importance of putting theory into practice. The book highlights current capabilities for planning and monitoring experiments, scientific data management and interpretation, chemical characterization, problem solving, and methods for encoding chemical data. It also examines the challenges as well as requirements, strategies, and considerations for implementing expert systems effectively in an existing laboratory software environment.

Expert Systems in Chemistry Research covers various artificial intelligence technologies used to support expert systems, including nonlinear statistics, wavelet transforms, artificial neural networks, genetic algorithms, and fuzzy logic. This definitive text provides researchers, scientists, and engineers with a cornerstone resource for developing new applications in chemoinformatics, systems design, and other emerging fields.

Fresenius University of Applied Sciences, Cologne, Germany

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