Artificial neural networks applied to FTIR and FT-Raman spectra in biomedical applications

1998 ◽  
Author(s):  
J. Schmitt ◽  
T. Udelhoven ◽  
T. Löchte ◽  
H.-C. Flemming ◽  
D. Naumann
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Raman Spectra ◽  
Biomedical Applications ◽  
Artificial Neural ◽  
Ft Raman

scholarly journals MINIATURIZATION OF A PIFA ANTENNA FOR BIOMEDICAL APPLICATIONS USING ARTIFICIAL NEURAL NETWORKS

2018 ◽  
Vol 70 ◽  
pp. 1-10
Author(s):  
Asma Djellid ◽  
Lionel Pichon ◽  
Stavros Koulouridis ◽  
Farid Bouttout
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Biomedical Applications ◽  
Artificial Neural ◽  
Pifa Antenna

scholarly journals Artificial Intelligence Aided Design of Microtextured Surfaces: Application to Controlling Wettability

Nanomaterials ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 2287
Author(s):  
Andrés Díaz Lantada ◽  
Francisco Franco-Martínez ◽  
Stefan Hengsbach ◽  
Florian Rupp ◽  
Richard Thelen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Biomedical Applications ◽  
Innovative Materials ◽  
Artificial Neural ◽  
The Creation ◽  
Aided Design ◽  
Microtextured Surfaces ◽  
And Training

Artificial intelligence (AI) has emerged as a powerful set of tools for engineering innovative materials. However, the AI-aided design of materials textures has not yet been researched in depth. In order to explore the potentials of AI for discovering innovative biointerfaces and engineering materials surfaces, especially for biomedical applications, this study focuses on the control of wettability through design-controlled hierarchical surfaces, whose design is supported and its performance predicted thanks to adequately structured and trained artificial neural networks (ANN). The authors explain the creation of a comprehensive library of microtextured surfaces with well-known wettability properties. Such a library is processed and employed for the generation and training of artificial neural networks, which can predict the actual wetting performance of new design biointerfaces. The present research demonstrates that AI can importantly support the engineering of innovative hierarchical or multiscale surfaces when complex-to-model properties and phenomena, such as wettability and wetting, are involved.

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