AI Medical Compendium Journal:
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

Showing 101 to 110 of 184 articles

NIR spectroscopy combined with 1D-convolutional neural network for breast cancerization analysis and diagnosis.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Oct 22, 2022
Near-infrared (NIR) spectroscopy with deep penetration can characterize the composition of biological tissue based on the vibration of the X-H group in a rapid and high-specificity way. Deep learning is proven helpful for rapid and automatic identifi...
Humans Neural Networks, Computer Cluster Analysis Discriminant Analysis Spectroscopy, Near-Infrared Reproducibility of Results
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Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Sep 20, 2022
According to the limited molecular information reflected by single spectroscopy, and the complementarity of FTIR spectroscopy and Raman spectroscopy, we propose a novel diagnostic technology combining multispectral fusion and deep learning. We used s...
Spectroscopy, Fourier Transform Infrared Lung Neoplasms Carcinoma, Non-Small-Cell Lung Deep Learning Spectrum Analysis, Raman Humans Technology
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Research on non-destructive testing of hotpot oil quality by fluorescence hyperspectral technology combined with machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Aug 28, 2022
Eating repeatedly used hotpot oil will cause serious harm to human health. In order to realize rapid non-destructive testing of hotpot oil quality, a modeling experiment method of fluorescence hyperspectral technology combined with machine learning a...
Support Vector Machine Fluorescence Machine Learning Technology Algorithms Humans
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Near infrared spectroscopy quantification based on Bi-LSTM and transfer learning for new scenarios.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Aug 13, 2022
This study proposed a deep transfer learning methodology based on an improved Bi-directional Long Short-Term Memory (Bi-LSTM) network for the first time to address the near infrared spectroscopy (NIR) model transfer issue between samples. We tested i...
Neural Networks, Computer Deep Learning Cattle Spectroscopy, Near-Infrared Animals Manure
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A powerful tool for near-infrared spectroscopy: Synergy adaptive moving window algorithm based on the immune support vector machine.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jul 14, 2022
Traditional trial-and-error methods are time-consuming and inefficient, especially very unfriendly to inexperienced analysts, and are sometimes still used to select preprocessing methods or wavelength variables in near-infrared spectroscopy (NIR). To...
Spectroscopy, Near-Infrared Least-Squares Analysis Support Vector Machine Algorithms Gasoline
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Diagnosis of urogenital cancer combining deep learning algorithms and surface-enhanced Raman spectroscopy based on small extracellular vesicles.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jul 7, 2022
PURPOSE: To identify and compare the capacities of serum and serum-derived small extracellular vesicles (EV) in diagnosis of common urogenital cancer combining Surface-enhanced Raman spectroscopy (SERS) and Convolutional Neural Networks (CNN).
Deep Learning Carcinoma, Renal Cell Male Spectrum Analysis, Raman Kidney Neoplasms Extracellular Vesicles Humans Algorithms
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A deep learning model designed for Raman spectroscopy with a novel hyperparameter optimization method.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jun 25, 2022
Raman spectroscopy is a spectroscopic technique typically used to determine vibrational modes of molecules and is commonly used in chemistry to provide a structural fingerprint by which molecules can be identified. With the help of deep learning, Ram...
Neural Networks, Computer Algorithms Spectrum Analysis, Raman Deep Learning
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Raman spectroscopy for on-line monitoring of botanical extraction process using convolutional neural network with background subtraction.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jun 9, 2022
Aqueous extraction is the most common and cost-effective means of obtaining active ingredients from medicinal plants. However, botanical extracts generally contain high pigment content and complex chemical composition posing a challenge for the proce...
Least-Squares Analysis Neural Networks, Computer Spectrum Analysis, Raman Plant Extracts
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Can machine learning methods accurately predict the molar absorption coefficient of different classes of dyes?

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy May 30, 2022
In this article, we provide a convenient tool for all researchers to predict the value of the molar absorption coefficient for a wide number of dyes without any computer costs. The new model is based on RFR method (ALogPS, OEstate + Fragmentor + QNPR...
Coloring Agents Machine Learning
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Olive oil classification with Laser-induced fluorescence (LIF) spectra using 1-dimensional convolutional neural network and dual convolution structure model.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy May 21, 2022
Laser-induced fluorescence (LIF) spectroscopy is widely used for the analysis and classification of olive oil. This paper proposes the classification of LIF data using a specific 1-dimensional convolutional neural network (1D-CNN) model, which does n...
Lasers Fluorescence Neural Networks, Computer Olive Oil Support Vector Machine
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