A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2018.12.003
Recognition and sensing of organic compounds using analytical methods, chemical sensors, and pattern recognition approaches
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2018.12.008
Image-based process monitoring using deep learning framework
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.03.008
A conceptual view to the area correlation constraint in multivariate curve resolution
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.04.009
iPredCNC: Computational prediction model for cancerlectins and non-cancerlectins using novel cascade features subset selection
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103876
Data-driven supervised fault diagnosis methods based on latent variable models: a comparative study
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.02.006
Using polarized Total Synchronous Fluorescence Spectroscopy (pTSFS) with PARAFAC analysis for characterizing intrinsic protein emission
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103871
Probe technique-based generalized multivariate standard addition strategy for the analysis of fluorescence signals with matrix effects
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.05.006
Automatic segmentation method for CFU counting in single plate-serial dilution
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103889
Spectra data classification with kernel extreme learning machine
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.103815
EEMlab: A graphical user-friendly interface for fluorimetry experiments based on the drEEM toolbox
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.03.001
RMet: An automated R based software for analyzing GC-MS and GC×GC-MS untargeted metabolomic data
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103866
Comparison of multi-response prediction methods
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.05.004
Variable selection using statistical non-parametric tests for classifying production batches into multiple classes
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103830
Introducing the monotonicity constraint as an effective chemistry-based condition in self-modeling curve resolution
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.04.002
Rock lithological classification by hyperspectral, range 3D and color images
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.04.006
A user-friendly excel spreadsheet for dealing with spectroscopic and chromatographic data
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103816
A method for gene essentiality in miRNA-TF-mRNA co-regulatory network and its application on prostate cancer
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.05.007
Supervised classification of monomodal and multimodal hyperspectral data in vibrational microspectroscopy: A comprehensive comparison
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2018.11.013
Basil leaves disease classification and identification by incorporating survival of fittest approach
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.01.006
Supervised projection pursuit - A dimensionality reduction technique optimized for probabilistic classification
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103867
On the internal correlations of protein sequences probed by non-alignment methods: Novel signatures for drug and antibody targets via the Burrows-Wheeler Transform
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.07.008
Ridge regression with self - Paced learning algorithm in interpretation of voltammetric signals
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/J.CHEMOLAB.2019.06.008
Semi-supervised learning in multivariate calibration
来源期刊:Chemometrics and Intelligent Laboratory SystemsDOI:10.1016/j.chemolab.2019.103868