scholarly journals Comparison and optimization of machine learning methods for automated classification of circulating tumor cells

2016 ◽  
Vol 89 (10) ◽  
pp. 922-931 ◽  
Author(s):  
Timothy B. Lannin ◽  
Fredrik I. Thege ◽  
Brian J. Kirby
Keyword(s):  
Machine Learning ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Automated Classification ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Application of machine learning methods for automated classification and routing in ITIL

2021 ◽  
Vol 2091 (1) ◽  
pp. 012041
Author(s):  
VV Nikulin ◽  
S D Shibaikin ◽  
A N Vishnyakov
Keyword(s):  
Machine Learning ◽  
Human Factor ◽  
Gradient Boosting ◽  
Automated Classification ◽  
It Services ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Text Information ◽  
Comparison Of The Results

Abstract The article analyzes the application of machine learning methods for automated classification and routing in ITIL library. ITSM technology and ITIL are considered. The definitions of the incident and IT services are given. Then, the vectorization and extraction of keywords in the information written in natural language is carried out and lemmatization and TF-IDF measure will be used. A comparative analysis of the application of machine learning methods is given as well as a comparison of the results of automatic classification of text information using gradient boosting and a convolutional neural network. Various parameters of these methods are considered and the most effective method of machine learning is determined. The results of using machine learning methods for automated classification of incidents allows high-precision routing of requests for restoring the operability of IT services, reducing response time and errors associated with the human factor.

scholarly journals TOWARDS AUTOMATED CLASSIFICATION OF PRODUCT DATA BASED ON MACHINE LEARNING

2020 ◽  
Vol 1 ◽  
pp. 375-382
Author(s):  
S. Schleibaum ◽  
S. Kehl ◽  
P. Stiefel ◽  
J. P. Müller
Keyword(s):  
Machine Learning ◽  
Product Lifecycle Management ◽  
Supervised Machine Learning ◽  
Automated Classification ◽  
Learning Methods ◽  
Industrial Case Study ◽  
Machine Learning Methods ◽  
Novel Approach ◽  
Lifecycle Management

AbstractModern machine learning methods have the potential to supply industrial product lifecycle management (PLM) with automated classification of product components. However, there is only little work in the literature on this topic. We propose to apply supervised machine learning on component meta-data. By analysing an industrial case study, we identify requirements and opportunities for automating classification, e.g. in part numbers and product structures. We validate our novel approach through a classification experiment comparing four machine learning methods on a realistic component dataset.

Machine Learning methods for micro-FTIR imaging classification of human skin tumors

2021 ◽  
Author(s):  
Matheus del Valle ◽  
Kleber Stancari ◽  
Pedro Arthur Augusto de Castro ◽  
Moises Oliveira dos Santos ◽  
Denise Maria Zezell
Keyword(s):  
Machine Learning ◽  
Human Skin ◽  
Skin Tumors ◽  
Learning Methods ◽  
Ftir Imaging ◽  
Machine Learning Methods ◽  
Imaging Classification

scholarly journals Classification of HIV-1 Protease Inhibitors by Machine Learning Methods

ACS Omega ◽  
2018 ◽  
Vol 3 (11) ◽  
pp. 15837-15849 ◽  
Author(s):  
Yang Li ◽  
Yujia Tian ◽  
Zijian Qin ◽  
Aixia Yan
Keyword(s):  
Machine Learning ◽  
Protease Inhibitors ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Hiv 1

scholarly journals Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0166898 ◽  
Author(s):  
Monique A. Ladds ◽  
Adam P. Thompson ◽  
David J. Slip ◽  
David P. Hocking ◽  
Robert G. Harcourt
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods
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