Semi-supervised Machine Learning Algorithm in Near Infrared Spectral Calibration: A Case Study to Determine Cetane Number and Total Aromatics of Diesel Fuels

2012 ◽  
Author(s):  
Songjing Wang ◽  
Di Wu ◽  
Kangsheng Liu
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Learning Algorithm ◽  
Cetane Number ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Diesel Fuels ◽  
Spectral Calibration ◽  
Infrared Spectral

Abstract TP61: Detection of Atherosclerotic Plaque Using Non-contact and Label-free Near-infrared Hyper-spectral Imaging

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Hideo Chihara ◽  
Naoya Oishi ◽  
Akira Ishii ◽  
Toshihiro Munemitsu ◽  
Daisuke Arai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vascular Surgery ◽  
Atherosclerotic Plaque ◽  
Near Infrared ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Atherosclerotic Plaques ◽  
Machine Learning Algorithm ◽  
Label Free

Background and Aims: Detecting detailed atherosclerotic plaques is important to reduce risk factors during vascular surgery. However, there are few methods to evaluate them during surgery. The aim of this study was to establish an in vivo, non-contact, and label-free imaging method for identifying atherosclerotic plaque lesions from outside vessels with a diffuse-reflectance near-infrared (NIR) hyperspectral imaging (HSI) system. Method: NIR spectra between 1000 and 2350 nm were measured using an NIR HSI imaging system outside the exposed abdominal aorta in 5 Watanabe Heritable Hyperlipidemic (WHHL) rabbits in vivo. Preprocessed data were input to a supervised machine learning algorithm called a support vector machine (SVM) to create pixel-based images that can predict atherosclerotic plaques within a vessel. The images were compared with histological findings. Result: Absorbance was significantly higher in plaques than in normal arteries at 1000-1380, 1580-1810, and 1880-2320 nm. Overall predictive performance showed a sensitivity of 0.814 ± 0.017, a specificity of 0.836 ± 0.020, and an accuracy of 0.827 ± 0.008. The area under the receiver operating characteristic curve was 0.905 (95% confidence interval = 0.904-0.906). Conclusion: The NIR HSI system combined with a machine learning algorithm enabled accurate detection of atherosclerotic plaques within an internal vessel with high spatial resolution from outside the vessel. The findings indicate that the NIR HSI system can provide non-contact, label-free, and precise localization of atherosclerotic plaques during vascular surgery.

Twitter Users' Classification Based on Interest

2020 ◽  
Vol 10 (1) ◽  
pp. 1-12
Author(s):  
Noura A. AlSomaikhi ◽  
Zakarya A. Alzamil
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Knowledge Sharing ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Software System ◽  
Web Based ◽  
Twitter Users

Microblogging platforms, such as Twitter, have become a popular interaction media that are used widely for different daily purposes, such as communication and knowledge sharing. Understanding the behaviors and interests of these platforms' users become a challenge that can help in different areas such as recommendation and filtering. In this article, an approach is proposed for classifying Twitter users with respect to their interests based on their Arabic tweets. A Multinomial Naïve Bayes machine learning algorithm is used for such classification. The proposed approach has been developed as a web-based software system that is integrated with Twitter using Twitter API. An experimental study on Arabic tweets has been investigated on the proposed system as a case study.

scholarly journals AI3 PREDICTION OF BREAST CANCER USING K-NEAREST NEIGHBOUR: A SUPERVISED MACHINE LEARNING ALGORITHM

2020 ◽  
Vol 23 ◽  
pp. S1
Author(s):  
S. Pandey ◽  
A. Sharma ◽  
M.K. Siddiqui ◽  
D. Singla ◽  
J. Vanderpuye-Orgle
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Nearest Neighbour
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