A Longitudinal Comparison of Supervised and Unsupervised Learning Approaches to Iso-Resource Grouping for Acute Healthcare in Australia

2005 ◽  
pp. 283-303
Author(s):  
Eu-Gene Siew ◽  
Kate A. Smith ◽  
Leonid Churilov ◽  
Jeff Wassertheil
Keyword(s):  
Unsupervised Learning ◽  
Learning Approaches ◽  
Supervised And Unsupervised Learning ◽  
Longitudinal Comparison ◽  
Acute Healthcare

scholarly journals Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification

2020 ◽  
Vol 2 (4) ◽  
pp. 513-528
Author(s):  
Rossella Aversa ◽  
Piero Coronica ◽  
Cristiano De Nobili ◽  
Stefano Cozzini
Keyword(s):  
Unsupervised Learning ◽  
Clustering Analysis ◽  
Feature Learning ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Clustering Methods ◽  
Data Set ◽  
Sem Image ◽  
Supervised And Unsupervised Learning ◽  
Scanning Electron

In this paper, we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope (SEM). This is done by coupling supervised and unsupervised learning approaches. We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy. Then, we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales (from 1 μm to 2 μm). Finally, we compare different clustering methods to uncover intrinsic structures in the images.

scholarly journals Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches

2019 ◽  
Vol 38 (8) ◽  
pp. 1777-1787 ◽  
Author(s):  
Sarfaraz Hussein ◽  
Pujan Kandel ◽  
Candice W. Bolan ◽  
Michael B. Wallace ◽  
Ulas Bagci
Keyword(s):  
Deep Learning ◽  
Unsupervised Learning ◽  
Pancreatic Tumor ◽  
Learning Approaches ◽  
Tumor Characterization ◽  
Supervised And Unsupervised Learning

scholarly journals A novel Approach Using “Supervised and Unsupervised learning” to prevent the Adequacy of Intrusion Detection Systems

2018 ◽  
Vol 7 (3.34) ◽  
pp. 474
Author(s):  
Pradeep Kumar Mallick ◽  
Bibhu Prasad Mohanty ◽  
Sudan Jha ◽  
Kuhoo .
Keyword(s):  
Intrusion Detection ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Critical Role ◽  
Intrusion Detection Systems ◽  
Detection Methods ◽  
The Internet ◽  
Learning Approaches ◽  
Detection Systems ◽  
Supervised And Unsupervised Learning

Countering digital dangers, particularly assault detection, is a testing region of research in the field of data affirmation. Intruders utilize polymorphic instruments to disguise the assault payload and dodge the detection methods. Many supervised and unsupervised learning comes closer from the field of machine learning and example acknowledgments have been utilized to expand the adequacy of intrusion detection systems (IDSs). Supervised learning approaches utilize just marked examples to prepare a classifier, however getting adequate named tests is lumbering, and requires the endeavors of area specialists. Notwithstanding, un-marked examples can without much of a stretch be acquired in some genuine issues. Contrasted with super-vised learning approaches, semi-supervised learning (SSL) addresses this issue by considering expansive number of unlabeled examples together with the marked examples to fabricate a superior classifier. In today’s age security is a big issue and every day when we are on the internet we are exposed to a huge number of threats where our personal information can be leaked. The information security and the Intrusion Detection System (IDS) play a critical role in the internet. IDS isan essential tool for detecting different kinds of attacks in a network and maintaining data integrity, confidentiality, and system availability against possible threats. In this paper, we are proposing a modified Elitist approach where the value of fitness is multiplied by the times a variable which is determined on the basis of the value of Kappa (K).  

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