An Unsupervised Machine Learning Technique for Recommendation Systems

2020 ◽  
Author(s):  
Rupesh Babu Shrestha ◽  
Mahsa Razavi ◽  
P.W.C Prasad
Keyword(s):  
Machine Learning ◽  
Recommendation Systems ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique

Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique

2021 ◽  
Author(s):  
Alexandre Oliveira Marques ◽  
Aline Nonato Sousa ◽  
Veronica Pereira Bernardes ◽  
Camila Hipolito Bernardo ◽  
Danielle Monique Reis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brazilian Coast ◽  
Ecological Distribution ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique

scholarly journals Utilizing Unsupervised Machine Learning in BSM Physics Searches At The LHC

2020 ◽  
Vol 245 ◽  
pp. 06021
Author(s):  
Adam Leinweber ◽  
Martin White
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Hadron Collider ◽  
New Physics ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique ◽  
Bsm Physics ◽  
Supersymmetric Particles ◽  
One Machine

Recent searches for supersymmetric particles at the Large Hadron Collider have been unsuccessful in detecting any BSM physics. This is partially because the exact masses of supersymmetric particles are not known, and as such, searching for them is very difficult. The method broadly used in searching for new physics requires one to optimise on the signal being searched for, potentially suppressing sensitivity to new physics which may actually be present that does not resemble the chosen signal. The problem with this approach is that, in order to detect something with this method, one must already know what to look for. I will showcase one machine-learning technique that can be used to define a “signal-agnostic” search. This is a search that does not make any assumptions about the signal being searched for, allowing it to detect a signal in a more general way. This method is applied to simulated BSM physics data and the results are explored.

scholarly journals uniForest: an unsupervised machine learning technique to detect outliers and restrict variance in microbiome studies

2021 ◽  
Author(s):  
Robert J. Leigh ◽  
Richard A. Murphy ◽  
Fiona Walsh
Keyword(s):  
Machine Learning ◽  
Variance Reduction ◽  
Principal Component ◽  
Alpha Diversity ◽  
Anatomical Site ◽  
Desktop Computer ◽  
Machine Learning Technique ◽  
Healthy Human ◽  
Unsupervised Machine Learning ◽  
Learning Technique

Isolation Forests is an unsupervised machine learning technique for detecting outliers in continuous datasets that does not require an underlying equivariant or Gaussian distribution and is suitable for use on small datasets. While this procedure is widely used across quantitative fields, to our knowledge, this is the first attempt to solely assess its use for microbiome datasets. Here we present uniForest, an interactive Python notebook (which can be run from any desktop computer using the Google Colaboratory web service) for the processing of microbiome outliers. We used uniForest to apply Isolation Forests to the Healthy Human Microbiome project dataset and imputed outliers with the mean of the remaining inliers to maintain sample size and assessed its prowess in variance reduction in both community structure and derived ecological statistics (alpha-diversity). We also assessed its functionality in anatomical site differentiation (pre- and postprocessing) using principal component analysis, dissimilarity matrices, and ANOSIM. We observed a minimum variance reduction of 81.17% across the entire dataset and in alpha diversity at the Phylum level. Application of Isolation Forests also separated the dataset to an extremely high specificity, reducing variance within taxa samples by a minimum of 81.33%. It is evident that Isolation Forests are a potent tool in restricting the effect of variance in microbiome analysis and has potential for broad application in studies where high levels of microbiome variance is expected. This software allows for clean analyses of otherwise noisy datasets.

Conjugate Thermal Optimization with Unsupervised Machine Learning

2021 ◽  
Author(s):  
Reid Smith ◽  
Sandip Dutta
Keyword(s):  
Machine Learning ◽  
Film Cooling ◽  
Thermal Stresses ◽  
Optimization Technique ◽  
Leading Edge ◽  
Element Model ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Temperature Variations ◽  
Learning Technique

Abstract With advances in additive manufacturing of metal components, commercial production of complex turbine components is becoming feasible. Thus, designers are not constrained to the limitations of conventional manufacturing methods. A new conjugate optimization technique is proposed, which is not computationally demanding and can be used when several heat transfer modes are working simultaneously. For this study, film cooling holes in the leading edge of a gas turbine airfoil are optimized without trial and error simulations. Since the machine learning technique is not dependent on thermal analysis, the optimization technique can be applied to any non-linear problem. Film hole sizes are optimized to minimize coolant flowrate while reducing the temperature variations in the stationary vane. The technique used a transfer function based iterative optimization process with unsupervised machine learning that has been termed Nonlinear Optimization with Replacement Strategy (NORS). It uses a grading metric to replace the worst performing hole combinations with one that has been optimized with a given objective and several constraints. The results show significant reductions in vertical temperature variations along the leading edge while minimizing coolant flow-rate. Reduced temperature variation results in reduced thermal stresses. The finite element model and the associated correlations are not part of the unsupervised machine learning technique; therefore, the proposed optimization model can be generalized for any engineering design with multiple inputs for learning and multiple outputs for grading.

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