Notice of Removal: Comparison of the effectiveness of tree algorithms in the diagnosis of spongy tissue

2019 ◽  
Author(s):  
Roza Dzierzak ◽  
Zbigniew Omiotek ◽  
Waldemar Wojcik
Keyword(s):  
Spongy Tissue ◽  
Tree Algorithms

scholarly journals Preconditioning methods based on spanning tree algorithms

2021 ◽  
Vol 1715 ◽  
pp. 012005
Author(s):  
D V Perevozkin ◽  
G A Omarova
Keyword(s):  
Spanning Tree ◽  
Tree Algorithms

scholarly journals Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

2021 ◽  
Vol 12 (2) ◽  
pp. 317-334
Author(s):  
Omar Alaqeeli ◽  
Li Xing ◽  
Xuekui Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Classification Tree ◽  
Area Under The Curve ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Tree Algorithms ◽  
R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

scholarly journals Development of revised diagnostic criteria for Fuchs’ uveitis syndrome in a Chinese population

2021 ◽  
pp. bjophthalmol-2021-319343
Author(s):  
Peizeng Yang ◽  
Wanyun Zhang ◽  
Zhijun Chen ◽  
Han Zhang ◽  
Guannan Su ◽  
...  
Keyword(s):  
Diagnostic Criteria ◽  
Clinical Symptoms ◽  
Southern China ◽  
High Sensitivity ◽  
Northern China ◽  
Chinese Patients ◽  
Tertiary Referral Centre ◽  
Tree Algorithms ◽  
Vitreous Opacities ◽  
Fuchs Uveitis Syndrome

Background/aimsFuchs’ uveitis syndrome (FUS) is one of the frequently misdiagnosed uveitis entities, which is partly due to the absence of internationally recognised diagnostic criteria. This study was performed to develop and evaluate a set of revised diagnostic criteria for FUS.MethodsThe clinical data of Chinese patients with FUS and patients with non-FUS were collected and analysed from a tertiary referral centre between April 2008 and December 2020. A total of 593 patients with FUS and 625 patients with non-FUS from northern China were enrolled for the development of diagnostic criteria for FUS. Three hundred and seventy-seven patients with FUS and 503 patients with non-FUS from southern China were used to validate the criteria. Clinical symptoms and ocular signs were collected from all patients with FUS and patients with non-FUS. Multivariate two-step cluster analysis, logistic regression and decision tree algorithms in combination with the clinical judgement of uveitis experts were used to revise diagnostic criteria for FUS.ResultsThree essential findings including diffuse iris depigmentation, absence of posterior synechiae, mild inflammation in the anterior chamber at presentation and five associated findings including mostly unilateral involvement, cataract, vitreous opacities, absence of acute symptoms and characteristic iris nodules were used in the development of FUS diagnostic criteria. All essential findings were required for the diagnosis of FUS, and the diagnosis was further strengthened by the presence of associated findings.ConclusionRevised diagnostic criteria for FUS were developed and validated by analysing data from Chinese patients and showed a high sensitivity (96.55%) and specificity (97.42%).

Analysis of tree algorithms for the simulation event list

1985 ◽  
Vol 22 (1) ◽  
pp. 15-33 ◽  
Author(s):  
Jeffrey H. Kingston
Keyword(s):  
Tree Algorithms

Novel Approach for Battery Type Determination: A Mere Electrical Alternative

2021 ◽  
Author(s):  
İsmail Can Dikmen ◽  
Teoman Karadağ
Keyword(s):  
Integrated Circuits ◽  
Decision Tree ◽  
Statistical Significance ◽  
High Capacity ◽  
Electrical Energy ◽  
Machine Learning Algorithms ◽  
Battery Management ◽  
Separation Function ◽  
Novel Approach ◽  
Tree Algorithms

Abstract Today, the storage of electrical energy is one of the most important technical challenges. The increasing number of high capacity, high-power applications, especially electric vehicles and grid energy storage, points to the fact that we will be faced with a large amount of batteries that will need to be recycled and separated in the near future. An alternative method to the currently used methods for separating these batteries according to their chemistry is discussed in this study. This method can be applied even on integrated circuits due to its ease of implementation and low operational cost. In this respect, it is also possible to use it in multi-chemistry battery management systems to detect the chemistry of the connected battery. For the implementation of the method, the batteries are connected to two different loads alternately. In this way, current and voltage values ​​are measured for two different loads without allowing the battery to relax. The obtained data is pre-processed with a separation function developed based on statistical significance. In machine learning algorithms, artificial neural network and decision tree algorithms are trained with processed data and used to determine battery chemistry with 100% accuracy. The efficiency and ease of implementation of the decision tree algorithm in such a categorization method are presented comparatively.

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