Classification of the Aroma Quality of Pyrazine Derivatives using Random Forest Tree Technique

There are various ways to improve the quality of someone's education, one of them is reading. By reading, insight and knowledge of various kinds of things can increase. But, the ability and someone's understanding of reading is different. This can be a problem for readers if the reading material exceeds his comprehension ability. Therefore, it is necessary to determine the load of reading material using Lexile Levels. Lexile Levels are a value that gives a size the complexity of reading material and someone's reading ability. Thus, the reading material will be classified based a value on the Lexile Levels. Lexile Levels will cluster the reading material into 2 clusters which is easy, and difficult. The clustering process will use the k-means method. After the clustering process, reading material will be classified using the reading load Random Forest method. The k-means method was chosen because of the method has a simple computing process and fast also. Random Forest algorithm is a method that can build decision tree and it’s able to build several decision trees then choose the best tree. The results of this experiment indicate that the experiment scenario uses 2 cluster and SMOTE and GIFS preprocessing are carried out shows good results with an accuracy of 76.03%, precision of 81.85% and recall of 76.05%.

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Precise detection of Acrs in prokaryotes using only six features

10.1101/2020.05.23.112011 ◽

2020 ◽

Author(s):

Chuan Dong ◽

Dong-Kai Pu ◽

Cong Ma ◽

Xin Wang ◽

Qing-Feng Wen ◽

...

Keyword(s):

Species Diversity ◽

Random Forest ◽

Cross Validation ◽

Forest Tree ◽

Whole Genome ◽

Random Forest Tree ◽

Genome Scale ◽

Random States ◽

Fold Cross Validation

ABSTRACTAnti-CRISPR proteins (Acrs) can suppress the activity of CRISPR-Cas systems. Some viruses depend on Acrs to expand their genetic materials into the host genome which can promote species diversity. Therefore, the identification and determination of Acrs are of vital importance. In this work we developed a random forest tree-based tool, AcrDetector, to identify Acrs in the whole genomescale using merely six features. AcrDetector can achieve a mean accuracy of 99.65%, a mean recall of 75.84%, a mean precision of 99.24% and a mean F1 score of 85.97%; in multi-round, 5-fold cross-validation (30 different random states). To demonstrate that AcrDetector can identify real Acrs precisely at the whole genome-scale we performed a cross-species validation which resulted in 71.43% of real Acrs being ranked in the top 10. We applied AcrDetector to detect Acrs in the latest data. It can accurately identify 3 Acrs, which have previously been verified experimentally. A standalone version of AcrDetector is available at https://github.com/RiversDong/AcrDetector. Additionally, our result showed that most of the Acrs are transferred into their host genomes in a recent stage rather than early.

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Detecting neutral hydrogen at z ≳ 3 in large spectroscopic surveys of quasars

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa2388 ◽

2020 ◽

Vol 498 (2) ◽

pp. 1951-1962

Author(s):

Michele Fumagalli ◽

Sotiria Fotopoulou ◽

Laura Thomson

Keyword(s):

Random Forest ◽

Neutral Hydrogen ◽

Sloan Digital Sky Survey ◽

Broad Absorption Line ◽

Quasar Spectra ◽

Sky Survey ◽

William Herschel ◽

High Column

ABSTRACT We present a pipeline based on a random forest classifier for the identification of high column density clouds of neutral hydrogen (i.e. the Lyman limit systems, LLSs) in absorption within large spectroscopic surveys of z ≳ 3 quasars. We test the performance of this method on mock quasar spectra that reproduce the expected data quality of the Dark Energy Spectroscopic Instrument and the WHT (William Herschel Telescope) Enhanced Area Velocity Explorer surveys, finding ${\gtrsim}90{{\ \rm per\ cent}}$ completeness and purity for $N_{\rm H\,\rm{\small I}} \gtrsim 10^{17.2}~\rm cm^{-2}$ LLSs against quasars of g < 23 mag at z ≈ 3.5–3.7. After training and applying our method on 10 000 quasar spectra at z ≈ 3.5–4.0 from the Sloan Digital Sky Survey (Data Release 16), we identify ≈6600 LLSs with $N_{\rm H\,\rm{\small I}} \gtrsim 10^{17.5}~\rm cm^{-2}$ between z ≈ 3.1 and 4.0 with a completeness and purity of ${\gtrsim}90{{\ \rm per\ cent}}$ for the classification of LLSs. Using this sample, we measure a number of LLSs per unit redshift of ℓ(z) = 2.32 ± 0.08 at z = [3.3, 3.6]. We also present results on the performance of random forest for the measurement of the LLS redshifts and H i column densities, and for the identification of broad absorption line quasars.

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