Assessing the probable distribution of the potentially invasive Chinese mystery snail, Cipangopaludina chinensis, in Nova Scotia using a random forest model approach

Recognizing emotions through the brain wave approach with facial or sound expression is widely used, but few use text stimuli. Therefore, this study aims to analyze the emotion recognition experiment by stimulating sentiment-tones using EEG. The process of classifying emotions uses a random forest model approach which is compared with two models, namely Support Vector Machine and decision tree as benchmarks. The raw data used comes from the results of scrapping Twitter data. The dataset of emotional annotation was carried out manually based on four classifications, specifically: happiness, sadness, fear, and anger. The annotated dataset was tested using an Electroencephalogram (EEG) device attached to the participant's head to determine the brain waves appearing after reading the text. The results showed that the random forest model has the highest accuracy level with a rate of 98% which is slightly different from the decision tree with 88%. Meanwhile, in SVM the accuracy results are less good with a rate of 32%. Furthermore, the match level of angry emotions from the three models above during manual annotation and using the EEG device showed a high number with an average value above 90%, because reading with angry expressions is easier to perform. For this reason, this study aims to test the emotion recognition experiment by stimulating sentiment-tones using EEG. The process of classifying emotions uses a random forest model approach which is compared with two models, namely SVM and decision tree as benchmarks. The dataset used comes from the results of scrapping Twitter data.

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Spatial modeling of gully head erosion on the Loess Plateau using a certainty factor and random forest model

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.147040 ◽

2021 ◽

Vol 783 ◽

pp. 147040

Author(s):

Chengcheng Jiang ◽

Wen Fan ◽

Ningyu Yu ◽

Enlong Liu

Keyword(s):

Random Forest ◽

Loess Plateau ◽

Spatial Modeling ◽

Random Forest Model ◽

Certainty Factor ◽

The Loess Plateau ◽

Forest Model ◽

Gully Head

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Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

Scientometrics ◽

10.1007/s11192-021-04111-w ◽

2021 ◽

Author(s):

Christian Thiele ◽

Gerrit Hirschfeld ◽

Ruth von Brachel

Keyword(s):

Clinical Trials ◽

Random Forest ◽

Random Forest Model ◽

Scientometric Analysis ◽

Data Set ◽

The Public ◽

Forest Model ◽

Clinical Trial Registries ◽

Multiple Primary ◽

Clinical Trials Registry

AbstractRegistries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

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Discrimination of the geographic origins and varieties of wine grapes using high-throughput sequencing assisted by a random forest model

LWT ◽

10.1016/j.lwt.2021.111333 ◽

2021 ◽

pp. 111333

Author(s):

Feifei Gao ◽

Guihua Zeng ◽

Bin Wang ◽

Jing Xiao ◽

Liang Zhang ◽

...

Keyword(s):

Random Forest ◽

High Throughput ◽

High Throughput Sequencing ◽

Random Forest Model ◽

Wine Grapes ◽

Forest Model ◽

Geographic Origins

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Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10080503 ◽

2021 ◽

Vol 10 (8) ◽

pp. 503

Author(s):

Hang Liu ◽

Riken Homma ◽

Qiang Liu ◽

Congying Fang

Keyword(s):

Land Use ◽

Random Forest ◽

Cellular Automata ◽

Land Use Change ◽

Urban Land ◽

Urban Land Use ◽

Random Forest Model ◽

Growth Trend ◽

Related Factors ◽

Forest Model

The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

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Estimates of daily ground-level NO2 concentrations in China based on Random Forest model integrated K-means

Advances in Applied Energy ◽

10.1016/j.adapen.2021.100017 ◽

2021 ◽

pp. 100017

Author(s):

Xinyu Dou ◽

Cuijuan Liao ◽

Hengqi Wang ◽

Ying Huang ◽

Ying Tu ◽

...

Keyword(s):

Random Forest ◽

Ground Level ◽

Random Forest Model ◽

Forest Model

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Improving satellite-based estimation of surface ozone across China during 2008–2019 using iterative random forest model and high-resolution grid meteorological data

Sustainable Cities and Society ◽

10.1016/j.scs.2021.102807 ◽

2021 ◽

pp. 102807

Author(s):

Gongbo Chen ◽

Jiang Chen ◽

Guang-hui Dong ◽

Bo-yi Yang ◽

Yisi Liu ◽

...

Keyword(s):

High Resolution ◽

Random Forest ◽

Meteorological Data ◽

Surface Ozone ◽

Random Forest Model ◽

Forest Model

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Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning

Journal of International Medical Research ◽

10.1177/0300060521993980 ◽

2021 ◽

Vol 49 (3) ◽

pp. 030006052199398

Author(s):

Jinwu Peng ◽

Zhili Duan ◽

Yamin Guo ◽

Xiaona Li ◽

Xiaoqin Luo ◽

...

Keyword(s):

Random Forest ◽

Hydatid Disease ◽

Characteristic Curve ◽

Receiver Operator Characteristic Curve ◽

Random Forest Model ◽

Hepatic Hydatid Disease ◽

Forest Model ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Microarray Profiling

Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.

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