Prediction of Torpedo Initial Velocity Based on Random Forests Regression

Pu-kai, Wang; Jiang, Tianyuan; Gao, Fan; Dan, Caihong

doi:10.1109/ihmsc.2015.17

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…It can effectively reduce the risk of overfitting and is more conducive to obtain a robust model. Therefore, it is a novel and efficient method to establish QSAR nonlinear models ( Zhang et al, 2015a ; Fang et al, 2022 .).…”

Section: Methodsmentioning

confidence: 99%

Prediction of histone deacetylase inhibition by triazole compounds based on artificial intelligence

Wang,

Zhang

2023

Front. Pharmacol.

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A quantitative structure-activity relationship (QSAR) study was conducted to predict the anti-colon cancer and HDAC inhibition of triazole-containing compounds. Four descriptors were selected from 579 descriptors which have the most obvious effect on the inhibition of histone deacetylase (HDAC). Four QSAR models were constructed using heuristic algorithm (HM), random forest (RF), radial basis kernel function support vector machine (RBF-SVM) and support vector machine optimized by particle swarm optimization (PSO-SVM). Furthermore, the robustness of four QSAR models were verified by K-fold cross-validation method, which was described by Q2. In addition, the R2 of the four models are greater than 0.8, which indicates that the four descriptors selected are reasonable. Among the four models, model based on PSO-SVM method has the best prediction ability and robustness with R2 of 0.954, root mean squared error (RMSE) of 0.019 and Q2 of 0.916 for the training set and R2 of 0.965, RMSE of 0.017 and Q2 of 0.907 for the test set. In this study, four key descriptors were discovered, which will help to screen effective new anti-colon cancer drugs in the future.

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Section: Methodsmentioning

confidence: 99%

Prediction of histone deacetylase inhibition by triazole compounds based on artificial intelligence

Wang,

Zhang

2023

Front. Pharmacol.

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“…The reason for choosing this method for this study is that it is a powerful method that can make unbiased and successful predictions in complex data [25]. The RFR method was implemented in the Python 3.7 scripting language using the Pandas, SciKit Learn, Numpy, Math, and Matplotlib libraries in the Spyder 3 editor.…”

Section: B 1 Random Forest Regression (Rfr)mentioning

confidence: 99%

Stock Closing Price Prediction with Machine Learning Algorithms: PETKM Stock Example In BIST

TOPRAK

Çağıl

Kökçam

2023

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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This study predicts the stock price of Petkim Petrokimya Holding Corp. (PETKM), which is listed in Borsa Istanbul (BIST), using PETKM stock price, US dollar (USD/TRY) price and BIST Chemical, Petroleum & Plastic (XKMYA) index price. A time series data set with three inputs and one output is created using these data. Random Forest Regression (RFR), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) algorithms are used in the prediction model. The success of these methods is compared using performance metrics such as MSE, RMSE, MAE, and R2. According to the calculated error metrics, LSTM and RFR algorithms gave better results than CNN with an MSE value less than 0.02. However, the fact that the R2 values of the most successful models created with all three algorithms were greater than 95% revealed that all the algorithms mentioned could be used to estimate this data set.

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“…If N is the number of samples for constructing a tree, and M is the number of variables. Each tree will be constructed based on lowest classification error among finite number of predictors on a particular node in the following two steps (Wang et al, 2015):…”

Section: Random Forestmentioning

confidence: 99%

Liver Disease Detection

Mukhopadhyay

Samanta

Pan³

2023

International Journal of Healthcare Information Systems and Informatics

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In recent times, intelligent predictive systems are showing greater levels of accuracy and effectiveness in early detection of the critical diseases of cancer in the liver, lungs, etc. Predictive models assist medical practitioners to identify the diseases based on symptoms and health indicators like hormones, enzymes, age, blood counts, etc. This article focuses on proposing an optimal classification model to detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics. The article proposes an enhanced framework on the original study by Ramana et al. It uses measures like precision and balanced accuracy to choose the most efficient classification algorithm in Indian and USA patient datasets using various factors like enzymes, age, etc. Using Youden's index, individual thresholds for each model were identified to increase the power of sensitivity and specificity, respectively. The study proposes a framework for highly accurate automated disease detection in the medical industry and helps in strategizing preventive measures for patients.

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Prediction of Torpedo Initial Velocity Based on Random Forests Regression

Cited by 4 publications

References 4 publications

Prediction of histone deacetylase inhibition by triazole compounds based on artificial intelligence

Prediction of histone deacetylase inhibition by triazole compounds based on artificial intelligence

Stock Closing Price Prediction with Machine Learning Algorithms: PETKM Stock Example In BIST

Liver Disease Detection

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