Treatment duration is one of the most important factors that patients consider when deciding whether to undergo orthodontic treatment or not. This study aimed build and compare machine learning (ML) models for prediction length identify affecting using ML approach. Records 518 who had successfully finished were used in this study. Seventy percent patient data training models, thirty testing the...

The paper reports three new ensembles of supervised learning predictors for managing medical insurance costs. open dataset is used data analysis methods development. usage artificial intelligence in the management financial risks will facilitate economic wear time and money protect patients’ health. Machine associated with many expectations, but its quality determined by choosing a good algorit...

We demonstrate that, for a range of state-of-the-art machine learning algorithms, the differences in generalisation performance obtained using default parameter settings and using parameters tuned via cross-validation can be similar in magnitude to the differences in performance observed between state-of-the-art and uncompetitive learning systems. This means that fair and rigorous evaluation of...

Random forests are one of the most successful ensemble methods which exhibits performance on the level of boosting and support vector machines. The method is fast, robust to noise, does not overfit and offers possibilities for explanation and visualization of its output. We investigate some possibilities to increase strength or decrease correlation of individual trees in the forest. Using sever...

As cyber-attacks become remarkably sophisticated, effective Intrusion Detection Systems (IDSs) are needed to monitor computer resources and provide alerts regarding unusual or suspicious behavior. Despite using several machine learning (ML) data mining methods achieve high effectiveness, these systems have not proven ideal. Current intrusion detection algorithms suffer from dimensionality, redu...

This thesis takes the historical weather time series of Chongqing as experimental samples. Firstly, this uses wavelet transform to organize data, and then divides sample data into training test sets verify accuracy evaluation Naive Bayes Model. Secondly, Model is compared with currently used machine learning models such SVM, XGBoost, bagging, random forest. Finally, results show that has high s...