Reversible Database Watermarking Based on Random Forest and Genetic Algorithm

The random forest(RF) algorithm is a very efficient and excellent ensemble classification algorithm. In this paper, we improve the random forest algorithm and propose an algorithm called ‘Bayesian Weighted Random Forest’(B-RF), focus on the problem that inaccurate decision tree classification caused by the same voting weights in the traditional random forest model. The main idea underlying the proposed model is to replace the supermajority voting of random forests into weighted voting, fully consider the difference of classification ability of each decision tree, using the Bayesian formula to dynamically update the weight value for each tree, so that the strong classifier has higher voting power and effectively improves the overall performance of classification. Through the verification of UCI database, the results show that the classification accuracy of the proposed weighted random forest model is higher. This illustrate the outperformance of the proposed model in comparison with the general random forest algorithm.

Skin disease is a very vulnerable and severe issue in today's world. Skin disorder classification is crucial for diagnosis. Several new data mining algorithms have been developed to classify and interpret medical images. The functionality of the K-Nearest Neighbors (KNN) Random Forest (RF) Algorithm is described in this article, along with an analysis of its results. Furthermore, this study demonstrates a high-performing approach that saves both effort and money. The proposed model is designed based on KNN and the Random Forest algorithm. The patient can use this model to classify his skin disease as a primary detection, and the doctor also can ensure his judgment by using this proposed model. Traditional skin disease diagnosis is an expensive and time-consuming procedure. This paper's proposed classification model will identify ten different skin diseases. The Random Forest algorithm has a testing accuracy of 94.22 percent, and K-Nearest Neighbors (KNN) has a testing accuracy of 95.23 percent. The KNN algorithm has an F1 Score of 95.98 percent, whereas the Random Forest (RF) algorithm has an F1 Score of 95.94 percent. It can be increased by expanding the dataset and more feature extraction. This approach may benefit individuals with skin illness who are looking to save money and time as well as avoid skin cancer by identifying cancer at an early stage.

Application of random forest algorithm in hail forecasting over Shandong Peninsula

Accurate estimation of dew point temperature (T_dew) plays a very important role in the fields of water resource management, agricultural engineering, climatology and energy utilization. However, there are few studies on the applicability of local T_dew algorithms at regional scales. This study evaluated the performance of a new machine learning algorithm, i.e., gradient boosting on decision trees with categorical features support (CatBoost) to estimate daily T_dew using limited local and cross-station meteorological data. The random forests (RF) algorithm was also assessed for comparison. Daily meteorological data from 2016 to 2019, including maximum, minimum and average temperature (T_max, T_min and T_mean), maximum, minimum and average relative humidity (RH_max, RH_min and RH_mean), maximum, minimum and average global solar radiation (Rs_max, Rs_min and Rs_mean) from three weather stations in Hunan of China were used to evaluate the CatBoost and RF algorithms. The results showed that both algorithms achieved satisfactory estimation accuracy at the target stations (on average RMSE = 1.020°C, R²=0.969, MAE = 0.718°C and NRMSE = 0.087) in the absence of complete meteorological parameters (with only temperature data as input). The CatBoost algorithm (on average RMSE = 1.900°C and R²=0.835) was better than the RF algorithm (on average RMSE = 2.214°C and R²=0.828). The accuracy and stability of the CatBoost and RF algorithms were positively correlated with the number of input parameters, and the three-parameter algorithms achieved higher estimation accuracy than the two-parameter algorithms. The developed methodology is helpful to predict T_dew at regional scale.

To enhance the safety and reliability of fuel cell vehicles, a remote monitoring system based on 5th generation (5G) mobile networks and controller area networks (CANs) was designed, and a random forest (RF) algorithm for the fault diagnosis for eight typical malfunctions of its powertrain system was incorporated. Firstly, the information on the powertrain system was obtained through a 5G-based monitoring terminal, and the Alibaba Cloud IoT platform was utilized for data storage and remote monitoring. Secondly, a fault diagnosis model based on the RF algorithm was constructed for fault classification; its parameters were optimized with a genetic algorithm (GA), and it was applied on the Alibaba Cloud PAI platform. Finally, the performance of the proposed RF fault diagnosis model was evaluated by comparing it with three other classification models: random search conditioning, grid search conditioning, and Bayesian optimization. Results show that the model accuracy, F1 score, and kappa value of the optimized RF fault classification model are higher than the other three. The model achieves an F1 value of 97.77% in identifying multiple typical faults of the powertrain system, as validated by vehicle malfunction data. The method demonstrates the feasibility of remote monitoring and fault diagnosis for the powertrain system of fuel cell vehicles.

With the continuous development of science and technology, the issue of learning copyright knowledge in music copyright education has become increasingly severe. In response to the problems highlighted above, this paper proposes a study on optimizing the effectiveness of copyright knowledge learning based on the random forest algorithm. By integrating music copyright education issues and three principles for constructing an evaluation system, this study establishes an evaluation indicator system for copyright knowledge learning effectiveness. Under the influence of the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation Method, the evaluation indicators are quantified. Based on this, the Random Forest algorithm is utilized to optimize the quantified data of each evaluation indicator, followed by an exploration of the optimization effects of the Random Forest algorithm. The optimization values of the four algorithms are 3.9365, 3.7213, 3.7323, and 3.7088, respectively. It can be concluded that the optimization effect of the RF (random forest) algorithm is higher than that of the DT (decision tree algorithm), LSTM (long short-term memory network), and LR (logistic regression), verifying the practical applicability and application value of the research scheme proposed in this paper, which has practical strategic significance for improving music copyright education.

Coronavirus disease 2019 (COVID-19) has rapidly become a global pandemic. Age is an independent factor in death from the disease, and predictive models to stratify patients according to their mortality risk are needed. To compare the laboratory parameters of the younger (≤70) and the elderly (>70) groups, and develop death prediction models for the two groups according to age stratification. A retrospective, single-center observational study. This study included 437 hospitalized patients with laboratory-confirmed COVID-19 from Tongji Hospital in Wuhan, China, 2020. Epidemiological information, laboratory data and outcomes were extracted from electronic medical records and compared between elderly patients and younger patients. First, recursive feature elimination (RFE) was used to select the optimal subset. Then, two random forest (RF) algorithms models were built to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients' clinical prognoses. Comparisons of the laboratory data of the two age groups revealed many different laboratory indicators. RFE was used to select the optimal subset for analysis, from which 11 variables were screened out for the two groups. The RF algorithm were built to predict the prognoses of COVID-19 patients based on the best subset, and the area under ROC curve (AUC) of the two groups is 0.874 (95% CI: 0.833-0.915) and 0.842 (95% CI: 0.765-0.920). Two prediction models for COVID-19 were developed in the patients with COVID-19 based on random forest algorithm, which provides a simple tool for the early prediction of COVID-19 mortality.

This research aims to predict the Heath care Insurance Premi by analyzing the customer characteristics Random Forest Algorithm. Secondly, this research will determine feature importance from customer characteristics that drive the closing decision. This research methodology is quantitative, and data mining methodology and data will be derived from primary data, and 202 data will be processed and cleaned into 148 data. This research also uses supervisory learning and using the random forest algorithm. The novelty of this research, Firstly, the health care insurance premium can be predicted by the machine learning random forest algorithm. Secondly, the sequence of importance from the customer characteristics can be determined by the feature of importance function at the random forest. A random forest algorithm can predict the prospect data test with an accurate value of 63,33 %. First, the crucial question that has to be made is how much the income is; second is age, is between 45-55 years old, and the third is how many dependent. The decision tree graph helps construct the questioner's level, leading to a monthly target premium of 3 million to 5 million. Keywords: Health care Insurance Premi Prediction, Random forest Algorithm, The feature Importance.

Context.As an increasing number of spectroscopic surveys become available, an automated approach to spectral classification becomes necessary. Due to the significance of the massive stars, it is of great importance to identify the phenomenological parameters of these stars (e.g., the spectral type), which can be used as proxies to their physical parameters (e.g., mass and temperature).Aims.In this work, we aim to use the random forest (RF) algorithm to develop a tool for the automated spectral classification of OB-type stars according to their sub-types.Methods.We used the regular RF algorithm, the probabilistic RF, which is an extension of RF that incorporates uncertainties, and we introduced the KDE – RF method which is a combination of the kernel-density estimation and the RF algorithm. We trained the algorithms on the equivalent width (EW) of characteristic absorption lines measured in high-quality spectra (signal-to-noise (S/N)≳50) from large Galactic (LAMOST, GOSSS) and extragalactic surveys (2dF, VFTS) with available spectral types and luminosity classes. By following an adaptive binning approach, we grouped the labels of these data in 11 spectral classes within the O2-B9 range. We examined which of the characteristic spectral lines (features) are more important for the classification based on a number of feature selection methods, and we searched for the optimal hyperparameters of the classifiers to achieve the best performance.Results.From the feature-screening process, we find that the full set of 17 spectral lines is needed to reach the maximum performance per spectral class. We find that the overall accuracy score is ∼70%, with similar results across all approaches. We apply our model in other observational data sets providing examples of the potential application of our classifier to real science cases. We find that it performs well for both single massive stars and for the companion massive stars in Be X-ray binaries, especially for data of similar quality to the training sample. In addition, we propose a reduced ten-features scheme that can be applied to large data sets with lowerS/N ∼ 20 − 50.Conclusions.The similarity in the performances of our models indicates the robustness and the reliability of the RF algorithm when it is used for the spectral classification of early-type stars. The score of ∼70% is high if we consider (a) the complexity of such multiclass classification problems (i.e., 11 classes), (b) the intrinsic scatter of the EW distributions within the examined spectral classes, and (c) the diversity of the training set since we use data obtained from different surveys with different observing strategies. In addition, the approach presented in this work is applicable to products from different surveys in terms of quality (e.g., different resolution) and different formats (e.g., absolute or normalized flux), while our classifier is agnostic to the luminosity class of a star, and, as much as possible, it is metallicity independent.

In order to improve the efficiency and adaptability of classical random forest algorithm in large data environment, an improved random forest algorithm based on Spark is proposed. Firstly, an improved random forest algorithm (FRF) based on the Fayyad boundary point principle is proposed to deal with the shortcomings of classical random forest algorithm in the process of discretization of continuous attributes. Then, using Spark's memory computing advantages, an improved random forest parallelization algorithm based on Spark is proposed. The experimental results show that FRF can effectively shorten the construction time while maintaining the computational accuracy, and it has good scalability and high efficiency in the distributed environment, and can perform fast classification task in large data background.

The aim of this paper is to develop an effective classification approach based on Random Forest (RF) algorithm. Three fruits; i.e., apples, Strawberry, and oranges were analysed and several features were extracted based on the fruits' shape, colour characteristics as well as Scale Invariant Feature Transform (SIFT). A preprocessing stages using image processing to prepare the fruit images dataset to reduce their color index is presented. The fruit image features is then extracted. Finally, the fruit classification process is adopted using random forests (RF), which is a recently developed machine learning algorithm. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB environment. Experiments were tested and evaluated using a series of experiments with 178 fruit images. It shows that Random Forest (RF) based algorithm provides better accuracy compared to the other well know machine learning techniques such as K-Nearest Neighborhood (K-NN) and Support Vector Machine (SVM) algorithms. Moreover, the system is capable of automatically recognize the fruit name with a high degree of accuracy.

As a guiding mechanism for protecting cultivated land, achieving intensive and economical land use, and improving the ecological environment, the potential analysis of comprehensive land consolidation in the whole area relies on the accuracy of land use classification. In karst regions, such as the Li River Basin, the unique topography and geomorphology often render traditional land-cover classification methods prone to uncertainties, thereby affecting the consistency of conclusions in potential analysis for comprehensive land consolidation in the whole region. This study developed an improved random forest classification algorithm optimized for land-cover classification in the Li River Basin. Based on this algorithm, a potential evaluation model of comprehensive land consolidation in the whole area was constructed, and the spatial distribution characteristics of areas requiring consolidation under different potential levels for various land use types in the Li River Basin were analyzed in depth. The results indicate that the improved random forest algorithm performs best in land use classification in the Li River Basin, with overall accuracy and the kappa coefficient enhanced by 3% and 4%, respectively, compared with the standard random forest algorithm. The spatial distribution of potential levels of comprehensive land consolidation in the whole area across various land use types in the Li River Basin shows significant differences, ranking as ecological land > agricultural land > construction land. Compared to traditional evaluation models, the new model identifies consolidation areas for various land use types, covering 94.97% of the total area in the Li River Basin. This study can facilitate a comprehensive consolidation of land use types in karst regions from all elements, dimensions, and periods, thus provides scientific basis for land use and ecological protection in such areas. 

The exponential growth of data in the information system has led the concept of big data. High dimensional data imposes serious challenges to the useful information. It limits the traditional machine learning algorithms and techniques to preprocess, learn, and analyze the data. The optimal subset selection is an effective solution. Thus, feature and instance selection are used for data reduction. In this paper, we have chosen instance subset selection to increase the accuracy of data analysis. The study is to propose an approach to select the optimal set of instances using Genetic Algorithm and is also aimed to optimize the Random Forest using GA for the boosted accuracy in the classification. The study introduced an approach that uses Genetic Algorithm to select the optimal instance subset. GA is also implemented with the Random Forest algorithm to select the best parameters that are stored in the DEAP library for the accurate classification. The experimentation results show that Genetic Algorithm effectively selects the best instance subset, and the combination of Random Forest and Genetic Algorithm outperforms with 85.8%. This method not only shows how evolutionary techniques can be used for data-driven optimization, but it also makes the model more efficient by decreasing data redundancy. The findings provide credence to the idea that combining Genetic Algorithms with more conventional machine learning techniques can improve performance.

When the traditional random forest (RF) algorithm is used to select feature elements in biostatistical data, a large amount of noise data and parameters can affect the importance of the selected feature elements, making the control of feature selection difficult. Therefore, it is a challenge for the traditional RF algorithm to preserve the accuracy of algorithm results in the presence of noise data. Generally, directly removing noise data can result in significant bias in the results. In this study, we develop a new algorithm, standardized threshold, and loops based random forest (STLBRF), and apply it to the field of gene expression data for feature gene selection. This algorithm, based on the traditional RF algorithm, combines backward elimination and K-fold cross-validation to construct a cyclic system and set a standardized threshold: error increment. The algorithm overcomes the shortcomings of existing gene selection methods. We compare ridge regression, lasso regression, elastic net regression, the traditional RF algorithm, and our improved RF algorithm using three real gene expression datasets and conducting a quantitative analysis. To ensure the reliability of the results, we validate the effectiveness of the genes selected by these methods using the Random Forest classifier. The results indicate that, compared to other methods, the STLBRF algorithm achieves not only higher effectiveness in feature gene selection but also better control over the number of selected genes. Our method offers reliable technical support for feature expression analysis and research on biomarker selection.

Fusion of diverse data sources for flood extent mapping and risk assessment in Sindh: A comparative study of inundation mapping approaches