Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower

This paper states an estimation method for lawn grass lengths or ground conditions based on random forest algorithm. This relates to Digital Twin and Virtual Twin of Hybrid Twin approach. Recently, the robotic lawn mowers are becoming popular with the advent of efficient sensors and embedded systems. However, the length of lawn grasses or such ground conditions as dirt, gravel, or concrete, etc., are not recognized. As a result, the motor for cutting lawn grasses is running with constant rotation speed from the beginning to the end of operation of robotic lawn mower. In order to precisely control the rotation speed of motor, the lawn grass lengths and ground conditions are estimated by using the effective sensor data. By applying the random forest algorithm, the combination of sensing parameters attained more than 90% correct estimation ratio is shown through some experiments. Now, the suggested algorithm and the sensor fusion are evaluated against wide range of lawn and grounds.

In order to explore the applicability of random forest algorithm in the detection of alcoholic foreign matter and to improve the identification of alcoholic products. In this paper, based on the random forest algorithm, the feature values of random forest are fuzzified using the fuzzy comprehensive evaluation method, and the application model of alcohol foreign body detection anomaly based on random forest fuzzy tree nodes is established. And the reliability of the random forest algorithm is verified by the quantitative analysis of the three test indexes (i.e., recall, precision, and accuracy) and the anomaly proportion coefficient of the test data set by the algorithm in this paper. The results show that the recall, precision, and accuracy of the random forest-based anomaly detection for alcoholic beverage foreign objects are 99.65%, 95.49%, and 97.19%, respectively, and the average value of the three eigenvalues of this paper’s algorithm is 97.44%, which is 59.89%, 43.98%, and 1.92% higher than the other three algorithms, respectively. In terms of the anomaly proportion coefficient, the stability of the algorithm in this paper is the best when the coefficient takes values between [0.2, 0.6]. It can be shown that the algorithm based on random forest can be applied to the foreign matter detection of wine, and through the detection of anomalies, the quality of the wine currently undergoing detection can be clearly and explicitly analyzed, which also provides a new direction for the application of the random forest algorithm.

Application of random forest algorithm in hail forecasting over Shandong Peninsula

To establish a random forest algorithm for identifying and classifying different brands of Xiasangju granules, and provide effective reference for identifying multi-index complex fingerprint. HPLC method was used to collect the fingerprint of 83 batches of Xiasangju granules from different manufacturers. The classification of Xiasangju granules samples based on chromatographic fingerprints was identified by chemometric methods including principal component analysis (PCA), partial least squares discriminate analysis (PLS-DA) and random forest analysis (RF). The superiority of the above three chemometric methods was compared. The results showed that the fingerprints of 83 batches of Xiasangju granules were established in this study. PCA could only explicate 56.52% variance contribution rate and could not completely classify the samples; PLS-DA analysis was superior to PCA, explicating 63.43% variance contribution rate and could obtain certain separation; RF could well classify the samples into 3 types, and the predication accuracy of the proposed method was 96.5%. Therefore, The results indicate that RF combined with HPLC fingerprint could effectively construct traditional Chinese medicine quality control and analysis system.

This paper presents a suitable algorithm to classify vehicles detected by a multiple inductive loop system, developed for measuring traffic parameters in a heterogeneous and no-lane disciplined traffic. The proposed classification scheme employs Random Forest (RF) algorithm. This scheme is suited not only for classifying the detected vehicles as bicycle, motorcycle, scooter, car and bus but also for counting them accurately under a mixed traffic condition. The algorithm has been implemented and tested. Its performance has also been compared with other algorithms based on threshold values and signature patterns. The threshold, signature and RF based algorithms use the number of loops a vehicle occupies as an important factor for classification. Results from a prototype system developed show that the RF based algorithm provides better accuracy compared to the threshold based and signature based methods.

This study constructs a predictive model based on the random forest algorithm, focusing on the challenge of predicting outcomes in complex systems. The research first performs time-series structured organization of historical data and extracts key features. A dynamic time-segmentation strategy is employed to divide the dataset into training and testing sets. Model parameters are optimized using information gain calculations and the cross-entropy loss function, enabling efficient capture of nonlinear relationships in high-dimensional data. The model uses key variables such as quantitative features and trend changes as inputs for predictive analysis, and incorporates a logistic regression model to hierarchically refine the results, forming a multi-model collaborative predictive framework. This study leverages the complementary advantages of multi-dimensional data processing workflows and algorithms to establish a predictive framework tailored for complex systems. It demonstrates significant adaptability in high-dimensional nonlinear data scenarios, providing a methodological framework with both theoretical value and practical significance for predictive analysis and feature variable impact assessment in similar complex systems.

This research investigates the integration of the Random Forest Algorithm within the framework of English Innovative Talent Cultivation Mode in higher education institutions. The study uses a performance evaluation approach to examine the algorithm's impact on student learning outcomes and instructional methodologies. The algorithm's usefulness in improving outcomes and personalizing instructional approaches is examined using descriptive statistical analysis. The findings demonstrate the algorithm's potential to change English language education by providing useful insights into student performance that can drive instructional decisions. This study emphasizes the relevance of refining talent development tactics and cultivating innovative English language skills among students, thus contributing to the growth of educational practices in colleges and universities. As a result, using the Random Forest Algorithm in this scenario has the potential to revolutionize teaching practices while also encouraging a culture of continual improvement and innovation in English language acquisition.

In the development of modern urban gardens, delving into visitor experience perception to address homogenization issues is a crucial means to enhance garden space characteristics and meet diverse demands. This study selected nine representative modern gardens in the Taihu Lake region as samples. Utilizing machine learning techniques combined with image semantic segmentation and principal component analysis, it systematically assessed various microscopic features of gardens and explored their relationship with actual visitor experience ratings. The aim is to reveal emotional resonance and aesthetic preferences during the touring process. The results indicate: (1) Comfort holds a primary position in influencing visitor touring experiences; (2) Microscopic features such as water features, tall vegetation, architecture, and ornametal elements significantly contribute to visitor comfort; (3) The random forest model unveils complex interactions between microscopic features and the overall visitor experience, indicating limitations in traditional linear model evaluations for such relationships. Through analyzing the relationship between microscopic garden features and visitor experiences in the selected cases, this study highlights the importance of adopting non-linear models to optimize garden landscape design and enhance the visitor touring experience.

Tornadoes are highly destructive small-scale extreme weather processes in the troposphere. The weather radar is one of the most effective remote sensing devices for the monitoring and early warning of tornadoes. The existing tornado detection algorithms based on radar data are unsupervised and have strict multi-altitude constraints, such as the tornado detection algorithm based on tornado vortex signatures (TDA-TVS), which may lead to high false alarm rates, and the performance of the detection algorithm is greatly affected by the radar data quality control algorithm. A novel TDA-RF algorithm based on the random forest (RF) classification algorithm is proposed for real-time tornado identification of the S-band China new generation of Doppler weather radar (CINRAD-SA). The TDA-RF algorithm uses velocity features to identify tornadoes and adds features related to reflectivity and velocity spectrum width in radar level-II data. Historical CINRAD-SA tornado data from 2006–2015 are used to construct the tornado dataset and train the TDA-RF model. The performance of TDA-RF is evaluated using CINRAD-SA data from five tornadoes of 2016–2020 with enhanced Fujita(EF) scale ratings ranging from EF0 to EF4 and distances from 10 to 130 km to the radar. TDA-RF performs well overall with the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of 71%, 29%, and 55%, respectively. Moreover, the TDA-RF improves POD and CSI, and reduces FAR compared to the TDA-TVS. The maximum tornado early-warning time of TDA-RF is 17 min, and the average is 6 min; TDA-RF can provide classification probability according to the tornado generation and development process to facilitate tracking ability.

Application of Random Forest Algorithm and Multi-Temporal Satellite Data for Forest Types Classification in Chiang Mai Province

Application of Random Forest Algorithm in Financial Intelligence Analysis

Due to serious health concerns, recently air quality monitoring and advance prediction model development have become a key focus for researchers. In view of prediction models, Ensemble data mining tool Random Forest was employed to predict the concentration of Nitrogen Dioxide (NO2) in air. For this paper, the monitoring dataset of emission inventory and meteorological parameters was analyzed as input predictors. While, the prediction performance of the model was calculated by using evaluation measures namely Correlation coefficient ( $\boldsymbol{R}^{2}$ ), Root Mean Squared Error (RMSE) and Relative Absolute Error (RAE). The model performance was validated by comparing the results with two well-known classification algorithms known as M5P and Support Vector Machines (SVM) for regression. The models were further assessed under different filtering techniques such as Principle Component Analysis and Relief Attribute Evaluator. The results obtained suggest that the performance of Random Forest to predict the atmospheric concentration of NO2 is better than other two data mining approaches.

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.

Solar-driven Joule cycle reciprocating Ericsson engines for small scale applications. From improper operation to high performance

The advancing information technology is playing more and more important role in data mining of relational database.1 The transfer and sharing of databases cause the copyright-related security threats. Database watermarking technology can effectively solve the problem with copyright protection and traceability, which has been attracting researchers' attention. In this paper, we proposed a novel, robust and reversible database watermarking technique, named histogram shifting watermarking based on random forest and genetic algorithm (RF-GAHCSW). It greatly improves the watermark capacity by means of histogram width reduction and eliminates the impact of the prediction error attack. Meanwhile, random forest algorithm is used to select important attributes for watermark embedding, and genetic algorithm is employed to find the optimal secret key for the database grouping and determine the position of watermark embedding to improve the watermark capacity and reduce data distortion. The experimental results show that the robustness of RF-GAHCSW is greatly improved, compared with the original HSW, and the distortion has little effect on the usability of database.