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The 7th International Conference on Management and Service Science(MASS 2013)

作者： Shaochun Jia Xiuzhen Hu Lixia Sun Department of Physics College of Sciences Inner Mongolia University of Technology

Based on the research of predicting β-hairpin motifs in proteins,we apply random forest and Support Vector Machine algorithm to predict β-hairpin motifs in ArchDB40 *** motifs with the loop length of 2 to 8 amino acid residues are extracted as research object and the fixed-length pattern of 12 amino acids are *** using the same characteristic parameters and the same test method,random forest algorithm is more effective than Support Vector *** addition,because of random forest algorithm doesn't produce overfitting phenomenon while the dimension of characteristic parameters is higher,we use random forest based on higher dimension characteristic parameters to predict β-hairpin *** better prediction results are obtained;the overall accuracy and Matthew's correlation coefficient of 5-fold cross-validation achieve 83.3% and 0.59,respectively.

关键词： random forest algorithm Support Vector Machine algorithm β-Hairpin Motif Increment of Diversity Scoring Function Predicted Secondary Structure Information

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Power Anomaly Data Detection algorithm Based on User Tag and random forest

Power Anomaly Data Detection Algorithm Based on User Tag and...

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作者： JianXun Guo Bo Liu Hongyan Zhang Qiang Li Shaoyong Guo Yang Yang State Grid Henan Electric Power Company State Grid Henan Information & Telecommunication company State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

In the context of the allocation of electricity big data,in order to solve the problem that the power consumption anomaly detection algorithm is less efficient due to the diverse sources,various types and large data volume of the power consumption data,this paper proposes a power anomaly data detection algorithm based on user tag and random *** analyzing the power data,each data is marked with a power type,including a normal type,a less bur type,a large downward shift type,and a multi-burr *** on the tags,three types of user power data tags,such as basic information tags,environmental information tags,and power information tags,are constructed as attribute sets of user power ***,a user data anomaly data analysis algorithm based on user tag and random forest is *** the experimental part,the traditional the random forest algorithm is compared with the proposed algorithm,and the performance index of the algorithm in both accuracy and false alarm rate is better than the traditional random forest algorithm.

关键词： power distribution and consumption data big data anomaly detection user tag random forest algorithm

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Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches

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forest SCIENCE AND TECHNOLOGY 2025年第1期21卷 110-122页

作者： Antunez, Pablo Wehenkel, Christian Basave-Villalobos, Erickson Calixto-Valencia, Celi Gloria Valenzuela-Encinas, Cesar Ruiz-Aquino, Faustino Sarmiento-Bustos, David Univ Sierra Juarez Div Estudios Posgrad Inst Estudios Ambientales Av Univ S-N Oaxaca Mexico Univ Juarez Estado Durango Inst Silvicultura & Ind Madera Durango Mexico Campo Expt Valle Guadiana CIR Norte Ctr INIFAP Durango Mexico Campo Expt Iguala INIFAP CIR Pacif-Sur Iguala De La Independenci Guerrero Mexico Univ Sierra Juarez Inst Estudios Ambientales Av Univ S-N Oaxaca Mexico

This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics-stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)-for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees. The novelty of this study lies in applying five machine learning algorithms-random forest, Neural Networks, Gradient Boosting Machines, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN)-to predict these metrics, using data from the destructive analysis of 98 individual trees aged from eight months to five years. For comparison, we also applied univariate allometric models, adjusted with nonlinear least squares and quantile regression. The results indicate that random forest, k-NN, and SVM outperformed the other algorithms, demonstrating superior predictive accuracy for both biomass and volume. A key innovation of this study is its demonstration of how machine learning, with its ability to model complex, nonlinear relationships, can serve as a powerful tool for forest management. Quantile regression, combined with nonlinear least squares, proves most effective when the relationships are well-defined, allowing for tailored parameter adjustments that enhance predictions, particularly in the presence of heteroscedasticity.

关键词： forest management machine learning algorithms in forestry predicting forest biomass random forest algorithm allometric modeling quantile regression in forest management

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Improved Prediction of Significant Prostate Cancer Following Repeated Prostate Biopsy by the random forest Classifier

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JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING 2023年第1期43卷 83-92页

作者： Stojadinovic, Miroslav Milicevic, Bogdan Jankovic, Slobodan Clin Ctr Kragujevac Dept Urol Clin Urol & Nephrol ZmajJovina 30 Kragujevac 34000 Serbia Univ Kragujevac Fac Med Sci Kragujevac Serbia BioIRCKragujevac Bioengn Res & Dev Ctr Kragujevac Serbia Univ Kragujevac Fac Engn Kragujevac Serbia Univ Kragujevac Fac Med Sci Pharmacol & Toxicol Dept Kragujevac Serbia

Purpose Numerous strategies and diagnostic tests were proposed in patients suspected of clinically significant (cs) prostate cancer (PCa) after an initial negative prostate biopsy. The study aimed to create a random forest (RF) classifier for predicting the probability of csPCa in specimens taken by the repeated systematic prostate biopsy (SBx), and to determine its diagnostic accuracy and clinical utility. Methods This retrospective, single-center study included patients who underwent repeated SBx due to clinical suspicion of cancer. Data on patient age, serum prostate-specific antigen (PSA) levels, prostate volume, digital rectal examination, first-degree family history, and histology findings from the SBx were collected for all patients. The area under the curve (AUC), and secondary metrics of clinical prediction models were used to assess their discriminative abilities. Clinical usefulness of final model was tested by the decision curve analysis (DCA). The explainability and website placement of the ML model were also performed. Results In total, 204 patients were eligible for analysis. The csPCa was detected in 26% (n = 53) patients. The AUC, accuracy, sensitivity, and specificity for detection of csPCa were 0.94, 0.91, 0.84, and 0.98, respectively. With an optimal threshold of 0.8, about 34% of unnecessary biopsies would be avoided, but correct diagnosis would be delayed in 4.4% csPC cases. PSA level, prostate volume, and age were the top-ranked variables in the RF model. Conclusion The RF classifier predicts csPCa with good accuracy and may help urologists when deciding whether the repeated biopsy is necessary to avoid being too invasive.

关键词： Prostate cancer Repeated prostate biopsy Risk calculator random forest algorithm Web-based model

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Layered management and hybrid control strategy based on hybrid automata and random forest for microgrid

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IET RENEWABLE POWER GENERATION 2019年第16期13卷 3113-3123页

作者： Dou, Chunxia Teng, Shengqi Zhang, Tengfei Zhang, Bo Ma, Kai Yanshan Univ Inst Elect Engn Qinhuangdao Hebei Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Jiangsu Peoples R China

A layered management and hybrid control strategy based on hybrid automata and random forest for the microgrid is proposed in this study. It is used to realise energy management and multi-modal coordinated switching of microgrid system to ensure high security, stability and high efficiency. Management and control strategies are implemented through a three-layer microgrid architecture based on multi-agent system theory. An energy management strategy is designed for the microgrid system in the upper layer agent structure. A coordinated control strategy is designed in the middle layer agent structure. A local control strategy is designed into the bottom layer agent structure. In the middle layer coordinated control strategy, the hybrid automata is used to characterise the complex logical features of each underlying unit and trigger the switching of working modes according to the condition change. The random forest algorithm is used to analyse the running state and reduce the number of switching. Finally, the simulation results verify the effectiveness of the proposed control strategy in different scenarios.

关键词： multi-agent systems distributed power generation power system control energy management systems hybrid control strategy hybrid automata multimodal coordinated switching microgrid system control strategies three-layer microgrid architecture multiagent system theory energy management strategy upper layer agent structure coordinated control strategy middle layer agent structure local control strategy random forest algorithm layered management

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Multi-Model Fusion Short-Term Load Forecasting Based on random forest Feature Selection and Hybrid Neural Network

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IEEE ACCESS 2021年 9卷 69002-69009页

作者： Xuan, Yi Si, Weiguo Zhu, Jiong Sun, Zhiqing Zhao, Jian Xu, Mingjie Xu, Shouliang State Grid Zhejiang Elect Power Co Ltd Hangzhou Power Supply Co Hangzhou 310007 Peoples R China Shanghai Univ Elect Power Elect Power Engn Shanghai 200090 Peoples R China

In an increasingly open electricity market environment, short-term load forecasting (STLF) can ensure the power grid to operate safely and stably, reduce resource waste, power dispatching, and provide technical support for demand-side response. Recently, with the rapid development of demand side response, accurate load forecasting can better provide demand side incentive for regional load of prosumer groups. Traditional machine learning prediction and time series prediction based on statistics failed to consider the non-linear relationship between various input features, resulting in the inability to accurately predict load changes. Recently, with the rapid development of deep learning, extensive research has been carried out in the field of load forecasting. On this basis, a feature selection algorithm based on random forest is first used in this paper to provide a basis for the selection of the input features of the load forecasting model. After the input features are selected, a hybrid neural network STLF algorithm based on multi-model fusion is proposed, of which the main structure of the hybrid neural network is composed of convolutional neural network and bidirectional gated recurrent unit (CNN-BiGRU). The input data is obtained by using long sliding time windows of different steps, then multiple CNN-BiGRU models are trained respectively. The forecasting results of multiple models are averaged to get the final forecasting load value. The load datasets come from a region in New Zealand and a region in Zhejiang, China, are used as load forecast examples. Finally, a variety of load forecasting algorithms are introduced for comparison. The experimental results show that our method has a higher accuracy than comparison models.

关键词： Load modeling Feature extraction random forests Load forecasting Data models Predictive models Vegetation Short-term load forecasting prosumer random forest algorithm convolutional neural network bidirectional GRU multi-model fusion

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Predicting the performance and emissions of an HCCI-DI engine powered by waste cooking oil biodiesel with Al2O3 and FeCl3 nano additives and gasoline injection - A random forest machine learning approach

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FUEL 2024年第PartB期357卷

作者： Leo, G. M. Lionus Jayabal, Ravikumar Srinivasan, D. Das, M. Chrispin Ganesh, M. Gavaskar, Thodda St Josephs Coll Engn Dept Mech Engn Chennai Tamil Nadu India SIMATS Saveetha Sch Engn Dept Thermal Engn Chennai Tamil Nadu India Loyola Inst Technol Dept Mech Engn Chennai Tamil Nadu India St Josephs Inst Technol Dept Mech Engn Chennai Tamil Nadu India

This study presents a novel method for predicting the performance and emissions of a Homogeneous Charge Compression Ignition-Direct Injection (HCCI-DI) engine fuelled by waste cooking oil biodiesel (WCOB) blended with Aluminum oxide (Al2O3) and Ferric chloride (FeCl3) nano additives and premixed with gasoline. The test fuels considered were pure diesel, WCOB, B50 (a 50-50 blend of diesel and WCOB), and the nano-additives at concentrations of 50 ppm and 100 ppm. All experiments were performed on a 4.4 kW HCCI-DI engine operating at 1500 rpm. The results revealed that, utilization of WCOB showed a substantial reduction in emissions. Hydrocarbon (HC), carbon monoxide (CO) and smoke emissions decreased by 54.17 %, 50 %, and 22.69 %. Nevertheless, oxides of nitrogen (NOx) emissions increased by 18.32 %. When introducing gasoline (20 %) as a premix in HCCI-DI engines, a favourable shift in emission and efficiency metrics was observed. The brake thermal efficiency (BTE) increased 4.23 %. Moreover, NOx and smoke emissions decreased by 4.3 % and 39.21 %. Furthermore, compared to conventional diesel-fueled combustion, the integration of the nano additives manifested promising results. After introducing Al2O3 into neat fuel, The BTE improved by 11.27 % for direct injection (DI) combustion and 18.31 % for HCCI-DI combustion. Additionally, there was a marked decrease in exhaust emissions. FeCl3 nano additive into the test fuel significantly reduced HC, CO, and smoke emissions. Furthermore, the random forest machine learning approach demonstrated an extensive accuracy in forecasting both engine performance and emissions for the HCCI-DI engine. The innovative combination of cutting-edge machine learning techniques and combustion technology used. This model to understand the complicated correlations between input parameters and engine outputs and account for the system's intrinsic non-linearities and interactions. This dramatically improves standard prediction approaches, often ass

关键词： Waste cooking oil biodiesel Aluminum oxide nano additive Ferric chloride nano additive Emissions random forest algorithm

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Optimizing maize germination forecasts with random forest and data fusion techniques

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2468-e2468页

作者： Wu, Lili Xing, Yuqing Yang, Kaiwen Li, Wenqiang Ren, Guangyue Zhang, Debang Fan, Huiping Henan Agr Univ Coll Sci Zhengzhou Peoples R China Henan Univ Sci & Technol Coll Food & Bioengn Luoyang Peoples R China Zhengzhou Wangu Machinery Co LTD Zhengzhou 450041 Peoples R China Henan Agr Univ Coll Food Sci & Technol Zhengzhou Peoples R China

Traditional methods for detecting seed germination rates often involve lengthy experiments that result in damaged seeds. This study selected the Zheng Dan-958 maize variety to predict germination rates using multi-source information fusion and a random forest (RF) algorithm. Images of the seeds and internal cracks were captured with a digital camera. In contrast, the dielectric constant of the seeds was measured using a fl at capacitor and converted into voltage readings. Features such as color, shape, texture, crack count, and normalized voltage were used to form feature vectors. Various prediction algorithms, including random forest (RF), radial basis function (RBF), neural networks (NNs), support vector machine (SVM), and extreme learning machine (ELM), were developed and tested against standard germination experiments. The RF model stood out, with a training time of 5.18 s and the highest accuracy of 92.88%, along with a mean absolute error (MAE) of 0.913 and a root mean square error (RMSE) of 1.163. The study concluded that the RF model, combined with multi-source information fusion, offers a feasible and nondestructive method for quickly and accurately predicting maize seed germination rates.

关键词： Maize seeds Germination rate Non-destructive prediction random forest algorithm Image processing

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Modified parallel random forest for intrusion detection systems

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JOURNAL OF SUPERCOMPUTING 2016年第6期72卷 2235-2258页

作者： Masarat, Saman Sharifian, Saeed Taheri, Hassan Amirkabir Univ Technol Switching & Network Lab Tehran Polytech Tehran 15914 Iran Amirkabir Univ Technol Dept Elect & Elect Tehran Polytech Tehran 15914 Iran

Intrusion detection system (IDS) is one of the important elements for providing the security in networks. Increasing the number of network-based applications on the one hand and increasing the data volumes on the other hand forced the designers to conduct some research on the novel methods for improving network security. One of the recent efforts to improve IDS performance is developing the machine learning algorithms. random forest is one of the powerful algorithms employed in data mining. It operates based on classifier fusion principles and is implemented as detection engine in some anomaly-based IDSs. In this paper, we present a novel parallel random forest algorithm for intrusion detection systems. The original random forest algorithm has some weaknesses in feature selection, selecting efficient numbers of classifiers, number of random features for training and also in combination steps. In this research we investigate aforementioned challenges and propose solutions for them. The simulation results show the superiority of our method regarding performance, scalability and cost of misclassified samples in our method in comparison with the original random forest algorithm and Hadoop-based version of the random forest.

关键词： random forest algorithm Intrusion detection system Classifier ensemble Decision tree Hadoop

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Robustness of random forest-based gene selection methods

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BMC BIOINFORMATICS 2014年第1期15卷 1-8页

作者： Kursa, Miron Bartosz Univ Warsaw Interdisciplinary Ctr Math & Computat Modelling PL-02106 Warsaw Poland

Background: Gene selection is an important part of microarray data analysis because it provides information that can lead to a better mechanistic understanding of an investigated phenomenon. At the same time, gene selection is very difficult because of the noisy nature of microarray data. As a consequence, gene selection is often performed with machine learning methods. The random forest method is particularly well suited for this purpose. In this work, four state-of-the-art random forest-based feature selection methods were compared in a gene selection context. The analysis focused on the stability of selection because, although it is necessary for determining the significance of results, it is often ignored in similar studies. Results: The comparison of post-selection accuracy of a validation of random forest classifiers revealed that all investigated methods were equivalent in this context. However, the methods substantially differed with respect to the number of selected genes and the stability of selection. Of the analysed methods, the Boruta algorithm predicted the most genes as potentially important. Conclusions: The post-selection classifier error rate, which is a frequently used measure, was found to be a potentially deceptive measure of gene selection quality. When the number of consistently selected genes was considered, the Boruta algorithm was clearly the best. Although it was also the most computationally intensive method, the Boruta algorithm's computational demands could be reduced to levels comparable to those of other algorithms by replacing the random forest importance with a comparable measure from random Ferns (a similar but simplified classifier). Despite their design assumptions, the minimal optimal selection methods, were found to select a high fraction of false positives.

关键词： Gene Selection Importance Measure Importance Score random forest Classifier random forest algorithm

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