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14th International Conference on Intelligent Tutoring Systems (ITS)

作者： Cheng, Xiaoxiao Zhu, Zhengzhou Liu, Xiao Yuan, Xiaofang Guo, Jiayu Guo, Qun Li, Deqi Zhu, Ruofei Peking Univ Beijing Peoples R China

ISBN: (纸本)9783319914640;9783319914633

The learning early-warning is an effective way to optimize the teaching effect and teach students in accordance of their aptitude. At present, the learning early-warning faces low accuracy, high value of MSE and MAE. We propose a novel learning early-warning model: LEWM-RFA. The model divides students' learning behaviors data into three dimensions: knowledge, behavior and attitude. Then the model uses random forest algorithm to extract features that can affect students' grades, and then predicts students' final exam scores. Students are divided into three warning levels according to their grades. Compared with the model based on the linear regression algorithm, the LEWM-RFA's MSE decreases by 27.498% and the LEWM-RFA's MAE decreases by 26.960%.

关键词： Learning early-warning random forest algorithm Prediction

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Detection of DNS DDoS Attacks with random forest algorithm on Spark 15

Detection of DNS DDoS Attacks with Random Forest Algorithm o...

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15th International Conference on Mobile Systems and Pervasive Computing (MobiSPC) / 13th International Conference on Future Networks and Communications (FNC)

作者： Chen, Liguo Zhang, Yuedong Zhao, Qi Geng, Guanggang Yan, ZhiWei Univ Chinese Acad Sci Sch Comp & Control Beijing 100190 Peoples R China China Comp Network Informat Ctr Beijing 100190 Peoples R China

Domain Name System(DNS) is one of the most foundational and essential services on the Internet, the security and robustness of DNS are of great significance. However, the stable operation of DNS has been threatened by Distributed Denial of Service(DDoS) for quite a long time, especially when the number of registered names of .CN are over 20 million on November 11, 2016. According to our observation, the frequency of volume-based DDoS attacks increased rapidly in recent years, and when the attack happened, not only the authoritative servers were affected, servers of Top Level Domain(TLD) also suffered a lot. In this paper, a model based on random forest([1]) is applied to traffic classification with an accuracy of 99.2% on Spark. The result shows that the model could be used to deal with large-scale DNS query flows, which is fast enough to be used in practice. (C) 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the 13th International Conference on Future Networks and Communications, FNC-2018 and the 15th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2018.

关键词： DNS DDoS Traffic Filtering random forest algorithm Spark

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Hybrid Approach to Improving the Results of the SVM Classification Using the random forest algorithm 13

Hybrid Approach to Improving the Results of the SVM Classifi...

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13th International Symposium on Intelligent Systems (INTELS)

作者： Demidova, L. A. Klyueva, I. A. Pylkin, A. N. Ryazan State Radio Engn Univ 59-1 Gagarina Str Ryazan 390005 Russia

The aim of this work is to improve the results of the SVM classification (Support Vector Machine) by hybridizing the SVM classifier with the random forest classifier (random forest, RF) used as the auxiliary. Specification of the classification decisions obtained on the basis of the SVM classifier is performed for the objects located in the experimentally determined subareas near the hyperplane separating the classes and including both correctly and erroneously classified objects. In the case of improving the quality of the objects classification from the initial dataset, the proposed hybrid approach to the objects classification can be recommended for classification of new objects. When developing the SVM classifier, the fixed default parameters values are used. A comparative analysis of the classification results obtained during the computational experiments in the hybridization of the SVM classifier with two auxiliary classifiers the random forest classifier (RF classifier) and the k nearest neighbor classifier (kNN classifier), for which the parameters values are determined randomly, confirms the expediency of using of these classifiers to increase the SVM classification quality. It was found that in most cases, the random forest classifier works better in terms of improving the SVM classification quality in comparison with the kNN classifier. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： intelligent classification algorithm SVM algorithm k-nearest neighbors algorithm random forest algorithm support vector kernel function kernel function parameter regularization parameter hybridization

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Research on the Classification of High Dimensional Imbalanced Data based on the Optimization of random forest algorithm 2018

Research on the Classification of High Dimensional Imbalance...

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International Conference on Big Data Engineering and Technology (BDET)

作者： Ma Xiaojuan Tianjin Transportat Vocat Coll Tianjin Peoples R China

ISBN: (纸本)9781450365826

The random forest is stochastic a forest establishment, there are many decision trees;there is no correlation between each decision tree random forest. The establishment of each decision tree, using the random sampling process is put back, and then uses the voting form of classification and prediction. The algorithm can solve the bottleneck in the performance of a single classifier, so it is widely used in many aspects. Of course, the algorithm also has some room for improvement, according to the random forest algorithm to deal with unbalanced data set when running low efficiency, this paper puts forward approaches to the problem are not a new balance at the same time as the calculation process, showing the growth of the index value, how to improve the prediction speed and shorten the running time, according to the characteristics of the random forest algorithm in the construction process is put forward Based on the domestic and foreign literatures, this paper mainly studies the optimization of random forest from two aspects. random forest algorithm is an ensemble learning method in the field of machine learning. It is integrated with the classification results of multiple decision trees to form a global classifier. The random forest algorithm compared with other classification algorithms have many advantages, the classified effect advantage is reflected in the classification accuracy and the generalization error is small and has the ability to deal with high dimensional data, the training process of the advantages of learning algorithm of quick and easy parallelization. Based on these two advantages, random forest algorithm has been widely used, and it has become one of the priorities to deal with classification problem. However, when the data type of the unbalanced distribution of the situation, that is the number one category of samples is far less than other types of samples, random forest algorithm will appear ineffective, the generalization error of variable c

关键词： Imbalanced data classification random forest algorithm optimization algorithm subspace selection model selection

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Knowledge Acquisition of Online Static Voltage Stability Margin Assessment Based on random forest algorithm 3

Knowledge Acquisition of Online Static Voltage Stability Mar...

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3rd IEEE International Electrical and Energy Conference (CIEEC)

作者： Fang, Hui Xu, Ruilin Zhu, Xiaojun Zhao, Ke Zhang, Pei Ren, Chao Li, Qiaoqiao Xu, Yan State Grid Chongqing Elect Power Co Elect Power Res Inst Chongqing Peoples R China State Grid Chongqing Elect Power Co Chongqing Peoples R China Beijing Jiaotong Univ Sch Elect Engn Beijing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781728116754

Modern power system has produced a large amount of offline data during the operational planning studies. In order to extract the evaluation rules of voltage stability margin from massive data, a knowledge acquisition model based on random forest (RF) algorithm is proposed in this paper. Firstly, the training set of the random forest is obtained by PV curve analysis and calculation of voltage stability margin, based on the simulation data under n-k fault. Then, the online evaluation model of the voltage stability margin is trained using the RF algorithm. Finally, by using the proposed RF rule extraction method, some voltage stability margin evaluation rules are extracted with high accuracy and coverage. The proposed method is verified on IEEE 3-machine 9-bus system. Simulation result shows that the obtained knowledge of voltage stability margin has high accuracy, and can effectively evaluate the real-time operation status of the power grid.

关键词： Voltage stability PV curve random forest algorithm Knowledge acquisition

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Predicting the Outer/Inner BetaStrands in Protein Beta Sheets Based on the random forest algorithm

Predicting the Outer/Inner BetaStrands in Protein Beta Sheet...

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10th International Conference on Intelligent Computing (ICIC)

作者： Tang, Li Zhao, Zheng Zhang, Lei Zhang, Tao Gao, Shan Nankai Univ Minist Educ Key Lab Bioact Mat Coll Life Sci Tianjin 300071 Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China Tianjin Univ Finance & Econ Informat Sci & Technol Dept Tianjin Peoples R China

ISBN: (纸本)9783319093307;9783319093291

The beta sheet, as one of the three common second form of regular secondary structure in proteins plays an important role in protein function. The best strands in a beta sheet can be classified into the outer or inner strands. Considering the protein primary sequences have determinant information to arrange the strands in the beta sheet topology, we introduce an approach by using the random forest algorithm to predict outer or inner arrangement of a beta strand. We use nine features to describe a strand based on the hydrophobicity, the hydrophilicity, the side-chain mass and other properties of the beta strands. The random forest classifiers reach the best prediction accuracy 89.45% with 10-fold cross-validation among five machine learning methods. This result demonstrates that there are significant differences between the outer beta strands and the inner ones in beta sheets. The finding in this study can be used to arrange beta strands in a beta sheet without any prior structure information. It can also help better understanding the mechanisms of protein beta sheet formation.

关键词： beta sheet beta strand protein secondary structure random forest algorithm

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Fault Diagnosis Method of Photovoltaic Array Based on random forest algorithm 39

Fault Diagnosis Method of Photovoltaic Array Based on Random...

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39th Chinese Control Conference (CCC)

作者： Gong, Sizhe Wu, Xunhao Zhang, Ziwen Northeastern Univ Coll Informat Sci & Engn Shenyang 110000 Peoples R China

ISBN: (纸本)9789881563903

This paper proposes a fault diagnosis method for photovoltaic array based on random forest algorithm. The random forest algorithm used in this paper is based on classification regression tree (CART). And bootstrap sampling method was used to generate multiple training sets from the original training set, and random attributes were introduced in the training process to generate multiple CART trees. When troubleshooting, the final troubleshooting results are determined by voting according to the diagnosis results of each CART tree. By building a photovoltaic array model in Matlab/Simulink simulation module, use it to summarize the output characteristics of photovoltaic array in the common fault states (partial shading, abnormal component aging, open circuit, short circuit). And considering the main natural factors that affect the output of photovoltaic array, the open circuit voltage, short circuit current, maximum power point voltage, maximum power point current, environmental temperature and light irradiance were selected as the input parameters of the random forest algorithm. Then, a 2X2 small photovoltaic array experiment platform and a field data acquisition system were designed and built to collect data in order to further verify the feasibility of the random forest algorithm. The test results of random forest were compared with those of single CART decision tree, K nearest neighbor (KNN) and support vector machine (SVM) algorithms. The results showed that random forest could complete the fault diagnosis task of photovoltaic array better.

关键词： photovoltaic array fault diagnosis random forest algorithm Matlab/Simulink simulation photovoltaic array experimental platform

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Prediction of compressive strength of nano-silica concrete by using random forest algorithm

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Asian Journal of Civil Engineering 2024年第7期25卷 5205-5213页

作者： Nigam, Mayank Verma, Manvendra Department of Civil Engineering GLA University Mathura India

The prediction of compressive strength in concrete is an important part of civil engineering because it influences structural design and durability. Recent improvements have brought nano-silica as an addition, improving concrete’s mechanical qualities. This study investigates the use of the random forest (RF) method to estimate the compressive strength of nano-silica concrete, taking advantage of its ability to manage complicated, non-linear interactions among factors. We used a large dataset with varying concentrations of nano-silica and other concrete mix components. Normalisation and feature selection were used as preprocessing techniques to improve the model’s performance. The RF model, which is known for its resilience and accuracy, was trained and verified, displaying better predictive performance than typical linear regression models. The results showed that the RF method could efficiently capture the subtle correlations between nano-silica content and compressive strength, resulting in accurate predictions over a wide range of concrete compositions. This prediction model is a vital tool for civil engineers, allowing for more precise and efficient design of nano-silica concrete mixtures, resulting in stronger and more durable projects. A complete dataset with varying concentrations of nano-silica and other concrete mix components was used. Preprocessing techniques, such as normalisation and feature selection, were used to improve the model's predicted accuracy. The RF model was trained and tested, demonstrating higher performance over established prediction methods like linear regression. The results showed that the RF algorithm could successfully forecast the compressive strength of nano-silica concrete while also capturing the complex interactions between the mix ingredients. This predictive capability is critical for civil engineers since it provides a powerful tool for creating concrete mixtures with improved performance characteristics. To summarise, inc

关键词： Concrete composites Machine learning techniques Mechanical properties Nano-silica concrete random forest algorithm

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Vehicle Accident Foresight System using Bigdata Intelligent random forest algorithm 10

Vehicle Accident Foresight System using Bigdata Intelligent ...

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10th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

作者： Aburas, Abdurazzag A. Eyono, Scott Naidoo, Omesan Univ KwaZulu Natal Dept Comp Engn Durban South Africa

ISBN: (数字)9781728109749

ISBN: (纸本)9781728109749

commercial vehicles are used to improve people life. One of the main concern is the safety of these commercial vehicle during accidents. This research paper presents the idea of a Vehicle Accident Foresight System (VAFS). Accident datasets for VAFS are based on actual vehicle accident datasets extracted from open-source datasets. VAFS aims to analyze past accident data using the random forest algorithm to predict future accidents. One thousand decision trees are used in implementation. The implemented prototype consists of a dashboard which can specify accident locations according to time, it can also identify the elements which contribute the most to accident severity. The prediction model of VAFS has a minimum accuracy of 69% and a maximum accuracy of 100%.

关键词： Vehicle accident Precognition insurance datasets random forest algorithm

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Multilevel random forest algorithm in image recognition for various scientific applications

Multilevel Random forest algorithm in image recognition for ...

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73rd IEEE National Aerospace and Electronics Conference (NAECON)

作者： Karmakar, Sayantani Karmakar, Supriya Portland State Univ Portland OR 97207 USA SUNY Farmingdale State Coll Farmingdale NY 11735 USA

ISBN: (纸本)9781665448598

Artificial Intelligence (AI) is a field of research where researchers and scientists are trying to understand our nature and incorporate that understanding in the machine and computer to serve our society. Machine learning (ML) is another aspect of AI. Several algorithms are inverted, modified and updated to characterize the understanding of the nature and implement intelligence artificially within the machine. One major challenge of AI is to understand the object. The first identification of an object is visual information. There are several mechanisms and algorithms to identify an object. Every algorithm has some pros and cons. Among these algorithms, decision tree and random forest algorithm, which is a combination of decision tree algorithms, can play a very effective role. This work shows the application of the multilevel random forest algorithm (MRFA) which is a modified application of the random forest algorithm is introduced for image classification.

关键词： Artificial Intelligence (AI) Image classification Decision tree random forest algorithm Multilevel random forest algorithm (MRFA) Machine Learning (ML)

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