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Automatic classification of multi-source and multi-granularity teaching resources based on random forest algorithm

INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2023年第2-3期33卷 177-191页

作者： Li, Dahui Qu, Peng Jin, Tao Chen, Changchun Bai, Yunfei Qiqihar Univ Sch Comp & Control Engn Qiqihar 161006 Heilongjiang Peoples R China

In traditional teaching resource classification methods, the classification accuracy is low and the RDV value of classification convergence is high. Through fuzzy information mining and fusion clustering method, multi-source and multi-granularity teaching resource data is obtained. With the help of incremental orthogonal component analysis method, the dimension of multi-source and multi-granularity teaching resource data is reduced. First, the teaching resource data is brought into random forest. Then, the filtering error of teaching resource is determined according to the classification parameter nonlinear feature recognition results. Finally, the multi-source and multi-granularity teaching resource classification is completed. The experimental results show that the highest classification accuracy is about 98%, and the lowest RDV is about 0.015.

关键词： random forest algorithm multi-source multi-granularity teaching resources automatic automatic classification

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Salt Land classification based on pansharpening images using random forest algorithm

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JOURNAL OF SPATIAL SCIENCE 2024年第2期69卷 621-647页

作者： Diastarini, Diastarini Virtriana, Riantini Harto, Agung Budi Bandung Inst Technol Fac Earth Sci & Technol Geodesy & Geomat Engn Bandung Indonesia

The analysis and policymaking of national salt production require quality Salt Land maps with more detailed object information. This study successfully classified Salt Land into Active and Inactive based on pansharpening images using random forest algorithm. The results show: 1) the best pansharpening method is UNB PanSharp;2) the texture variables have more significant contributions to enhance classification accuracy;3) scheme 5 is the most efficient and recommended scheme in research, achieves an OA of 66.699% and a Kappa coefficient of 39.4%. This study can provide a good reference for developing Salt Land mapping and policies for practitioners or stakeholders.

关键词： Salt Land pansharpening random forest algorithm

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Research on Prediction of Dam Seepage and Dual Analysis of Lag-Sensitivity of Influencing Factors Based on MIC Optimizing random forest algorithm

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KSCE JOURNAL OF CIVIL ENGINEERING 2023年第2期27卷 508-520页

作者： Liu, Yongtao Zheng, Dongjian Wu, Xin Chen, Xingqiao Georgakis, Christos T. Qiu, Jianchun Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ State Key Lab Hydrol Water Resources & Hydraul Eng Nanjing 210098 Peoples R China Aarhus Univ Dept Civil & Architectural Engn DK-8000 Aarhus C Denmark Yangzhou Univ Coll Hydraul Sci & Engn Yangzhou 225009 Peoples R China

The seepage of the dam is an important representation of the operation characteristics of the dam, and there are many factors affecting the seepage with a certain lag. It is still difficult to predict its change and sensitivity because of complex operating conditions. At present, the lag-sensitivity of influence factors of the dam seepage has not been studied. The time series influence factors of seepage are determined by HTRT (hydrostatic-thermal-rainfall-time) model in this paper. To avoid the pseudo fitting of conventional methods, HTRT model nested random forest algorithm is used to establish the predicting model of the dam seepage. And MIC algorithm is used to achieve the dual purposes of time lag and sensitivity analysis. Firstly, the time lag of relationship between seepage and its influencing factors is characterized, and the most appropriate lag time of the HTRT model factors is determined. Secondly, independent correlation analysis on all influencing factors is carried out and the sensitivity of each factor is analyzed by MIC. Meanwhile, the sensitivity of the factors to seepage is quantitatively analyzed by the two parameters of %IncMSE and IncNodePurity of RF algorithm. The sensitivity of influencing factors is analyzed by comparing MIC results with RF results. Combined with the case, taking the error of fitting prediction as the evaluation index of seepage prediction, the prediction accuracy of MIC-RF model, RF model and MIC-BPNN (Back Propagation neural network) model is calculated and compared. Case study showed that MIC- RF monitoring model has high prediction accuracy, strong adaptability and high robustness in dam seepage, and the sensitivity and time lag of influencing factors can be quantitatively analyzed. The water pressure and rainfall of the lag time are 14 days and 16 days respectively. The sensitivity study of the time series influencing factors of seepage showed that the water pressure component is the main controlling factor of seepage, a

关键词： Seepage prediction Dam MIC random forest algorithm Influencing factors Time lag

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Optimization of parallel random forest algorithm based on distance weight

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1951-1963页

作者： Wang, Qinge Chen, Huihua Cent South Univ Sch Civil Engn Changsha Peoples R China

In order to overcome the problems of long execution time and low parallelism of existing parallel random forest algorithms, an optimization method for parallel random forest algorithm based on distance weights is proposed. The concept of distance weights is introduced to optimize the algorithm. Firstly, the training sample data are extracted from the original data set by random selection. Based on the extracted results, a single decision tree is constructed. The single decision tree is grouped together according to different grouping methods to form a random forest. The distance weights of the training sample data set are calculated, and then the weighted optimization of the random forest model is realized. The experimental results show that the execution time of the parallel random forest algorithm after optimization is 110 000 ms less than that before optimization, and the operation efficiency of the algorithm is greatly improved, which effectively solves the problems existing in the traditional random forest algorithm.

关键词： Distance weights parallel algorithm random forest algorithm algorithm optimization

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Rapid quantitative analysis of coal composition using laser-induced breakdown spectroscopy coupled with random forest algorithm

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ANALYTICAL SCIENCES 2024年第9期40卷 1709-1722页

作者： Du, Hongkun Ke, Shaoying Zhang, Wei Qi, Dongfeng Sun, Tengfei Shandong Univ Technol Ctr Adv Laser Mfg CALM Sch Mech Engn Zibo 255000 Shandong Peoples R China Minnan Normal Univ Sch Phys & Informat Engn Key Lab Light Field Manipulat & Syst Integrat Appl Zhangzhou 363000 Fujian Peoples R China

Coal is the primary energy source in China, widely used in energy production, industrial processes, and chemical engineering. Due to the complexity and diversity of coal quality, there is an urgent need for new technologies to achieve rapid and accurate detection and analysis of coal, aiming to improve coal resource utilization and reduce pollutant emissions. This study proposes a rapid quantitative analysis of coal using laser-induced breakdown spectroscopy combined with the random forest algorithm. Firstly, a Q-switched Nd: YAG laser at 1064 nm was employed to ablate coal samples, generating plasma, and spectral data were collected using a spectrometer. Secondly, the study explores the impact of different parameters in the preprocessing method (wavelet transform) on the predictive performance of the random forest model. It identifies elements related to coal ash content and calorific value along with their spectral information. Subsequently, to further validate the predictive performance of the model, a comparison is made with models established using support vector machine, artificial neural network, and partial least squares. Finally, under optimal parameters for spectral information preprocessing (wavelet transform with Db4 as the base function and 3 decomposition levels), a model combining wavelet transform with random forest is established to predict and analyze the ash content and calorific value of coal. The results demonstrate that the Wavelet Transform-random forest model exhibits excellent predictive performance (coal ash content: R2 = 0.9470, RMSECV = 4.8594, RMSEP = 4.8450;coal calorific value: R2 = 0.9485, RMSECV = 1.5996, RMSEP = 1.5949). Therefore, laser-induced breakdown spectroscopy combined with the random forest algorithm is an effective method for rapid and accurate detection and analysis of coal. The predicted coal composition values show high accuracy, providing insights and methods for coal composition monitoring and analysis.

关键词： Laser-induced breakdown spectroscopy random forest algorithm Coal analysis

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Automatic gender determination from 3D digital maxillary tooth plaster models based on the random forest algorithm and discrete cosine transform

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017年 143卷 59-65页

作者： Akkoc, Betul Arslan, Ahmet Kok, Hatice Selcuk Univ Fac Engn Dept Comp Engn Aleaddin Keykubad Campus TR-42075 Konya Turkey Konya Food & Agr Univ Dept Comp Engn Konya Turkey Necmettin Erbakan Univ Dept Orthodont Konya Turkey

Background and Objective: One of the first stages in the identification of an individual is gender determination. Through gender determination, the search spectrum can be reduced. In disasters such as accidents or fires, which can render identification somewhat difficult, durable teeth are an important source for identification. This study proposes a smart system that can automatically determine gender using 3D digital maxillary tooth plaster models. Methods: The study group was composed of 40 Turkish individuals (20 female, 20 male) between the ages of 21 and 24. Using the iterative closest point (ICP) algorithm, tooth models were aligned, and after the segmentation process, models were transformed into depth images. The local discrete cosine transform (DCT) was used in the process of feature extraction, and the random forest (RF) algorithm was used for the process of classification. Results: Classification was performed using 30 different seeds for random generator values and 10 fold cross-validation. A value of 85.166% was obtained for average classification accuracy (CA) and a value of 91.75% for the area under the ROC curve (AUC). Conclusions: A multi-disciplinary study is performed here that includes computer sciences, medicine and dentistry. A smart system is proposed for the determination of gender from 3D digital models of maxillary tooth plaster models. This study has the capacity to extend the field of gender determination from teeth. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Gender determination Feature extraction Image processing random forest algorithm

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UWB Rectangular Microstrip Patch Antenna Design in Matching Liquid and Evaluating the Classification Accuracy in Data Mining Using random forest algorithm for Breast Cancer Detection with Microwave

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2019年第5期14卷 2127-2136页

作者： Aydin, Emine Avsar Keles, Mumie Kaya Adana Alparslan Turkes Sci & Technol Univ Dept Aerosp Engn Adana Turkey Adana Alparslan Turkes Sci & Technol Univ Dept Comp Engn Adana Turkey

The most common type of cancer for a female is breast cancer in the world. Regular checks and effective-timely treatment are noteworthy parameters for patients' survival struggle . Against existing imaging methods, microwave imaging method has been considered more powerful and effective method by many researchers. In this paper, comprehensive design equations and parameters of rectangular microstrip patch antenna (RMPA) are given for microwave breast cancer detection. The layered breast model with a spherical tumor that was placed into the fibro-glandular layer was created by using CST Microwave Studio Software, and it was embedded in canola oil to decrease the distorted signals between the transmitting and receiving antennas. The RMPA has a wideband performance from 3 to 18GHz. The simulation results show that differences in the electric field and reflection coefficients might more efficiently give a possibility to assign the tumor in the breast model. In addition, in this study, the data obtained from these experiments are classified by using the random forest algorithm from the data mining methods. According to the classification result, the random forest algorithm can diagnose breast cancer by classifying the tumor as 94% accuracy.

关键词： Breast cancer detection Classification Data mining random forest algorithm Rectangular microstrip patch antenna

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Analysis on the spatial dynamic characteristics of land use in the urban agglomeration in central Yunnan based on random forest algorithm

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL TECHNOLOGY AND MANAGEMENT 2024年第1-2期27卷 92-109页

作者： Niu, Lede Lin, Jingzhi Zhou, Lifang Zhou, Yan Yunnan Normal Univ Fac Geog Kunming 650500 Peoples R China

In order to improve the accuracy of land use spatial analysis results, this paper takes the urban agglomeration in central Yunnan as an example, and proposes a land use spatial dynamic characteristics analysis method based on random forest algorithm. Using GIS technology to collect and process land remote sensing image data, we extract sparse description features of remote sensing images through the dictionary learning method, build a random forest classification model, classify land use space, and analyse dynamic features. The detailed analysis of land use change in the study area from 2005 to 2020 shows that the cultivated land area in this area has increased by 3,661 km(2), the dry land area has increased by 3,704 km(2), and the grassland area has decreased by 2,727 km(2), with the highest annual change rate of 0.62%.

关键词： remote sensing image land use spatial dynamic feature random forest algorithm sparse description feature

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Evaluation model of multimedia-aided teaching effect of physical education course based on random forest algorithm

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JOURNAL OF INTELLIGENT SYSTEMS 2022年第1期31卷 555-567页

作者： Liu, Gang Zhuang, Hongbo Xiangtan Med & Hlth Vocat Coll Inst Med Technol Xiangtan 411102 Peoples R China Hunan Univ Sci & Technol Coll Phys Educ Xiangtan 411201 Peoples R China Krirk Univ Int Coll Bangkok 10220 Thailand

The multimedia technology and computer technology supported by the development of modern science and technology provide an important platform for the development of college physical education teaching activities. To better play the role of network auxiliary teaching platform in college sports teaching and improve the effectiveness of college sports teaching, the construction method of multimedia auxiliary teaching effect evaluation model based on the random number forest algorithm is proposed. Through the specification of the random forest algorithm and the optimization of the teaching quality evaluation index, the auxiliary teaching level of the college physical education network is analyzed, and the evaluation of the multimedia auxiliary teaching effect of the physical education courses is completed. The experimental results verify the effectiveness of the evaluation model designed in this article, with a user satisfaction of 72%. Teachers and students can use the evaluation model to improve the teaching quality and teaching efficiency, improve the management work, and promote the scientific, standardization, and specialization of physical education teaching management in colleges and universities.

关键词： random forest algorithm physical education curriculum auxiliary teaching effect evaluation

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Research on Diagnosis Method of Common Knee Diseases Based on Subjective Symptoms and random forest algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第4期15卷 156-168页

作者： Wang, Guangjun Hu, Mengxia Lv, Linlin Zhang, Hanyuan Sun, Yining Su, Benyue Ma, Zuchang Chinese Acad Sci Hefei Inst Phys Sci Inst Intelligent Machines Anhui Prov Key Lab Med Phys & Technol Hefei 230031 Peoples R China Univ Sci & Technol China Grad Sch Sci Isl Branch Hefei 230026 Peoples R China Anqing Normal Univ Univ Key Lab Intelligent Percept & Comp Anhui Pro Anqing 246013 Peoples R China Anhui Med Univ Affiliated Hosp 1 Dept Sports Med & Arthroscop Surg Hefei 230022 Peoples R China

Knee diseases are common diseases in the elderly, and timely and effective diagnosis of knee diseases is essential for disease treatment and rehabilitation training. In this study, we construct a diagnostic model of common knee diseases based on subjective symptoms and random forest algorithm to realize patients' self-initial diagnosis. In this paper, we first constructed a questionnaire of subjective symptoms of knee, and set up a questionnaire system to guide users to fill out the questionnaire correctly. Then clinical data collection is carried out to obtain clinical questionnaire data. Finally, the diagnostic analysis of three common diseases of knee joint is carried out by random forest machine learning method. Through leave-one-out cross validation, the accuracy of meniscus injury, anterior cruciate ligament injury and knee osteoarthritis diseases are 0.79, 0.84, 0.81 respectively;the sensitivity is 0.79, 0.84, 0.88 respectively;and the specificity is 0.80, 0.84, 0.79 respectively. The results show that the method can achieve a good effect of self- diagnosis, and can provide a knee joint disease screening a convenient and effective approach.

关键词： Knee diseases subjective symptoms random forest algorithm self-diagnosis

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