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IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)

作者： Dai, Jianhua Guo, Xiao Jin, Libo Commun Univ China Sch Econ & Management Beijing Peoples R China

ISBN: (纸本)9781728181172

With the rise of major video websites, the scale of video website users is expanding. Users are the core of all video websites, so it is of great significance to study video website DAU to evaluate and guide its operation. Based on the data of a video website DAU and platform layout from January 2017 to December 2018, this article analyzes the influencing factors of the video website DAU, selects 10 influencing factors from broadcasting arrangement, content level, content mode, and winter and summer vacations, constructs a random forest model, makes regression prediction on the daily average DAU of the video website and calculates the importance of 10 variables. The results show that the most important predictor of DAU is the self-produced drama, followed by the number of variety shows S-level, variety shows A-level and TV series. Video websites can use the method to predict DAU, and improve their operation from the above predictors.

关键词： video websites random forest algorithm DAU

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Analysis of Precision Agriculture based on random forest algorithm by using Sensor Networks 5

Analysis of Precision Agriculture based on Random Forest Alg...

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5th International Conference on Inventive Computation Technologies (ICICT)

作者： Pavithra, K. Jayalakshmi, M. Natl Engn Coll Comp Sci & Engn Kovilpatti Tamil Nadu India

ISBN: (纸本)9781728146850

India is the largest country where most of the human lives depends upon agriculture. Due to the shortage of water facilities, the yield of irrigation is affected. To reduce this shortage of water, different types of methods are invented to supply the correct level of water to the agricultural lands. Here precision irrigation system is used to maintain the perfect water supply from this method wastage of water can be reduced. By using this technique, we can achieve better harvesting results. The total amount of water for lands and plants can be calculated by using the PH value. To detect the PH level, a random forest algorithm is used. There is a direct relationship between the number of plants in the land and the results it can get. It improves the prediction accuracy.

关键词： Irrigation Precision Agriculture random forest algorithm Water Prediction

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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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Microbiome analysis and random forest algorithm-aided identification of the diacetyl-producing microorganisms in the stacking fermentation stage of Maotai-flavor liquor production

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FOOD BIOTECHNOLOGY 2019年第4期33卷 338-352页

作者： Wang, Li Liu, Yanfeng Yang, Fan Li, Jianghua Chen, Bi Du, Guocheng China Kweichow Moutai Distillery Co Ltd Zunyi Guizhou Peoples R China Jiangnan Univ Key Lab Carbohydrate Chem & Biotechnol Minist Educ Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Key Lab Ind Biotechnol Minist Educ Wuxi Jiangsu Peoples R China

Diacetyl imparts a characteristic buttery aroma to foods and beverages. However, when present at high levels in Maotai-flavor liquor, it generates an undesirable buttery off-odor. The aim of this study was to identify the diacetyl-producing microorganisms in Maotai-flavor liquor with an ultimate aim to develop methods that could reduce diacetyl production. To explore the microbial origin of diacetyl in Maotai-flavor liquor, the off-odor intensity and diacetyl content of stacking fermented grains, which are used in the production of Maotai-flavor liquor, were evaluated. Then, 16S rRNA V3-V4 high-throughput sequencing and the random forest algorithm were combined to analyze the potential diacetyl producers. The featured selection results indicated that Lactobacillus was the major diacetyl contributor in the fermented grains, followed by Staphylococcus, Weissella, Pediococcus, and Klebsiella. To verify this, 123 lactic acid bacteria (LAB) isolates were selected from the high diacetyl off-odor group samples, and the genus Lactobacillus accounted for more than 90% of the identified isolates. The results of our study showed that L. plantarum, L. pentosus, and L. fermentum were the major diacetyl-producing microorganisms (>90 mg/L). Moreover, the fermentation characteristics of the high-yield strain, L. plantarum MTL-09, showed that temperature and pH had strong effects on diacetyl production, which may offer a strategy for inhibiting the off-odor by controlling the pH or temperature during fermentation. The methods may also be useful for identification of key microorganisms for fermented foods and alcoholic beverages.

关键词： Lactic acid bacteria high-throughput sequencing diacetyl random forest algorithm Maotai-flavor liquor of China

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Insulation Defect Diagnosis Using a random forest algorithm with Optimized Feature Selection in a Gas-Insulated Line Breaker

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Electronics 2025年第10期14卷 1940-1940页

作者： Gyeong-Yeol Lee Gyung-Suk Kil Department of Electrical and Electronics Engineering Korea Maritime and Ocean University Busan 49112 Republic of Korea

Fault diagnosis based on the partial discharge (PD) recognition has been widely applied on a gas-insulated line breaker (GILB) and gas-insulated switchgear (GIS) as a reliable online condition monitoring method. This paper dealt with insulation defect diagnosis based on a random forest (RF) algorithm with an optimized feature selection method. Four different types of insulation defect models, such as the free-moving particle (FMP) defect, the protrusion-on-conductor (POC) defect, the protrusion-on-enclosure (POE) defect, and the delamination defect, were prepared to simulate representative PD single pulses and PRPD patterns generated from the GILB. The PD signals generated from defect models were detected using the PRPD sensor which can detect phase-synchronized PD signals with the applied high-voltage (HV) signals without the need for additional equipment. Various statistical PD features were extracted from PD single pulses and PRPD patterns according to four kinds of PD defect models, and optimized features were selected with respect to variance importance analysis. Two kinds of PD datasets were established using all statistical features and top-ranked features. From the experimental results, the RF algorithm achieved accuracy rates exceeding 92%, and the PD datasets using only half of the statistical PD features could reduce the computational times while maintaining the accuracy rates.

关键词： random forest algorithm PRPD sensor insulation defect optimal feature

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MapReduce-based adaptive random forest algorithm for multi-label classification

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NEURAL COMPUTING & APPLICATIONS 2019年第12期31卷 8239-8252页

作者： Wu, Qinghua Wang, Haihui Yan, Xuesong Liu, Xiaobo Wuhan Inst Technol Fac Comp Sci & Engn Wuhan 430205 Hubei Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China

Due to the complexity of data characteristics, multi-label learning in data mining has been proposed by scholars to solve the problem of information knowledge in the era of big data. In the era of big data, the complexity of the data structures makes it impossible for traditional single-label learning methods to meet the needs of technological development. Moreover, the importance of multi-label learning is gradually becoming evident. The random forest (RF) algorithm is regarded as one of the best classification algorithms. In this study, the traditional decision tree algorithm was improved, and the traditional RF method was converted into an adaptive RF (ARF) method for multi-label classification. By experiments, the effectiveness of the proposed method was verified. The RF method may not be able to classify massive data in a short time, but Hadoop, which was by Apache, is suitable for data-intensive tasks. On this basis, we modified the MapReduce programming mode to make it suitable for the proposed ARF method. This method was implemented on the cloud platform, and the time effectiveness of the parallel model was verified by experiments.

关键词： Multi-label classification random forest algorithm Hadoop MapReduce

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Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm

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MEASUREMENT & CONTROL 2019年第1-2期52卷 37-45页

作者： Balli, Serkan Sagbas, Ensar Arif Peker, Musa Mugla Sitki Kocman Univ Fac Technol Dept Informat Syst Engn TR-48000 Mugla Turkey

Background: Detecting of human movements is an important task in various areas such as healthcare, fitness and eldercare. It is now possible to achieve this aim using mobile applications. These applications provide users, doctors and related persons a better understanding about daily physical activities. It can also lead to various useful habits by following the activities of the users in their daily life. In addition, dangerous actions such as the fall of elderly people or young children are identified and necessary precautions are taken as soon as possible. Classification of human motions with motion sensor data is among the current topics of study. Smart watches have these sensors built-in. Thus, it is possible to follow the activities of a user carrying only a smart watch. Methods: The purpose of this work is to detect human movements using smart watch sensor data and machine learning methods. The data are obtained from the accelerometer, gyroscope, step counter and heart rate sensors of the smart watch. The obtained data have been divided into 2 s windows and a data set containing 500 patterns for each class has been created for each class. Results and Discussion: After the features were determined, the data set to which the principal component analysis has been applied was classified by random forest, support vector machine, C4.5 and k-nearest neighbor methods, and their performances were compared. The most successful result was obtained from the random forest method.

关键词： Smart watch classification of human activity principal component analysis 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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Identification of SARS-CoV-2 origin: Using Ngrams, principal component analysis and random forest algorithm

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Informatics in Medicine Unlocked 2021年 24卷 100577页

作者： El Boujnouni, Hamoucha Rahouti, Mohamed El Boujnouni, Mohamed Research Center of Plant and Microbial Biotechnologies Biodiversity and Environment Faculty of Sciences Mohammed V University in Rabat PO Box 1014 Morocco Laboratory of Information Technologies National School of Applied Sciences Chouaib Doukkali University in El Jadida PO Box 1166 Morocco

COVID-19 is an infectious disease caused by the newly discovered SARS-CoV-2 virus. This virus causes a respiratory tract infection, symptoms include dry cough, fever, tiredness and in more severe cases, breathing difficulty. SARS-CoV-2 is an extremely contagious virus that is spreading rapidly all over the world and the scientific community is working tirelessly to find an effective treatment. This paper aims to determine the origin of this virus by comparing its nucleic acid sequence with all members of the coronaviridae family. This study uses a new approach based on the combination of three powerful techniques which are: Ngrams (For text categorization), Principal Component Analysis (For dimensionality reduction) and random forest algorithm (For supervised classification). The experimental results have shown that a large set of SARS-CoV-2 genomes, collected from different locations around the world, present significant similarities to those found in pangolins. This finding confirms some previous results obtained by other methods, which also suggest that pangolins should be considered as possible hosts in the emergence of the new coronavirus. © 2021 The Authors

关键词： Bioinformatics COVID-19 Genomes Ngrams Principal component analysis random forest algorithm SARS-CoV-2

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Passenger Travel Behavior in Public Transport Corridor After the Operation of Urban Rail Transit: A random forest algorithm Approach

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IEEE ACCESS 2020年 8卷 211303-211314页

作者： Li, Xiaofei Gao, Yueer Zhang, Huizhen Liao, Yanqing Huaqiao Univ Coll Architecture Xiamen 361021 Peoples R China Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China

A competitive relationship has been generated between urban rail transit and bus transit since the operation of the former. Despite different roles in providing services in public transport corridor, they affect each other in actual situations. In terms of urban transportation planning and policy formulation, it is necessary to explore and master the rules of passengers' travel mode choice from different means. In order to study their choices between the rail transit and the bus transit after the operation of the former, taking Xiamen, China as an example, this article analyzed the overall travel features of passenger flow before and after the operation of rail transit by using the public transit IC card data from two consecutive weeks in November 2017 and November 2018. Some features of travel distance, travel time, travel cost, whether to travel in peak hours, the number of collinear stations between bus transit and rail transit or that of rail transit stations are sorted out. With random forest algorithm, a model is set up for the travel mode choice of passengers after urban rail transit is put into use to find out the impact of different travel features. The result shows that travel cost is the most crucial factor that affects passengers' decisions, followed by the number of collinear stations between bus transit and rail transit or that of rail transit stations, travel time and travel distance. Whether to travel in peak hours have less impact on their choices. This study is constructive for cities in the stage of facing competition between newly opened rail transit and bus transit and support transportation decision-making.

关键词： Travel mode choice feature importance after the operation of newly urban rail transit random forest algorithm

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