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2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS )

作者： Zhu, Chengji Liu, Guozhong Liu, Lu Gao, Feng Gao, Qi Beijing Informat Sci & Technol Univ Sch Instrument Sci & Opto Elect Engn Beijing Peoples R China Beijing Kedong Elect Control Syst Co Ltd Beijing Peoples R China

ISBN: (纸本)9781450390200

Aiming at the problem of traditional substation operation and maintenance training method, this paper proposes a new method for identifying substation operation and maintenance actions based on Kinect depth camera. The method is to establish a data set through a hybrid feature extraction method of human bone joint angles and the normalized distances of joint points;establishing three models (cart, KNN and BP) to identify ten typical operation and maintenance actions of substations. The experimental results show that the accuracy of using hybrid feature recognition is higher than that of single feature, and the recognition accuracy of cart algorithm based on hybrid features reaches 98.1%. Its effect is better than KNN algorithm and BP neural network method, and it can accurately identify ten selected substation operation and maintenance actions.

关键词： Depth camera substation operation and maintenance hybrid features cart algorithm action recognition

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Applying the Regression Tree Method to Predict Students’ Science Achievement

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Trends in Psychology 2020年第1期28卷 99-117页

作者： Gomes, Cristiano Mauro Assis Amantes, Amanda Jelihovschi, Enio G. Department of Psychology Laboratory for Cognitive Architecture Mapping (LAICO)/Federal University of Minas Gerais Belo Horizonte Brazil Department of Education Science and Mathematics Educational Group (ENCIMA)/Federal University of Bahia Salvador Brazil Department of Exact and Technological Sciences State University of Santa Cruz Ilhéus Brazil

Science teaching is one of the most important tools for developing individuals’ critical thinking skills. Therefore, studies about achievement predictors of science teaching are increasingly being performed in order to provide evidence of what could influence students’ academic performance. As a contribution to those studies, this paper applies a predictive analysis to students’ science achievement in the 2011 National Examination for Secondary Education (ENEM). The sample is composed of Brazilian students who took the 2-day exam ENEM in 2011. The cart algorithm was applied through a model with 53 predictors. The model explained 24.50% of the science achievement variance. The results showed a lower achievement for those students who (1) were not enrolled in a private school to attend Secondary Education;(2) are female;(3) live in the North, North East, and Center-West regions of Brazil;(4) were strongly motivated to take the exam to obtain a Secondary Education certificate or scholarship;(5) had not yet finished Secondary Education until 2011;and (6) whose family income was equal or lower than a 1.5 minimum wage, as well as equal or lower than 5 minimum wages, depending on the type of school attended by the student in Secondary Education. © 2020, Associação Brasileira de Psicologia.

关键词： cart algorithm ENEM Prediction Regression tree Science

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Heart Disease Prediction Model Using Tree-based Methods 2

Heart Disease Prediction Model Using Tree-based Methods

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2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC)

作者： Li, Yanran Liu, Yitong Luo, Jin Sun, Xiao Xian Jiaotong Liverpool Univ Dept Appl Math Suzhou Jiangsu Peoples R China Univ Toronto Dept Stat Toronto ON Canada Univ Toronto Dept Econ Toronto ON Canada Univ Glasgow Stat Sch Math & Stat Glasgow Lanark Scotland Zhejiang Univ Sch Math Sci Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781510655201;9781510655195

Heart disease has become a global trend illness, with high mortality and death toll. Based on the accumulated case data, data analysis can be used to predict potential heart disease patients more accurately and optimize the treatment. Comparing with other research, this study uses the most basic algorithm in the tree-based methods to predict the data. In the research, some exploratory analysis on the heart disease data set is done firstly. By analyzing the relationship between each variable, the symptom factors related to heart disease are obtained preliminarily. Then this study develops two heart disease prediction models using two kinds of tree methods and compares the error of the two models. It turns out that the random forest model fits better than the decision tree model which can predict the data much more accurate. This can help medical professionals predict heart disease status based on a patient's clinical data and improve patient treatment efficiency. Also, the study gives hints about the causes of the heart diseases, which will be useful in preventing these fatal illnesses.

关键词： Heart disease tree-based methods cart algorithm

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Study on prediction model of stroke risk based on decision tree and regression model

Study on prediction model of stroke risk based on decision t...

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9th IEEE International Conference on Big Data (IEEE BigData)

作者： Liu, Yunfan Ma, Baoying Wang, Yan Hubei Univ Econ Law & Business Coll Wuhan Peoples R China Hubei Univ Chinese Med Informat Engn Coll Wuhan Peoples R China

ISBN: (纸本)9781665439022

In this study, the risk prediction model of stroke disease was established based on the data set of pre-cohort follow-up samples provided by the National Population Health Science Data Warehouse platform, and demographic data, living habits, past medical history and physical examination indexes as variable *** relationship between the influencing factors and the risk degree of stroke was discussed. Chi-square test and ANOVA were performed on the integrated data. The influential factors were selected through single-factor analysis results, and the final variables were determined based on previous research conclusions. The results showed that the incidence increased with age and significantly higher in males than in females. Among the lifestyle related factors, long-term smoking was a serious risk factor for stroke, and the incidence of hypertension with physiological indicators and other related factors over 65 years old was 28. Compared with Logistic regression model, there was no statistical difference between the two models, and the risk prediction model of cart algorithm could be used for disease prediction.

关键词： Stroke Prediction model Logistic regression cart algorithm

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An Evaluation Method of ATR algorithm Based on Decision Tree 8

An Evaluation Method of ATR Algorithm Based on Decision Tree

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8th International Conference on Digital Image Processing (ICDIP)

作者： Zhang, Yifei Zhou, Bin Dou, Hao Ming, Delie Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Natl Key Lab Sci & Technol Aerosp Intelligence Co Beijing 100854 Peoples R China Beijing Aerosp Automat Control Inst Beijing 100854 Peoples R China

ISBN: (纸本)9781510605039;9781510605046

Combined with the characteristics of the forward-looking template matching algorithm, this paper proposed an evaluation method for ATR algorithm based on decision tree. The image features of the simulated real-time images and the template images are extracted as the input attribute, and the recognition results of the real-time images are used as the label. The input attribute and the label make up the training samples, and a series of decision trees are generated through the cart algorithm. As the real-time image of the target area is difficult to obtain, the image features of the simulated real-time images of the target area are extracted, and then the recognition results and their confidence level are predicted by using different decision trees corresponding to different ATR algorithms. The ATR algorithms are evaluated by comparing the prediction results and their confidence level.

关键词： cart algorithm Decision Tree Image features Confidence level

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Wavelet based algorithm for acoustic detection of moving ground and airborne targets

Wavelet based algorithm for acoustic detection of moving gro...

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Conference on Wavelet Applications in Signal and Image Processing VIII

作者： Averbuch, AZ Zheludev, VA Kozlov, I Tel Aviv Univ Dept Comp Sci IL-69978 Tel Aviv Israel

ISBN: (纸本)0819437646

We detect the presence of a vehicle or an air borne target from a certain class via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. To achieve this detection with no false alarms we construct the acoustic signatures of certain targets to be found by the distribution of the energies among blocks which consist of wavelet packet coefficients. We developed an efficient procedure for adaptive selection of the characteristic blocks. We modified the cart algorithm in order to utilize it as a decision unit in our scheme. A wide series of field experiments manifested a remarkable efficiency of the algorithm. The detection had been achieved practically with no false alarms even under severe conditions such as the acoustic recording of sought-after object was a superposition of the acoustics emitted from other targets that belong to other classes. The detection was even immune to severe noisy surroundings.

关键词： wavelet packets discriminant blocks acoustic signature cart algorithm

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Alternative Methodology To Household Activity Matching In TRANSIMS

Alternative Methodology To Household Activity Matching In TR...

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作者： Paradkar, Rajan Virginia Tech | University

TRANSIMS (Transportation Analysis and Simulation System) developed at the Los Alamos National Laboratory, is an integrated system of travel forecasting models designed to give transportation planners accurate and complete information on traffic impacts, congestion, and pollution. TRANSIMS is a micro-simulation model which uses census data to generate a synthetic population and assigns activities using activity survey data to each person of every household of the synthetic population. The synthetic households generated from the census data are matched with the survey households based on their demographic characteristics. The activities of the survey household individuals are then assigned to the individuals of the matched synthetic households. The cart algorithm is used to match the households. With the use of cart algorithm a classification tree is built for the activity survey households based on some dependent and independent variables from the demographic data. The TRANSIMS model assumes activity times as dependent variables for building the classification tree.

The topic of this research is to compare the TRANSIMS approach of using times spent in executing the activities as dependent variables, compared to match the alternative of using travel times for trips between activities as dependent variables i.e. to use the travel time pattern instead of activity time pattern to match the persons in the survey households with the synthetic households. Thus assuming that if the travel time patterns are the same then we can match the survey households to the synthetic population i.e. people with similar demographic characteristics tend to have similar travel time patterns.

The algorithm of the Activity Generator module along with the original set of dependent variables, were first used to generate a base case scenario. Further tests were carried out using an alternative set of dependent variables in the algorithm. A sensitivity analysis was also carried out to test

关键词： TRANSIMS Activity Based Modeling cart algorithm Thesis

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Use of two hybrid algorithms in the investigation and prediction of the values of five quantitative traits of guar beans in different deficit irrigation methods

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APPLIED WATER SCIENCE 2022年第12期12卷 273-273页

作者： Mirhashemi, Seyed Hassan Univ Zabol Fac Water & Soil Dept Water Engn Zabol Iran

Nowadays, deficit irrigation is of particular importance in areas facing the water shortage and drought. This study focused on the investigation and prediction of the values of five quantitative traits of guar beans under different deficit irrigation methods. Deficit irrigation methods were carried out at the initial, development, mid, and late plant growth stages. The experiment was carried out in 25 treatments each with four replications in 2018 and 2019. Initially, the values of five quantitative traits of guar beans were divided into three categories, the values of which were clustered using the K-means algorithm. Then, clusters were predicted using a combination of K-means and cart algorithms. Finally, the relationship between different deficit irrigation methods and clusters was investigated by a combination of K-means and Apriori algorithms. The results of two hybrid algorithms determined that the amount of irrigation in the mid-stage of plant growth significantly affected the five quantitative traits of guar beans. After the mid-stage of the plant growth, the amount of irrigation in the development, initial, and late growth stages had the greatest effect on the quantitative traits of guar beans. Among the deficit irrigation methods, irrigation rates of 60% in the primary stage, 80% in the development stage, 100% in the mid-stage, and 40% in the late stage of the plant growth were the best deficit irrigation methods in the four stages of growth.

关键词： Apriori algorithm cart algorithm Dry areas K-means algorithm Plant growth stages

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Use of a combination of two clustering and tree algorithms for investigating and predicting alfalfa yield under different conditions of deficit irrigation

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SCIENCE OF THE TOTAL ENVIRONMENT 2023年 904卷 166836页

作者： Mirhashemi, Seyed Hassan Univ Zabol Fac Water & Soil Dept Water Engn Zabol Iran

The largest amount of water consumption in Iran is related to agriculture. Considering that Iran has suffered a drought in recent years, the optimal use of water is necessary, especially in the agricultural sector. For this reason, in this research, the deficit irrigation regarding alfalfa crop was investigated. Alfalfa was cultivated in three years with four replications. Deficit irrigation methods including 40 %, 70 % and 100 % full irrigation (FI) were employed at different stages of alfalfa growth. First, crop per drop (CPD), benefit per drop (BPD) and net benefit per drop (NBPD) values related to alfalfa were clustered. The results of clustering were introduced to the tree algorithm as target data. The tree algorithm predicted the target data according to the factors of 1) the type of cropping year, 2) the time of harvest and 3) the level of deficit irrigation at different stages of alfalfa growth. According to the results of the combination of two algorithms, it was found that The lowest values of CPD, BPD and NBPD are equal to 1.2 (kg/m3), 125 (1000 Rial/m3) and 115 (1000 Rial/m3) respectively in the second harvest and deficit irrigation which was predicted in the two regrowth (RG) and budding (BU) stages. The highest values of CPD, BPD and NBPD are equal to 1.8 (kg/m3), 185 (1000 Rial/m3) and 175 (1000 Rial/m3) respectively in the third harvest and deficit irrigation which was predicted in the branching stage (BR). Thus, to increase the yield, it is better not to perform severe deficit irrigation at RG and BU stages. Also, according to the predictor importance of the software, it was found that the three factors of harvest time, BU and the type of cropping year had the greatest effect on the prediction, respectively.

关键词： Arid regions cart algorithm CPD K-means algorithm Plant growth stages

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Gas Station Recognition Method Based on Monitoring Data of Heavy-Duty Vehicles

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ENERGIES 2021年第23期14卷 8011页

作者： Ding, Yan Ji, Zhe Liu, Peng Wu, Zhiqiang Li, Gang Cui, Dingsong Wu, Yizhong Xu, Sha Chinese Res Inst Environm Sci State Environm Protect Key Lab Vehicle Emiss Cont Beijing 100012 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Anhui Inst Opt & Fine Mech Key Lab Environm Opt & Technol Hefei 230031 Peoples R China Beijing Inst Technol Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China Beijing Bitnei Corp Ltd Beijing 100081 Peoples R China

With the requirement of reduced carbon emissions and air pollution, it has become much more important to monitor the oil quality used in heavy-duty vehicles, which have more than 2/3 transportation emissions. Some gas stations may provide unqualified fuel, resulting in uncontrollable emissions, which is a big challenge for environmental protection. Based on this focus, a gas station recognition method is proposed in this paper. Combining the cart algorithm with the DBSCAN clustering algorithm, the locations of gas stations were detected and recognized. Then, the oil quality analysis of these gas stations could be effectively evaluated from oil stability and vehicle emissions. Massive real-world data operating in Tangshan, China, collected from the Heavy-duty Vehicle Remote Emission Service and Management Platform, were used to verify the accuracy and robustness of the proposed model. The results illustrated that the proposed model can not only accurately detect both the time and location of the refueling behavior but can also locate gas stations and evaluate the oil quality. It can effectively assist environmental protection departments to monitor and investigate abnormal gas stations based on oil quality analysis results. In addition, this method can be achieved with a relatively small calculation effort, which makes it implementable in many different application scenarios.

关键词： gas stations recognition oil quality evaluation heavy-duty vehicles DBSCAN clustering cart algorithm real-world data

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