Research on the Intelligent Distribution System of College Dormitory Based on the Decision Tree Classification Algorithm

The trend toward designing an intelligent distribution system based on students’individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system,which neglects the students’personality traits,causes dormitory disputes,and affects the students’quality of life and academic quality.This paper collects freshmen's data according to college students’personal preferences,conducts a classification comparison,uses the decision tree classification algorithm based on the information gain principle as the core algorithm of dormitory allocation,determines the description rules of students’personal preferences and decision tree classification preferences,completes the conceptual design of the database of entity relations and data dictionaries,meets students’personality classification requirements for the dormitory,and lays the foundation for the intelligent dormitory allocation system.

Tree species classification in an extensive forest area using airborne hyperspectral data under varying light conditions

Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification,it has not been widely used over large areas.A comprehensive process based on multi-flightline airborne hyperspectral data is lacking over large,forested areas influenced by both the effects of bidirectional reflectance distribution function(BRDF)and cloud shadow contamination.In this study,hyperspectral data were collected over the Mengjiagang Forest Farm in Northeast China in the summer of 2017 using the Chinese Academy of Forestry's LiDAR,CCD,and hyperspectral systems(CAF-LiCHy).After BRDF correction and cloud shadow detection processing,a tree species classification workflow was developed for sunlit and cloud-shaded forest areas with input features of minimum noise fraction reduced bands,spectral vegetation indices,and texture information.Results indicate that BRDF-corrected sunlit hyperspectral data can provide a stable and high classification accuracy based on representative training data.Cloud-shaded pixels also have good spectral separability for species classification.The red-edge spectral information and ratio-based spectral indices with high importance scores are recommended as input features for species classification under varying light conditions.According to the classification accuracies through field survey data at multiple spatial scales,it was found that species classification within an extensive forest area using airborne hyperspectral data under various illuminations can be successfully carried out using the effective radiometric consistency process and feature selection strategy.

Tree species classification using deep learning and RGB optical images obtained by an unmanned aerial vehicle

The diversity of tree species and the complexity of land use in cities create challenging issues for tree species classification.The combination of deep learning methods and RGB optical images obtained by unmanned aerial vehicles(UAVs) provides a new research direction for urban tree species classification.We proposed an RGB optical image dataset with 10 urban tree species,termed TCC10,which is a benchmark for tree canopy classification(TCC).TCC10 dataset contains two types of data:tree canopy images with simple backgrounds and those with complex backgrounds.The objective was to examine the possibility of using deep learning methods(AlexNet,VGG-16,and ResNet-50) for individual tree species classification.The results of convolutional neural networks(CNNs) were compared with those of K-nearest neighbor(KNN) and BP neural network.Our results demonstrated:(1) ResNet-50 achieved an overall accuracy(OA) of 92.6% and a kappa coefficient of 0.91 for tree species classification on TCC10 and outperformed AlexNet and VGG-16.(2) The classification accuracy of KNN and BP neural network was less than70%,while the accuracy of CNNs was relatively higher.(3)The classification accuracy of tree canopy images with complex backgrounds was lower than that for images with simple backgrounds.For the deciduous tree species in TCC10,the classification accuracy of ResNet-50 was higher in summer than that in autumn.Therefore,the deep learning is effective for urban tree species classification using RGB optical images.

Groundwater level prediction of landslide based on classification and regression tree

According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the change of groundwater level, the influential factors of groundwater level were selected. Then the classification and regression tree（CART） model was constructed by the subset and used to predict the groundwater level. Through the verification, the predictive results of the test sample were consistent with the actually measured values, and the mean absolute error and relative error is 0.28 m and 1.15%respectively. To compare the support vector machine（SVM） model constructed using the same set of factors, the mean absolute error and relative error of predicted results is 1.53 m and 6.11% respectively. It is indicated that CART model has not only better fitting and generalization ability, but also strong advantages in the analysis of landslide groundwater dynamic characteristics and the screening of important variables. It is an effective method for prediction of ground water level in landslides.

Retrieval of Antarctic sea ice freeboard and thickness from HY-2B satellite altimeter data

Antarctic sea ice is an important part of the Earth’s atmospheric system,and satellite remote sensing is an important technology for observing Antarctic sea ice.Whether Chinese Haiyang-2B(HY-2B)satellite altimeter data could be used to estimate sea ice freeboard and provide alternative Antarctic sea ice thickness information with a high precision and long time series,as other radar altimetry satellites can,needs further investigation.This paper proposed an algorithm to discriminate leads and then retrieve sea ice freeboard and thickness from HY-2B radar altimeter data.We first collected the Moderate-resolution Imaging Spectroradiometer ice surface temperature(IST)product from the National Aeronautics and Space Administration to extract leads from the Antarctic waters and verified their accuracy through Sentinel-1 Synthetic Aperture Radar images.Second,a surface classification decision tree was generated for HY-2B satellite altimeter measurements of the Antarctic waters to extract leads and calculate local sea surface heights.We then estimated the Antarctic sea ice freeboard and thickness based on local sea surface heights and the static equilibrium equation.Finally,the retrieved HY-2B Antarctic sea ice thickness was compared with the CryoSat-2 sea ice thickness and the Antarctic Sea Ice Processes and Climate(ASPeCt)ship-based observed sea ice thickness.The results indicate that our classification decision tree constructed for HY-2B satellite altimeter measurements was reasonable,and the root mean square error of the obtained sea ice thickness compared to the ship measurements was 0.62 m.The proposed sea ice thickness algorithm for the HY-2B radar satellite fills a gap in this application domain for the HY-series satellites and can be a complement to existing Antarctic sea ice thickness products;this algorithm could provide long-time-series and large-scale sea ice thickness data that contribute to research on global climate change.

A Statistical Analysis of Textual E-Commerce Reviews Using Tree-Based Methods

With the increasing interest in e-commerce shopping, customer reviews have become one of the most important elements that determine customer satisfaction regarding products. This demonstrates the importance of working with Text Mining. This study is based on The Women’s Clothing E-Commerce Reviews database, which consists of reviews written by real customers. The aim of this paper is to conduct a Text Mining approach on a set of customer reviews. Each review was classified as either a positive or negative review by employing a classification method. Four tree-based methods were applied to solve the classification problem, namely Classification Tree, Random Forest, Gradient Boosting and XGBoost. The dataset was categorized into training and test sets. The results indicate that the Random Forest method displays an overfitting, XGBoost displays an overfitting if the number of trees is too high, Classification Tree is good at detecting negative reviews and bad at detecting positive reviews and the Gradient Boosting shows stable values and quality measures above 77% for the test dataset. A consensus between the applied methods is noted for important classification terms.

High-resolution remote sensing data can predict household poverty in pastoral areas,Inner Mongolia,China

The accurate prediction of poverty is critical to efforts of poverty reduction,and high-resolution remote sensing(HRRS)data have shown great promise for facilitating such prediction.Accordingly,the present study used HRRS with 1 m resolution and 238 households data to evaluate the utility and optimal scale of HRRS data for predicting household poverty in a grassland region of Inner Mongolia,China.The prediction of household poverty was improved by using remote sensing indicators at multiple scales,instead of indicators at a single scale,and a model that combined indicators from four scales(building land,household,neighborhood,and regional)provided the most accurate prediction of household poverty,with testing and training accuracies of 48.57%and 70.83%,respectively.Furthermore,building area was the most efficient indicator of household poverty.When compared to conducting household surveys,the analysis of HRRS data is a cheaper and more time-efficient method for predicting household poverty and,in this case study,it reduced study time and cost by about 75%and 90%,respectively.This study provides the first evaluation of HRRS data for the prediction of household poverty in pastoral areas and thus provides technical support for the identification of poverty in pastoral areas around the world.

Spectral indices derived,non-parametric Decision Tree Classification approach to lithological mapping in the Lake Magadi area,Kenya

Here,we demonstrate the application of Decision Tree Classification(DTC)method for lithological mapping from multi-spectral satellite imagery.The area of investigation is the Lake Magadi in the East African Rift Valley in Kenya.The work involves the collection of rock and soil samples in the field,their analyses using reflectance and emittance spectroscopy,and the processing and interpretation of Advanced Spaceborne Thermal Emission and Reflection Radiometer data through the DTC method.The latter method is strictly non-parametric,flexible and simple which does not require assumptions regarding the distributions of the input data.It has been successfully used in a wide range of classification problems.The DTC method successfully mapped the chert and trachyte series rocks,including clay minerals and evaporites of the area with higher overall accuracy(86%).Higher classification accuracies of the developed decision tree suggest its ability to adapt to noise and nonlinear relations often observed on the surface materials in space-borne spectral image data without making assumptions on the distribution of input data.Moreover,the present work found the DTC method useful in mapping lithological variations in the vast rugged terrain accurately,which are inherently equipped with different sources of noises even when subjected to considerable radiance and atmospheric correction.

Introducing a Novel Approach for Oil-Oil Correlation based on Asphaltene Structure: X-ray Diffraction

Asphaltenes have always been an attractive subject for researchers.However,the application of this fraction of the geochemical field has only been studied in a limited way.In other words,despite many studies on asphaltene structure,the application of asphaltene structures in organic geochemistry has not so far been assessed.Oil-oil correlation is a wellknown concept in geochemical studies and plays a vital role in basin modeling and the reconstruction of the burial history of basin sediments,as well as accurate characterization of the relevant petroleum system.This study aims to propose the Xray diffraction(XRD)technique as a novel method for oil-oil correlation and investigate its reliability and accuracy for different crude oils.To this end,13 crude oil samples from the Iranian sector of the Persian Gulf region,which had previously been correlated by traditional geochemical tools such as biomarker ratios and isotope values,in four distinct genetic groups,were selected and their asphaltene fractions analyzed by two prevalent methods of XRD and Fouriertransform infrared spectroscopy(FTIR).For oil-oil correlation assessment,various cross-plots,as well as principal component analysis(PCA),were conducted,based on the structural parameters of the studied asphaltenes.The results indicate that asphaltene structural parameters can also be used for oil-oil correlation purposes,their results being completely in accord with the previous classifications.The average values of distance between saturated portions(d_(r))and the distance between two aromatic layers(d_(m))of asphaltene molecules belonging to the studied oil samples are 4.69Aand 3.54A,respectively.Furthermore,the average diameter of the aromatic sheets(L_(a)),the height of the clusters(L_(c)),the number of carbons per aromatic unit(C_(au)),the number of aromatic rings per layer(R_(a)),the number of sheets in the cluster(M_(e))and aromaticity(f_(a))values of these asphaltene samples are 10.09A,34.04A,17.42A,3.78A,10.61Aand 0.26A,respectively.The results of XRD parameters indicate that plots of dr vs.d_(m),d_(r) vs.M_(e),d_(r) vs.f_(a),d_(m) vs.L_(c),L_(c) vs.L_(a),and f_(a) vs.L_(a) perform appropriately for distinguishing genetic groups.A comparison between XRD and FTIR results indicated that the XRD method is more accurate for this purpose.In addition,decision tree classification,one of the most efficacious approaches of machine learning,was employed for the geochemical groups of this study for the first time.This tree,which was constructed using XRD data,can distinguish genetic groups accurately and can also determine the characteristics of each geochemical group.In conclusion,the obtaining of structural parameters for asphaltene by the XRD technique is a novel,precise and inexpensive method,which can be deployed as a new approach for oil-oil correlation goals.The findings of this study can help in the prompt determination of genetic groups as a screening method and can also be useful for assessing oil samples affected by secondary processes.

Comparative Study on Tool Fault Diagnosis Methods Using Vibration Signals and Cutting Force Signals by Machine Learning Technique

The state of cutting tool determines the quality of surface produced on the machined parts.A faulty tool produces poor sur face,inaccurate geometry and non-economic production.Thus,it is necessary to monitor tool condition for a.machining process to have superior quality and economic production.In the pre-sent study,fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique.Cutting force and vibration signals were acquired to monitor tool condition during machining.A set of four tooling conditions namely healthy,worn flank,broken insert and extended tool overhang have been considered for the study.The machine learning technique was applied to both vibration and cutting force signals.Discrete wavelet features of the signals have been extracted using discrete wavelet trans formation(DWT).This transformation represents a large dataset into approximation coeffcients which contain the most useful information of the dataset.Significant features,among features extracted,were selected using J48 decision tree technique.Clas-sification of tool conditions was carried out us ing Naive Bayes algorithm.A 10 fold cross validation was incorporated to test the validity of classifier.A comparison of performance of classifier was made between cutting force and vibration signal to choose the best signal acquisition method in classifying tool fault conditions using machine learning technique.

The Derivation of Nutrient Criteria for the Adjacent Waters of Yellow River Estuary in China

Ecological protection and high-quality development of the Yellow River basin are becoming part of the national strategy in recent years.The Yellow River Estuary has been seriously affected by human activities.Especially,it has been severely polluted by the nitrogen and phosphorus from land sources,which have caused serious eutrophication and harmful algal blooms.Nutrient criteria,however,was not developed for the Yellow River Estuary,which hindered nutrient management measures and eutrophication risk assessment in this key ecological function zone of China.Based on field data during 2004-2019,we adopted the frequency distribution method,correlation analysis,Linear Regression Model(LRM),Classification and Regression Tree(CART)and Nonparametric Changepoint Analysis(nCPA)methods to establish the nutrient criteria for the adjacent waters of Yellow River Estuary.The water quality criteria of dissolved inorganic nitrogen(DIN)and soluble reactive phosphorus(SRP)are recommended as 244.0μg L^(−1) and 22.4μg L^(−1),respectively.It is hoped that the results will provide scientific basis for the formulation of nutrient standards in this important estuary of China.

The predicted effects of climate change on local species distributions around Beijing,China

To assist conservationists and policymakers in managing and protecting forests in Beijing from the effects of climate change,this study predicts changes for 2012–2112 in habitable areas of three tree species—Betula platyphylla,Quercus palustris,Platycladus orientalis,plus other mixed broadleaf species—in Beijing using a classification and regression tree niche model under the International Panel on Climate Change’s A2 and B2 emissions scenarios(SRES).The results show that climate change will increase annual average temperatures in the Beijing area by 2.0–4.7℃,and annual precipitation by 4.7–8.5 mm,depending on the emissions scenario used.These changes result in shifts in the range of each of the species.New suitable areas for distributions of B.platyphylla and Q.palustris will decrease in the future.The model points to significant shifts in the distributions of these species,withdrawing from their current ranges and pushing southward towards central Beijing.Most of the ranges decline during the initial 2012–2040 period before shifting southward and ending up larger overall at the end of the 88-year period.The mixed broadleaf forests expand their ranges significantly.The P.orientalis forests,on the other hand,expand their range marginally.The results indicate that climate change and its effects will accelerate significantly in Beijing over the next 88 years.Water stress is likely to be a major limiting factor on the distribution of forests and the most important factor affecting migration of species into and out of existing nature reserves.There is a potential for the extinction of some species.Therefore,long-term vegetation monitoring and warning systems will be needed to protect local species from habitat loss and genetic swamping of native species by hybrids.

Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy

BACKGROUND Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning.The global community has recently witnessed an increase in the mortality rate due to liver disease.This could be attributed to many factors,among which are human habits,awareness issues,poor healthcare,and late detection.To curb the growing threats from liver disease,early detection is critical to help reduce the risks and improve treatment outcome.Emerging technologies such as machine learning,as shown in this study,could be deployed to assist in enhancing its prediction and treatment.AIM To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection,diagnosis,and reduction of risks and mortality associated with the disease.METHODS The dataset used in this study consisted of 416 people with liver problems and 167 with no such history.The data were collected from the state of Andhra Pradesh,India,through https://www.kaggle.com/datasets/uciml/indian-liver-patientrecords.The population was divided into two sets depending on the disease state of the patient.This binary information was recorded in the attribute"is_patient".RESULTS The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36%and 73.24%,respectively,which was much better than the conventional method.The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis(scarring)and to enhance the survival of patients.The study showed the potential of machine learning in health care,especially as it concerns disease prediction and monitoring.CONCLUSION This study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease.However,relevant authorities have to invest more into machine learning research and other health technologies to maximize their potential.

Application of intelligent algorithms in Down syndrome screening during second trimester pregnancy

BACKGROUND Down syndrome(DS)is one of the most common chromosomal aneuploidy diseases.Prenatal screening and diagnostic tests can aid the early diagnosis,appropriate management of these fetuses,and give parents an informed choice about whether or not to terminate a pregnancy.In recent years,investigations have been conducted to achieve a high detection rate(DR)and reduce the false positive rate(FPR).Hospitals have accumulated large numbers of screened cases.However,artificial intelligence methods are rarely used in the risk assessment of prenatal screening for DS.AIM To use a support vector machine algorithm,classification and regression tree algorithm,and AdaBoost algorithm in machine learning for modeling and analysis of prenatal DS screening.METHODS The dataset was from the Center for Prenatal Diagnosis at the First Hospital of Jilin University.We designed and developed intelligent algorithms based on the synthetic minority over-sampling technique(SMOTE)-Tomek and adaptive synthetic sampling over-sampling techniques to preprocess the dataset of prenatal screening information.The machine learning model was then established.Finally,the feasibility of artificial intelligence algorithms in DS screening evaluation is discussed.RESULTS The database contained 31 DS diagnosed cases,accounting for 0.03%of all patients.The dataset showed a large difference between the numbers of DS affected and non-affected cases.A combination of over-sampling and undersampling techniques can greatly increase the performance of the algorithm at processing non-balanced datasets.As the number of iterations increases,the combination of the classification and regression tree algorithm and the SMOTETomek over-sampling technique can obtain a high DR while keeping the FPR to a minimum.CONCLUSION The support vector machine algorithm and the classification and regression tree algorithm achieved good results on the DS screening dataset.When the T21 risk cutoff value was set to 270,machine learning methods had a higher DR and a lower FPR than statistical methods.

Application of Machine Learning for Tool Condition Monitoring in Turning

The machining process is primarily used to remove material using cutting tools.Any variation in tool state affects the quality of a finished job and causes disturbances.So,a tool monitoring scheme(TMS)for categorization and supervision of failures has become the utmost priority.To respond,traditional TMS followed by the machine learning(ML)analysis is advocated in this paper.Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation.In the current study,investigation on the single point cutting tool is carried out while turning a stainless steel(SS)workpeice on the manual lathe trainer.The vibrations developed during this activity are examined for failure-free and various failure states of a tool.The statistical modeling is then incorporated to trace vital signs from vibration signals.The multiple-binary-rule-based model for categorization is designed using the decision tree.Lastly,various tree-based algorithms are used for the categorization of tool conditions.The Random Forest offered the highest classification accuracy,i.e.,92.6%.

Predicting Electric Energy Consumption for a Jerky Enterprise

Wholesale and retail markets for electricity and power require consumers to forecast electricity consumption at different time intervals. The study aims to increase economic efficiency of the enterprise through the introduction of algorithm for forecasting electric energy consumption unchanged in technological process. Qualitative forecast allows you to essentially reduce costs of electrical energy, because power cannot be stockpiled. Therefore, when buying excess electrical power, costs can increase either by selling it on the balancing energy market or by maintaining reserve capacity. If the purchased power is insufficient, the costs increase is due to the purchase of additional capacity. This paper illustrates three methods of forecasting electric energy consumption: autoregressive integrated moving average method, artificial neural networks and classification and regression trees. Actual data from consuming of electrical energy was used to make day, week and month ahead prediction. The prediction effect of prediction model was proved in Statistica simulation environment. Analysis of estimation of the economic efficiency of prediction methods demonstrated that the use of the artificial neural networks method for short-term forecast allowed reducing the cost of electricity more efficiently. However, for mid- range predictions, the classification and regression tree was the most efficient method for a Jerky Enterprise. The results indicate that calculation error reduction allows decreases expenses for the purchase of electric energy.

Extraction of Planting Information of Winter Wheat in a Province Based on GF-1/WFV Images

In order to explore the adaptability of domestic high-resolution GF-1 satellite images in the extraction of planting information of crops especially in a province, based on the 16-meter remote sensing images of a multi-spectral wide-spectrum camera （WFV） carried by the GF-1 satellite as well as land use type and field survey data of Shandong Province, the planting area and distribution regions of winter wheat in Shandong Province （the main producing area of winter wheat in China） in 2016 were extracted by decision tree classification method and supervised classification- maximum likelihood classification method, and the accuracy of the classification results was verified based on ground survey data and data published by the statistics bureau. The results showed that the method of taking the GF-1/WFV images as the main source of data, introducing multi-source information into the decision tree and supervised classification models, and then calculating the planting area of winter wheat in the province was feasible. The total accuracy of remote sensing interpretation of winter wheat in Shandong Province in 2016 reached 92.1 %, and Kappa coefficient was 0.806. The planting area of winter wheat extracted based on the remote sensing images in the province was slightly smaller than the area pro-vided by the statistics department, and the extraction accuracy of the area was 93.0%. Research indicates that GF-1/WFV images have great po-tential for development and application in remote sensing monitoring of planting information of crops in a province.

Mechanical Eye Trauma Epidemiology, Prognostic Factors, and Management Controversies—An Update

Purpose of Review: The management of eye injuries is both difficult and argumentative. This study attempts to highlight the management of ocular trauma using currently available information in the literature and author experience. This review presents a workable framework from the first presentation, epidemiology, classification, investigations, management principles, complications, prognostic factors, final visual outcome and management debates. Review Findings: Mechanical ocular trauma is a leading cause of monocular blindness and possible handicap worldwide. Among several classification systems, the most widely accepted is Birmingham Eye Trauma Terminology (BETT). Mechanical ocular trauma is a topic of unsolved controversy. Patching for corneal abrasion, paracentesis for hyphema, the timing of cataract surgery and intraocular lens implantation are all issues in anterior segment injuries. Regarding posterior segment controversies, the timing of vitrectomy, use of prophylactic cryotherapy, the necessity of intravitreal antibiotics in the absence of infection, the use of vitrectomy vs vitreous tap in traumatic endophthalmitis is the issues. The pediatric age group needs to be approached by a different protocol due to the risk of amblyopia, intraocular inflammation, and significant vitreoretinal adhesions. The various prognostic factors have a role in the final visual outcome. B scan is used to exclude R.D, Intraocular foreign body (IOFB), and vitreous haemorrhage in hazy media. Individual surgical strategies are used for every patient according to the classification and extent of the injuries. Conclusion: This article examines relevant evidence on the management challenges and controversies of mechanical trauma of the eye and offers treatment recommendations based on published research and the authors’ own experience.

Analysis of NIR spectroscopic data using decision trees and their ensembles

Decision trees and their ensembles became quite popular for data analysis during the past decade.One of the main reasons for that is current boom in big data,where traditional statistical methods(such as,e.g.,multiple linear regression)are not very efficient.However,in chemometrics these methods are still not very widespread,first of all because of several limitations related to the ratio between number of variables and observations.This paper presents several examples on how decision trees and their ensembles can be used in analysis of NIR spectroscopic data both for regression and classification.We will try to consider all important aspects including optimization and validation of models,evaluation of results,treating missing data and selection of most important variables.The performance and outcome of the decision tree-based methods are compared with more traditional approach based on partial least squares.

Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models

Land cover monitoring using digital Earth data requires robust classification methods that allow the accurate mapping of complex land cover categories.This paper discusses the crucial issues related to the application of different up-to-date machine learning classifiers:classification trees(CT),artificial neural networks(ANN),support vector machines(SVM)and random forest(RF).The analysis of the statistical significance of the differences between the performance of these algorithms,as well as sensitivity to data set size reduction and noise were also analysed.Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land cover categories in south Spain.Overall,statistically similar accuracies of over 91%were obtained for ANN,SVM and RF.However,the findings of this study show differences in the accuracy of the classifiers,being RF the most accurate classifier with a very simple parameterization.SVM,followed by RF,was the most robust classifier to noise and data reduction.Significant differences in their performances were only reached for thresholds of noise and data reduction greater than 20%(noise,SVM)and 25%(noise,RF),and 80%(reduction,SVM)and 50%(reduction,RF),respectively.