Random Tree Model Research Articles

The asymptotic distribution of cluster sizes for supercritical percolation on random split trees

AbstractWe consider the model of random trees introduced by Devroye, the so‐called random split trees. The model encompasses many important randomized algorithms and data structures. We then perform supercritical Bernoulli bond‐percolation on those trees and obtain a precise weak limit theorem for the sizes of the largest clusters. We also show that the approach developed in this work may be useful for studying percolation on other classes of trees with logarithmic height, for instance, we also study the case of d‐regular trees.

Experimental and Modeling Study of Peanut Drying in a Solar Dryer with a Novel Type of a Drying Chamber

ABSTRACT The aim of the present study is to investigate the experimental performance of a solar dryer by using a novel type of a drying chamber for increasing the drying performance and ensuring homogeneous drying. The drying system (SDS) consisted of a new type of a drying chamber and a solar air collector, which were designed, made, and tested in Osmaniye, Turkey. The analyses of Computational Fluid Dynamics were carried out to demonstrate the advantage of the designed drying chamber over the tunnel type drying chamber, which is commonly used in peanut drying. The drying experiments were conducted using in-shell peanut to observe the performance of the system. The drying procedure was performed in the form of open sun drying (OSD), and the results of drying kinetics such as moisture ratio (MR), drying rate (DR), and heat transfer coefficient (hc) obtained from both drying methods were compared. The MR and DR values obtained during the drying experiments were estimated using different models such as mathematical, multiple linear regression (MLR), decision tree (DT), and the support vector machine (SVM). It was observed from the numerical results that the products can dry more homogenously and effectively with the newly designed drying chamber compared to the conventional tunnel type drying chamber. The results of the drying experiments showed that the products dried earlier and more regularly through the SDS compared to the OSD. The DR and hc values were found 0.0051E-01 (gw/gdm)/min and 1.5727 W/m2°C for SDS and 0.0039E-01 (gw/gdm)/min and 1.4664 W/m2°C for OSD, respectively. The models that best estimated the experimentally obtained MR and DR values for peanuts dried with the SDS proved to be the Random Tree Model (R2 = 0.9972) and Quintic Model (R2 = 0.8551), respectively.

Spatial modeling of snow avalanche susceptibility using hybrid and ensemble machine learning techniques

In this study, snow avalanche susceptibility maps of Uzungöl Basin, which is a special environmental protected zone located in Eastern Black Sea Basin of Turkeywere conducted. For this, the belief function values of snow avalanche conditioning factors extracted from snow avalanche inventory data exploring geographical information system software were used. Classes of snow avalanche conditioning factors based on J48, random tree (RT), random forest (RF), and AdaBoost M1, Bagging, Real AdaBoost, and Random Subspace ensemble models of J48, RT, and RF models were implemented. Then snow avalanche susceptibility maps of each model were generated based on the combination rule of Dempster-Shafer theory. The model predictions were validated using a receiver operating characteristics curve, seed cell area index, and statistical tests. AdaBoost M1 ensemble of RT model was found to be the best performing results. However, ensemble models did not improve the prediction results unexpectedly. It can be expected that findings of this study will lead to the implementation of dynamic snow avalanche modeling studies on avalanche paths highlighted in the prepared maps, planning of land-use change projections, controlling snow avalanche movement, and mitigating the risks of loss of life and possible snow avalanches.

Scaling limits of multi-type Markov Branching trees

We introduce multi-type Markov Branching trees, which are simple random population tree models where individuals are characterized by their size and their type and give rise to (size,type)-children in a Galton–Watson fashion, with the rule that the size of any individual is at least the sum of the sizes of its children. Assuming that the macroscopic size-splittings are rare, we describe the scaling limits of multi-type Markov Branching trees in terms of multi-type self-similar fragmentation trees. We observe three different regimes according to whether the probability of type change of a size-biased child is proportional to the probability of macroscopic splitting (the critical regime, in which we get in the limit multi-type fragmentation trees with indeed several types), smaller than the probability of macroscopic splitting (the solo regime, in which the limit trees are monotype as we never see a type change), or larger than the probability of macroscopic splitting (the mixing regime, in which case the types mix in the limit and we get monotype fragmentation trees). This framework allows us to unify models which may a priori seem quite different, a strength which we illustrate with two notable applications. The first one concerns the description of the scaling limits of growing models of random trees built by gluing at each step on the current structure a finite tree picked randomly in a finite alphabet of trees, extending Rémy’s well-known algorithm for the generation of uniform binary trees to a fairly broad framework. We are then either in the critical regime with multi-type fragmentation trees in the scaling limit, or in the solo regime. The second application concerns the scaling limits of large multi-type critical Galton–Watson trees when the offspring distributions all have finite second moments. This topic has already been studied but our approach gives a different proof and we improve on previous results by relaxing some hypotheses. We are then in the mixing regime: the scaling limits are always multiple of the Brownian CRT, a pure monotype fragmentation tree in our framework.

Weighted instances handler wrapper and rotation forest-based hybrid algorithms for sediment transport modeling

Sediment transport modeling has been known as an essential issue and challenging task in water resources and environmental engineering. In order to minimize the adverse impacts of the continues sediment deposition that is known as a main source of pollution in the urban area, the self-cleansing method is widely utilized for designing the sewer pipes to create a condition to keep the bottom of channel clean from sedimentation. In the present study, an extensive data range is utilized for modeling the sediment transport in non-deposition with clean bed condition. Regarding the effective parameters involved, four different scenarios are considered for the modeling. To this end, four standalone methods including the M5P, reduced error pruning tree (REPT), random forest (RF) and random tree (RT) and two hybrid models based on rotation forest (ROF) and weighted instances handler wrapper (WIHW) techniques are developed and result compared with three empirical equations. Based on the results, the hybrid WIHW-RT and WIHW-RF models provide better performance in particle Froude number estimation in comparison to other standalone and hybrid models. Performances of the most of the models are found accurate except RT and REPT standalone models. The outcomes revealed that the empirical models have considerable overestimation. Generally, hybrid data mining methods yield more precise estimations of sediment transport in contrast to the regression equations and standalone models. Particularly, both WIHW-RT and WIHW-RF models provide almost the same performances however, as WIHW-RT can better capture the extreme particle Froude number values, it slightly outperforms WIHW-RF. Promising findings of the current study may encourage the implementation of the recommended approaches in alternative hydrological problems.

Intelligent computing of positive switching impulse breakdown voltage of rod-plane air gap based on extremely randomized trees algorithm

The insulating strength of an air gap is an important basis for external insulation design of power transmission and transformation project, and both gap structure and meteorological conditions have a great influence on the breakdown voltage of air gap. The temperature, humidity and other meteorological conditions in the actual operating environment of power transmission and transformation projects may change greatly with time, so presently, there is no better method to calculate the breakdown voltage of air gaps in various meteorological conditions. In this paper, a prediction model for positive switching impulse breakdown voltage of rod-plane air gap based on extremely randomized trees is proposed. All samples are divided into training set and test set by Self-Organizing Mapping neural network and stratified sampling method. In the test set, the predicted breakdown voltage results of rod-plane air gap based on decision trees, random forest, extremely randomized trees and support vector machine are compared, with reference to the rectification results of atmospheric correction method. The result shows that the maximum absolute relative error of the model is 4.3% and the mean absolute percentage error is 1.6%. Then, the importance of features of import variables is calculated with the trained extremely randomized trees model. Finally, the breakdown voltage of rod-plane air gap in correspondence to different gap distances in four extreme meteorological conditions is calculated with the model. The method can provide reference for calculation of the breakdown voltage of air gap in various meteorological conditions.

Utilidad diagnóstica y validez predictiva del uso conjunto de Fototest y Mini-Cog en deterioro cognitivo

IntroductionThe Fototest and Mini-Cog include all the domains that are necessary in a cognitive assessment. This study aims to evaluate the diagnostic accuracy of the combined use of both instruments for detecting cognitive impairment. MethodsWe performed a phase iii diagnostic accuracy study with 2 independent samples: STUDY, which included 448 participants randomly allocated to 2 datasets (BASE [80%] and TEST [20%]); and EXTERNAL, which included 61 participants. The index test was consecutive administration of the Fototest and Mini-Cog, and the reference test was formal cognitive assessment. We evaluated the diagnostic accuracy of two-step vs. consecutive application of the tests and simple (Comb-Simple), logistic regression (Comb-LR), and random decision tree (Comb-RDT) models of their combined use for detecting cognitive impairment (Global Deterioration Scale score ≥ 3). We performed an exploratory analysis of the BASE dataset, selecting criteria that maximise accuracy; a pre-specified analysis was used to evaluate the selected criteria in the TEST and EXTERNAL datasets. ResultsThe diagnostic accuracy (95% confidence interval) of the combined models in the BASE dataset (Comb-Simple: 88.3 [88.5-91.4]; Comb-LR: 91.6 [88.2-94.3]; Comb-RDT 95.2 [92.5-97.2]) was significantly higher than the individual values observed for the Mini-Cog and Fototest (81.6 [77.1-85.4] and 84.9 [80.8-88.5], respectively). These results were replicated in the TEST (Comb-Simple: 88.9; Comb-LR: 95.6; Comb-RDT: 92.2) and EXTERNAL datasets (Comb-Simple: 91.8; Comb-LR: 90.2; Comb-RDT: 88.5). Two-step application had the same diagnostic accuracy than consecutive application but required less time (mean [SD] of 197.3 s [56.7] vs. 233.9 s [45.2]; P<.0001). ConclusionsCombined application of the Fototest and Mini-Cog takes less than 4minutes and improves the diagnostic accuracy of both instruments. Two-step application is more efficient as it requires less time while maintaining the same diagnostic accuracy.

A Diagnostic Model for Screening Diabetic Retinopathy Using the Hand-Held Electroretinogram Device RETeval.

PurposeTo construct a proper model to screen for diabetic retinopathy (DR) with the RETeval.MethodThis was a cross-sectional study. Two hundred thirty-two diabetic patients and seventy controls were recruited. The DR risk assessment protocol was performed to obtain subjects’ DR risk score using the RETeval. Afterwards, the receiver operating characteristic (ROC) curve was used to determine the best cutoff for diagnosing DR. Random forest and decision tree models were constructed.ResultsWith increasing DR severity, the DR score gradually increased. When the DR score was used to diagnose DR, the ROC curve had an area under the curve of 0.881 (95% confidence interval: 0.836-0.927, P < 0.001), with a best cutoff value of 22.95, a sensitivity of 74.3% (95 CI: 66.0%~82.6%), and a specificity of 90.6% (95 CI: 83.7% ~94.8%). The top four risk factors selected by the random forest were used to construct the decision tree for diagnosing DR, which had a sensitivity of 93.3% (95% CI: 86.3%~97.0%) and a specificity of 80.3% (95% CI: 72.1% ~86.6%).ConclusionsThe DR risk assessment protocol combined with the decision tree model was innovatively used to evaluate the risk of DR, improving the sensitivity of diagnosis, which makes this method more suitable than the current protocol for DR screening.

The Random Forest Model Has the Best Accuracy Among the Four Pressure Ulcer Prediction Models Using Machine Learning Algorithms.

PurposeBuild machine learning models for predicting pressure ulcer nursing adverse event, and find an optimal model that predicts the occurrence of pressure ulcer accurately.Patients and MethodsRetrospectively enrolled 5814 patients, of which 1673 suffer from pressure ulcer events. Support vector machine (SVM), decision tree (DT), random forest (RF) and artificial neural network (ANN) models were used to construct the pressure ulcer prediction models, respectively. A total of 19 variables are included, and the importance of screening variables is evaluated. Meanwhile, the performance of the prediction models is evaluated and compared.ResultsThe experimental results show that the four pressure ulcer prediction models all achieve good performance. Also, the AUC values of the four models are all greater than 0.95. Besides, the comparison of the four models indicates that RF model achieves a higher accuracy for the prediction of pressure ulcer.ConclusionThis research verifies the feasibility of developing a management system for predicting nursing adverse event based on big data technology and machine learning technology. The random forest and decision tree model are more suitable for constructing a pressure ulcer prediction model. This study provides a reference for future pressure ulcer risk warning based on big data.

Detection of Phishing in Internet of Things Using Machine Learning Approach

Phishing attacks are growing in the similar manner as e-commerce industries are growing. Prediction and prevention of phishing attacks is a very critical step towards safeguarding online transactions. Data mining tools can be applied in this regard as the technique is very easy and can mine millions of information within seconds and deliver accurate results. With the help of machine learning algorithms like random forest, decision tree, neural network, and linear model, we can classify data into phishing, suspicious, and legitimate. The devices that are connected over the internet, known as internet of things (IoT), are also at very high risk of phishing attack. In this work, machine learning algorithms random forest classifier, support vector machine, and logistic regression have been applied on IoT dataset for detection of phishing attacks, and then the results have been compared with previous work carried out on the same dataset as well as on a different dataset. The results of these algorithms have then been compared in terms of accuracy, error rate, precision, and recall.

Graphon convergence of random cographs

AbstractWe study the behavior of random labeled and unlabeled cographs with n vertices as n tends to infinity. We show that both models admit a novel random graphon W1/2 as distributional limit. Our main tool is an enhanced skeleton decomposition of the random Pólya tree with n leaves and no internal vertices having only one child. As a byproduct, we obtain limits describing the asymptotic shape of this model of random trees.

High-Resolution Landslide Susceptibility Map Generation using Machine Learning (Case Study in Pacitan, Indonesia)

Landslide, one of the most disastrous natural hazards, causes damage to infrastructure worldwide and local communities. Pacitan, Indonesia is one city with high susceptibility to landslides occurrence. The conditions of landslide occurrence are assumed to be the same in the future. This study’s objective is to produce a landslide susceptibility map by using machine learning methods based on topographical factors including elevation, slope, aspect, profile curvature, plan curvature, Topographic Wetness Index (TWI), distance to the river, and geological map as independent variables, whereas the landslide inventory map derived from Sentinel-2A and Landsat 7 were used as the dependent variables in the model construction. This study's datasets were constructed in three different compositions where each composition was treated as input in Random Forest, Decision Tree, and Logistic regression model. The first dataset was composed of a 70:30 ratio for training and testing sample points, the second dataset with a 60:40 ratio, and the third with a 50:50 ratio. The performance of each model using each dataset composition was analyzed using various accuracy assessments. This study also considered each topographical factor's effect on model performance by excluding several factors in model construction. From the results, random forest with the first dataset appeared to give the best performance for mapping landslide susceptibility area, shown by the highest Area Under Curve (AUC) value, Coefficient Correlation (CC), and Cohen’s Kappa of 0.96, 0.92 (92%) and 0.84, respectively. Elevation and geological maps were considered as essential variables shown by significant drops in model accuracy assessment when these two factors were separately excluded, while profile curvature was the least essential variable based on the insignificant drop in the model accuracy assessment result.

Enhancing MNIST Digit Recognition with Ensemble Learning Techniques

&#x0D; &#x0D; &#x0D; &#x0D; Abstract&#x0D; The classification task known as MNIST digit recognition involves identifying handwritten numbers into their corresponding values. Although there are numerous approaches proposed for this type of task, they typically face issues in achieving high accuracy. One method that can improve single models' performance is through ensemble learning. The goal of this study is to explore the use of various learning techniques, such as boosting and bagging, in combination with random forest models and decision trees, to improve the performance of MNIST digit recognition with regard to accuracy. We then perform evaluations on these methods using various metrics, such as recall, precision, accuracy, and F1. The findings of this study provide valuable insight into the various advantages of ensemble methods for the MNIST digit recognition task. It also highlights the need to explore these techniques in the context of machine learning. The objective of this study is to investigate the use of ensembles in improving the accuracy of MNIST digit recognition. We performed evaluations on two popular methods, namely boosting and bagging, with random forest and decision tree models. The evaluation parameters included F1 score, recall, accuracy, and precision. The results of the evaluations revealed that both boosting and bagging methods performed well in terms of their evaluation metrics. In most cases, the decision tree performed better than the random forest. However, the random forest method was able to achieve the highest accuracy, which is 99 percent. The findings of the evaluation revealed that ensembles can help improve single models' accuracy in MNIST digit recognition. On the other hand, the random forest method is a promising option for this task. The exact results of the evaluations will vary depending on the evaluation and implementation metrics. More research is needed to confirm their generalizability. The study emphasizes the value of exploring ensembles in machine learning systems, as well as the potential advantages of performing MNIST digit recognition using them.&#x0D; &#x0D; &#x0D; &#x0D;

Extending the EOS Long-Term PM2.5 Data Records Since 2013 in China: Application to the VIIRS Deep Blue Aerosol Products

PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> is hazardous to human health, and high-quality data are thus needed on a routine basis. An attempt is made here to improve the accuracy of near-surface PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> estimates using the newly released aerosol product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite with the Deep Blue retrieval algorithm. A high-quality PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> data set is generated at a spatial resolution of 6 km from 2013 to 2018 by applying the space-time extremely randomized trees (STET) model, which also aims to extend the Earth Observing System (EOS) long-term PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> data records in China. The PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> estimates are highly consistent with ground-based measurements, with an out-of-sample cross-validation coefficient of determination (CV-R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of 0.88, a root-mean-square error (RMSE) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16.52~\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula> , and a mean absolute error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10~\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula> at the national scale. Spatiotemporal PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> variations at monthly scales are also well captured (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2} =0.91$ </tex-math></inline-formula> –0.94, RMSE = 5.8– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$11.6~\mu \text{g}/\text{m}^{3})$ </tex-math></inline-formula> . PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> varied greatly at regional and seasonal scales across China. Benefiting from emission reduction and air pollution controls, PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> pollution has reduced dramatically in China with an average of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$- 5.6~\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula> /yr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> during 2013–2018. Significant regional reductions are also seen, in particular, in the Beijing–Tianjin–Hebei region ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$- 6.6~\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula> /yr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p &lt; 0.001$ </tex-math></inline-formula> ), and the Deltas of Yangtze River ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$- 6.3~\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula> /yr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p &lt; 0.001$ </tex-math></inline-formula> ) and Pearl River Delta ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$- 4.5~\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula> /yr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p &lt; 0.001$ </tex-math></inline-formula> ). Our study improved the accuracy of near-surface PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> estimates in terms of their spatiotemporal variations at a relatively long-term record, which is important for future air pollution and health studies in China.

A note on the independence number, domination number and related parameters of random binary search trees and random recursive trees

We identify the mean growth of the independence number of random binary search trees and random recursive trees and show normal fluctuations around their means. Similarly we also show normal limit laws for the domination number and variations of it for these two cases of random tree models. Our results are an application of a recent general theorem of Holmgren and Janson on fringe trees in these two random tree models.

Feature Engineering Algorithms for Traffic Dataset

As a result of an increase in the human popula-tion globally, traffic congestion in the urban area is becoming worse, which leads to time-consuming, waste of fuel, and, most importantly, the emission of pollutants. Therefore, there is a need to monitor and estimate traffic density. The emergence of an automatic traffic management system allows us to record and monitor motor vehicles’ movement in a road segment. One of the challenges researchers face is when the historical traffic data is given as an annual average that contains incomplete data. The annual average daily traffic (AADT) is an average number of traffic volumes at the roadway segment in a specific location over a year. An example of AADT data is the one given by Road Traffic Volume Malaysia (RTVM), and this data is incomplete. The RTVM provides an average of daily traffic data and one peak hour. The recorded traffic data is for sixteen hours, and the only hourly data given is one hour, from 8.00 am to 9.00 am. Hence there is a need to estimate hourly traffic volume for the remaining hours. Feature engineering can be used to overcome the issue of incomplete data. This paper proposed feature engineering algorithms that can efficiently estimate hourly traffic volume and generate features from the existing dataset for all traffic census stations in Malaysia using queuing theory. The proposed feature engineering algorithms were able to estimate the hourly traffic volume and generate features for three years in Jalan Kepong census station, Kuala Lumpur, Malaysia. The algorithms were evaluated using the Random Forest model and Decision Tree Models. The result shows that our feature engineering algorithms improve machine learning algorithms’ performance except for the prediction of N􀀀2 using Random Forest, which shows the highest MAE, MSE, and RMSE when traffic data was included for prediction. The algorithm is applied in one of the traffic census stations in Kuala Lumpur, and it can be used for the other stations in Malaysia. Additionally, the algorithm can also be used for any annual average daily traffic data if it includes average hourly data.

A Hybrid Model to Predict Monthly Streamflow Using Neighboring Rivers Annual Flows

The issue of predicting monthly streamflow data is an important issue in water resources engineering. In this paper, a hybrid model was proposed to generate monthly streamflow data for a river from the annual streamflow data. To develop the proposed hybrid model, a combination of K-Nearest Neighbor (KNN) and Random Tree (RT) algorithms was used. The hybrid structure was designed to predict the annual flow data for the target river using the annual flow data from neighboring rivers by applying the KNN model, and then generated the monthly flow data for the target river using the predicted annual flow by applying the random tree model. The hybrid model was applied to three rivers in Iraq. The accuracy of the proposed model was tested using two statistical indices, namely, the degree of determination and the efficiency coefficient. The results of the statistical indices indicated a good performance of the proposed hybrid model to generate monthly streamflow using annual streamflow data that the values of degree of determination and efficiency coefficient were greater than 0.91 in the training phase and greater than 0.79 in the validating phase.

Big-ECG: Cardiographic Predictive Cyber-Physical System for Stroke Management

Electrocardiogram (ECG) is sensitive to autonomic dysfunction and cardiac complications derived from ischemic or hemorrhage stroke and is supposed to be a potential prognostic tool in stroke identification and post-stroke treatment. ECG data generated cannot be real-time accumulated, processed, and used for enterprise-level healthcare and wellness services with the existing cardiovascular monitoring system used in hospitals. This study aims to assess the feasibility of a cyber-physical cardiac monitoring system to classify stroke patients with altered cardiac activity and healthy adults. Here, we propose Big-ECG, a cyber-physical cardiac monitoring system for stroke management, consisting of a wearable ECG sensor, data storage and data analysis in a big data platform, and health advisory services using data analytics and medical ontology. We investigated our proposed ECG-based patient monitoring system with 45 stroke patients (average age 70.8 years old, 68% men) admitted to the rehabilitation center of the hospital and 40 healthy elderly volunteers (average age 75.4 years old, 38% men). We recorded ECG at resting state using a single-channel ECG patch within three months of diagnosis of ischemic stroke (clinically confirmed). In statistical results, ECG fiducial features, RR-I, QRS, QT, ST, and heart rate variability (HRV) features, SDSD, LF/HF, LF/(LF + HF), and HF/(LF + HF) are observed as significantly distinctive biomarkers for the stroke group relative to the healthy control group. The Random Trees model presented the best classification performance (overall accuracy: 95.6%) utilizing ECG fiducial variables. This system may assist healthcare enterprises in prognosis and rehabilitation management during post-stroke treatment.

Classification of Biodegradable Substances Using Balanced Random Trees and Boosted C5.0 Decision Trees.

Substances that do not degrade over time have proven to be harmful to the environment and are dangerous to living organisms. Being able to predict the biodegradability of substances without costly experiments is useful. Recently, the quantitative structure–activity relationship (QSAR) models have proposed effective solutions to this problem. However, the molecular descriptor datasets usually suffer from the problems of unbalanced class distribution, which adversely affects the efficiency and generalization of the derived models. Accordingly, this study aims at validating the performances of balanced random trees (RTs) and boosted C5.0 decision trees (DTs) to construct QSAR models to classify the ready biodegradation of substances and their abilities to deal with unbalanced data. The balanced RTs model algorithm builds individual trees using balanced bootstrap samples, while the boosted C5.0 DT is modeled using cost-sensitive learning. We employed the two-dimensional molecular descriptor dataset, which is publicly available through the University of California, Irvine (UCI) machine learning repository. The molecular descriptors were ranked according to their contributions to the balanced RTs classification process. The performance of the proposed models was compared with previously reported results. Based on the statistical measures, the experimental results showed that the proposed models outperform the classification results of the support vector machine (SVM), K-nearest neighbors (KNN), and discrimination analysis (DA). Classification measures were analyzed in terms of accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, F1 score, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUROC).

Parking on a random rooted plane tree

In this paper, we investigate a parking process on a uniform random rooted plane tree with $n$ vertices. Every vertex of the tree has a parking space for a single car. Cars arrive at independent uniformly random vertices of the tree. If the parking space at a vertex is unoccupied when a car arrives there, it parks. If not, the car drives towards the root and parks in the first empty space it encounters (if there is one). We are interested in asymptotics of the probability of the event that all cars can park when $\lfloor\alpha n\rfloor$ cars arrive, for $\alpha>0$. We observe that there is a phase transition at $\alpha_{c}:=\sqrt{2}-1$: if $\alpha<\alpha_{c}$ then the event has positive limiting probability, whereas for $\alpha>\alpha_{c}$ its probability tends to 0. Analogous results have been proved by Lackner and Panholzer (J. Combin. Theory Ser. A 142 (2016) 1–28), Goldschmidt and Przykucki (Combin. Probab. Comput. 28 (2019) 23–45) and Jones (J. Appl. Probab. 56 (2019) 1065–1085) for different underlying random tree models.