Tree Technique Research Articles

Evaluation and optimization of basketball tactics training effect in physical education: Application research using Decision Tree (DT) algorithm

In this study, the decision tree (DT) technique is developed to assess the basketball strategies’ training impact on physical education (PE). The recommended DT method effectively guesses the tactics of two teams based on the locations of basketball players with only a small amount of training data. Next, using data concerning ball possession, the interaction of these teams’ tactics, and the distinctive features of basketball strategies, the DT approach changes the team’s strategies. Therefore, effective findings from prediction could be produced because the suggested DT approach predicts the team strategies that meet these requirements. The suggested DT approach is implemented using the Python platform to analyze efficiency in terms of several metrics. Additionally, the DT method’s effectiveness is contrasted with that of other approaches. Based on the results of the experiments, the suggested approach outperforms the current approaches in assessing the impact of basketball strategy instruction in physical education.

FUZZY-PROPORTIONAL INTEGRAL DERIVATIVE CONTROLLER WITH INTERACTIVE DECISION TREE

The STATCOM is extensively used in the power system to address the power quality issues by actively compensating the reactive power requirements of the load. However, the STATCOM performance depends on the underlying controller operation to handle sudden load changes and disturbances optimally with faster response. To solve this issue, this paper presents the intelligent algorithm-optimized fuzzy rule-based Proportional Integral Derivative (PID) controller to enhance the performance of the STATCOM. The proposed controller consists of a fuzzy-PID controller capable of handling non-linear dynamics and sudden changes in the load. The interactive decision tree (IDT) technique detects weaker metrics and improves their influence in control scenarios. The Biogeography-based optimization (BBO) algorithm minimizes the controller's integral absolute error to tune the fuzzy-PID controller parameters. The algorithm terminates once all the metrics are tuned to satisfaction with the operator’s consent. The proposed IDT-based fuzzy PID controller is tested on a power system containing a nonlinear load, and its performance is compared with the existing controller and simulated using the MATLAB/Simulink tool. The proposed controller provides fast control action, reduces the reactive power from the source by 42.5%, and improves the power factor by 1.5%.

의사결정나무 분석에 대한 국내 연구 동향 탐색

This study explores the basic system and applicability of decision tree techniques by examining domestic research trends in this field. We analyzed 1,075 academic papers using domestic decision tree analysis from 2001 to 2023, categorized by year, journal, topic field, author, paper frequency, and frequency of algorithms used. Key findings reveal that the frequency of papers began with one in 2001, increasing to 87 in 2023. The distribution across fields was as follows: social science (377 papers, 35.07%), natural science (233 papers, 21.67%), and engineering (230 papers, 21.40%). Among the algorithms, CHAID was used most frequently (315 times, 36.42%), followed by CART (310 times, 35.84%), ensemble techniques like random forest (124 times, 14.34%), and C5.0 (94 times, 10.87%). The Quest algorithm was rarely used (10 times, 1.16%). The annual usage trends indicate a growing preference for ensemble techniques to enhance decision tree prediction rates. The paper's keywords included “decision trees” (687), “data mining” (239), “machine learning” (105), “logistic” (98), and “neural networks” (68). Notably, keywords related to health, such as “suicide,” “depression,” “high blood pressure,” and “health,” emerged prominently, signifying the extensive use of decision trees in medical research. Based on these findings, we propose implications and directions for follow-up studies.

Using Decision Tree Technique to Predict the Financial Collapse of Iraqi Private Banks: An Applied Study

Predicting the Financial Collapse (FC) using modern technologies and scientific methods for Iraqi private banks is considered an early warning to detect their failure in the future. Therefore, these proposals, represented by scientific methods and modern technologies, are submitted to the specialized bodies represented by (the Central Bank of Iraq) in order to present them to the banks in the research sample to raise their capacity through the use of modern technologies and in order to adopt them as a scientific basis for predicting the FC. The following will be followed: Evaluating the performance of banks and classifying them based on the Camels standard (CS) and predicting the FC based on modern technologies (by relying on the decision tree algorithm). The research relied on the deductive approach to complete the theoretical aspect and the applied (experimental) approach to complete the practical aspect. The researcher also processed and prepared the data and then evaluated the performance of banks and classified them based on the CS equations consisting of five indicators and on the averages of these results as inputs for prediction using DT. Different computer programs were also adopted, as the Excel program was used to unload the data, the (SPSS) program to apply the equations, and the (Matlab) program to predict modern technologies. The most prominent conclusions reached by the research are as follows: Modern technologies represented by decision trees are characterized by providing more accurate information

AirNet: predictive machine learning model for air quality forecasting using web interface

Air is one of the most significant elements of the environment. The increasing global air pollution crisis poses an unavoidable threat to human health, environmental sustainability, ecosystems, and the earth's climate. Air pollution has been referred to as a silent killer due to its insidious nature. Its indirect impact on human health further underscores its dangerous effects. Early detection of air quality can potentially save millions of lives globally. A unique and transformative approach can harness the power of machine learning to combat air pollution. This research presents a manual and web-based automatic prediction system that provides real-time alerts on air quality status and can help prevent premature deaths, chronic diseases, and other health problems. Air pollutants, including carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM 2.5), are used in this study for feature analysis and extraction. The system utilizes publicly available data from 23,463 different cities worldwide. Data preprocessing was performed before feeding the data into the machine learning models for feature correlation and evaluation. The proposed research uses various machine learning models to predict air quality, including Random Forest (100%), Logistic Regression (79%), Decision Tree (100%), Support Vector Machine (93%), Linear SVC (98%), K-Nearest Neighbor (99%), and Multinomial Naïve Bayes (52%). A user-friendly Django-based web interface offers an accessible platform for users to monitor air quality in real-time, based on the two best-performing models: Random Forest and Decision Tree techniques.

Sectoral Impact of Corporate Social Responsibility on Social Dialogue in Cote d’Ivoire: Empirical Evidence from Agro-industrial Companies

The aim of this article is to study the effect of Corporate Social Responsibility (CSR) on social dialogue through their dimensions. The methodology is based on structural equations to highlight these links using data from a sample of 812 workers in agro-industrial firms in Côte d'Ivoire. In particular, the results reveal that environmental, social and cultural CSR dimensions have a significant impact on all dimensions of social dialogue reorganized by cultural negotiation. In the background, the contribution to the protection of mountains, rivers and sacred forests, to the celebration of religious events, both happy and unhappy, and to the resolution of certain conflicts, requires the mobilization of traditional negotiation tools: Inter-ethnic alliances, birthright, traditional chiefs and the palaver tree technique to resolve certain organizational conflicts. The result is a contribution to the protection of beliefs and collectivism, the pillars of organizational durability in Africa.

Digging into the Interior of Hot Cores with ALMA (DIHCA). IV. Fragmentation in High-mass Star-forming Clumps

Fragmentation contributes to the formation and evolution of stars. Observationally, high-mass stars are known to form multiple-star systems, preferentially in cluster environments. Theoretically, Jeans instability has been suggested to determine characteristic fragmentation scales, and thermal or turbulent motion in the parental gas clump mainly contributes to the instability. To search for such a characteristic fragmentation scale, we have analyzed Atacama Large Millimeter/submillimeter Array (ALMA) 1.33 mm continuum observations toward 30 high-mass star-forming clumps taken by the Digging into the Interior of Hot Cores with ALMA survey. We have identified 573 cores using the dendrogram algorithm and measured the separation of cores by using the Minimum Spanning Tree technique. The core separation corrected by projection effects has a distribution peaked around 5800 au. In order to remove biases produced by different distances and sensitivities, we further smooth the images to a common physical scale and perform completeness tests. Our careful analysis finds a characteristic fragmentation scale of ∼7000 au, comparable to the thermal Jeans length of the clumps. We conclude that thermal Jeans fragmentation plays a dominant role in determining the clump fragmentation in high-mass star-forming regions, without the need to invoke turbulent Jeans fragmentation.

Clinical outcomes of retrograde intramedullary multiple pinning for proximal humeral fractures using a modified palm tree technique

BackgroundRetrograde intramedullary multiple pinning using a modified palm tree technique for proximal humeral fractures has attracted interest from surgeons because of its minimal invasiveness into the soft tissue around the shoulder joints. We aimed to evaluate the clinical and radiological outcomes of this procedure. MethodsThis retrospective study included 21 patients who underwent surgery using a modified palm tree technique for proximal humeral fractures between March 2010 and March 2022. Patients with two- and three-part proximal humeral fractures that could be reduced by closed manipulation under general anesthesia were included in this study. All patients were clinically evaluated using the Japanese Orthopaedic Association (JOA) and University of California at Los Angeles (UCLA) shoulder scores. Postoperative radiographic findings, including fracture healing and signs of pinning-related complications, were evaluated. ResultsTwenty-one shoulders in 21 patients (9 men and 12 women) with mean age at surgery of 66.3 ± 19.3 years were evaluated. The mean period until bone union was 2.6 ± 0.7 months. The mean JOA and UCLA shoulder scores at the final follow-up were 84.8 ± 12.1 and 27.5 ± 5.4, respectively. Perforation of the humeral head by Kirschner (K) wires was observed in 9 of the 21 (42.9 %) shoulders. The JOA and UCLA shoulder scores at the final follow-up were not significantly different between the groups with and without perforations (p = 0.41 and 0.27, respectively). The oblique or Y-view detected significantly more wires with perforation of the humeral head than did the anteroposterior view (6 vs. 15 wires, p < 0.01). ConclusionsThe modified palm tree technique for proximal humeral fractures demonstrated good postoperative clinical outcomes and early fracture healing. However, modifications in postoperative management and surgical techniques should be considered to reduce the high rate of perforation of the humeral head by K-wires.

Differential Classification of Dengue, Zika, and Chikungunya Using Machine Learning—Random Forest and Decision Tree Techniques

Differential Classification of Dengue, Zika, and Chikungunya Using Machine Learning—Random Forest and Decision Tree Techniques

Predicting the Views of TED Talks using Machine Learning Model

A forecast of such accuracy would have significant implications in this respect for content creators, marketers, and researchers alike. The paper questions whether machine learning models can predict the views that any certain TED Talks receive with a dataset containing features on the duration of the talks, characteristics of the speaker, the date of publication, metrics indicating audience engagement, and so on. In this work, we employ techniques of multiple regression, decision trees, and ensemble methods to identify empirically which factors have the most influence and to come up with the best models for the prediction. Indeed, the results obtained easily indicate that the ensemble models-especially Random Forest and Gradient Boosting- perform way above the rest. These features help to emphasize the fact that machine learning will help guide a content strategy to better reach audiences and give insight into a deeper understanding of how digital content is consumed. Keywords: TED Talks, machine learning, predictive modeling, view prediction, Random Forest, Gradient Boosting, digital content analysis, audience engagement.

Deep Learning Model for Detection and Severity Analysis of Car Accidents

Abstract On-road car accidents are immensely unfortunate but quite common occurrences worldwide. Instant data-centric and informed decisions of crisis management are rarely experienced due to the absence of real-time car accident detection and severity analysis mechanisms. On this background, the current paper presents a deep learning model for car accident detection and analysis of its severity so that the crisis management activities might follow without any delay saving invaluable human lives. The existing works lack in using time-series data, the proper learning model for accurate prediction, and minimizing the time taken in post-accident scenarios for the victims to receive immediate medical help. This paper introduces the Long Short Term Memory (LSTM) model in conjunction with the Gradient Boosted Regression Trees (GBRT) technique for the determination of car accidents with different levels of severity. The proposed model works with the accelerometer and gyroscopic data collected through an application installed in the smartphones of the users inside the car. The LSTM-GBRT hybrid model is proposed to achieve higher accuracy than LSTM which deals with time-variant data. The satisfactory performance of the proposed technique has been reported and the results are extensively investigated in comparison with another hybrid technique such as LSTM with Random Forest (RF) as well. The statistics confirm the superiority of the proposed model over other parallel models in terms of several performance metrics, like Accuracy, Precision, etc.

Identifying Influential Variables on Health Expenditure of the Organisation for Economic Co-Operation and Development (OECD) Countries.

Health expenditures of countries have an increasing trend in general and identifying variables affecting health expenditure is an important step toward budget planning for financial sustainability. This study aimed to examine the health expenditure of the Organisation for Economic Co-operation and Development (OECD) countries and identify influential variables. The data for the years 2000-2018 of OECD countries' current health expenditure (% of GDP) and economic, demographic, and health variables, considered to affect the health expenditure, to include in the analysis were extracted using the World Bank database (World Bank 2021). Data analys using Chi-Squared Automatic Interaction Detection (CHAID) decision tree technique. Fifteen variables in economic, demographic, and health categories are selected to build the CHAID decision tree. As a result of CHAID analysis, five variables are identified as influential on current health expenditure, which are gross domestic product per capita, life expectancy at birth, death rate, out-of-pocket expenditure, and fertility rate. Thirty-seven OECD countries are classified into eleven groups by the decision rules in terms of the current health expenditure. The high value of the correlation coefficient between the predicted values and the actual values of health expenditure of countries indicates good prediction performance. Moreover, the regression models built using the identified influential variables as explanatory variables give good forecast accuracy. As an effective tool, the CHAID decision tree technique provides a rule-based model in the form of a tree with nodes and branches, illustrating the splitting process graphically with identified variables and their cut-off points for classification and prediction.

Arbitrary order spline representation of cohomology generators for isogeometric analysis of eddy current problems

Common formulations of the eddy current problem involve either vector or scalar potentials, each with its own advantages and disadvantages. An impasse arises when using scalar potential-based formulations in the presence of conductors with non-trivial topology. A remedy is to augment the approximation spaces with generators of the first cohomology group. Most existing algorithms for this require a special, e.g., hierarchical, finite element basis construction. Using insights from de Rham complex approximation with splines, we show that additional conditions are here unnecessary. Spanning tree techniques can be adapted to operate on a hexahedral mesh resulting from isomorphisms between spline spaces of differential forms and de Rham complexes on an auxiliary control mesh.

Water Contaminant Detection and Water Purification of Household Drinking Water Using a New Stacking Ensemble Model

ABSTRACTContaminated drinking water sources pose a significant health risk worldwide. Monitoring programs for drinking water quality aims to ensure safe water supply by informing management practices. Improved online monitoring of water systems is necessary as current lab‐based methods are slow and do not offer real‐time public health protection. Rapid detection and response to potential contamination events are crucial to mitigate health risks. Mark of‐purpose water treatment strategies offer a reasonable method for upgrading drinking water quality at the family level and forestalling waterborne illnesses. This study focuses on collecting household drinking water and utilizing various sensors to measure parameters such as pH, turbidity, water level, temperature, and humidity. A consistent water quality noticing system using the stacking outfit model, which solidifies Bayesian association and decision tree techniques, is proposed in this article. Bayesian network analyzes the input data attained from sensors collecting real‐time data and concludes whether the data represents the contamination event. DTs are utilized to demonstrate the connections between multivariate water boundaries utilized in the review. Afterward, a multiobjective, such as a biobjective optimization model and a nondominated genetic algorithm (NGA) are used in this work of optimization to minimize the volume of contaminated water. After the pollution in the water is identified, water decontamination processes are done given point of purpose medicines like ceramic channels and solar water disinfection (SODIS). The method outlined is executed through Python software. The findings indicate that the estimated values for PH, temperature, and turbidity are 7.3, 31.8, and 0.77, respectively. However, the proposed method is compared with the existing C‐NSGA‐II, while compared to this method, the proposed system produces improved cost functions. Consequently, suitable water treatment and supply should be considered to reduce the effects on people's health as well as to improve living conditions, respectively.

High-Fidelity, Multiqubit Generalized Measurements with Dynamic Circuits

Generalized measurements, also called positive operator-valued measures (POVMs), can offer advantages over projective measurements in various quantum information tasks. Here, we realize a generalized measurement of one and two superconducting qubits with high fidelity and in a single experimental setting. To do so, we propose a hybrid method, the “Naimark-terminated binary tree,” based on a hybridization of Naimark’s dilation and binary tree techniques that leverages emerging hardware capabilities for midcircuit measurements and feed-forward control. Furthermore, we showcase a highly effective use of approximate compiling to enhance POVM fidelity in noisy conditions. We argue that our hybrid method scales better toward larger system sizes than its constituent methods and demonstrate its advantage by performing detector tomography of symmetric, informationally complete POVM (SIC POVM). Detector fidelity is further improved through a composite error-mitigation strategy that incorporates twirling and a newly devised conditional readout error mitigation. Looking forward, we expect improvements in approximate compilation and hardware noise for dynamic circuits to enable generalized measurements of larger multiqubit POVMs on superconducting qubits. Published by the American Physical Society 2024

Application of Ensemble Tree Algorithm for Installment Payment Arrears Prediction at Makmur Bersama Credit Union

&lt;span lang="EN-US"&gt;One of the widely used machine learning techniques is the ensemble tree method, which is a combination of several classification trees where the final decision is based on the combined predictions of each tree. This approach produces better accuracy than a single classification tree. Two common methods used in the ensemble tree technique are boosting and bagging. This research will predict the status of installment payments at CU Makmur Bersama Credit Union. The method used is the bagging tree method, namely random forest and boosting, namely AdaBoost. To get optimal results, hyperparameter tuning is also carried out. The results showed that the boosting and bagging ensemble tree methods were able to handle the classification of cooperative loan installment payment status better than the distance approach, namely kNN (single classification). The performance of the boosting ensemble tree with the AdaBoost model has an accuracy of 72.89% better than the bagging ensemble tree with the random forest model whose accuracy is 72.08%.&lt;/span&gt;

Quantitative assessment of population risk to tropical cyclones using hybrid modeling combining GAM and XGBoost: A case study of Hainan Province

Global climate change is expected to increase the proportion of intense tropical cyclones (TCs) in the Northwest Pacific. This study focused on how factors, especially extreme events, may affect disaster losses. To address this issue, an event-based multivariate TC risk assessment model, which employs copulas, a generalized additive model, and undersampling extreme gradient boosting decision tree techniques, is developed to enhance the accuracy of disaster loss prediction. The results suggest that on Hainan Island, the rate of the affected population is positively correlated with the maximum wind speed and maximum daily rainfall while negatively correlated with the gross domestic product and elevation. The study also shows that the TC risk in the cities of Hainan Island increases as the return period expands, and each return period scenario shows a unique geospatial distribution of TC risk on Hainan Island, with higher risks in the coastal and eastern regions. These results highlight the importance of implementing effective disaster management strategies to mitigate the impact of severe TCs in the region.

Planning trajectories for connected and automated vehicle platoon on curved roads: A two-dimensional cooperative approach

This paper presents a cooperative two-dimensional trajectory planning algorithm for connected and automated vehicle (CAV) platoons. Specifically, the proposed algorithm generates two-dimensional optimal trajectories for CAVs with car-following relationships cooperatively within a complex road geometry. By extending the simplified Newell’s car-following model, we propose a two-dimensional Newell’s car-following model as an equilibrium car-following policy for CAVs. Based on this, a multi-objective constrained optimization is systematically formulated under a Cartesian coordinate. Due to the constraint’s complexity, a new solving algorithm based on the rapid random tree (RRT) technique is proposed. To test the effectiveness of our proposed models and algorithm, numerical simulation experiments with a real-world road geometry are conducted. Results indicate that our proposed method is able to generate trajectories for CAV platoons which are close to the equilibrium condition with smooth controls, while avoiding road obstacles. We further extend the definition of a one-dimensional car-following control string stability to a two-dimensional case. By this definition, we find that the proposed method can achieve empirical two-dimensional string stability, ensuring that both lateral and longitudinal disturbances are attenuated through vehicular strings.

Predicting lower limb lymphedema after cervical cancer surgery using artificial neural network and decision tree models

PurposeThis study aimed to develop and validate accessible artificial neural network and decision tree models to predict the risk of lower limb lymphedema after cervical cancer surgery. MethodsWe selected 759 patients who underwent cervical cancer surgery at the Hunan Cancer Hospital from January 2010 to January 2020, collecting demographic, behavioral, clinicopathological, and disease-related data. The artificial neural network and decision tree techniques were used to construct prediction models for lower limb lymphedema after cervical cancer surgery. Then, the models’ predictive efficacies were evaluated to select the optimal model using several methods, such as the area under the receiver operating characteristic curve and accuracy, sensitivity, and specificity tests. ResultsIn the training set, the artificial neural network and decision tree model accuracies for predicting lower limb lymphedema after cervical cancer surgery were 99.80% and 88.14%, and the sensitivities 99.50% and 74.01%, respectively; the specificities were 100% and 95.20%, respectively. The area under the receiver operating characteristic curve was 1.00 for the artificial neural network and 0.92 for the decision tree model. In the test set, the artificial neural network and decision tree models’ accuracies were 86.70% and 82.02%, and the sensitivities 65.70% and 67.11%, respectively; the specificities were 96.00% and 89.47%, respectively. ConclusionBoth models had good predictive efficacy for lower limb lymphedema after cervical cancer surgery. However, the predictive performance and stability were superior in the artificial neural network model than in the decision tree model.

IORSim: A Mathematical Workflow for Field-Scale Geochemistry Simulations in Porous Media

Reservoir modeling consists of two key components: the reproduction of the historical performance and the prediction of the future reservoir performance. Industry-standard reservoir simulators must run fast on enormous and possibly unstructured grids while yet guaranteeing a reasonable representation of physical and chemical processes. However, computational demands limit simulators in capturing involved physical and geochemical mechanisms, especially when chemical reactions interfere with reservoir flow. This paper presents a mathematical workflow, implemented in IORSim, that makes it possible to add geochemical calculations to porous media flow simulators without access to the source code of the original host simulator. An industry-standard reservoir simulator calculates velocity fields of the fluid phases (e.g., water, oil, and gas), while IORSim calculates the transport and reaction of geochemical components. Depending on the simulation mode, the geochemical solver estimates updated relative and/or capillary pressure curves to modify the global fluid flow. As one of the key innovations of the coupling mechanism, IORSim uses a sorting algorithm to permute the grid cells along flow directions. Instead of solving an over-dimensionalized global matrix calling a Newton–Raphson solver, the geochemical software tool treats the species balance as a set of local nonlinear problems. Moreover, IORSim applies basis swapping and splay tree techniques to accelerate geochemical computations in complex full-field reservoir models. The presented work introduces the mathematical IORSim concept, verifies the chemical species advection, and demonstrates the IORSim computation efficiency. After validating the geochemical solver against reference software, IORSim is used to investigate the impact of seawater injection on the NCS Ekofisk reservoir chemistry.Article HighlightsThe IORSim sorting algorithm decouples the nonlinear geochemical reaction calculations into recurring one-dimensional problems to assure numerical stability and computation efficiency. To the best of our knowledge, this work presents the mathematical concept, implementation, and application of topological sorting for the first time on (industry) field-scale problems.IORSim combines topological sorting with basis swapping and splay trees to significantly reduce computation times. Moreover, a high-speed forward simulation mode was developed to allow the post-advection of chemical components to visualize species distribution, water chemistry, and mineral interactions. If the geochemical reactions interfere with the fluid flow, the IORSim backward mode uses relative permeability curves to update the global fluid flow at each time step.We validate the implemented topological scheme on a reservoir grid, show the computation efficiency, and compare the impact of explicit, implicit, and grid refinement on numerical dispersion.The decoupled flow simulator and geochemical reaction calculations allow seamless integration of full-field reservoir models that contain complex geological structures, a large number of wells, and long production histories.The computation capabilities of IORSim are demonstrated by simulating and reproducing the impact of seawater injection in the southern segment of the giant Ekofisk field (more than 50 years of injection and production history). IORSim shows that seawater injection changed the Ekofisk mineralogy and impacted the produced water chemistry. In the investigated Ekofisk case, seawater promoted calcite dissolution and led to the precipitation of magnesite and anhydrite. Moreover, surface complexation modeling revealed that sulfate is adsorbed on the calcite surface.