Tree Algorithm 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.

Characterizing EEG signal dynamics in healthy, seizure-free, and seizure states using the chaos decision tree algorithm

Abstract Epilepsy is a multifaceted neurological condition marked by repetitive seizures that arise from irregular electrical activity in the brain. To understand this condition, a thorough examination of brain signals captured in different states is needed. In order to examine the dynamic behavior of brain signals in three different conditions: healthy, seizure-free, and seizure periods, this study uses the chaos decision tree algorithm. The findings show notable variations in these situations’ dynamics. Chaos is evident during seizure moments, showing extremely chaotic activity. The signals mostly exhibit stochastic behavior in the healthy condition, which is consistent with typical brain dynamics. It is noteworthy that an intermediate state exhibiting a blend of stochastic and chaotic signal dynamics is exhibited throughout the seizure-free time. Furthermore, the research shows that the frequency of chaotic signals rises with increasing proximity to the epileptogenic zone. These discoveries clarify the complex nature of epilepsy and offer insightful information about the dynamic properties of brain signals in various stages, aiding in improved understanding and potential diagnostic approaches.

Autonomous and ubiquitous in-node learning algorithms of active directed graphs and its storage behavior

The brain’s memory system is extraordinarily complex, evidenced by the multitude of neurons involved and the intricate electrochemical activities within them, as well as the complex interactions among neurons. Memory research spans various levels, from cellular and molecular to cognitive behavioral studies, each with its own focus, making it challenging to fully describe the memory mechanism. Many details of how biological neuronal networks encode, store, and retrieve information remain unknown. In this study, we model biological neuronal networks as active directed graphs, where each node is self-adaptive and relies on local information for decision-making. To explore how these networks implement memory mechanisms, we propose a parallel distributed information access algorithm based on the node scale of the active directed graph. Here, subgraphs are seen as the physical realization of the information stored in the active directed graph. Unlike traditional algorithms with global perspectives, our algorithm emphasizes global node collaboration in resource utilization through local perspectives. While it may not achieve the global optimum like a global-view algorithm, it offers superior robustness, concurrency, decentralization, and biological feasibility. We also tested network capacity, fault tolerance, and robustness, finding that the algorithm performs better in sparser network structures.

Estimating coffee crop parameters through multispectral imaging and machine learning algorithms

Abstract. Brazil plays a crucial role in the global economy due to its significant contribution to the agricultural sector, particularly in coffee production, where it stands out as the largest producer and exporter of processed coffee. Various disturbances can influence coffee plants, causing abnormalities that can hinder their successful growth. Parameters such as plant height and canopy diameter play an essential role in assessing the health and productivity of the plants, reflecting their growth, development, and ability to capture sunlight. Additionally, height is also related to the balanced distribution of nutrients and water, providing valuable information about overall performance and the capacity for healthy production. In this regard, the application of methodologies involving remote sensing and machine learning algorithms has shown promising results in the rapid and safe acquisition of information about agricultural systems. This study evaluates different machine learning algorithms, using radiometric values from multispectral images obtained by remote sensing platforms as input datasets for estimating plant height and canopy diameter in coffee cultivation. The best performance was observed for architectures that showed lower RMSE and RMSE% values. For the plant height parameter (m), the RGB sensor exhibited the best performance using the Random Tree algorithm, with an RMSE (0.27) and RMSE% (8.80). For the canopy diameter (m), the sensor showed the best performance using the Random Forest algorithm, with an RMSE (0.15) and RMSE% (8.16).

Predictors and Profile of Severe Infectious Complications in Multiple Myeloma Patients Treated with Daratumumab-Based Regimens: A Machine Learning Model for Pneumonia Risk

Background: Daratumumab (Dara) is the first monoclonal antibody introduced into clinical practice to treat multiple myeloma (MM). It currently forms the backbone of therapy regimens in both newly diagnosed (ND) and relapsed/refractory (RR) patients. However, previous reports indicated an increased risk of infectious complications (ICs) during Dara-based treatment. In this study, we aimed to determine the profile of ICs in MM patients treated with Dara-based regimens and establish predictors of their occurrence. Methods: This retrospective, real-life study included MM patients treated with Dara-based regimens between July 2019 and March 2024 at our institution. Infectious events were evaluated using the Terminology Criteria for Adverse Events (CTCAE) version 5.0. Results: The study group consisted of a total of 139 patients, including 49 NDMM and 90 RRMM. In the RR setting, the majority (60.0%) of patients received the Dara, bortezomib, and dexamethasone (DVd) regimen, whereas ND patients were predominantly (98%) treated with the Dara, bortezomib, thalidomide, and dexamethasone (DVTd) regimen. Overall, 55 patients (39.6%) experienced ICs. The most common IC was pneumonia (37.5%), followed by upper respiratory tract infections (26.8%). Finally, twenty-five patients had severe ICs (grade ≥ 3) and required hospitalization, and eight patients died due to ICs. In the final multivariable model adjusted for setting (ND/RR) and age, hemoglobin level (OR 0.77, 95% CI: 0.61–0.96, p = 0.0037), and Eastern Cooperative Oncology Group (ECOG) >1 (OR 4.46, 95% CI: 1.63–12.26, p = 0.0037) were significant factors influencing severe IC occurrence. Additionally, we developed predictive models using the J48 decision tree, gradient boosting, and random forest algorithms. After conducting 10-fold cross-validation, these models demonstrated strong performance in predicting the occurrence of pneumonia during treatment with daratumumab-based regimens. Conclusions: Simple clinical and laboratory assessments, including hemoglobin level and ECOG scale, can be valuable in identifying patients vulnerable to infections during Dara-based regimens, facilitating personalized prophylactic strategies.

Combined Gradient Boosting Decision Tree and Differential Evolution Algorithm for Optimization Design of Permanent Magnet Arc Motor

Combined Gradient Boosting Decision Tree and Differential Evolution Algorithm for Optimization Design of Permanent Magnet Arc Motor

Heart rate variability analysis for the prediction of pre-arousal during propofol-remifentanil general anaesthesia: A feasibility study.

Accidental awareness during general anaesthesia is a major complication. Despite the routine use of continuous electroencephalographic monitoring, accidental awareness during general anaesthesia remains relatively frequent and constitutes a significant additional cost. The prediction of patients' arousal during general anaesthesia could help preventing accidental awareness and some researchers have suggested that heart rate variability (HRV) analysis contains valuable information about the patient arousal during general anaesthesia. We conducted pilot study to investigate HRV ability to detect patient arousal. RR series and the Bispectral IndexTM (BISTM) were recorded during general anaesthesia. The pre-arousal period T0 was defined as the time at which the BISTM exceeded 60 at the end of surgery. HRV parameters were computed over several time periods before and after T0 and classified as "BISTM<60" or "BISTM≥60". A multivariate logistic regression model and a classification and regression tree algorithm were used to evaluate the HRV variables' ability to detect "BISTM≥60". All the models gave high specificity but poor sensitivity. Excluding T0 from the classification increased the sensitivity for all the models and gave AUCROC>0.7. In conclusion, we found that HRV analysis provided encouraging results to predict arousal at the end of general anaesthesia.

An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems

Knowing the type of buried object before excavation prevents unnecessary excavation. Moreover, it saves time and money. In this study, an experiment set was prepared for the detection of buried objects. The experimental set was composed of an antenna that sends and receives electromagnetic waves in a wide frequency band, software that records and processes reflections, and a sandbox. In the study, metallic and non-metallic objects with different depths, sizes and shapes were buried in this sand pool and measurements were taken along a profile. 2D images were created from the measurements and image processing techniques were applied to these images. Classification algorithms were used to detect the type of bruied object from processed images. To increase the success of the algorithms, correlation-based attribute selection (CFS) and Principal Component Analysis (PCA) were used as attribute selection techniques. Genetic algorithm (GA), Particle Swarm Optimization (PSO), Harmony search (HA), and Evolutionary search (EA), which are among the metaheuristic optimization algorithms, were preferred as search methods in attribute selection with CFS. The performance of the algorithms was analyzed using the 10-fold cross-validation method. As a result, it was understood that the use of the PCA algorithm in attribute selection increases the classification success more than metaheuristic algorithms. The most successful among the classification algorithms used is the Random tree algorithm. After PCA, the accuracy value of this algorithm was 95.8 Therefore, a hybrid approach is proposed in which PCA and Random tree algorithms are used in the software embedded in the measurement system.

A Semantic and Optimized Focused Crawler Based on Semantic Graph and Genetic Algorithm

A focused crawler automatically retrieves, organizes, and extracts specific topic-related information from the internet for analysis and application. Currently, most focused crawlers assess the relevance of web pages to a given topic through methods such as keyword matching, semantic analysis, and link structures. However, these existing focused crawlers suffer from issues such as misleading directions and reduced accuracy due to the lack of semantic analysis of topic terms, as well as biased computation of topic relevance caused by the absence of effective weighting factors. To solve the above-mentioned problems, this study proposes a semantic and optimized focused crawler based on Semantic Graph and Genetic Algorithm. The proposed crawler eliminates ambiguous terms by constructing a semantic graph, optimizes the weighting factors of topic relevance with asymmetry by using a genetic algorithm, and combines both above two points to predict the priority of each unvisited hyperlink. The experiment results indicate that the proposed SG-GA Crawler improves the evaluation indicators compared with the other three focused crawlers, including VSM Crawler, SSRM Crawler, and SG Crawler. More specifically, the percentage improvement achieved by the proposed method exceeds 19%, 19%, and 13% in terms of three evaluation indicators, including the number of relevant web pages, acquisition rate, and average relevance, respectively. In conclusion, the proposed focused crawler can grab more quantity and higher quality topic-related web pages from the Internet.

AI techniques applied to network intrusion detection system by using genetic approaches

The ability to identify and assess potential security risks to a communication network is a critical function of network intrusion detection systems. The data it provides regarding the frequency and type of assaults is a valuable addition to other network security measures, including firewalls. Typically, an NIDS will have a sensor that scans all incoming and outgoing packets on the monitored network, flags those that seem suspicious, and then sends the flagged packets and an alert message to a server system, which will then store them and analyse them in conjunction with other events. First, we standardised the protocol structure. Then, we used a genetic approach to generate mutation and cross-over values for device identification. Finally, we used a modified J48 decision tree algorithm to conduct our search. The developed algorithms/ones outperform the existing methods in terms of performance. As an initial step in detecting an intrusion, the 64-byte structured protocol standardisation technique is created. The wire shark tool is used to monitor the communication process network, taking into account all potential transactions. An array is created from the recorded packets. Included in the array are details about the frames, as well as the protocol type, hardware device type, source and destination IP addresses, MAC addresses, and data. This information is transformed by the 64-byte protocol structured standardisation. Because all of the necessary attributes are determined in the same place by this common protocol structure procedure, finding the MAC and IP addresses in packets should take as little time as possible. As a second step, the product and device details are supplied by the least and most important 16 bits of the MAC address. Using the cross-over and mutation functions, the genetic iv method compares the invader device to the Current Active Directory List (CADL).

Kinematic Constrained RRT Algorithm with Post Waypoint Shift for the Shortest Path Planning of Wheeled Mobile Robots

This paper presents a rapidly exploring random tree (RRT) algorithm with an effective post waypoint shift, which is suitable for the path planning of a wheeled mobile robot under kinematic constraints. In the growth of the exploring tree, the nearest node that satisfies the kinematic constraints is selected as the parent node. Once the distance between the new node and the target is within a certain threshold, the tree growth stops and a target connection based on minimum turning radius arc is proposed to generate an initial complete random path. The most significant difference from traditional RRT-based methods is that the proposed method optimizes the path based on Dubins curves through a post waypoint shift after a random path is generated, rather than through parent node selection and rewiring during the exploring tree growth. Then, it is proved that the method can obtain an optimal path in terms of the shortest length. The optimized path has good convergence and almost does not depend on the state of the initial random path. The comparative test results show that the proposed method has significant advantages over traditional RRT-based methods in terms of the sampling point number, the tree node number, and the path node number. Subsequently, an efficient method is further proposed to avoid unknown obstacles, which utilizes the original path information and thus effectively improves the new path planning efficiency. Simulations and real-world tests are carried out to demonstrate the effectiveness of this method.

Application of a decision tree approach to predict energy consumption in lightweight buildings under subtropical climate

PurposeLightweight building systems have emerged as alternatives to reduce the high environmental impact of conventional masonry. However, in subtropical climates, the low thermal inertia of lightweight envelopes negatively affects energy performance. The purpose of this paper is to investigate the thermophysical parameters that influence heating and cooling energy consumption in lightweight residential buildings under subtropical climates and develop a model to predict these parameters using statistical and machine learning tools.Design/methodology/approachA database was created with computer simulation data on the energy performance of 2048 building conditions generated by factorial combination of 10 parameters. Sensitivity analysis was performed to identify which parameters contribute most to energy performance indicators. Subsequently, decision trees were created using a classification and regression tree (CART) algorithm to visualize parameters and improve energy performance indicators, particularly cooling energy consumption.FindingsLow thermal transmittance and ground contact are interesting strategies for low thermal capacity buildings. Furthermore, the findings showed that relying only on the most influential properties does not ensure good energy performance; rather, it is the adequate combination of envelope properties that leads to good energy efficiency. The tree developed by CART can be used as a guide to assist designers and researchers in the initial selection of building envelopes, demonstrating the impact of each choice on electrical energy consumption for indoor climate control.Originality/valueBy adopting a global approach to assess the thermal performance of lightweight buildings, this study makes a significant contribution to synthesizing the results of a complex and time-consuming methodology into a guide for optimizing envelope design decisions and directing efforts and resources toward efficient strategies.

Time Irreversibility of Complex Time Series with Detrended Cross-Correlation Analysis Based on Visibility Graph

In this paper, we introduce detrended cross-correlation analysis (DCCA) method into visibility graph (VG) algorithm and propose VGDCCA method to reflect the time series irreversibility from a new perspective. The validity and reliability of the proposed VGDCCA method is supported by numerical simulations on synthesized short-term correlated chaotic systems and long-term correlated fractal processes, and by the empirical analysis on stock indices and traffic parameter. The VGDCCA planes suggest that autoregressive fractionally integrated moving average (ARFIMA) and fractional Gaussian noise (FGN) series show time reversible behavior, whatever the long-term correlation of the series is or however strong the persistence or anti-persistence of the series is. Meanwhile, Logistic map with [Formula: see text] and Hénon map show more time irreversible behaviors than those of ARFIMA, FGN series and other Logistic maps. It can be found that the relationship between cross-correlation of ingoing degree sequence and outgoing degree sequence for the simulated series with different parameters is consistent with the complexity and autocorrelation behavior in the corresponding definition of the time series. For the empirical analysis, VGDCCA method declares the similarity and dissimilarity between stock indices on time series irreversibility and captures the time irreversibility of traffic time series recorded by the detectors in different locations.

A Fast and Cost-Effective Electronic Nose Model for Methanol Detection Using Ensemble Learning

Methanol, commonly used to cut costs in the production of counterfeit alcohol, is extremely harmful to human health, potentially leading to severe outcomes, including death. In this study, an electronic nose system was designed using 11 inexpensive gas sensors to detect the proportion of methanol in an alcohol mixture. A total of 168 odor samples were taken and analyzed from eight types of ethanol–methanol mixtures prepared at different concentrations. Only 4 features out of 264 were selected using the feature selection method based on feature importance. These four features were extracted from the data of MQ-3, MQ-4, and MQ-137 sensors, and the classification process was carried out using the data of these sensors. A Voting Classifier, an ensemble model, was used with Linear Discriminant Analysis, Support Vector Machines, and Extra Trees algorithms. The Voting Classifier achieved 85.88% classification accuracy before and 81.85% after feature selection. With its cost effectiveness, fast processing time, and practicality, the recommended system shows great potential for detecting methanol, which threatens human health in counterfeit drink production.

Amoeba Monte Carlo algorithms for random trees with controlled branching activity: Efficient trial move generation and universal dynamics

Amoeba Monte Carlo algorithms for random trees with controlled branching activity: Efficient trial move generation and universal dynamics

On 3-Coloring Circle Graphs

Given a graph $G$ with a fixed vertex order $\prec$, one obtains a circle graph $H$ whose vertices are the edges of $G$ and where two such edges are adjacent if and only if their endpoints are pairwise distinct and alternate in $\prec$. Therefore, the problem of determining whether $G$ has a $k$-page book embedding with spine order $\prec$ is equivalent to deciding whether $H$ can be colored with $k$ colors. Finding a $k$-coloring for a circle graph is known to be NP-complete for $k \geq 4$ [9] and trivial for $k \leq 2$. For $k = 3$, Unger (1992) claims an efficient algorithm that finds a 3-coloring in $O(n \log n)$ time, if it exists. Given a circle graph $H$, Unger's algorithm (1) constructs a 3-\textsc{Sat} formula $\Phi$ that is satisfiable if and only if $H$ admits a 3-coloring and (2) solves $\Phi$ by a backtracking strategy that relies on the structure imposed by the circle graph. However, the extended abstract misses several details and Unger refers to his PhD thesis (in German) for details. In this paper we argue that Unger's algorithm for 3-coloring circle graphs is not correct and that 3-coloring circle graphs should be considered as an open problem. We show that step (1) of Unger's algorithm is incorrect by exhibiting a circle graph and its representation whose formula $\Phi$ is satisfiable but that is not 3-colorable. We further show that Unger's backtracking strategy for solving $\Phi$ in step (2) may produce incorrect results and give empirical evidence that it exhibits a runtime behaviour that is not consistent with the claimed running time.

Unravelling COVID-19 social mortality patterns using conditional inference tree algorithm

Abstract Background The COVID-19 pandemic led to significant excess mortality in 2020 in Belgium. By using microlevel cause-specific mortality data for the total Belgian adult population in 2020, three outcomes were considered in this study aiming at predicting sociodemographic (SD) and socioeconomic (SE) patterns of 1) COVID-19 specific death compared to survival; 2) all other causes of death (OCOD) compared to survival; 3) COVID-19 specific deaths compared to all OCOD. Methods Multivariable logistic regression models were fitted for the three outcomes. In addition, we computed conditional inference tree (CIT) algorithms, complementing regression models, to identify and rank the strongest SD and SE predictors of the three outcomes. Results Older people, males, people living in collectivities, first-generation migrants, and deprived SE groups experienced higher odds of dying from COVID-19 compared to survival; living in collectivities was identified by the CIT as the strongest predictor followed by age and sex. Education emerged as one of the strongest predictors for people not living in collectivities. Overall, similar patterns were observed for all OCOD except for first- and second-generation migrants having lower odds of all OCOD compared to survival; age group was identified by the CIT as the strongest predictor. Older people, males, people living in collectivities, first- and second-generation migrants, and people with lower levels of education had higher odds of COVID-19 death compared to all OCOD; living in collectivities was identified by the CIT as the strongest predictor followed by age, sex, and migration status. Education and income emerged as the strongest predictors among people not living in collectivities. Conclusions This study identified important SD and SE disparities in COVID-19 mortality underlying the importance of implementing preventive measures, particularly within the most vulnerable populations, in infectious disease pandemic preparedness. Key messages • Living in collectivities appeared to be the strongest predictor of COVID-19 death across all age groups, highlighting the importance of preventive measures in reducing the transmission of the virus. • Poor education plays a crucial role in predicting COVID-19 death among people not living in collectivities, highlighting the importance of targeting preventive measures towards low educated groups.

Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm

Background: Tsunami caused by volcanic collapse are categorized as non-seismic uncommon events, unlike tsunamis caused by earthquakes, which are common events. The traditional tsunami early warning based on the seismic sensor (e.g. earthquake detectors) may not be applicable to volcanic tsunamis because they do not generate seismic waves. Consequently, these tsunamis cannot be detected in advance, and warnings cannot be issued. New methods should be explored to address these non-seismic tsunamis caused by volcanic collapse. Objective: This study explored the potential of machine learning algorithms in supporting early warning level issuing for non-seismic tsunamis, specifically volcanic tsunamis. The Anak Krakatau volcano event in Indonesia was used as a case study. Methods: This study generated a database of 160 collapse scenarios using numerical simulation as input sequences. A classification model was constructed by defining the worst tsunami elevation and its arrival time at the coast. The database was supervised by labeling the warning levels as targets. Subsequently, a decision tree algorithm was employed to classify the warning levels. Results: The results demonstrated that the classification model performs very well for the Major Tsunami, Minor Tsunami, and Tsunami classes, achieving high precision, recall, and F1-Score with very high accuracy of 98%. However, the macro average indicates uneven performance across classes, as there are instances of ‘No Warning’ in some coastal gauges. Conclusion: To improve the model performance in the ‘No Warning’ class, it is necessary to balance the dataset by including more ‘No Warning’ scenarios, which can be achieved by simulating additional scenarios involving very small-volume collapse. Additionally, exploring additional collapse parameters such as dip angle and outlier volume could contribute to developing a more robust classification model. Keywords: Machine Learning, Classification, Volcanic Tsunamis, Early Warning, Decision Tree

A Hybrid Machine Learning‐Based Data‐Centric Cybersecurity Detection in the 5G‐Enabled IoT

ABSTRACTBy increasing smart objects as high‐potential computing devices and 5G technology, the Internet of Things (IoT) has emerging technology to provide a data‐centric infrastructure for detecting safe device‐to‐device communications by supporting security and privacy issues. As a 5G‐enabled communication technology, vehicle‐to‐vehicle (V2V) communication provides a wireless exchange information connection between smart vehicles, intelligent devices, and a cloud‐edge computing environment. This connection should be established with a safe and secured run‐time protection system to avoid many critical anomalies and misbehavior problems. Detecting run‐time malicious transformations with data‐centric misbehaving reactions is a main challenge for autonomous vehicle communications with 5G‐enabled communication technology. This paper provides a hybrid genetic algorithm‐based ensemble bagged trees (GA‐EBT) algorithm for a data‐centric misbehavior detection approach to support the V2V communications against malicious and misbehavior transactions. In this hybrid algorithm, GA provides a beneficial performance on the training accuracy factor based on a single‐objective fitness function and EBT supports a high‐degree prediction and training process using multiple decision tree models with bootstrapped samples of the training data. For the evaluation of the proposed algorithm, four real test cases are applied for messaging injection attacks in the V2V environments in comparison to the state‐of‐the‐art machine learning algorithms. The experimental results show that the proposed hybrid approach can achieve an optimal high rate accuracy factor of 99.999, precision and recall factors of 100%, and an F1‐Score factor of 100% to detect unexpected cyber‐attacks for the V2V communications in the IoT environment.

Rationalization of Energy Expenditure: Household Behavior in Poland

Background: The implementation of the EU climate and energy policy, along with changes in the legal environment, has led to a significant increase in energy prices in Poland. Consequently, energy expenditures are now a larger part of household budgets. These rising energy costs and the evolving legal landscape are compelling households to invest in energy-saving solutions and modify their energy consumption habits. This article aims to identify the activities of households in Poland regarding the rationalization of energy expenditures. It formulates the following research hypothesis: households invest in energy-saving appliances to rationalize energy expenditures and/or change their behaviors to reduce energy consumption. Methods: The paper is based on primary research conducted using an online questionnaire survey on a sample of 331 respondents in Poland in March and April 2023. Results: A classification tree algorithm was used to identify the level of investment activities and behavioral changes made by households to reduce energy expenditures. The authors found that low-income households and people who fear further energy price increases are the first of all to change their behaviors for more energy-efficient ones. Medium- and high-income households take investment measures. They replace household appliances with more energy-efficient ones and install heat pumps and photovoltaic panels. These investments are motivated by responsible consumption, environmental protection, cleanliness, and the ease of use of the appliances.