Tree Model Research Articles

Global risk dynamics of Borrelia miyamotoi in the context of climate change.

The impact of Borrelia miyamotoi on human health, facilitated by the expanding geographical distribution and increasing population of Ixodes ticks, remains obscure in the context of global climate change. We employed multiple models to evaluate the effect of global climate change on the risk of B. miyamotoi worldwide across various scenarios. The habitat suitability index of four primary vector tick species for B. miyamotoi, including Ixodes persulcatus, Ixodes ricinus, Ixodes pacificus and Ixodes scapularis, was projected using a boosted regression tree model, considering multiple shared socio-economic pathway scenarios over various time periods. The modelling analysis reveals that, apart from I. scapularis, future global warming will result in a northward shift in the other three vector tick species and a gradual reduction in suitable habitats. Random forest models indicate consistent changes in B. miyamotoi and its primary tick species, with potential risk areas shrinking and shifting northward, particularly in the eastern USA, northeastern and northern Europe and northeast Asia. These findings highlight the urgent need for enhanced active surveillance of B. miyamotoi infection in primary vector tick species across projected potential risk areas. The effect of climate change on B. miyamotoi distribution might have significant implications for public health decision-making regarding tick-borne pathogens.

Leveraging a decade of Landsat-8 spectral records for mapping blue carbon storage in tidal salt marshes

Tidal salt marsh ecosystems are known to accumulate and store large amounts of “blue” carbon, making them an important component of regional carbon cycle processes and a potential target for ecosystem-based carbon crediting efforts. However, blue carbon content in salt marshes can vary substantially at relatively small spatial scales. Understanding spatial variations of blue carbon storage at the landscape or local scale is important for developing carbon inventories, guiding ecological restorations, and informing habitat management strategies. We investigated the potential of spectral index records from the Landsat-8 Operational Land Imager spanning from 2014 to 2023 for mapping blue carbon storage in the soils of two tidal salt marsh systems in the Mid-Atlantic United States. The decadal mean of a non-photosynthetic vegetation index and standard deviations of a normalized difference vegetation index and a modified normalized difference water index were identified as predictors of blue carbon. These predictors were used to train a gradient boosted trees model for predicting soil organic matter content that achieved a testing set r-squared value of 0.67. We estimated that the two study marshes stored a combined 133-208 gigagrams of organic carbon in the top 30 cm of soil. We emphasize the need for better quantification of deep soil carbon in tidal salt marsh systems, which is likely quite high, and demonstrate the potential for satellite-based mapping of blue carbon within individual tidal wetland systems.

Modeling High Pan Evaporation Losses Using Support Vector Machine, Gaussian Processes, and Regression Tree Models

Modeling High Pan Evaporation Losses Using Support Vector Machine, Gaussian Processes, and Regression Tree Models

Accuracy of distinguishing benign, high-risk lesions and malignancies with inductive machine learning models in BIRADS 4 and BIRADS 5 lesions on breast MR examinations

Rationale and ObjectivesThe aim of this study is to explore the utility of Inductive Decision Tree models (IDTs) in distinguishing between benign, malignant, and high-risk (B3) breast lesions. Materials and MethodsWe analyzed 124 histologically confirmed lesions in 114 patients who underwent breast MR with BI-RADS 4 or 5 assessment. The dataset comprised 10 imaging parameters and one clinical observation. Using the IDTs method (algorithm C5.0) combined with the data balancing method SMOTE (Synthetic Minority Oversampling Technique) and a corresponding new method called LCC (Leveling of Cases per Class), we developed corresponding 3-class classification models (Benign, B3, or Malignant). The training set used for classification model development consists of 112 cases with 11 variables, and the model’s performance was assessed using 10-fold Cross-Validation and Leave One Out methods (utilizing the training set) and the Use Test Set method (testing on an unknown (for the models) dataset of 12 cases with 12 variables). ResultsThis preliminary study demonstrates the potential for IDTs to accurately distinguish between benign, B3 and Malignant lesions based on extracted data from breast MRI exams with a high classification accuracy (88.70%), mean sensitivity of 97.18% and specificity of 98.59% achieved by the optimal classification model, derived from the combination of the IDTs method and the LCC data balancing method. ConclusionThe present study represents a pioneering approach for three-category classification of breast lesions in MRI images using Artificial Intelligence (AI) which can be utilized as second-opinion tool in daily clinical practice by radiologists.

Optimal MMSE and MoCA cutoffs for cognitive diagnoses in Parkinson's disease: A data-driven decision tree model

BackgroundDetecting cognitive impairment in Parkinson's disease (PD) is challenging due to diverse manifestations and outdated diagnostic criteria. Cognitive screening tools, as Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), are adopted worldwide, but despite several cutoffs has been proposed for PD, no consensus has been reached, hindered by limited sample sizes, lack of validation, and inconsistent age- and education-adjustments. ObjectivesDetermine the optimal MMSE and MoCA cutoffs in a large PD cohort, spanning from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD), and develop a decision tree model to assist physicians in cognitive workups. MethodsOur retrospective Italian multicenter study involves 1780 PD, cognitively diagnosed with a level-II assessment: PD-NC(n = 700), PD-MCI(n = 706), and PDD(n = 374). Optimal cutoffs (for raw scores) were determined through ROC analysis. Then, a machine learning approach—decision trees—was adopted to validate and analyze the possible inclusion of other relevant clinical features. ResultsThe decision tree model selected as primary feature a MMSE cutoff ≤24 to predict dementia, and a score ≤ 27 for PD-MCI. To enhance PD-MCIvs.PD-NC accuracy, it also recommends including a MoCA score ≤ 22 for PD-MCI, and > 22 for PD-NC. Age and education were not selected as relevant features for the cognitive workup. Both MoCA and MMSE cutoffs exhibited high sensitivity and specificity in detecting PD cognitive statues. ConclusionsFor the first time, a clinical decision tree model based on robust MMSE and MoCA cutoffs has been developed, allowing to diagnose PD-MCI and/or PDD with a high accuracy and short administration time.

Explainable Machine Learning Models to Predict Gibbs-Donnan Effect During Ultrafiltration and Diafiltration of High-Concentration Monoclonal Antibody Formulations.

Evaluating the Gibbs-Donnan and volume exclusion effects during protein ultrafiltration and diafiltration (UF/DF) is crucial in biopharmaceutical process development to precisely control the concentration of the drug substance in the final formulation. Understanding the interactions between formulation excipients and proteins under these conditions requires a domain-specific knowledge of molecular-level phenomena. This study developed gradient boosted tree models to predict the Gibbs-Donnan and volume exclusion effects for amino acids and therapeutic monoclonal antibodies using simple molecular descriptors. The models' predictions were interpreted by information gain and Shapley additive explanation (SHAP) values to understand the modes of action of the antibodies and excipients and to validate the models. The results translated feature effects in machine learning to real-world molecular interactions, which were cross-referenced with existing scientific literature for verification. The models were validated in pilot-scale manufacturing runs of two antibody products requiring high levels of concentration. By only requiring a molecule's biophysicochemical descriptors and process conditions, the proposed models provide an in silico alternative to conventional UF/DF experiments to accelerate process development and boost process understanding of the underlying molecular mechanisms through rational interpretation of the models.

Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in Djibouti

At this time, as the world and nations move to reduce the use of fossil fuels, research is oriented toward improving the energy consumption of people and buildings. Recent methods, mainly computing techniques such as deep learning, are proposed in the literature. This paper proposes a model that integrates the autoencoder with the decision tree model using six months of meteorological data, including solar radiation, wind speed, temperature, and other meteorological data. This study aims to advance the energy consumption prediction of loads connected to the microgrid. A case study of a microgrid park of a campus in Djibouti is presented to test the proposed model. Autoencoders were exploited to extract the main features of the meteorological data. Then the decision tree is proposed to predict the energy consumption using the resulting encoded features. The evaluation of the training of the autoencoder model gave a favorable result with a mean squared error of 0.002 and an R2 value of 0.99. Hyperparameter tuning scenarios facilitated the exploration of the decision tree model. Ensemble decision trees performed better than individual trees in this model, achieving a mean absolute error of 1.4 % and an R2 value of 0.997. It showed that hyperparameter tuning improved the results due to the best architecture fit for the decision tree. Moreover, the proposed model was validated by comparing it with the literature on KNN, SVR, and MLP models commonly used in microgrid energy management. The autoencoder decision tree outperformed other compared methods, achieving an explained variance of 0.997 and an MAE of 1.7 % in the standard decision tree regressor scenario. These results demonstrated the capabilities of machine learning and weather data. Combining an autoencoder and the decision tree model will open a new door to energy management improvement.

Significance-Based Decision Tree for Interpretable Categorical Data Clustering

Numerous clustering algorithms prioritize accuracy, but in high-risk domains, the interpretability of clustering methods is crucial as well. The inherent heterogeneity of categorical data makes it particularly challenging for users to comprehend clustering outcomes. Currently, the majority of interpretable clustering methods are tailored for numerical data and utilize decision tree models, leaving interpretable clustering for categorical data as a less explored domain. Additionally, existing interpretable clustering algorithms often depend on external, potentially non-interpretable algorithms and lack transparency in the decision-making process during tree construction. In this paper, we tackle the problem of interpretable categorical data clustering by growing a decision tree in a statistically meaningful manner. We formulate the evaluation of candidate splits as a multivariate two-sample testing problem, where a single p-value is derived by combining significance evidence from all individual categories. This approach provides a reliable and controllable method for selecting the optimal split while determining its statistical significance. Extensive experimental results on real-world data sets demonstrate that our algorithm achieves comparable performance in terms of cluster quality, running efficiency, and explainability relative to its counterparts.

Quantum state transfer between cells and binary tree model

Discussing quantum state transfer on a graph is a very difficult task, when it has a relatively large number of vertices. In this paper, we construct cells so that it contain a number of vertices by partitioning the vertices, so that a finite number of cells can be used to cover all vertices on the graph. Subsequently, we define the adjacency case between cells and the orthonormal basis on the complex Hilbert space. In this framework, the quantum state transfer based on binary tree model is studied by using Laplacian operator.

Battery Condition Monitoring of Quadrotor UAV Using Machine Learning Classification Algorithm

Unmanned aerial vehicle flight performance and efficiency rely on various factors. Flight instabilities can happen due to malfunctions inside the system and disturbances from the external environment. Battery status plays a significant role in healthy flight conditions. A weak battery will affect the performance of propellers and motors, and the presence of wind disturbance can contribute towards inefficient flying capabilities. Therefore, investigation of fault at the early stage is crucial to maintain the great performance of the UAV. This paper aims to investigate the best prediction system from the existing machine learning algorithm such as Decision Tree (DT), Linear Discriminant (LD), Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Neural Network (NN) to classify the battery condition of the quadrotor by extracting the features from the displacement time series dataset. By using recorded flight data, it will be statistically analyzed to extract the flying condition features. The extracted features are the Euclidian distance (ED), speed, acceleration, Periodogram Power spectral density (PSD) and Fast Fourier Transform (FFT) of the signal. The result shows that the two best classifier algorithms are the Decision Tree and Neural Network models with training accuracy of 98% and 93% in Set A and B, respectively.

AnxietyNet: social media trends with fuzzy logic trees

In the digital age, social media platforms have become integral parts of daily life, facilitating communication, information sharing, and community building on a global scale. However, alongside the benefits of connectivity, concerns regarding public anxiety within these virtual communities have emerged. This research paper delves into the phenomenon of public anxiety on social media and proposes a novel approach utilizing fuzzy tree logic to analyze and address this issue. Through a comprehensive review of literature, theoretical frameworks, and practical applications, this paper aims to provide insights into the complex dynamics of public anxiety and offer potential solutions leveraging machine learning techniques. The proposed methodology involves the development of a cascading model to dynamically compute individual anxiety scores within social media networks. By analyzing structural influences and user interactions, this model aims to provide a nuanced understanding of anxiety dynamics within online communities. Additionally, a probabilistic model is designed to measure anxiety scores of social network messages, utilizing a generalized user profile. The integration of fuzzy tree logic enables the construction of a tree structure to effectively compute anxiety scores, offering a more flexible and human-like decision-making process. Preliminary experiments demonstrate the effectiveness of the proposed approach in capturing and analyzing public anxiety on social media platforms. The fuzzy tree model shows promise in accurately assessing anxiety levels and identifying key factors contributing to social anxiety within online communities. Moreover, the integration of machine learning techniques enhances the scalability and adaptability of the model, allowing for real-time analysis and proactive intervention strategies. This research underscores the importance of addressing public anxiety on social media through innovative analytical frameworks. By leveraging fuzzy tree logic and machine learning techniques, we can gain deeper insights into the dynamics of anxiety within online communities and develop targeted interventions to support individuals experiencing distress. Collaborative efforts in this endeavor hold the potential to foster societal well-being and resilience in the digital age.

Using Supervised and Unsupervised Machine Learning Models to Analyze Students Academic Performance

Examination result repositories generated by most universities can serve as machine learning datasets for training various models to gain insights from the data. These datasets can train multiple linear regression models to determine a student's cumulative grade point average (CGPA), or the score that a student will get in specific courses. Additionally, classification-based supervised machine learning models can use these datasets to provide insights into the class result that a student will obtain. These insights can be invaluable for academic advising and early intervention. Moreover, these datasets can train clustering-based unsupervised machine learning models, such as the K-means clustering model, to understand how student results are grouped into various clusters. This information can be crucial for planning and evaluating the quality of the university. This paper uses the dataset of undergraduate students' examination results from the Department of Computer Science at the University of Nigeria, Nsukka, to train three supervised machine learning models and one unsupervised machine learning model, utilizing Jupyter Notebook as the Python IDE. The training results showed acceptable accuracies of 91.5% for the Naïve Bayes model and 95.1% for the Decision Tree model. The linear regression model demonstrated a negligible root mean square error of 8.23×10−18, while the K-means clustering model exhibited an acceptable Silhouette metric of 0.12.

Fast CU decision method based on texture characteristics and decision tree for depth map intra-coding

As the demand for higher quality 3D videos continues to grow, the 3D extensions of the Efficient Video Coding (3D-HEVC) standard are gradually unable to meet the needs of users. Versatile Video Coding Standard (H. 266/VVC), as an advanced video coding standard, adopts the nested Multi-Type Tree quadtree (QTMT) partitioning structure that current fast depth map coding unit (CU) partitioning methods cannot apply. Therefore, we have designed a fast intra-frame CU partitioning algorithm for VVC 3D video depth maps. Our proposed algorithm in this article consists of two steps, including two sub-algorithms. First, by analyzing the relationship between image entropy and variance and depth map CU division, we establish a bi-criterion decision algorithm to determine whether the texture complexity of the current CU is low enough to terminate its partitioning process. Then, for CUs that have been determined by the first algorithm to need further partitioning, we use a decision tree model based on Light Gradient Boosting Machine (LGBM) to predict which direction of Rate-Distortion Optimization (RDO) calculation can be skipped, which can avoid some unnecessary RDO calculations in a certain direction. The final experiment demonstrated the effect of the proposed algorithm, which can reduce 47.65% complexity of VVC 3D video intra-coding with negligible 0.23% Bjøntegaard Delta Bitrate (BDBR) increase, superior to other advanced methods.

Machine Learning-Based Estimation of Reinforced Concrete Columns Stiffness Modifiers for Improved Accuracy in Linear Response History Analysis

ABSTRACT This study examines machine learning to estimate the stiffness modifiers of rectangular reinforced concrete columns. It involves training and validating these models on experimental data. The extremely randomized trees model outcomes are utilized as the optimal case results to evaluate their influence on the earthquake response of single-degree-of-freedom systems during linear response history analysis compared to existing empirical methods. The novelty lies in integrating machine learning to enhance the accuracy during seismic design. The results demonstrate that using empirical models’ stiffness modifiers overestimated the seismic response by 40% compared to the experimental values, while the proposed model showed high agreement.

Inverse design of high-strength medium-Mn steel using a machine learning-aided genetic algorithm approach

To develop medium-Mn steels with an ultimate tensile strength (UTS) exceeding 2 GPa and excellent ductility, we created a highly accurate UTS prediction machine learning (ML) model using a boosted decision tree model and 1520 dataset of tensile properties of medium-Mn steels with micro-alloying elements. We also optimized the hyper-parameters of a genetic algorithm (GA) using the Shannon diversity index to enhance search efficiency while retaining diversity. In a high-dimensional search space with millions of potential combinations, the ML-GA approach efficiently identified diverse chemical compositions and austenitizing conditions to achieve UTSs above 2 GPa. The k-means clustering method then grouped them into five distinct specimens based on similarities. These five specimens, fabricated using inversly designed chemical compositions and austenitizing temperatures, successfully exhibited UTSs exceeding 2 GPa and greater ductility compared to hot-stamped C steels. These excellent tensile properties were attributed to grain refinement resulting from the low austenitizing temperature and the pinning effect of micro-alloying element carbides, such as TiC and VC.

In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks

This study aims to develop a robust and efficient system to identify ties and ballasts in motion using a variety of non-contact sensors mounted on a robotic rail cart. The sensors include distance LiDAR sensors and inductive proximity sensors for ferrous materials to collect data while traversing railroad tracks. Many existing tie/ballast health monitoring devices cannot be mounted on Hyrail vehicles for in-motion inspection due to their inability to filter out unwanted targets (i.e., ties or ballasts). The system studied here addresses that limitation by exploring several approaches based on distance LiDAR sensors. The first approach is based on calculating the running standard deviation of the measured distance from LiDAR sensors to tie or ballast surfaces. The second approach uses machine learning (ML) methods that combine two primary algorithms (Logistic Regression and Decision Tree) and three preprocessing methods (six models in total). The results indicate that the optimal configuration for non-contact, in-motion differentiation of ties and ballasts is integrating two distance LiDAR sensors with a Decision Tree model. This configuration provides rapid, accurate, and robust tie/ballast differentiation. The study also facilitates further sensor and inspection research and development in railroad track maintenance.

Particle Swarm Optimization and Tree Models (M5P) as Cost Estimation Tool for Construction Project

Particle Swarm Optimization and Tree Models (M5P) as Cost Estimation Tool for Construction Project

Century‐Long Variations in Surface Incident Solar Radiation Over Japan——Characterized by Observations and Reanalyses

ABSTRACTSurface incident solar radiation (Rs) is of great importance in determining the energy balance in the Earth system. In this study, we use century‐long homogenized observations over Japan to constrain five 20th century reanalyses to explore their performance in reproducing Rs variation on different time scales (high‐frequency components [HFCs], for signals with cycles less than 10 years; low‐frequency components [LFCs], for signals with cycles more than 10 years) by ensemble empirical mode decomposition (EEMD) method and to quantify the impact factors on the Rs estimations by the sum of tree (SOT) model. It is found that ERA20C, ERA20CM and CERA20C overestimated Rs by 0.48–1.49 W/m−2, while 20CRv2c and 20CRv3 underestimated Rs by −0.63 and −1.18 W/m−2, respectively for 1931–2010. Poor correlation coefficient (R) was found to be 0.10 for ERA20CM and 0.22 for 20CRv2c. 20CRv2c failed for 1931–1960 but improved considerably for 1961–2010. 20CRv3 uses an upgraded model and assimilates more observations compared with its predecessor 20CRv2c; however, only the original components and HFCs in Rs were improved, with nearly no improvement in the LFCs. In general, CERA20C Rs, with small biases and higher R of 0.73 for original signals, 0.83 for HFCs and LFCs, is superior to other reanalyses. No obvious trend in clear sky Rs demonstrated that reanalysed Rs are insensitive to the aerosol forcings. Therefore, cloud cover and water vapour maybe the main factors influenced reanalysed Rs. Most of time, Rs is more sensitive to cloud cover than vapour pressure for all reanalyses except original signals (with contribution ratio of 0.29 for cloud cover and 0.71 for vapour pressure) and LFCs (with contribution ratio of 0.41 for cloud cover and 0.59 for vapour pressure) in CERA20C, and original signals (with contribution ratio of 0.37 for cloud cover and 0.63 for vapour pressure) in ERA20C. This work pointed out that aerosol related processes such as aerosol forcings or aerosol radiative effect in reanalyses should be improved in the future, which will ultimately improve the interaction between aerosol and cloud in Rs simulations.

Predictors of preparedness for well-dying among middle-aged and older adults.

This study investigated psychosocial factors related to preparedness for well-dying among middle-aged and older adults and verified a model that can predict it along with psychosocial factors and demographic profiles to provide useful information for intervention. The participants were 340 middle-aged and older Korean adults aged 40-79 years. The predictive models were verified using stepwise regression and decision tree analyses. The results revealed that personality, meaning of life, hardiness, intrinsic religiosity, death anxiety, family support, subjective well-being, and expectations for future life were significantly correlated with preparedness for well-dying among middle-aged and older adults. A stepwise regression analysis revealed that meaning of life accounted for the greatest variance in preparedness for well-dying. A decision tree model predicting preparedness for well-dying included search for meaning, the presence of a spouse, family support, having a job, tenacity, conscientiousness, and positive emotions. These findings suggest that search for meaning and tenacity in middle-aged and older adults as well as the presence of a spouse or occupation and family support may play important roles in preparing for well-dying.

Optimization scheme design in production process based on multi-stage decision-making

This paper studies the control of defective rate in the electronic industry chain, designs a small sample binomial distribution sampling detection scheme to verify the supplier 's defective rate statement and optimize the detection cost. Based on the defective rate, the linear programming and dynamic programming / decision tree model are used to optimize the multi-stage production process, including parts inspection, finished product inspection and unqualified product treatment, so as to maximize the profit of the enterprise. A phased optimization strategy is proposed for complex multi-process situations. Finally, according to the sampling test results, the defect rate estimation is adjusted, the problem model is re-optimized, and the cost control reference scheme is provided.