Random Forest Feature Selection Research Articles

Stock market prediction employing ensemble methods: the Nifty50 index

<span lang="EN-US">Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions.</span>

Abstract PO2-27-05: Composite biomarkers for the prediction of progression-free survival with CDK4/6 inhibitors in metastatic HR+/HER2- breast cancer

Abstract CDK4/6 inhibitors are widely used in combination with endocrine therapy in the treatment of HR+/HER2- metastatic breast cancer. Generally the CDK4/6 inhibitors lead to a doubling of progression-free survival (PFS) over single agent endocrine therapy. In spite of a known mechanism of action, biomarkers to define the duration of response to CDK4/6 inhibitors with either aromatase inhibitors (AI) or fulvestrant (FUL) remain lacking. A cohort of over 300 patients receiving standard of care CDK4/6 inhibitor combination therapy for metastatic disease were consented for participation (NCT04526587). The duration of PFS in both AI and FUL treated sub-groups were comparable to that observed in randomized clinical trials. In this cohort progesterone receptor-status (HR 0.70, p=0.0123), visceral disease (HR 1.55, p=0.0013), prior endocrine therapy (HR 2.34, p< 0.001), and type of endocrine therapy (HR 2.16, p< 0.001) were associated with duration of PFS. To develop biomarkers, gene expression analyses was performed on 313 tumor samples representing over 200 individual patients. Focusing on the pre-treatment metastatic biopsy, established signatures associated with prognosis in HR+/HER2- breast cancer were employed to determine association with response to combination therapy in the metastatic setting. Proliferation-associated signatures were associated with shorter PFS. For example, an established RB loss signature was associated with PFS in both AI (HR 1.9, p=0.009) and FUL (HR 2.8, p=0.007) treated patients. Clinically employed signatures (e.g. OncotypeDx and Mammaprint) harbored variable associations with outcomes. Random Survival Forest feature selection was applied to individual and combined prognostic signatures. This approach resulted in a potent classifier for progression-free survival (HR 10, p< 1E-20). To enable applicability to different manifestations of disease, significant clinical pathological variables were incorporated into the model so that there was equivalent performance in AI and FUL treated patients for prediction of PFS. This predictive algorithm was also strongly associated with overall survival (HR 4.6, p=2.7E-8). Ongoing studies are evaluating the algorithms prospectively in NCT04526587. Citation Format: Agnieszka Witkiewicz, Jianxin Wang, Erik Knudsen, Ellis Levine, Tracey O'Connor. Composite biomarkers for the prediction of progression-free survival with CDK4/6 inhibitors in metastatic HR+/HER2- breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-27-05.

A maximal overlap discrete wavelet packet transform coupled with an LSTM deep learning model for improving multilevel groundwater level forecasts

Developing precise groundwater level (GWL) forecast models is essential for the optimal usage of limited groundwater resources and sustainable planning and management of water resources. In this study, an improved forecasting accuracy for up to 3 weeks ahead of GWLs in Bangladesh was achieved by employing a coupled Long Short Term Memory (LSTM) network-based deep learning algorithm and Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) data preprocessing. The coupled LSTM-MODWPT model’s performance was compared with that of the LSTM model. For both standalone LSTM and LSTM-MODWPT models, the Random Forest feature selection approach was employed to select the ideal inputs from the candidate GWL lags. In the LSTM-MODWPT model, input GWL time series were decomposed using MODWPT. The ‘Fejér-Korovkin’ mother wavelet with a filter length of 18 was used to obtain a collection of scaling coefficients and wavelets for every single input time series. Model performance was assessed using five performance indices: Root Mean Squared Error; Scatter Index; Maximum Absolute Error; Median Absolute Deviation; and an a-20 index. The LSTM-MODWPT model outperformed standalone LSTM models for all time horizons in GWL forecasting. The percentage improvements in the forecasting accuracies were 36.28%, 32.97%, and 30.77%, respectively, for 1-, 2-, and 3-weeks ahead forecasts at the observation well GT3330001. Accordingly, the coupled LSTM-MODWPT model could potentially be used to enhance multiscale GWL forecasts. This research demonstrates that the coupled LSTM-MODWPT model could generate more precise GWL forecasts at the Bangladesh study site, with potential applications in other geographic locations globally.

Predictive models for flexible pavement fatigue cracking based on machine learning

Pavement performance prediction is crucial for ensuring the longevity and safety of road networks. In our extensive study, we employ a diverse array of techniques to enhance fatigue performance models in flexible pavements. The methodology begins with Random Forest feature selection, identifying the top 15 critical variables that significantly impact pavement performance. These variables form the basis for subsequent model development. Our investigation into model performance indicates the superiority of advanced machine learning methods such as Regression Trees (RT), Gaussian Process Regression (GPR), Support Vector Machines (SVM), Ensemble Trees (ET), and Artificial Neural Networks (ANN) over traditional linear regression methods. This consistent outperformance underscores their potential to reshape forecasting accuracy. Through extensive model optimization, we reveal robust performance across both complete and selected feature sets, emphasizing the importance of meticulous feature selection in enhancing forecast accuracy. The accuracy of our best optimized machine learning model is highlighted by its Performance Measurement metrics: RMSE of 22.416, MSE of 502.46, R-squared of 0.80848, and MAE of 8.9958. Additionally, comparative analysis with previous empirical models demonstrates that our best optimized machine learning model outperforms existing empirical models. This work underscores the significance of feature curation in pavement performance prediction, highlighting the potential of sophisticated modeling methodologies. Embracing cutting-edge technologies facilitates data-driven decisions, ultimately contributing to the development of more robust road networks, enhancing safety, and prolonging lifespan.

A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning

The estimation of forest above-ground biomass (AGB) can be significantly improved by leveraging remote sensing (RS) and deep learning (DL) techniques. In this process, it is crucial to obtain appropriate RS features and develop a suitable model. However, traditional methods such as random forest (RF) feature selection often fail to adequately consider the complex relationships within high-dimensional RS feature spaces. Moreover, challenges related to parameter selection and overfitting inherent in DL models may compromise the accuracy of AGB estimation. Therefore, this study proposes a novel framework based on freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data. Firstly, we designed new indices through the formula analogous with vegetation index calculation to integrate multidimensional spectral and structural information. Then, leveraging the simplicity of computational principles, a pigeon-inspired optimization algorithm (PIO) was introduced into a bi-directional long short-term memory neural network (PIO-BiLSTM), which achieved the set objective function through repeated iteration and validation to obtain the optimal model parameters. Finally, to verify the framework’s effect, we conducted experiments in two different tree species and compared another seven classical optimization algorithms and machine learning models. The results indicated that the new indices significantly improved the inversion accuracy of all models in both categories, and the PIO-BiLSTM model achieved the highest accuracy (Category-1: R2 = 0.8055, MAE = 8.8475 Mg·ha−1, RMSE = 12.2876 Mg·ha−1, relative RMSE = 18.1715%; Category-2: R2 = 0.7956, MAE = 1.7103 Mg·ha−1, RMSE = 2.2887 Mg·ha−1, relative RMSE = 9.3000%). Compared with existing methods, the proposed framework greatly reduced the labor costs in parameter selection, and its potential uncertainty also decreased by up to 9.0%. Furthermore, the proposed method has a strong generalization ability and is independent of tree species, indicating its great potential for future forest AGB inversion in wider regions with diverse forest types.

Value of multi-center 18 F-FDG PET/CT radiomics in predicting EGFR mutation status in lung adenocarcinoma.

Accurate, noninvasive, and reliable assessment of epidermal growth factor receptor (EGFR) mutation status and EGFR molecular subtypes is essential for treatment plan selection and individualized therapy in lung adenocarcinoma (LUAD). Radiomics models based on 18 F-FDG PET/CT have great potential in identifying EGFR mutation status and EGFR subtypes in patients with LUAD. The validation of multi-center data, model visualization, and interpretation are significantly important for the management, application and trust of machine learning predictive models. However, few EGFR-related research involved model visualization and interpretation, and multi-center trial. To develop explainable optimal predictive models based on handcrafted radiomics features (HRFs) extracted from multi-center 18 F-FDG PET/CT to predict EGFR mutation status and molecular subtypes in LUAD. Baseline 18 F-FDG PET/CT images of 383 LUAD patients from three hospitals and one public data set were collected. Further, 1808 HRFs were extracted from the primary tumor regions using Pyradiomics. Predictive models were built based on cross-combination of seven feature selection methods and seven machine learning algorithms. Yellowbrick and explainable artificial intelligence technology were used for model visualization and interpretation. Receiver operating characteristic curve, classification report and confusion matrix were used for model performance evaluation. Clinical applicability of the optimal models was assessed by decision curve analysis. STACK feature selection method combined with light gradient boosting machine (LGBM) reached optimal performance in identifying EGFR mutation status ([area under the curve] AUC=0.81 in the internal test cohort; AUC=0.62 in the external test cohort). Random forest feature selection method combined with LGBM reached optimal performance in predicting EGFR mutation molecular subtypes (AUC=0.89 in the internal test cohort; AUC=0.61 in the external test cohort). Explainable machine learning models combined with radiomics features extracted from multi-center/scanner 18 F-FDG PET/CT have certain potential to identify EGFR mutation status and subtypes in LUAD, which might be helpful to the treatment of LUAD.

Hyperspectral Image Classification: A Hybrid Approach Integrating Random Forest Feature Selection and Convolutional Neural Networks for Enhanced Accuracy

Hyperspectral Image Classification: A Hybrid Approach Integrating Random Forest Feature Selection and Convolutional Neural Networks for Enhanced Accuracy

Predicting the need for a replan in oropharyngeal cancer: A radiomic, clinical, and dosimetric model.

Patients with oropharyngeal cancer (OPC) treated with chemoradiation can experience weight loss and tumor shrinkage, altering the prescribed treatment. Treatment replanning ensures patients do not receive excessive doses to normal tissue. However, it is a time- and resource-intensive process, as it takes 1 to 2 weeks to acquire a new treatment plan, and during this time, overtreatment of normal tissues could lead to increased toxicities. Currently, there are limited prognostic factors to determine which patients will require a replan. There remains an unmet need for predictive models to assist in identifying patients who could benefit from the knowledge of a replan prior to treatment. We aimed to develop and evaluate a CT-based radiomic model, integrating clinical and dosimetric information, to predict the need for a replan prior to treatment. A dataset of patients (n=315) with OPC treated with chemoradiation was used for this study. The dataset was split into independent training (n=220) and testing (n=95) datasets. Tumor volumes and organs at risk (OARs) were contoured on planning CT images. PyRadiomics was used to compute radiomic image features (n=1218) on the original and filtered images from each of the primary tumor, nodal volumes, and ipsilateral and contralateral parotid glands. Nine clinical features and nine dose features extracted from the OARs were collected and those significantly (p<0.05) associated with the need for a replan in the training dataset were used in a baseline model. Random forest feature selection was applied to select the optimal radiomic features to predict replanning. Logistic regression, Naïve Bayes, support vector machine, and random forest classifiers were built using the non-correlated selected radiomic, clinical, and dose features on the training dataset and performance was assessed in the testing dataset. The area under the curve (AUC) was used to assess the prognostic value. A total of 78 patients (25%) required a replan. Smoking status, nodal stage, base of tongue subsite, and larynx mean dose were found to be significantly associated with the need for a replan in the training dataset and incorporated into the baseline model, as well as into the combined models. Five predictive radiomic features were selected (one nodal volume, one primary tumor, two ipsilateral and one contralateral parotid gland). The baseline model comprised of clinical and dose features alone achieved an AUC of 0.66 [95% CI: 0.51-0.79] in the testing dataset. The random forest classifier was the top-performing radiomics model and achieved an AUC of 0.82 [0.75-0.89] in the training dataset and an AUC of 0.78 [0.68-0.87] in the testing dataset, which significantly outperformed the baseline model (p=0.023, testing dataset). This is the first study to use radiomics from the primary tumor, nodal volumes, and parotid glands for the prediction of replanning for patients with OPC. Radiomic features augmented clinical and dose features for predicting the need for a replan in our testing dataset. Once validated, this model has the potential to assist physicians in identifying patients that may benefit from a replan, allowing for better resource allocation and reduced toxicities.

Predictive modeling for the development of diabetes mellitus using key factors in various machine learning approaches

AimsMachine learning (ML) approaches are beneficial when automatic identification of relevant features among numerous candidates is desired. We investigated the predictive ability of several ML models for new onset of diabetes mellitus. MethodsIn 10,248 subjects who received annual health examinations, 58 candidates including fatty liver index (FLI), which is calculated by using waist circumference, body mass index and levels of triglycerides and γ-glutamyl transferase, were used. ResultsDuring a 10-year follow-up period (mean period: 6.9 years), 322 subjects (6.5 %) in the training group (70 %, n=7,173) and 127 subjects (6.2 %) in the test group (30 %, n=3,075) had new onset of diabetes mellitus. Hemoglobin A1c, fasting glucose and FLI were identified as the top 3 predictors by random forest feature selection with 10-fold cross-validation. When hemoglobin A1c and FLI were used as the selected features, C-statistics analogous in receiver operating characteristic curve analysis in ML models including logistic regression, naïve Bayes, extreme gradient boosting and artificial neural network were 0.874, 0.869, 0.856 and 0.869, respectively. There was no significant difference in the discriminatory capacity among the ML models. ConclusionsML models incorporating hemoglobin A1c and FLI provide an accurate and straightforward approach for predicting the development of diabetes mellitus.

Multivariate and Dimensionality-Reduction-Based Machine Learning Techniques for Tumor Classification of RNA-Seq Data

Cancer, a genetic disease, is considered one of the leading causes of death globally and affects people of all ages. Ribonucleic acid sequencing (RNA-Seq) is a technique used to quantify the expression of genes of interest and can be used to classify cancer tumor types. This paper describes a machine learning technique to classify cancer tissue samples by tumor type, such as breast cancer, lung cancer, colon cancer, and others. More than 60,000 RNA-Seq features were analyzed using six different machine learning classification algorithms, both individually and as an ensemble. Numerous dimensionality reduction techniques addressed the challenges of working with enormous amounts of genetic data. In particular, we were able to reduce the number of features from over 60,000 to 660 in the random forest feature selection and to 68 factor features using factor analysis with an accuracy of 99% in classifying tumor types.

Application of dimension reduction methods on propeller performance prediction model

Efficient and effective prediction of propeller performance, especially for hydrodynamic performance and fluctuating pressure, have always been areas of interest for ship engineers and researchers. With the rapid blooming of machine learning based surrogate models, it is important to identify proper features that can represent crucial information from numerous features of propellers' geometry design. This paper used five different dimension reduction methods to conduct the feature selection process from propellers geometry. The reduced features are then used as inputs for a random forest based surrogate model to predict both the hydrodynamic performance and fluctuation pressure under cavitation. The experiment results on three test propellers showed that dimension reduction methods such as factor analysis, principal component analysis and random forest feature selection could yield a higher precision for the surrogate model compare to using all features or the main features selected by experience, while manifold learning methods such as isometric feature mapping and locally linear embedding were not good at extracting propellers' geometry. This paper innovatively proposes dimension reduction methods that can automatically extract main features from propellers' complex geometry. With these main features selected, Artificial Intelligence models can yield a higher precision on propellers’ performance predication. Furthermore, these dimension reduction methods have high generalization ability on different types of propellers, which enables a smarter and more cost-efficient way for the preliminary design process of ship propellers.

Crop mapping through a hybrid machine learning and deep learning method

A major function of satellite imagery is crop identification and separation, which is of key importance in acreage estimation, yield estimation, and agricultural management. This study aimed to examine the capability of the object-based method and machine learning and deep learning algorithms in combining optical and radar images to generate crop-type maps. For this purpose, Sentinel-1 and Sentinel-2 satellite images were used for the 2019 crop year in the northwest of Ardabil, Iran. Image segmentation was performed through the multiscale method and various spectral, textural, and radar backscattering characteristics. The main features were identified using Random Forest feature selection and fed to support vector machine, random forest, and convolutional neural network algorithms. The results of these algorithms were combined via the majority voting method. The results showed that the convolutional neural network algorithm performed better than other methods with an overall accuracy of 88.03% and a kappa coefficient of 85.99%. Meanwhile, the support vector machine method showed poor performance compared to the other two methods. According to the findings, combining the results of these algorithms through the majority voting method yielded the best crop identification result with an overall accuracy of 90.34% and a Kappa coefficient of 88.72%.

Characterization of gene expression and biological pathways in Wilms tumor

BackgroundAlthough mortality for low stage (I/II) Wilms tumor (WT) has greatly improved with dose-intense multimodality and multi-agent interventions, advanced stage (III/IV) tumors remain associated with relapse, mortality and increased treatment-related morbidity. We aim to identify targets unique to the microenvironment of advanced stage WT to develop novel treatments. MethodsRetrospective review of patients treated for WT at a single institution from 2000 to 2021. Following review by a pediatric pathologist, samples underwent bulk RNA-sequencing with differential gene expression and pathway enrichment analysis. Groups analyzed included low vs advanced stage and neoadjuvant chemotherapy-treated vs treatment-naïve tumors. In neoadjuvant chemotherapy-treated patients with subsequent recurrence, genes predictive of recurrence were identified using Boruta Random Forest Feature Selection and whole-exome DNA-Sequencing. ResultsInitial analysis of 15 samples showed enrichment for cell cycle progression E2F genes with downregulation of interferon alpha and KRAS signaling genes in advanced compared to low stage tumors. Subsequent analysis of 51 tumors with stratification of RNA-sequencing profiles by treatment status showed notable differences among neoadjuvant chemotherapy-treated tumors. A subcluster of advanced stage tumors lacked enrichment of immune-related genes and pathways observed within a subcluster of immune-enriched low stage tumors, including an activating variant of Catenin beta-1. Among patients with recurrence, transmembrane protein 31 was downregulated and flavin containing dimethylaniline monoxygenase 3 upregulated. ConclusionsGenetic and cellular composition differ between low and advanced stage WT, most notably a significant reduction in immune-related genes/pathways in neoadjuvant chemotherapy-treated tumors. Significant variability exists between same stage tumors, requiring further investigation for reliable treatment targets.

Radiomics as a non-invasive adjunct to Chest CT in distinguishing benign and malignant lung nodules

In an observational study conducted from 2016 to 2021, we assessed the utility of radiomics in differentiating between benign and malignant lung nodules detected on computed tomography (CT) scans. Patients in whom a final diagnosis regarding the lung nodules was available according to histopathology and/or 2017 Fleischner Society guidelines were included. The radiomics workflow included lesion segmentation, region of interest (ROI) definition, pre-processing, and feature extraction. Employing random forest feature selection, we identified ten important radiomic features for distinguishing between benign and malignant nodules. Among the classifiers tested, the Decision Tree model demonstrated superior performance, achieving 79% accuracy, 75% sensitivity, 85% specificity, 82% precision, and 90% F1 score. The implementation of the XGBoost algorithm further enhanced these results, yielding 89% accuracy, 89% sensitivity, 89% precision, and an F1 score of 89%, alongside a specificity of 85%. Our findings highlight tumor texture as the primary predictor of malignancy, emphasizing the importance of texture-based features in computational oncology. Thus, our study establishes radiomics as a powerful, non-invasive adjunct to CT scans in the differentiation of lung nodules, with significant implications for clinical decision-making, especially for indeterminate nodules, and the enhancement of diagnostic and predictive accuracy in this clinical context.

Pericoronary radiomics texture features associated with hypercholesterolemia on a photon-counting-CT

IntroductionPericoronary adipose tissue (PCAT) stands in complex bidirectional interaction with the surrounding arteries and is known to be connected to many cardiovascular diseases involving vascular inflammation. PCAT texture may be influenced by other cardiovascular risk factors such as hypercholesterolemia. The recently established photon-counting CT could improve texture analysis and help detect those changes by offering higher spatial resolution and signal-to-noise ratio.MethodsIn this retrospective, single-center, IRB-approved study, PCAT of the left and right coronary artery was manually segmented and radiomic features were extracted using pyradiomics. The study population consisted of a test collective and a validation collective. The collectives were each divided into two groups defined by the presence or absence of hypercholesterolemia, taken from self-reported conditions and confirmed by medical records. Mean and standard deviation were calculated with Pearson correlation coefficient for correlation of features and visualized as boxplots and heatmaps using R statistics. Random forest feature selection was performed to identify differentiating features between the two groups. 66 patients were enrolled in this study (34 female, mean age 58 years).ResultsTwo radiomics features allowing differentiation between PCAT texture of the groups were identified (p-values between 0.013 and 0.24) and validated. Patients with hypercholesterolemia presented with a greater concentration of high-density values as indicated through analysis of specific texture features as “gldm_HighGrayLevelEmphasis” (23.95 vs. 22.99) and “glrlm_HighGrayLevelRunEmphasis” (24.21 vs. 23.31).DiscussionTexture analysis of PCAT allowed differentiation between patients with and without hypercholesterolemia offering a potential imaging biomarker for this specific cardiovascular risk factor.

Mortality Prediction in Emergency Department Using Machine Learning Models

Background: Diagnosing patient deterioration and preventing unexpected deaths in the emergency department is a complex task that relies on the expertise and comprehensive understanding of emergency physicians concerning extensive clinical data. Objectives: Our study aimed to predict emergency department mortality and compare different models. Methods: During a one-month period, demographic information and records were collected from 1,000 patients admitted to the emergency department of a selected hospital in Tehran. We rigorously followed The Cross Industry Standard Process for data mining and methodically progressed through its sequential steps. We employed Cat Boost and Random Forest models for prediction purposes. To prevent overfitting, Random Forest feature selection was employed. Expert judgment was utilized to eliminate features with an importance score below 0.0095. To achieve a more thorough and dependable assessment, we implemented a K-fold cross-validation method with a value of 5. Results: The Cat Boost model outperformed Random Forest significantly, showcasing an impressive mean accuracy of 0.94 (standard deviation: 0.03). Ejection fraction, urea (body waste materials), and diabetes had the greatest impact on prediction. Conclusions: This study sheds light on the exceptional accuracy and efficiency of machine learning in predicting emergency department mortality, surpassing the performance of traditional models. Implementing such models can result in significant improvements in early diagnosis and intervention. This, in turn, allows for optimal resource allocation in the emergency department, preventing the excessive consumption of resources and ultimately saving lives while enhancing patient outcomes.

Random forest feature selection for partial label learning

Partial Label Learning (PLL) aims to induce a multi-class classifier to deal with the problem that each training instance is associated with a set of candidate labels, among which only one is valid but unknown. Feature selection, which choses some features related to the task (e.g., classification) and omits the unrelated ones, is challenging for PLL due to the ambiguous labeling information. In this paper, a random forest based method, namely, RFUTE, is developed to deal with the feature selection challenge in PLL. Due to the fact that the ground-truth labels are inaccessible in the training process, RFUTE first disambiguates the candidate labels, and then operates feature selection by calculating and sorting the total change of information entropy over all trees in the forest for all features. More importantly, a supplement approach is proposed after disambiguation to avoid the class imbalance problem. Comprehensive experiments over both synthetic and real-world partial label data sets empirically justify the effectiveness of RFUTE in improving the generalization performance of well-established PLL methods.

Developing a machine learning algorithm to predict the probability of aseptic loosening of the glenoid component after anatomical total shoulder arthroplasty: protocol for a retrospective, multicentre study

IntroductionDespite technological advancements in recent years, glenoid component loosening remains a common complication after anatomical total shoulder arthroplasty (ATSA) and is one of the main causes of revision surgery. Increasing...

Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests

The information acquisition about potassium, which affects the quality and yield of crops, is of great significance for crop nutrient management and intelligent decision making in smart agriculture. This article proposes a method for predicting the rice leaf potassium content (LKC) using spectral characteristics and random forests (RF). The method screens spectral characteristic variables based on the linear correlation analysis results of rice LKC and four transformed spectra (original reflectance (R), first derivative reflectance (FDR), continuum-removed reflectance (CRR), and normalized reflectance (NR)) of leaves and the PCA dimensionality reduction results of vegetation indices. Following a second screening of the correlated single band and vegetation index variables of the four transformed spectra, the RF is used to obtain the mixed variable (MV), and regression models are developed to achieve an accurate prediction of rice LKC. Additionally, the effect of potassium spectral sensitivity bands, indices, spectral transformation form, and different modeling methods on rice LKC prediction accuracy is assessed. The results showed that the mixed variable obtained with the second screening using the random forest feature selection method could effectively improve the prediction accuracy of rice LKC. The regression models based on the single band variables (BV) and the vegetation index variables (IV), FDR–RF and IV–RF, with R2 values of 0.62301 and 0.7387 and RMSE values of 0.24174 and 0.15045, respectively, are the best models. In comparison to the previous two models, the MV–RF validation had a higher R2 and a lower RMSE, reaching 0.77817 and 0.14913, respectively. It can be seen that the RF has a better processing ability for the MV that contains vegetation indices and IV than for the BV. Furthermore, the results of different variable screening and regression analyses also revealed that the single band’s range of 1402–1428 nm and 1871–1907 nm, as well as the vegetation indices constituted of reflectance 1799–1881 nm and 2276–2350 nm, are of great significance for predicting rice LKC. This conclusion can provide a reference for establishing a universal vegetation index related to potassium.

Identification of Risk Influential Factors for Fishing Vessel Accidents Using Claims Data from Fishery Mutual Insurance Association

This research aims to identify and analyze the significant risk factors contributing to accidents involving fishing vessels, a crucial step towards enhancing safety and promoting sustainable practices in the fishing industry. Using a data-driven Bayesian network (BN) model that incorporates feature selection through the random forest (RF) method, we explore these key factors and their interconnected relationships. A review of past academic studies and accident investigation reports from the Fishery Mutual Insurance Association (FMIA) revealed 17 such factors. We then used the random forest model to rank these factors by importance, selecting 11 critical ones to build the Bayesian network model. The data-driven Bayesian network (BN) model is further utilized to delve deeper into the central factors influencing fishing vessel accidents. Upon validation, the study results show that incorporating the random forest feature selection method enhances the simplicity, reliability, and precision of the BN model. This finding is supported by a thorough performance evaluation and scenario analysis.