Sequential Backward Selection Research Articles

Flexible Ureteroscopy Lithotripsy Operative Time Prediction Model for the Treatment of Kidney Stones

Effective time and resource management is crucial not only in the operating room but also in healthcare supply chains. Healthcare supply chains involve the movement of medical supplies, equipment, and medications from manufacturers to healthcare providers. Effective management is crucial to ensuring that patients receive the care they need promptly. In the operating room, it is essential to have an information process in place to effectively manage time and resources during the current surgical procedure. This paper focuses on developing a predictive model for the operating time of flexible ureteroscopy for kidney stones. The model can forecast surgical and preoperative time based on patient characteristics and surgeon experience. The model can assist in planning ureteroscopy procedures and preventing surgical complications, which is crucial not only for the operating room but also for healthcare supply chains. The paper presents a study that compares different feature selection methods and regression techniques. The study found that sequential backward selection combined with the extra tree regressor was the most effective approach.

Prediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort.

Our study aimed to develop machine learning algorithms capable of predicting red blood cell (RBC) transfusion during valve replacement surgery based on a preoperative dataset of the non-anemic cohort. A total of 423 patients who underwent valvular replacement surgery from January 2015 to December 2020 were enrolled. A comprehensive database that incorporated demographic characteristics, clinical conditions, and results of preoperative biochemistry tests was used for establishing the models. A range of machine learning algorithms were employed, including decision tree, random forest, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), support vector classifier and logistic regression (LR). Subsequently, the area under the receiver operating characteristic curve (AUC), accuracy, recall, precision, and F1 score were used to determine the predictive capability of the algorithms. Furthermore, we utilized SHapley Additive exPlanation (SHAP) values to explain the optimal prediction model. The enrolled patients were randomly divided into training set and testing set according to the 8:2 ratio. There were 16 important features identified by Sequential Backward Selection for model establishment. The top 5 most influential features in the RF importance matrix plot were hematocrit, hemoglobin, ALT, fibrinogen, and ferritin. The optimal prediction model was CatBoost algorithm, exhibiting the highest AUC (0.752, 95% CI: 0.662-0.780), which also got relatively high F1 score (0.695). The CatBoost algorithm also showed superior performance over the LR model with the AUC (0.666, 95% CI: 0.534-0.697). The SHAP summary plot and the SHAP dependence plot were used to visually illustrate the positive or negative effects of the selected features attributed to the CatBoost model. This study established a series of prediction models to enhance risk assessment of intraoperative RBC transfusion during valve replacement in no-anemic patients. The identified important predictors may provide effective preoperative interventions.

Comparing Performances of Predictive Models of Toxicity after Radiotherapy for Breast Cancer Using Different Machine Learning Approaches.

Different ML models were compared to predict toxicity in RT on a large cohort (n = 1314). The endpoint was RTOG G2/G3 acute toxicity, resulting in 204/1314 patients with the event. The dataset, including 25 clinical, anatomical, and dosimetric features, was split into 984 for training and 330 for internal tests. The dataset was standardized; features with a high p-value at univariate LR and with Spearman ρ>0.8 were excluded; synthesized data of the minority were generated to compensate for class imbalance. Twelve ML methods were considered. Model optimization and sequential backward selection were run to choose the best models with a parsimonious feature number. Finally, feature importance was derived for every model. The model's performance was compared on a training-test dataset over different metrics: the best performance model was LightGBM. Logistic regression with three variables (LR3) selected via bootstrapping showed performances similar to the best-performing models. The AUC of test data is slightly above 0.65 for the best models (highest value: 0.662 with LightGBM). No model performed the best for all metrics: more complex ML models had better performances; however, models with just three features showed performances comparable to the best models using many (n = 13-19) features.

ANFIS-based Framework for the Prediction of Bearing’s Remaining Useful Life

Bearings are critical components extensively used in rotary machines, often being the leading cause of unexpected machine shutdowns. To mitigate system failures, it is crucial to implement effective maintenance strategies. This paper introduces a novel methodology for bearing prognostics, employing Wavelet Packet Decomposition (WPD) for data preprocessing, Sequential Backward Selection (SBS) for feature selection, and Adaptive Neuro-Fuzzy Inference System (ANFIS) networks for prognostic modeling. The proposed approach consists of two key steps. Firstly, the data undergoes preprocessing through Wavelet Packet Decomposition, enhancing the quality and extracting relevant features. Subsequently, the Remaining Useful Life (RUL) of the bearing is predicted using a degradation model. The accuracy of the proposed method is evaluated using a bearing life dataset obtained from a run-to-failure test (IMS dataset). The results demonstrate the remarkable capability of the ANFIS model to learn and accurately estimate the system’s RUL. By leveraging the combined power of WPD, SBS, and ANFIS, this methodology showcases its potential as an effective prognostic tool for bearing health assessment and proactive maintenance planning.

New health indicators for the monitoring of bearing failures under variable loads

Bearings are one of the most critical components in rotating machines. Unexpected failure of this components may cause serious damages and unplanned breakdowns. In this paper, a new method is proposed for bearing fault diagnosis under various loads based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the sequential backward selection (SBS). The CEEMDAN is used for the automatic selection of intrinsic mode functions from vibration signals to build a bank of health indicators. Then, the SBS algorithm is used to select the most relevant indicators of the bearing different failure modes. To test the proposed method accuracy, it was first applied to the Case Western Reserve University dataset. Then, data collected from a dedicated test bench of the Laboratory of Signal and Industrial Process Analysis were introduced to this method to classify different bearing health states. The obtained results show that the proposed method is effective in identifying and classifying bearing faults under various loads with high accuracy. This method can be used for condition monitoring of bearings and prognostics in real industrial applications.

Predicting radiographic outcomes of vertebral body tethering in adolescent idiopathic scoliosis patients using machine learning.

Anterior Vertebral Body Tethering (AVBT) is a growing alternative treatment for adolescent idiopathic scoliosis (AIS), offering an option besides spinal fusion. While AVBT aims to correct spinal deformity through growth correction, its outcomes have been mixed. To improve surgical outcomes, this study aimed to develop a machine learning-based tool to predict short- and midterm spinal curve correction in AIS patients who underwent AVBT surgery, using the most predictive clinical, radiographic, and surgical parameters. After institutional review board approval and based on inclusion criteria, 91 AIS patients who underwent AVBT surgery were selected from the Shriners Hospitals for Children, Philadelphia. For all patients, longitudinal standing (PA or AP, and lateral) and side bending spinal Radiographs were retrospectively obtained at six visits: preop and first standing, one year, two years, five years postop, and at the most recent follow-up. Demographic, radiographic, and surgical features associated with curve correction were collected. The sequential backward feature selection method was used to eliminate correlated features and to provide a rank-ordered list of the most predictive features of the AVBT correction. A Gradient Boosting Regressor (GBR) model was trained and tested using the selected features to predict the final correction of the curve in AIS patients. Eleven most predictive features were identified. The GBR model predicted the final Cobb angle with an average error of 6.3 ± 5.6 degrees. The model also provided a prediction interval, where 84% of the actual values were within the 90% prediction interval. A list of the most predictive features for AVBT curve correction was provided. The GBR model, trained on these features, predicted the final curve magnitude with a clinically acceptable margin of error. This model can be used as a clinical tool to plan AVBT surgical parameters and improve outcomes.

Machine learning-based prediction of effluent total suspended solids in a wastewater treatment plant using different feature selection approaches: A comparative study

Accurately predicting the characteristics of effluent, discharged from wastewater treatment plants (WWTPs) is crucial for reducing sampling requirements, labor, costs, and environmental pollution. Machine learning (ML) techniques can be effective in achieving this goal. To optimize ML-based models, various feature selection (FS) methods are employed. This study aims to investigate the impact of six FS methods (categorized as Wrapper, Filter, and Embedded methods) on the accuracy of three supervised ML algorithms in predicting total suspended solids (TSS) concentration in the effluent of a municipal wastewater treatment plant. Based on the features proposed by each FS method, five distinct scenarios were defined. Within each scenario, three ML algorithms, namely artificial neural network-multi layer perceptron (ANN-MLP), K-nearest neighbors (KNN), and adaptive boosting (AdaBoost) were applied. The features utilized for predicting TSS concentration in the WWTP effluent included BOD5, COD, TSS, TN, NH3 in the influent, and BOD5, COD, residual Cl2, NO3, TN, NH4 in the effluent. To construct the models, the dataset was randomly divided into training and testing subsets, and K-fold cross-validation was employed to control overfitting and underfitting. The evaluation metrics that are used are root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R2). The most efficient scenario was identified as Scenario IV, with the Sequential Backward Selection FS method. The features selected by this method were CODe, BOD5e, BOD5i, TNi. Furthermore, the ANN-MLP algorithm demonstrated the best performance, achieving the highest R2 value. This algorithm exhibited acceptable performance in both the training and testing subsets (R2 = 0.78 and R2 = 0.8, respectively).

Efficient prediction of SOC and aggregate OC components by continuous wavelet transform spectra under different feature selection methods

Soil Organic Carbon (SOC) fixation plays a vital role in reducing greenhouse gas emissions and the formation of soil aggregates is recognized as a crucial process in SOC fixation. In the current study, we characterized spectral information on SOC and aggregate Organic carbon (OC) components by using a rapid and robust method. Soil samples were collected from different layers to obtain the SOC and aggregate OC components to record corresponding spectra. The Continuous Wavelet Transform (CWT) spectra were then combined with various characteristic wavelength selection methods to build Multivariate Linear Regression (MLR), BP neural network (BP), and Random Forest (RF) models. Finally, the optimal combination of characteristic wavelengths based on the best-performing model for each index was determined. The results demonstrated that the model's accuracy, which was built using the set of characteristic wavelengths of the best model for all metrics, was higher than that of the best model for a single metric. This was due to the correlation between SOC and aggregate OC components. The best set of characteristic wavelengths were those selected by the Sequential Backward Selection method with wavelet decomposition scales of 23, 26, and 27, and those selected by the Minimum Redundancy Maximum Relevance method with wavelet decomposition scale 27. The RF model for Free silt and clay particles organic carbon (FSP-OC) showed the highest accuracy among the four SOC prediction models. In addition, it was discovered that as the wavelet decomposition scales become more similar, the extracted characteristic wavelengths also become more similar, resulting in similar model predictions. In summary, the accuracy of the predictive model for SOC and its constituents can be demonstrably improved by reasonable combination of characteristic wavelengths selection method and wavelet decomposition scale.

Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets.

Current quantum systems have significant limitations affecting the processing of large datasets with high dimensionality, typical of high energy physics. In the present paper, feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods, trained both in the reduced and the complete datasets. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets. Sequential Backward Selection and Principal Component Analysis techniques were used for feature's selection and while the former can produce the better quantum machine learning models in specific cases, it is more unstable. Additionally, we show that such variability in the results is caused by the use of discrete variables, highlighting the suitability of Principal Component analysis transformed data for quantum machine learning applications in the high energy physics context.

Prediction of malaria positivity using patients’ demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment

BackgroundCurrent malaria diagnosis methods that rely on microscopy and Histidine Rich Protein-2 (HRP2)-based rapid diagnostic tests (RDT) have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all these limitations. Consequently, the addition of automated detection and classification of malaria using laboratory methods can provide patients with more accurate and faster diagnosis. Therefore, this study used a machine-learning model to predict Plasmodium falciparum (Pf) antigen positivity (presence of malaria) based on sociodemographic behaviour, environment, and clinical features.MethodData from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward feature selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models. Outcomes were classified as Pf-positive or Pf-negative, corresponding to the presence or absence of malaria, respectively.ResultsAmong the three machine learning models examined, the penalised logistic regression model had the best area under the receiver operating characteristic curve for the training set (AUC = 84%; 95% confidence interval [CI]: 75–93%) and test set (AUC = 83%; 95% CI: 63–100%). Increased odds of malaria were associated with higher body weight (adjusted odds ratio (AOR) = 4.50, 95% CI: 2.27 to 8.01, p < 0.0001). Even though the association between the odds of having malaria and body temperature was not significant, patients with high body temperature had higher odds of testing positive for the Pf antigen than those who did not have high body temperature (AOR = 1.40, 95% CI: 0.99 to 1.91, p = 0.068). In addition, patients who had bushes in their surroundings (AOR = 2.60, 95% CI: 1.30 to 4.66, p = 0.006) or experienced fever (AOR = 2.10, 95% CI: 0.88 to 4.24, p = 0.099), headache (AOR = 2.07; 95% CI: 0.95 to 3.95, p = 0.068), muscle pain (AOR = 1.49; 95% CI: 0.66 to 3.39, p = 0.333), and vomiting (AOR = 2.32; 95% CI: 0.85 to 6.82, p = 0.097) were more likely to experience malaria. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI: 0.41 to 0.90, p = 0.012) and BMI (AOR = 0.47, 95% CI: 0.26 to 0.80, p = 0.006).ConclusionNewly developed routinely collected baseline sociodemographic, environmental, and clinical features to predict Pf antigen positivity may be a valuable tool for clinical decision-making.

Investigating the feasibility of differentiating MS active lesions from inactive ones using texture analysis and machine learning methods in DWI images

BackgroundMagnetic resonance imaging (MRI) is commonly used in conjunction with a gadolinium-based contrast agent (GBCA) to distinguish active multiple sclerosis (MS) lesions. However, recent studies have raised concerns regarding the long-term effects of the accumulation of GBCA in the body. Thus, the purpose of this study is to investigate the possibility of using texture analysis in diffusion-weighted imaging (DWI) and machine learning algorithms to discriminate active from inactive MS lesions without the use of GBCA. MethodsTo achieve this purpose, we introduce an image processing pipeline. In the proposed pipeline, following registration and alignment of slices, MS lesions from DWI images are segmented and quantized. Next, different texture analysis methods are employed to extract texture features from the lesions. Then, a two-stage feature reduction method is applied, in which the first stage involves a statistical t-test and the second stage relies on principal component analysis (PCA), sequential forward selection (SFS), sequential backward selection (SBS), and ReliefF algorithms. Finally, we use five classifiers logistic regression (LR), support vector machine (SVM), decision tree (DT), K nearest neighbor (KNN), and linear discriminant analysis (LDA) in a 5-fold cross-validation procedure to determine active and inactive MS lesions. ResultsIn this study, we collected and prepared 255 active/inactive MS lesions from MRI scans of 34 patients diagnosed with MS, with a mean age of 35.56±10.89. Among 89 texture features extracted, 63 features showed statistically significant differences between the means of active and inactive lesions (P<0.05). The SVM classifier with the PCA feature reduction algorithm demonstrated the best performance with an average accuracy of 0.960 (±0.024), specificity and precision of 1.0, sensitivity of 0.913 (±0.053), and AUC of 0.957 (±0.027). ConclusionOur study indicates that DWI changes detected using texture analysis-based machine learning models can precisely differentiate active from inactive MS lesions. This finding provides valuable clinical information for the early diagnosis and effective monitoring of MS disease.

An interpretable machine learning model for predicting bond strength of CFRP-steel epoxy-bonded interface

This study develops an interpretable machine learning model for predicting the bond strength of CFRP-steel epoxy bonding interfaces and reveals key bond parameters. A total of 302 sets of experimental data were collected from the existing literature, including 16 influencing factors (i.e. the “features”) for the bond strength of the CFRP-steel bonded interface. Firstly, the sequential backward selection algorithms based on random forest (RF), maximum information coefficient (MIC), and distance coefficient (DC) were assessed. Then, Catboost and RF-based models were optimized and evaluated to determine the best prediction model. Finally, Shapley Additive Explanation (SHAP) was used to interpret the Catboost-based model. The results showed that the average Coefficient of Determination (R2) of the prediction for the test set by the Catboost and RF-based models was 95.5 % and 91.5 %, respectively, indicating the Catboost-based model has a higher prediction accuracy. The SHAP analysis indicates that the bond length, bond width, and the elastic modulus of the adhesive are the critical bonding parameters governing the bond strength of CFRP-steel epoxy-bonded interfaces. The Pareto diagram shows that the effective bond length is about 120 mm. This study provides valuable guidance for the design of bond performance and process optimization of CFRP-steel epoxy bonding interfaces.

Sequential backward feature selection for optimizing permanent strain model of unbound aggregates

This study proposes a novel framework for identifying the optimal feature set required to predict the permanent strain of unbound aggregates. An experimental database consisting of 16 input features is preprocessed and the performance of 10 machine learning models is evaluated. The best-performing model is then paired with a sequential backward selection algorithm to determine the optimal feature set for predicting the permanent strain. Finally, the selected features are used to predict the permanent strain, and the performance is compared with those obtained from the principal components analysis. Six features are selected as the optimal feature set. Furthermore, the selected features accurately predict permanent strain with a root mean square error value of 0.014, which is smaller than those obtained from principal components analysis. Thus, the feature selection approach for machine learning models effectively predicts the permanent strain of unbound aggregates using a limited set of input features.

Early Detection of Coronary Microvascular Dysfunction Using Machine Learning Algorithm Based on Vectorcardiography and Cardiodynamicsgram Features

PurposeAs a main etiology of myocardial ischemia, coronary microvascular dysfunction (CMD) can occur in patients with or without obstructive coronary artery disease. Currently, there is a lack of a non-invasive approach for early detection of CMD. AimWe aim to develop a multilayer perceptron (MLP) algorithm to achieve non-invasive early detection of CMD based on vectorcardiography (VCG) and cardiodynamicsgram (CDG) features. MethodsElectrocardiograms of 82 CMD patients and 107 healthy controls were collected and synthesized into VCGs. The VCGs' ST-T segments were extracted and fed into a deterministic learning algorithm to develop CDGs. Temporal heterogeneity index, spatial heterogeneity index, sample entropy, approximate entropy, and complexity index were extracted from VCGs' ST-T segments and CDGs, entitled as STT- and CDG-based features, respectively. The most effective feature subsets were determined from CDG-based, STT-based, and the combined features (i.e., all features) via the sequential backward selection algorithm as inputs for CDG-, STT-, and CDG-STT-based MLP models optimized with an improved sparrow search algorithm, respectively. Finally, the classification capacity of the corresponding models was evaluated via five-fold cross-validations and tested on a testing dataset to verify the optimal one. ResultsThe CDG-STT-based MLP model had significantly higher evaluated metrics than CDG- and STT-based ones on the validation dataset, with the accuracy, sensitivity, specificity, F1 score, and AUC of 0.904, 0.925, 0.870, 0.870, and 0.897 on the testing dataset respectively. ConclusionsThe MLP model based on VCG and CDG features showed high efficiency in identifying CMD. The CDG-STT-based MLP model may afford a potential computer-aided tool for non-invasive detection of CMD.

A receiver-optimized total focusing method for detectability enhancement of small defects in coarse grained materials

The identification and localization of small defects are among the main objectives of ultrasonic non-destructive testing (NDT). In particular, the inspection of polycrystalline materials remains a challenge due to ultrasonic attenuation and backscatter noise caused by grain scattering. An adaptive ultrasonic array imaging algorithm is proposed in this paper, which is capable of enhancing the signal-to-noise ratio (SNR) of small subwavelength defects. The proposed approach adopts an iterative optimization procedure based on sequential backward selection, and is termed receiver optimized total focusing method (ROTFM). ROTFM is formulated in a baseline subtraction (BS) setting, and it is shown to achieve higher defect SNRs compared to TFM results obtained by directly applying the BS method to measured array data. More importantly, the performance of the proposed ROTFM is robust to time and spatial misalignments, and hence, it alleviates the need for complex compensation schemes when performing imaging computations. Compared to conventional TFM, ROTFM achieved an average SNR improvement of 3.41 dB and 6.05 dB in experiments for four test defects (cracks and holes of sizes 0.5 mm and 1 mm) at the frequencies of 5 MHz and 7 MHz, respectively.

Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets

Drug-induced intrahepatic cholestasis (DIC) is a main type of hepatic toxicity that is challenging to predict in early drug development stages. Preclinical animal studies often fail to detect DIC in humans. In vitro toxicogenomics assays using human liver cells have become a practical approach to predict human-relevant DIC. The present study was set up to identify transcriptomic signatures of DIC by applying machine learning algorithms to the Open TG-GATEs database. A total of nine DIC compounds and nine non-DIC compounds were selected, and supervised classification algorithms were applied to develop prediction models using differentially expressed features. Feature selection techniques identified 13 genes that achieved optimal prediction performance using logistic regression combined with a sequential backward selection method. The internal validation of the best-performing model showed accuracy of 0.958, sensitivity of 0.941, specificity of 0.978, and F1-score of 0.956. Applying the model to an external validation set resulted in an average prediction accuracy of 0.71. The identified genes were mechanistically linked to the adverse outcome pathway network of DIC, providing insights into cellular and molecular processes during response to chemical toxicity. Our findings provide valuable insights into toxicological responses and enhance the predictive accuracy of DIC prediction, thereby advancing the application of transcriptome profiling in designing new approach methodologies for hazard identification.

Micro-expression recognition based on EEG signals

Research into micro-expression recognition is increasingly motivated by its potential applications to mental-health diagnosis and policing. Thus far, micro-expression recognition research and applications have been based on computer vision processing technology, with notable limitations. For instance, changes in ambient lighting or in the head posture and facial occlusion can all affect micro-expression recognition. The high temporal resolution of EEG technology captures brain activity associated with micro-expressions, and hence objectively identifies micro-expressions from a neurophysiological perspective. Herein, we report a real-time supervision and emotional expression suppression (SEES) experimental paradigm. We collected micro-expression video and EEG data from 68 subjects under positive emotion induction. Thereafter, the EEG frequency-band power, statistical features, autoregressive (AR) model parameters, and wavelet entropy were extracted as features. We ranked all 128 channels based on the micro-expression recognition task classification accuracy using all the features extracted for each channel. Based on the channel ranking, different sizes of channel combinations were chosen. Subsequently, the optimal subset of these features from different combinations was selected using a sequential backward selection (SBS) algorithm and fed into different classifiers, including a support vector machine (SVM), K-nearest neighbor (KNN), gradient-boosted decision tree (GBDT), and back-propagation (BP) neuron network. The different classifiers achieve promising accuracy for micro-expression recognition, with GBDT exhibiting the highest accuracy of 85.96 % when 11 channels were selected. This demonstrates the feasibility of recognizing micro-expressions based on EEG signals.

Performance Analysis of a new Filter and Wrapper Sequence for the Survivability Prediction of Breast Cancer Patients

Feature selection is an essential preprocessing step for removing redundant or irrelevant features from multidimensional data to improve predictive performance. Currently, medical clinical datasets are increasingly large and multidimensional and not every feature helps in the necessary predictions. So, feature selection techniques are used to determine relevant feature set that can improve the performance of a learning algorithm. This study presents a performance analysis of a new filter and wrapper sequence involving the intersection of filter methods, Mutual Information and Chi-Square followed by one of the wrapper methods: Sequential Forward Selection and Sequential Backward Selection to obtain a more informative feature set for improved prediction of the survivability of breast cancer patients from the clinical breast cancer dataset, SEER. The improvement in performance due to this filter and wrapper sequence in terms of Accuracy, False Positive Rate, False Negative Rate and Area under the Receiver Operating Characteristics curve is tested using the Machine learning algorithms: Logistic Regression, K-Nearest Neighbour, Decision Tree, Random Forest, Support Vector Machine and Multilayer Perceptron. The performance analysis supports the Sequential Backward Selection of the new filter and wrapper sequence over Sequential Forward Selection for the SEER dataset.

Exploring the power of machine learning in analyzing the gas minimum miscibility pressure in hydrocarbons

Minimum Miscibility Pressure (MMP) plays a crucial role in subsurface gas injection processes. Hence, the accurate determination and analysis of the effective parameters on MMP are vital for a successful injection project. In this study, different Machine Learning (ML) algorithms are used to identify the most influential parameters on the MMP and develop reliable predictive models. A comprehensive database containing 812 samples (almost all the available experimental data set published from 1961 to 2022) is collected from 66 open literature studies. Six algorithms were employed for feature selection: Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection (SFFS), Sequential Backward Floating Selection (SBFS), Lasso Regression (LR), and Random Forest Feature Importance (RFFI). These feature selection algorithms were evaluated using a Decision Tree (DT) regressor. The most important features from 42 potential features were x C₅, x C₆, x C₂-C₆, MW C₇⁺, MW Gas, TC, and T, selected using the SBFS method based on the Root Mean Squared Error (RMSE). Using the best-selected features, six predictive models were developed, including LR, DT, Random Forest (RF), Extra Trees (ET), Stacking Regressor (SR), and Voting Regressor (VR). The SR predictive model performed the best with RMSE and R2 values of 18.37 bars and 0.96, respectively, for the testing dataset. The outcomes of this research can be employed for any industrial process involving gas injection into hydrocarbon reservoirs to select the most relevant features in designing the experimental and field trials.

Monitoring the Severity of Rubber Tree Infected with Powdery Mildew Based on UAV Multispectral Remote Sensing

Rubber tree powdery mildew (PM) is one of the most devastating leaf diseases in rubber forest plantations. To prevent and control PM, timely and accurate detection is essential. In recent years, unmanned Aerial Vehicle (UAV) remote sensing technology has been widely used in the field of agriculture and forestry, but it has not been widely used to detect forest diseases. In this study, we propose a method to detect the severity of PM based on UAV low-altitude remote sensing and multispectral imaging technology. The method uses UAVs to collect multispectral images of rubber forest canopies that are naturally infected, and then extracts 19 spectral features (five spectral bands + 14 vegetation indices), eight texture features, and 10 color features. Meanwhile, Pearson correlation analysis and sequential backward selection (SBS) algorithm were used to eliminate redundant features and discover sensitive feature combinations. The feature combinations include spectral, texture, and color features and their combinations. The combinations of these features were used as inputs to the RF, BPNN, and SVM algorithms to construct PM severity models and identify different PM stages (Asymptomatic, Healthy, Early, Middle and Serious). The results showed that the SVM model with fused spectral, texture, and color features had the best performance (OA = 95.88%, Kappa = 0.94), as well as the highest recognition rate of 93.2% for PM in early stages.