Boosting Algorithm Research Articles

An intelligent diabetes classification and perception framework based on ensemble and deep learning method.

Sugar in the blood can harm individuals and their vital organs, potentially leading to blindness, renal illness, as well as kidney and heart diseases. Globally, diabetic patients face an average annual mortality rate of 38%. This study employs Chi-square, mutual information, and sequential feature selection (SFS) to choose features for training multiple classifiers. These classifiers include an artificial neural network (ANN), a random forest (RF), a gradient boosting (GB) algorithm, Tab-Net, and a support vector machine (SVM). The goal is to predict the onset of diabetes at an earlier age. The classifier, developed based on the selected features, aims to enable early diagnosis of diabetes. The PIMA and early-risk diabetes datasets serve as test subjects for the developed system. The feature selection technique is then applied to focus on the most important and relevant features for model training. The experiment findings conclude that the ANN exhibited a spectacular performance in terms of accuracy on the PIMA dataset, achieving a remarkable accuracy rate of 99.35%. The second experiment, conducted on the early diabetes risk dataset using selected features, revealed that RF achieved an accuracy of 99.36%. Based on our experimental results, it can be concluded that our suggested method significantly outperformed baseline machine learning algorithms already employed for diabetes prediction on both datasets.

A machine learning-based assessment model for defect diagnosis in XLPE power cables

Cross-linked polyethylene (XLPE) power cables are commonly used in flexible DC transmission applications. The diagnosis of the defects of XLPE power cable has consistently been a focal point of research as it is an essential equipment for power delivery. Previous research works have seldom employed machine learning methods to deal with this problem. Therefore, this work presents a machine learning-based assessment model that combines the Categorical Boosting (CatBoost) algorithm and Aquila optimizer. The CatBoost model is developed to examine the stages of the cable defect. The load condition, corrosion condition, operating life, and partial discharge are the inputs into the CatBoost model. The Aquila optimizer is employed to optimize parameters in an automated manner during the training of the CatBoost model. The performance comparison is made between the proposed model and the existing models. The proposed model demonstrates a high level of accuracy, precision, recall, and F1-score, all higher than 99 %. The model exhibits at least 1 % more achievement in accuracy, precision, recall, and F1-score when compared to the next best model, XGBoost. These results suggest that the proposed model is capable of effectively assessing the stages of cable defects. In addition to this, these experimental results of the proposed model are superior to those of the existing models, which demonstrate that the proposed model is superior to the existing models in terms of its ability to assess the defect stages of cables.

Approaches of Combining Machine Learning with NMR-Based Pore Structure Characterization for Reservoir Evaluation

Tight gas, a category of unconventional natural gas, relies on advanced intelligent monitoring methods for their extraction. Conventional logging for reservoir evaluation relies on logging data and the manual setting of evaluation criteria to classify reservoirs. However, the complexity and heterogeneity of tight reservoirs pose challenges in accurately identifying target layers by using traditional well-logging techniques. Machine learning may hold the key to solving this problem, as it enables computers to learn without being explicitly programmed and manually adding rules. Therefore, it is possible to make reservoir evaluations using machine learning methods. In this paper, the reservoir quality index (RQI) and porous geometric parameters obtained from the optimized inversion of the spherical–tubular model are adopted to evaluate the reservoir. Then, three different machine learning approaches, the random forest (RF) algorithm, support vector machine (SVM) algorithm, and extreme gradient boosting (XGB) algorithm, are utilized for reservoir classification. The selected dataset covers more than 7000 samples from five wells. The data from four wells are arranged as the training dataset, and the data of the remaining one well is designed as the testing dataset to calculate the prediction accuracies of different machine learning algorithms. Among them, accuracies of RF, SVM, and XGB are all higher than 90%, and XGB owns the highest result by reaching 97%. Machine learning based approaches can greatly assist reservoir prediction by implementing the well-logging data. The research highlights the application of reservoir classification with a higher prediction accuracy by combining machine learning algorithms with NMR-logging-based pore structure characterization, which can provide a guideline for sweet spot identification within the tight formation. This not only optimizes resource extraction but also aligns with the global shift towards clean and renewable energy sources, promoting sustainability and reducing the carbon footprint associated with conventional energy production. In summary, the fusion of machine learning and NMR-logging-based reservoir evaluation plays a crucial role in advancing both energy efficiency and the transition to cleaner energy sources.

Characteristics and risk factors for invasive fungal infection in hospitalized patients with acute-on-chronic hepatitis B liver failure: a retrospective cohort study from 2010 to 2023.

The global burden of invasive fungal infections (IFIs) is emerging in immunologic deficiency status from various disease. Patients with acute-on-chronic hepatitis B liver failure (ACHBLF) are prone to IFI and their conditions are commonly exacerbated by IFI. However, little is known about the characteristics and risk factors for IFI in hospitalized ACHBLF patients. A total of 243 hospitalized ACHBLF patients were retrospectively enrolled from January 2010 to July 2023. We performed restricted cubic spline analysis to determine the non-linear associations between independent variables and IFI. The risk factors for IFI were identified using logistic regression and the extreme gradient boosting (XGBoost) algorithm. The effect values of the risk factors were determined by the SHapley Additive exPlanations (SHAP) method. There were 24 ACHBLF patients (9.84%) who developed IFI on average 17.5 (13.50, 23.00) days after admission. The serum creatinine level showed a non-linear association with the possibility of IFI. Multiple logistic regression revealed that length of hospitalization (OR = 1.05, 95% CI: 1.02-1.08, P = 0.002) and neutrophilic granulocyte percentage (OR = 1.04, 95% CI: 1.00-1.09, P = 0.042) were independent risk factors for IFI. The XGBoost algorithm showed that the use of antibiotics (SHAP value = 0.446), length of hospitalization (SHAP value = 0.406) and log (qHBV DNA) (SHAP value = 0.206) were the top three independent risk factors for IFI. Furthermore, interaction analysis revealed no multiplicative effects between the use of antibiotics and the use of glucocorticoids (P = 0.990). IFI is a rare complication that leads to high mortality in hospitalized ACHBLF patients, and a high neutrophilic granulocyte percentage and length of hospitalization are independent risk factors for the occurrence of IFI.

Construction of mathematical model for integration of engineering cost prediction and multiple algorithms

As a key link in engineering construction, reasonable evaluation of engineering cost can effectively control the budget and save costs. Therefore, the reliability of the engineering cost estimation will directly affect the economic status of the whole project. However, traditional prediction models are based on a single machine learning method, which is not generalized enough and has low accuracy. In view of this, a mathematical model for engineering cost prediction is constructed by combining a random forest algorithm, ridge regression algorithm, and extreme gradient boosting (XG Boost) algorithm to obtain a prediction model with higher generalization and accuracy, and to evaluate the cost of engineering projects reasonably and scientifically. The average relative error between predicted and actual values was only 0.872%. The root mean square error and average percentage error of the fusion model were relatively small. The superiority of the proposed mathematical model of prediction cost is verified, and the model possesses a certain application value in construction engineering, providing practical reference and guidance for engineering cost prediction.

Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H2O2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.).

Industrial hemp (Cannabis sativa L.) is a highly recalcitrant plant under in vitro conditions that can be overcome by employing external stimuli. Hemp seeds were primed with 2.0-3.0% hydrogen peroxide (H2O2) followed by culture under different Light Emitting Diodes (LEDs) sources. Priming seeds with 2.0% yielded relatively high germination rate, growth, and other biochemical and enzymatic activities. The LED lights exerted a variable impact on Cannabis germination and enzymatic activities. Similarly, variable responses were observed for H2O2 × Blue-LEDs combination. The results were also analyzed by multiple regression analysis, followed by an investigation of the impact of both factors by Pareto chart and normal plots. The results were optimized by contour and surface plots for all parameters. Response surface optimizer optimized 2.0% H2O2 × 918 LUX LEDs for maximum scores of all output parameters. The results were predicted by employing Multilayer Perceptron (MLP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Moreover, the validity of these models was assessed by using six different performance metrics. MLP performed better than RF and XGBoost models, considering all six-performance metrics. Despite the differences in scores, the performance indicators for all examined models were quite close to each other. It can easily be concluded that all three models are capable of predicting and validating data for cannabis seeds primed with H2O2 and grown under different LED lights.

Hybrid machine learning with optimization algorithm and resampling methods for patch load resistance prediction of unstiffened and stiffened plate girders

This paper aims to propose a new hybrid Machine Learning (ML) with Egret Swarm Optimization Algorithm (ESOA) and Weighted Relevance-based Combination Strategy (WERCS) methods for predicting the patch loading resistance (PLR) of both longitudinally unstiffened and stiffened plate girders. A dataset of both stiffened and unstiffened plate girders under patch loading are collected and used for training and testing to generate the proposed models. Firstly, the WERCS method is used to resample the training set to improve the performance of training data. After that, four ML models, including Gradient Boosted Tree (GTB), Extreme Gradient Boosting algorithm (XGB), Artificial neural network (ANN), and CATBoost regression (CAT) are employed. The hyperparameter of all above ML models are optimized by using ESOA algorithm to choose the best performance models. It was found that the WERCS influences considerably the performance of hybrid ML models, and the WERCS + ESOA + XGB model presents the best performance compared to others. The accuracy of the WERCS + ESOA + XGB model is validated by comparing its predictive results with the existing design codes and formulae. Additionally, the Local Interpretable Model-Agnostic Explanations (LIME) method is used to assess the importance and contributions of each input variable in the proposed model. This algorithm can explain the global and local effects of features with each range and create favourable foundations for practical designing. A Graphical User Interface (GUI) tool is developed to conveniently estimate both the patch loading resistance of longitudinally unstiffened and stiffened plate girders. A Python library has been developed and uploaded to PyPi for more convenient use without the need for accessing the source code.

Multi-objective optimization design of recycled aggregate concrete mixture proportions based on machine learning and NSGA-II algorithm

This paper employs Support Vector Regression (SVR), Random Forest Regression (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) algorithms to establish the compressive strength prediction models for Recycled Aggregate Concrete (RAC) and analyze the influence of ten inputs on RAC compressive strength. Combined with the best prediction model, the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-II) is applied for multi-objective optimization of mixture proportions in RAC addressing cost, carbon emissions, and compressive strength as key objectives. The results demonstrate that the RAC compressive strength prediction model using the GB algorithm exhibits the highest accuracy, with the R2 value of 0.99 for the training set and 0.85 for the testing set. Feature importance and SHAP analysis reveal that cement and water contents are the primary factors affecting the compressive strength of RAC. Among the three mineral admixtures including Silica Fume (SF), Fly Ash (FA), and Ground Granulated Blast Furnace Slag (GGBFS), SF exhibits superior improvement in the compressive strength of RAC compared to FA and GGBFS. Partial dependency plot analysis indicates that concrete strength remains unaffected by recycled coarse aggregate content within the range of 0 to 300 kg/m3. Pareto fronts of a tri-objective mixture optimization problem for RAC are successfully obtained through the GB model combined with the NSGA-II algorithm, which can guide the optimization of RAC preparation.

Forecasting regional water demand using multi-fidelity data and harris hawks optimization of generalized regression neural network models – A case study of Heilongjiang Province, China

Accurate forecasting of water demand plays a crucial role in decision makers to effectively allocate regional water resources and promotes the rational development and utilization of water resources. To address the challenges posed by limited raw data availability and the difficulty in selecting parameters for water demand forecasting models, this study proposes a coupled model called MF-HHO-GRNN (Multi-Fidelity Data, Hybrid Harris Hawks Optimization, and Generalized Regression Neural Network). Initially, multi-fidelity data was constructed employing water demand factor data for Heilongjiang Province, China (1999–2020) as high-fidelity data, alongside data from Liaoning and Jilin Provinces of China as low-fidelity data. Adaptive boosting algorithms, data concatenation, and data dimensionality reduction techniques were employed to construct multi-fidelity data, thereby improving the availability of the original data. Subsequently, Harris hawk optimization algorithms were implemented to optimize the smoothing factor of the GRNN to resolve the challenge of selecting suitable parameters for water demand forecasting models. Finally, the MF-HHO-GRNN model was compared with other prediction models to analyze the prediction accuracy. The optimized MF-HHO-GRNN model meeting accuracy requirements was utilized to forecast agricultural irrigation, industrial, domestic, ecological water demand, and total water demand in Heilongjiang Province for the next 5 years. Results demonstrated that the proposed MF-HHO-GRNN model attained predictive accuracy of 98 %, 98 %, and 97 % for total water demand under three distinct time horizons (1 year, 3 years, and 5 years), proving excellent predictive capabilities. Furthermore, a comprehensive comparison was made between the predicted total water demand and user-specific water demands for the next 5 years in Heilongjiang Province and the regional development plans. The results showed that the predictions of the coupled model aligned with the future development trends of Heilongjiang Province, validating the practicality of the MF-HHO-GRNN model. This study provides decision-makers with an effective method for forecasting water demand, contributing to the future realization of more intelligent and accurate water resource management and decision-making.

Prediction of pCR based on clinical-radiomic model in patients with locally advanced ESCC treated with neoadjuvant immunotherapy plus chemoradiotherapy.

The primary objective of this research is to devise a model to predict the pathologic complete response in esophageal squamous cell carcinoma (ESCC) patients undergoing neoadjuvant immunotherapy combined with chemoradiotherapy (nICRT). We retrospectively analyzed data from 60 ESCC patients who received nICRT between 2019 and 2023. These patients were divided into two cohorts: pCR-group (N = 28) and non-pCR group (N = 32). Radiomic features, discerned from the primary tumor region across plain, arterial, and venous phases of CT, and pertinent laboratory data were documented at two intervals: pre-treatment and preoperation. Concurrently, related clinical data was amassed. Feature selection was facilitated using the Extreme Gradient Boosting (XGBoost) algorithm, with model validation conducted via fivefold cross-validation. The model's discriminating capability was evaluated using the area under the receiver operating characteristic curve (AUC). Additionally, the clinical applicability of the clinical-radiomic model was appraised through decision curve analysis (DCA). The clinical-radiomic model incorporated seven significant markers: postHALP, ΔHB, post-ALB, firstorder_Skewness, GLCM_DifferenceAverage, GLCM_JointEntropy, GLDM_DependenceEntropy, and NGTDM_Complexity, to predict pCR. The XGBoost algorithm rendered an accuracy of 0.87 and an AUC of 0.84. Notably, the joint omics approach superseded the performance of solely radiomic or clinical model. The DCA further cemented the robust clinical utility of our clinical-radiomic model. This study successfully formulated and validated a union omics methodology for anticipating the therapeutic outcomes of nICRT followed by radical surgical resection. Such insights are invaluable for clinicians in identifying potential nICRT responders among ESCC patients and tailoring optimal individualized treatment plans.

Revolutionizing the used car market: Predicting prices with XGBoost

In contemporary dynamic automobile industry, informed decision-making is crucial for success. Accurate sales predictions are essential for optimizing operations and resource allocation in the used car market. This article explores the application of machine learning, specifically the eXtreme Gradient Boosting(XGBoost) algorithm, in predicting used car prices in the UK. The dataset consists of over 110,000 data points from various brands, including features like model, year, mileage, and more. Data preprocessing, including outlier removal and feature scaling, is performed to prepare the dataset. XGBoost, a powerful machine learning algorithm, is selected for price prediction and compared with traditional regression models. The results show that eXtreme Gradient Boosting(XGBoost) outperforms other models in terms of R2 score, Mean Absolute Error, and Mean Square Error. Its accuracy and efficiency make it a valuable tool for both buyers and sellers in the used car market. However, challenges such as data quality, feature engineering complexity, and model interpretability remain. Future research directions include improving data quality, exploring advanced feature engineering techniques, and integrating external data sources. This study highlights the potential of machine learning in enhancing decision-making in the dynamic used car market.

Enhancing Customer Analytics: A Comprehensive Framework for Effective Churn Prediction

This paper presents a comprehensive framework designed to forecast churn rates within organizations and provide visual representations for proactive and reactive analyses. Churning is the process of customer discontinuation from a product or service due to dissatisfaction or shifting needs of customers. Since keeping current clients comes with a considerably cheaper cost, acquiring new ones is no longer a smart business plan. By leveraging historical customer data encompassing demographic, behavioral, and transactional attributes, predictive models are developed and evaluated. Given that we already have all of the current customer's data, retention is far more affordable and successful. This paper aims at improving the accuracy and gives a churn prediction model which is suitable for all organizations. There are Five main components in this framework: Data preprocessing, pattern recognition, exploratory data analysis(EDA), Gradient Boost algorithm (ML algorithm to identify whether the customer is satisfied with the service or not) and Churn prediction. The churn prediction will give the results as reactive and proactive analysis. We have used the Gradient Boost algorithm ( ensemble learning technique) along with SMOTE-ENN in this paper which gives us an accuracy of 95%.This gradient boost algorithm gives the best accuracy when compared with other machine learning algorithms like, Logistic Regression, Random Forest Classifier and Decision Tree Classifier. Visualization of churned data and risk analysis is facilitated through Power BI.

Intelligent method to predict intensive care unit admission after drainage operation in patients with deep neck space abscess: A multicenter retrospective study.

A deep neck space abscess (DNSA) is a critical condition resulting from infection of deep neck fascia and soft issue, leading to high morbidity and mortality. Therefore, intensive care can be very significant for patients with DNSA. This study aimed to develop models to predict the need for postoperative intensive care in patients with DNSA. We retrospectively analyzed the records of 332 patients with DNSA who received drainage operation between 2015 and 2020. Multivariate logistic regression analysis and the eXtrem Gradient Boosting (XGBoost) algorithm were used to develop predictive models. We developed two predictive models, the nomogram and the XGBoost model. The area under the curve (AUC) of the nomogram was 0.911 and of the XGBoost model was 0.935. We developed two predictive models for guiding clinical decision making for postoperative ICU admission for DNSA patients, which may help improve prognosis and optimize intensive care resource allocation.

Determining the Landing Error Scoring System after a Jump by Artificial Intelligence

Objective: The study aims to examine the predictability of the Landing Error Scoring System (LESS) results after the jump with the Adaptive Boosting (AdaBoost) algorithm.
 Materials and Methods: A model has been developed by artificial intelligence to shorten the scoring system significantly. In the data preprocessing stage, 17 different items contained in the original dataset were reduced to 13. A total of 3790 data items were included in the dataset used in the study, and the dataset was divided into 4 different sub-datasets. AdaBoost was chosen to give the highest accuracy tested in five different machine learning used for regression. The model's reliability was evaluated by testing the proposed AdaBoost model with performance metrics.
 Results: The error score given by the clinician in the LESS was in the range of 0-86.6%. Recommended AdaBoost model for Sub1, Sub2, Sub3, and Sub4 respectively 98%, 87%, 88%, 89% accuracy has been achieved. 
 Conclusions: The score given to the LESS's 8th, 10th, 16th, and 17th items can be predicted with high accuracy, and the total score can be reached through the model proposed in the research.

Phthalate metabolites and sex steroid hormones in relation to obesity in US adults: NHANES 2013-2016.

Obesity and metabolic syndrome pose significant health challenges in the United States (US), with connections to disruptions in sex hormone regulation. The increasing prevalence of obesity and metabolic syndrome might be associated with exposure to phthalates (PAEs). Further exploration of the impact of PAEs on obesity is crucial, particularly from a sex hormone perspective. A total of 7780 adult participants in the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2016 were included in the study. Principal component analysis (PCA) coupled with multinomial logistic regression was employed to elucidate the association between urinary PAEs metabolite concentrations and the likelihood of obesity. Weighted quartiles sum (WQS) regression was utilized to consolidate the impact of mixed PAEs exposure on sex hormone levels (total testosterone (TT), estradiol and sex hormone-binding globulin (SHBG)). We also delved into machine learning models to accurately discern obesity status and identify the key variables contributing most to these models. Principal Component 1 (PC1), characterized by mono(2-ethyl-5-carboxypentyl) phthalate (MECPP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), and mono(2-ethyl-5-oxohexyl) phthalate (MEOHP) as major contributors, exhibited a negative association with obesity. Conversely, PC2, with monocarboxyononyl phthalate (MCNP), monocarboxyoctyl phthalate (MCOP), and mono(3-carboxypropyl) phthalate (MCPP) as major contributors, showed a positive association with obesity. Mixed exposure to PAEs was associated with decreased TT levels and increased estradiol and SHBG. During the exploration of the interrelations among obesity, sex hormones, and PAEs, models based on Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms demonstrated the best classification efficacy. In both models, sex hormones exhibited the highest variable importance, and certain phthalate metabolites made significant contributions to the model's performance. Individuals with obesity exhibit lower levels of TT and SHBG, accompanied by elevated estradiol levels. Exposure to PAEs disrupts sex hormone levels, contributing to an increased risk of obesity in US adults. In the exploration of the interrelationships among these three factors, the RF and XGBoost algorithm models demonstrated superior performance, with sex hormones displaying higher variable importance.

Analyzing prehospital delays in recurrent acute ischemic stroke: Insights from interpretable machine learning

ObjectiveThis study investigates prehospital delays in recurrent Acute Ischemic Stroke (AIS) patients, aiming to identify key factors contributing to these delays to inform effective interventions. MethodsA retrospective cohort analysis of 1419 AIS patients in Shenzhen from December 2021 to August 2023 was performed. The study applied the Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP) for identifying determinants of delay. ResultsLiving with others and lack of stroke knowledge emerged as significant risk factors for delayed hospital presentation in recurrent AIS patients. Key features impacting delay times included residential status, awareness of stroke symptoms, presence of conscious disturbance, diabetes mellitus awareness, physical weakness, mode of hospital presentation, type of stroke, and presence of coronary artery disease. ConclusionPrehospital delays are similarly prevalent among both recurrent and first-time AIS patients, highlighting a pronounced knowledge gap in the former group. This discovery underscores the urgent need for enhanced stroke education and management. Practice implicationThe similarity in prehospital delay patterns between recurrent and first-time AIS patients emphasizes the necessity for public health initiatives and tailored educational programs. These strategies aim to improve stroke response times and outcomes for all patients.

A Neural Network model for Indian Supreme Court legal judgment prediction

Industry experts are motivated to collect, collate, and analyse historical data in the legal sector in attempt to predict court case outcomes as the amount of historical data available in this field has increased over time. But using judicial data to predict and defend court judgements is no simple undertaking. Using Machine Learning (ML) models and traditional approaches for categorical feature encoding, previous research on predicting court outcomes using limited experimental datasets produced a number of unexpected predictions. The paper proposes an ensemble model combining Convolutional Neural Network (CNN), attention mechanism and eXtreme Gradient Boosting (XGB) algorithm. This model is primarily based on a self-attention network, which could simultaneously capture linguistic relationships over lengthy sequences like RNN (Recurrent Neural Network) and is nevertheless speedy to train like CNN. C-XGB can obtain accuracy that surpasses the state-of-art model on numerous classification/prediction tasks simultaneously as being twice as speedy to train. The proposed C-XGB model is designed to process the documents hierarchically and calculates the attention weights. Two convolutional layers are used to calculate the attention weights, one at the word level and another at the sentence level. And finally, at the last layer, the XGB algorithm predicts the input case file’s outcome. The experimental results shows that the proposed model outperforms the existing model with 4.67% improvement in accuracy value.

Mapping Forest Tree Species Using Sentinel-2 Time Series by Taking into Account Tree Age

Accurate classification of forest tree species holds great significance in the context of forest biodiversity assessment and the management of forest resources. In this study, we utilized Sentinel-2 time series data with high temporal and spatial resolution for tree species classification. To address potential classification errors stemming from spectral differences due to tree age variations, we implemented the Continuous Change Detection and Classification (CCDC) algorithm to estimate tree ages, which were integrated as additional features into our classification models. Four different combinations of classification features were created for both the random forest (RF) algorithm and extreme gradient boosting (XGB) algorithm: spectral band (Spec), spectral band combined with tree age feature (SpecAge), spectral band combined with spectral index (SpecVI), and spectral band combined with spectral index and tree age feature (SpecVIAge). The results demonstrated that the XGB-based models outperformed the RF-based ones, with the SpecVIAge model achieving the highest accuracy at 78.8%. The incorporation of tree age as a classification feature led to an improvement in accuracy by 2% to 3%. The improvement effect on classification accuracy varies across tree species, due to the varying uniformity of tree age among different tree species. These results also showed it is feasible to accurately map regional tree species based on a time-series multi-feature tree species classification model which takes into account tree age.

Virtual analysis of machine learning models for diseases prediction in muskmelon

Muskmelon, a crop prized for its economic potential, has a relatively brief growth cycle. Disease susceptibility during this period can have a profound impact on yields, posing challenges for farmers. Environmental conditions are pivotal in disease occurrence. Unfavorable conditions reduce the likelihood of pathogens infecting vulnerable host plants as temperature and humidity influence pathogen behavior, including toxin synthesis, virulence protein production, and reproduction. Pathogens can lie dormant in the soil until suitable conditions activate them. When the right environment and host plants align, these dormant pathogens can cause outbreaks. Disease prediction becomes possible by analyzing environmental variables. Real-time data collected via strategically placed sensors focused on viral, fungal, and bacterial infections. Results indicated that the extreme gradient boosting (XGBoost) algorithm, with a maximum tree depth of 4 and 30 trees per iteration, achieved remarkable performance, yielding an accuracy of 97%. For comparison, the XGBoost model outperformed an 8-layer Backpropagation network with 7 nodes per layer, which achieved 95% accuracy. These findings underscore XGBoost's efficacy in forecasting and mitigating muskmelon plant diseases, offering the potential for improved crop yields and agricultural sustainability.

Data-driven PSO-CatBoost machine learning model to predict the compressive strength of CFRP- confined circular concrete specimens

This work articulates the development of a sophisticated machine-learning model for the prediction of compressive strength in Carbon Fiber-Reinforced Polymer Confined-Concrete (CFRP-CC) specimens. Despite extensive empirical studies conducted over the last three decades, prevailing predictive models predominantly rooted in linear or nonlinear regression analyses are constrained by their dependency on limited data scopes. Addressing this deficiency, our research delineates the formulation of an innovative Particle Swarm Optimization- Categorical Boosting (PSO-CatBoost) algorithm, underpinned by an expansive database encompassing 916 experimental outcomes from 105 scholarly articles, spanning the period from 1991 to mid-2023. This innovative approach effectively combines the strengths of Particle Swarm Optimization and the CatBoost algorithm. It carefully evaluates various vital factors that affect the compressive strength of CFRP-CC. The uniqueness of our approach is further accentuated through the application of SHapley Additive exPlanations (SHAP) and Permutation Feature Importance (PFI) methodologies, thereby elucidating the relative importance of each contributory feature. In an unprecedented comparative analysis, the PSO-CatBoost model is rigorously benchmarked against six contemporary machine learning paradigms: CatBoost, XgBoost, AdaBoost, GBoost, Extra Trees, and Random Forest. Furthermore, this model is assessed against six empirical models for further comparison. The model exhibits superior predictive efficacy, evidenced by an exemplary coefficient of determination R-squared of 0.9847, surpassing the methodologies. This research introduces a new predictive model for CFRP-CC and represents a significant shift in concrete research, moving towards a more sophisticated, data-driven, and machine learning-focused methodology. This work thus establishes a new benchmark in the predictive modeling realm for CFRP-CC compressive strength, offering a robust and comprehensive analytical tool for both researchers and practitioners in the field. Lastly, a graphical user interface was designed for modeling the compressive strength of CFRP-CC to facilitate practical use.