Random Forest Tree Research Articles

Predicting groundwater level based on remote sensing and machine learning: a case study in the Rabat-Kénitra region

ABSTRACT Groundwater is essential for sustaining water needs, industrial growth, agriculture, and ecosystems, particularly in arid regions. This study uses data from GRACE and MODIS satellites, integrating environmental variables like land surface temperature, soil moisture, terrestrial water storage, precipitation, and vegetation indices to predict groundwater levels in Morocco’s Rabat-Salé Kenitra region. These environmental variables serve as input parameters, with the output being the predicted groundwater level. Advanced machine learning models, including Gradient Boosting Regression (GBR), Support Vector Regression (SVR), Random Forest (RF), and Decision Tree (DT) were employed to capture the relationships between these variables and groundwater levels. The GBR model showed superior performance with an R2 value of 0.99, a Mean Absolute Error (MAE) of 1.94, and a Root Mean Squared Error (RMSE) of 2.98, significantly improving over traditional methods that struggle with non-linear relationships and data noise. Compared to existing methods, our approach offers enhanced accuracy and robustness due to the GBR model’s ability to handle complex and non-linear relationships. This study demonstrates the advantages of integrating diverse environmental datasets with advanced machine learning techniques, improving groundwater management strategies and prediction reliability, especially in regions facing significant water scarcity and climate change impacts.

Data-driven shear capacity analysis of headed stud in steel-UHPC composite structures

This study employs machine learning (ML) techniques for shear capacity analysis of headed stud in steel-UHPC composite structure. 194 experimental and numerical results of push-out tests are collected and serve as the dataset for ML models training and testing. Six ML algorithms are implemented to train ML models. Comparison analysis is conducted to compare the performance of three empirical formulae and ML models. The results demonstrate that both the Random Forest and eXtreme Gradient Boosting Trees (XGBoost) models exhibit excellent performance, surpassing an R2 value of 97 % on both training and testing datasets. In contrast, the empirical formulae perform less effectively. Besides, the study incorporates the Shapley additive explanations algorithm to ranking the importance of each feature, and carried out parametric analysis to investigate the correlation between each feature and shear capacity using all samples in the collected dataset. Notably, the most influential variables include diameter and ultimate strength of the headed studs, followed by stud height and thickness of UHPC slab. Cover thickness of UHPC layer and steel fiber volume fractions shows little influence on the shear capacity. Furthermore, based on findings of parametric analysis, design recommendations are provided to avoid shear capacity reduction caused by group effect of studs and UHPC damage. Finally, a user-friendly interactive software is developed and provided to facilitate the shear capacity prediction and design of headed studs in steel-UHPC composite structures.

Identifying freshness of various chilled pork cuts using rapid imaging analysis.

Determining the freshness of chilled pork is of paramount importance to consumers worldwide. Established freshness indicators such as total viable count, total volatile basic nitrogen and pH are destructive and time-consuming. Color change in chilled pork is also associated with freshness. However, traditional detection methods using handheld colorimeters are expensive, inconvenient and prone to limitations in accuracy. Substantial progress has been made in methods for pork preservation and freshness evaluation. However, traditional methods often necessitate expensive equipment or specialized expertise, restricting their accessibility to general consumers and small-scale traders. Therefore, developing a user-friendly, rapid and economical method is of particular importance. This study conducted image analysis of photographs captured by smartphone cameras of chilled pork stored at 4 °C for 7 days. The analysis tracked color changes, which were then used to develop predictive models for freshness indicators. Compared to handheld colorimeters, smartphone image analysis demonstrated superior stability and accuracy in color data acquisition. Machine learning regression models, particularly the random forest and decision tree models, achieved prediction accuracies of more than 80% and 90%, respectively. Our study provides a feasible and practical non-destructive approach to determining the freshness of chilled pork. © 2024 Society of Chemical Industry.

Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3

BackgroundSepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database.MethodsA cohort of 4,240 Sepsis-3 patients was analyzed, with 783 experiencing 30-day mortality and 3,457 surviving. Fifteen biomarkers were selected using feature ranking methods, including Extreme Gradient Boosting (XGBoost), Random Forest, and Extra Tree, and the Logistic Regression (LR) model was used to assess their individual predictability with a fivefold cross-validation approach for the validation of the prediction. The dataset was balanced using the SMOTE-TOMEK LINK technique, and a stacking-based meta-classifier was used for 30-day mortality prediction. The SHapley Additive explanations analysis was performed to explain the model’s prediction.ResultsUsing the LR classifier, the model achieved an area under the curve or AUC score of 0.99. A nomogram provided clinical insights into the biomarkers' significance. The stacked meta-learner, LR classifier exhibited the best performance with 95.52% accuracy, 95.79% precision, 95.52% recall, 93.65% specificity, and a 95.60% F1-score.ConclusionsIn conjunction with the nomogram, the proposed stacking classifier model effectively predicted 30-day mortality in Sepsis patients. This approach holds promise for early intervention and improved outcomes in treating Sepsis cases.

Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes

The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and documenting their accuracy. This information is stored in a CSV file, which is then used by AI classifiers to predict the accuracy of new, unseen datasets. By evaluating feature information and various data segmentations, we recommend the configuration that achieves the highest accuracy. This approach eliminates the need for exhaustive algorithm reruns, relying on pre-trained models to estimate outcomes based on dataset characteristics. Our experimentation spans various configurations, including different training–testing splits and feature sets across multiple dataset sizes, meticulously evaluated through key performance metrics such as accuracy, precision, recall, and F-measure. The experimental results underscore the efficiency of our model, with significant improvements in predictive accuracy and resource utilization, demonstrated through comparative performance analysis against traditional methods. This paper highlights the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions. The results reveal that the proposed system can accurately predict a near-optimal machine learning algorithm and data structure for crop data with an accuracy of 89.38%, 87.61%, and 84.27% for decision tree, random forest, and random tree algorithms, respectively.

Productivity Prediction Model of Tight Oil Reservoir Based on Particle Swarm Optimization–Back Propagation Neural Network

Single-well productivity is a crucial metric for assessing the effectiveness of petroleum reservoir development. The accurate prediction of productivity is essential for achieving the efficient and economical development of oil–gas reservoirs. Traditional productivity prediction methods (empirical formulae and numerical simulation) are limited to specific reservoir types. There are few influencing factors, and a large number of ideal assumptions are made for the assumed conditions when predicting productivity. The application scenario is ideal. However, in tight oil reservoirs, numerous factors affect productivity, and their interactions exhibit significant complexity. Continuing to use traditional reservoir productivity prediction methods may result in significant calculation errors and lead to economic losses in oilfield development. To enhance the accuracy of tight reservoir productivity predictions and achieve economical and efficient development, this paper investigates the tight reservoir in the WZ block of the Beibuwan area, considering the impact of geological and engineering factors on productivity; the random forest tree and Spearman correlation coefficient are used to analyze the main influencing factors of productivity. The back propagation neural network optimized by particle swarm optimization was employed to develop a productivity prediction model (PSO-BP model) for offshore deep and ultra-deep tight reservoirs. The actual test well data of the oilfield are substituted into this model. The analysis results of the example application yielded an RMSE of 0.032, an MAE of 1.209, and an R2 value of 0.919. Compared with traditional productivity prediction methods, this study concludes that the model is both reasonable and practical. The calculation speed is faster and the calculation result is more accurate, which can greatly reduce productivity errors. The model constructed in this paper is well suited for predicting the productivity of tight reservoirs within the WZ block. It offers substantial guidance for predicting the productivity of similar reservoirs and supports the economical and efficient development of petroleum reservoirs.

The credibility assessment of Twitter/X users based organization objectives by heterogeneous resources in big data life cycle

Social network data, such as Twitter/X, is of Big Social Data type. Big social data describes people's social behaviors and interactions. They have high business value for decision-making in organizations. However, because of the anonymous nature of social network users, their credibility is ambiguous. Credibility expresses the accuracy and value of big social data. Despite extensive research on the credibility of big social data, most methods have not paid sufficient attention to the important dimensions of their assessment, including user expertise based on topic, selecting social network features, and labeling them. Furthermore, these methods cannot manage the time, high volume, and speed of big social data. To address these issues, this paper presents a novel model for assessing the credibility of Twitter/X users by integrating Twitter/X with Google Scholar. The model automatically defines users' credibility labels using Google Scholar. Machine learning feature selection methods also select features that affect the credibility of Twitter/X users based on the topic. This study uses Google Scholar and the BerTopic algorithm for effective topic modeling on Twitter/X. The model considers unrelated data management, dynamic user credibility, and organizing activities based on the Big Data lifecycle. Finally, using Linear Regression, Support Vector Regression, K-Nearest Neighbor, Random Forest, Classification and Regression Trees algorithms, the model predicts the credibility of Twitter/X users and proves that it performed better than similar models through Classification and Regression Trees. In addition, the model is generalizable for all organizational purposes due to the integration of heterogeneous resources and feature selection methods.

Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations

AbstractAgriculture constitutes the foundational pillar of the global economy, engaging a substantial segment of the workforce and making a considerable contribution to the Gross Domestic Product (GDP). However, agricultural productivity faces numerous challenges, including varying climatic conditions, soil types, and limited access to modern farming practices. Developing intelligent agricultural systems becomes imperative to address these challenges and enhance agricultural productivity. Therefore, this paper aims to present a Machine Learning (ML) based crop recommendation system tailored for the farming landscape. The proposed system utilizes historical data on climatic conditions, soil properties, crop yields, and farmer preferences to provide personalized crop recommendations. The goal of this study is to appraise the efficacy of nine distinct ML models—Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Bagging (BG), AdaBoost (AB), Gradient Boosting (GB), and Extra Trees (ET) to generate practical recommendations for crop selection. Numerous preprocessing methods are employed to cleanse and normalize the data, thereby ensuring its appropriateness for model training. The ML models are trained using historical data sets, including temperature, rainfall, humidity, soil pH, and nutrient levels, where crop yields are correlated with environmental and agronomic factors. The models undergo fine-tuning through methods such as cross-validation to enhance their performance and ensure robustness. Among those models, Radom Forest has achieved the highest accuracy (99.31%). The proposed Machine Learning-based crop recommendation system offers a promising approach to addressing the challenges faced by the farmers. By leveraging advanced data analytics and artificial intelligence techniques, the system empowers farmers with timely and personalized recommendations, ultimately leading to improved agricultural productivity, food security, and economic prosperity.

Using machine learning to identify risk factors for short-term complications following thumb carpometacarpal arthroplasty

BackgroundThumb carpometacarpal (CMC) joint osteoarthritis is among the most common degenerative hand diseases. Thumb CMC arthroplasty, or trapeziectomy with or without tendon augmentation, is the most frequently performed surgical treatment and has a strong safety profile. Though adverse outcomes are infrequent, the ability to predict risk for complications has substantial clinical benefits. In the present study, we evaluated a well-known surgical database with machine learning (ML) techniques to predict short-term complications and reoperations after thumb CMC arthroplasty. MethodsA retrospective study was conducted using data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) years 2005–2020. Outcomes were 30-day wound and medical complications and 30-day return to the operating room. We used three ML algorithms — a Random Forest (RF), Elastic-Net Regression (ENet), and Extreme Gradient Boosted Tree (XGBoost), and a deep learning Neural Network (NN). Feature importance analysis was performed in the highest performing model for each outcome to identify predictors with the greatest contributions. ResultsWe included a total of 7711 cases. The RF was the best performing algorithm for all outcomes, with an AUC score of 0.61±0.03 for reoperations, 0.55±0.04 for medical complications, and 0.59±0.03 for wound complications. On feature importance analysis, procedure duration was the highest weighted predictor for reoperations. In all outcomes, procedure duration, older age, and female sex were consistently among the top five predictors. ConclusionsWe successfully developed ML algorithms to predict reoperations, wound complications, and medical complications. RF models had the highest performance in all outcomes.

Machine Learning Models for Gold Price Prediction: A Comparative Analysis and Evaluation

The prediction of gold prices is meaningful for multiple industries. Gold is a relatively stable store of value, and its price is closely linked to stocks, exchange rates, and monetary policy. Observing the trend of gold prices can promote the stable development of financial businesses. In this article, the daily opening and closing prices of gold over the past ten years were chosen as the data support. Seven different models were trained to predict prices on a yearly basis with a half-year interval. These predictions were then compared to actual data, and evaluations and analyses were conducted to identify the most suitable model for gold price forecasting. The machine learning models used included Ridge Regression, Linear Regression, Decision Tree Random Forest, Lasso Regression, Support Vector Machine, and K-Nearest Neighbors. In the evaluation process, Mean Squared Error (MSE) was used as the criterion to assess the accuracy of the model predictions.

Prediction of heart disease based on logistic regression

Heart disease is a major threat to human health, with a variety of contributing factors, and is not easily cured. This paper will present a dataset from a cardiovascular study of residents of Framingham, Massachusetts. First, the validity of the three models, logistic regression, random forest, and decision tree, is estimated by comparing information such as accuracy, precision, recall, and F1 values. The optimal model, i.e., the logistic regression model, was selected by plotting ROC curves and using AUC as a reference criterion for assessing the predictive effectiveness of the models. Then the raw data and data were preprocessed, including dealing with missing values. Finally, a logistic regression model was developed to analyze the influencing factors of heart disease. The purpose of this study was to use the results of the logistic model to help doctors and patients in heart disease treatment. The results show that the model has a good predictive effect.

Prediction of heart disease based on logistic regression

Heart disease is a major threat to human health, with a variety of contributing factors, and is not easily cured. This paper will present a dataset from a cardiovascular study of residents of Framingham, Massachusetts. First, the validity of the three models, logistic regression, random forest, and decision tree, is estimated by comparing information such as accuracy, precision, recall, and F1 values. The optimal model, i.e., the logistic regression model, was selected by plotting ROC curves and using AUC as a reference criterion for assessing the predictive effectiveness of the models. Then the raw data and data were preprocessed, including dealing with missing values. Finally, a logistic regression model was developed to analyze the influencing factors of heart disease. The purpose of this study was to use the results of the logistic model to help doctors and patients in heart disease treatment. The results show that the model has a good predictive effect.

Enhanced machine learning approaches for OSA patient screening: model development and validation study

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7–84.1%), 69.9% (95% CI 69.8–69.9%), 86.5% (95% CI 81.6–91.3%), 61.5% (95% CI 56.6–66.4%), 53.2% (95% CI 47.7–58.7%), 65.9% (95% CI 60.2–71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.

Comparative Analysis of Supervised Machine Learning Algorithms for Diabetes Prediction

Diabetes Mellitus is a chronic disease with far-reaching consequences, necessitating innovative diagnostic approaches. Current methods have limitations, highlighting the need for advanced techniques. This research leveraged machine learning algorithms to predict diabetes using the Pima Indian Diabetes Dataset. Six algorithms were developed and compared: Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Random Forest, and Decision Tree. Standard evaluation metrics assessed their performance. The Random Forest Classifier emerged as the top-performing algorithm, achieving an impressive 91% accuracy and demonstrating exceptional reliability. This study showcases machine learning's potential to transform diabetes diagnosis and management. The developed web application provides a user-friendly interface for predicting diabetes, facilitating timely intervention and improved health outcomes. The findings contribute significantly to the development of intelligent disease prediction systems, promoting early detection and enhanced patient care. By harnessing machine learning, this research pioneers a new frontier in diabetes diagnosis, paving the way for improved patient outcomes and enhanced healthcare services.

Stacked machine learning approach for predicting evolved hydrogen from sugar industry wastewater

The wastewater generated by the sugar industry contains high levels of organic pollutants, posing significant environmental and health risks. Traditional treatment methods often fall short in effectively managing these pollutants, necessitating innovative approaches for wastewater treatment and hydrogen production. In this study, machine learning (ML) algorithms are explored to predict hydrogen evolution from sugar industry wastewater treated via photocatalytic oxidation using solar-driven photocatalysts. By leveraging ML-driven models, we aim to streamline experimental procedures, reduce costs, and enhance energy efficiency. Our proposed stacking model, which combines Random Forest, Gradient Boosting, XGBoost, and Extra Trees, achieved superior results with R2 values of 0.9933, 0.9866, and 0.9906, and mean squared errors (MSE) of 0.0350, 0.0412, and 0.0412 for GLFO, GBFO, and GLRO, respectively. These results demonstrate the significant advantages of using ensemble techniques, particularly our proposed stacking method, which outperformed all individual models. The blending model, which averaged predictions from XGBoost and Gradient Boosting, also performed well with R2 values of 0.9866, 0.9811, and 0.9820, and MSEs of 0.0702, 0.0815, and 0.0795 for GLFO, GBFO, and GLRO, respectively. This study highlights the efficacy of ensemble methods in predicting hydrogen production, providing a novel and effective approach for wastewater treatment and renewable energy generation.

Model development using hybrid method for prediction of drug release from biomaterial matrix

A comprehensive multi-scale computational strategy was developed in this study based on mass transfer and machine learning for simulation of drug concentration distribution in a biomaterial matrix. The controlled release was modeled and validated via the hybrid model. Mass transfer equations along with kinetics models were solved numerically and the results were then used for machine learning models. We investigated the performance of three regression models, namely Decision Tree (DT), Random Forest (RF), and Extra Tree (ET) in predicting medicine concentration (C) based on r and z data. Hyper-parameter optimization is conducted using Glowworm Swarm Optimization (GSO). Results revealed high predictive accuracy across all models, with ET demonstrating superior performance, achieving a coefficient of determination value (R2) of 0.99854, an RMSE of 1.1446E-05, and a maximum error of 6.49087E-05. DT and RF also exhibit notable performance, with coefficients of determination equal to 0.99571 and 0.99655, respectively. These results highlight the effectiveness of ensemble tree-based methods in accurately predicting chemical concentrations, with Extra Tree (ET) Regression emerging as the most promising model for this specific dataset.

Optimal Mapping of Soil Erodibility in a Plateau Lake Watershed: Empirical Models Empowered by Machine Learning

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R2 of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion.

Effectively removal of PPCPs by catalytic activated biochar derived from hazelnut shell: Modeled and predicted by machine learning

As a new type of environmentally friendly adsorbent, biochar has been increasingly applied in water pollution treatment. Herein, a catalyzed and activated biochar adsorbent (CABC) which was derived from hazelnut shell and modified by FeCl3 and ZnCl2 was synthesized successfully. The CABC was applied to remove two typical Pharmaceutical and Personal Products (PPCPs), sulfamethoxazole (SMX) and diclofenac (DCF). The CABC obtained high specific surface area of 1413.02 m2/g. The removal rates for SMX and DCF by CABC could reach 97.04 % and 98.65 %, respectively. The adsorption process was consistent with the pseudo-second-order kinetic model and the Langmuir isothermal model, which illustrated that the adsorption process conformed to chemical adsorption and monolayer adsorption. The thermodynamics indicated that the adsorption process was endothermic and could occur spontaneously. After 5 cycles, the CABC still remained a high removal efficiency of 77.7 % and 80.5 % for SMX and DCF, which showed a good reusability. The removal rates of CABC for multiple PPCPs had achieved over 77.7–98.0 %. In different actual wastewater, the removal rates of CABC for SMX and DCF ranged from 56.7 % to 97 %, demonstrating its excellent universality and practical value for utilization. Furthermore, three machine learning models based on Decision Tree, Random Forest and Gradient Boosting Decision Tree algorithm were applied to predict the removal efficiency and determine the relative importance of different influencing factors. The gradient boosting regressor model obtained the best predict performance with R2 at 0.9806 and 0.9840 for training set and testing set. And the importance analysis had shown that pH was the most important influencing factor with the importance value over 0.35. The adsorption mechanism between CABC and pollutants included pore adsorption, electrostatic interactions, intramolecular hydrogen bonding, the reaction with oxygen-containing functional groups and metal complexation. The prepared biochar adsorbent CABC possessed the potential for PPCPs removing. In addition, the machine learning method can play more and more undeniable role in subsequent water treatment. In future research, machine learning can be used to predict the adsorption performance of materials, enabling targeted design of materials. This will greatly save experimental time and cost losses, and can improve research efficiency.

Modeling of land use and land cover changes using google earth engine and machine learning approach: implications for landscape management

A precise and up-to-date Land Use and Land Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now the most advanced open-source global platform for rapid and accurate LULC classification. Thus, this study explores the dynamics of the LULC changes between 1993 and 2023 using Landsat imagery and the machine learning algorithms in the Google Earth Engine (GEE) platform. Focus group discussion and key informant interviews were also used to get further data regarding LULC dynamics. Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) were demonstrated for LULC classification. Six LULC types (agricultural land, grazingland, shrubland, built-up area, forest and bareland) were identified and mapped for 1993, 2003, 2013, and 2023. The overall accuracy and kappa coefficient demonstrated that the RF using images comprising auxiliary variables (spectral indices and topographic data) performed better than SVM and CART. Despite being the most common type of LULC, agricultural land shows a trend of shrinking during the study period. The built-up area and bareland exhibits a trend of progressive expansion. The amount of forest and shrubland has decreased over the last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, agricultural land expansion, fuelwood collection, charcoal production, built-up areas expansion, illegal settlement and intervention are among causes of LULC shifts. This study provides reliable information about the patterns of LULC in the Robit watershed, which can be used to develop frameworks for watershed management and sustainability.

Ensemble of Deep Learning Architectures with Machine Learning for Pneumonia Classification Using Chest X-rays.

Pneumonia is a severe health concern, particularly for vulnerable groups, needing early and correct classification for optimal treatment. This study addresses the use of deep learning combined with machine learning classifiers (DLxMLCs) for pneumonia classification from chest X-ray (CXR) images. We deployed modified VGG19, ResNet50V2, and DenseNet121 models for feature extraction, followed by five machine learning classifiers (logistic regression, support vector machine, decision tree, random forest, artificial neural network). The approach we suggested displayed remarkable accuracy, with VGG19 and DenseNet121 models obtaining 99.98% accuracy when combined with random forest or decision tree classifiers. ResNet50V2 achieved 99.25% accuracy with random forest. These results illustrate the advantages of merging deep learning models with machine learning classifiers in boosting the speedy and accurate identification of pneumonia. The study underlines the potential of DLxMLC systems in enhancing diagnostic accuracy and efficiency. By integrating these models into clinical practice, healthcare practitioners could greatly boost patient care and results. Future research should focus on refining these models and exploring their application to other medical imaging tasks, as well as including explainability methodologies to better understand their decision-making processes and build trust in their clinical use. This technique promises promising breakthroughs in medical imaging and patient management.