Search

Machine learning classifiers are increasingly widely used. This research note explores how a particular widely used classifier, the Random Forest, performs when faced with samples which are imbalanced and noisy data. Both are known to affect accuracy, but if their effects are independent or not has not been explored. Based on an experiment using synthetic data generated for the study we find that the effects of noise and sample balance interact with each other; classification accuracy is worse when faced with both noisy data and sample imbalance. This has implications for the use of RF in market research, but also for how methods to address either sample imbalance or noise are assessed. [ABSTRACT FROM AUTHOR]

This article presents an approach for predicting Lockout/Tagout (LOTO) procedure sheets, which are commonly used in the manufacturing industry to prevent premature equipment restart during maintenance. The prediction problem of energetic devices to lock from machine names is regarded as a multi-task classification problem. The dataset was obtained by processing LOTO sheets in Portable Document Format (PDF). The K-Nearest Neighbours (KNN), Random Forest (RF), and Deep Neural Network (DNN) algorithms were compared for this problem. The best prediction performance was achieved with the DNN method, with top-1 accuracies exceeding 63% and top-2 accuracies exceeding 90% for all devices. The sensitivity analysis conducted on the results indicates that the approach is robust and reliable, regardless of the industrial sector considered. In other words, the approach is not significantly affected by variations in the industry or its specific characteristics. These results suggest that the proposed approach can be used to assist workers in drafting LOTO sheets, and offers strong potential for concrete applications in safety management in the era of smart manufacturing. [ABSTRACT FROM AUTHOR]

Processes or products are typically complex systems with numerous interrelated procedures and interdependent components. This results in complex relationships between responses and input factors, as well as complex nonlinear correlations among multiple responses. If the two types of complex correlations in the quality design cannot be properly dealt with, it will affect the prediction accuracy of the response surface model, as well as the accuracy and reliability of the recommended optimal solutions. In this paper, we combine kernel trick-based kernel principal component analysis, spline-based Bayesian semiparametric additive model, and normal boundary intersection-based evolutionary algorithm to address these two types of complex correlations. The effectiveness of the proposed method in modeling and optimisation is validated through a simulation study and a case study. The results show that the proposed Bayesian semiparametric additive model can better describe the process relationships compared to least squares regression, random forest regression, and support vector basis regression, and the proposed multi-objective optimisation method performs well on several indicators mentioned in the paper. [ABSTRACT FROM AUTHOR]

The competition among Food & Beverage companies has substantially increased in today's age of digitization. Sales forecasting is one of their main challenges. Due to space limitations, employee shortages, and rising online demand, retail sales forecasting became extremely important for Food and Beverage companies. This research analyzed the sales data of a multinational Food & Beverage Company. It proposed a framework using Gaussian Mixture Model (GMM) clustering, Hierarchical Agglomerative Clustering (HAC), and Random Forest algorithm for forecasting sales. This model analyzes the impact of the weekends, holidays, promotional activities, customer sentiments, festivals, and socio-economic situations in sales data and is able to forecast sales ranging from one to 15 months. An investigation of the suggested model's performance compared to numerous cutting-edge sales forecasting techniques is carried out to show its efficacy. Here, we demonstrate that the proposed hybrid model surpasses current predicting and computing efficiency methods. The results of this study can help retail managers to allocate resources and manage inventories in well-informed ways. The findings suggest that combining many strategies may produce the most precise forecasts. [ABSTRACT FROM AUTHOR]

Solar interfacial evaporation, as a novel practical freshwater production method, requires continuous research on how to improve the evaporation rates to increase water production. In this study, sets of data were obtained from molecule dynamics simulation and literature, in which the parameters included height, diameter, height–radius ratio, evaporation efficiency, and evaporation rate. Initially, the correlation between the four input parameters and the output of the evaporation rate was examined through traditional pairwise plots and Pearson correlation analysis, revealing weak correlations. Subsequently, the accuracy and generalization performance of the evaporation rate prediction models established by neural network and random forest were compared, with the latter demonstrating superior performance and reliability confirmed via random data extraction. Furthermore, the impact of different percentages (10%, 20%, and 30%) of the data on the model performance was explored, and the result indicated that the model performance is better when the test set is 20% and all the constructed model converge. Moreover, the mean absolute error and mean squared error of the evaporation rate prediction model for the three ratios were calculated to evaluate their performance. However, the relationship between the height- radius ratio and optimal evaporation rate was investigated using the enumeration method, and it was determined that the evaporation efficiency was optimal when the height–radius ratio was 6. Finally, the importance of height, diameter, height– radius ratio, and evaporation efficiency were calculated to optimize evaporator structure, increase evaporation rate, and facilitate the application of interfacial evaporation in solar desalination. [ABSTRACT FROM AUTHOR]

In this article, we present a machine learning model to obtain fast and accurate estimates of the molecular Hessian matrix. In this model, based on a random forest, the second derivatives of the energy with respect to redundant internal coordinates are learned individually. The internal coordinates together with their specific representation guarantee rotational and translational invariance. The model is trained on a subset of the QM7 dataset but is shown to be applicable to larger molecules picked from the QM9 dataset. From the predicted Hessian, it is also possible to obtain reasonable estimates of the vibrational frequencies, normal modes, and zero point energies of the molecules. [ABSTRACT FROM AUTHOR]

Purpose: The efficacy of isolated and relative performance indicators (PIs) has been compared in rugby union; the latter more effective at discerning match outcomes. However, this methodology has not been applied in women's rugby. The aim of this study was to identify PIs that maximize prediction accuracy of match outcome, from isolated and relative data sets, in women's rugby union. Methods: Twenty-six PIs were selected from 110 women's international rugby matches between 2017 and 2022 to form an isolated data set, with relative data sets determined by subtracting corresponding opposition PIs. Random forest classification was completed on both data sets, and feature selection and importance were used to simplify models and interpret key PIs. Models were used in prediction on the 2021 World Cup to evaluate performance on unseen data. Results: The isolated full model correctly classified 75% of outcomes (CI, 65%–82%), whereas the relative full model correctly classified 78% (CI, 69%–86%). Reduced respective models correctly classified 74% (CI, 65%–82%) and 76% (CI, 67%–84%). Reduced models correctly predicted 100% and 96% of outcomes for isolated and relative test data sets, respectively. No significant difference in accuracy was found between data sets. In the relative reduced model, meters made, clean breaks, missed tackles, lineouts lost, carries, and kicks from hand were significant. Conclusions: Increased relative meters made, clean breaks, carries, and kicks from hand and decreased relative missed tackles and lineouts lost were associated with success. This information can be utilized to inform physical and tactical preparation and direct physiological studies in women's rugby. [ABSTRACT FROM AUTHOR]

Pre-tightening torque has a significant impact on the performance of a bolt-clamped high-frequency piezoelectric ultrasonic transducer widely used in wire bonding. To develop a vibratory response-based method for the determination of optimal pre-tightening torque, the vibration amplitude signals of the transducer under different excitation conditions were monitored, and the characteristic parameters of the response, involving the resonance frequency, settling time within the initial transient response, and vibration amplitude of the steady-state stage, were extracted. Based on a series of in-depth tests, analyses, and discussions, the influences that the pre-tightening torque has on the vibratory response properties were investigated. Then, the experimental measurement and the random forest method were combined to determine the optimal pre-tightening torque. The performance test results show that the developed method can give the optimal pre-tightening torque according to the desired vibrational response properties, and it is feasible, effective, and reliable as well as improves the adaptability and flexibility of the high-frequency ultrasonic transducer in the application. [ABSTRACT FROM AUTHOR]

This study developed an innovative biosensor strategy for the sensitive and selective detection of canine mammary tumor biomarkers, cancer antigen 15–3 (CA 15–3) and mucin 1 (MUC-1), integrating green silver nanoparticles (GAgNPs) with machine learning (ML) algorithms to achieve high diagnostic accuracy and potential for noninvasive early detection. The GAgNPs-enhanced electrochemical biosensor demonstrated selective detection of CA 15–3 in serum and MUC-1 in tissue homogenates, with limits of detection (LODs) of 0.07 and 0.11 U mL−1, respectively. The nanoscale dimensions of the GAgNPs endowed them with electrochemically active surface areas, facilitating sensitive biomarker detection. Experimental studies targeted CA 15–3 and MUC-1 biomarkers in clinical samples, and the biosensor exhibited ease of use and good selectivity. Furthermore, ML algorithms were employed to analyze the electrochemical data and predict biomarker concentrations, enhancing the diagnostic accuracy. The Random Forest algorithm achieved 98% accuracy in tumor presence prediction, while an Artificial Neural Network attained 76% accuracy in CA 15–3-based tumor grade classification. The integration of ML techniques with the GAgNPs-based biosensor offers a promising approach for noninvasive, accurate, and early detection of canine mammary tumors, potentially revolutionizing veterinary diagnostics. This multilayered strategy, combining eco-friendly nanomaterials, electrochemical sensing, and ML algorithms, holds significant potential for advancing both biomedical research and clinical practice in the field of canine mammary tumor diagnostics. [ABSTRACT FROM AUTHOR]

Faced with the terrible spread of electronic attacks and intrusions, which tend to increase, securing computer systems has become an urgent necessity for enterprises. In this work, we are interested in a very specific security problem, which is the identity theft of business experts that causes major problems for the company, such as loss of money, fraud, information leakage, customer distrust of using the enterprise's services, etc. The implementation of an implicit authentication system based on the study of the expert behavior is a very effective way to fight this problem. The objective of this work is to propose a system dedicated to business experts, which will be able to detect impostors and false profiles during the authentication phase and during the negotiation phase in a continuous manner. The proposed system relies on machine learning algorithms, behavioral authentication, the keystroke dynamics of real experts and their behaviors during the negotiation phase, as well as their ways of interacting with the application, in order to prevent imposters to infiltrate the system by trying to login with the account of a real expert. So, we followed the following steps: Data acquisition, Criteria extraction, Model creation and Storage. We Have used three machine learning algorithms, which are: K Nearest Neighbor, Random Forest and Logistic Regression. [ABSTRACT FROM AUTHOR]

The primary goal of the present research work is to build machine learning based classification model for classifying an individual with different degrees of autism that can yield better performance than traditional autism assessment scale used in India. The study aimed to find how significant are the selected additional 6 attributes for better identification and understanding of Autism spectrum disorder (ASD). This work focuses on a unique screening tool developed for the Indian population –Indian Scale for assessment of Autism(ISAA) to build the classification model. For better performance few additional significant features are also considered following proper procedure. Selective machine learning models are built and finally performance evaluation is done on all classifier results. As an alternative approach, a binary classifier multi-layer perceptron (MLP) model has also been trained on both the data sets to distinguish between people with autism who need less attention and those who need more attention for assessment and further intervention. Overall performance of 3 best classifiers (KNN, Random Forest, SVM) suggests that after considering additional 6 attributes SVM produced better efficacy in identification of degree of autism over conventional ISAA parameters with higher weighted F1 score of 85.7% and accuracy of 90% respectively. It is also reconfirmed with the results of the MLP model. The basic model converges after 71 epochs with training accuracy 97.78% wherein the extended model converges after 61 epochs with training accuracy 98.89%. The best model functions better than conventional scale of assessment which also made the screening task unbiased, accurate and time efficient. [ABSTRACT FROM AUTHOR]

In the era of advanced cyber developments, intrusions becomes a common event in any network. Although there are research studies and developers found ways to improve the detection models, there is some problem that persists in the intrusion models such as extracting key features from a large dataset, and delayed detection is a critical issue that needs to be addressed. Hence the proposed study aimed to develop a model that could extract key features from the dataset and use them effectively in the detection of threats. The study incorporates two approaches, one is feature extraction by the K-Nearest Neighbourhood, and feature selection by the K-Best approach. And the other is the balanced Gini-Entropy approach for the Random Forest (RF) classifier. This combined approach by KNN, K-best, and RF is referred to as (KK-RF). This combined approach of feature extraction, selection, and classification results in an effective threat detection model with high accuracy of about 99.61%. Moreover the proposed model has achieved precision and the recall rates of 97.3 and 96.6% respectively. Concurrently, the model attained markable F1-score of 96.6 respectively. Also, from the comparison results, it is observed that the proposed model had higher performance. [ABSTRACT FROM AUTHOR]

The majority of people are using connecting media in recent times, like Facebook, Twitter, LinkedIn, etc. People like to communicate on social websites as compared to direct conversation due to the lack of time, fast communication, minimizing long distance, etc. but more fraud such as fake accounts, cloned attacks, etc. are generated due to growing data on social connecting media. In our proposed approach, we are using the Bernoulli RBM_RF Classifier (BRRC) to detect cloned attacks in connecting media. This model is based on deep learning algorithms and more research is under process but still, there is no such kind of model to provide high accuracy. In this model, we have used multiple algorithms Artificial Neural Networks (ANN), Logistic Regression, Decision Trees, K-Nearest Neighbour Classification, Random Forest, and Bernoulli RBM with extra add-on features of the random forest we have created a Bernoulli pipeline also known as BRRC. Our goal is to establish a clear neural network connection between our target attributes. Finally, we created a restricted Boltzmann machine using a RBM Bernoulli pipeline with additional add-on random forest features after using random forest, where we employed n number of decision trees for classification and provided the same in depth. Our model can extract higher-level features from the given data and helps in the dimensionality Reduction of the input data by learning a compressed representation. Accuracy can be improved by using a big dataset with particular connecting media attributes that are either not currently available or are only sparingly present. We can conclude that Bernoulli Restricted Boltzmann Machine is the best fit and suitable for our model with 93% accuracy. [ABSTRACT FROM AUTHOR]

The ever-growing 1.15 million new cases of cancer on a yearly basis alone in India is a major cause of concern for the experts and researchers working in various biomedical organizations. The advent of modern text engineering strategies and NLP techniques can play a crucial role in the discovery and analysis of pre-existing knowledge present in the cancer related biomedical archives. The available 10 Cancer hallmarks can provide key insights and make significant impact in the ongoing cancer research. It is extremely important to identify and classify required information due to time and resource crunch which needs to be quickly accessed. This article introduces a novel machine learning framework called Cancer Hallmark Classification and Topic Modeling (CHCTM), designed for supervised learning. The CHCTM framework is capable of semantically learning, categorizing, and extracting significant topics and their combinations related to the hallmarks of cancer (HoC) from a dataset comprising 1499 PubMed documents. The key contributions of this research include the creation of an innovative ensemble classification model using a meta-classifier based on Random Forest (RF). Additionally, it introduces an Enhanced Latent Dirichlet Allocation (ELDA) topic modeling strategy to generate relevant mixtures of topics. The performance of the CHCTM framework is evaluated using precision, recall, accuracy, and F-score parameters. Comparative analysis with other biomedical baseline methods reveals an 8% improvement in F-score. The coherence values acquired for ELDA are tallied and weighted against PLSA and LDA models to demonstrate the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

The use of social media has grown exponentially in recent years up to become a reflection of human social attitudes and to represent today the main channel for conducting discussions and sharing opinions. For this reason, the vast amount of information generated is often used for predicting outcomes of real-world events in different fields, including business, politics, and health, as well as in the entertainment industry. In this paper, we focus on how data from Twitter can be used to predict ratings of a large set of TV shows regardless of their specific genre. Given a show, the idea is to exploit features concerning the pre-release hype on Twitter for rating predictions. We propose a novel machine learning-based approach to the genre-independent TV show popularity prediction problem. We compared the performance of several well-known predictive methods, and as a result, we discovered that LSTM and Random Forest can predict the ratings in the USA entertainment market, with a low mean squared error of 0.058. Furthermore, we tested our model by using data of "never seen" shows, by deriving interesting results in terms of error rates. Finally, we compared performance against relevant solutions available in the literature, with discussions about challenges arousing from the analysis of shows in different languages. [ABSTRACT FROM AUTHOR]

Understanding the intricate pathways of protein secretion in bacteria is crucial for advancing research on bacterial diseases and their potential treatments, particularly in the case of non-classical protein secretion pathways. These pathways pose unique challenges due to the complex signaling mechanisms involved. To address this, we employed advanced machine learning techniques and gathered physical–chemical characteristics of amino acids from the AA index site. Through a meticulous six-step methodology, we curated a comprehensive dataset by filtering raw genome data and juxtaposing it with a positive dataset comprising 141 proteins from authoritative literature sources. Leveraging a conventional Random Forest machine learning algorithm, we achieved an impressive accuracy rate of approximately 91% in classifying non-classical secreted proteins. This validation was conducted on a dataset of 14 positive and 92 negative proteins, resulting in a sensitivity of 91% and a specificity of 86%. Notably, our study distinguishes itself by its rapid execution of non-classical secretion pathway analyses, rendering it particularly suitable for large datasets. This speed does not compromise accuracy, allowing for comprehensive Omics analyses. Consequently, our research underscores the significance of carefully selecting appropriate descriptors and constructing a robust training dataset to enhance Omics analyses of bacterial protein secretion via non-classical pathways. For further details, please refer to the complete study available at https://github.com/santosardr/non-CSPs. [ABSTRACT FROM AUTHOR]

Polycystic Ovary Syndrome (PCOS) is a hormonal state that affects women within the reproductive age group. Early and accurate diagnosis of medical conditions is crucial for the successful implementation of medication and prevention of significant health problems. In recent times, there has been an increasing amount of evidence suggesting that machine learning (ML) methods have significant potential in facilitating the diagnosis and classification of PCOS. The objective of this research is to evaluate the viability of using several machine learning algorithms, such as Random Forests, Decision Trees, Artificial Neural Networks, and XGBoost, for the purpose of classifying PCOS using clinical and laboratory features. Furthermore, the use of the Sine Cosine Algorithm was implemented for the purpose of selecting features in order to improve the accuracy and efficiency of the model. A sample of 500 individuals was selected for the purpose of training and assessing the models, with half of the participants (250) diagnosed with PCOS and the other half not exhibiting the disease. The findings suggest that the classification algorithms had strong performance in classifying PCOS, with XGBoost having the best level of performance. In addition, the use of the Sine Cosine Algorithm resulted in enhanced model precision and effectiveness via the identification of the most pertinent features for the classification objective. The results of this study emphasise the significant capabilities of machine learning methods, namely XGBoost and the Sine Cosine Algorithm, in aiding the diagnosis and classification of PCOS in a medical setting. Additional investigation is required in order to substantiate the models and prove their clinical significance in the provision of patient care. [ABSTRACT FROM AUTHOR]

Nitrogen is a key nutrient for crop growth. Excessive or insufficient nitrogen affects crop growth, yield, and quality. Additionally, excessive nitrogen fertilizer can cause soil and water pollution. Applying panicle fertilizer during the late jointing stage can promote rice panicle growth. Therefore, accurately and timely monitoring of nitrogen status in rice fields during the late jointing stage and timely optimizing fertilization strategies is crucial for ensuring rice yield and environmental protection. This paper integrates multimodal data from unmanned aerial vehicle (UAV) remote sensing and ground observations to construct inversion models for leaf nitrogen content (LNC) and plant nitrogen content (PNC) of rice at the late jointing stage. The research was conducted at the Shapu Experimental Base of the Agricultural Science Research Institute in Zhaoqing City, Guangdong Province, with two field experiments carried out during the late rice seasons of 2021 and 2022. Each of Experiment 1 (2021) and Experiment 2 (2022) included 30 experimental plots, designed with 5 nitrogen fertilizer gradients, 2 planting densities, and 3 replications. Phosphorus and potassium fertilizers were applied uniformly across all plots. UAVs equipped with multispectral and RGB cameras were used to acquire remote sensing images of rice canopies during the late jointing stage. Vegetation indices (VIs) and texture feature values (TFVs) were extracted from the multispectral images, with TFVs derived using the gray level co-occurrence matrix (GLCM) method. Texture indices (TIs) were then constructed by combining TFVs. RGB images were used to generate digital surface models (DSM) for bare ground (pre-transplant) and rice fields (late jointing stage). These DSMs, combined with ground reference methods, were used to construct crop surface models (CSM) to derive estimated canopy heights (ECH) for each plot. Manually collected data included measured canopy height (MCH) and field nitrogen management data (FN) used as ground observations. For each experimental plot, three representative rice plants were selected as samples. After removing the roots, the leaves and stems were separated and dried at 85℃ to a constant weight, which was recorded as the aboveground biomass of the leaves and stems. The true values of leaf nitrogen content and stem nitrogen content were obtained using the Kjeldahl method. Combining these values with the dry weight data, the true values of plant nitrogen content were calculated. The maximal information coefficient (MIC) was used as an evaluation metric for feature assessment and selection. Random forest regression algorithms were employed to construct inversion models for rice LNC and PNC, respectively, using the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) as model evaluation metrics. The analysis and experimental results indicate: TIs constructed using combinations of TFVs significantly enhanced the correlation between texture information and LNC and PNC. When the UAV flight height was 100 m, the Ratio Texture Index constructed using a 9×9 sliding window size in the GLCM method showed the best performance, improving the MIC value by 11.48% compared to the best TFV. For conventional machine-transplanted rice planting density, the correlation between TIs and LNC and PNC was best when the GLCM sliding window size was set to 9×9 or 11×11 at a UAV flight height of 100 m. The ECH derived from the CSM showed a high correlation with the manual MCH in the field. Including canopy height (MCH or ECH) as an input feature in the random forest regression model significantly improved the inversion accuracy of rice nitrogen content. The ECH extracted from the CSM showed high estimation accuracy (R² = 0.77, RMSE = 3.4 cm, MAE = 2.8 cm). The inclusion of canopy height (MCH or ECH) in the model construction improved the inversion accuracy for PNC more significantly compared to LNC. Integrating UAV remote sensing and ground observation multimodal data, the random forest regression algorithm significantly improved the inversion accuracy of rice LNC and PNC at the late jointing stage. Considering both inversion accuracy and operational convenience, it is recommended to use a feature combination of VI+TI+ECH+FN in field production. Compared to using VI alone, this feature combination improved R² by 6.73%, and reduced RMSE and MAE by 13.62% and 14.68%, respectively, for LNC inversion. For PNC inversion, R2 was improved by 12.53%, and RMSE and MAE were reduced by 22.09% and 19.80%, respectively. The results demonstrate that constructing random forest regression models by integrating UAV remote sensing and ground observation multimodal data can accurately detect rice LNC and PNC, providing a scientific basis for rice field management and fertilization decision-making. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is a common cancer that causes millions of deaths worldwide each year. At present, numerous studies have confirmed that intestinal microbes play a crucial role in the process of CRC. Additionally, studies have shown that CRC can be divided into several consensus molecular subtypes (CMS) based on tumor gene expression, and CRC microbiomes have been reported related to CMS. However, most previous studies on intestinal microbiome of CRC have only compared patients with healthy controls, without classifying of CRC patients based on intestinal microbial composition. Results: In this study, a CRC cohort including 339 CRC samples and 333 healthy controls was selected as the discovery set, and the CRC samples were divided into two subgroups (234 Subgroup1 and 105 Subgroup2) using PAM clustering algorithm based on the intestinal microbial composition. We found that not only the microbial diversity was significantly different (Shannon index, p-value < 0.05), but also 129 shared genera altered (p-value < 0.05) between the two CRC subgroups, including several marker genera in CRC, such as Fusobacterium and Bacteroides. A random forest algorithm was used to construct diagnostic models, which showed significantly higher efficiency when the CRC samples were divided into subgroups. Then an independent cohort including 187 CRC samples (divided into 153 Subgroup1 and 34 Subgroup2) and 123 healthy controls was chosen to validate the models, and confirmed the results. Conclusions: These results indicate that the divided CRC subgroups can improve the efficiency of disease diagnosis, with various microbial composition in the subgroups. [ABSTRACT FROM AUTHOR]

Background: Coronary artery disease (CAD) is a major global cardiovascular health threat and the leading cause of death in many countries. The disease has a significant impact in China, where it has become the leading cause of death. There is an urgent need to develop non-invasive, rapid, cost-effective, and reliable techniques for the early detection of CAD using machine learning (ML). Methods: Six hundred eight participants were divided into three groups: healthy, hypertensive, and CAD. The raw data of pulse wave from those participants was collected. The data were de-noised, normalized, and analyzed using several applications. Seven ML classifiers were used to model the processed data, including Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extra Trees (ET), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), and Unbiased Boosting with Categorical Features (CatBoost). Results: The Extra Trees classifier demonstrated the best classification performance. After tunning, the results performance evaluation on test set are: 0.8579 accuracy, 0.9361 AUC, 0.8561 recall, 0.8581 precision, 0.8571 F1 score, 0.7859 kappa coefficient, and 0.7867 MCC. The top 10 feature importances of ET model are w/t1, t3/tmax, tmax, t3/t1, As, hf/3, tf/3/tmax, tf/5, w and tf/3/t1. Conclusion: Radial artery pulse wave can be used to identify healthy, hypertensive and CAD participants by using Extra Trees Classifier. This method provides a potential pathway to recognize CAD patients by using a simple, non-invasive, and cost-effective technique. [ABSTRACT FROM AUTHOR]

Background: The population diagnosed with renal cell carcinoma, especially in Asia, represents 36.6% of global cases, with the incidence rate of renal cell carcinoma in Korea steadily increasing annually. However, treatment options for renal cell carcinoma are diverse, depending on clinical stage and histologic characteristics. Hence, this study aims to develop a machine learning based clinical decision-support system that recommends personalized treatment tailored to the individual health condition of each patient. Results: We reviewed the real-world medical data of 1,867 participants diagnosed with renal cell carcinoma between November 2008 and June 2021 at the Pusan National University Yangsan Hospital in South Korea. Data were manually divided into a follow-up group where the patients did not undergo surgery or chemotherapy (Surveillance), a group where the patients underwent surgery (Surgery), and a group where the patients received chemotherapy before or after surgery (Chemotherapy). Feature selection was conducted to identify the significant clinical factors influencing renal cell carcinoma treatment decisions from 2,058 features. These features included subsets of 20, 50, 75, 100, and 150, as well as the complete set and an additional 50 expert-selected features. We applied representative machine learning algorithms, namely Decision Tree, Random Forest, and Gradient Boosting Machine (GBM). We analyzed the performance of three applied machine learning algorithms, among which the GBM algorithm achieved an accuracy score of 95% (95% CI, 92–98%) for the 100 and 150 feature sets. The GBM algorithm using 100 and 150 features achieved better performance than the algorithm using features selected by clinical experts (93%, 95% CI 89–97%). Conclusions: We developed a preliminary personalized treatment decision-support system (TDSS) called "RCC-Supporter" by applying machine learning (ML) algorithms to determine personalized treatment for the various clinical situations of RCC patients. Our results demonstrate the feasibility of using machine learning-based clinical decision support systems for treatment decisions in real clinical settings. [ABSTRACT FROM AUTHOR]

Background: Predicting mortality and relapse in children with acute lymphoblastic leukemia (ALL) is crucial for effective treatment and follow-up management. ALL is a common and deadly childhood cancer that often relapses after remission. In this study, we aimed to apply and evaluate machine learning-based models for predicting mortality and relapse in pediatric ALL patients. Methods: This retrospective cohort study was conducted on 161 children aged less than 16 years with ALL. Survival status (dead/alive) and patient experience of relapse (yes/no) were considered as the outcome variables. Ten machine learning (ML) algorithms were used to predict mortality and relapse. The performance of the algorithms was evaluated by cross-validation and reported as mean sensitivity, specificity, accuracy and area under the curve (AUC). Finally, prognostic factors were identified based on the best algorithms. Results: The mean accuracy of the ML algorithms for prediction of patient mortality ranged from 64 to 74% and for prediction of relapse, it varied from 64 to 84% on test data sets. The mean AUC of the ML algorithms for mortality and relapse was above 64%. The most important prognostic factors for predicting both mortality and relapse were identified as age at diagnosis, hemoglobin and platelets. In addition, significant prognostic factors for predicting mortality included clinical side effects such as splenomegaly, hepatomegaly and lymphadenopathy. Conclusions: Our results showed that artificial neural networks and bagging algorithms outperformed other algorithms in predicting mortality, while boosting and random forest algorithms excelled in predicting relapse in ALL patients across all criteria. These results offer significant clinical insights into the prognostic factors for children with ALL, which can inform treatment decisions and improve patient outcomes. [ABSTRACT FROM AUTHOR]

ABSTRACT Geographical random forest (GRF) is a recently developed and spatially explicit machine learning model. With the ability to provide more accurate predictions and local interpretations, GRF has already been used in many studies. The current GRF model, however, has limitations in its determination of the local model weight and bandwidth hyperparameters, potentially insufficient numbers of local training samples, and sometimes high local prediction errors. Also, implemented as an R package, GRF currently does not have a Python version which limits its adoption among machine learning practitioners who prefer Python. This work addresses these limitations by introducing theory‐informed hyperparameter determination, local training sample expansion, and spatially weighted local prediction. We also develop a Python‐based GRF model and package, PyGRF, to facilitate the use of the model. We evaluate the performance of PyGRF on an example dataset and further demonstrate its use in two case studies in public health and natural disasters. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusion Length of stay (LOS) is an outcome measure and is assumed to be related to quality. The objective of this study is to examine the quality of birth care and risk factors associated with LOS after birth.A nationwide population‐based Turkish Demographic and Health Survey (TDHS) was used for the year 2018. A total of 1849 women ages 15–49 were included. Explanatory factor analysis and machine learning predictors such as Random Forest, Support Vector Machine, Neural Network, k‐Nearest Neighbor, and Naïve Bayes were used to identify the quality of birth care and risk factors associated with LOS after birth.As a result of the explanatory factor analysis, factor structures of quality of birth care, antenatal check‐ups and supplements, and risk factors associated with birth were obtained using the Categorical Component Analysis method. The type of delivery, place of delivery, age, and type of place, which are under the quality of birth care, and risk factors associated with birth factors were found to be the variables that had the highest impact on LOS estimation. Random forest (Accuracy = 0.5789), support vector machine (radial) (Accuracy = 0.5766), and neural network (Accuracy = 0.5750) models outperformed, respectively.Type of delivery which is an indicator of quality of birth care is a strong predictor of LOS after birth according to the Random Forest model. We demonstrated that machine learning techniques offer precise LOS prediction after birth. Further studies assessing the effect of quality of birth care on predicting LOS at birth would be beneficial. [ABSTRACT FROM AUTHOR]

As a crucial node in the East Asia–Australasia migratory bird network, the Liaohe River Estuary Wetland (LREW) contributes significantly to coastal ecological balance and biodiversity. Accurate and timely mapping of the LREW is essential for monitoring ecological changes and understanding shifts in the wetland's ecosystem structure and functions. In this study, an innovative approach that integrates radar (Sentinel-1), optical (Sentinel-2), and DEM data on Google Earth Engine (GEE) is proposed to achieve a more optimized and accurate wetland mapping. Recursive Feature Elimination (RFE) is employed to improve feature collection, facilitating the construction of an optimal feature set for Random Forest (RF) classification to extract detailed LREW information. Mapping conducted from 2017 to 2023 using the proposed approach demonstrates its effectiveness and stability in mapping the LREW, achieving overall accuracies ranging from 89.84% to 93.73% and Kappa from 0.85 to 0.91. Compared to single-source methods, the integration of multi-source data substantially enhances mapping accuracy, while RF-RFE effectively eliminates redundant features, further refining classification precision. Texture features (Sum Average, SAVG) and the Water Index 2019 (WI2019) were found to significantly influence wetland mapping. The importance of different feature types is ranked as follows: spectral features > vegetation/water body index > PCA features > SAR texture features > red-edge index > other spectral index > spectral texture features > backscatter coefficient > H-Alpha. During the study period, the wetland area increased from 849.60 km2 to 979.49 km2. This expansion was caused primarily by wetland protection policies that led to the dismantling of coastal aquaculture ponds, resulting in a reduction of 90.28 km2 in waterbodies over 7 years, with many areas transitioning to tidal flats and Suaeda salsa habitats. In conclusion, the proposed method is effective in mapping the LRE wetlands and provides reliable data support for wetland ecological protection and restoration. [ABSTRACT FROM AUTHOR]

Secondary metabolites have high ecological and economic values. Measuring the amounts of these metabolites in plants through laboratory analysis is time-consuming and expensive. On the other hand, the laboratory results cannot be generalized to a wide area of rangelands with diverse climates, topography, and soil. Therefore, the present study aims to evaluate the potential of PRISMA hyperspectral data to estimate the rangeland phenol concentration at landscape scale in western Isfahan province, Iran. Phenol concentration and spectral signatures were extracted from 42 plant individuals, including Astragalus adscendens, Astragalus verus, Daphne mucronata, Anabasis aphylla, and Phlomis olivieri using laboratory methods and spectroscopy in the range of 350 to 2500 nm. Phenol-sensitive bands were identified by establishing a relationship between phenol concentration and all spectral bands by the Partial Least Square Regression (PLSR) method. The map of phenol spatial changes was then produced with the Bayesian Regularized Artificial Neural Network Model (BRNN), Random Forest (RF), Generalized Linear Model (GAM), Generalized Additive Model (GLM), and Gaussian Process Regression (GPR) in rangelands. The results showed that the phenol concentration in different individuals was between 3.65 and 20.13 (mgGAE/gDW), and species spectral absorptions ranged from 1.650 to 1.660. The very high coefficient of determination of phenol concentration with spectral data (R2 = 0.94) confirmed the use of these data in phenol mapping at the landscape scale. The maximum estimated phenol concentration by GLM, GAM, RF, BRNN, and GPR models was 36.40, 26.76, 18.07, 22.74, and 32.92 GAE/g DW, respectively; which RF model had the highest (R2 = 0.955) and GLM had the lowest accuracy (R2 = 0.816). The findings show that hyperspectral data can estimate secondary metabolites at the landscape scale and can supplement laboratory methods in managing rangeland plants. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) has seen rapid expansion, but this growth brings significant privacy challenges due to the large amounts of data generated by myriad IoT devices. To address these challenges, this study introduces Smart Anonymity, a method that determines the optimal data anonymization algorithm for a dataset by assessing its unique features. The solution leverages OWL ontologies grounded in description logic (DL), which facilitates inconsistency checks and the discovery of new facts for data validation. Additionally, machine learning (ML) is incorporated to improve the accuracy of these classifications. ML is also instrumental in recommending suitable anonymization algorithms, with the random forest algorithm being employed explicitly for this purpose. The findings from this research indicate that Smart Anonymity effectively improves user privacy. [ABSTRACT FROM AUTHOR]

For a long time, decision-making in auditing was limited to a risk-oriented recommendation and consisted of the rigorous analysis of a sample of data. The new trend in the audit decision process focuses on the use of decision support systems (DSSs) founded on data analytics (DA) to better concentrate on in-depth analysis. This study examines how DA can improve the audit decision-making approach in the banking sector. We show that DA techniques can improve the quality of audit decision-making within banks and highlight the advantages associated with mastering these techniques, which results in a more effective and efficient audit of digital banking transactions. We propose an artifact-based data analytics-driven decision support system (DA-DSS) for an automatic fraud detection system supported by DA. The proposed DA-DSS artifact with a random forest classifier at its core is a promising innovation in the field of electronic transaction fraud detection. The results show that the random forest classifier can accurately classify the data generated by this artifact with an accuracy varying from 88 to 93% using transaction data collected from 2021 to 2022. Other classifiers including k-nearest neighbors (KNN) are also used, with a classification rate ranging from 66 to 88% for the same transaction datasets. These results show that the proposed DA-DSS with random forest can significantly improve auditing by reducing the time required for data analysis and increasing the results' accuracy. Management can use the proposed artifact to enhance and speed up the decision-making process within their organization. [ABSTRACT FROM AUTHOR]

High costs and time consuming associated with experimental trial-and-error result in low efficiency, creating an urgent need for a more effective strategy for ultra-high temperature ceramic matrix composites (UHTCMCs) development. Inspired by the exceptional performance of machine learning (ML) algorithms across various domains, this work employs ML algorithms to construct models and conduct in-depth analysis of the key factors and their patterns influencing the ablation resistance of UHTCMCs. A set of 26 dimensional features that could potentially impact the ablation resistance of UHTCMCs were established based on domain knowledge. Eight typical ML models were used to build and predict the linear ablation rate (LAR) of UHTCMCs. Results show that the random forest model has optimal generalization performance, with mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R 2) being 2.75 μm s−1 and 7.3 (μm s−1)2, and 0.71 respectively. The Shapley additive explanations values based on the random forest model reveal that the key features affecting the LAR of UHTCMCs are ranked as average melting point of ceramics (AMPC) > thermal conductivity of material (TCM) > thermal expansion coefficient of oxides (TECO) > fabrication temperature of material (FTM), all showing a negative influence on the LAR. Symbolic regression further indicates that AMPC, TCM, and TECO have an exponential negative correlation with LAR. These data-driven conclusions have been thoroughly validated through the use of C f /(TiZrHfNbTa)C composites. The established model can accelerate the discovery of material knowledge and provide reliable guidance for UHTCMC development. [ABSTRACT FROM AUTHOR]

Metamaterial absorbers (MMAs) have tremendous potential for controlling and modulating Terahertz electromagnetic (EM) waves. It is challenging to design MMAs for optimal performance using conventional methods due to their time-consuming and computationally demanding nature. Machine learning (ML) regressor approaches have emerged as potential tools for optimizing the predictive modeling of MMAs in recent years. This article examined different regression techniques, such as Decision Tree, k-Nearest Neighbors, Random Forest, Extra Trees (ET), Extreme Gradient Boosting, Bagging, and Categorical Boosting. The primary goal is to assess the effectiveness of each regressor technique in forecasting the performance of MMAs using various performance metrics. The study focuses on a representative spectrum of MMAs, chosen for its key features that are common across a wide range of MMAs. The extra tree Regressor outperforms other models in the comparative analysis due to its capacity to integrate multiple decision trees and include randomness into the feature selection process, resulting in improved predictive skills. The ET regressor forecasts MMA performance remarkably well for a 70 % test size, with a Root Mean Squared Error of 0.0251, a Mean Absolute Error of 0.0074, a Mean Squared Error of 0.006, and an Adjusted R-squared of 0.9873. These findings can assist researchers in optimizing metamaterial absorber designs for Terahertz applications using ML regressor techniques and provide insights into how these methods can be generalized to other spectra with similar characteristics. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Conditioning process intensification is necessary for improving the fly ash flotation effect. Understanding the relationship between conditioning parameters and flotation results is crucial for optimizing the conditioning-flotation process. This study developed a machine learning framework for predicting the yield, loss on ignition, and removal rate of unburned carbon using data from conditioning-flotation experiments. Feature variables of conditioning speed, impeller type, stirred tank diameter, collector dosage, and frother dosage were employed to develop five machine learning models. After tuning the hyper-parameters with GridSearchCV technique, it was found that Random Forest Regression model, eXtreme Gradient Boosting Regression model, and Gradient Boosting Regression model demonstrated good agreements between the predicted data and test data for the yield, loss on ignition, and removal rate of unburned carbon. The maximum R2 values were 0.9184, 0.8339, and 0.9434, respectively, while the minimum MSE values were 3.30, 5.82, and 20.59, respectively. The interpretable analysis using SHapley Additive exPlanations (SHAP) method indicated that conditioning speed was the most significant feature variable affecting the yield, loss on ignition, and removal rate of unburned carbon, while impeller type had a secondary impact. This investigation is expected to reveal the important role of conditioning process intensification in optimizing fly ash flotation. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Aortic dissection (AD) is a life‐threatening cardiovascular emergency that is often misdiagnosed as other chest pain conditions. Physiologically, AD may cause abnormalities in peripheral blood flow, which can be detected using pulse oximetry waveforms.This study aimed to assess the feasibility of identifying AD based on pulse oximetry waveforms and to highlight the key waveform features that play a crucial role in this diagnostic method.This prospective study employed high‐risk chest pain cohorts from two emergency departments. The initial cohort was enriched with AD patients (n = 258, 47% AD) for model development, while the second cohort consisted of chest pain patients awaiting angiography (n = 71, 25% AD) and was used for external validation. Pulse oximetry waveforms from the four extremities were collected for each patient. After data preprocessing, a recognition model based on the random forest algorithm was trained using patients' gender, age, and waveform difference features extracted from the pulse oximetry waveforms. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). The importance of features was also assessed using Shapley Value and Gini importance.The model demonstrated strong performance in identifying AD in both the training and external validation sets. In the training set, the model achieved an area under the ROC curve of 0.979 (95% CI: 0.961–0.990), sensitivity of 0.918 (95% CI: 0.873–0.955), specificity of 0.949 (95% CI: 0.912–0.985), and accuracy of 0.933 (95% CI: 0.904–0.959). In the external validation set, the model attained an area under the ROC curve of 0.855 (95% CI: 0.720–0.965), sensitivity of 0.889 (95% CI: 0.722–1.000), specificity of 0.698 (95% CI: 0.566–0.812), and accuracy of 0.794 (95% CI: 0.672–0.878). Decision curve analysis (DCA) further showed that the model provided a substantial net benefit for identifying AD. The median mean and median variance of the four limbs' signals were the most influential features in the recognition model.This study demonstrated the feasibility and strong performance of identifying AD based on peripheral pulse oximetry waveforms in high‐risk chest pain populations in the emergency setting. The findings also provided valuable insights for future human fluid dynamics simulations to elucidate the impact of AD on blood flow in greater detail. [ABSTRACT FROM AUTHOR]

Motivation: The association between weather conditions and stroke incidence has been a subject of interest for several years, yet the findings from various studies remain inconsistent. Additionally, predictive modelling in this context has been infrequent. This study explores the relationship of extremely high ischaemic stroke incidence and meteorological factors within the Slovak population. Furthermore, it aims to construct forecasting models of extremely high number of strokes. Methods: Over a five-year period, a total of 52,036 cases of ischemic stroke were documented. Days exhibiting a notable surge in ischemic stroke occurrences (surpassing the 90th percentile of historical records) were identified as extreme cases. These cases were then scrutinized alongside daily meteorological parameters spanning from 2015 to 2019. To create forecasts for the occurrence of these extreme cases one day in advance, three distinct methods were employed: Logistic regression, Random Forest for Time Series, and Croston's method. Results: For each of the analyzed stroke centers, the cross-correlations between instances of extremely high stroke numbers and meteorological factors yielded negligible results. Predictive performance achieved by forecasts generated through multivariate logistic regression and Random Forest for time series analysis, which incorporated meteorological data, was on par with that of Croston's method. Notably, Croston's method relies solely on the stroke time series data. All three forecasting methods exhibited limited predictive accuracy. Conclusions: The task of predicting days characterized by an exceptionally high number of strokes proved to be challenging across all three explored methods. The inclusion of meteorological parameters did not yield substantive improvements in forecasting accuracy. [ABSTRACT FROM AUTHOR]

In an era marked by pervasive digital connectivity, cybersecurity concerns have escalated. The rapid evolution of technology has led to a spectrum of cyber threats, including sophisticated zero-day attacks. This research addresses the challenge of existing intrusion detection systems in identifying zero-day attacks using the CIC-MalMem-2022 dataset and autoencoders for anomaly detection. The trained autoencoder is integrated with XGBoost and Random Forest, resulting in the models XGBoost-AE and Random Forest-AE. The study demonstrates that incorporating an anomaly detector into traditional models significantly enhances performance. The Random Forest-AE model achieved 100% accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (MCC), outperforming the methods proposed by Balasubramanian et al., Khan, Mezina et al., Smith et al., and Dener et al. When tested on unseen data, the Random Forest-AE model achieved an accuracy of 99.9892%, precision of 100%, recall of 99.9803%, F1 score of 99.9901%, and MCC of 99.8313%. This research highlights the effectiveness of the proposed model in maintaining high accuracy even with previously unseen data. [ABSTRACT FROM AUTHOR]

ABSTRACT Aims Design Methods Results Conclusion Impact Patient or Public Contribution The aim of our study was to formulate and validate a prediction model using machine learning algorithms to forecast the risk of pressure injuries (PIs) in children undergoing living donor liver transplantation (LDLT).A retrospective cohort study.The research was carried out at China's largest paediatric liver transplantation centre. A total of 438 children who underwent LDLT between June 2021 and December 2022 constituted the study cohort. The dataset was partitioned randomly into 70% for training datasets (306 cases) and 30% for testing datasets (132 cases). Utilising four machine learning algorithms—Decision Tree, Random Forest, Gradient Boosting Decision Tree and eXtreme Gradient Boosting—we identified risk factors and constructed predictive models.Out of 438 children, 42 developed PIs, yielding an incidence rate of 9.6%. Notably, 94% of these cases were categorised as Stage 1, and 54% were localised on the occiput. Upon evaluating the four prediction models, the Decision Tree model emerged as the most effective. The primary contributors to pressure injury in the Decision Tree model were identified as operation time, intraoperative corticosteroid administration, preoperative skin protection measures and preoperative skin conditions. A visualisation elucidating the logical inference process for the 10 variables within the Decision Tree model was presented. Ultimately, based on the Decision Tree model, a predictive system was developed.Machine learning algorithms facilitate the identification of crucial factors, enabling the creation of an effective Decision Tree model to forecast pressure injury development in children undergoing LDLT.With this predictive model at their disposal, nurses can assess the pressure injury risk level in children more intuitively. Subsequently, they can implement tailored preventive strategies to mitigate the occurrence of PIs.Paediatric patients contributed electronic health records datasets. [ABSTRACT FROM AUTHOR]

Summary: Mobile networks have advanced in many fields to share information with others. But finding the required resources for each user to share the data is a complex task because of the moving environment. So, the present research article has introduced a novel Chimp‐based Random Forest Peer Identification Framework (CbRFPIF) in the mobile environment. Initially, the required nodes were designed dynamically in the moving background. Hence, the designed wireless moving environment has included sender, peer, and receiver nodes. Moreover, the Chimp fitness model predicted the users' requested resources in the primary stage, then the nearest peer and the user locations were predicted. Consequently, the requested resource by users was distributed to the particular closest peers, and the peer forwarded the data to the specific users. Finally, the planned model is simulated in the NS2 environment, and the communication parameters were calculated and compared with other associated models, which gained the highest throughput and search rate. [ABSTRACT FROM AUTHOR]

Summary: Indoor location based on Wi‐Fi fingerprint has attracted extensive attention recently, since it only requires a mobile device and existing network; it does not require additional infrastructure and hardware. However, localization using only received signal strength fingerprint is susceptible to dynamic environments and device heterogeneity. Localization using signal strength differences fingerprint and hyperbolic location fingerprint which extracted from received signal strength fingerprint is a powerful measure to overcome the device heterogeneity. Existing fusion‐based methods do not fully use of the mutual fusion function of multiple fingerprints, which leads to low positioning accuracy. In order to further improve the positioning accuracy, we propose a multifingerprint multiclassifier and two‐layer fusion weight positioning model. The proposed approach first creates a multiple fingerprints group by gleaning signal strength differences fingerprint and hyperbolic location fingerprint from received signal strength fingerprint. In order to deal with the accuracy decline caused by a single classifier in complex scenes, three typical classifiers, random forest (RF), K‐nearest neighbor (KNN), and support vector machine (SVM), are adopted, and a fusion weight selection algorithm is proposed to improve the localization accuracy by choosing fusion weights intelligently from the two‐layer fusion weights. Experiment results show that the proposed scheme is effective to resolve device heterogeneity, and the position accuracy is improved about 16.9% and 5.8%, respectively, compared with single fingerprint and classifier schemes. The results show that, compared with the single fingerprint scheme, the position accuracy is improved at least 16.9% and 28.4% for the homogeneous and heterogeneous equipments, respectively; as for the proposed multiclassifier fusion method, it achieves at least 5.8% and 8.7% accuracy improvement for the cases with homogeneous and heterogeneous equipments, respectively, compared with other traditional classifiers such as RF, KNN, and SVM. [ABSTRACT FROM AUTHOR]

For survival analysis applications we propose a novel procedure for identifying subgroups with large treatment effects, with focus on subgroups where treatment is potentially detrimental. The approach, termed forest search, is relatively simple and flexible. All‐possible subgroups are screened and selected based on hazard ratio thresholds indicative of harm with assessment according to the standard Cox model. By reversing the role of treatment one can seek to identify substantial benefit. We apply a splitting consistency criteria to identify a subgroup considered "maximally consistent with harm." The type‐1 error and power for subgroup identification can be quickly approximated by numerical integration. To aid inference we describe a bootstrap bias‐corrected Cox model estimator with variance estimated by a Jacknife approximation. We provide a detailed evaluation of operating characteristics in simulations and compare to virtual twins and generalized random forests where we find the proposal to have favorable performance. In particular, in our simulation setting, we find the proposed approach favorably controls the type‐1 error for falsely identifying heterogeneity with higher power and classification accuracy for substantial heterogeneous effects. Two real data applications are provided for publicly available datasets from a clinical trial in oncology, and HIV. [ABSTRACT FROM AUTHOR]

In recent years, the application of machine learning methods in the prediction of hydrological processes such as precipitation has been widely considered. These methods can analyze large volumes of data and detect the existing trends and patterns. Therefore, in the present study, machine learning methods, including random forests (RF), Kstar algorithm and Gaussian process regression (GPR), were used to predict the precipitation of Sindh River basin in India during period of 1901 to 2020. In the next step, three distinct input scenarios include (i) using monthly precipitation data and considering the memory of time series up to 5 months delay, (ii) adding periodic term to the first scenario inputs and (iii) decomposing the data using the Daubechies 4 wavelet function and creating hybrid wavelet-learning machine (W-ML) models, were prepared and introduced to the models. The performance of each method was evaluated using the root mean square error (RMSE), mean absolute error (MAE), Kling–Gupta efficiency score (KGE) and Willmott index (WI). The results showed that single models with the first scenario inputs (without taking into account the periodicity of the data) did not have good accuracy, but by adding the periodicity, the performance of these models was significantly improved, and the average value of KGE index for all studied stations increased from 0.466 to 0.672. It was also found that the GPR model for all stations could not have good performance and RF and Kstar models are the most appropriate methods for predicting precipitation in the Sindh River basin, respectively. With the application of the third scenario and the development of W-ML hybrid models, the accuracy of precipitation forecasting was significantly improved, especially the maximum precipitation values were estimated with higher accuracy than standalone models. [ABSTRACT FROM AUTHOR]

Background: To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma. Methods: This retrospective study included pulmonary GGN patients who were histologically confirmed to have adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma cancer (IAC) from 2020 to 2023. CT images of all patients were automatically segmented and 107 radiomic features were obtained for each patient. Classification models were developed using random forest (RF) and cross-validation, including three one-versus-others models and one three-class model. For each model, features were ranked by normalized Gini importance, and a minimal subset was selected with a cumulative importance exceeding 0.9. These selected features were then used to train the final models. The models' performance metrics, including area under the curve (AUC), accuracy, sensitivity, and specificity, were computed. AUC and accuracy were compared to determine the final optimal method. Results: The study comprised 193 patients (mean age 54 ± 11 years, 65 men), including 65 AIS, 54 MIA, and 74 IAC, divided into one training cohort (N = 154) and one test cohort (N = 39). The final three-class RF model outperformed three individual one-versus-others models in distinguishing each class from the other two. For the multiclass classification model, the AUC, accuracy, sensitivity, and specificity were 0.87, 0.79, 0.62, and 0.88 for AIS; 0.90, 0.79, 0.54, and 0.89 for MIA; and 0.87, 0.69, 0.73, and 0.67 for IAC, respectively. Conclusions: A radiomics-based multiclass RF model could effectively differentiate three types of pulmonary GGN, which enabled early diagnosis of GGO pulmonary adenocarcinoma. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop a deep learning model to predict skeletal malocclusions with an acceptable level of accuracy using airway and cephalometric landmark values obtained from analyzing different CBCT images. Background: In orthodontics, multitudinous studies have reported the correlation between orthodontic treatment and changes in the anatomy as well as the functioning of the airway. Typically, the values obtained from various measurements of cephalometric landmarks are used to determine skeletal class based on the interpretation an orthodontist experiences, which sometimes may not be accurate. Methods: Samples of skeletal anatomical data were retrospectively obtained and recorded in Digital Imaging and Communications in Medicine (DICOM) file format. The DICOM files were used to reconstruct 3D models using 3DSlicer (slicer.org) by thresholding airway regions to build up 3D polygon models of airway regions for each sample. The 3D models were measured for different landmarks that included measurements across the nasopharynx, the oropharynx, and the hypopharynx. Male and female subjects were combined as one data set to develop supervised learning models. These measurements were utilized to build 7 artificial intelligence-based supervised learning models. Results: The supervised learning model with the best accuracy was Random Forest, with a value of 0.74. All the other models were lower in terms of their accuracy. The recall scores for Class I, II, and III malocclusions were 0.71, 0.69, and 0.77, respectively, which represented the total number of actual positive cases predicted correctly, making the sensitivity of the model high. Conclusion: In this study, it is observed that the Random Forest model was the most accurate model for predicting the skeletal malocclusion based on various airway and cephalometric landmarks. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to explore the feasibility of using machine learning approaches to objectively differentiate the mobilization patterns, measured via accelerometer sensors, of patients pre- and post-intervention. Methods: The intervention tested the implementation of a Frailty Care Bundle to improve mobilization, nutrition and cognition in older orthopedic patients. The study recruited 120 participants, a sub-group analysis was undertaken on 113 patients with accelerometer data (57 pre-intervention and 56 post-intervention), the median age was 78 years and the majority were female. Physical activity data from an ankle-worn accelerometer (StepWatch 4) was collected for each patient during their hospital stay. These data contained daily aggregated gait variables. Data preprocessing included the standardization of step counts and feature computation. Subsequently, a binary classification model was trained. A systematic hyperparameter optimization approach was applied, and feature selection was performed. Two classifier models, logistic regression and Random Forest, were investigated and Shapley values were used to explain model predictions. Results: The Random Forest classifier demonstrated an average balanced accuracy of 82.3% (± 1.7%) during training and 74.7% (± 8.2%) for the test set. In comparison, the logistic regression classifier achieved a training accuracy of 79.7% (± 1.9%) and a test accuracy of 77.6% (± 5.5%). The logistic regression model demonstrated less overfitting compared to the Random Forest model and better performance on the hold-out test set. Stride length was consistently chosen as a key feature in all iterations for both models, along with features related to stride velocity, gait speed, and Lyapunov exponent, indicating their significance in the classification. Conclusion: The best performing classifier was able to distinguish between patients pre- and post-intervention with greater than 75% accuracy. The intervention showed a correlation with higher gait speed and reduced stride length. However, the question of whether these alterations are part of an adaptive process that leads to improved outcomes over time remains. [ABSTRACT FROM AUTHOR]

Word recognition has garnered significant attention due to its wide-ranging real-time applications, including postal automation, signature verification, bank check processing and writer identification. Handwritten word recognition poses a formidable challenge in computer vision, primarily due to the diverse writing styles exhibited by individuals. Presently, researchers are increasingly focused on developing holistic word recognition approaches. This holistic approach treats entire words as single, indivisible entities for the purpose of feature extraction and recognition, rendering it well-suited for identifying words with overlapping characters. In this study, authors have proposed a holistic approach for recognizing offline handwritten words in the Devanagari script, employing VGG16 (Visual Geometry Group) as a versatile feature extractor. Three classifiers, namely Gaussian Naive Bayes (Gaussian NB), eXtreme Gradient Boosting (XGBoost) and Random Forest (RF), are considered for classifying these word images. Based on the extracted features and the aforementioned classifiers, words are categorized into one of 50 distinct word classes. Various performance evaluation metrics, including Accuracy, Precision, Recall, F1-score and Area Under the Curve (AUC), are employed to assess the effectiveness of the proposed system. It is worth noting that the Random Forest (RF) based classification technique achieved exceptional accuracy, with impressive recall, high precision and a remarkable F1-score. Additionally, the XGBoost based classification exhibited comparable performance, achieving the highest possible AUC in this study. Furthermore, to underscore the significance of this work, authors have presented a comparative analysis of the proposed approach with existing state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

Recently, every human being has been busy keeping pace with fast changes and is unable to put his attention on a healthy lifestyle. This unhealthy lifestyle leads to several fatal diseases like heart disease (HD), cancer disease, allergies, asthma, breast cancer, etc. These diseases are increasing the morbidity and mortality rate every year, and out of these HD is more fetal. Earlier/Timely prediction of HD is regarded as one of the most crucial tasks in the medical field. In the healthcare industry, a big collection of datasets is available free of cost. Motivated by existing challenges, the authors proposed two intelligent models i.e., HyOPTRF (Model 1), and HyOPTXGBoost Classifier (Model 2), implemented on the Statlog HD dataset with default and hyper-tuned parameters. Both the models recorded maximum Accuracy of 92.59%, Specificity 100%, Precision 100%, Negative Predicted Value 100%, Geometric mean 93.93% F1 Score 93.75%, Training Time 0.02s, and Testing Time 0.005s for the HyOPTRF on Trial no. 2nd and Accuracy of 96.30%, Specificity 100%, Precision 100%, Negative Predicted Value 100%, Geometric mean 96.82%, F1 Score 96.77%, Training Time 0.009s, and Testing Time 0.002s for the HyOPTXGBoost Classifier on Trial no. 33rd. The proposed models were validated by the Stratify Kfold Cross-Validation technique and compared with the other existing models. [ABSTRACT FROM AUTHOR]

Case-based reasoning (CBR) is widely used in medical decision support systems because of its similarity to human reasoning. Despite the effectiveness of the latter, its performance can be improved when reinforced by other automatic learning techniques. This is what induced us to propose a hybrid model 3FS-CBR-IRF (Three feature selection- Case based reasoning- Improved random forest) that combines case-based reasoning and random forests. Firstly, a feature selection step is used by three techniques such as: Correlation, Dropping Constant Features and Chi-square to select the relevant features. Secondly, we have modeled the retrieval phase of the CBR system by the modified random forests; in this step two pruning methods (Pre-pruning and Post-pruning) are applied only to the highly ranked features to improve the performance of the CBR system. Thirdly, the adaptation phase of the retrieval cases based on the adaptation rules is proposed. The developed approach is evaluated on 13 medical databases using the precision and several other performance criteria, the results obtained are satisfactory with an average precision of 91% for the first model (CBR-FS-RF) which improves the CBR system by feature selection techniques with classic random forest, 97% for the model (CBR-FS-IRF) which improve the CBR system by feature selection techniques with improved random forests and 98% for the second model (CBR-FS-IRF) with adaptation. The results showed that the proposed pruning method selects the best tree in less time and with better precision than hybridization with classical random forest that means an improvement of the performance of the CBR system. [ABSTRACT FROM AUTHOR]

Background and purpose: Early detection, diagnosis, and treatment of colorectal cancer and its precancerous lesions can significantly improve patients' survival rates. The purpose of this research is to identify methylation markers specific to colorectal cancer tissues and validate their diagnostic capability in colorectal cancer and precancerous changes by measuring the level of DNA methylation in stool samples. Method: We analyzed samples from six cancer tissues and adjacent normal tissues and fecal samples from 758 participants, including 62 patients with interfering diseases. Bioinformatics databases were used to screen for candidate biomarkers for CRC, and quantitative methylation-specific PCR methods were applied for identification. The methylation levels of the candidate biomarkers in fecal and tissue samples were measured. Logistic regression and random forest models were built and validated using fecal sample data from one of the centers, and the independent or combined diagnostic value of the candidate biomarkers in fecal samples for CRC and precancerous lesions was analyzed. Finally, the diagnostic capability and stability of the model were validated at another medical center. Results: This study identified two colorectal cancer CpG sites with tissue specificity. These two biomarkers have certain diagnostic power when used individually, but their diagnostic value for colorectal cancer and colorectal adenoma is more significant when they are used in combination. Conclusion: The results indicate that a DNA methylation biomarker combined diagnostic model based on two CpG sites, cg13096260 and cg12587766, has the potential for screening and diagnosing precancerous lesions and colorectal cancer. Additionally, compared to traditional diagnostic models, machine learning algorithms perform better but may yield more false-positive results, necessitating further investigation. [ABSTRACT FROM AUTHOR]

Background: Sepsis 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. Methods: A 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. Results: Using 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. Conclusions: In 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. [ABSTRACT FROM AUTHOR]

Bridge construction collapse is one of the most common bridge safety accidents. At present, evaluation results are often affected by the ability and experience of the assessor. Therefore, it is difficult to quickly, accurately and effectively evaluate the risk in the process of bridge construction. Moreover, key factors that can prevent accidents can hardly find from the existing bridge construction safety management and evaluation method. This paper analyzes and classifies the artificial and environmental risk factors that affect the bridge construction stage, and establishes 26 risk factors in 5 categories according to the characteristics of bridge construction and the actual situation of the project. Random forest (RF) algorithm is a non-parametric machine learning method based on decision tree, which does not need to be scored by experts in advance and avoids the influence of subjective factors. Compared with other analysis methods, random forest algorithm has the advantages of accurate and robust risk assessment results. Based on the advantages of random forest algorithm and the characteristics of bridge construction risk, this paper uses random forest algorithm to evaluate the bridge construction risk, and ranks the importance of indicators, and identify the index that has a greater influence on the risk. In order to verify the applicability and feasibility of the proposed method, a typical urban complex pedestrian bridge was taken as an example for actual engineering evaluation and verification. The results obtained are basically consistent with the actual risk assessment results of the pedestrian bridge. [ABSTRACT FROM AUTHOR]

Alpine natural heritage sites hold significant value due to their unique global resources. Studying land cover changes in these areas is crucial for maintaining and preserving multiple their values. This study takes Kalajun-Kuerdening, one of the components of Xinjiang Tianshan World Natural Heritage Site, as an example to analyze land cover changes and their driving factors in alpine heritage sites. Highlights include: (1) Between 1994 and 2023, Forest and Grassland increased by 55.96 km2 and 18.16 km2, with notable forest growth from 2007 to 2017. Trends in Forest changes align with forest protection policies, and a substantial amount of Bareland converted to Grassland indicates an increase in vegetation cover. (2) Elevation, precipitation, temperature, and evapotranspiration are key drivers of land cover changes, as validated by Random Forest algorithm and Geodetector model. (3) Favorable conditions for Grassland to Forest transition include annual precipitation between 275 and 375 mm, annual temperature between −2 and 3 °C, annual evapotranspiration between 580 and 750 mm, elevation between 1800 and 2600 m, and aspect between 0 to 110° and 220 to 259.9°. Continuous monitoring of land cover changes and their driving factors in mountain heritage sites contributes to the protection of the ecological environment and provides data and information support for addressing climate change, resource management, and policy making. [ABSTRACT FROM AUTHOR]