Ensemble Machine Learning Model Research Articles

Safety assurance and nutritional quality enhancement of Phyllospora comosa biomass using hydrothermal treatment derived ensemble machine learning models

One of the major barriers to the mass industrial utilisation of brown seaweeds as food sources stem from the food safety risks associated with their iodine and arsenic concentrations, which typically exceed regulatory limits. Hydrothermal treatments might effectively reduce the high iodine and arsenic concentration of Phyllospora comosa below the Australian maximum residual limits (iodine = 1 mg/g and arsenic = 0.00667 mg/g; dry weight) set for brown seaweeds. The experimental hydrothermal treatments dictated that the 82 °C–250 s treatment reduced the iodine concentration from 2.76 mg/g to 0.88 mg/g (68% reduction) and arsenic concentration from 0.01693 mg/g to 0.00965 mg/g (43% reduction). Machine learning models predicted that blanching at 100 °C for ∼4 minutes will reduce the arsenic concentration below its maximum residual limit. Additive log-ratio transformations showed that around 50% (dw) of the hydrothermally treated Phyllospora comosa samples were leached out during the highest treatment intensity, 82 °C–250 s. Even though, a half of the biomass is lost, hydrothermally treated Phyllospora comosa products are safer for human consumption and thus may permit the expansion of seaweed production and consumption in Australia.

Deep serum lipidomics identifies evaluative and predictive biomarkers for individualized glycemic responses following low-energy diet-induced weight loss: a PREVention of diabetes through lifestyle Intervention and population studies in Europe and around the World (PREVIEW) substudy

BackgroundWeight loss through lifestyle interventions, notably low-energy diets, offers glycemic benefits in populations with overweight-associated prediabetes. However, >50% of these individuals fail to achieve normoglycemia after weight loss. Circulating lipids hold potential for evaluating dietary impacts and predicting diabetes risk. ObjectivesThis study sought to identify serum lipids that could serve as evaluative or predictive biomarkers for individual glycemic changes following diet-induced weight loss. MethodsWe studied 104 participants with overweight-associated prediabetes, who lost ≥8% weight via a low-energy diet over 8 wk. High-coverage lipidomics was conducted in serum samples before and after the dietary intervention. The lipidomic recalibration was assessed using differential lipid abundance comparisons and partial least squares discriminant analyses. Associations between lipid changes and clinical characteristics were determined by Spearman correlation and Bootstrap Forest of ensemble machine learning model. Baseline lipids, predictive of glycemic parameters changes postweight loss, were assessed using Bootstrap Forest analyses. ResultsWe quantified 439 serum lipid species and 9 related organic acids. Dietary intervention significantly reduced diacylglycerols, ceramides, lysophospholipids, and ether-linked phosphatidylethanolamine. In contrast, acylcarnitines, short-chain fatty acids, organic acids, and ether-linked phosphatidylcholine increased significantly. Changes in certain lipid species (e.g., saturated and monounsaturated fatty acid-containing glycerolipids, sphingadienine-based very long-chain sphingolipids, and organic acids) were closely associated with clinical glycemic parameters. Six baseline bioactive sphingolipids primarily predicted changes in fasting plasma glucose. In addition, a number of baseline lipid species, mainly diacylglycerols and triglycerides, were predictive of clinical changes in hemoglobin A1c, insulin and homeostasis model assessment of insulin resistance. ConclusionsNewly discovered serum lipidomic alterations and the associated changes in lipid-clinical variables suggest broad metabolic reprogramming related to diet-mediated glycemic control. Novel lipid predictors of glycemic outcomes could facilitate early stratification of individuals with prediabetes who are metabolically less responsive to weight loss, enabling more tailored intervention strategies beyond 1-size-fits-all lifestyle modification advice.The PREVIEW lifestyle intervention study was registered at clinicaltrials.gov as NCT01777893 (https://clinicaltrials.gov/study/NCT01777893).

Ensemble Machine Learning on the Fusion of Sentinel Time Series Imagery with High-Resolution Orthoimagery for Improved Land Use/Land Cover Mapping

In the United States, several land use and land cover (LULC) data sets are available based on satellite data, but these data sets often fail to accurately represent features on the ground. Alternatively, detailed mapping of heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences among scenes, landscape heterogeneity, and computational limitations. Various machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study were to evaluate bagging (Random Forest, RF), boosting (Gradient Boosting Machines [GBM] and extreme gradient boosting [XGB]), and stacking ensemble ML models. We used these techniques on a time series of Sentinel 2A data and NAIP orthoimagery to create a LULC map of a portion of Irion and Tom Green counties in Texas (USA). We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. We then compared accuracy based on random cross-validation without accounting for spatial autocorrelation and target-oriented cross-validation accounting for spatial structures of the training data set. Comparison of random and target-oriented cross-validation results showed that autocorrelation in the training data offered overestimation ranging from 2% to 3.5%. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base learners. We show that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use/land cover classification using geographic object-based image analysis.

PyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data.

High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet, the extent of noise, including false positives, associated with these methodologies, is difficult to quantify as experimental approaches for validating the results are generally low throughput. To address this, we introduce pyRBDome, a pipeline for enhancing RNA-binding proteome data in silico. It aligns the experimental results with RNA-binding site (RBS) predictions from distinct machine-learning tools and integrates high-resolution structural data when available. Its statistical evaluation of RBDome data enables quick identification of likely genuine RNA-binders in experimental datasets. Furthermore, by leveraging the pyRBDome results, we have enhanced the sensitivity and specificity of RBS detection through training new ensemble machine-learning models. pyRBDome analysis of a human RBDome dataset, compared with known structural data, revealed that although UV-cross-linked amino acids were more likely to contain predicted RBSs, they infrequently bind RNA in high-resolution structures. This discrepancy underscores the limitations of structural data as benchmarks, positioning pyRBDome as a valuable alternative for increasing confidence in RBDome datasets.

Application of hybridized ensemble learning and equilibrium optimization in estimating damping ratios of municipal solid waste

The dynamic analysis of municipal solid waste (MSW) is essential for optimizing landfills and advancing sustainable development goals. Assessing damping ratio (D), a critical dynamic parameter, under laboratory conditions is costly and time-consuming, requiring specialized equipment and expertise. To streamline this process, this research leveraged several novel ensemble machine learning models integrated with the equilibrium optimizer algorithm (EOA) for the predictive analysis of damping characteristics. Data were gathered from 153 cyclic triaxial experiments on MSW, which examined the age, shear strain, weight, frequency, and percentage of plastic content. Analysis of a correlation heatmap indicated a significant dependence of D on shear strain within the collected MSW data. Subsequently, five advanced machine learning methods—adaptive boosting (AdaBoost), gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost), random forest (RF), and cubist regression—were employed to model D in landfill structures. Among these, the GBRT-EOA model demonstrated superior performance, with a coefficient of determination (R2) of 0.898, root mean square error of 1.659, mean absolute error of 1.194, mean absolute percentage error of 0.095, and an a20-index of 0.891 for the test data. A Shapley additive explanation analysis was conducted to validate these models further, revealing the relative contributions of each studied variable to the predicted D-MSW. This holistic approach not only enhances the understanding of MSW dynamics but also aids in the efficient design and management of landfill systems.

Comparative study on the prediction of the unconfined compressive strength of the one-part geopolymer stabilized soil by using different hybrid machine learning models

With the development of green, low-carbon, and sustainable economic systems, the issues of high pollution and energy consumption in construction materials have become increasingly prominent. This study focuses on adopting one-part geopolymer (OPG) in soil stabilization for underground engineering, which exhibits environmental and low-carbon advantages. The unconfined compressive strength (UCS) serves as a crucial parameter for assessing stabilized soil’s performance. However, it is necessary to conduct a large number of experiments, inducing high costs and time consumption. In this study, one multiple linear regression model, one Decision Tree (DT) model, five ensemble machine learning (ML) models (i.e. Random Forest [RF], Extra Tree [ET], Gradient Boosting [GB], Gradient Boosting Decision Tree [GBDT], and Extreme Gradient Boosting [XGBoost]), and hybrid models of those single ensemble models with Particle Swarm Optimization (PSO) (i.e. PSO-RF, PSO-ET, PSO-GB, PSO-GBDT, and PSO-XGBoost) were adopted and compared to achieve better prediction on the UCS of the OPG-stabilized soil. Furthermore, the interpretable method including SHAP and PDP (1D and 2D), was employed to investigate the precise mechanisms by which input parameters influenced the output label. The results revealed that the multiple linear regression model delivered the lowest accuracy, and PSO-XGBoost and PSO-ET exhibited the best performance on the prediction of the UCS with R2 value of 0.9964 and 0.9928, respectively. In addition, Curing time exerted the most significant impact on the UCS, followed by FA/GGBFS, Molarity, Water/Binder, and NaOH/Precursor. Compared to the SHAP method, the PDP offered a more intuitive approach to reveal the relationship between the inputs and output. The outcome of the study shed new light on the application of ML models in the prediction of the OPG-stabilized soil’s performance in underground engineering.

An ensemble machine learning model for predicting one-year mortality in elderly coronary heart disease patients with anemia

ObjectiveThis study was designed to develop and validate a robust predictive model for one-year mortality in elderly coronary heart disease (CHD) patients with anemia using machine learning methods.MethodsDemographics, tests, comorbidities, and drugs were collected for a cohort of 974 elderly patients with CHD. A prospective analysis was performed to evaluate predictive performances of the developed models. External validation of models was performed in a series of 112 elderly CHD patients with anemia.ResultsThe overall one-year mortality was 43.6%. Risk factors included heart rate, chronic heart failure, tachycardia and β receptor blockers. Protective factors included hemoglobin, albumin, high density lipoprotein cholesterol, estimated glomerular filtration rate (eGFR), left ventricular ejection fraction (LVEF), aspirin, clopidogrel, calcium channel blockers, angiotensin converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs), and statins. Compared with other algorithms, an ensemble machine learning model performed the best with area under the curve (95% confidence interval) being 0.828 (0.805–0.870) and Brier score being 0.170. Calibration and density curves further confirmed favorable predicted probability and discriminative ability of an ensemble machine learning model. External validation of Ensemble Model also exhibited good performance with area under the curve (95% confidence interval) being 0.825 (0.734–0.916) and Brier score being 0.185. Patients in the high-risk group had more than six-fold probability of one-year mortality compared with those in the low-risk group (P < 0.001). Shaley Additive exPlanation identified the top five risk factors that associated with one-year mortality were hemoglobin, albumin, eGFR, LVEF, and ACEIs/ARBs.ConclusionsThis model identifies key risk factors and protective factors, providing valuable insights for improving risk assessment, informing clinical decision-making and performing targeted interventions. It outperforms other algorithms with predictive performance and provides significant opportunities for personalized risk mitigation strategies, with clinical implications for improving patient care.

Leveraging ensemble machine learning and multimodal video complexity for better prediction of video difficulty in second language

ABSTRACT Research into multimodal language learning has shown that video materials are engaging and effective in teaching and learning language skills. While previous studies have predominantly focused on how to make learning from video more effective, little research has been directed at estimating video content difficulty. This study, therefore, aims to examine the efficacy of developing predictive models using different configurations of ensemble machine learning approaches (averaging, bagging, boosting, and stacking) and video complexity features (acoustic, phonological, lexical, syntactic, and visual complexity features). A corpus of 640 videos that were subjectively rated for content difficulty by 322 English language learners was used to build and evaluate predictive models. The results demonstrated that ensemble machine learning models substantially outperformed baseline regression models in predicting video difficulty. The findings are discussed in terms of their applications in second language learning and assessment.

Assessing the risk of subsoil compaction in croplands using the soil compaction stratification ratio as an indicator

AbstractAccurate prediction of subsoil compaction in cropland areas is important for food security and sustainable agricultural production development. This study evaluated the validity and advantages of the soil compaction stratification ratio (SR) as an indicator for assessing the risk of subsoil compaction and presented a theoretical framework for predicting and evaluating subsoil compaction risk. Geographic information system and remote sensing technologies were utilized in combination with a state‐of‐the‐art ensemble machine learning model to quantitatively predict the spatial distribution and uncertainty of subsoil compaction risk in croplands. The results showed that the subsoil bulk density (BD) was significantly greater than the topsoil BD in Shaanxi Province, China. Compared with that of the subsoil BD, the stratification ratio of the bulk density (SRBD) exhibited greater sensitivity to subsoil compaction, and it accurately reflected spatial variations in subsoil compactness. The SRBD exhibited obvious spatial heterogeneity, and the mean annual precipitation, elevation, and mean land surface temperature substantially influenced its spatial distribution. The critical thresholds of the SRBD for the low‐risk, medium‐risk, and high‐risk groups were 1.01, 1.21, and 1.32, respectively. Low subsoil compaction dominated in the mountainous and hilly areas, whereas the Guanzhong Plain and Hanzhong Basin exhibited medium levels of subsoil compaction. Some croplands in the central Guanzhong Plain experienced severe subsoil compaction issues. In conclusion, the soil compaction SR was found to be an effective indicator of subsoil compaction by effectively eliminating soil background differences. This approach has potential applications in cropland management, digital soil mapping, and early warning of soil compaction risk.

AI-driven design for the compressive strength of ultra-high performance geopolymer concrete (UHPGC): From explainable ensemble models to the graphical user interface

Ultra-high performance geopolymer concrete (UHPGC) has become of interest in recent years as a more economical and sustainable alternative while offering similar mechanical performance to ordinary ultra-high performance concrete (UHPC). The lack of an effective mix design methodology has inhibited the widespread use of UHPGC, despite its potential. This paper adopted an artificial intelligence (AI)-based approach to accurately model the compressive strength (CS) of UHPGC, a critical parameter to ensure structural integrity and reliability. Ensemble machine learning (ML) models such as RF, XGBoost, LightGBM and AdaBoost, which have been very popular lately, were selected as AI algorithms. For the establishment of these models, a comprehensive and reliable dataset of 181 test results was used, including 13 input features. Additionally, feature importance and Shapley additive explanations (SHAP) analyses were used to ensure the explainability of the prediction models and tackle the "black box" challenge of ML models. The results obtained revealed that all ensemble models successfully predicted the CS of UHPGC; in particular, the XGBoost model consistently exhibited the best overall performance in terms of higher R2 (0.948) and lower RMSE (6.68), MAE (4.73), MAPE (4 %), and mean error value (1.095), in the test phase. Moreover, feature importance and SHAP analyses revealed that the most influential features on the CS of UHPGC were age, fiber and silica fume, sodium silicate (Na2SiO3), and water content. Lastly, a graphical user interface (GUI) was developed to easily predict the CS of UHPGC in practical applications.

Hyperbolic mode resonance-based acetone optical sensors powered by ensemble learning

The current work describes and compares the performance of hyperbolic mode resonance (HMR)-based sensors for the detection of acetone at parts per billion (ppb) concentrations using ensemble machine learning (EML) techniques. A pair of HMR based-sensors with resonances located in the visible (VIS) and mid infrared (MIR) regions were obtained in order to train a set of ensemble machine learning models. The response of the detection system formed by both devices in the VIS and MIR regions, with the help of the EML system, allowed the limit of detection (LoD) of the sensors to be reduced by an order of magnitude. It is the first time that HMR-based sensors are shown in practical applications, at the same time that their performance is improved using EML techniques. This opens new avenues for the use of this type of HMR-based sensors for the detection of other substances, in addition to improving the performance of any optoelectronic sensor using EML techniques.

Improving forest age prediction performance using ensemble learning algorithms base on satellite remote sensing data

Forest age plays a crucial role in assessing forest structure, carbon sinks, and other ecological functions. How to estimate forest age by satellite remote sensing data has been a hot research topic. This study focused on the forests of Zhejiang Province, utilizing Landsat 5 as the remote sensing data source to extract distribution information of broadleaf and coniferous forests. Then, the remote sensing feature variables were screened, and the age of broadleaf forests and coniferous forests was estimated by using the multiple linear regression model MLR, the machine learning model (K-nearest neighbor method regression model KNN, support vector regression model SVR), and the ensemble learning model (adaptive boosting model AdaBoost, random forest model RF, and eXtreme gradient boosting XGBoost). After analyzing the forest age estimation results from different models, the best-performing model was selected to create a spatial distribution map of forest age in Zhejiang Province. The study shows that the ensemble machine learning model can better realize the remote sensing inversion of forest age. The optimal model for broadleaf forests is the XGBoost model, with a coefficient of determination R2 of 0.832, a root mean square error (RMSE) of 5.823a and a relative root mean square error (rRMSE) of 21.009%. And the top model for coniferous forests is the RF model, with R2 of 0.800, RMSE of 5.076a and rRMSE of 19.782%. Compared with the MLR model, the best broadleaf and coniferous forest age inversion models improved the R2 by 75.120% and 82.500%, and reduced the RMSE by 52.674% and 47.540%, and reduced the rRMSE by 52.703% and 47.480% respectively. Additionally, the analysis revealed that 50% of the remote sensing feature variables involved in forest age inversion are texture features, indicating that texture is an important feature variable for the construction of forest age remote sensing inversion models.

CommentClass: A Robust Ensemble Machine Learning Model for Comment Classification

Enormous amounts of data are generated in the form of feedback or comments from online platforms such as social media, e-commerce, education, and programming. This feedback and comments hold significant value for making important strategic decisions; therefore, effectively analyzing them poses a major challenge. This research addresses the imperative need for an efficient comment classification model. To fill this research gap, we propose a robust ensemble machine learning (ML) model called CommentClass (RF+AdaBoost+SVM+Soft-Voting), specifically designed for the comment classification task. First, we developed eight (08) pipelines using various combinations of ML algorithms. Next, the fundamental ensemble techniques such as stacking, blending, hard-voting, soft-voting, and averaging are incorporated into these pipelines to improve comment classification performance. These ensemble models are able to discern the latent characteristics of diverse text comments, classifying them to achieve superior accuracy. The proposed CommentClass ensemble model achieved an impressive accuracy and F1-score of approximately 98% for comment classification on the YouTube dataset. This result represents an improvement in accuracy by approximately +3% compared to prior research on the same dataset. Moreover, the proposed CommentClass model obtained higher F1-scores of 90.26%, 87.04%, and 75.74%, on the Spambase, IMDB, and Twitter datasets, respectively, compared to other sophisticated models. Furthermore, the proposed CommentClass model exhibited significant accuracy on the SMS dataset and two distinct synthetic datasets.

Improving Shear Strength Prediction in Steel Fiber Reinforced Concrete Beams: Stacked Ensemble Machine Learning Modeling and Practical Applications

Existing machine learning (ML) models often encounter challenges in accurately predicting the shear strength of steel fiber reinforced concrete (SFRC) beams, mainly due to a lack of generalization. This study introduces an advanced stacked ensemble ML architecture to overcome this limitation by utilizing a comprehensive data set of 394 experimental observations and a 20-feature matrix. The model exhibits exceptional performance with a mean absolute error of 0.391 and a correlation coefficient (R2) of 93.7%, and surpasses traditional ML algorithms. Furthermore, a sensitivity analysis of the developed model yields that shear strength is highly responsive to the shear span-to-effective depth ratio, with an increase from 1 to 4 resulting in a significant reduction (about 50%) in strength. Increasing the percentage of longitudinal steel from 1 to 2% leads to a 14.6% gain, whereas doubling its yield strength has a more modest 3.7% effect. Increasing the compressive strength of concrete from 25 to 50 MPa, notably increases the shear strength by 19.6%. Fiber length, diameter, and aspect ratio exhibit varying impacts, with shear strength most influenced by the fiber volume fraction, which leads to a peak enhancement of 30.7% at 2% fibrous volume; however, the tensile strength of fibers minimally affects the shear strength. Additionally, this research presents a simplified empirical model to predict the shear strength of SFRC beams based on the key determinants. This model employs the iterative Gauss–Newton algorithm, demonstrates reasonable predictive capability, and boasts an R2 of 83.3% and mean prediction-tested strengths of around 1.039. The practical implications of these findings are substantial for the construction industry as they enable a more accurate and reliable design of SFRC beams, optimize material usage, and potentially reduce construction costs as well as enhance structural safety.

Enhancing local road pavement condition prediction using Bayesian-optimized ensemble machine learning and adaptive synthetic sampling technique

ABSTRACT Pavement condition prediction helps road agencies to schedule maintenance, rehabilitation, and reconstruction, and to allocate limited funds and resources to such activities. Compared to state highways, pavement performance prediction for local roads has received relatively little attention in the literature due to perceptions of low importance, low levels of investment in data collection, poor data quality, and high variation within the data elements. Additionally, local road pavement condition data may suffer from dataset imbalance, often leading to unreliable condition predictions. Hence, this paper introduces a methodology to predict local pavement condition using various single estimator and ensemble machine learning (ML) models along with the adaptive synthetic sampling method. The study develops nine (9) Bayesian-optimised ML models: category boosting (CatBoost), adaptive boosting, decision tree, extra trees, gradient boosting, light gradient-boosting machine, k-nearest neighbour, random forest, and artificial neural network. The ensemble ML and CatBoost were found to exhibit the best model performance, with an average testing accuracy of 0.82, specificity of 0.81, sensitivity of 0.63, and F-measure of 0.61. These results underscore the efficacy of ensemble ML models in pavement condition prediction. The proposed approach can be beneficial to local road agencies in their long-term planning, scheduling, and budgeting.

Predicting sexually transmitted infections among men who have sex with men in Zimbabwe using deep learning and ensemble machine learning models.

There is a substantial increase in sexually transmitted infections (STIs) among men who have sex with men (MSM) globally. Unprotected sexual practices, multiple sex partners, criminalization, stigmatisation, fear of discrimination, substance use, poor access to care, and lack of early STI screening tools are among the contributing factors. Therefore, this study applied multilayer perceptron (MLP), extremely randomized trees (ExtraTrees) and XGBoost machine learning models to predict STIs among MSM using bio-behavioural survey (BBS) data in Zimbabwe. Data were collected from 1538 MSM in Zimbabwe. The dataset was split into training and testing sets using the ratio of 80% and 20%, respectively. The synthetic minority oversampling technique (SMOTE) was applied to address class imbalance. Using a stepwise logistic regression model, the study revealed several predictors of STIs among MSM such as age, cohabitation with sex partners, education status and employment status. The results show that MLP performed better than STI predictive models (XGBoost and ExtraTrees) and achieved accuracy of 87.54%, recall of 97.29%, precision of 89.64%, F1-Score of 93.31% and AUC of 66.78%. XGBoost also achieved an accuracy of 86.51%, recall of 96.51%, precision of 89.25%, F1-Score of 92.74% and AUC of 54.83%. ExtraTrees recorded an accuracy of 85.47%, recall of 95.35%, precision of 89.13%, F1-Score of 92.13% and AUC of 60.21%. These models can be effectively used to identify highly at-risk MSM, for STI surveillance and to further develop STI infection screening tools to improve health outcomes of MSM.

Defoliator outbreaks track with warming across the Pacific coastal temperate rainforest of North America

The biogeography of irruptive insect herbivores is determined by host availability and climate conditions. As such, outbreak distributions are sensitive to climatic change, especially across large latitudinal gradients. Here, we investigate the outbreak distributions of two understudied defoliators, hemlock sawfly Neodiprion tsugae (Hymenoptera) and western blackheaded budworm Acleris gloverana (Lepidoptera), that have both recently impacted the greatest land area recorded across the Pacific coastal temperate rainforest since the establishment of aerial survey programs. We compiled polygon‐based estimates of insect damage collected by aerial observers, forest inventory, and downscaled climatic data to develop gridded estimates of bioclimatic conditions across the extent of the Pacific coastal temperate rainforest, including the continental United States, British Columbia and Alaska. We leveraged these data to develop ensemble machine learning models with the goal of predicting the outbreak distribution of each insect. In this manuscript we: 1) describe the historical patterns of defoliator outbreaks, 2) identify and describe climatic conditions associated with outbreaks in both species and 3) assess whether historic outbreaks have tracked geographic shifts in climate conditions across the region. We demonstrate that outbreaks of hemlock sawfly and western blackheaded budworm have been observed across the Pacific coastal temperature rainforests of North America in each decade since the establishment of the Canadian and United States aerial survey programs. The distribution of outbreaks by both insects were best explained by host availability, a limited range of spring, summer, and winter temperatures, and minimum precipitation. Finally, we demonstrate that outbreaks have tracked the poleward shift in suitable climate over the last century. This study establishes a baseline understanding of the climatic constraints and biogeographic patterns of historic sawfly and budworm outbreaks across the Pacific coastal temperate rainforest and emphasizes the overarching importance of climate in driving the irruptive dynamics of these defoliator species.Keywords: climate envelope, defoliators, hemlock sawfly, Pacific coastal temperate rainforest, population dynamics, western blackheaded budworm

Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review.

With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-making by such algorithms is not trustworthy for clinicians and is considered a black-box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer-reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model-agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP model primarily explained tree-based ensemble machine learning models. The most common reason is that SHAP is model agnostic, which makes it both popular and useful for explaining any model prediction. Additionally, it is relatively easy to implement effectively and completely suits performant models, such as tree-based models. Explainable AI improves the transparency, interpretability, fairness, and trustworthiness of AI-enabled health systems and medical devices and, ultimately, the quality of care and outcomes.

Automated signal‐based evaluation of dynamic cone resistance via machine learning for subsurface characterization

AbstractDynamic cone resistance (DCR) is a recently introduced soil resistance index that has the unit of stress. It is determined from the dynamic response at the tip of an instrumented dynamic cone penetrometer. However, DCR evaluation is generally a manual, time‐consuming, and error‐prone process. Thus, this study investigates the feasibility of determining DCR using a stacked ensemble (SE) machine learning (ML) model that utilizes signals obtained from dynamic cone penetration testing. Two ML experiments revealed that using only force signals provides more accurate predictions of DCR. In addition, the SE technique outperformed the base learning algorithms in both cases. Overall, the findings suggest that ML techniques can be used to automate the analysis of DCR with force and acceleration signals.

Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis: Evidence from Shimla district of North-west Indian Himalayan region

Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis: Evidence from Shimla district of North-west Indian Himalayan region