Machine Learning-Based Classification of Suspension Droplet-Solid Wall Impacts for Control of Droplet Fragmentation

Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental condition that affects millions of individuals globally. This study leverages the publicly available UCI Machine Learning ASD Screening Dataset, which contains behavioral questionnaire responses from children aged 4-11 years, to develop and compare multiple machine learning (ML) models for early ASD diagnosis. This study applies SHAP (SHapley Additive exPlanations) to interpret and analyze the feature importance of various attributes in the dataset. Advanced ML models like Extreme Gradient Boosting (XGBoost), Random Forest (RF), Extra Trees Classifier (ETC), Support Vector Machines (SVM), and Multilayer Perceptrons (MLPs) were implemented to evaluate and compare their performance in predicting early ASD. The developed ASD model underwent a rigorous evaluation through comprehensive performance metrics, including accuracy, precision, recall, F1-score, specificity, and Matthews correlation coefficient. Ranking-based metrics, such as area under the receiver operating characteristic curve (AUROC), provided additional insights into classification robustness, while 10-fold cross-validation ensured reliability and generalizability. Results indicate that XGBoost achieved the highest diagnostic performance (accuracy = 97.8%, AUROC = 0.99), followed closely by MLP, with RF and ETC offering balanced performance and interpretability. SHAP analysis identified key behavioral features influencing model predictions, enabling explainable AI-driven insights. These findings suggest that ML-based tools can support clinicians in identifying ASD earlier, allowing for timelier interventions in disability services, particularly in underserved populations. While results are promising, the use of secondary data and the lack of clinical validation remain limitations, highlighting the need for real-world pilot testing. These findings underscore the transformative potential of ML techniques in enhancing ASD diagnostics. Despite their promise, the results are constrained by reliance on secondary data and the absence of clinical validation, underscoring the need for real-world pilot testing. Nonetheless, incorporating these models into clinical practice could accelerate early detection, facilitate personalized interventions, and optimize resource allocation for improved ASD management outcomes.

Examining the electrical discharge machining (EDM) process is challenging in manufacturing technology due to the complexity of the physical events that take place in the gaps between electrodes. In this study, we examined the EDM process in detail and developed multiple machine learning (ML) models to describe the relationship between the EDM independent (process parameters) and dependent (responses) variables. The collected experimental data was used to train the machine learning models. According to the results, the GPR model outperformed other ML models across different materials, with average RMSE values of 0.9234 and 3.0216 for the material removal rate (MRR) and surface roughness (Sa), respectively, highlighting the effectiveness of ML tools at modeling complex machining processes, such as EDM. In addition, as a practical implication, this study opens the door to employing the developed ML models to predict the EDM process performance.

Purpose: Accurate survival prediction is essential for optimizing the treatment planning in patients with castration-resistant prostate cancer (CRPC). However, the traditional statistical models often underperform due to limited variable inclusion and an inability to account for complex, multidimensional data interactions. Methods: We retrospectively collected 46 clinical, laboratory, and pathological variables from 801 patients with CRPC, covering the disease course from the initial disease diagnosis to CRPC progression. Multiple machine learning (ML) models, including random survival forests (RSFs), XGBoost, LightGBM, and logistic regression, were developed to predict cancer-specific mortality (CSM), overall mortality (OM), and 2- and 3-year survival status. The dataset was split into training and test cohorts (80:20), with 10-fold cross-validation. The performance was assessed using the C-index for regression models and the AUC, accuracy, precision, recall, and F1-score for classification models. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: Over a median follow-up of 24 months, 70.6% of patients experienced CSM. RSFs achieved the highest C-index in the test set for both CSM (0.772) and OM (0.771). For classification tasks, RSFs demonstrated a superior performance in predicting 2-year survival, while XGBoost yielded the highest F1-score for 3-year survival. The SHAP analysis identified time to first-line CRPC treatment and hemoglobin and alkaline phosphatase levels as key predictors of survival outcomes. Conclusion: The RSF and XGBoost ML models demonstrated a superior performance over that of traditional statistical methods in predicting survival in CRPC. These models offer accurate and interpretable prognostic tools that may inform personalized treatment strategies. External validation and the integration of emerging therapies are warranted for broader clinical applicability.

Recognition and prediction of individual thermal comfort requirement based on local skin temperature

An ensemble approach for enhanced Day-Ahead price forecasting in electricity markets

BackgroundOsteogenesis imperfecta (OI) is a genetic disorder characterized by low bone mass, bone fragility and short stature. There is a significant gap in knowledge regarding the growth patterns across different types of OI, and the prediction of height in individuals with OI was not adequately addressed. In this study, we described the growth patterns and predicted the height of individuals with OI employing multiple machine learning (ML) models. Accurate height prediction enables effective monitoring and facilitates the development of personalized intervention plans for managing OI.MethodThis study included cross-sectional data for 323 participants with OI, and the median height Z-score for OI types I, III and IV were − 0.62 (-5.93 ~ 3.24), -3.97 (-10.44 ~ -0.02) and − 1.64 (-6.67 ~ 2.44), respectively. Based on the cross-sectional data of participants, the height curves across different gender and OI types were plotted and compared. Subsequently, feature selection techniques, specifically the filter and wrapper methods, were employed to identify predictive factors for the height of participants. Finally, multiple machine learning (ML) models were constructed for height prediction, and the performance of each model was systematically evaluated.ResultsThe analysis of height curves revealed that male with OI are significantly taller than female with OI from the age of 14 (p = 0.045), individuals with OI type III are statistically shorter than those with OI types I and IV starting from 3 years old (p = 0.006), and those with OI type IV are statistically shorter than those with OI type I from the age of 10 (p = 0.028). The application of filter and wrapper methods identified gender (p = 0.001), age (p < 0.001), Sillence types (p = 0.007), weight Z-score (p < 0.001) and aBMD Z-score (p = 0.021) as significant predictive factors for height. The optimal performance of predictive models was registered by gradient boosting classifier (GB) (bias = 5.783, accuracy = 92.59%, R2 = 0.828), random forest (RF) (bias = 6.155, accuracy = 90.12%, R2 = 0.788), ensemble machine learning (EML) (bias = 6.250, accuracy = 91.36%, R2 = 0.825) and deep neuron networks (DNNs) (bias = 6.223, accuracy = 90.12%, R2 = 0.821).ConclusionThis study analyzed a large cohort of individuals with OI and provided detailed height patterns across different gender and OI types that are crucial for assessing overall growth. Gender, age, Sillence types, weight Z-score and aBMD Z-score were identified as predictive factors for height. The predictive models of GB, RF, EML and DNNs had higher accuracy to evaluate the height of individuals with OI. This study allows guardians and physicians to timely monitor the height parameters, and facilitate the creation of personalized intervention schedules tailored to the needs of individuals with OI.

Multiple machine learning (ML) algorithms were developed using artificial intelligence, including Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN), to predict the yield of biodiesel production in an acid-catalyzed alcoholysis process using green seed canola oil. Catalyst loading, methanol-to-oil (M/O) molar ratio, and reaction time were considered as input parameters, while the yield of biodiesel production was selected as the output parameter. The performance of the developed ML models was assessed using evaluation metrics such as the coefficient of determination (R2) and the root mean squared error (RMSE). The R2 values obtained for LR, RF, DT, and KNN models were 0.80, 0.95, 0.97, and 0.84, respectively. Furthermore, the corresponding RMSE values for these models were 2.48, 1.51, 0.89, and 4.51, respectively. According to the results, the DT model exhibited superior accuracy and reliability for predicting biodiesel production compared to the other models. The values of the input variables to potentially yield the highest biodiesel output were identified through a systematic trial-and-error approach using the DT model. The results showed that a biodiesel yield of 88 % can be achieved with 5 wt% catalyst loading, a 22 M/O molar ratio, and a reaction time of 5 hours.

Robust quantification of greenhouse gas (GHG) balances in tea plantations is critical for evaluating their contribution to agricultural carbon neutrality. This study aimed to develop data-driven models to quantify soil organic carbon (SOC) sequestration and N2O emissions in Chinese tea plantations, evaluate their net GHG balance at the national scale, and assess the mitigation potential under alternative nitrogen management scenarios. Using a comprehensive national dataset, we compared multiple machine learning (ML) approaches with a conventional multiple linear regression (MLR) model to simulate N2O emissions and SOC changes in Chinese tea plantations. All ML models substantially outperformed the MLR model, with the Random Forest (RF) algorithm achieving the highest predictive accuracy. The RF models yielded R2 values of 0.68 for N2O emissions and 0.67 for SOC changes, with no significant prediction bias. Variable importance and marginal effect analyses revealed strong non-linear controls. Mineral N fertilizer input was the dominant driver of N2O emissions, followed by organic N input, soil clay content, and SOC. In contrast, SOC dynamics were primarily regulated by organic carbon inputs, tea plantation age, climate variables, and soil pH. National-scale simulations indicated an average N2O emission intensity of 9.03 kg N2O ha−1 yr−1 and a mean SOC sequestration rate of 0.88 t C ha−1 yr−1. Overall, SOC sequestration offset N2O emissions, rendering Chinese tea plantations a net GHG sink (−2525 Gg CO2-eq yr−1). Scenario analyses showed that mineral N reduction increased net GHG uptake by 1804 Gg CO2-eq, while organic fertilizer substitution achieved a substantially larger mitigation potential of 5961 Gg CO2-eq. By integrating SOC sequestration and N2O emissions within a unified modeling framework and applying machine-learning-based national-scale simulations, this study provides a more comprehensive and data-driven quantification of GHG balances in tea ecosystems, offering a scientific basis for evaluating their role in agricultural carbon neutrality strategies.

Agriculture plays a crucial role in the global economy and social stability, and accurate crop yield prediction is essential for rational planting planning and decision-making. This study focuses on crop yield Time-Series Data prediction. Considering the crucial significance of agriculture in the global economy and social stability and the importance of accurate crop yield prediction for rational planting planning and decision-making, this research uses a dataset containing multiple crops, multiple regions, and data over many years to deeply explore the relationships between climatic factors (average rainfall, average temperature) and agricultural inputs (pesticide usage) and crop yield. Multiple hybrid machine learning models such as Linear Regression, Random Forest, Gradient Boost, XGBoost, KNN, Decision Tree, and Bagging Regressor are adopted for yield prediction. After evaluation, it is found that the Random Forest and Bagging Regressor models perform excellently in predicting crop yield with high accuracy and low error.As agricultural data becomes increasingly rich and time-series prediction techniques continue to evolve, the results of this study contribute to advancing the practical application of crop yield prediction in agricultural production management. The integration of time-series analysis allows for more dynamic, data-driven decision-making, enhancing the accuracy and reliability of crop yield forecasts over time.

&amp;lt;p&amp;gt;To meet the sustainable development goals and enable protection of surface waters, there is a strong need to plan and align forest management with the needs of the environment. The number one tool to succeed in sustainable spatial planning is accurate and detailed maps. High resolution soil moisture mapping over spatial large extent remains a consistent challenge despite its substantial value in practical forestry and land management. Here we present a novel technique combining LIDAR-derived terrain indices and machine learning to model soil moisture at 2 m spatial resolution across the Swedish forest landscape with high accuracy. We used field data from about 20,000 sites across Sweden to train and evaluate multiple machine learning (ML) models. The predictor features included a suite of terrain indices generated from national LIDAR digital elevation model and other ancillary environmental features, including surficial geology, climate, land use information, allowing for adjustment of soil moisture maps to regional/local conditions. In our analysis, extreme gradient boosting (XGBoost) outperformed the other tested ML methods (Kappa = 0.69, MCC= 0.68), namely Artificial Neural Network, Random Forest, Support Vector Machine, and Na&amp;amp;#239;ve Bayes classification. The depth to water index, topographic wetness index, and wetlands derived from Swedish property maps were the most important predictors for all models. With the presented technique, it was possible to generate a multiclass model with 3 classes with Kappa and MCC of 0.58. Besides the classified moisture maps, we also investigated the potential of producing a continuous map from dry to wet soils. We argue that the probability of a pixel being classified as wet from the 2-class model can be used as an index of soil moisture from 0% &amp;amp;#8211; dry to 100% &amp;amp;#8211; wet and that such maps hold more valuable information for practical forest management than classified maps.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The soil moisture map was developed to support the need for land use management optimization by incorporating landscape sensitivity and hydrological connectivity into a framework that promotes the protection of soil and water quality. The soil moisture map can be used to address fundamental considerations, such as;&amp;lt;/p&amp;gt;&amp;lt;ul&amp;gt;&amp;lt;li&amp;gt;(i) locating areas where different land use practices can be conducted with minimal impacts on water quality;&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;(ii) guiding the construction of vital infrastructure in high flood risk areas;&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;(iii) designing riparian protection zones to optimize the protection of water quality and biodiversity.&amp;lt;/li&amp;gt; &amp;lt;/ul&amp;gt;

An Artificial Intelligence-Based Support Tool for Lumbar Spinal Stenosis Diagnosis from Self-Reported History Questionnaire

To evaluate if a machine learning approach can accurately predict antidepressant treatment outcome using electronic health records (EHRs) from patients with depression. This study examined 808 patients with depression at a New York City-based outpatient mental health clinic between June 13, 2016 and June 22, 2020. Antidepressant treatment outcome was defined based on trend in depression symptom severity over time and was categorized as either "Recovering" or "Worsening" (i.e., non-Recovering), measured by the slope of individual-level Patient Health Questionnaire-9 (PHQ-9) score trajectory spanning 6 months following treatment initiation. A patient was designated as "Recovering" if the slope is less than 0 and as "Worsening" if the slope was no less than 0. Multiple machine learning (ML) models including L2 norm regularized Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting Decision Tree (GBDT) were used to predict treatment outcome based on additional data from EHRs, including demographics and diagnoses. Shapley Additive Explanations were applied to identify the most important predictors. The GBDT achieved the best results of predicting "Recovering" (AUC: 0.7654±0.0227; precision: 0.6002± 0.0215; recall: 0.5131±0.0336). When excluding patients with low PHQ-9 scores (<10) at baseline, the results of predicting "Recovering" (AUC: 0.7254±0.0218; precision: 0.5392±0.0437; recall: 0.4431±0.0513) were obtained. Prior diagnosis of anxiety, psychotherapy, recurrent depression, and baseline depression symptom severity were strong predictors. The results demonstrate the potential utility of using ML in longitudinal EHRs to predict antidepressant treatment outcome. Our predictive tool holds the promise to accelerate personalized medical management in patients with psychiatric illnesses.

Predicting epileptic seizures is a significantly more challenging task compared to seizure detection. However, most publicly available electroencephalography (EEG) datasets are geared towards detection because the ictal phase (main symptomatic period) is annotated. In contrast, prediction requires the availability of annotated preictal and interictal phases. To this end, we designed and developed a method called MLSPred-Bench that can be used for converting any EEG big data annotated for detection into ML-ready data suitable for prediction. We apply our methods to the existing EEG data corpus to generate 12 ML-ready benchmarks comprising data for training, validating, and testing seizure prediction models. Our strategy uses different variations of seizure prediction horizon (SPH) and the seizure occurrence period (SOP) to produce >150GB of ML-ready data. To illustrate the usefulness of the generated data, we technically validate all the benchmarks using multiple machine learning (ML) and deep learning (DL) models. We hope that the generated benchmarking data will be utilized by various computational groups for their seizure prediction model development.The work can be summarized as follows:1.Extract short preictal and interictal segments from long-duration annotated EEG montages.2.Generate a comprehensive list of ML-ready benchmarks with varying SPH and SOP.3.Technically validate the generated data with multiple ML and DL models with up-to 88.73 % validation accuracy4.Opensource code and related materials are available at https://github.com/pcdslab/MLSPred-Bench.

This research is concerned with the following question: How a model trained on mental state detection using stress inducer X would perform on detecting a stress stimulated by inducer Y? This is a scenario that can be faced often in real-life. In vision applications, detection in the wild usually implies a real-life setting with minimal constraints. In this research, we investigate the generalization between a classical stress inducer, namely, mental arithmetic stressor (MAE), and the stress induced by immersive technologies, particularly virtual reality (VR)-VR-induced stress. On a group of three participants, we acquired EEG recordings following their exposure to the two types of stressors mentioned above. Using multiple machine learning (ML) classifiers, we show that even basic ML models, when trained to differentiate between the MAE stressor and the non-stress state, can generalize well when tested on differentiating between VR-based stress and the non-stress state. Significant cross-detection performance is reported using accuracy, precision, recall, and F1-score. This research provides preliminary insights on the capacity of machine learning models to generalize well in the contexts of mental state detection. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

Sedimentary exhalative (SEDEX) and volcanogenic massive sulfide (VMS) deposits represent two major classes of metallic ore systems. They commonly form in comparable geologic settings and may exhibit overlapping metallogenic signatures. Trace element compositions of pyrite and chalcopyrite can provide critical constraints for distinguishing their genetic origins. Here we compile a global geochemical dataset of pyrite and chalcopyrite and evaluate multiple machine learning (ML) classifiers, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Stacking models. Classification accuracies reach 0.96 (RF), 0.96 (XGBoost), 0.95 (MLP), and 0.99 (Stacking) for chalcopyrite, and 0.96 (RF), 0.98 (XGBoost), 0.96 (MLP), and 0.98 (Stacking) for pyrite. Key discriminative trace elements include Ni, Pb, and Se in chalcopyrite, and Ni, Se, Pb, and Sb in pyrite, reflecting distinct enrichment mechanisms in SEDEX and VMS systems. To validate model performance, we conducted LA-ICP-MS and XRF analyses of chalcopyrite and pyrite from the Dongshengmiao Zn-Pb-Cu deposit (Inner Mongolia, China). The trained ML classifiers consistently classify fine-grained pyrite and chalcopyrite from Dongshengmiao as VMS-related. Integrated with previous petrological, geochemical, and isotopic evidence, these results suggest that submarine volcanic activity played a dominant role in ore formation. Our findings demonstrate the robustness of ML-based mineral chemistry discrimination and provide new insights into the genesis of the Dongshengmiao deposit.