Non-linear Machine Learning Algorithms Research Articles

Neural networks for geospatial data

–Analysis of geospatial data has traditionally been model-based, with a mean model, customarily specified as a linear regression on the covariates, and a Gaussian process covariance model, encoding the spatial dependence. While non-linear machine learning algorithms like neural networks are increasingly being used for spatial analysis, current approaches depart from the model-based setup and cannot explicitly incorporate spatial covariance. We propose NN-GLS, embedding neural networks directly within the traditional Gaussian process (GP) geostatistical model to accommodate non-linear mean functions while retaining all other advantages of GP, like explicit modeling of the spatial covariance and predicting at new locations via kriging. In NN-GLS, estimation of the neural network parameters for the non-linear mean of the Gaussian Process explicitly accounts for the spatial covariance through use of the generalized least squares (GLS) loss, thus extending the linear case. We show that NN-GLS admits a representation as a special type of graph neural network (GNN). This connection facilitates the use of standard neural network computational techniques for irregular geospatial data, enabling novel and scalable mini-batching, backpropagation, and kriging schemes. We provide methodology to obtain uncertainty bounds for estimation and predictions from NN-GLS. Theoretically, we show that NN-GLS will be consistent for irregularly observed spatially correlated data processes. We also provide a finite sample concentration rate, which quantifies the need to accurately model the spatial covariance in neural networks for dependent data. To our knowledge, these are the first large-sample results for any neural network algorithm for irregular spatial data. We demonstrate the methodology through numerous simulations and an application to air pollution modeling. We develop a software implementation of NN-GLS in the Python package geospaNN.

Sex determination based on craniometric parameters: a comparative approach between linear and non-linear machine learning algorithms

Introduction: Determining sex based on cranial characteristics is of great relevance in forensic anthropology. Most studies have employed linear methods (such as logistic regression) for this estimation with accuracies around 70%, rarely exceeding 90% accuracy. Several authors have tested non-linear models such as neural networks, support vector machines, and decision trees with good results, surpassing linear models. Objective: To compare linear models (logistic regression, linear regression, and linear discriminant analysis) with non-linear models (neural networks, extreme gradient boosting, support vector machine, naive Bayes, random forest, decision tree, k-nearest neighbors, and adaptive multivariate spline regression). Materials and Methods: 241 skulls used in this study were obtained from the collection of Center for Study and Research in Anatomy and Forensic Anthropology at Tiradentes University, Farolândia campus in Aracaju, Sergipe. Each skull in the collection has secure detailed records. Eighty-nine skulls with signs of craniotomy (n=58) or damage (n=30) and one unidentified were excluded. The 152 eligible skulls underwent cranial measurements. Using the Anaconda platform and Jupyter editor, the data were divided into a training group (80% of the sample) and then were tested (20% of the sample). Eleven machine learning algorithms, including both linear and non-linear models, were applied. Results: The best machine learning algorithm was a neural network with average accuracy of 93%, after 50 runs. The difference to logistic regression, which had an accuracy of 68%, was significantly (p-value of 0.01016). Conclusion: This study demonstrated the potential of the neural network for solving the sex classification problem. The study has a limitation in that neural networks perform better with a large volume of data, and this study used data from a single center. Nevertheless, in the future, more studies should be conducted testing neural networks with larger samples and skulls from other continents.

The non-linear dynamics of South Australian regional housing markets: A machine learning approach

Traditional linear models often struggle to capture regional housing markets' complex, non-linear dynamics. This study addresses this gap by developing and applying advanced machine learning algorithms to unlock unique insights into South Australian housing price behavior. Leveraging a comprehensive dataset of over 10,000 regional house sales from 2010 to 2021, we explore the non-linear relationships between housing prices and microeconomic factors (e.g., house size, land area, building quality) and socioeconomic characteristics (e.g., proximity to amenities and income levels). Our analysis employs a multi-step approach, including feature engineering, spatial data integration, correlation tests, multilevel modeling, and non-linear machine learning algorithms including Decision Tree, Random Forest, Gradient-Boosted Tree, and Artificial Neural Network. The key finding is that machine learning models outperform traditional econometric models in predicting regional housing prices, with higher accuracy and greater goodness of fit. Furthermore, we identify specific non-linear relationships, such as the increasing marginal impact of proximity to the sea on house prices as distance decreases. These findings offer valuable insights for policymakers, real estate professionals, and stakeholders, informing regional planning, infrastructure provision, and economic development strategies. This study sheds light on the complex dynamics of South Australian housing markets and lays the foundation for further research.

Autonomous flight performance optimization of fixed-wing unmanned aerial vehicle with morphing wingtip

PurposeThe purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.Design/methodology/approachThe main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.FindingsLongitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.Originality/valueThis paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

Forecasting Stock Market Realized Volatility using Random Forest and Artificial Neural Network in South Africa

Volatility is often used as a key input into several financial models, yet there is still no consensus on the best-performing model in forecasting stock market returns volatility. Conventional time series models such as GARCH are the preferred models in the literature. However, this project aims to first adopt two novel non-linear machine learning algorithms, namely the Random Forest and Artificial Neural Network (ANN). The project then compares the performance of these two models in predicting stock market realized volatility for the JSE Basic Material Index (JBIND) and the JSE Financials Index (JFIN) over a period of five years. Based on the results of the project, the Random Forest model outperformed the ANN model for both the JFIN and JBIND index. Lastly, the COVID effect on the model’s performance was also considered and the results show that the negative impact of COVID on the model’s performance is ambiguous.

Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms

The corrosion of materials poses a significant challenge in various industries, leading to substantial economic impacts. In this context, pyrimidine compounds emerge as promising, non-toxic, cost-effective, and versatile corrosion inhibitors. However, conventional methods for identifying such inhibitors are typically time-consuming, expensive, and labor-intensive. Addressing this challenge, our study leverages machine learning (ML) to predict pyrimidine compounds corrosion inhibition efficiency (CIE). Using a quantitative structure-property relationship (QSPR) model, we compared 14 linear and 12 non-linear ML algorithms to identify the most accurate predictor of CIE. The bagging regressor model demonstrated superior performance, achieving a root mean square error (RMSE) of 5.38, a mean square error (MSE) of 28.93, a mean absolute error (MAE) of 4.23, and a mean absolute percentage error (MAPE) of 0.05 in predicting the CIE values for pyrimidine compounds. This research marks a significant advancement in corrosion science, offering a novel and efficient ML-based approach as an alternative to traditional experimental methods. It shows that machine learning can quickly and accurately determine how well organic chemical inhibitors like pyrimidine stop material corrosion. This method gives the industry a new perspective and a workable solution to a problem that has existed for a long time.
 Keywords: machine learning, corrosion inhibition, pyrimidine, QSPR model, predictive analysis

Snapshots of daily life: Situations investigated through the lens of smartphone sensing.

Daily life unfolds in a sequence of situational contexts, which are pivotal for explaining people's thoughts, feelings, and behaviors. While situational data were previously difficult to collect, the ubiquity of smartphones now opens up new opportunities for assessing situations in situ, that is, while they occur. Seizing this opportunity, the present study demonstrates how smartphones can help establish associations between the psychological perception and physical reality of situations. We employed an intensive longitudinal sampling design and investigated 9,790 situational snapshots experienced by 455 participants for 14 consecutive days. These snapshots combined self-reported situation characteristics from experience samplings with their corresponding objective cues obtained via smartphone sensing. More precisely, we extracted a total of 1,356 granular cues from different sensing modalities to account for the complexity of real-world situations. We applied linear and nonlinear machine learning algorithms to examine how well these cues predicted the perceived characteristics in terms of the Situational Eight Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception, Sociality (DIAMONDS), finding significant out-of-sample predictions for the five dimensions reflecting the situations' Duty, Intellect, Mating, pOsitivity, and Sociality. In a series of follow-up analyses, we further explored the data patterns captured by our models, revealing, for example, that those cues related to time and location were particularly informative of the respective situation characteristics. We conclude by interpreting the mapping between cues and characteristics in real-world situations and discussing how smartphone-based situational snapshots may push the boundaries of psychological research on situations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Estimating ammonium changes in pilot and full-scale constructed wetlands using kinetic model, linear regression, and machine learning

Constructed wetlands (CWs) are a widely utilized nature-based wastewater treatment method for various effluents. However, their application has been more focused on pilot and full-scale CWs with substantial surface areas and extended operation times, which hold greater relevance in practical scenarios. This study used kinetics, linear regression (LR), and machine learning (ML) models to estimate effluent ammonium in pilot and full-scale CWs. From screening 1476 papers, 24 pilot and full-scale CW studies were selected to extract data containing 15 features and 975 data points. Nine models were fit to this data, revealing that linear models were less effective in capturing CW effluent compared to nonlinear ML algorithms. For training data, the Monod kinetic model predicted the poorest performance with an RMSE of 41.84 mg/L and R2 of 0.34, followed by simple LR (RMSE 24.29 mg/L and R2 0.77) and multiple LR (RMSE 22.63 mg/L and R2 0.80). In contrast, Cubist and Random Forest achieved high performances, with an average RMSE of 12.01 ± 5.38 and an average R2 of 0.93 ± 0.07 for Cubist, and an average RMSE of 15.94 ± 10.69 and an average R2 of 0.91 ± 0.08 for RF. The trained Random Forest performed the best for new data, with an R2 of 0.93 and RMSE of 13.48 mg/L. This ML-based model is a valuable tool for efficiently estimating effluent ammonium concentration in pilot and full-scale CWs, thereby facilitating the design of systems.

Effect of data preprocessing and machine learning hyperparameters on mass spectrometry imaging models

The self-organizing map (SOM) is a nonlinear machine learning algorithm that is particularly well suited for visualizing and analyzing high-dimensional, hyperspectral time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data. Previously, we compared the capabilities of the SOM with more traditional linear techniques using ToF-SIMS imaging data. Although SOMs perform well with minimal data preprocessing and negligible hyperparameter optimization, it is important to understand how different data preprocessing methods and hyperparameter settings influence the performance of SOMs. While these investigations have been reported outside of the ToF-SIMS field, no such study has been reported for hyperspectral MSI data. To address this, we used two labeled ToF-SIMS imaging datasets, one of which was a polymer microarray dataset, while the other was semisynthetic hyperspectral data. The latter was generated using a novel algorithm that we describe here. A grid-search was used to evaluate which data preprocessing methods and SOM hyperparameters had the largest impact on the performance of the SOM. This was assessed using multiple linear regression, whereby performance metrics were regressed onto each variable defining the preprocessing-hyperparameter space. We found that preprocessing was generally more important than hyperparameter selection. We also found statistically significant interactions between several parameters studied, suggesting a complex interplay between preprocessing and hyperparameter selection. Importantly, we identified interesting trends, both dataset specific and dataset agnostic, which we describe and discuss in detail.

Modeling time series radon inventory and constraints on the submarine groundwater discharge mass balance of a well-mixed, highly dynamic estuary

Although often diffuse and over-shadowed by surface-water inputs, submarine groundwater discharge (SGD) represents a significant source of freshwater and solutes to many sensitive marine environments. Radon (222Rn) is an effective naturally occurring tracer of SGD due to its non-reactive nature and dominant terrestrial source. However, limited continuous measurements and poor constraints on temporal 222Rn variability in shallow-turbulent estuaries introduce large uncertainties to SGD rate estimation. We hypothesize that nonlinear regression analysis (e.g., generalized additive model [GAM]) and machine learning (ML) algorithms could explain radon trends and resolve limitations of the traditionally used mass balance for estimating SGD in shallow and vertically mixed estuaries, thus enabling preliminary insights related to wind-driven mixing losses using sporadic groundwater tracer measurements and publicly available hydroclimatic parameters. Cross-correlation functions and a generalized additive model [GAM] were employed to quantify atmospheric and hydrologic controls and predict, respectively, 222Rn inventory variability modulated by windspeed and direction. Two ML models, neural network (deep-learning neural network [DNN]) and decision tree (random forest [RF]) based models, were trained on an intermittent 24-month 222Rn (n = 10,660, 30 min interval) dataset (2019–2021) collected from a well-mixed northwestern Gulf of Mexico estuary (Corpus Christi Bay, Texas) with publicly available inputs (e.g., windspeed/direction, tide levels, water temperature, creek discharge and barometric pressure, n = 35,088). Both ML models can predict observed radon concentrations with r2 > 0.90, however, only the RF model is able to predict within the observed range of the 222Rn magnitude dataset and finds the relevant hydroclimatic parameters (e.g., streamflow and wind direction) highly responsible for the seasonal and daily variability in the inventory, respectively. The DNN model can more closely predict extremely high inventories, but it overpredicts lows, resulting in numerous negative values, which suggests that large data gaps in the groundwater tracer cause overfitting and underperformance of this model in a wind-mixed estuary. Using the GAM predicted 222Rn dependency on wind direction and speed, the adjusted mass balance reduces overestimation of wind-mixing losses and results in overall lower fluxes. This research provides new opportunities to: 1) predict radon inventories in regions with large temporal data gaps using only publicly available hydroclimatic parameters and 2) better constrain SGD estimates in windy coastal areas, allowing for insights that can be used to plan field excursions and management strategies for coastal water and solute budgets.

Unmanned aerial vehicle (UAV) imaging and machine learning applications for plant phenotyping

The availability of timely and accurate information about plant conditions is critical for making informed decisions for maintaining or improving agricultural productivity. However, field data collection is often laborious and time-consuming, underscoring the need for accurate and efficient methods to monitor crop growth and development. Therefore, the objective of this study was to evaluate the effectiveness of combining unmanned aerial vehicle (UAV)-based imaging and machine learning (ML) techniques for monitoring sweet corn (Zea mays var. saccharata) height, biomass, and yield. The study was conducted at the Tropical Research and Education Center (TREC) during the winter (dry) season of 2020–2021 using 16 experimental plots planted with sweet corn. The treatments were set up in a completely randomized block design (RCBD) with four irrigation treatments of 25%, 50%, 75%, and 100% full irrigation with four replications each. Field data collection included plant height, fresh and dry biomass, and yield. In addition, UAV images were collected using the DJI Matrice 210 v2 UAV (SZ DJI Technology Co., Ltd., Shenzhen, China) equipped with a MicaSense RedEdge-MX multispectral sensor (MicaSense, Seattle, WA, USA). Image processing was done with Pix4Dmapper 4.7.5 (Pix4D S.A., Prilly, Switzerland). A crop surface model, representing estimated plant height (UAVH), was calculated based on pixel-to-pixel differences between digital surface and terrain models. A simple linear regression model was used to estimate sweet corn biomass and yield from UAV images estimated plant height (UAVH). In addition, two linear algorithms known as a linear model (LM) and Lasso and elastic-net regularized generalized linear model (GLMNET) and three non-linear ML algorithms including random forest (RF), support vector machine (SVM), and k-nearest neighbor (kNN) were used to predict plant height and biomass. These algorithms were chosen due to their reliable performance and ability to learn complex non-linear relationships. Eight vegetation indices with UAVH were also used to evaluate the ML models' performance to predict plant height and biomass. Results confirmed that UAV imaging could be effectively used to estimate plant height (d = 0.99, r2 = 0.99, and MAE = 5 cm). Estimated fresh and dry biomass from UAV imaging also had good agreements with measured data with r2 values of 0.75 and 0.70, respectively. A statistically significant linear correlation between measured fresh yield at harvest and UAVH was found with d, adjusted r2, and MAE of 0.84, 0.66, and 67 g m−2, respectively. Evaluation of ML algorithms revealed that all models performed well for plant biomass estimation with d values between 0.88 and 0.99. However, the kNN and SVM outperformed all other models for biomass estimation. GMLNET performed better than other models for plant height estimation. Overall, results revealed that UAV imaging and ML models could be effectively used for monitoring plant phenotypic characteristics such as height, yield, and biomass.

Effects of sex, age, and body mass index on serum bicarbonate.

Obesity hypoventilation syndrome (OHS) is often underdiagnosed, with significant morbidity and mortality. Bicarbonate, as a surrogate of arterial carbon dioxide, has been proposed as a screening tool for OHS. Understanding the predictors of serum bicarbonate could provide insights into risk factors for OHS. We hypothesized that the bicarbonate levels would increase with an increase in body mass index (BMI), since the prevalence of OHS increases with obesity. We used the TriNetX Research Network, an electronic health record database with de-identified clinical data from participating healthcare organizations across the United States, to identify 93,320 adults without pulmonary or advanced renal diseases who had serum bicarbonate and BMI measurements within 6 months of each other between 2017 and 2022. We used linear regression analysis to examine the associations between bicarbonate and BMI, age, and their interactions for the entire cohort and stratified by sex. We also applied a non-linear machine learning algorithm (XGBoost) to examine the relative importance of age, BMI, sex, race/ethnicity, and obstructive sleep apnea (OSA) status on bicarbonate. This cohort population was 56% women and 72% white and 80% non-Hispanic individuals, with an average (SD) age of 49.4 (17.9) years and a BMI of 29.1 (6.1) kg/m2. The mean bicarbonate was 24.8 (2.8) mmol/L, with higher levels in men (mean 25.2 mmol/L) than in women (mean 24.4 mmol/L). We found a small negative association between bicarbonate and BMI, with an expected change of -0.03 mmol/L in bicarbonate for each 1 kg/m2 increase in BMI (p < 0.001), in the entire cohort and both sexes. We found sex differences in the bicarbonate trajectory with age, with women exhibiting lower bicarbonate values than men until age 50, after which the bicarbonate levels were modestly higher. The non-linear machine learning algorithm similarly revealed that age and sex played larger roles in determining bicarbonate levels than the BMI or OSA status. Contrary to our hypothesis, BMI is not associated with elevated bicarbonate levels, and age modifies the impact of sex on bicarbonate.

Machine learning methods for endocrine disrupting potential identification based on single-cell data

Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms. This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ERα), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ERα pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ERα agonists and antagonists. The results show that Support Vector Machines classify the unseen chemicals correctly with more than 96% accuracy using the proposed framework, where the preprocessing and the PCA steps play a key role in suppressing experimental noise and unraveling hidden patterns in the dataset.

The influence of nitrogen and variety on rice grain moisture content dry-down

Rice field management around maturity and harvest are some of the most difficult decisions growers face. Field drainage and harvest timing affect quality, yield, and post-harvest drying costs. These decisions are informed by grain moisture content (MC). Over three years, three sites and three varieties, we studied the field dry-down rate and time to optimal harvest MC. We showed that field-specific parameters significantly affected these characteristics, including rice variety, Nitrogen applied (NA), mid-season N uptake (NU) and dry matter (DM). Increased N and DM is associated with increased MC and thus delays time to harvest. We developed models based on linear regression and nonlinear machine learning (ML) algorithms, including parameters describing these field-specific conditions. Cross validation across the three years provided a realistic expectation of model prediction errors. A linear model with the addition of nonlinear predictors achieved competitive performance compared with more complex and less interpretable ML models. When MC was modeled as a function of days since heading, similar or better accuracy was achieved to using accumulated weather parameters. Moisture content was predicted with mean absolute error of 2.1 %. The predicted time from heading to harvest MC was improved by the inclusion of field-specific parameters (N and variety) from mean absolute error of 6.8 days to 5.7 days. The final linear regression model explained 80 % of the moisture variability in the dataset, and provided estimates of dry-down rates, moisture as a function of time, and time to reach harvest moisture. This study shows the importance of including field-specific parameters when estimating of rice harvest timing, and provides methods to model these effects.

Comparison of linear and non-linear machine learning models for time-dependent readmission or mortality prediction among hospitalized heart failure patients

Although many models are available to predict prognosis of heart failure patients, most tools combining survival analysis are based on proportional hazard model. Non-linear machine learning algorithms would overcome the limitation of the time-independent hazard ratio assumption and provide more information in readmission or mortality prediction among heart failure patients.The present study collected the clinical information of 1796 hospitalized heart failure patients surviving during hospitalization in a Chinese clinical center from December 2016 to June 2019. A traditional multivariate Cox regression model and three machine learning survival models were developed in derivation cohort. Uno's concordance index and integrated Brier score in validation cohort were calculated to evaluate the discrimination and calibration of different models. Time-dependent AUC and Brier score curves were plotted to assess the performance of models at different time phases.

Applicability of machine learning techniques in predicting wheat yield based on remote sensing and climate data in Pakistan, South Asia

Machine learning (ML) algorithms perform better than classical statistical approaches to explore hidden nonlinear relationships. In this context, the goal of this research is to predict wheat yield utilizing remote sensing and climatic data in southern part of Pakistan. Four remote sensing indices, viz.., Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI) are integrated with five climatic variables, i.e., Maximum Temperature (Tmax), Minimum Temperature (Tmin), Rainfall (R), Relative humidity (RH) and windspeed (WS) and one drought index, i.e., Standardized Precipitation Evapotranspiration Index (SPEI). Eight model combinations are built within two scenarios of wheat season, i.e., Whole Seasonal mean (WSM) (SC1), and Peak of Seasonal Mean (POSM) (SC2). Two nonlinear ML algorithms, i.e., Random Forest (RF), and Support Vector Machines (SVM), and one linear model, i.e., LASSO is being employed for wheat yield prediction to find the best combination and ML algorithm in two scenarios. Results revealed that in SC1, RF regression for the model combination (GNDVI +Tmax+ Tmin + R + RH + WS) outperformed other models (R2 = 0.71, RMSE = 2.365). Similarly, in SC2 RF regression outperformed SVM with model combination (GNDVI + Tmax+ Tmin + R + RH + WS) performed highest with R2 = 0.78, and lowest RMSE = 2.07, followed by (GNDVI + SPEI + RH + WS; R2 = 0.75). Interestingly, linear LASSSO also performed equally with RF with R2 = 0.77–0.73 in both scenarios. However, the output of this research recommends using SC2 for yield prediction in ML models. Overall, this research reveals the significance and potential of ML techniques for timely prediction of crop yield in different stages of crop growth that provide a solid foundation for food security in the region.

Prognosis prediction for glioblastoma multiforme patients using machine learning approaches: Development of the clinically applicable model

Background and purposeWe aimed to develop a clinically applicable prognosis prediction model predicting overall survival (OS) and progression-free survival (PFS) for glioblastoma multiforme (GBM) patients. Materials and methodsAll 467 patients treated with concurrent chemoradiotherapy at Yonsei Cancer Center from 2016 to 2020 were included in this study. We developed a conventional linear regression, Cox proportional hazards (COX), and non-linear machine learning algorithms, random survival forest (RSF) and survival support vector machine (SVM) based on 16 clinical variables. After backward feature selection and hyperparameter tuning using grid search, we repeated 100 times of cross-validations to combat overfitting and enhance the model performance. Harrell's concordance index (C-index) and integrated brier score (IBS) were employed as quantitative performance metrics. ResultsIn both predictions, RSF performed much better than COX and SVM. (For OS prediction: RSF C-index = 0.72 90%CI [0.71–0.72] and IBS = 0.12 90%CI [0.10–0.13]; For PFS prediction: RSF C-index = 0.70 90%CI [0.70–0.71] and IBS = 0.12 90%CI [0.10–0.14]). Permutation feature importance confirmed that MGMT promoter methylation, extent of resection, age, cone down planning target volume, and subventricular zone involvement are significant prognostic factors for OS. The importance of the extent of resection and MGMT promoter methylation was much higher than other selected input factors in PFS. Our final models accurately stratified two risk groups with root mean square errors less than 0.07. The sensitivity analysis revealed that our final models are highly applicable to newly diagnosed GBM patients. ConclusionOur final models can provide a reliable outcome prediction for individual GBM. The final OS and PFS predicting models we developed accurately stratify high-risk groups up to 5-years, and the sensitivity analysis confirmed that both final models are clinically applicable.

An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance.

In this work, a machine learning application was constructed to predict the logistics performance index based on economic attributes. The prediction procedure employs both linear and non-linear machine learning algorithms. The macroeconomic panel dataset is used in this investigation. Furthermore, it was combined with the microeconomic panel dataset obtained through the data envelopment analysis method for evaluating financial efficiency. The procedure was implemented in six ASEAN member countries. The non-linear algorithm of an artificial neural network performed best on the complex pattern of a collective instance of these six countries, followed by the penalized linear of the Ridge regression method. Due to the limited amount of training data for each country, the artificial neural network prediction procedure is only applicable to the datasets of Singapore, Malaysia, and the Philippines. Ridge regression fits the Indonesia, Thailand and Vietnam datasets. The results provide precise trend forecasting. Macroeconomic factors are driving up the logistics performance index in Vietnam in 2020. Malaysia logistics performance is influenced by the logistics business's financial efficiency. The results at the country level can be used to track, improve, and reform the country's short-term logistics and supply chain policies. This can bring significant gains in national logistics and supply chain capabilities, as well as support for global trade collaboration, all for the long-term development of the region.

Land use regression model established using Light Gradient Boosting Machine incorporating the WRF/CMAQ model for highly accurate spatiotemporal PM2.5 estimation in the central region of Thailand

The level of fine particulate matter (PM2.5) in central Thailand has exceeded the national air quality standard in the dry season for the years. The limited number of monitoring stations makes it difficult to obtain spatiotemporal PM2.5 distributions in the region. In this study, we first developed a land use regression (LUR) model using a non-linear machine learning (ML) algorithm incorporating the Weather Research and Forecasting and Community Multiscale Air Quality (WRF/CMAQ) model to predict daily ambient PM2.5 levels in Thailand. We aimed to evaluate the PM2.5 estimation performance of the integrated LUR modeling approach in developing countries. The Light Gradient Boosting Machine (LightGBM) was used as the non-linear ML algorithm. CMAQ-simulated PM2.5 concentrations, WRF-simulated meteorological parameters, population density, and land use variables were used as predictors of the LUR model. A 5-fold site-based cross-validation (CV) technique was performed to evaluate the prediction performances of the LUR model using the coefficient of determination (R2). The daily PM2.5 concentrations were estimated by the LUR model at a 1-km grid resolution. Our LUR model exhibited a CV-R2 of 0.71. Moreover, the LUR model effectively illustrated PM2.5 distributions at a high spatiotemporal resolution over the central Thailand. Our findings demonstrate the advantages of the integrated LUR model for accurately estimating the daily ambient PM2.5 level, which is influenced by transboundary and local pollution. This modeling approach could be implemented for future air pollutant estimation in Southeast Asia and other developing countries.

ElectroPredictor: An Application to Predict Mayr's Electrophilicity E through Implementation of an Ensemble Model Based on Machine Learning Algorithms.

Electrophilicity (E) is one of the most important parameters to understand the reactivity of an organic molecule. Although the theoretical electrophilicity index (ω) has been associated with E in a small homologous series, the use of w to predict E in a structurally heterogeneous set of compounds is not a trivial task. In this study, a robust ensemble model is created using Mayr's database of reactivity parameters. A combination of topological and quantum mechanical descriptors and different machine learning algorithms are employed for the model's development. The predictability of the model is assessed using different statistical parameters, and its validation is examined, including a training/test partition, an applicability domain, and a y-scrambling test. The global ensemble model presents a Q5-fold2 of 0.909 and a Qext2 of 0.912, demonstrating an excellent predictability performance of E values and showing that w is not a good descriptor for the prediction of E, especially for the case of neutral compounds. ElectroPredictor, a noncommercial Python application (https://github.com/mmoreno1/ElectroPredictor), is developed to predict E. QM9, a well-known large dataset containing 133885 neutral molecules, is used to perform a virtual screening (94.0% coverage). Finally, the 10 most electrophilic molecules are analyzed as possible new Mayr's electrophiles, which have not yet been experimentally tested. This study confirms the necessity to build an ensemble model using nonlinear machine learning algorithms, topographic descriptors, and separating molecules into charged and neutral compounds to predict E with precision.