Machine Learning Modeling Approach Research Articles

Machine learning for design and optimization of organic Rankine cycle plants: A review of current status and future perspectives

AbstractThe organic Rankine cycle (ORC) is widely acknowledged as a sustainable power cycle. However, the traditional approach commonly adopted for its optimal design involves sequential consideration of working fluid selection, plant configuration, and component types, before the optimization of state parameters. This way, the design process fails to achieve an optimal design in most cases, since the process relies heavily on empirical judgments. To improve the design process, researchers have been exploring lately the suitability of machine learning techniques. It is however not clear yet if data‐driven designs of ORC plants are practically viable and accurate. To bridge this gap, this article reviews literature studies in the field. Overviews were first presented on the ORC technology and machine learning modeling approaches. Next, studies that applied machine‐learning methods for the design and performance prediction of ORC plants were discussed. Furthermore, studies that focused on ORC machine learning optimizations were discussed. The artificial neural network (ANN) approach was observed as the technique most frequently applied for ORC design and optimization. Additionally, researchers agree in general that machine‐learning methods can achieve accurate results, with significant reductions of computational time and cost. However, there is the risk of using inadequate data size in the machine learning design approach, or insufficient data set training time, all of which can affect accuracy. It is hoped that this effort would spur the practical implementation of machine learning techniques in the future design and optimization of ORC plants, toward the achievement of more sustainable energy technology.This article is categorized under: Sustainable Energy > Energy Efficiency Sustainable Energy > Other Renewables

Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm.

To develop the extreme gradient boosting (XG Boost) machine learning (ML) model for predicting gestational diabetes mellitus (GDM) compared with a model using the traditional logistic regression (LR) method. A case-control study was carried out among pregnant women, who were assigned to either the training set (these women were recruited from August 2019 to November 2019) or the testing set (these women were recruited in August 2020). We applied the XG Boost ML model approach to identify the best set of predictors out of a set of 33 variables. The performance of the prediction model was determined by using the area under the receiver operating characteristic (ROC) curve (AUC) to assess discrimination, and the Hosmer-Lemeshow (HL) test and calibration plots to assess calibration. Decision curve analysis (DCA) was introduced to evaluate the clinical use of each of the models. A total of 735 and 190 pregnant women were included in the training and testing sets, respectively. The XG Boost ML model, which included 20 predictors, resulted in an AUC of 0.946 and yielded a predictive accuracy of 0.875, whereas the model using a traditional LR included four predictors and presented an AUC of 0.752 and yielded a predictive accuracy of 0.786. The HL test and calibration plots show that the two models have good calibration. DCA indicated that treating only those women whom the XG Boost ML model predicts are at risk of GDM confers a net benefit compared with treating all women or treating none. The established model using XG Boost ML showed better predictive ability than the traditional LR model in terms of discrimination. The calibration performance of both models was good.

A Comprehensive Study on the Styrene–GTR Radical Graft Polymerization: Combination of an Experimental Approach, on Different Scales, with Machine Learning Modeling

The study of the styrene–Ground Tire Rubber (GTR) graft radical polymerization is particularly challenging due to the complexity of the underlying kinetic mechanisms and nature of GTR. In this work, an experimental study on two scales (∼10 mL and ∼100 mL) and a machine learning (ML) modeling approach are combined to establish a quantitative relationship between operating conditions and styrene conversion. The two-scale experimental approach enables to verify the impact of upscaling on thermal and mixing effects that are particularly important in this heterogeneous system, as also evidenced in previous works. The adopted experimental setups are designed in view of multiple data production, while paying specific attention in data reliability by eliminating the uncertainty related to sampling for analyses. At the same time, all the potential sources of uncertainty, such as the mass loss along the different steps of the process and the precision of the experimental equipment, are also carefully identified and monitored. The experimental results on both scales validate previously observed effects of GTR, benzoyl peroxide initiator and temperature on styrene conversion but, at the same time, reveal the need of an efficient design of the experimental procedure in terms of mixing and of monitoring uncertainties. Subsequently, the most reliable experimental data (i.e., 69 data from the 10 mL system) are used for the screening of a series of diverse supervised-learning regression ML models and the optimization of the hyperparameters of the best-performing ones. These are gradient boosting, multilayer perceptrons and random forest with, respectively, a test R2 of 0.91 ± 0.04, 0.90 ± 0.04 and 0.89 ± 0.05. Finally, the effect of additional parameters, such as the scaling method, the number of folds and the random partitioning of data in the train/test splits, as well as the integration of the experimental uncertainties in the learning procedure, are exploited as means to improve the performance of the developed models.

Predicting survival of NSCLC patients treated with immune checkpoint inhibitors: Impact and timing of immune-related adverse events and prior tyrosine kinase inhibitor therapy.

Immune checkpoint inhibitors (ICIs) produce a broad spectrum of immune-related adverse events (irAEs) affecting various organ systems. While ICIs are established as a therapeutic option in non-small cell lung cancer (NSCLC) treatment, most patients receiving ICI relapse. Additionally, the role of ICIs on survival in patients receiving prior targeted tyrosine kinase inhibitor (TKI) therapy has not been well-defined. To investigate the impact of irAEs, the relative time of occurrence, and prior TKI therapy to predict clinical outcomes in NSCLC patients treated with ICIs. A single center retrospective cohort study identified 354 adult patients with NSCLC receiving ICI therapy between 2014 and 2018. Survival analysis utilized overall survival (OS) and real-world progression free survival (rwPFS) outcomes. Model performance matrices for predicting 1-year OS and 6-month rwPFS using linear regression baseline, optimal, and machine learning modeling approaches. Patients experiencing an irAE were found to have a significantly longer OS and rwPFS compared to patients who did not (median OS 25.1 vs. 11.1 months; hazard ratio [HR] 0.51, confidence interval [CI] 0.39- 0.68, P-value <0.001, median rwPFS 5.7 months vs. 2.3; HR 0.52, CI 0.41- 0.66, P-value <0.001, respectively). Patients who received TKI therapy before initiation of ICI experienced significantly shorter OS than patients without prior TKI therapy (median OS 7.6 months vs. 18.5 months; P-value < 0.01). After adjusting for other variables, irAEs and prior TKI therapy significantly impacted OS and rwPFS. Lastly, the performances of models implementing logistic regression and machine learning approaches were comparable in predicting 1-year OS and 6-month rwPFS. The occurrence of irAEs, the timing of the events, and prior TKI therapy were significant predictors of survival in NSCLC patients on ICI therapy. Therefore, our study supports future prospective studies to investigate the impact of irAEs, and sequence of therapy on the survival of NSCLC patients taking ICIs.

Mapping seagrass habitats of potential suitability using a hybrid machine learning model

Seagrass meadows provide essential ecosystem services globally in the context of climate change. However, seagrass is being degraded at an accelerated rate globally due to ocean warming, ocean acidification, aquaculture, and human activities. The need for more information on seagrasses’ spatial distribution and health status is a serious impediment to their conservation and management. Therefore, we propose a new hybrid machine learning model (RF-SWOA) that integrates the sinusoidal chaos map whale optimization algorithm (SWOA) with a random forest (RF) model to accurately model the suitable habitat of potential seagrasses. This study combines in situ sampling data with multivariate remote sensing data to train and validate hybrid machine learning models. It shows that RF-SWOA can predict potential seagrass habitat suitability more accurately and efficiently than RF. It also shows that the two most important factors affecting the potential seagrass habitat suitability on Hainan Island in China are distance to land (38.2%) and depth to sea (25.9%). This paper not only demonstrates the effectiveness of a hybrid machine learning model but also provides a more accurate machine learning model approach for predicting the potential suitability distribution of seagrasses. This research can help identify seagrass suitability distribution areas and thus develop conservation strategies to restore healthy seagrass ecosystems.

Abstract WMP68: Machine Learning Approach In Prediction Of Nasoenteral Tube In Acute Ischemic Stroke

Introduction: Dysphagia occurs in about 20 to 50% of acute stroke patients, and it may persist longer than six months after stroke. Nasoenteral tube feeding (NTF) must be judiciously prescribed to avoid dysphagia complications and, at the same time, prevent its unnecessary usage, which is not free of adverse events. Objective: Our aim was to state independent predictive factors associated with dysphagia and nasoenteral tube feeding. Besides, we aimed to develop a prediction model for nasoenteral tube feeding through a machine learning modeling approach. Methods: This is a prospective cohort study. All consecutive ischemic acute stroke patients were included. All patients had phonoaudiological evaluation for dysphagia screening. Data analyzed included age, sex, Glasgow Coma Scale, NIHSS, Aspects score, Seattle comorbid index, Rankin scale at discharge, TOAST classification for stroke subtypes, presence of major stroke risk factors and data from CT scan of patients. Results: We studied 1101 acute stroke patients. Twenty-eight percent of stroke patients received NTF. They were older (p&lt;0.001), had a more severe stroke (p&lt;0.001), and presented consciousness disturbance and dysarthria more frequently (p&lt;0.001 for both measures). These findings independently predicted nasoenteral tube feeding in acute stroke. The decision tree model disclosed a sensitivity of 75% and specificity of 87%, with 84% accuracy for predicting nasoenteral tube feeding. On the other hand, the artificial neural network predicted 83% accurately, disclosing a sensitivity of 70% and specificity of 86%. Conclusions: Dysphagia occurred in one third of cases. Older age, stroke severity, dysarthria, reduced conscious level at onset independently predict the need for nasoenteral tube. The tree decision model is an accurate tool for predicting NTF in acute ischemic stroke patients.

Use of multivariate analysis and machine learning methods to characterize traits contributing to wheat yield diversity

Aim of study: Regarding the third largest staple food crop in the world, determining the factors affecting wheat yield is of great importance. This study aimed to determine useful subsets of agronomic traits and evaluate the order of importance of traits in grain yield. Area of study: Fars province, Iran. Material and methods: In total, the data corresponding to 22 agronomic traits was collected from six different regions (Darab, Kavar, Marvdasht, Fasa, Lar, and Khonj) of 90 farms of Fars province, Iran as the most important wheat-growing regions. Multivariate statistical analysis (correlation, stepwise regression, and principal component analysis (PCA)) and machine learning modeling approaches, such as partial least squares regression (PLSR) and support vector regression (SVR) models, were applied to agronomic traits. Main results: The findings, based on integrated approaches such as correlation, stepwise regression, and PCA, highlighted that number of spikes m-2, grain number spike-1, and thousand-grain weight had a major impact on the yield followed by awn length, spike length, narrow leaf herbicide, broadleaf herbicide, time to plant maturity (month), and soil salinity. Besides, PLSR with nine inputs (nine selected traits) displayed better prediction capability (R2=85 %, RMSE=0.32, MSE=0.10, and BIAS=-0.05) than that with all twenty-two input traits. Research highlights: Integrated multivariate statistical analyses and machine learning regression methods could be a powerful tool in determining traits that have a significant impact on yield. These achievements can be considered for future breeding programs.

Machine learning reveals the complex ecological interplay of microbiome in a full-scale membrane bioreactor wastewater treatment plant

Membrane bioreactor (MBR) systems are one of the most widely used wastewater treatment processes for various municipal and industrial waste streams. The present study aimed to advance the understanding of ecologically important keystone taxa that play an important role in full-scale MBR systems. A machine-learning (ML) modeling framework based on microbiome data was developed to successfully predict, with an average accuracy of >91.6%, the operational characteristics of three representative full-scale wastewater systems: an MBR, a conventional activated sludge system, and a sequencing batch reactor. ML-based feature-importance analysis identified Ferruginibacter as a keystone organism in the MBR system. The phylogeny and known ecophysiology of members of Ferruginibacter supported their role in metabolizing complex organic polymers (e.g., extracellular polymeric substances) in MBR systems characterized by high concentrations of mixed liquor suspended solids and a high solid retention time. ML regression modeling also revealed temporal patterns of Ferruginibacter in response to water temperature. ML modeling was thus successfully employed in the present study to investigate complex/non-linear relationships between keystone taxa and environmental conditions that cannot be detected using conventional approaches. Overall, our microbiome-data-enabled ML modeling approach represents a methodological advance for identifying keystone taxa and their complex ecological interactions, which has implications for the sustainable and predictive management of MBR systems.

Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India

Groundwater is an essential source of water especially in arid and semi-arid regions of the world. The demand for water due to exponential increase in population has created stresses on available groundwater resources. Further, climate change has affected the quantity of water globally. Many parts of Indian cities are experiencing water scarcity. Thus, assessment of groundwater potential is necessary for sustainable utilization and management of water resources. We utilized a novel ensemble approach using artificial neural network multi-layer perceptron (ANN-MLP), random forest (RF), M5 prime (M5P) and support vector machine for regression (SMOReg) models for assessing groundwater potential in the Parbhani district of Maharashtra in India. Ten site-specific influencing factors, elevation, slope, aspect, drainage density, rainfall, water table depth, lineament density, land use land cover, geomorphology, and soil types, were integrated for preparation of groundwater potential zones. The results revealed that the largest area of the district was found under moderate category GWP zone followed by poor, good, very good and very poor. Spatial distribution of GWP zones showed that Poor GWPZs are spread over north, central and southern parts of the district. Very poor GWPZs are mostly found in the north-western and southern parts of the district. The study calls for policy implications to conserve and manage groundwater in these parts. The ensembled model has proved to be effective for assessment of GWP zones. The outcome of the study may help stakeholders efficiently utilize groundwater and devise suitable strategies for its management. Other geographical regions may find the methodology adopted in this study effective for groundwater potential assessment.

Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach.

Objectives: Our aim was to test if machine learning algorithms can predict cancer mortality (CM) at an ecological level and use these results to identify statistically significant spatial clusters of excess cancer mortality (eCM). Methods: Age-standardized CM was extracted from the official databases of Brazil. Predictive features included sociodemographic and health coverage variables. Machine learning algorithms were selected and trained with 70% of the data, and the performance was tested with the remaining 30%. Clusters of eCM were identified using SatScan. Additionally, separate analyses were performed for the 10 most frequent cancer types. Results: The gradient boosting trees algorithm presented the highest coefficient of determination (R 2 = 0.66). For total cancer, all algorithms overlapped in the region of Bagé (27% eCM). For esophageal cancer, all algorithms overlapped in west Rio Grande do Sul (48%-96% eCM). The most significant cluster for stomach cancer was in Macapá (82% eCM). The most important variables were the percentage of the white population and residents with computers. Conclusion: We found consistent and well-defined geographic regions in Brazil with significantly higher than expected cancer mortality.

Developing Machine learning models for hyperlocal traffic related particulate matter concentration mapping

Recently, estimating air pollution concentrations and contributions from various sources has become a major research focus. Our research adds to the body of knowledge by developing machine learning (ML) models to avoid intermediate modeling steps using traffic and meteorological data. The ML model's overall performance in predicting air pollution concentrations at receptors outperforms previous methods. Our best model, Convolutional Long Short-Term Memory (ConvLSTM), has a Mean Relative Error (MRE) of 38.9%, which is lower than the 47.5% MRE for the single hidden layer model, the 63.2% MRE for the Convolutional Neural Network model, and the 41.5% MRE for ConvLSTM with time-series data. Memory cells help the ConvLSTM model predict a large number of spatially correlated observations. The novel ML modeling approach has low data requirements and is computationally efficient, which makes it promising for future transportation planning, epidemiology, and environmental justice assessments.

Stochastic Free Vibration and Impact Responses of Functionally Graded Plates: A Support Vector Machine Learning Model Approach

Stochastic Free Vibration and Impact Responses of Functionally Graded Plates: A Support Vector Machine Learning Model Approach

Integrating machine learning and multi-linear regression modeling approaches in groundwater quality assessment around Obosi, SE Nigeria

Integrating machine learning and multi-linear regression modeling approaches in groundwater quality assessment around Obosi, SE Nigeria

Investigating the Use of Machine Learning Methods in Direct Ridership Models for Bus Transit

This test paper develops and tests 13 direct ridership models (DRMs) for transit sketch planning the Dallas–Fort Worth region. We explore both, machine learning modeling approaches (e.g., ridge regression and random forest) and traditional statistical models (e.g., linear regression and multiplicative regression). This effort provides a detailed description of modeling workflows and of the preprocessing of input data including general transit feed specification (GTFS), employment, socio-demographic, and ridership data. We also describe metrics to compare model performance; in our experiments the ridge regression framework using a Yeo-Johnson power transformation led to the most accurate predictions with an [Formula: see text] of 0.88. The sensitivity of the DRM model to errors in the service-related predictor variables is within acceptable limits with the root mean squared error (RMSE) increasing by less than 20% for a 25% error in any one of the input predictors. Our findings suggest that DRMs can be a powerful complement to the four-step planning process, providing an alternative that is easier to maintain and run, and which may lead to more accurate ridership estimates given the limitations of transit modeling in traditional regional models. To illustrate the benefits of DRMs, this effort describes the deployment of trained models using a web-based framework which allows practitioners to obtain ridership estimates by drawing prospective routes on a map and providing a small number of service attributes as input.

Features Importance and Their Impacts on the Properties of Asphalt Mixture Modified with Plastic Waste: A Machine Learning Modeling Approach

Plastic asphalt mixtures (PAM) have attracted extensive attention lately; however, their application in the field has not been so common because of the lack of clear understanding of the behavior of the asphalt mix after modification. In the interest of closing this gap, a modeling tool able to estimate the plastic effect on asphalt mixtures properties is needed. Nevertheless, the suggestion of a generalized model is complex due to the numerous variables involved. To facilitate this process, the present article aims to expand the current knowledge about PAM modeling by providing a clearer understanding of what variables have the highest impact on PAM properties. To do so, data from previous articles have been gathered and machine learning and shapley additive explanation values have been applied. PAM properties assessed were air voids, Marshall stability, Marshall flow, indirect tensile strength, and tensile strength ratio. Overall, the features with the highest impact are plastic type and content (35%), aggregates gradation (35%), aggregates absorption (9%), bitumen content (8%), mixing technique (4%), and bitumen penetration (3%). The final proposed models extend the application of previous machine learning models and feature importance understanding, and, in the field, they can serve as an initial estimate of the plastic effect on different asphalt mixture types. It is suggested that future articles intending to model PAM should consider these critical features during model formulation, and articles evaluating new types of PAM should clearly report these properties, for they might be the basis of these generalized future models.

Application of artificial neural networks in horizontal luminous efficacy modeling

Harnessing daylight for energy-efficient designs requires the availability of daylight illuminance data. In the absence of measured daylight data, deriving daylight using the luminous efficacy model is an alternative. This paper presents an approach for modeling diffuse and global luminous efficacies using accessible measured climatic data. The methodology explored machine learning, sensitivity analysis and empirical modeling approaches. Twelve (12) luminous efficacy models were proposed. These models consisted of six artificial neural networks (ANN) and six empirical models. All models also cover the all-sky, overcast and non-overcast sky conditions. The intent of using ANN lies in the need for more accurate daylight predictions and its ease in explaining complex relationships between complex atmospheric variables. Findings from the study show that diffuse fraction is crucial in global and diffuse luminous efficacy modeling. Furthermore, the performance of all models was statistically assessed, and the results show that all twelve proposed models could offer acceptable predictions of daylight. In particular, the ANN models outperformed the empirical models.

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches

BackgroundThe number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches.MethodsWe used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control.ResultsFrom the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside.ConclusionsOur layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions.

Characterization and Classification of Citrus reticulata var. Keprok Batu 55 Using Image Processing and Artificial Intelligence

Citrus reticulata var. Keprok Batu 55 is one of the superior varieties of citrus originating from Batu City, East Java, which has a slightly sour-sweet taste with a sweetness level of 10-12^obrix. Prediction of citrus maturity as a monitoring activity for pre- and post-harvest quality management is still done manually, whereas human judgment of the maturity level is subjective. One alternative to increase the monitoring productivity is the development of a portable system with image processing and destructive measurements of physico-chemical properties such as hardness, brix, and pH. This study aims to develop an image-based classification model and characterize the quality parameters of citrus. Measurement of maturity on Citrus reticulata var. Keprok Batu 55 has been carried out for image analysis with color index (RGB, L*a*b. and HSV). The image of citrus will be taken with the camera, which will later be taken partially (cropping) on the skin, which will then extract the color characteristics and calculate the level of color content from RGB and then converted it to HSV. A sufficient number of images with various conditions are needed to train the artificial intelligent model so that it can perform segmentation, calculation, and grade classification. A prediction model then was developed using color features and several machine learning modeling approaches.

Development of SiGe Indentation Process Control for Gate-All-Around FET Technology Enablement

Methodologies for characterization of the lateral indentation of silicon-germanium (SiGe) nanosheets using different non-destructive and in-line compatible metrology techniques are presented and discussed. Gate-all-around nanosheet device structures with a total of three sacrificial SiGe sheets were fabricated and different etch process conditions used to induce indent depth variations. Scatterometry with spectral interferometry and x-ray fluorescence in conjunction with advanced interpretation and machine learning algorithms were used to quantify the SiGe indentation. Solutions for two approaches, average indent (represented by a single parameter) as well as sheet-specific indent, are presented. Both scatterometry with spectral interferometry as well as x-ray fluorescence measurements are suitable techniques to quantify the average indent through a single parameter. Furthermore, machine learning algorithms enable a fast solution path by combining x-ray fluorescence difference data with scatterometry spectra, therefore avoiding the need for a full optical model solution. A similar machine learning model approach can be employed for sheet-specific indent monitoring; however, reference data from cross-section transmission electron microscopy image analyses are required for training. It was found that scatterometry with spectral interferometry spectra and a traditional optical model in combination with advanced algorithms can achieve a very good match to sheet-specific reference data.

A comprehensive review of trip generation models based on land use characteristics

• Existing researches on the impact of land use on trip generation has been explored. • Advanced data collection techniques for travel data collection were investigated. • Numerous approaches to trip generation modelling have been discussed. To assess the impact of any proposed development, it is necessary to estimate the trips that are likely to be generated because of such development. In case of developing countries, land use pattern varies significantly and it becomes very challenging to model the trip rates. In view of this, an effort has been made to synthesize the available literature related to modelling of trip generation rates. This paper provides a comprehensive review of studies related to impact of land use on trip rates, data collection techniques and modelling approaches. Major challenges associated with modelling of trip generation includes the unavailability of standard reference databases for multimodal trip generation, consideration of land use evolvement, less explored machine learning modelling approaches, and the feasibility of using emerging data collection techniques in travel surveys. Finally, a framework is proposed for modelling multimodal trip generation rates considering the above identified issues.