Machine Learning Regression Research Articles

PREDICTION OF TARIFF RATE USING MULTIPLE LINEAR REGRESSIONPREDICTION OF TARIFF RATE USING MULTIPLE LINEAR REGRESSION

A tariff is a list of costs incurred during the movement of commodities across different distances. Seasonal and non-seasonal variables also affect tariffs. The goal of this study is to use multiple linear regression, a machine learning method, to estimate truck load tariff rates. We will attempt to forecast the truck tariff rates by using a few variables and the machine learning regression procedure previously discussed. By doing this, we can assist the industries in estimating the tariff rates so they can take the appropriate steps and manage and control the cost of transportation to run their businesses profitably

Stock price prediction portfolio optimization using different risk measures on application of genetic algorithm for machine learning regressions

Stock price prediction portfolio optimization using different risk measures on application of genetic algorithm for machine learning regressions

Prediction of end-point phosphorus content of molten steel in BOF with machine learning models

The main task in the production of steel in the basic oxygen furnace (BOF) is dephosphorization Therefore, the prediction and control of the end-point phosphorus content of molten steel is of great significance. Four machine learning regression models (Lasso, Random Forest, Xgboost, and Neural Network) were established to predict the end-point phosphorus content of molten steel in the BOF based on raw and auxiliary material data, process parameters, and production quality data. The prediction effect of the four models was further compared, and their prediction results were interpreted based on the interpretability of the models and the permutation importance method. The results showed that compared with linear regression and neural network regression model, two types of ensemble tree model have higher prediction accuracy, better stability with small data sets, and lower data preprocessing requirements. The factors influencing the end-point phosphorus (P) content in BOF were ranked in order of importance as: Tapping temperature > Turning down times > Steel scrap quantity> Operation habits of different working groups > Amount of oxygen injection> Sulfur and phosphorus content of molten iron > Addition amount of lime, limestone, and lightly burnt dolomite in the slag > Slag-splashing amount.

Development of Machine Learning Regression Models for Predicting the Performance of Nanofibrous Scaffolds for Skin Tissue Engineering

Development of Machine Learning Regression Models for Predicting the Performance of Nanofibrous Scaffolds for Skin Tissue Engineering

Global zenith wet delay modeling with surface meteorological data and machine learning

The tropospheric delay is a major error source for space geodetic techniques, and the performance of its modeling is significantly limited due to the high spatiotemporal variability of the moisture in the lower atmosphere. In this study, global modeling of the tropospheric zenith wet delay (ZWD) was realized based on surface meteorological data obtained from radiosondes and Global Positioning System (GPS) radio occultation (RO) measurements through the random forest (RF) and backpropagation neural network (BPNN) regression analysis. The modeling performance was further validated based on two kinds of global atmospheric profiles for the year 2020. Our results show that the ZWD modeling accuracy gained by two machine learning regression approaches is significantly improved by taking into account surface meteorological parameters, especially the surface water vapor pressure when compared to the Global Pressure and Temperature 3 (GPT3) model. When surface meteorological data are available, the RF-B model yields ZWD estimations with an overall agreement of 3.1 cm in comparison with the sounding profiles and 2.4 cm in contrast to the GPS RO atmospheric profiles. The RF-B is superior to other models based on surface meteorological parameters for ZWD calculation, e.g., the accuracy improves by 21.8–23.8% against the approach by Saastamoinen and 7–12.2% against the formula by Askne and Nordius.

Power Consumption and Processing Time Estimation of CNC Machines Using Explainable Artificial Intelligence (XAI)

Due to environmental issues such as climate change, companies are required to optimize their resource and energy consumption in their production process. Predicting power consumption and processing time of all production facilities is essential for manufacturing to develop mechanisms to prevent energy and resource waste and optimize their use. Machine learning is a powerful tool for prediction tasks using data in digitalized environments. In this paper, we present power consumption and processing time prediction of CNC milling machines using five machine learning regression models, i.e., decision tree, random forest, support vector regression (SVR), extreme gradient boosting (XGBoost), and artificial neural network (ANN). Since most of those models are black-box, we applied two explainable artificial intelligence (XAI) approaches, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to give post-hoc explanations of the predictions given by the machine learning models. Our experiments indicated that random forest regression performed the best in predicting power consumption and processing time. The explanation showed that the number of axis rotations and the number of travels to the machine's zero point in rapid traverse were the most important factors that affected the processing time and power consumption. The companies using CNC milling machines can use our prediction models to optimally plan and schedule the operation of the milling machines in a time and energy-efficient manner. They can also optimize the factors that affect power consumption and processing time the most.

A Machine Learning Model for Lightning-Related Deaths in Brazil

Abstract Brazil presents the highest number of lightning-related deaths in the world. This study aimed to identify the key victims’ characteristics associated with such fatalities in Brazil and to develop a model that predicts the number of deaths as function of the victims’ data. The dataset provided by the Department of Informatics of the Unified Health System in Brazil (DATASUS) was analyzed and machine learning regression techniques were applied. The gradien-boosting regressor (GBR) model was found to be the most effective, achieving a prediction accuracy of 97%. Through the analysis of 34 initial variables, 10 variables were identified as having the greatest influence on the model’s outcomes. These variables included race, gender, age group, occupational accidents, education, and location of death. Understanding these characteristics is crucial for implementing targeted prevention and safety strategies in various regions, helping to mitigate the risk of lightning-related deaths worldwide. Additionally, the methodology used in this study can serve as a framework for similar research in different locations, allowing for the identification of important factors specific to each region. By adapting the machine learning regression techniques and incorporating local datasets, researchers can gain valuable insights into the determinants of lightning-related fatalities, enabling the development of effective prevention and safety measures tailored to specific geographical areas. Significance Statement This study presents a machine learning approach using the gradient-boosting regressor (GBR) method to estimate the weekly number of lightning-related deaths with an impressive 97% prediction accuracy. The research includes a comprehensive analysis of various factors, such as race, gender, age group, occupational accidents, education, and location of death, providing valuable insights for targeted preventive strategies and safety measures. The findings significantly contribute to understanding lightning-related fatalities in Brazil. The proposed machine learning model demonstrates a robust and accurate fit to the data, allowing for a comprehensive understanding of patterns and underlying trends in lightning fatalities.

Evaluation of covid-19 mortality using machine learning regression methods based on health system indicators

Evaluation of covid-19 mortality using machine learning regression methods based on health system indicators

Forecasting hourly WTI oil front monthly price volatility densities

<p>Financial commodity markets have an impact on company values and cash flows, where price movements within frequent time intervals can be both significant and random. Understanding highly frequent price movements is both important and difficult. In this paper, I measured and forecasted volatility for high-frequency (mostly twelve hours per day) WTI Oil front month price movements from 2012 to 2024 (about 40,500 observations). I created a new stochastic volatility model for extracting latent volatility using the non-linear Kalman filter. Stable and strictly ergodic hourly price series, along with the BIC optimal non-linear (generic) general method of moments model coefficients, enabled this process. The latent volatility seemed to separate into two volatility factors. One factor that was very persistent suggested slow mean reversion, and one choppy strongly mean-reverting factor. The data dependence found in the factor volatility series suggested forecasting ability. I applied classical static forecasts and three machine learning regression techniques to predict one-step-ahead volatility. The two volatility factors and one summarizing exponential factor were reported, along with several fit measures, including the root mean square error and Theil's measure of covariance.</p>

Multi-output machine learning regression for climate prediction: a comparative study of precipitation and temperature forecasting in Jakarta and East Kalimantan, Indonesia

Multi-output machine learning regression for climate prediction: a comparative study of precipitation and temperature forecasting in Jakarta and East Kalimantan, Indonesia

Diagnosis and remaining useful life prediction of end-of-life battery systems for second-life applications

The realization of a circular economy for lithium-ion batteries demands technological advancements capable of performing diagnosis on end-of-life batteries. This will ensure improvement of the life cycle performance of battery systems, considering the most diverse value chains. A key challenge is considering the variety of battery conditions at their end-of-life and the multiple reuse options in second-life applications. This paper presents the different approaches of software-based State of Health (SOH) determination and remaining useful life (RUL) prediction on battery cells. This includes the use and performance comparison of classical regression methods, machine learning regression methods and physical battery models in determining the SOH and degradation process of battery cells. In addition, we consider the resulting RUL conditions of typical second-life applications of electric vehicles (EVs), forklifts, grid stabilisation storage systems, and home storage systems. These results enable the selection and use of electrical and software characterisation methods for efficient battery management diagnostics and end-of-life characterisation.

Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring.

Current blood pressure (BP) estimation methods have not achieved an accurate and adaptable approach for ambulatory diagnosis and monitoring applications of populations at risk of cardiovascular disease, generally due to a limited sample size. This paper introduces an algorithm for BP estimation solely reliant on photoplethysmography (PPG) signals and demographic features. It automatically obtains signal features and employs the Markov Blanket (MB) feature selection to discern informative and transmissible features, achieving a robust space adaptable to the population shift. This approach was validated with the Aurora-BP database, compromising ambulatory wearable cuffless BP measurements for over 500 individuals. After evaluating several machine-learning regression methods, Gradient Boosting emerged as the most effective. According to the MB feature selection, temporal, frequency, and demographic features ranked highest in importance, while statistical ones were deemed non-significant. A comparative assessment of a generic model (trained on unclassified BP data) and specialized models (tailored to each distinct BP population), demonstrated a consistent superiority of our proposed MB feature space with a mean absolute error of [Formula: see text] for systolic BP and [Formula: see text] for diastolic BP on the whole dataset. Moreover, we present a first comparison of in-clinic vs. ambulatory models, with performance significantly lower for the latter with a drop of [Formula: see text] in systolic ( ) and [Formula: see text] for diastolic ( ) estimation errors. This work contributes to the resilient understanding of BP estimation algorithms from PPG signals, providing causal features in the signal and quantifying the disparities between ambulatory and in-clinic measurements.

Suitability of different machine learning methods for high-speed flow modeling issues

In the present study, machine learning algorithms are applied for modeling transport coefficients in strongly nonequilibrium reacting gas flows. As a model case, the problem of a hypersonic flow of a five-component air mixture around a sphere is considered. Various approaches for an application of machine learning methods, such as linear regression, k-nearest neighbors, support vector machine, regression tree, random forest, gradient boosting, and neural network (multilayer perceptron) are investigated. For the transport coefficients regression modeling the combination of machine learning methods with the finite volume method is constructed. The machine learning regressors are trained on the accurate numerical data given by one-temperature approach of the kinetic theory. The results of trained models are compared with approximate formulae of Blottner-Eucken-Wilke model. The results of different machine learning methods are analyzed in terms of the relationship between the obtained accuracy of calculations and the overall speed of calculations. The overall time of dataset formation and model training is estimated. The design of the constructed multilayer perceptron is discussed. The machine learning methods considered in the article can be used for the engineering problem such as design of high-speed aircraft, as well as for modeling of flows around complex shape bodies.

Prediction and Interpretation of Total N and Its Key Drivers in Cultivated Tropical Peat using Machine Learning and Game Theory

Currently, there is a growing interest among research communities in the development of statistical learning-based pedotransfer functions/PtFs to predict mineral soil nutrients; however, similar studies in peatlands are relatively rare. Moreover, extracting meaningful information from these ‘black-box’ models is crucial, particularly concerning their algorithmic complexity and the non-linear nature of the soil covariate interrelationships. This study employed the Pulunggono (2022a) dataset and the bootstrapping method, to (1) develop and evaluate seven PtF models, including both general linear models (GLM) and machine learning (ML) regressors for estimating total nitrogen (N) in tropical peat that has been drained and cultivated for oil palm (OP) in Riau, Indonesia and (2) explaining model functioning by incorporating Shapley Additive Explanation (SHAP), a tool derived from coalitional game theory. This study demonstrated the superior predictive performance of ML-based PtFs in estimating total N compared to GLM algorithms. The top-performing algorithms for PtF models were identified as GBM, XGB, and Cubist. The SHAP method revealed that sampling depth and organic C were consistently identified as the most important covariates across all models, irrespective of their algorithmic capabilities. Additionally, ML algorithms identified the total Fe, pH, and bulk density (BD) as significant covariates. Local explanations based on Shapley values indicated that the behavior of PtF-based algorithms diverged from their global explanations. This study emphasized the critical role of ML algorithms and game theory in accurately predicting total N in peatlands subjected to drainage and cultivation for OP and explaining their model behavior in relation to soil biogeochemical processes.

MAKİNE ÖĞRENMESİNDE REGRESYON MODELLERİNİN TAHMİN PERFORMANSLARININ KARŞILAŞTIRILMASI: TÜRKİYE ÜRÜN İHTİSAS BORSASI BUĞDAY ENDEKSİ ÜZERİNE BİR UYGULAMA

Agricultural commodity prices have significant impacts on economies by leading to changes and regulations in both fiscal and monetary policies. These also have effects on household economies and consumer purchasing power particularly in developing countries. Thereby, instability and variability in these prices constitute adverse effects on these economies. On the other hand, assets of the commodity markets become popular just as bonds and stocks. Because of this growing interest, needs for managing risks, stable prices and lowering transaction costs has led to establishment of the commodity exchanges. In this context, Turkey put the licensed warehousing system into operation by founding the Turkish Mercantile Exchange (TMEX) to operate trades of Electronic Warehouse Receipts (EWRs). In this study, a model including US Dollar-Turkish Lira exchange rate (USD/TRY), Brent crude-oil prices, overnight interest rate and a daily dataset for the 01/04/2021-20/02/2023 period were used to assess several machine learning regression methods in predicting the TMEX Wheat Index (TMXWHT). As verified by comparisons with actual values and considering performance evaluation criteria, all methods yielded successful outcomes, furthermore, tree-based methods revealed better overall performance.

Yield Prediction and Recommendation of Crops in the Northeastern Region Using Machine Learning Regression Models

Agriculture has a big impact on society because it is essential for a large percentage of our food. The issue of hunger is getting worse by a growing population in many nations, resulting in food shortages or insufficiencies. To meet the world's food needs, it is ever more crucial to provide crop protection, conduct detailed land surveys, and predict crop yields. To calculate the estimated number of crops that are produced in a year, this research focuses on the use of machine learning techniques to predict crop yield and recommend crops with the highest yield in the Northeast region of India. The crop market's fluctuations in prices may be controlled with the aid of this information. To estimate agricultural crop yields, this study accurately evaluates a range of machine learning regression models, such as Linear Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost (eXtreme Gradient Boosting), and AdaBoost. With a 0.98 R2 score for the XGBoost and 0.96 for the Random Forest, they performed better than the other models.

Optimal power distribution control in modular power architecture using hydraulic free piston engines

Vehicle modularization has become an emerging trend in the automotive industry, leading to research on modular configuration, composition, and related control strategies. In this paper, we propose a modular power system with a hydraulic free piston engine (HFPE) as the power unit and develop a power distribution control strategy to enhance the overall efficiency of the system. Firstly, we determine the configuration scheme of the modular power system and establish a simulation model of the HFPE using MATLAB/Simulink. We conduct principle verification of the simulation model. Secondly, based on the simulation model of HFPE, we research the power unit control strategy using the machine learning regression prediction algorithm, enabling dynamic working condition switching of the power unit. Next, we propose a power distribution optimization algorithm which is named as the Rule Based Double Iterative Optimization Algorithm (RBDI) and compare it with several mature optimization algorithms under the framework of model predictive control, considering related constraints. Finally, we validate the performance of the proposed power distribution control strategy using a hardware-in-loop system. The results demonstrate that the output power of the modular power system can be effectively ensured. Compared with the average distribution algorithm (AVE), the genetic algorithm (GA), and the ameliorated particle swarm optimization algorithm (APSO), the overall working efficiency of the modular power system using the proposed control strategy is increased by 6.57%, 6.13%, and 5.59%, respectively, under the three test driving cycles.

Evolution Model, Mechanism, and Performance of Urban Park Green Areas in the Grand Canal of China

Urban park green areas are part of territorial space planning, shouldering the mission of providing residents with high-quality ecological products and public space. Using a combination of several measurement models such as the BCG (Boston Consulting Group) matrix, ESDA (Exploratory Spatial Data Analysis), MLR (Machine Learning Regression), GWR (Geographically Weighted Regression), and GeoDetector, this paper presents an empirical study on the changes in Urban Park Green Areas (UPGAs) in the Grand Canal of China. By quantitatively measuring the spatio–temporal evolution patterns of UPGAs, this study reveals the driving mechanisms behind them and proposes policy recommendations for planning and management based on performance evaluation. The spatio–temporal evolution of UPGAs and their performance in China’s Grand Canal are characterized by significant spatial heterogeneity and correlation, with diversified development patterns such as HH (High-scale–High-growth), HL (High-scale–Low-growth), LH (Low-scale–High-growth), and LL (Low-scale–Low-growth) emerging. The evolution performance is dominated by positive oversupply and positive equilibrium, where undersupply coexists with oversupply. Therefore, this paper recommends the implementation of a zoning strategy in the future spatial planning of ecological green areas, urban parks, and green infrastructure. It is also recommended to design differentiated construction strategies and management policies for each zoning area, while promoting inter-city mutual cooperation in the joint preparation and implementation of integrated symbiosis planning. Furthermore, the spatio–temporal evolution of the UPGAs in the Grand Canal of China is influenced by many factors with very complex dynamic mechanisms, and there are significant differences in the nature, intensity, spatial effects, and interaction effects between different factors. Therefore, in the future management of ecological green areas, urban parks, and green infrastructure, it is necessary to interconnect policies to enhance their synergies in population, aging, industry and economy, and ecological civilization to maximize the policy performance.

Inhibitor design for TMPRSS2: insights from computational analysis of its backbone hydrogen bonds using a simple descriptor

Transmembrane protease serine 2 (TMPRSS2) is an important drug target due to its role in the infection mechanism of coronaviruses including SARS-CoV-2. Current understanding regarding the molecular mechanisms of known inhibitors and insights required for inhibitor design are limited. This study investigates the effect of inhibitor binding on the intramolecular backbone hydrogen bonds (BHBs) of TMPRSS2 using the concept of hydrogen bond wrapping, which is the phenomenon of stabilization of a hydrogen bond in a solvent environment as a result of being surrounded by non-polar groups. A molecular descriptor which quantifies the extent of wrapping around BHBs is introduced for this. First, virtual screening for TMPRSS2 inhibitors is performed by molecular docking using the program DOCK 6 with a Generalized Born surface area (GBSA) scoring function. The docking results are then analyzed using this descriptor and its relationship to the solvent-accessible surface area term ΔGsa of the GBSA score is demonstrated with machine learning regression and principal component analysis. The effect of binding of the inhibitors camostat, nafamostat, and 4-guanidinobenzoic acid (GBA) on the wrapping of important BHBs in TMPRSS2 is also studied using molecular dynamics. For BHBs with a large increase in wrapping groups due to these inhibitors, the radial distribution function of water revealed that certain residues involved in these BHBs, like Gln438, Asp440, and Ser441, undergo preferential desolvation. The findings offer valuable insights into the mechanisms of these inhibitors and may prove useful in the design of new inhibitors.

Geospatial Big Data Approaches to Estimate Granular Level Poverty Distribution in East Java, Indonesia using Machine Learning and Deep Learning Regressions

One of the economic development the focus of the Indonesian government's efforts is for reducing poverty. In Indonesia, collecting poverty data uses the conventional method, the name is National Socio-Economic Survey (SUSENAS) which takes a large cost, time, and effort. To overcome these limitations, there is a need for additional data to provide more detailed poverty data. Recent studies show that the use of geospatial big data could identify poverty at a granular level, with a lower cost and faster update because of their unique and unbiased capacity to identify physical and socioeconomic phenomena. The integrated multi-source satellite imagery data such as the normalized difference vegetation index (NDVI) for detecting rural areas based on vegetation, built-up index (BUI) for identifying urban areas through building distribution, normalized difference water index (NDWI) for land cover detection, day time land surface temperature (LST) for identifying urban regions based on surface temperature, and pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) to evaluate economic activities based on pollution. Additionally, point of interest (POI) density and minimum POI distance are used to measure area accessibility. Therefore, the contribution of this research is to implement the utilization of geospatial big data to estimate the numbers of poverties at a granular level to the 666 sub-districts in East Java Province using machine learning and deep learning regression models. The evaluation results to estimate sub-district level poverty shows that the best model development using Support Vector Regression (SVR) in machine learning was the best root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values of 0.365, 0.293, and 0.032 with R-squared of 0.59 and MLP in deep learning algorithm with 0.444, 0.345, and 0.039 values of RMSE, MAE, and MAPE with R2 0.52. In addition, the results of visual identification revealed that high estimates of lower poverty are typically found in urban areas with high accessibility, and these areas are not spatially deprived areas with limited accessibility.