Non-linear Machine Learning Methods Research Articles

In-line NIR coupled with machine learning to predict mechanical properties and dissolution profile of PLA-Aspirin

In the production of polymeric drug delivery devices, dissolution profile and mechanical properties of the drug loaded polymeric matrix are considered important Critical Quality Attributes (CQA) for quality assurance. However, currently the industry relies on offline testing methods which are destructive, slow, labour intensive, and costly. In this work, a real-time method for predicting these CQAs in a Hot Melt Extrusion (HME) process is explored using in-line NIR and temperature sensors together with Machine Learning (ML) algorithms. The mechanical and drug dissolution properties were found to vary significantly with changes in processing conditions, highlighting that real-time methods to accurately predict product properties are highly desirable for process monitoring and optimisation. Nonlinear ML methods including Random Forest (RF), K-Nearest Neighbours (KNN) and Recursive Feature Elimination with RF (RFE-RF) outperformed commonly used linear machine learning methods. For the prediction of tensile strength RFE-RF and KNN achieved R2 values 98% and 99%, respectively. For the prediction of drug dissolution, two time points were considered with drug release at t = 6 h as a measure of the extent of burst release, and t = 96 h as a measure of sustained release. KNN and RFE-RF achieved R2 values of 97% and 96%, respectively in predicting the drug release at t = 96 h. This work for the first time reports the prediction of drug dissolution and mechanical properties of drug loaded polymer product from in-line data collected during the HME process.

Revisiting the nature and strength of the personality-job performance relations: New insights from interpretable machine learning.

Prior research on the relations between the five-factor model (FFM) of personality traits and job performance has suggested mixed findings: Some studies pointed to linear relations, while other studies revealed nonlinear relations. This study addresses these gaps using machine learning (ML) methods that can model complex relations between the FFM traits and job performance in a more generalizable way, particularly interpretable ML techniques that can more effectively reveal the nature (linear, curvilinear, interactive) and strength (feature/relative importance) of the personality-job performance relations. Overall, the results based on a sample of 1,190 employees suggest that nonlinear ML methods perform slightly yet consistently better than linear regression methods in modeling the relation of job performance with FFM facets, but not with factors. On the factor level, conscientiousness exhibits a noticeable curvilinear relation with job performance, and it also interacts with other FFM factors to predict job performance. Conscientiousness displays the strongest feature importance across job types, followed by agreeableness. On the facet level, most FFM facets show limited evidence for curvilinear and interactive (with other facets) relations with job performance. While several conscientiousness facets (order, deliberation, self-discipline) display the strongest feature importance in predicting job performance, some agreeableness (straightforwardness, altruism) and extraversion (positive emotionality) facets also emerge as important features for different sales job types (corporate vs. individual sales). We discuss the implications of these findings for research and practice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Improving Subseasonal Soil Moisture and Evaporative Stress Index Forecasts through Machine Learning: The Role of Initial Land State versus Dynamical Model Output

Abstract The effect of machine learning and other enhancements on statistical–dynamical forecasts of soil moisture (0–10 and 0–100 cm) and a reference evapotranspiration fraction [evaporative stress index (ESI)] on subseasonal time scales (15–28 days) are explored. The predictors include the current and past land surface conditions and dynamical model hindcasts from the Subseasonal to Seasonal Prediction project (S2S). When the methods are enhanced with machine learning and other improvements, the increases in skill are almost exclusively coming from predictors drawn from observations of current and past land surface states. This suggests that operational S2S flash drought forecasts should focus on optimizing use of information on current conditions rather than on integrating dynamically based forecasts, given the current state of knowledge. Nonlinear machine learning methods lead to improved skill over linear methods for soil moisture but not for ESI. Improvements for both soil moisture and ESI are realized by increasing the sample size by including surrounding grid points in training and increasing the number of predictors. In addition, all the improvements in the soil moisture forecasts predominantly impact soil moistening rather than soil drying—i.e., prediction of conditions moving away from drought rather than into drought—especially when the initial soil state is drier than normal. The physical reasons for the nonlinear machine learning improvements are also explored. Significance Statement Rapidly intensifying droughts pose extra challenges for predictability. Here, dynamical forecast model output is combined with nonlinear machine learning methods to improve forecasts of rapid changes in soil moisture and the evaporative stress index (ESI).

Using machine learning to identify environmental factors that collectively determine microbial community structure of activated sludge

Activated sludge (AS) microbial communities are influenced by various environmental variables. However, a comprehensive analysis of how these variables jointly and nonlinearly shape the AS microbial community remains challenging. In this study, we employed advanced machine learning techniques to elucidate the collective effects of environmental variables on the structure and function of AS microbial communities. Applying Dirichlet multinomial mixtures analysis to 311 global AS samples, we identified four distinct microbial community types (AS-types), each characterized by unique microbial compositions and metabolic profiles. We used 14 classical linear and nonlinear machine learning methods to select a baseline model. The extremely randomized trees demonstrated optimal performance in learning the relationship between environmental factors and AS types (with an accuracy of 71.43%). Feature selection identified critical environmental factors and their importance rankings, including latitude (Lat), longitude (Long), precipitation during sampling (Precip), solids retention time (SRT), effluent total nitrogen (Effluent TN), average temperature during sampling month (Avg Temp), mixed liquor temperature (Mixed Temp), influent biochemical oxygen demand (Influent BOD), and annual precipitation (Annual Precip). Significantly, Lat, Long, Precip, Avg Temp, and Annual Precip, influenced metabolic variations among AS types. These findings emphasize the pivotal role of environmental variables in shaping microbial community structures and enhancing metabolic pathways within activated sludge. Our study encourages the application of machine learning techniques to design artificial activated sludge microbial communities for specific environmental purposes.

Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland.

The total fertility rate is influenced over an extended period of time by shifts in population socioeconomic characteristics and attitudes and values. However, it may be impacted by macroeconomic trends in the short term, although these effects are likely to be minimal when fertility is low. With the objective of forecasting monthly deliveries, this study concentrates on the analysis of registered births in Scotland. Through this approach, we examine the significance of precisely forecasting fertility trends, which can subsequently aid in the anticipation of demand in diverse sectors by allowing policymakers to anticipate changes in population dynamics and customize policies to tackle emerging demographic challenges. Consequently, this has implications for fiscal stability, national economic accounts and the environment. In conducting our analysis, we incorporated non-linear machine learning methods alongside traditional statistical approaches to forecast monthly births in an out-of-sample exercise that occurs one step in advance. The outcomes underscore the efficacy of machine learning in generating precise predictions within this particular domain. In sum, this research will comprehensively demonstrate a cutting-edge model of machine learning that utilizes several attributes to assist in clinical decision-making, predict potential complications during pregnancy and choose the appropriate delivery method, as well as help in medical diagnosis and treatment.

Employing machine learning (ML) and explainable artificial intelligence (XAI) to predict and explain suicidal ideation among patients with prostate cancer.

e23086 Background: It is anticipated that implementing machine learning (ML) strategies will impact the clinical decision-making process in the future. This study aimed to compare the accuracy of various machine learning algorithms in predicting suicidal ideation in patients who were receiving treatment for prostate cancer (PC). Further, to predict suicidal ideation and explain the burden of illness (BOI) by utilizing ML algorithms and XAI-based interpretability. Methods: We analyzed the United States 2017 National Inpatient Sample database for patients hospitalized for PC using linear and non-linear ML methods. Using techniques such as forward selection (FS) and backward elimination (BE), Random Forest (RF), decision trees, Multivariate Adaptive Regression Splines, and Gradient Boosting Machine (GBM), we determined subsets and features. We used linear and non-linear MLs-- Lasso, Ridge, RF, and Neural Networks (NN). A 70% and 30% partitioning was performed on the training and test datasets. The performance of the model was tested using discrimination (C-statistics), the Receiver-Operating Characteristics (ROC) curve, and the Hosmer-Lemeshow tests on both the training data and the test data. Several XAI approaches, including permutation significance, global surrogate marker, feature interpretation, interactivity, and local interpretability, were also utilized to examine the BOI (Length of stay (LOS)) among PC cancer cohort with suicide ideation. Results: We identified 680 patients with suicidal ideation among 208,730 PC patients. The most important variable derived through FS and BE were depression, drug abuse, psychiatric disorder, alcohol dependence, race, Medicaid beneficiaries, cardiac arrhythmias, metastasis, weight loss, congestive heart failure, and un-complicated hypertension, further confirmed through the GBM and other methods. There was successful documentation of demographic, socioeconomic, and clinical characteristics through feature selection approaches. Linear models, Lasso, and Ridge demonstrated an excellent area under the ROC for all cohorts ( > 0.9 in train and test datasets). On the other hand, tree-based models, such as RF, performed poorly (AUC 0.72 for train and 0.66 for test). The findings of the XAI methodologies showed that among PC suicide ideation, higher BOI was associated with patients staying in low-income neighborhoods, Whites, Medicaid beneficiaries, and those with weight loss. Conclusions: We showcase the increasing capability of predictive analytics, particularly XAI, in predicting suicidal ideation. The combination of the vast amount of data generated by the digitalization of healthcare systems with the advanced processing power of modern servers enables the development of machine learning-based care pathways, which has the potential to revolutionize clinical decision-making in cancer.

Performance of double-arch tunnels under internal BLEVE

Double-arch tunnels, as one of the popular forms of tunnels, might be exposed to boiling liquid expanding vapour explosions (BLEVEs) associated with transported liquified petroleum gas (LPG), which could cause damage to the tunnel and even catastrophic collapse of the tunnel in extreme cases. However, very limited study has investigated the performance of double-arch tunnels when exposed to internal BLEVEs and in most analyses of tunnel responses to accidental explosions. The TNT-equivalence method was used to approximate the explosion load, which may lead to inaccurate tunnel response predictions. This study numerically investigates the response of typical double-arch tunnels to an internal BLEVE resulting from the instantaneous rupture of a 20 m3 LPG tank. Effects of various factors, including in-situ stresses, BLEVE locations, and lining configurations on tunnel responses are examined. The results show that the double-arch tunnels at their early-operation ages are more vulnerable to severe damage when exposed to the BLEVE due to the low action of in-situ stress of rock mass on the response of early-age tunnels. It is also found that directing the LPG tank to different driving lanes inside tunnels can affect the BLEVE-induced tunnel response more significantly than varying the configurations of tunnel lining. Moreover, installing section-steel arches in the mid-wall can effectively improve the blast resistance of the double-arch tunnels against the internal BLEVE. In addition, the prediction models based on multi-variate nonlinear regressions and machine learning methods are developed to predict the BLEVE-induced damage levels of the double-arch tunnels without and with section-steel arches.

Processing alcoholism eeg signals using neural networks

Alcoholism is one of the most common diseases, which creates problems for contemporary society. To date, there are still no standardized methods and tests to detect this disease. This study presents a new methodology based on an integrated approach to the study of Electroencephalography (EEG) signals, using a combination of nonlinear dynamics and machine learning methods. We applied signal encoding in the form of a 16-channel image, which was fed into the ResNet20 and GoogLeNet models for training. Wavelet transforms and fast Fourier transforms were considered for data preprocessing. In addition to the application of neural networks, the study of EEG signals was carried out by computing Lyapunov exponents and multiscale entropy. The question of choosing the most effective maternal wavelet was previously investigated. The wavelet transformations of Morlet, Meyer, Mexican Hat, Daubechies, and Gauss were studied. A spectrum of five Lyapunov exponents was calculated using the Sano – Savada method. To confirm the reliability of the results, the senior Lyapunov exponent was previously calculated by three more methods: Wolf, Kantz, Rosenstein. Previously, the methods were tested on such classical problems as the hyperchaotic Hénon map, logistic map, Rössler attractor and Lorenz attractor. The use of several approaches for the analysis of EEG signals confirmed the reliability of the results. The study demonstrates that the GoogLeNet model is more efficient than ResNet50. The experiment results showed that, based on the developed methodology, the GoogLeNet model is more efficient than ResNet50. The results of the experiment show that the wavelet transform, compared to the Fourier transform, gives higher accuracy for the analysis of EEG signals of patients with alcoholism.

Development of pyramid neural networks for prediction of significant wave height for renewable energy farms

Significant wave height (Hs) is a critical parameter in the design, operation, and maintenance of nearshore and offshore wind and wave farms. In this study, the original pyramid neural network (PNN) was introduced and applied to predict Hs for North Sea renewable energy farms. The performance of PNN was compared with state-of-the-art algorithms. The results show that the machine learning (ML) models trained by the network datasets can accurately predict Hs at any point of nearshore or offshore energy farms, significantly reducing wave monitoring costs. The PNN models predict Hs with high accuracy. The results predicted by PNN, with SRMSE of 0.256 and R2 of 0.873, are more accurate than the corresponding results obtained by the deep neural network (DNN) model, which show that PNNs with their far simpler structures and lower number of parameters are attractive alternatives for complex and time-consuming DNN models. The ensemble of regular models, with SRMSE of 0.247, is ranked as the most accurate method. The results also show that the applied state-of-the-art algorithms including MLR, SNN, DNN, ANFIS, and SVR models predict non-physical negative values of Hs. The PNNs developed in this study predict Hs with positive values. Moreover, the analysis shows that the scatter of the results obtained by the derived nonlinear models is less pronounced than the scatter of corresponding results obtained by other methods. The results also show that to evaluate the performance of nonlinear ML methods for large datasets, the analysis of standard statistical indices is not sufficient. For large datasets standard statistical indices provide inadequate or insufficient information and additional calculations, and statistical analysis are required.

China’s inflation forecasting in a data-rich environment: based on machine learning algorithms

ABSTRACT Inflation forecasting stands as a central concern in macroeconomics. This paper focuses on predicting China’s inflation within a data-rich environment. Specifically, we compile a large panel of China’s monthly macroeconomic and financial variables, employing various machine learning models on this predictor panel to forecast China’s inflation, encompassing both CPI and PPI, across various forecasting horizons extending up to 24 months. Our findings indicate the following: First, the machine learning models, when coupled with a large dataset of macroeconomic and financial predictors, demonstrate a superior ability to forecast China’s inflation compared to benchmark time series models and principal component regression models. These advantages become even more notable at medium-to-long horizons and during periods of high inflation volatility, as well as CPI/PPI divergence periods. Second, our experiments reveal that penalized linear regression models, such as ridge and elastic net, consistently outperform the benchmarks and nonlinear machine learning methods in most cases. Lastly, variables related to price, stock, and money & credit are identified as the most crucial factors for forecasting inflation in China.

A predictive study on the impact of board characteristics on firm performance of Chinese listed companies based on machine learning methods

Purpose This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms. Design/methodology/approach This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance. Findings The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises. Practical implications The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors. Originality/value The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

Enhanced model tree for quantifying output variances due to random data sampling: Productivity prediction applications

The data used for productivity modeling represents a sample of a company's performance taken at a particular time, which leads to varying model parameters and results in a variance of the predicted productivity. This research enhances the capability of the nonlinear machine learning method called Model Tree by integrating error propagation theory to determine the variance for the predicted productivity based on a relatively large dataset available for productivity analysis. The enhanced model tree is formalized and applied in modeling structural steel fabrication productivity and piping spool fabrication productivity. The enhanced model tree is preferred over artificial neural networks due to the model's capability to explain the productivity model concerning the variance of its predicted point value and the underlying reasoning logic. The enhanced model tree will potentially find its applications in construction beyond predicting labor productivity and make a further impact on implementing explainable artificial intelligence (XAI) in wide-ranging engineering domains.

Long-Term Characteristics of Surface Soil Moisture over the Tibetan Plateau and Its Response to Climate Change

Soil moisture over the Tibetan Plateau (TP) can affect hydrological cycles on local and remote scales through land–atmosphere interactions. However, TP long-term surface soil moisture characteristics and their response to climate change are still unclear. In this study, we firstly evaluate two satellite-based products—SSM/I (the Special Sensor Microwave Imagers) and ECV COMBINED (the Essential Climate Variable combined)—and three reanalysis products—ERA5-Land (the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis), MERRA2 (the second version of Modern-Era Retrospective Analysis for Research and Applications), and GLDAS Noah (the Noah land surface model driven by Global Land Data Assimilation System)—against two in situ observation networks. SSM/I and GLDAS Noah outperform the other soil moisture products, followed by MERRA2 and ECV COMBINED, and ERA5-Land has a certain degree of uncertainty in evaluating TP surface soil moisture. Analysis of long-term soil moisture characteristics during 1988–2008 shows that annual and seasonal mean soil moisture have similar spatial distributions of soil moisture decreasing from southeast to northwest. Additionally, a significant increasing trend of soil moisture is found in most of the TP region. With a non-linear machine learning method, we quantify the contribution of each climatic variable to warm-season soil moisture. It indicates that precipitation dominates soil moisture changes rather than air temperature. Pixel-wise partial correlation coefficients further show that there are significant positive correlations between precipitation and soil moisture over most of the TP region. The results of this study will help to understand the role of TP soil moisture in land–atmosphere coupling and hydrological cycles under climate change.

Fragment-based structural exploration and chemico-biological interaction study of HDAC3 inhibitors through non-linear pattern recognition, chemical space, and binding mode of interaction analysis

HDAC3 is an emerging target for the identification and discovery of novel drug candidates against several disease conditions including cancer. Here, a fragment-based non-linear machine learning (ML) method along with chemical space exploration followed by a structure-based binding mode of interaction analysis study was carried out on some HDAC3 inhibitors to obtain the key structural features modulating HDAC3 inhibition. Both the ML and chemical space analysis identified several physicochemical and structural properties namely lipophilicity, polar and relative polar surface area, arylcarboxamide moiety, bulky fused aromatic group, n-alkyl, and cinnamoyl moieties, the higher number of oxygen atoms, π-electrons for the substituted tetrahydrofuronaphthodioxolone moiety favorable for higher HDAC3 inhibition. Moreover, hydrogen bond forming capabilities, the length and substitution position of the linker moiety, the importance of phenyl ring in the linker motif, the contribution of heterocyclic cap moieties for effective inhibitor binding at the HDAC3 catalytic site that correspondingly affects the HDAC3 inhibitory potency. Again, macrocyclic ring structure and cyclohexyl cap moiety are responsible for lower HDAC3 inhibition. The MD simulation study of selected compounds explained strong binding patterns at the HDAC3 active site as evidenced by the lower RMSD and RMSF values. Nevertheless, it also explained the importance of the crucial structural fragments derived from the fragment-based analysis during ligand-enzyme interactions. Therefore, the outcomes of this current structural analysis will be a useful tool for fragment-based drug discovery of effective HDAC3 inhibitors for clinical therapeutics in the future. Communicated by Ramaswamy H. Sarma

Improving Hybrid Models for Precipitation Forecasting by Combining Nonlinear Machine Learning Methods

AbstractPrecipitation forecast is key for water resources management in semi-arid climates. The traditional hybrid models simulate linear and nonlinear components of precipitation series separately. But they do not still provide accurate forecasts. This research aims to improve hybrid models by using an ensemble of linear and nonlinear models. Preprocessing configurations and each of the Gene Expression Programming (GEP), Support Vector Regression (SVR), and Group Method of Data Handling (GMDH) models were used as in the traditional hybrid models. They were compared against the proposed hybrid models with a combination of all these three models. The performance of the hybrid models was improved by different methods. Two weather stations of Tabriz and Rasht in Iran with respectively annual and monthly time steps were selected to test the improved models. The results showed that Theil’s coefficient, which measures the inequality degree to which forecasts differ from observations, improved by 9% and 15% for SVR and GMDH relative to GEP for the Tabriz station. The applied error criteria indicated that the proposed hybrid models have a better representation of observations than the traditional hybrid models. Mean square error decreased by 67% and Nash Sutcliffe increased by 5% in the Rasht station when we combined the three machine learning models using genetic algorithm instead of SVR. Generally, the representation of the nonlinear models within the improved hybrid models showed better performance than the traditional hybrid models. The improved models have implications for modeling highly nonlinear systems using the full advantages of machine learning methods.

A machine learning approach to predicting 30-day mortality following paediatric cardiac surgery: findings from the Australia New Zealand Congenital Outcomes Registry for Surgery (ANZCORS).

We aim to develop the first risk prediction model for 30-day mortality for the Australian and New Zealand patient populations and examine whether machine learning (ML) algorithms outperform traditional statistical approaches. Data from the Australia New Zealand Congenital Outcomes Registry for Surgery, which contains information on every paediatric cardiac surgical encounter in Australian and New Zealand for patients aged <18 years between January 2013 and December 2021, were analysed (n = 14343). The outcome was mortality within the 30-day period following a surgical encounter, with ∼30% of the observations randomly selected to be used for validation of the final model. Three different ML methods were used, all of which employed five-fold cross-validation to prevent overfitting, with model performance judged primarily by the area under the receiver operating curve (AUC). Among the 14343 30-day periods, there were 188 deaths (1.3%). In the validation data, the gradient-boosted tree obtained the best performance [AUC = 0.87, 95% confidence interval = (0.82, 0.92); calibration = 0.97, 95% confidence interval = (0.72, 1.27)], outperforming penalized logistic regression and artificial neural networks (AUC of 0.82 and 0.81, respectively). The strongest predictors of mortality in the gradient boosting trees were patient weight, STAT score, age and gender. Our risk prediction model outperformed logistic regression and achieved a level of discrimination comparable to the PRAiS2 and Society of Thoracic Surgery Congenital Heart Surgery Database mortality risk models (both which obtained AUC = 0.86). Non-linear ML methods can be used to construct accurate clinical risk prediction tools.

A combinatorial machine-learning-driven approach for predicting glass transition temperature based on numerous molecular descriptors

ABSTRACT Glass transition temperature (Tg ) is one of the most significant thermophysical property which is hard to measure experimentally. With the development of machine learning, many molecular presentation methods and prediction algorithms have been proposed for predicting Tg . However, most descriptors of these algorithms are linear, while the values of molecular descriptors obtained from experiments or simulation software may have nonlinear relationships. General nonlinear machine learning methods are difficult to apply for the super high dimensional characteristics of molecular descriptors. In this paper, molecular descriptors are defined in manifold space and manifold learning is used for dimension reduction. Different from other linear algorithms for selecting molecular descriptors, Locally Linear Embedding (LLE) is utilised to reduce dimensionalities by extracting new features from numerous molecular descriptors. The features with dimensionality reduction are fed to the eXtreme Gradient Boosting (XGBoost) model. This ensemble algorithm is suitable for predicting glass transition temperature due to its high adaptability and nonlinear approximation ability. Genetic algorithm (GA) is adopted for optimising the parameters of XGBoost. The combinatorial approach consists of dimensionality reduction, prediction and optimisation. Experimental results show that the proposed machine learning approach LLE-XGBoost-GA has higher accuracy in Tg prediction of polymers.

Research on Data Classification Method of Optimized Support Vector Machine Based on Gray Wolf Algorithm

The data classification method based on support vector machine (SVM) has been widely used in various studies as a non-linear, high precision, and good generalization ability machine learning method. Among them, the kernel function and its parameters have a great impact on the classification accuracy. In order to find the optimal parameters to improve the classification accuracy of SVM, this paper proposes a data multi-classification method based on gray wolf algorithm optimized SVM(GWO-SVM). In this paper, the iris data set is used to test the performance of GWO-SVM, and the classification result is compared with those based on genetic algorithm (GA), particle swarm optimization (PSO) and the original SVM model. The test results show that the GWO-SVM model has a higher recognition and classification accuracy than the other three models, and has the shortest running time, which has obvious advantages and can effectively improve the classification accuracy of SVM. This method has practical significance in image classification, text classification, and fault detection.

Distributed optical fiber sensor temperature dynamic correction method based on building fire temperature-time curve

To enhance the applicability of standard fiber sensors in building fire scenarios, this study conducted the temperature rise experiments of common single-mode and multi-mode fiber sensors based on the temperature-time curve of typical fire experiments. Moreover, the statistical method was employed to analyze its temperature sensing performance and obtain the fiber sensor suitable for building fire warning. After that, the temperature error correction models were established by nonlinear fitting and machine learning methods. The results show that the temperature measurement stability of the multi-mode fiber sensor is better than the single-mode one. The model built by polynomial fitting performs best above 600 °C, reducing the error to a minimum of 1.5%; and the model built by ANN performs best below 600 °C, reducing the error to a minimum of 3.5%. Furthermore, the generalization of the model was verified by subsequent random temperature offset warming experiments.

Analysis of Influencing Factors and Distribution Simulation of Budget Hotel Room Pricing Based on Big Data and Machine Learning from a Spatial Perspective

The goal of investors in the hotel business is to maximize profits, and the price is an important means of achieving this goal. This has attracted many scholars to study the spatiotemporal relationship between hotel room prices and their possible influencing factors from different perspectives. However, most existing studies adopt the linear assumption of the hedonic model, with limited features and a lack of feature selection procedures. Additionally, there are few forecasts of hotel pricing from a spatial perspective. To overcome these gaps, this study adopts linear and nonlinear machine learning methods based on the “big data” of Sanya City to explore the influencing factors of budget hotel pricing. Based on the spatial perspective, 81 potential factors were considered. They are further selected using a feature extraction model called recursive feature elimination. Six machine-learning algorithms were evaluated and compared: random forest, extreme gradient boosting, multi-linear regression, support vector regression, multilayer perceptron regression, and K-nearest neighbor regression. The optimal value was used to further calculate the feature importance. They disclosed 40 important impact characteristics and predicted the spatial distribution of hotel pricing.