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The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using machine learning algorithms

PROCESSES 2024年第3期12卷 441页

作者： Pan, Tao Song, Chengmin Gao, Zhiyu Xia, Tian Wang, Tianqi Cent Iron & Steel Res Inst Inst Struct Steel Res Beijing 100081 Peoples R China Shenyang Ligong Univ Sch Mat Sci & Engn Shenyang 110159 Peoples R China Liaoning Tech Univ Coll Mat Sci & Engn Fuxin 123000 Peoples R China

The constitutive model refers to the mapping relationship between the stress and deformation conditions (such as strain, strain rate, and temperature) after being loaded. In this work, the hot deformation behavior of a Ni-Cr-Mo steel was investigated by conducting isothermal compression tests using a Gleeble-3800 thermal simulator with deformation temperatures ranging from 800 degrees C to 1200 degrees C, strain rates ranging from 0.01 s-1 to 10 s-1, and deformations of 55%. To analyze the constitutive relation of the Ni-Cr-Mo steel at high temperatures, five machine learning algorithms were employed to predict the flow stress, namely, back-propagation artificial neural network (BP-ANN), Random Committee, Bagging, k-nearest neighbor (k-NN), and a library for support vector machines (libSVM). A comparative study between the experimental and the predicted results was performed. The results show that correlation coefficient (R), root mean square error (RMSE), mean absolute value error (MAE), mean square error (MSE), and average absolute relative error (AARE) obtained from the Random Committee on the testing set are 0.98897, 8.00808 MPa, 5.54244 MPa, 64.12927 MPa2 and 5.67135%, respectively, whereas the metrics obtained via other algorithms are all inferior to the Random Committee. It suggests that the Random Committee can predict the flow stress of the steel more effectively.

关键词： Ni-Cr-Mo steel hot deformation flow stress constitutive model machine learning algorithm error evaluation

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Applicability of machine learning algorithms in predicting chloride diffusion in concrete: Modeling, evaluation, and feature analysis

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CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 21卷

作者： Liu, Weizheng Liu, Guiyong Zhu, Xiaolin Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China Jiangxi Univ Technol Coll Urban Construct Nanchang 330098 Peoples R China

The resistance to chloride diffusion is one of the most crucial durable properties of concrete. However, traditional methods to evaluate this property are time-consuming and inefficient. In this research, backpropagation-artificial neural network (BP-ANN), support vector regression (SVR), genetic programming (GP), extreme gradient boost (XGBoost), and random forest (RF) models were optimized using particle swarm optimization (PSO) to predict the chloride diffusion coefficient of concretes containing silica fume. A database was also compiled, consisting of various features related to materials composition, curing, and exposure conditions. Statistical assessments were made to evaluate the predictive efficacy of every model. In addition, the distribution of errors and the consistency of each model were scrutinized. The findings indicate that the XGBoost model outperformed the standard models, achieving an R-2 value of 0.9382 and an MSE of 3.0162. The models' predictive precision was notably enhanced following their integration with PSO. The PSO algorithm can also decrease the occurrence of significant error points in the predicted values and enhance the consistency of predictive performance across the range of experimental data. Finally, the PSO-XGBoost demonstrated the best comprehensive performance and proved to be the most efficient among the other PSO-synthesized (PSOS) models.

关键词： Concrete Durability machine learning algorithm Chloride Diffusion Coefficient Model Evaluation Feature Analysis

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Selective Detection of VOCs With WO₃ Nanoplates-Based Single Chemiresistive Sensor Device Using machine learning algorithms

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IEEE SENSORS JOURNAL 2021年第5期21卷 5771-5778页

作者： Acharyya, Snehanjan Nag, Sudip Guha, Prasanta Kumar Indian Inst Technol Kharagpur Adv Technol Dev Ctr Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

The paper presents the integration of single metal-oxide based chemiresistive sensor device and machine learning tools for selective discrimination of different volatile organic compounds (VOCs) for indoor air quality monitoring applications. Tungsten oxide (WO3) nanoplates has been employed as the gas sensing material which were obtained by acidification followed by low temperature hydrothermal process. Synthesized WO3 nanoplate structure was confirmed by different characterization tools explaining surface morphology and structural properties. The sensor device was fabricated by using a simple drop coating technique on top of aluminum based interdigitated electrodes. An extensive gas sensing study was carried out where adequate sensor response was observed for each target VOC. The sensing mechanism has been discussed to realize the behavior of the sensor towards the introduction of target VOCs. Collective data obtained from the sensor device were engaged with machine learning algorithms (best results shown by multilayer perceptron) to discriminate the target VOCs accurately. Furthermore, concentrations of tested VOCs were predicted in a quantitative manner using a regression model with fair accuracy.

关键词： Sensors Gas detectors Prediction algorithms Temperature sensors machine learning algorithms Classification algorithms Chemical sensors Indoor air quality volatile organic compound WO₃ nanoplates hydrothermal method selectivity machine learning algorithm

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PrePCF ML: Prediction of photonic crystal fiber parameters using machine learning algorithms 2

PrePCF ML: Prediction of photonic crystal fiber parameters u...

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2nd IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (IEEE ICAECT)

作者： Das, Tushar Hossen, Md Nazmul Rahman, S. K. Mushfiqur Parvin, Tarunnum Ahmed, Kawsar Bui, Francis M. Mawlana Bhashani Sci & Technol Univ MBSTU Dept Informat & Commun Technol Tangail 1902 Bangladesh Birla Insitute Technol BIT Dept Elect & Commun Engn Patna Campus Patna 800014 Bihar India Univ Saskatchewan USASK Dept Elect & Comp Engn Saskatoon SK Canada

ISBN: (纸本)9781665411202

To create a functioning photonic crystal fiber, it must first be accurately designed, and then simulations with various parameters must be performed to find the optimal one. However, simulation is a time-consuming and labor-intensive endeavor for both humans and machines. The outstanding strategy of machine learning (ML) may expedite the lengthy procedure and reduce the arduous effort. In this work, we first prepared a custom dataset after getting data from the COMSOL Multiphysics simulation tool. After that, we experimented with numerous machine learning algorithms using the datasets to predict the design parameters of photonic crystal fiber. In each machine learning algorithm, the input features were wavelength, core radius, cladding radius, analyte, and pitch, and the output was the prediction of real and imaginary (x-direction, y-direction) values. The predicted values were used to look at the PCF's sensitivity and confinement loss. Furthermore, for each algorithm, the R squared score, mean square error (MSE), and mean average error (MAE) were assessed. Among the experimented algorithms, random forest regression obtained the highest R squared score and also the lowest MSE and MAE. In the sphere of optical sensing, this strategy might be a boon.

关键词： machine learning algorithm Photonic Crystal Fiber Linear Regression Random Forest Decision Tree Optical Sensor Design Loss profile

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Classification of Esophageal Adenocarcinoma, Esophageal Squamous Cell Carcinoma, and Stomach Adenocarcinoma Based on machine learning algorithms

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SENSORS AND MATERIALS 2021年第8期33卷 2639-2654页

作者： Chen, Xiaoping Zheng, Lihui Yao, Jianqi Yang, Cheng-Fu Fujian Normal Univ Coll Math & Stat Fuzhou 350000 Peoples R China Fujian Normal Univ FJKLMAA Fuzhou 350000 Peoples R China Fujian Normal Univ Ctr Appl Math Fujian Prov Fuzhou 350000 Peoples R China Natl Univ Kaohsiung Dept Chem & Mat Engn Kaohsiung 811 Taiwan Chaoyang Univ Technol Dept Aeronaut Engn Taichung 413 Taiwan

Esophageal and gastric cancers are common malignant tumors. In medicine, it is difficult to differentiate the sickness symptoms of esophageal adenocarcinoma (EAC), esophageal squamous cell carcinoma (ESCC), and stomach adenocarcinoma (SAC). In particular, the molecular characteristics of EAC and SAC are very similar, which makes them difficult to distinguish. Information collected by sensors can be analyzed by machine learning. In this study, we used cancer data published in Nature in 2017, which were downloaded from cBioPortal, to classify the three types of cancer by five machine learning algorithms, and we compared the classification effects for different models by calculating confusion matrices. According to the research data in this paper, the random forest (RF) model is the best of the five machine learning classification models for the overall classification effect of the three types of cancer. More specifically, the classification effect of this model is the best for EAC, whereas the classification effect for ESCC is not ideal. The classification based on the RF model can effectively enhance the differentiation between the symptoms of EAC, SAC, and ESCC, enabling cancer patients to receive more accurate treatment and have an improved prognosis.

关键词： EAC ESCC SAC machine learning algorithm confusion matrix

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Predictive modeling of COVID-19 mortality risk in chronic kidney disease patients using multiple machine learning algorithms

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Luo, Lin Gao, Peng Yang, Chunhui Yu, Sha Dalian Med Univ Affiliated Hosp 2 Dept Clin Lab 467Zhongshan Rd Dalian 116027 Liaoning Peoples R China

The coronavirus disease 2019 (COVID-19) has a significant impact on the global population, particularly on individuals with chronic kidney disease (CKD). COVID-19 patients with CKD will face a considerably higher risk of mortality than the general population. This study developed a predictive model for assessing mortality in COVID-19-affected CKD patients, providing personalized risk prediction to optimize clinical management and reduce mortality rates. We developed machine learning algorithms to analyze 219 patients' clinical laboratory test data retrospectively. The performance of each model was assessed using a calibration curve, decision curve analysis, and receiver operating characteristic (ROC) curve. It was found that the LightGBM model showed the most satisfied performance, with an area under the ROC curve of 0.833, sensitivity of 0.952, and specificity of 0.714. Prealbumin, neutrophil percent, respiratory index in arterial blood, half-saturated pressure of oxygen, carbon dioxide in serum, glucose, neutrophil count, and uric acid were the top 8 significant variables in the prediction model. Validation by 46 patients demonstrated acceptable accuracy. This model can serve as a powerful tool for screening CKD patients at high risk of COVID-19-related mortality and providing decision support for clinical staff, enabling efficient allocation of resources, and facilitating timely and targeted management for those who need the relevant interference urgently.

关键词： Chronic kidney disease COVID-19 machine learning algorithm LightGBM (light gradient-boosting machine) Prediction model

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Evaluating strength behavior and failure mode of ice materials based on large-scale database and machine learning algorithms

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OCEAN ENGINEERING 2024年第Part1期313卷

作者： Ai, Xihui Li, Jiao Zhou, Zhiwei Ma, Wei Chinese Acad Sci Northwest Inst Ecoenvironm & Resources State Key Lab Frozen Soil Engn Lanzhou 730000 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The strength characteristic and failure mode of ice materials are widely used to analyze the interaction between ice and structure to ensure the construction stability in ocean engineering and ice engineering. This study establishes a large-scale database (comprising 5542 testing samples) for the ice mechanics to study the influences of six effective features (grain size, density, porosity, salinity, temperature, and strain rate) on mechanical behaviors. The correlation degrees between effective features and strength behavior (failure mode) are investigated through correlation analysis. The Regression algorithm and Classification algorithm of four machine learning models are respectively used to evaluate and predict the strength and deformation behaviors of ice materials. Four evaluation parameters (R-2, RMSE, MAE, and MBE) are adopted to further investigate the predictive ability of those machine learning models. Based on the partial dependence interpretation, the contributions of effective features to strength prediction are quantitatively described. The results indicate that temperature is the most important factor for strength behavior. In the classification prediction for failure mode, the prediction accuracy for ductile behavior is enhanced in the Random Forest algorithm to improve the overall classification accuracy, and the Random Forest algorithm exhibits well performance compared to the other three algorithms.

关键词： Ice material Strength prediction Failure mode classification Correlation analysis machine learning algorithm

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Identification and experimental validation of key genes in osteoarthritis based on machine learning algorithms and single-cell sequencing analysis

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HELIYON 2024年第17期10卷 e37047页

作者： Yu, Enming Zhang, Mingshu Xi, Chunyang Yan, Jinglong Harbin Med Univ Affiliated Hosp 2 Dept Orthoped Harbin Peoples R China

Purpose: Osteoarthritis (OA) is a prevalent cause of disability in older adults. Identifying diagnostic markers for OA is essential for elucidating its mechanisms and facilitating early diagnosis. Methods: We analyzed 53 synovial tissue samples (n = 30 for OA, n = 23 for the control group) from two datasets in the Gene Express Omnibus (GEO) database. We identified differentially expressed genes (DEGs) between the groups and applied dimensionality reduction using six machine learning algorithms to pinpoint characteristic genes (key genes). We classified the OA samples into subtypes based on these key genes and explored the differences in biological functions and immune characteristics among subtypes, as well as the roles of the key genes. Additionally, we constructed a protein-protein interaction network to predict small molecules that target these genes. Further, we accessed synovial tissue sample data from the single-cell RNA dataset GSE152805, categorized the cells into various types, and examined variations in gene expression and their correlation with OA progression. Validation of key gene expression was conducted in cellular experiments using the qPCR method. Results: Four genes AGMAT, MAP3K8, PERT, and XIST, were identified as characteristic genes of OA. All can independently predict the occurrence of OA. With these genes, the OA samples can be clustered into two subtypes, which showed significant differences in functional pathways and immune infiltration. Eight cell types were obtained by analyzing the single-cell RNA data, with synovial intimal fibroblasts (SIF) accounting for the highest proportion in each sample. The key genes were found over-expressed in SIF and significantly correlated with OA progression and the content of immune cells (ICs). We validated the relative levels of key genes in OA and normal cartilage tissue cells, which showed an expression trend consistency with the bioinformatics result except for XIST. Conclusion: Four genes, AGMAT, MAP

关键词： Osteoarthritis machine learning algorithm Differentially expressed genes Key genes Immune response

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Open switch fault diagnosis of cascade H-bridge multi-level inverter in distributed power generators by machine learning algorithms

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ENERGY REPORTS 2021年 7卷 8929-8942页

作者： Ali, Murad Din, Zakiud Solomin, Evgeny Cheema, Khalid Mehmood Milyani, Ahmad H. Che, Zhiyuan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China South Ural State Univ Dept Elect Stat Grid & Syst Power Supply Chelyabinsk Russia King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia

In recent years, multi-level inverters have had remarkable applications in renewable energy sources, high voltage, and other high-power applications. The multi-level inverter has advantages like minimum harmonic distortion and can operate on several voltage levels. A multi-level inverter is being utilized for multipurpose applications such as transportation, communication, industrial manufacturing, aerospace active power filter, Static Var Compensator, and machine drive. Power electronics equipment reliability is very important, and to ensure a multi-level inverter system's stable operation;it is important to detect and locate faults as quickly as possible. It is difficult to diagnose a fault in a multi-level inverter using a mathematical model because it consists of many switching devices, in this context and to improve fault diagnosis accuracy and efficiency of a cascaded multi-level inverter (CHMLI), a fault diagnosis strategy based on the probability principal component analysis (PPCA) might be utilized. Different machine learning algorithms are used to classify and diagnose the faults under different conditions in a cascaded H-bridge multi-level inverter (CHMLI). This paper presents the comparison of two different machine learning algorithms, such as support vector machine (SVM) and k-Nearest neighbors algorithm (k-NN), based on probabilistic principal component analysis (PPCA) for the effective open switch fault diagnosis in CHMLI employed in distributed generator units. PPCA is a useful technique used for optimizing and data processing without changing the input data's original properties and characteristics. Using the phase shift pulse width modulation technique, the output voltage signals under different switching fault conditions if the CHMLI are taken as fault features. Both algorithms are used to identify and locate the fault under different modes in CHMLI of distributed generator units. The proposed fault diagnosis methods are compared using simulations

关键词： Renewable energy resources Multi-level inverter Distributed generator Fault diagnosis machine learning algorithm

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Implementation of machine learning for the design of spiral shaped multiband monopole antenna for MICS/IEEE802.11a/IEEE802.11b applications

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2025年第3期39卷 318-343页

作者： Chatterjee, Debajyoti Kundu, Anjan Kumar Univ Engn & Management Dept CSE Jaipur India Univ Calcutta Inst Radio Phys & Elect 92 APC Rd Kolkata 700009 W Bengal India

This paper presents a method to reduce the size of a compact antenna for MICS and IEEE 802.11a/b applications. Initially a monopole antenna (45 x 40 x 1.6 mm(3)) is designed for the ISM band (2.4-2.5 GHz). A spiral meandered single patch is incorporated to lower the operational frequency to 600 MHz assuring significant antenna size reduction. Further enhancements include a two-sided spiral extension and a T-shaped arm around the microstrip feed, enabling operation across three frequency bands achieving an overall 84% size reduction with improved gain. The prototype meets FCC standards for Specific Absorption Rate (SAR). To optimize gain, machine learning models along-with LASSO, Ridge, and Random Forest regression algorithm are used. The LASSO model proves most effective, achieving gains of 6.6629 dBi at 400 MHz, 7.6225 dBi at 2.45 GHz, and 8.7569 dBi at 5.5 GHz, with fractional bandwidths of 22%, 27%, and 8% respectively.

关键词： Miniaturization multiband antenna MICS/IEEE802.11a/IEEE802.11b specific absorption rate (SAR) machine learning algorithm regression model

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