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A Comparative Analysis of machine learning algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System

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BUILDINGS 2024年第6期14卷 1659页

作者： Lu, Shengze Cui, Mengying Gao, Bo Liu, Jianhua Ni, Ji Liu, Jiying Zhou, Shiyu Shandong Jianzhu Univ Sch Thermal Engn Jinan 250101 Peoples R China Sichuan Inst Bldg Res Chengdu 610084 Peoples R China Shandong GRAD Grp Corp Ltd Dezhou 253000 Peoples R China

machine learning algorithms have proven to be practical in a wide range of applications. Many studies have been conducted on the operational energy consumption and thermal comfort of radiant floor systems. This paper conducts a case study in a self-designed experimental setup that combines radiant floor and fan coil cooling (RFCFC) and develops a data monitoring system as a source of historical operational data. Seven machine learning algorithms (extreme learning machine (ELM), convolutional neural network (CNN), genetic algorithm-back propagation (GA-BP), radial basis function (RBF), random forest (RF), support vector machine (SVM), and long short-term memory (LSTM)) were employed to predict the behavior of the RFCFC system. Corresponding prediction models were then developed to evaluate operative temperature (T-op) and energy consumption (Eh). The performance of the model was evaluated using five error metrics. The obtained results showed that the RF model had very high performance in predicting T-op and E-h, with high correlation coefficients (>0.9915) and low error metrics. Compared with other models, it also demonstrated high accuracy in E-h prediction, yielding maximum reductions of 68.1, 82.4, and 43.2% in the mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), respectively. A sensitivity ranking algorithm analysis was also conducted. The obtained results demonstrated the importance of adjusting parameters, such as the radiant floor supply water temperature, to enhance the indoor comfort. This study provides a novel and effective method for evaluating the energy efficiency and thermal comfort of radiant cooling systems. It also provides insights for optimizing the efficiency and thermal comfort of RFCFC systems, and lays a theoretical foundation for future studies integrating machine learning algorithms in this field.

关键词： machine learning algorithm radiant floor fan coil cooling thermal comfort energy consumption

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Prediction of Loan Pricing on the basis of Area Location using K-Nearest Neighbour and Support Vector machine learning algorithms

Prediction of Loan Pricing on the basis of Area Location usi...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Saravanan, M.S. Kumar, K. Kalyan Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350313987

-Estimating the Loan amount settlement depends on the area of location and issuance of Loan for the customer of the bank is the risk-taking process. This research aims to employ a novel K-Nearest Neighbour (KNN) approach, in contrast to the Support Vector machine (SVM) algorithm, for estimating loan prices based on geographical factors. The objective is to enhance the accuracy of loan price predictions within the banking system. Datasets were downloaded from *** and used to train utilising Novel KNN algorithms on 346 records with 10 attributes, with a sample size of 10. The model will utilize advanced K-Nearest Neighbour and Support Vector machines to analyze and forecast loan defaulters across diverse geographical locations. KNN and SVM have a significance level less than 0.05 with two tailed independent sample tests with value (p © 2023 IEEE.

关键词： Area of location Loan Pricing machine learning algorithm Novel K-Nearest Neighbour Support Vector machine

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machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2022年第1期22卷 205-205页

作者： Lei, Nuo Zhang, Xianlong Wei, Mengting Lao, Beini Xu, Xueyi Zhang, Min Chen, Huifen Xu, Yanmin Xia, Bingqing Zhang, Dingjun Dong, Chendi Fu, Lizhe Tang, Fang Wu, Yifan Guangzhou Univ Chinese Med Clin Med Coll 2 Guangzhou Peoples R China Guangzhou Univ Chinese Med Dept Nephrol Guangdong Prov Hosp Chinese Med Affiliated Hosp 2 Guangzhou Peoples R China Guangzhou Univ Chinese Med Guangdong Prov Hosp Chinese Med Chron Dis Management Dept Affiliated Hosp 2 Guangzhou Peoples R China

Background Kidney disease progression rates vary among patients. Rapid and accurate prediction of kidney disease outcomes is crucial for disease management. In recent years, various prediction models using machine learning (ML) algorithms have been established in nephrology. However, their accuracy have been inconsistent. Therefore, we conducted a systematic review and meta-analysis to investigate the diagnostic accuracy of ML algorithms for kidney disease progression. Methods We searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials, the Chinese Biomedicine Literature Database, Chinese National Knowledge Infrastructure, Wanfang Database, and the VIP Database for diagnostic studies on ML algorithms' accuracy in predicting kidney disease prognosis, from the establishment of these databases until October 2020. Two investigators independently evaluate study quality by QUADAS-2 tool and extracted data from single ML algorithm for data synthesis using the bivariate model and the hierarchical summary receiver operating characteristic (HSROC) model. Results Fifteen studies were left after screening, only 6 studies were eligible for data synthesis. The sample size of these 6 studies was 12,534, and the kidney disease types could be divided into chronic kidney disease (CKD) and Immunoglobulin A Nephropathy, with 5 articles using end-stage renal diseases occurrence as the primary outcome. The main results indicated that the area under curve (AUC) of the HSROC was 0.87 (0.84-0.90) and ML algorithm exhibited a strong specificity, 95% confidence interval and heterogeneity (I-2) of (0.87, 0.84-0.90, [I-2 99.0%]) and a weak sensitivity of (0.68, 0.58-0.77, [I-2 99.7%]) in predicting kidney disease deterioration. And the the results of subgroup analysis indicated that ML algorithm's AUC for predicting CKD prognosis was 0.82 (0.79-0.85), with the pool sensitivity of (0.64, 0.49-0.77, [I-2 99.20%]) and pool specificity of (0.84, 0.74-0.91, [I-2 99.84%]). The ML algor

关键词： Artificial intelligence machine learning algorithm Prediction models Chronic kidney disease CKD progression Immunoglobulin A nephropathy

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Intelligent Analysis Method of E-commerce Data Based on Various machine learning algorithms

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Procedia Computer Science 2025年 259卷 591-597页

作者： Bo Yang Lanzhou University of Resources and Environment Lanzhou 730000 Gansu China

Under the current rapid development of the e-commerce industry, most e-commerce companies are pursuing to enhance the clicks of products and its conversion rate to buy. And there are many machine learning algorithms for the intelligent analysis of e-commerce data, among which, the most widely used is the recurrent neural network (RNN) and collaborative filtering algorithm. Based on the use of multiple machine learning algorithms, this paper compares the differences in the clicks of products and the purchase conversion rates between the RNN algorithm and the collaborative filtering algorithm. The RNN algorithm can make full use of the behavior sequence time dependence and context information and the collaborative filtering algorithm is based on the similarities between user and product. The evaluation results are as follows: the products clicked by the RNN algorithm are between 18,000 and 25,000, which is significantly higher than the products clicked by the collaborative filtering algorithm. In order to improve user purchase decisions and overall sales efficiency, e-commerce operators can select the RNN algorithm to fully understand the user’s interests and needs, and provide accurate personalized product recommendations.

关键词： Intelligent Analysis of E-Commerce Data machine learning algorithm Collaborative Filtering Recurrent Neural Network

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

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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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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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Diagnostic and comparative performance for the prediction of tuberculous pleural effusion using machine learning algorithms

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INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2024年 182卷 105320-105320页

作者： Liu, Yanqing Liang, Zhigang Yang, Jing Yuan, Songbo Wang, Shanshan Huang, Weina Wu, Aihua Ningbo Univ Affiliated Hosp 1 Dept Lab Med Ningbo Zhejiang Peoples R China Ningbo Univ Dept Thorac Surg Affiliated Hosp 1 Ningbo Zhejiang Peoples R China Ningbo Univ Affiliated Hosp 1 Dept Resp & Crit Care Med Ningbo Zhejiang Peoples R China Ningbo Univ Dept Lab Med Affiliated Peoples Hosp Ningbo Zhejiang Peoples R China Ningbo Univ Affiliated Hosp 1 Dept Lab Med 59 Liuting St Ningbo 315010 Zhejiang Peoples R China Ningbo Univ Affiliated Hosp 1 Dept Resp & Crit Care Med 59 Liuting St Ningbo 315010 Zhejiang Peoples R China

Objective: Early diagnosis and differential diagnosis of tuberculous pleural effusion (TPE) remains challenging and is critical to the patients' prognosis. The present study aimed to develop nine machine learning (ML) algorithms for early diagnosis of TPE and compare their ***: A total of 1435 untreated patients with pleural effusions (PEs) were retrospectively included and divided into the training set (80%) and the test set (20%). The demographic and laboratory variables were collected, preprocessed, and analyzed to select features, which were fed into nine ML algorithms to develop an optimal diagnostic model for TPE. The proposed model was validated by an independently external data. The decision curve analysis (DCA) and the SHapley Additive exPlanations (SHAP) were also ***: Support vector machine (SVM) was the best model in discriminating TPE from non-TPE, with a balanced accuracy of 87.7%, precision of 85.3%, area under the curve (AUC) of 0.914, sensitivity of 94.7%, specificity of 80.7%, and F1-score of 86.0% among the nine ML algorithms. The excellent diagnostic performance was also validated by the external data (a balanced accuracy of 87.7%, precision of 85.2%, and AUC of 0.898). Neural network (NN) and K-nearest neighbor (KNN) had better net benefits in clinical usefulness. Besides, PE adenosine deaminase (ADA), PE carcinoembryonic antigen (CEA), and serum CYFRA21-1 were identified as the top three important features for diagnosing TPE. Conclusions: This study developed and validated a SVM model for the early diagnosis of TPE, which might help clinicians provide better diagnosis and treatment for TPE patients.

关键词： Tuberculous pleural effusion machine learning algorithm Diagnostic performance Decision curve analysis SHapley Additive exPlanations

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Can Reclaimed Asphalt Pavement Aggregates be Effectively Utilized in Designing Paving-Grade Geopolymer Concrete? An Experimental Study with Strength Prediction Using machine learning algorithms

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TRANSPORTATION RESEARCH RECORD 2024年第12期2678卷 1905-1922页

作者： Ghosh, Ayana Ransinchung, G. D. R. N. Kumar, Praveen Indian Inst Technol Roorkee Dept Civil Engn Roorkee India

Geopolymer concrete (GPC) has emerged as a prominent choice in the construction industry as a sustainable alternative binder. This research delves into the performance of paving-grade GPC by recycling reclaimed asphalt pavement (RAP) as a replacement for natural coarse aggregates. The experimental framework comprehensively examines its effect on the strength and elastic modulus (E), with results portraying more than a 20% reduction in 28 days of compressive strength with a 50% RAP inclusion. Similarly, complete replacement of RAP (100%) significantly affected the E value, revealing a substantial decrease of 46.67%. However, contrary to the existing literature, a Fourier transform infrared spectroscopy study indicated no notable change owing to RAP integration, suggesting a physical interaction at the molecular level. The acquired mechanical properties were used to design pavement thickness, which indicated that, even with a 50% RAP incorporation in the mix, the thickness could be reduced by approximately 9% compared to traditional cement concrete. In addition, an analysis of its impact on the stress ratio revealed an increase with the increment of RAP proportion. To understand the effect of relevant parameters such as NAOH molarity, RAP proportion (%), and curing age on the compressive strength of RAP-GPC, an in-depth analysis leveraging six supervised machine learning algorithms was carried out. The gradient booster validated with a k-fold cross-validation technique demonstrated the highest accuracy for both training and test data sets (89.5% and 96.6%, respectively). The minimal root mean square error (4.18%), mean square error (26.9%), and mean absolute error (4.16%) further substantiated the accuracy of the developed model.

关键词： RAP rigid pavement geopolymer concrete machine learning algorithm pavement design

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