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Artículos de revistas sobre el tema "Multioutput regression"

Autor: Grafiati

Publicado: 30 de noviembre de 2024

Última modificación: 31 de julio de 2025

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1

Tian, Qing, Meng Cao, Songcan Chen, and Hujun Yin. "Structure-Exploiting Discriminative Ordinal Multioutput Regression." IEEE Transactions on Neural Networks and Learning Systems 32, no. 1 (2021): 266–80. http://dx.doi.org/10.1109/tnnls.2020.2978508.

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Li, Shunlong, Huiming Yin, Zhonglong Li, Wencheng Xu, Yao Jin, and Shaoyang He. "Optimal sensor placement for cable force monitoring based on multioutput support vector regression model." Advances in Structural Engineering 21, no. 15 (2018): 2259–69. http://dx.doi.org/10.1177/1369433218772342.

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Cable force monitoring is an essential and critical part of structural health monitoring for cable-supported bridges. The quality of obtained information depends considerably on the number and location of limited sensors. The purpose of this article is to provide a method for optimal sensor placement for cable force monitoring in cable-supported bridges. Based on the spatial correlation between neighbouring or symmetrical cable forces, the structural information of non-monitored cables can be predicted by multioutput support vector regression models, established between monitored (input) and t

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3

Tuia, D., J. Verrelst, L. Alonso, F. Perez-Cruz, and G. Camps-Valls. "Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation." IEEE Geoscience and Remote Sensing Letters 8, no. 4 (2011): 804–8. http://dx.doi.org/10.1109/lgrs.2011.2109934.

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4

KONDO, Tadashi. "Multiinput-Multioutput Type GMDH Algorithm Using Regression-Principal Component Analysis." Transactions of the Institute of Systems, Control and Information Engineers 6, no. 11 (1993): 520–29. http://dx.doi.org/10.5687/iscie.6.520.

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5

Yun, Seokheon. "Performance Analysis of Construction Cost Prediction Using Neural Network for Multioutput Regression." Applied Sciences 12, no. 19 (2022): 9592. http://dx.doi.org/10.3390/app12199592.

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In a construction project, construction cost estimation is very important, but construction costs are affected by various factors, so they are difficult to predict accurately. However, with the recent development of ANN technology, it has become possible to predict construction costs with consideration of various influencing factors. Unlike previous research cases, this study aimed to predict the total construction cost by predicting seven sub-construction costs using a multioutput regression model, not by predicting a single total construction cost. In addition, analysis of the change in cons

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6

Wang, Yu, and Guohua Liu. "MLA-TCN: Multioutput Prediction of Dam Displacement Based on Temporal Convolutional Network with Attention Mechanism." Structural Control and Health Monitoring 2023 (August 25, 2023): 1–19. http://dx.doi.org/10.1155/2023/2189912.

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The displacement of concrete dams effectively reflects their structural integrity and operational status. Therefore, establishing a model for predicting the displacement of concrete dams and studying the evolution mechanism of dam displacement is essential for monitoring the structural safety of dams. Current data-driven models utilize artificial data that cannot reflect the actual status of dams for network training. They also have difficulty extracting the temporal patterns from long-term dependencies and obtaining the interactions between the targets and variables. To address such problems,

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7

Avani Dedhia. "Investigation of Multi-Output Regression Modeling in Predicting Concrete Mix Design." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 615–30. https://doi.org/10.52783/jisem.v10i43s.8458.

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Current research is a piece of an innovative approach to concrete mix-design prediction by implementing advanced regression techniques, that addressed the limitations of traditional methods in IS 10262 and ACI 318 standards which rely heavily on empirical relationships and require extensive trial batching. The study investigates eight important mix-design parameters namely water-cementratio, cement content, flyash content, fine aggregate content, 10 mm and 20 mm aggregate content, water content, and superplasticizer content. The methodology utilizes comprehensive Multioutput Regression with gr

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8

Wu, Shengbiao, Huaning Li, and Xianpeng Chen. "Parametric Model for Coaxial Cavity Filter with Combined KCCA and MLSSVR." International Journal of Antennas and Propagation 2023 (June 7, 2023): 1–10. http://dx.doi.org/10.1155/2023/2024720.

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Aiming at the problems of poor data effectiveness, low modeling accuracy, and weak generalization in the tuning process of microwave cavity filters, a parametric model for coaxial cavity filter using kernel canonical correlation analysis (KCCA) and multioutput least squares support vector regression (MLSSVR) is proposed in this study. First, the low-dimensional tuning data is mapped to the high-dimensional feature space by kernel canonical correlation analysis, and the nonlinear feature vectors are fused by the kernel function; second, the multioutput least squares support vector regression al

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9

Huang, Kai, Ming-Yi You, Yun-Xia Ye, Bin Jiang, and An-Nan Lu. "Direction of Arrival Based on the Multioutput Least Squares Support Vector Regression Model." Mathematical Problems in Engineering 2020 (September 30, 2020): 1–8. http://dx.doi.org/10.1155/2020/8601376.

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The interferometer is a widely used direction-finding system with high precision. When there are comprehensive disturbances in the direction-finding system, some scholars have proposed corresponding correction algorithms, but most of them require hypothesis based on the geometric position of the array. The method of using machine learning that has attracted much attention recently is data driven, which can be independent of these assumptions. We propose a direction-finding method for the interferometer by using multioutput least squares support vector regression (MLSSVR) model. The application

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10

Rosentreter, Johannes, Ron Hagensieker, Akpona Okujeni, Ribana Roscher, Paul D. Wagner, and Bjorn Waske. "Subpixel Mapping of Urban Areas Using EnMAP Data and Multioutput Support Vector Regression." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 5 (2017): 1938–48. http://dx.doi.org/10.1109/jstars.2017.2652726.

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11

Zhen, Xiantong, Heye Zhang, Ali Islam, Mousumi Bhaduri, Ian Chan, and Shuo Li. "Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression." Medical Image Analysis 36 (February 2017): 184–96. http://dx.doi.org/10.1016/j.media.2016.11.008.

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12

Hornbaker, Robert H., Bruce L. Dixon, and Steven T. Sonka. "Estimating Production Activity Costs for Multioutput Firms with a Random Coefficient Regression Model." American Journal of Agricultural Economics 71, no. 1 (1989): 167–77. http://dx.doi.org/10.2307/1241785.

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13

Hariguna, Taqwa, and Athapol Ruangkanjanases. "Adaptive sentiment analysis using multioutput classification: a performance comparison." PeerJ Computer Science 9 (May 9, 2023): e1378. http://dx.doi.org/10.7717/peerj-cs.1378.

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The primary objective of this research is to create a multi-output classification model for sentiment analysis through the combination of 10 algorithms: BernoulliNB, Decision Tree, K-nearest neighbor, Logistic Regression, LinearSVC, Bagging, Stacking, Random Forest, AdaBoost, and ExtraTrees. In doing so, we aim to identify the optimal algorithm performance and role within the model. The data utilized in this study is derived from customer reviews of cryptocurrencies in Indonesia. Our results indicate that LinearSVC and Stacking exhibit a high accuracy (90%) compared to the other eight algorith

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14

JAHANSEIR, Mercedeh, Seyed Kamaledin SETAREHDAN, and Sirous MOMENZADEH. "Estimation of the depth of anesthesia by using a multioutput least-square support vector regression." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 26, no. 6 (2018): 2793–802. http://dx.doi.org/10.3906/elk-1802-189.

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15

Cui, ZhenKai, Cheng Wang, Jianwei Chen, and Ting He. "Multipoint Vibration Response Prediction under Uncorrelated Multiple Sources Load Based on Elastic-Net Regularization in Frequency Domain." Shock and Vibration 2021 (March 2, 2021): 1–10. http://dx.doi.org/10.1155/2021/6614020.

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In order to solve the problems of large number of conditions at inherent frequencies and low prediction accuracy when using multiple multivariate linear regression methods for vibration response prediction alone, an elastic-net regularization method is proposed. Firstly, a multi-input and multioutput linear regression model of the multipoint frequency domain vibration response is trained using historical data at each frequency point. Secondly, the trained model under each frequency point is improved by the elastic regularization. Finally, the model is used in a working situation. The predicted

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16

Aydın, Yaren, Gebrail Bekdaş, Sinan Melih Nigdeli, Ümit Isıkdağ, Sanghun Kim, and Zong Woo Geem. "Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns." Applied Sciences 13, no. 7 (2023): 4117. http://dx.doi.org/10.3390/app13074117.

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CO2 emission is one of the biggest environmental problems and contributes to global warming. The climatic changes due to the damage to nature is triggering a climate crisis globally. To prevent a possible climate crisis, this research proposes an engineering design solution to reduce CO2 emissions. This research proposes an optimization-machine learning pipeline and a set of models trained for the prediction of the design variables of an ecofriendly concrete column. In this research, the harmony search algorithm was used as the optimization algorithm, and different regression models were used

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17

Bhakti, S. Pimpale, and A. Pandit Anala. "Multioutput Ensemble Machine Learning Algorithm: A Prediction Model of Acute Respiratory infection and Pneumonia Occurrence." Indian Journal of Science and Technology 16, no. 45 (2023): 4141–55. https://doi.org/10.17485/IJST/v16i45.1011.

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Abstract <strong>Objectives:</strong> To forecast daily OPD patients based on air pollution and weather parameters, the objective is to build a robust model that accurately predicts patient volume by considering major missing values and factors such as PM2.5 levels, temperature, humidity, wind speed, and rainfall, etc. thereby improving healthcare planning and delivery. <strong>Methods:</strong> To develop the multioutput ensemble model for forecasting daily OPD (out-patient department), we have used 13 machine learning techniques such as regression analysis, Extra tree regresso

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18

Moumouh, Jihane, Saad Benjelloun, Abderrazak Latifi, and Lhachmi Khamar. "Data-driven modeling and optimization of an industrial phosphoric acid production unit." MATEC Web of Conferences 379 (2023): 07008. http://dx.doi.org/10.1051/matecconf/202337907008.

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In this work, a supervised machine learning (ML) multi-output regression approach is investigated to build predictive models for an industrial unit of phosphoric acid production. More specifically, multioutput data-driven regression is applied to simultaneously estimate nine outputs (Reactor temperature, chemical yield (RC), P2O5 concentration in the phosphoric acid, and chemical losses in gypsum) under different operating conditions. The presented methods are linear regression and decision tree regression models. The use of decision tree regression provides high accuracy compared to linear re

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19

Panchbhai, Kamini G., Sarvesha S. Shetgaonkar, Pranay P. Morajkar, and Madhusudan G. Lanjewar. "Artificial intelligence with wrapper multioutput regression to identify chemical contents of fresh and dried manure samples using NIR spectroscopy." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 330 (April 2025): 125697. https://doi.org/10.1016/j.saa.2025.125697.

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20

Bu, Aiguo, and Jie Li. "A Learning-Based Framework for Circuit Path Level NBTI Degradation Prediction." Electronics 9, no. 11 (2020): 1976. http://dx.doi.org/10.3390/electronics9111976.

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Negative bias temperature instability (NBTI) has become one of the major causes for temporal reliability degradation of nanoscale circuits. Due to its complex dependence on operating conditions, it is a tremendous challenge to the existing timing analysis flow. In order to get the accurate aged delay of the circuit, previous research mainly focused on the gate level or lower. This paper proposes a low-runtime and high-accuracy machining learning framework on the circuit path level firstly, which can be formulated as a multi-input–multioutput problem and solved using a linear regression model.

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21

Zhang, Renhui, and Xutao Zhao. "Inverse Method of Centrifugal Pump Blade Based on Gaussian Process Regression." Mathematical Problems in Engineering 2020 (February 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/4605625.

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The inverse problem is always one of the important issues in the field of fluid machinery for the complex relationship among the blade shape, the hydraulic performance, and the inner flow structure. Based on Bayesian theory of posterior probability obtained from known prior probability, the inverse methods for the centrifugal pump blade based on the single-output Gaussian process regression (SOGPR) and the multioutput Gaussian process regression (MOGPR) were proposed, respectively. The training sample set consists of the blade shape parameters and the distribution of flow parameters. The hyper

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22

Kumari, Sumit, Vikas Siwach, Yudhvir Singh, Dheerdhwaj Barak, and Rituraj Jain. "A Machine Learning Centered Approach for Uncovering Excavators’ Last Known Location Using Bluetooth and Underground WSN." Wireless Communications and Mobile Computing 2022 (March 3, 2022): 1–11. http://dx.doi.org/10.1155/2022/9160031.

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Machine learning and data analytics are two of the most popular subdisciplines of modern computer science which have a variety of scopes in most of the industries ranging from hospitals to hotels, manufacturing to pharmaceuticals, mining to banking, etc. Additionally, mining and hospitals are two of the most critical industries where applications when deployed security, accuracy, and cost effectiveness are the major concerns, due to the huge involvement of man and machines. In this paper, the problem of finding out the location of man and machines has been focused on in case of an accident dur

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23

Aydın, Yaren, Farnaz Ahadian, Gebrail Bekdaş, and Sinan Melih Nigdeli. "Prediction of optimum design of welded beam design via machine learning." Challenge Journal of Structural Mechanics 10, no. 3 (2024): 86. http://dx.doi.org/10.20528/cjsmec.2024.03.001.

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Design optimization is an important engineering design topic. One of the important issues in structural design is to minimize the cost. This study based on an engineering problem of Welded Beam Design aims to minimize the cost of the beam with machine learning (ML) models depending on the constraints on applied load, shear stress, bending stress and end deflection. The data set to be used in this context was created using a metaheuristic optimization algorithm. This hybrid algorithm is based on the classical Jaya algorithm by adding the student phase of Teaching Learning Based Optimization. Th

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24

Zhang, Lu, Junbiao Zhang, Tao Xiong, and Chiao Su. "Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables." Discrete Dynamics in Nature and Society 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/5730295.

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This paper examines the interval forecasting of carbon futures prices in one of the most important carbon futures market. Specifically, the purpose of this study is to present a novel hybrid approach, which is composed of multioutput support vector regression (MSVR) and particle swarm optimization (PSO), in the task of forecasting the highest and lowest prices of carbon futures on the next trading day. Furthermore, we set out to investigate if considering some potential predictors, which have strong influence on carbon futures prices, in modeling process is useful for achieving better predicti

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25

Chen, Changrui, Sifan Li, Jinbi Ye, et al. "Soil Parameter Inversion in Dredger Fill Strata Using GWO-MLSSVR for Deep Foundation Pit Engineering." Buildings 15, no. 11 (2025): 1864. https://doi.org/10.3390/buildings15111864.

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Accurate determination of constitutive model parameters is crucial for reliable numerical simulation in deep foundation pit engineering. This study presents an inverse analysis method using Multioutput Least-Squares Support Vector Regression (MLSSVR) optimized by the Gray Wolf Optimization (GWO) algorithm to invert key parameters of the Hardening Soil (HS) model. A case study on a foundation pit in the dredger fill stratum of Xiamen Railway integrates finite element simulation with machine learning. The proposed GWO-MLSSVR model demonstrates high predictive accuracy, with lateral displacement

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26

Mashuri, M., H. Khusna, Wibawati, and F. D. Putri. "Mixed Multivariate EWMA-CUSUM (MEC) Chart based on MLS-SVR Model for Monitoring Drinking Water Quality." Journal of Physics: Conference Series 2123, no. 1 (2021): 012019. http://dx.doi.org/10.1088/1742-6596/2123/1/012019.

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Abstract Monitoring the quality of drinking water needs to be conducted considering the important role of water in human life. Mixed Multivariate EWMA-CUSUM (MEC) chart is a multivariate control chart developed for observing the mean process. Based on the previous studies, this chart has better performance in detecting a shift in the process mean. In this research, the MEC is applied to observe the grade of drinking water. However, there is autocorrelation in drinking water data which lead to more false alarm occurred. Therefore, the Multioutput Least Square Support Vector Regression (MLS-SVR)

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27

Ahsan, M., and T. R. Aulia. "Comparing the Performance of Several Multivariate Control Charts Based on Residual of Multioutput Least Square SVR (MLS-SVR) Model in Monitoring Water Production Process." Journal of Physics: Conference Series 2123, no. 1 (2021): 012018. http://dx.doi.org/10.1088/1742-6596/2123/1/012018.

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Abstract Water that is used as the basic human need, requires a processing process to get it. Water quality control in Tirtanadi Water Treatment Plant is still univariate, while theoretically the quality characteristics of water quality are correlated and there is also an autocorrelation due to the continuous process. In this study, quality control is performed on three main variables of water quality characteristics, namely acidity (pH), chlorine residual (ppm), and turbidity (NTU) using several multivariate control charts based on Multioutput Least Square Support Vector Regression (MLS-SVR)

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28

Fagin, Joshua, Ji Won Park, Henry Best, et al. "Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties." Astrophysical Journal 965, no. 2 (2024): 104. http://dx.doi.org/10.3847/1538-4357/ad2988.

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Abstract Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of A

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29

Chen, Zhiming. "Technology Focus: Formation Evaluation (August 2024)." Journal of Petroleum Technology 76, no. 08 (2024): 52–53. http://dx.doi.org/10.2118/0824-0052-jpt.

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Formation evaluation stands as a cornerstone in determining the potential of a wellbore in the exploration, development, and production of oil and gas resources. Using a suite of tools such as well logging, core analysis, formation testing, and well-testing analysis, formation evaluation provides critical insights into reservoir properties. For instance, well-test analysis enables reservoir-parameter inversions, well-productivity evaluation, and reservoir-boundaries identification for both conventional and unconventional reservoirs. The inherent subjectivity of human interpretation, however, c

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30

Jiang, Mingfeng, Feng Liu, Yaming Wang, Guofa Shou, Wenqing Huang, and Huaxiong Zhang. "A Hybrid Model of Maximum Margin Clustering Method and Support Vector Regression for Noninvasive Electrocardiographic Imaging." Computational and Mathematical Methods in Medicine 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/436281.

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Noninvasive electrocardiographic imaging, such as the reconstruction of myocardial transmembrane potentials (TMPs) distribution, can provide more detailed and complicated electrophysiological information than the body surface potentials (BSPs). However, the noninvasive reconstruction of the TMPs from BSPs is a typical inverse problem. In this study, this inverse ECG problem is treated as a regression problem with multi-inputs (BSPs) and multioutputs (TMPs), which will be solved by the Maximum Margin Clustering- (MMC-) Support Vector Regression (SVR) method. First, the MMC approach is adopted t

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31

Yu, Hang, Jie Lu, and Guangquan Zhang. "MORStreaming: A Multioutput Regression System for Streaming Data." IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 1–13. http://dx.doi.org/10.1109/tsmc.2021.3102978.

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32

Sun, Haoming. "Learning Rates of Multioutput Regression With Kernel Methods and ℓp$$ {\ell}^p $$‐Regularization". Mathematical Methods in the Applied Sciences, 9 липня 2025. https://doi.org/10.1002/mma.11217.

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ABSTRACTMultioutput learning aims at learning multiple outputs simultaneously from a given input. It has been extensively studied in the literature on machine learning. Among them, how to estimate learning rates for various learning problems remains a fundamental theoretical problem. By far, most of the existing research only focuses on single‐output learning. To strengthen the theoretical support of multioutput learning, we study the learning rates of multioutput regression with kernel methods and ‐regularization. We obtain an explicit learning rate, which reveals the quantitative dependency

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33

Zhen, Xiantong, Mengyang Yu, Ali Islam, Mousumi Bhaduri, Ian Chan, and Shuo Li. "Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression." IEEE Transactions on Neural Networks and Learning Systems, 2016, 1–13. http://dx.doi.org/10.1109/tnnls.2016.2573260.

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34

Tan, Chao, Sheng Chen, Genlin Ji, and Xin Geng. "Multilabel Distribution Learning Based on Multioutput Regression and Manifold Learning." IEEE Transactions on Cybernetics, 2020, 1–15. http://dx.doi.org/10.1109/tcyb.2020.3026576.

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Liu, Tong, Sheng Chen, Kang Li, Shaojun Gan, and Chris J. Harris. "Adaptive Multioutput Gradient RBF Tracker for Nonlinear and Nonstationary Regression." IEEE Transactions on Cybernetics, 2023, 1–14. http://dx.doi.org/10.1109/tcyb.2023.3235155.

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Zhong, Huaiyang, Margaret L. Brandeau, Golnaz Eftekhari Yazdi, et al. "Metamodeling for Policy Simulations with Multivariate Outcomes." Medical Decision Making, June 23, 2022, 0272989X2211050. http://dx.doi.org/10.1177/0272989x221105079.

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Purpose Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We develop a framework for metamodeling with policy simulations to accommodate multivariate outcomes. Methods: We combine 2 algorithm adaptation methods—multitarget stacking and regression chain with maximum correlation—with different base learners including linear regression (LR), elastic net (EE) with second-order terms, Gaussian process regression (GPR),

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37

Hua, Huichun, Jian Li, Donglin Fang, and Min Guo. "An Adaptive Multioutput Fuzzy Gaussian Process Regression Method for Harmonic Source Modeling." IEEE Transactions on Power Delivery, 2024, 1–11. http://dx.doi.org/10.1109/tpwrd.2024.3421609.

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38

Li, Ximing, Yang Wang, Zhao Zhang, Richang Hong, Zhuo Li, and Meng Wang. "RMoR-Aion: Robust Multioutput Regression by Simultaneously Alleviating Input and Output Noises." IEEE Transactions on Neural Networks and Learning Systems, 2020, 1–14. http://dx.doi.org/10.1109/tnnls.2020.2984635.

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39

Pretto, Tatiane, Fábio Baum, Rogério A. Gouvêa, Alexandre G. Brolo, and Marcos J. Leite Santos. "Optimizing the Synthesis Parameters of Double Perovskites with Machine Learning Using a Multioutput Regression Model." Journal of Physical Chemistry C, April 18, 2024. http://dx.doi.org/10.1021/acs.jpcc.3c06801.

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40

Nugroho, Waego Hadi, Samingun Handoyo, Hsing-Chuan Hsieh, Yusnita Julyarni Akri, Zuraidah -, and Donna DwinitaAdelia. "Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation." International Journal of Advanced Computer Science and Applications 13, no. 9 (2022). http://dx.doi.org/10.14569/ijacsa.2022.0130992.

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41

Mollick, Tajrian, Galib Hashmi, and Saifur Rahman Sabuj. "A multifaceted journey in coastal meteorological projections through multioutput regression: a two-layer stacking ensemble approach." Theoretical and Applied Climatology, March 16, 2024. http://dx.doi.org/10.1007/s00704-024-04923-9.

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42

Février, Stéphane, Lionel Mathelin, Stéphane Nachar, Frédéric Giordano, and Bérengère Podvin. "Data-Driven Model to Predict Aircraft Vibration Environment." AIAA Journal, July 25, 2023, 1–13. http://dx.doi.org/10.2514/1.j062735.

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Vibration levels that onboard equipment must be able to withstand throughout their lives for correct operation are mainly determined experimentally because predicting the dynamic behavior of a complete aircraft requires computational means and methods that are currently difficult to implement. We present a data-driven methodology that leverages flight-test accelerometer data to produce a predictive model. This model, based on an ensemble of artificial neural networks, performs a multioutput multivariate regression to estimate vibration spectra from a set of aircraft general parameters without

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43

Maruyama, Sho, Fumiya Mizutani, and Haruyuki Watanabe. "Novel approach for quality control testing of medical displays using deep learning technology." Biomedical Physics & Engineering Express, January 7, 2025. https://doi.org/10.1088/2057-1976/ada6bd.

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Abstract Objectives:&#xD;In digital image diagnosis using medical displays, it is crucial to rigorously manage display devices to ensure appropriate image quality and diagnostic safety. The aim of this study was to develop a model for the efficient quality control (QC) of medical displays, specifically addressing the measurement items of contrast response and maximum luminance as part of constancy testing, and to evaluate its performance. In addition, the study focused on whether these tasks could be addressed using a multitasking strategy.&#xD;Methods:&#xD;The model used in this s

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44

Yunika, Annisa, and Muhammad Ahsan. "Pengendalian Kualitas Proses Produksi Hasil Gula Kristal Putih di PG Djatiroto PTPN XI Menggunakan Diagram Kontrol Maximum Multivariate Cumulative Sum (Max-MCUSUM) Berbasis Residual Model Multioutput Least Square Support Vector Regression (MLS-SVR)." Jurnal Sains dan Seni ITS 12, no. 1 (2023). http://dx.doi.org/10.12962/j23373520.v12i1.101891.

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Alsaihati, Ahmed, Menhal Ismail, and Salaheldin Elkatatny. "Optimization of Drilling Parameters While Drilling Surface Holes Using Machine Learning and Differential Evolution." SPE Journal, December 1, 2024, 1–14. https://doi.org/10.2118/223965-pa.

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Summary Downhole vibrations while drilling surface hole sections can cause inefficient drilling. Downhole sensors can be used to provide real-time data on vibration levels encountered during drilling operations. This information helps the drilling crew to identify and address the factors causing excessive vibrations by adjusting drilling parameters based on real-time feedback to maintain or enhance the rate of penetration (ROP). The high cost, however, hinders the operator from using such sensors in each well. This research presents a workflow that coupled machine learning (ML) with an optimiz

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Lei, Chi-Un, Wincy Chan, and Yuyue Wang. "Evaluation of UN SDG-related formal learning activities in a university common core curriculum." International Journal of Sustainability in Higher Education, December 11, 2023. http://dx.doi.org/10.1108/ijshe-02-2023-0050.

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Purpose Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs. Design/methodology/approach In this report, the authors used machine learning techniques to tag the 166 common co

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Dong, Yukun, Shuaiwei Liu, Jiyuan Zhang, et al. "Prediction of Gas and Water Production of Coalbed Methane Wells by Integrating Light Gradient Boosting Machine and Multioutput Regressor." SPE Journal, June 1, 2025, 1–16. https://doi.org/10.2118/228316-pa.

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Summary The production of coalbed methane (CBM) significantly supplements unconventional natural gas supply. Accurate prediction of CBM well production is crucial for optimizing development strategies and evaluating project economics. Existing studies typically rely on historical production data and some influencing factors to forecast future CBM production. However, few studies directly predict the entire production dynamics solely based on influencing factors. To address this gap, we propose an improved light gradient boosting machine (LightGBM) model that integrates the multioutput regresso

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