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Journal articles on the topic 'Bayesian Machine Learning (BML)'

Author: Grafiati

Published: 6 September 2023

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1

Rigueira, Xurxo, María Pazo, María Araújo, Saki Gerassis, and Elvira Bocos. "Bayesian Machine Learning and Functional Data Analysis as a Two-Fold Approach for the Study of Acid Mine Drainage Events." Water 15, no. 8 (2023): 1553. http://dx.doi.org/10.3390/w15081553.

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Acid mine drainage events have a negative influence on the water quality of fluvial systems affected by coal mining activities. This research focuses on the analysis of these events, revealing hidden correlations among potential factors that contribute to the occurrence of atypical measures and ultimately proposing the basis of an analytical tool capable of automatically capturing the overall behavior of the fluvial system. For this purpose, the hydrological and water quality data collected by an automated station located in a coal mining region in the NW of Spain (Fabero) were analyzed with a

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Mobiny, Aryan, Aditi Singh, and Hien Van Nguyen. "Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis." Journal of Clinical Medicine 8, no. 8 (2019): 1241. http://dx.doi.org/10.3390/jcm8081241.

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Knowing when a machine learning system is not confident about its prediction is crucial in medical domains where safety is critical. Ideally, a machine learning algorithm should make a prediction only when it is highly certain about its competency, and refer the case to physicians otherwise. In this paper, we investigate how Bayesian deep learning can improve the performance of the machine–physician team in the skin lesion classification task. We used the publicly available HAM10000 dataset, which includes samples from seven common skin lesion categories: Melanoma (MEL), Melanocytic Nevi (NV),

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3

Oladyshkin, Sergey, Farid Mohammadi, Ilja Kroeker, and Wolfgang Nowak. "Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory." Entropy 22, no. 8 (2020): 890. http://dx.doi.org/10.3390/e22080890.

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Gaussian process emulators (GPE) are a machine learning approach that replicates computational demanding models using training runs of that model. Constructing such a surrogate is very challenging and, in the context of Bayesian inference, the training runs should be well invested. The current paper offers a fully Bayesian view on GPEs for Bayesian inference accompanied by Bayesian active learning (BAL). We introduce three BAL strategies that adaptively identify training sets for the GPE using information-theoretic arguments. The first strategy relies on Bayesian model evidence that indicates

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Zhou, Ting, Xiaohu Wen, Qi Feng, Haijiao Yu, and Haiyang Xi. "Bayesian Model Averaging Ensemble Approach for Multi-Time-Ahead Groundwater Level Prediction Combining the GRACE, GLEAM, and GLDAS Data in Arid Areas." Remote Sensing 15, no. 1 (2022): 188. http://dx.doi.org/10.3390/rs15010188.

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Accurate groundwater level (GWL) prediction is essential for the sustainable management of groundwater resources. However, the prediction of GWLs remains a challenge due to insufficient data and the complicated hydrogeological system. In this study, we investigated the ability of the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Evaporation Amsterdam Model (GLEAM) data, the Global Land Data Assimilation System (GLDAS) data, and the publicly available meteorological data in 1-, 2-, and 3-month-ahead GWL prediction using three traditional machine learning models

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Kim, Sungwon, Meysam Alizamir, Nam Won Kim, and Ozgur Kisi. "Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series." Sustainability 12, no. 22 (2020): 9720. http://dx.doi.org/10.3390/su12229720.

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Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigates the potential of novel ensemble approach, Bayesian model averaging (BMA), in streamflow forecasting using daily time series data from two stations (i.e., Hongcheon and Jucheon), South Korea. Six categories (i.e., M1–M6) of input combination using

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Najafi, Mohammad Reza, Zahra Kavianpour, Banafsheh Najafi, Mohammad Reza Kavianpour, and Hamid Moradkhani. "Air demand in gated tunnels – a Bayesian approach to merge various predictions." Journal of Hydroinformatics 14, no. 1 (2011): 152–66. http://dx.doi.org/10.2166/hydro.2011.108.

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High flowrate through gated tunnels may cause critical flow conditions, especially downstream of the regulating gates. Aeration is found to be the most effective and efficient way to prevent cavitation attack. Several experimental equations are presented to predict air demand in gated tunnels; however, they are restricted to particular model geometries and flow conditions and often provide differing results. In this study the current relationships are first evaluated, and then other approaches for air discharge estimation are investigated. Three machine learning techniques are compared based o

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Xu, Ren, Nengcheng Chen, Yumin Chen, and Zeqiang Chen. "Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin." Advances in Meteorology 2020 (March 9, 2020): 1–17. http://dx.doi.org/10.1155/2020/8680436.

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Downscaling considerably alleviates the drawbacks of regional climate simulation by general circulation models (GCMs). However, little information is available regarding the downscaling using machine learning methods, specifically at hydrological basin scale. This study developed multiple machine learning (ML) downscaling models, based on a Bayesian model average (BMA), to downscale the precipitation simulation of 8 Coupled Model Intercomparison Project Phase 5 (CMIP5) models using model output statistics (MOS) for the years 1961–2005 in the upper Han River basin. A series of statistical metri

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8

Shu, Meiyan, Shuaipeng Fei, Bingyu Zhang, et al. "Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits." Plant Phenomics 2022 (August 28, 2022): 1–17. http://dx.doi.org/10.34133/2022/9802585.

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High-throughput estimation of phenotypic traits from UAV (unmanned aerial vehicle) images is helpful to improve the screening efficiency of breeding maize. Accurately estimating phenotyping traits of breeding maize at plot scale helps to promote gene mining for specific traits and provides a guarantee for accelerating the breeding of superior varieties. Constructing an efficient and accurate estimation model is the key to the application of UAV-based multiple sensors data. This study aims to apply the ensemble learning model to improve the feasibility and accuracy of estimating maize phenotypi

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Quadeer, Ahmed A., Matthew R. McKay, John P. Barton, and Raymond H. Y. Louie. "MPF–BML: a standalone GUI-based package for maximum entropy model inference." Bioinformatics 36, no. 7 (2019): 2278–79. http://dx.doi.org/10.1093/bioinformatics/btz925.

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Abstract Summary Learning underlying correlation patterns in data is a central problem across scientific fields. Maximum entropy models present an important class of statistical approaches for addressing this problem. However, accurately and efficiently inferring model parameters are a major challenge, particularly for modern high-dimensional applications such as in biology, for which the number of parameters is enormous. Previously, we developed a statistical method, minimum probability flow–Boltzmann Machine Learning (MPF–BML), for performing fast and accurate inference of maximum entropy mo

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Soria-Olivas, E., J. Gomez-Sanchis, J. D. Martin, et al. "BELM: Bayesian Extreme Learning Machine." IEEE Transactions on Neural Networks 22, no. 3 (2011): 505–9. http://dx.doi.org/10.1109/tnn.2010.2103956.

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11

Biletskyy, B. "Distributed Bayesian Machine Learning Procedures." Cybernetics and Systems Analysis 55, no. 3 (2019): 456–61. http://dx.doi.org/10.1007/s10559-019-00153-4.

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12

Chen, Yarui, Jucheng Yang, Chao Wang, and DongSun Park. "Variational Bayesian extreme learning machine." Neural Computing and Applications 27, no. 1 (2014): 185–96. http://dx.doi.org/10.1007/s00521-014-1710-1.

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13

Suyama, Atsushi. "Introduction to Bayesian Machine Learning." Journal of the Robotics Society of Japan 40, no. 10 (2022): 857–62. http://dx.doi.org/10.7210/jrsj.40.857.

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14

Li, Yifen, Yun Wang, Zhiya Chen, and Runmin Zou. "Bayesian robust multi-extreme learning machine." Knowledge-Based Systems 210 (December 2020): 106468. http://dx.doi.org/10.1016/j.knosys.2020.106468.

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15

Gandhi, Shipra, Sarabjot Pabla, Mary Nesline, et al. "Algorithmic prediction of response to checkpoint inhibitors: Hyperprogressors versus responders." Journal of Clinical Oncology 35, no. 15_suppl (2017): 11565. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.11565.

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11565 Background: Predicting response to checkpoint inhibitors (CPIs) using biological knowledge-based decision processes with machine learning (ML) has a great potential to predict rapid progression in patients treated with checkpoint inhibitors (CPIs) (hyperprogressive disease (HPD)) as well as responders. ML models risk overfitting data and do not always evaluate the underlying biology, thus performing well in the initial training cohort but lack generalizability when extended to other cohorts. Biology-based decision may not perform as well initially due to limited understanding and a simpl

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16

Wang, Peipei, Xinqi Zheng, Junhua Ku, and Chunning Wang. "Multiple-Instance Learning Approach via Bayesian Extreme Learning Machine." IEEE Access 8 (2020): 62458–70. http://dx.doi.org/10.1109/access.2020.2984271.

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17

Wai Lam. "Bayesian network refinement via machine learning approach." IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no. 3 (1998): 240–51. http://dx.doi.org/10.1109/34.667882.

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18

Krems, R. V. "Bayesian machine learning for quantum molecular dynamics." Physical Chemistry Chemical Physics 21, no. 25 (2019): 13392–410. http://dx.doi.org/10.1039/c9cp01883b.

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19

Karandikar, Jaydeep, Andrew Honeycutt, Scott Smith, and Tony Schmitz. "Milling stability identification using Bayesian machine learning." Procedia CIRP 93 (2020): 1423–28. http://dx.doi.org/10.1016/j.procir.2020.04.022.

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20

Bew, David, Campbell R. Harvey, Anthony Ledford, Sam Radnor, and Andrew Sinclair. "Modeling Analysts’ Recommendations via Bayesian Machine Learning." Journal of Financial Data Science 1, no. 1 (2019): 75–98. http://dx.doi.org/10.3905/jfds.2019.1.1.075.

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21

Zhu, Jun, Jianfei Chen, Wenbo Hu, and Bo Zhang. "Big Learning with Bayesian methods." National Science Review 4, no. 4 (2017): 627–51. http://dx.doi.org/10.1093/nsr/nwx044.

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AbstractThe explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems and applications with Big Data. Bayesian methods represent one important class of statistical methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including non-parametric Bayesian meth

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22

Boyko, Nataliya, and Oleksandra Dypko. "Analysis of Machine Learning Methods Using Spam Filtering." Modeling Control and Information Technologies, no. 5 (November 21, 2021): 25–28. http://dx.doi.org/10.31713/mcit.2021.06.

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The paper considers methods of the naive Bayesian classifier. Experiments that show independence between traits are described. Describes the naive Bayesian classifier used to filter spam in messages. The aim of the study is to determine the best method to solve the problem of spam in messages. The paper considers three different variations of the naive Bayesian classifier. The results of experiments and research are given.

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23

J, Dr Visumathi, Tetala Durga Venkata Rama Reddy, Velagapudi Abhinandhan, and Panamganti Anil Kumar. "Multi-Disease Prediction Using Machine Learning Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 447–53. http://dx.doi.org/10.22214/ijraset.2023.50128.

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Abstract: In the medical sector, disease diagnosis is an essential duty, and prompt and accurate diagnosis is crucial to effective management and therapy. Machine learning techniques, including Naive Bayesian networks, have shown promise in disease prediction and diagnosis. In this study, we present a machine learning-based multi-disease prediction system that uses Naive Bayesian networks. The proposed methodology seeks to deliver precise illness prediction for several diseases instantaneously. In addition to describing the methods adopted, which included dataset selection, preprocessing, feat

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24

Tresp, Volker. "A Bayesian Committee Machine." Neural Computation 12, no. 11 (2000): 2719–41. http://dx.doi.org/10.1162/089976600300014908.

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The Bayesian committee machine (BCM) is a novel approach to combining estimators that were trained on different data sets. Although the BCM can be applied to the combination of any kind of estimators, the main foci are gaussian process regression and related systems such as regularization networks and smoothing splines for which the degrees of freedom increase with the number of training data. Somewhat surprisingly, we find that the performance of the BCM improves if several test points are queried at the same time and is optimal if the number of test points is at least as large as the degrees

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25

Geer, A. J. "Learning earth system models from observations: machine learning or data assimilation?" Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2194 (2021): 20200089. http://dx.doi.org/10.1098/rsta.2020.0089.

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Recent progress in machine learning (ML) inspires the idea of improving (or learning) earth system models directly from the observations. Earth sciences already use data assimilation (DA), which underpins decades of progress in weather forecasting. DA and ML have many similarities: they are both inverse methods that can be united under a Bayesian (probabilistic) framework. ML could benefit from approaches used in DA, which has evolved to deal with real observations—these are uncertain, sparsely sampled, and only indirectly sensitive to the processes of interest. DA could also become more like

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26

Sohail, Ayesha. "INFERENCE OF BIOMEDICAL DATA SETS USING BAYESIAN MACHINE LEARNING." Biomedical Engineering: Applications, Basis and Communications 31, no. 04 (2019): 1950030. http://dx.doi.org/10.4015/s1016237219500303.

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Due to the advancement in data collection and maintenance strategies, the current clinical databases around the globe are rich in a sense that these contain detailed information not only about the individual’s medical conditions, but also about the environmental features, associated with the individual. Classification within this data could provide new medical insights. Data mining technology has become an attraction for researchers due to its affectivity and efficacy in the field of biomedicine research. Due to the diverse structure of such data sets, only few successful techniques and easy t

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27

Malviya, Ravi Prakash. "A Bayesian Machine Learning Approach for Smart City." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 796–816. http://dx.doi.org/10.22214/ijraset.2021.39195.

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28

Gao, Haiping, Shifa Zhong, Wenlong Zhang, et al. "Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization." Environmental Science & Technology 56, no. 4 (2021): 2572–81. http://dx.doi.org/10.1021/acs.est.1c04373.

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29

Jun, Sunghae, #VALUE! #VALUE!, and #VALUE! #VALUE! "Regression Machine Learning using Bayesian Inference and Regularization." Journal of Korean Institute of Intelligent Systems 29, no. 5 (2019): 390–94. http://dx.doi.org/10.5391/jkiis.2019.29.5.390.

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30

Wu, Wei, Srikantan Nagarajan, and Zhe Chen. "Bayesian Machine Learning: EEG\/MEG signal processing measurements." IEEE Signal Processing Magazine 33, no. 1 (2016): 14–36. http://dx.doi.org/10.1109/msp.2015.2481559.

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31

Chakraborty, Sounak. "Bayesian semi-supervised learning with support vector machine." Statistical Methodology 8, no. 1 (2011): 68–82. http://dx.doi.org/10.1016/j.stamet.2009.09.002.

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32

Sarkar, Dripta, Michael A. Osborne, and Thomas A. A. Adcock. "Prediction of tidal currents using Bayesian machine learning." Ocean Engineering 158 (June 2018): 221–31. http://dx.doi.org/10.1016/j.oceaneng.2018.03.007.

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33

Wang, Jing, Lin Zhang, Juan-juan Cao, and Di Han. "NBWELM: naive Bayesian based weighted extreme learning machine." International Journal of Machine Learning and Cybernetics 9, no. 1 (2014): 21–35. http://dx.doi.org/10.1007/s13042-014-0318-1.

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34

Jiahua Luo, Chi-Man Vong, and Pak-Kin Wong. "Sparse Bayesian Extreme Learning Machine for Multi-classification." IEEE Transactions on Neural Networks and Learning Systems 25, no. 4 (2014): 836–43. http://dx.doi.org/10.1109/tnnls.2013.2281839.

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35

Song, Min-Jong, and Yong-Sik Cho. "Probabilistic Tsunami Heights Model using Bayesian Machine Learning." Journal of Coastal Research 95, sp1 (2020): 1291. http://dx.doi.org/10.2112/si95-249.1.

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36

Hobson, Michael, Philip Graff, Farhan Feroz, and Anthony Lasenby. "Machine-learning in astronomy." Proceedings of the International Astronomical Union 10, S306 (2014): 279–87. http://dx.doi.org/10.1017/s1743921314013672.

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AbstractMachine-learning methods may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. An intuitive and well-established approach to machine learning is the use of artificial neural networks (NNs), which consist of a group of interconnected nodes, each of which processes information that it receives and then passes this product on to other nodes via weighted connections. In particular, I discuss the first public release of the generic

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37

White, Brian S., Suleiman A. Khan, Muhammad Ammad-ud-din, et al. "Comparative Analysis of Independent Ex Vivo functional Drug Screens Identifies Predictive Biomarkers of BCL-2 Inhibitor Response in AML." Blood 132, Supplement 1 (2018): 2763. http://dx.doi.org/10.1182/blood-2018-99-111916.

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Abstract Introduction: Therapeutic options for patients with AML were recently expanded with FDA approval of four drugs in 2017. As their efficacy is limited in some patient subpopulations and relapse ultimately ensues, there remains an urgent need for additional treatment options tailored to well-defined patient subpopulations to achieve durable responses. Two comprehensive profiling efforts were launched to address this need-the multi-center Beat AML initiative, led by the Oregon Health & Science University (OHSU) and the AML Individualized Systems Medicine program at the Institute for M

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38

Chavan, Mr Vikram. "Malware Classification using Machine Learning Algorithms and Tools." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 69–73. http://dx.doi.org/10.22214/ijraset.2021.34353.

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The explosive growth of malware variants poses a major threat to information security. Malware is the one which frequently growing day by day and becomes major threats to the Internet Security. According to numerous increasing of worm malware in the networks nowadays, it became a serious danger that threatens our computers. Networks attackers did these attacks by designing the worms. A designed system model is needed to defy these threats, prevent it from multiplying and spreading through the network, and harm our computers. In this paper, we designed a classification on system model for this

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Nixon, Matthew C., and Nikku Madhusudhan. "Assessment of supervised machine learning for atmospheric retrieval of exoplanets." Monthly Notices of the Royal Astronomical Society 496, no. 1 (2020): 269–81. http://dx.doi.org/10.1093/mnras/staa1150.

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ABSTRACT Atmospheric retrieval of exoplanets from spectroscopic observations requires an extensive exploration of a highly degenerate and high-dimensional parameter space to accurately constrain atmospheric parameters. Retrieval methods commonly conduct Bayesian parameter estimation and statistical inference using sampling algorithms such as Markov chain Monte Carlo or Nested Sampling. Recently several attempts have been made to use machine learning algorithms either to complement or to replace fully Bayesian methods. While much progress has been made, these approaches are still at times unabl

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40

Lehto, M. R., and G. S. Sorock. "Machine Learning of Motor Vehicle Accident Categories from Narrative Data." Methods of Information in Medicine 35, no. 04/05 (1996): 309–16. http://dx.doi.org/10.1055/s-0038-1634680.

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Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ra

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41

Hwang, Ha-Eun, Yoon-Sang Cho, Seok-Cheol Hwang, and Seoung-Bum Kim. "Optimal Tire Design Using Machine Learning and Bayesian Optimization." Journal of the Korean Institute of Industrial Engineers 48, no. 4 (2022): 433–40. http://dx.doi.org/10.7232/jkiie.2022.48.4.433.

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42

Baggio, Giacomo, Algo Carè, Anna Scampicchio, and Gianluigi Pillonetto. "Bayesian frequentist bounds for machine learning and system identification." Automatica 146 (December 2022): 110599. http://dx.doi.org/10.1016/j.automatica.2022.110599.

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43

Williams, Dominic P., Stanley E. Lazic, Alison J. Foster, Elizaveta Semenova, and Paul Morgan. "Predicting Drug-Induced Liver Injury with Bayesian Machine Learning." Chemical Research in Toxicology 33, no. 1 (2019): 239–48. http://dx.doi.org/10.1021/acs.chemrestox.9b00264.

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44

Wang, Hui. "Finding patterns in subsurface using Bayesian machine learning approach." Underground Space 5, no. 1 (2020): 84–92. http://dx.doi.org/10.1016/j.undsp.2018.10.006.

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45

Wang, Jian, Ting Ran, Yadong Chen, and Tao Lu. "Bayesian machine learning to discover Bruton’s tyrosine kinase inhibitors." Chemical Biology & Drug Design 96, no. 4 (2020): 1114–22. http://dx.doi.org/10.1111/cbdd.13656.

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46

Garcia-Bonete, Maria-Jose, and Gergely Katona. "Bayesian machine learning improves single-wavelength anomalous diffraction phasing." Acta Crystallographica Section A Foundations and Advances 75, no. 6 (2019): 851–60. http://dx.doi.org/10.1107/s2053273319011446.

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Single-wavelength X-ray anomalous diffraction (SAD) is a frequently employed technique to solve the phase problem in X-ray crystallography. The precision and accuracy of recovered anomalous differences are crucial for determining the correct phases. Continuous rotation (CR) and inverse-beam geometry (IBG) anomalous data collection methods have been performed on tetragonal lysozyme and monoclinic survivin crystals and analysis carried out of how correlated the pairs of Friedel's reflections are after scaling. A multivariate Bayesian model for estimating anomalous differences was tested, which t

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47

Santucci, Raymond J., Christine E. Sanders, Hongyu Zhu, Kenneth D. Smith, and Robert G. Kelly. "Bayesian Network Machine Learning Approach to Atmospheric Corrosion Modelling." ECS Meeting Abstracts MA2022-02, no. 10 (2022): 693. http://dx.doi.org/10.1149/ma2022-0210693mtgabs.

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The performance degradation of materials exposed to corrosive atmospheric environments is a serious problem. Corrosion maintenance strategies and cycles are informed largely by historical trends in corrosivity dependent on the location of interest. Assessments are often made with sparse experimental data and are generalized to certain materials and baseline conditions observed in the past. The development of a predictive model which can better inform maintenance strategies and cycles offers opportunities for time savings and cost avoidance. Such a model would need to predict corrosion damage a

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48

Bessa, Miguel A., Piotr Glowacki, and Michael Houlder. "Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible." Advanced Materials 31, no. 48 (2019): 1904845. http://dx.doi.org/10.1002/adma.201904845.

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Chen, Hongyu, Xinyi Li, Zongbao Feng, et al. "Shield attitude prediction based on Bayesian-LGBM machine learning." Information Sciences 632 (June 2023): 105–29. http://dx.doi.org/10.1016/j.ins.2023.03.004.

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Chaturvedi, Iti, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino, and Erik Cambria. "Bayesian network based extreme learning machine for subjectivity detection." Journal of the Franklin Institute 355, no. 4 (2018): 1780–97. http://dx.doi.org/10.1016/j.jfranklin.2017.06.007.

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