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Academic Articles & Research Papers
Showing 20 of 85 academic articles
Shutao Li A, Weiwei Song, Leyuan Fang, Yushi Chen et al. • IEEE Transactions on Geoscience and Remote Sensing
(2019)
Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine ...
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Open Access
DOI: https://doi.org/10.1109/tgrs.2019.2907932
Xuedan Du, Yinghao Cai, Shuo Wang, Leijie Zhang • Youth Academic Annual Conference of Chinese Association of Automation (YAC)
(2016)
In recent years, deep learning has achieved great success in many fields, such as computer vision and natural language processing. Compared to traditional machine learning methods, deep learning has a strong learning ability and can make be...
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DOI: https://doi.org/10.1109/yac.2016.7804882
Polina Mamoshina, Armando S. Vieira, Eugene Lane, Alex A. Zhavoronkov • Molecular Pharmaceutics
(2016)
Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this ...
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DOI: https://doi.org/10.1021/acs.molpharmaceut.5b00982
Neil Thompson, Kristjan H. Greenewald, Keeheon Lee, Gabriel F. Manso (2023)
Deep learning's recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image classification, voice recognition, translation, and other tasks.But this progress has come with ...
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DOI: https://doi.org/10.21428/bf6fb269.1f033948
Gabriel Chartrand, Phillip M. Cheng, Eugene Vorontsov, Michal Drozdzal et al. • Radiographics
(2017)
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods prod...
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DOI: https://doi.org/10.1148/rg.2017170077
Christof Angermueller, Tanel Pärnamaa, Leopold Parts, Oliver Stegle • Molecular Systems Biology
(2016)
Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventiona...
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other-oa
DOI: https://doi.org/10.15252/msb.20156651
PMC: 4965871
Max Mowbray, Thomas Savage, Chufan Wu, Ziqi Song et al. • Biochemical Engineering Journal
(2021)
The field of machine learning is comprised of techniques, which have proven powerful approaches to knowledge discovery and construction of ‘digital twins’ in the highly dimensional, nonlinear and stochastic domains common to biochemical eng...
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DOI: https://doi.org/10.1016/j.bej.2021.108054
Xin Qian, Yu Li, Xianghui Zhang, Heng Guo et al. • Frontiers in Cardiovascular Medicine
(2022)
BackgroundCardiovascular diseases (CVD) are currently the leading cause of premature death worldwide. Model-based early detection of high-risk populations for CVD is the key to CVD prevention. Thus, this research aimed to use machine learni...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fcvm.2022.854287
William W. Hsieh • Cambridge University Press eBooks
(2009)
Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and th...
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DOI: https://doi.org/10.1017/cbo9780511627217
Gengke Lai, Yongguang Zhang • Geophysical Research Letters
(2025)
Abstract Canada experienced an unprecedented wildfire season in 2023. Here, we analyzed the exceptional scale, dominant driving factors, and potential impacts on permafrost of these wildfires using Moderate Resolution Imaging Spectroradiome...
DOAJ
Open Access (DOAJ)
DOI: 10.1029/2024GL114492
Richard J. Roiger • Chapman & Hall/CRC eBooks
(2020)
R is one of the top-rated problem-solving tools for machine learning, data science, data analytics, data mining, and statistical analysis. R ’s popularity has resulted in the development of thousands of tutorials. Unfortunately, it’s easy t...
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DOI: https://doi.org/10.1201/9781003006695-1
Yiming Mao, Ting Li, Wenqiang Lu, Chunhong Li et al. • Discover Oncology
(2025)
Abstract Glioblastoma (GBM) is an extremely aggressive and diverse type of brain tumor that is associated with a poor prognosis. Immunogenic cell death (ICD) is a particular form of cellular demise that is closely linked to antineoplastic i...
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Open Access (DOAJ)
DOI: 10.1007/s12672-025-03557-1
Hongyu Liu, Bo Lang • Applied Sciences
(2019)
Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in th...
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DOI: https://doi.org/10.3390/app9204396
Stephen Marsland (2009)
Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is r...
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Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola • The Annals of Statistics
(2008)
We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a ke...
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Open Access
DOI: https://doi.org/10.1214/009053607000000677
Alexander I. Kochegurov, Vladislav I. Denisov, Elizaveta A. Zadorozhnykh • Известия Томского политехнического университета: Инжиниринг георесурсов
(2024)
Relevance. Core studies in the oil and gas industry allow us to obtain some filtration and capacity characteristics of rocks, as well as to give an idea of the composition and structure of the subsoil. This information is extremely importan...
DOAJ
Open Access (DOAJ)
DOI: 10.18799/24131830/2024/9/4792
Shihao Gu, Bryan Kelly, Dacheng Xiu • National Bureau of Economic Research
(2018)
We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia.We demonstrate large economic gains to investors using machine learning forecasts, in some cases...
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Open Access
DOI: https://doi.org/10.3386/w25398
Yun Liu, Po-Hsuan Cameron Chen, Jonathan Krause, Lily H. Peng • JAMA
(2019)
In recent years, many new clinical diagnostic tools have been developed using complicated machine learning methods. Irrespective of how a diagnostic tool is derived, it must be evaluated using a 3-step process of deriving, validating, and e...
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DOI: https://doi.org/10.1001/jama.2019.16489
Haoxin Mai, Tu C. Le, Dehong Chen, David Alan Winkler et al. • Chemical Reviews
(2022)
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better unde...
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DOI: https://doi.org/10.1021/acs.chemrev.2c00061
Philomena Schlexer, Kirsten T. Winther, José Antonio Garrido Torres, Verena Streibel et al. • ChemCatChem
(2019)
Abstract Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the developme...
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DOI: https://doi.org/10.1002/cctc.201900595