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Academic Articles & Research Papers
Showing 20 of 94 academic articles
Charles Nickmilder, Anthony Tedde, Isabelle Dufrasne, Françoise Lessire et al. • Remote Sensing
(2021)
Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass express...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/rs13030408
Kuldeep Randhawa, Chu Kiong Loo, Manjeevan Seera, Chee Peng Lim et al. • IEEE Access
(2018)
Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2018.2806420
Thorsten Wuest, Daniel Weimer, Christopher Irgens, Klaus‐Dieter Thoben • Production & Manufacturing Research
(2016)
The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means av...
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DOI: https://doi.org/10.1080/21693277.2016.1192517
Steven L. Brunton, J. Nathan Kutz • Cambridge University Press eBooks
(2022)
Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering...
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DOI: https://doi.org/10.1017/9781009089517.019
Juliana Aparecida Anochi, Vinícius Albuquerque de Almeida, Haroldo Fraga de Campos Velho • Remote Sensing
(2021)
Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learni...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/rs13132468
Stanislav Mazurenko, Zbyněk Prokop, Jiřı́ Damborský • ACS Catalysis
(2019)
Abstract Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have b...
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other-oa
DOI: https://doi.org/10.1021/acscatal.9b04321
Joseph A. Cruz, David Scott Wishart • Cancer Informatics
(2006)
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, ...
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DOI: https://doi.org/10.1177/117693510600200030
PMC: 2675494
Hilal M. El Misilmani, Tarek Naous • International Conference on High Performance Computing & Simulation (HPCS)
(2019)
With the growth and wide variety of available data, advanced processing, and affordable data storage, machine learning is witnessing great attention in finding optimized solutions in various fields. Machine learning techniques are currently...
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DOI: https://doi.org/10.1109/hpcs48598.2019.9188224
Feiyun Cui, Yun Yue, Yi Zhang, Ziming Zhang et al. • ACS Sensors
(2020)
Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, es...
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DOI: https://doi.org/10.1021/acssensors.0c01424
Bardia Abbasi, Daniel M. Goldenholz • Epilepsia
(2019)
Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recogn...
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Open Access
DOI: https://doi.org/10.1111/epi.16333
PMC: 9897263
Sebastian Raschka, Joshua M. Patterson, Corey Nolet • Information
(2020)
Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the...
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DOI: https://doi.org/10.3390/info11040193
Yu‐Chen Lo, Stefano Rensi, Wen Torng, Russ Biagio Altman • Drug Discovery Today
(2018)
Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learn...
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DOI: https://doi.org/10.1016/j.drudis.2018.05.010
PMC: 6078794
Sendhil Mullainathan, Jann Spiess • The Journal of Economic Perspectives
(2017)
Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the presence y of a face from pixels x. This similarity to ...
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DOI: https://doi.org/10.1257/jep.31.2.87
Harry Surden • Colorado Law Scholarly Commons (University of Colorado Colorado Springs)
(2014)
This Article explores the application of machine learning techniques within the practice of law. Broadly speaking “machine learning” refers to computer algorithms that have the ability to “learn” or improve in performance over time on some ...
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Open Access
Mario D. Molina, Filiz Garip • Annual Review of Sociology
(2019)
Machine learning is a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data. Its applications increasingly find their way into economics, political science, and soci...
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Open Access
DOI: https://doi.org/10.1146/annurev-soc-073117-041106
Atılım Güneş Baydin, Barak A. Pearlmutter, Alexey Radul, Jeffrey Mark Siskind • Maynooth University ePrints and eTheses Archive (Maynooth University)
(2015)
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD) is a technique for calculating derivatives of numeric functions expressed as computer programs efficiently and acc...
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Open Access
Jing Wei, Xuan Chu, Xiangyu Sun, Kun Xu et al. • InfoMat
(2019)
Abstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to the...
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DOI: https://doi.org/10.1002/inf2.12028
JUDE W. SHAVLIK, Thomas E. Deitterich, Thomas G. Dietterich (1991)
From the Publisher:
The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous ...
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Peter de B. Harrington (2012)
SummaryMachine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to bui...
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Solveig Badillo, Balázs Bánfai, Fabian Birzele, Iakov I. Davydov et al. • Clinical Pharmacology & Therapeutics
(2020)
In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever-increasing amount of data and computational power as well as the discovery of improved learning algorithm...
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cc-by-nc-nd
DOI: https://doi.org/10.1002/cpt.1796
PMC: 7189875