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Editorial: Mathematical Fundamentals of Machine Learning

David Glickenstein, Keaton Hamm, Xiaoming Huo, Yajun Mei, Martin Stoll
2021 Frontiers in Applied Mathematics and Statistics  
Editorial on the Research Topic Mathematical Fundamentals of Machine Learning With an abundance of data originating from all aspects of life, machine learning, and in particular deep learning, has powered  ...  While the results are astounding, a deeper understanding of the fundamental principles of machine learning is needed in order to better understand the success and limitations of machine learning techniques  ...  While the results are astounding, a deeper understanding of the fundamental principles of machine learning is needed in order to better understand the success and limitations of machine learning techniques  ... 
doi:10.3389/fams.2021.674785 fatcat:h34x4ngz3rec5ornncndmrlrnu

Machine Learning in Medicine: A Primer

Matthew N. O Sadiku, Sarhan M. Musa, Adedamola Omotoso
2019 International Journal of Trend in Scientific Research and Development  
This paper provides a brief introduction to applying machine learning in medicine.  ...  Machine learning is the field that focuses on how computers learn from data. Today, machine learning is playing an integral role in the medical industry.  ...  Few applications of machine learning include [9, 10] : Oncology: Almost all works in this field apply machine learning techniques, which perform deep statistical analysis of a set of clinical cases supported  ... 
doi:10.31142/ijtsrd20255 fatcat:xxfids7dafchnodnjsjki7akya

Significant Enhancements in Machine Translation by Various Deep Learning Approaches

Alpana Upadhyay
2017 American Journal of Computer Science and Information Technology  
It describes and includes all the topics like integrating deep learning in statistical machine translation, developing end-to-end neural machine translation systems, introducing deep learning in machine  ...  Several research directions are drawn in terms of how deep learning can influence machine translation.  ...  One of the novice machine learning technique is deep learning that has been successfully applied to many extents of machine learning like image processing, speech processing and recognition, natural language  ... 
doi:10.21767/2349-3917.100008 fatcat:sc4mccb7ofdhjkhcka5c4jraza

Artificial Intelligence in Cardiology

Kipp W. Johnson, Jessica Torres Soto, Benjamin S. Glicksberg, Khader Shameer, Riccardo Miotto, Mohsin Ali, Euan Ashley, Joel T. Dudley
2018 Journal of the American College of Cardiology  
then explains how these methods could be applied to enable precision cardiology and improve patient outcomes.  ...  This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how  ...  This limitation applies to both classical statistical modeling and machine learning methods.  ... 
doi:10.1016/j.jacc.2018.03.521 pmid:29880128 fatcat:egzxsazumvgnfbtr4i2744blxy

Deep Adversarial Learning on Google Home devices [article]

Andrea Ranieri, Davide Caputo, Luca Verderame, Alessio Merlo, Luca Caviglione
2021 arXiv   pre-print
To cope with that, deep adversarial learning approaches can be used to build black-box countermeasures altering the network traffic (e.g., via packet padding) and its statistical information.  ...  Unfortunately, they are vulnerable to various privacy threats exploiting machine learning to analyze the generated encrypted traffic.  ...  Then, we used a set of deep adversarial learning techniques to alter the statistical information of out- bound traffic.  ... 
arXiv:2102.13023v1 fatcat:yi3noammfreyjgs5gm5itp3kee

When Econometrics Meets Machine Learning

Eric Zheng, Yong Tan, Paulo Goes, Ramnath Chellappa, D.J. Wu, Michael Shaw, Olivia Sheng, Alok Gupta
2017 Data and Information Management  
Their function as part of the literary portrayal and narrative technique.  ...  Their function as part of the literary portrayal and narrative technique.  ...  University of Singapore and Professor Han Zhang from Georgia Institute of Technology of the USA for their great support to this paper.  ... 
doi:10.1515/dim-2017-0012 fatcat:jowblljuhrgddde2pvsfkbmc5m

Book Review: Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd edition by Aurélien Géron

Michael J. J. Douglass
2020 Physical and Engineering Sciences in Medicine  
This chapter really highlights the subtleties of how Keras in particular works and what to look out for when training your model. Chapter 11 begins the section on deep learning applications.  ...  This chapter contains a very nice list of examples where machine learning could be applied and which chapter to read to guide the reader in designing their own applications.  ... 
doi:10.1007/s13246-020-00913-z pmid:32785882 fatcat:oxittfumnzendpvxogvzuanime

Machine Learning and AI for Risk Management [chapter]

Saqib Aziz, Michael Dowling
2018 Disrupting Finance  
A non-technical overview is first given of the main machine learning and AI techniques of benefit to risk management.  ...  We explore how machine learning and artificial intelligence (AI) solutions are transforming risk management.  ...  Deep learning and neural networks 2 are viewed as being at the forefront of machine learning techniques and are often classified separately to the machine learning techniques already described.  ... 
doi:10.1007/978-3-030-02330-0_3 fatcat:nn2745q6d5fbhbe4etvhy4oodq

Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study

Victor E. Staartjes, Carlo Serra, Giovanni Muscas, Nicolai Maldaner, Kevin Akeret, Christiaan H. B. van Niftrik, Jorn Fierstra, David Holzmann, Luca Regli
2018 Neurosurgical Focus  
Deep learning, a subset of machine learning, has recently established itself as highly effective in forecasting medical outcomes.  ...  Class imbalance adjustment, cross-validation, and random dropout were applied to prevent overfitting and ensure robustness of the predictive model.  ...  As with any statistical or machine learning model, overfitting is a possible problem when applying the trained model to external data.  ... 
doi:10.3171/2018.8.focus18243 fatcat:wwxowr3dnbh2lk3ijkepmbekwi

Data integration and machine learning

Xin Luna Dong, Theodoros Rekatsinas
2018 Proceedings of the VLDB Endowment  
approaches for accurate results and effective human-in-the-loop pipelines, (2) we review how end-to-end machine learning applications rely on data integration to identify accurate, clean, and relevant  ...  This tutorial focuses on three aspects of the synergistic relationship between data integration and machine learning: (1) we survey how state-of-the-art data integration solutions rely on machine learning-based  ...  A future direction is for a system to automatically identify when, where, and how to get human involved, by applying active learning, transitive learning, and reinforcement learning.  ... 
doi:10.14778/3229863.3229876 fatcat:atysarruwrdythlge46vwlkcxi

Supporting Humans in an Intelligent Manner with Awareness of the Human's State Using Artificial Intelligence & Machine Learning

Prof. Amar Nath Singh
2016 International Journal Of Engineering And Computer Science  
Machine learning as widely used concept in Artificial Intelligence. It is the concept which teaches machines to detect different patterns and to adapt to new circumstances.  ...  Machine Learning can be both experience and explanation based learning.  ...  As a consequence of this new interest in learning we are experiencing a new era in statistical and functional approximation techniques and their applications to domain such as computer visions.  ... 
doi:10.18535/ijecs/v5i11.39 fatcat:24azyvrvdzdglkcfsscfxdzqui

Smart anomaly detection in sensor systems: A multi-perspective review

L. Erhan, M. Ndubuaku, M. Di Mauro, W. Song, M. Chen, G. Fortino, O. Bagdasar, A. Liotta
2020 Information Fusion  
We taxonomize methods ranging from conventional techniques (statistical methods, time-series analysis, signal processing, etc.) to data-driven techniques (supervised learning, reinforcement learning, deep  ...  speed, and network/energy efficiency, to mention but the most pressing ones.  ...  Many machine learning techniques are often getting a deep approach or are combined with deep learning.  ... 
doi:10.1016/j.inffus.2020.10.001 fatcat:r65qp56ipzebnasd33o3wxkfo4

A Review of Grey Scale Normalization in Machine Learning and Artificial Intelligence for Bioinformatics using Convolution Neural Networks

Divya Kothari
2021 International Journal for Research in Applied Science and Engineering Technology  
The algorithms obtain an input value and by using some statistical techniques, forecast an output for this.  ...  To make computers function in a certain way without being specifically programmed, machine learning uses certain statistical algorithms.  ...  how you can attempt to incorporate deep learning into research activities to tackle both new and existing problems and develop improved, wiser user devices and applications.  ... 
doi:10.22214/ijraset.2021.33316 fatcat:arbchv2fxzdvzpvcn2mbvgty6y

Artificial Intelligence for Anesthesia

Michael R. Mathis, Sachin Kheterpal, Kayvan Najarian
2018 Anesthesiology  
Nevertheless, only recently has machine learning seen an exponential increase in growth, sophistication, and influence.  ...  accuracy of 97.53%). 1 In 2016 Google adopted a deep learning approach to language translation, using an algorithm which is fed massive amounts of data to effectively train itself to recognize patterns  ...  Acknowledgments Funding Statement: All work and partial funding attributed to the Department of Anesthesiology, University of Michigan Medical School (Ann Arbor, Michigan, USA).  ... 
doi:10.1097/aln.0000000000002384 pmid:30080689 pmcid:PMC6148374 fatcat:3qimsdtqonbidmpprvlqihty5q

What machine learning can do for developmental biology

Paul Villoutreix
2021 Development  
In this Spotlight, I introduce the key concepts, advantages and limitations of machine learning, and discuss how these methods are being applied to problems in developmental biology.  ...  Specifically, I focus on how machine learning is improving microscopy and single-cell 'omics' techniques and data analysis.  ...  Acknowledgements I would like to thank the anonymous reviewers for insightful comments on the various versions of the manuscript.  ... 
doi:10.1242/dev.188474 pmid:33431591 fatcat:g6umzaselzeznak4uu7lhx2nsy
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