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Academic Articles & Research Papers
Showing 20 of 227 academic articles
Andrew J. Reader, Guillaume Corda-D’Incan, Abolfazl Mehranian, Casper O. da Costa-Luis et al. • IEEE Transactions on Radiation and Plasma Medical Sciences
(2020)
This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for the reconstruction of images in positron emission tomography (PET). Deep learning can be used either directly or as a component of conventio...
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Open Access
DOI: https://doi.org/10.1109/trpms.2020.3014786
Lennart Ljung A, Carl R. Andersson, Koen Tiels, Thomas B. Schön • IFAC-PapersOnLine
(2020)
Deep learning is a topic of considerable interest today. Since it deals with estimating - or learning - models, there are connections to the area of System Identification developed in the Automatic Control community. Such connections are ex...
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Open Access
DOI: https://doi.org/10.1016/j.ifacol.2020.12.1329
Kaixuan Sun, Qi Yao, Yanhui Li • PLoS ONE
(2025)
The volatility of agricultural commodity prices significantly affects market stability and financial market dynamics, especially during periods of economic uncertainty and global shocks. Accurate price prediction, however, remains challengi...
DOAJ
Open Access (DOAJ)
DOI: 10.1371/journal.pone.0337103
Yifei Chen, Yi Li, Rajiv Narayan, Aravind Subramanian et al. • Bioinformatics
(2016)
MOTIVATION: Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dro...
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Open Access
DOI: https://doi.org/10.1093/bioinformatics/btw074
PMC: 4908320
Jianqing Fan, Cong Ma, Yiqiao Zhong • Statistical Science
(2021)
Deep learning has achieved tremendous success in recent years. In simple words, deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and labels. While neural networks have a l...
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Open Access
DOI: https://doi.org/10.1214/20-sts783
PMC: 8300482
Jing Li, Aixin Sun, Jianglei Han, Chenliang Li • IEEE Transactions on Knowledge and Data Engineering
(2020)
Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language ...
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Open Access
DOI: https://doi.org/10.1109/tkde.2020.2981314
Rocio Vargas, Amir Hosein Mosavi, Ramon Ruiz • Preprints.org
(2018)
Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level i...
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DOI: https://doi.org/10.20944/preprints201810.0218.v1
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
Miguel Marquez, Yingming Lai, Miao Liu, Elahe Memari et al. • PhotoniX
(2026)
Abstract Real-time dynamic imaging of microbubbles is crucial for understanding their microscale biophysical interactions and advancing ultrasound therapy. Despite progress in time-resolved optical imaging, existing techniques still face tr...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s43074-026-00232-8
Paschalis Bizopoulos, Dimitris Koutsouris • IEEE Reviews in Biomedical Engineering
(2018)
The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data....
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Open Access
DOI: https://doi.org/10.1109/rbme.2018.2885714
Haotian Wu, Yan Guo, Juliang Cao, Zhiming Xiong et al. • Aerospace
(2025)
In response to the issues of severe pitch oscillation and unstable roll attitude present in existing reinforcement learning-based aircraft cruise control methods during dynamic maneuvers, this paper proposes a precise control method for air...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/aerospace12080670
Natasha Dropka, Stefan Ecklebe, Martin Holena • Crystals
(2021)
The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs)...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/cryst11020138
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
Federico Amato, Fabian Guignard, Sylvain Robert, Mikhail Kanevski • Scientific Reports
(2020)
Abstract As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help ...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-020-79148-7
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
Mengyuan Zhu, Jiawei Wang, Xiao Yang, Yu Zhang et al. • Eco-Environment & Health
(2022)
With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven...
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cc-by-nc-nd
DOI: https://doi.org/10.1016/j.eehl.2022.06.001
PMC: 10702893
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
Nana Liu, Qisheng Wang, Mark M. Wilde, Zhicheng Zhang • npj Quantum Information
(2025)
Abstract Matrix geometric means between two positive definite matrices can be defined from distinct perspectives—as solutions to certain nonlinear systems of equations, as points along geodesics in Riemannian geometry, and as solutions to c...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41534-025-00973-7
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
Matthias Seeger • International Journal of Neural Systems
(2004)
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many dee...
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Open Access
DOI: https://doi.org/10.1142/s0129065704001899