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Academic Articles & Research Papers
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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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DOI: https://doi.org/10.15252/msb.20156651
PMC: 4965871
Anna Teresa Seiche, Lucas Wittstruck, Thomas Jarmer • Sensors
(2024)
In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effect...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/s24051544
Shiv Singh, Neelu Jyothi Ahuja • Discover Computing
(2026)
Abstract The application of deep learning models in resource-constrained environments requires a trade-off among accuracy, efficiency, and interpretability a trilemma frequently neglected in conventional knowledge distillation (KD) approach...
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Open Access (DOAJ)
DOI: 10.1007/s10791-026-10123-y
Wenqiang Liu, Li Yan, Ningning Ma, Gaozhou Wang et al. • Applied Sciences
(2024)
With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eli...
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Open Access (DOAJ)
DOI: 10.3390/app14020774
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)...
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Open Access (DOAJ)
DOI: 10.3390/cryst11020138
Hu Dong, Xin Tong, Gang Liu • Fractal and Fractional
(2026)
For high-intensity focused ultrasound (HIFU) thermal ablation to be safe and effective, real-time, high-precision monitoring of tissue coagulative necrosis is essential. However, decoding the ultra-long, non-stationary radio frequency (RF) ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/fractalfract10090609
Adam C. Mater, Michelle L. Coote • Journal of Chemical Information and Modeling
(2019)
Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns repres...
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DOI: https://doi.org/10.1021/acs.jcim.9b00266
S. Mostafa Mousavi, Gregory C. Beroza • Science
(2022)
Seismic waves from earthquakes and other sources are used to infer the structure and properties of Earth's interior. The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processin...
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DOI: https://doi.org/10.1126/science.abm4470
Li Deng, Dong Yu • Foundations and Trends® in Signal Processing
(2014)
This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise...
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Open Access
DOI: https://doi.org/10.1561/2000000039
John D. Kelleher • The MIT Press eBooks
(2019)
An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vi...
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DOI: https://doi.org/10.7551/mitpress/11171.001.0001
Samira Pouyanfar, Saad B Sadiq, Yilin Yan, Haiman Tian et al. • ACM Computing Surveys
(2018)
The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler d...
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DOI: https://doi.org/10.1145/3234150
Seonwoo Min, Byunghan Lee, Sungroh Yoon • Briefings in Bioinformatics
(2016)
In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-th...
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Open Access
DOI: https://doi.org/10.1093/bib/bbw068
Ian Goodfellow, Yoshua Bengio, Aaron C. Courville • The MIT Press eBooks
(2016)
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human c...
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DOI: https://doi.org/10.1016/b978-0-12-804291-5.00010-6
Mohammed Javeed Zaki, Wagner Meira • Cambridge University Press eBooks
(2020)
The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business an...
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DOI: https://doi.org/10.1017/9781108564175
Daniel Kirk, E.J. Kok, Michele Tufano, Bedir Teki̇nerdoğan et al. • Advances in Nutrition
(2022)
Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and ...
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DOI: https://doi.org/10.1093/advances/nmac103
PMC: 9776646
Vishal Kumar Goar, Nagendra Singh Yadav • Advances in business information systems and analytics book series
(2024)
This chapter focuses on providing a complete grasp of the foundations of machine learning (ML). Machine learning is a rapidly evolving domain with wide-ranging applications, from natural language processing to autonomous vehicles. This chap...
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DOI: https://doi.org/10.4018/979-8-3693-1598-9.ch002
Luigi Capogrosso, Federico Cunico, Dong Seon Cheng, Franco Fummi et al. • IEEE Access
(2024)
The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures. T...
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DOI: https://doi.org/10.1109/access.2024.3365349
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
Ahmed E. Radwan, David A. Wood, Ahmed A. Radwan • Journal of Rock Mechanics and Geotechnical Engineering
(2022)
Pore pressure is an essential parameter for establishing reservoir conditions, geological interpretation and drilling programs. Pore pressure prediction depends on information from various geophysical logs, seismic, and direct down-hole pre...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.jrmge.2022.01.012
Jakob Mannstadt, Arash Rahimi‐Iman • Advanced Sensor Research
(2025)
Abstract A machine‐learning (ML) non‐contact method to determine the temperature of a laser gain medium via its laser emission with a trained few‐layer neural‐network (NN) model is presented. The training of the feed‐forward NN enables the ...
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Open Access (DOAJ)
DOI: 10.1002/adsr.70009