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Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi • IEEE Communications Surveys & Tutorials (2019)

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traff...

OPENALEX Open Access DOI: https://doi.org/10.1109/comst.2019.2904897

Deep learning and education for sustainability

Kevin Warburton • International Journal of Sustainability in Higher Education (2003)

Deep learning is a key strategy by which students extract meaning and understanding from course materials and experiences. Because of the range and interconnectedness of environmental, social and economic issues, and the importance of inter...

OPENALEX DOI: https://doi.org/10.1108/14676370310455332

The Deep Learning Revolution

Terrence J. Sejnowski A B • The MIT Press eBooks (2018)

How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy.The deep learning revolution has brought us driverless cars, the greatly improved...

OPENALEX DOI: https://doi.org/10.7551/mitpress/11474.001.0001

Deep Learning

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...

OPENALEX DOI: https://doi.org/10.1016/b978-0-12-804291-5.00010-6

Machine learning in aerodynamic shape optimization

Jichao Li, Xiaosong Du, Joaquim R. R. A. Martins • Progress in Aerospace Sciences (2022)

Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and pr...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.paerosci.2022.100849

On the use of aspect-based sentiment analysis of Twitter data to explore the experiences of African Americans during COVID-19

Meghna Chaudhary, Kristin Kosyluk, Sylvia Thomas, Tempestt Neal • Scientific Reports (2023)

Abstract According to data from the U.S. Center for Disease Control and Prevention, as of June 2020, a significant number of African Americans had been infected with the coronavirus disease, experiencing disproportionately higher death rate...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-023-37592-1

Increased Atmospheric Aridity and Reduced Precipitation Drive the 2023 Extreme Wildfire Season in Canada

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

New Advances in Machine Learning

Yagang Zhang • InTech eBooks (2010)

The purpose of this book is to provide an up-to-date and systematical introduction to the principles and algorithms of machine learning. The definition of learning is broad enough to include most tasks that we commonly call “learning” tasks...

OPENALEX Open Access DOI: https://doi.org/10.5772/225

Machine Learning for Hackers

Drew Conway, J.M. White • CERN Document Server (European Organization for Nuclear Research) (2012)

If you're an experienced programmer interested in crunching data, this book will get you started with machine learning-a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John...

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Key factors and serial mediation analysis of anxiety and depression among university students: a cross-sectional study

Tan Jiang, Shijia Zhang, Shijia Zhang, Deyou Jiang • Frontiers in Psychiatry (2026)

BackgroundAnxiety and depressive symptoms are prevalent among university students; however, their risk factors are unlikely to operate in isolation. Instead, they may be organized in a multilevel structure that progressively approaches emot...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpsyt.2026.1809332

Machine Learning in Healthcare Communication

Sarkar Siddique, James C. L. Chow • Encyclopedia (2021)

Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelli...

OPENALEX cc-by DOI: https://doi.org/10.3390/encyclopedia1010021

Machine Learning with TensorFlow

Nishant Shukla • CERN Document Server (European Organization for Nuclear Research) (2018)

Summary Machine Learning with Tensor Flow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding Tensor Flow with Python. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub forma...

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Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis

Lin Wang, Jiaming Su, Zhongjie Liu, Shaowei Ding et al. • BioData Mining (2024)

Abstract Background Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-st...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13040-024-00369-x