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HIFU Tissue Degeneration Classification Based on Multifractal Detrending Fluctuation Analysis and Vision Transformer

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

Deep Learning

Hao Xing, Guigang Zhang, Shang Ma • International Journal of Semantic Computing (2016)

Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and th...

OPENALEX DOI: https://doi.org/10.1142/s1793351x16500045

Deep Learning

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...

OPENALEX DOI: https://doi.org/10.7551/mitpress/11171.001.0001

A Survey on Deep Learning

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...

OPENALEX DOI: https://doi.org/10.1145/3234150

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

Data Mining and Machine Learning

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...

OPENALEX DOI: https://doi.org/10.1017/9781108564175

A Machine Learning-Oriented Survey on Tiny Machine Learning

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...

OPENALEX cc-by DOI: https://doi.org/10.1109/access.2024.3365349

Machine learning and data-driven prediction of pore pressure from geophysical logs: A case study for the Mangahewa gas field, New Zealand

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

The Microbiome Composition of a Man's Penis Predicts Incident Bacterial Vaginosis in His Female Sex Partner With High Accuracy

Supriya D. Mehta, Dan Zhao, Stefan J. Green, Walter Agingu et al. • Frontiers in Cellular and Infection Microbiology (2020)

Background: We determined the predictive accuracy of penile bacteria for incident BV in female sex partners. In this prospective cohort, we enrolled Kenyan men aged 18–35 and their female sex partners aged 16 and older. We assessed BV at ba...

DOAJ Open Access (DOAJ) DOI: 10.3389/fcimb.2020.00433

Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques

Ognjen Radišić-Aberger, Peter Burggräf, Fabian Steinberg, Alexander Becher et al. • Supply Chain Analytics (2024)

By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.sca.2024.100087

A Cardiovascular Disease Prediction Model Based on Routine Physical Examination Indicators Using Machine Learning Methods: A Cohort Study

Xin Qian, Yu Li, Xianghui Zhang, Heng Guo et al. • Frontiers in Cardiovascular Medicine (2022)

BackgroundCardiovascular diseases (CVD) are currently the leading cause of premature death worldwide. Model-based early detection of high-risk populations for CVD is the key to CVD prevention. Thus, this research aimed to use machine learni...

DOAJ Open Access (DOAJ) DOI: 10.3389/fcvm.2022.854287

A novel framework for spatio-temporal prediction of environmental data using deep learning

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

Machine Learning Refined

Jeremy M. Watt, Reza Borhani, Aggelos K. Katsaggelos • Cambridge University Press eBooks (2020)

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuiti...

OPENALEX DOI: https://doi.org/10.1017/9781108690935

Machine Learning for Engineers

Osvaldo Simeone • Cambridge University Press eBooks (2022)

This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering probl...

OPENALEX DOI: https://doi.org/10.1017/9781009072205

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