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Showing 20 of 173 academic articles

Deep learning as a tool for ecology and evolution

Marek L. Borowiec, Rebecca B. Dikow, Paul B. Frandsen, Alexander J. McKeeken et al. • Methods in Ecology and Evolution (2022)

Abstract Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been use...

OPENALEX cc-by-nc DOI: https://doi.org/10.1111/2041-210x.13901

Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function

Georgios Petmezas, Grigorios-Aris Cheimariotis, Leandros Stefanopoulos, Bruno Rocha et al. • Sensors (2022)

Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient’s quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the ef...

DOAJ Open Access (DOAJ) DOI: 10.3390/s22031232

Deep Learning in Medical Ultrasound Analysis: A Review

Shengfeng Liu, Yi Wang, Xin Yang, Baiying Lei et al. • Engineering (2019)

Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variabil...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.eng.2018.11.020

Deep Learning for Cardiac Image Segmentation: A Review

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni et al. • Frontiers in Cardiovascular Medicine (2020)

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalit...

OPENALEX cc-by DOI: https://doi.org/10.3389/fcvm.2020.00025 PMC: 7066212

Computational Linguistics and Deep Learning

Christopher D. Manning • Computational Linguistics (2015)

Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences. However, some pun...

OPENALEX Open Access DOI: https://doi.org/10.1162/coli_a_00239

A deep learning-driven pipeline for differentiating hypertrophic cardiomyopathy from cardiac amyloidosis using 2D multi-view echocardiography

Xiaofeng Li, Bo Peng, Xinyu Li, Zhenghan Wang et al. • Scientific Reports (2026)

Abstract Hypertrophic Cardiomyopathy (HCM) and Cardiac Amyloidosis (CA) are two cardiac conditions that can advance to heart failure if left untreated, which present considerable diagnostic challenges due to their overlapping echocardiograp...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-025-15443-5

Deep Learning in Ultrasound Imaging

Ruud J. G. van Sloun, Regev Cohen, Yonina C. Eldar • Proceedings of the IEEE (2019)

In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on ...

OPENALEX Open Access DOI: https://doi.org/10.1109/jproc.2019.2932116

Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding

Shaorong Zhang, Shaorong Zhang, Qihui Wang, Benxin Zhang et al. • Frontiers in Neuroscience (2023)

IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses signi...

DOAJ Open Access (DOAJ) DOI: 10.3389/fnins.2023.1292724

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

Machine learning in acoustics: Theory and applications

Michael J. Bianco, Peter Gerstoft, James A. Traer, Emma Reeves Ozanich et al. • The Journal of the Acoustical Society of America (2019)

Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep lear...

OPENALEX Open Access DOI: https://doi.org/10.1121/1.5133944

Simulation of Flow-Induced Vibration and Dynamic Performance of Circular-Arc Helical Gear Pump under Background of Machine Learning

Xiaoling Wei, Yongbao Feng, Xiaoxia Han, Zhenxin He • International Transactions on Electrical Energy Systems (2022)

At present, with the continuous development and great improvement of mechanical manufacturing, processing, and assembly technology, mechanical flow-induced vibration (FIV) with a relatively concentrated frequency domain can be controlled by...

DOAJ Open Access (DOAJ) DOI: 10.1155/2022/9513357

Neural Networks for Machine Learning

Richard F. Lyon • Cambridge University Press eBooks (2017)

In order for a digital neocortex to learn a new skill, it will still require many iterations of education, just as a biological neocortex does, but once a single neocortex somewhere and at some time learns something, it can share that knowl...

OPENALEX Open Access DOI: https://doi.org/10.1017/9781139051699.031

Hyperparameter Search in Machine Learning

Marc Claesen, Bart De Moor • arXiv (Cornell University) (2015)

We introduce the hyperparameter search problem in the field of machine learning and discuss its main challenges from an optimization perspective. Machine learning methods attempt to build models that capture some element of interest based o...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1502.02127