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Deep Learning (CNN) and Transfer Learning: A Review

Jaya Gupta, Sunil Kumar Pathak, Gireesh Kumar • Journal of Physics Conference Series (2022)

Abstract Deep Learning is a machine learning area that has recently been used in a variety of industries. Unsupervised, semi-supervised, and supervised-learning are only a few of the strategies that have been developed to accommodate differ...

OPENALEX cc-by DOI: https://doi.org/10.1088/1742-6596/2273/1/012029

An overview of deep learning in the field of dentistry

Jae Joon Hwang, Yun‐Hoa Jung, Bong‐Hae Cho, Min-Suk Heo • Imaging Science in Dentistry (2019)

PURPOSE: Artificial intelligence (AI), represented by deep learning, can be used for real-life problems and is applied across all sectors of society including medical and dental field. The purpose of this study is to review articles about d...

OPENALEX cc-by-nc DOI: https://doi.org/10.5624/isd.2019.49.1.1 PMC: 6444007

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

A Comparison of Two Neural Network Based Methods for Human Activity Recognition

saeedeh zebhi, Seyed Mohammad Taghi AlModaressi, Vahid Abootalebi • AUT Journal of Electrical Engineering (2021)

In this paper, two different methods of human activity recognition based on video signals are introduced. The first method explores the effectiveness of combining feature descriptors obtained by local descriptors and artificial neural network ...

DOAJ Open Access (DOAJ) DOI: 10.22060/eej.2020.18517.5357

Ship Detection With SAR C-Band Satellite Images: A Systematic Review

Cyprien Alexandre, Rodolphe Devillers, David Mouillot, Raphael Seguin et al. • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024)

Detecting and tracking ships remotely is now required in a wide range of contexts, from military security to illegal immigration control, as well as the management of fisheries and marine protected areas. Among the available methods, radar ...

DOAJ Open Access (DOAJ) DOI: 10.1109/JSTARS.2024.3437187

Review of Deep Learning for Language Modeling

WANG Sili, ZHANG Ling, YANG Heng, LIU Wei • Nongye tushu qingbao xuebao (2023)

[Purpose/Significance] Deep learning for language modeling is one of the major methods and advanced technologies to enhance language intelligence of machines at present, which has become an indispensable important technical means for automa...

DOAJ Open Access (DOAJ) DOI: 10.13998/j.cnki.issn1002-1248.23-0251

Deep learning-based MRI model for predicting P53-mutated hepatocellular carcinoma

Lulu Jia, Qing Yang, Hanchen Jiang, Gang Huang et al. • BMC Medical Imaging (2025)

Abstract Background The P53-mutated Hepatocellular Carcinoma (HCC) is an aggressive variant associated with vascular endothelial growth factor (VEGF) overexpression and increased microvascular density. This study aimed to develop an MRI-bas...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12880-025-02045-w

Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery

Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens, Margriet Sitskoorn • BMC Medical Imaging (2025)

Abstract Background and purpose Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several ...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12880-025-01643-y

Using fuzzy and machine learning iterative optimized models to generate the flood susceptibility maps: case study of Prahova River basin, Romania

Romulus Costache, Hazem Ghassan Abdo, Arun Pratap Mishra, Subodh Chandra Pal et al. • Geomatics, Natural Hazards & Risk (2023)

AbstractIn this work, the vulnerability to flooding in the Prahova River basin was calculated and analyzed using advanced methods and techniques. Thus, 2 hybrid models represented by Iterative Classifier Optimizer – Multiclass Alternating D...

DOAJ Open Access (DOAJ) DOI: 10.1080/19475705.2023.2281241

Development and validation of a predictive model for diabetic peripheral neuropathy with type 2 diabetes mellitus in Xinjiang, China

Asiguli Adili, Maihesumu Aikemu, Karanvir Singh Rana, Yihan Wei et al. • Scientific Reports (2025)

Abstract This study aims to identify risk factors associated with diabetic peripheral neuropathy (DPN) in patients with type 2 diabetesmellitus (T2DM) and to develop a predictive model to support clinical decision-making. A total of 1,001 p...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-025-19922-7

Application of Machine Learning for Predicting Progression‐Free and Overall Survival in Patients With Renal Cell Carcinoma

Caroline W. Grant, Jerry Li, Swan Lin, Dana Nickens et al. • Clinical and Translational Science (2025)

ABSTRACT Patient outcomes in advanced renal cell carcinoma (RCC) remain poor, with five‐year survival rates ranging from ~10% to 30%. Early projections of therapeutic outcomes could optimize precision medicine and accelerate drug developmen...

DOAJ Open Access (DOAJ) DOI: 10.1111/cts.70348

Diversity in Machine Learning

Zhiqiang Gong, Ping Zhong, Weidong Hu • IEEE Access (2019)

Machine learning methods have achieved good performance and been widely applied in various real-world applications. They can learn the model adaptively and be better fit for special requirements of different tasks. Generally, a good machine...

OPENALEX Open Access DOI: https://doi.org/10.1109/access.2019.2917620

Machine learning in pain research

Jörn Lötsch, Alfred Ultsch • Pain (2017)

1. Introduction Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence.14 It has been accepted that pain is a complex phenomenon.2,32,72 Contemporary methods of compu...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1097/j.pain.0000000000001118 PMC: 5895117

A Survey on Distributed Machine Learning

Joost Verbraeken, Matthijs Wolting, Jonathan Katzy, Jeroen Kloppenburg et al. • ACM Computing Surveys (2020)

The demand for artificial intelligence has grown significantly over the past decade, and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, to increase the qual...

OPENALEX Open Access DOI: https://doi.org/10.1145/3377454

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

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

Foundations of Machine Learning

• The MIT Press eBooks (2012)

Foundations of Machine LearningSoon we will embark on a theoretical study of AdaBoost in order to understand its properties, particularly its ability as a learning algorithm to generalize, that is, to make accurate predictions on data not s...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.7551/mitpress/8291.003.0006