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Compilation of training datasets for use of convolutional neural networks supporting automatic inspection processes in industry 4.0 based electronic manufacturing

A. I. M. Schwebig, A. I. M. Schwebig, R. Tutsch • Journal of Sensors and Sensor Systems (2020)

Ensuring the highest quality standards at competitive prices is one of the greatest challenges in the manufacture of electronic products. The identification of flaws has the uppermost priority in the field of automotive electronics, particu...

DOAJ Open Access (DOAJ) DOI: 10.5194/jsss-9-167-2020

Communicative management in present-day university

Petryshyna Olga, Boyko Mariya • SHS Web of Conferences (2021)

In modern academic community not only communication management plays a great role. Monitoring of expectations, awareness of communicative needs, the course and results of communicative interaction, barriers hindering rational managerial dec...

DOAJ Open Access (DOAJ) DOI: 10.1051/shsconf/202110402012

HRCTCov19-a high-resolution chest CT scan image dataset for COVID-19 diagnosis and differentiation

Iraj Abedi, Mahsa Vali, Bentolhoda Otroshi, Maryam Zamanian et al. • BMC Research Notes (2024)

Abstract Introduction Computed tomography (CT) was a widely used diagnostic technique for COVID-19 during the pandemic. High-Resolution Computed Tomography (HRCT), is a type of computed tomography that enhances image resolution through the ...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13104-024-06693-z

Behavioral Motivation and Influencing Factors of Graduate Students Using AIGC Tool: An Empirical Analysis Based on Questionnaire Survey

Yijia WAN, Liping GU • Nongye tushu qingbao xuebao (2024)

[Purpose/Significance] To explore in depth the acceptance and usage habits of AIGC tools by graduate students in the process of academic research, and to promote the positive attention and application of emerging technologies by graduate st...

DOAJ Open Access (DOAJ) DOI: 10.13998/j.cnki.issn1002-1248.24-0716

Multimodal artificial intelligence for subepithelial lesion classification and characterization: a multicenter comparative study (with video)

Jiao Li, Xiaojuan Jing, Qin Zhang, Xiaoxiang Wang et al. • BMC Medical Informatics and Decision Making (2025)

Abstract Background Subepithelial lesions (SELs) present significant diagnostic challenges in gastrointestinal endoscopy, particularly in differentiating malignant types, such as gastrointestinal stromal tumors (GISTs) and neuroendocrine tu...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12911-025-03147-9

Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma

Hongfan Liao, Jiang Yuan, Chunhua Liu, Jiao Zhang et al. • Insights into Imaging (2023)

Abstract Objective This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC)...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13244-023-01553-z

Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess Plateau

Junpo Yu, Yajun Si, Wen Zhao, Zeyu Zhou et al. • Plants (2025)

As the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and...

DOAJ Open Access (DOAJ) DOI: 10.3390/plants14152391

Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning

Synho Do, Kyoung Doo Song, Joo Won Chung • Korean Journal of Radiology (2019)

Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years. Many articles on...

OPENALEX cc-by-nc DOI: https://doi.org/10.3348/kjr.2019.0312 PMC: 6960318

A review of deep learning in image recognition

Myeongsuk Pak, Sang-Hoon Kim • International Conference on Computer Applications and Information Processing Technology (CAIPT) (2017)

Deep learning has been the core topic in machine learning and convolutional neural network is one of the most prominent approaches. Convolutional neural network has won numerous competitions in recent years. It has outstanding results in im...

OPENALEX DOI: https://doi.org/10.1109/caipt.2017.8320684

Deep Learning for Spatio-Temporal Data Mining: A Survey

Senzhang Wang, Jiannong Cao, Philip S. Yu • IEEE Transactions on Knowledge and Data Engineering (2020)

With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temp...

OPENALEX Open Access DOI: https://doi.org/10.1109/tkde.2020.3025580

Deep Learning and Its Application to LHC Physics

D. Guest, Kyle Stuart Cranmer, Daniel Whiteson • Annual Review of Nuclear and Particle Science (2018)

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex ...

OPENALEX Open Access DOI: https://doi.org/10.1146/annurev-nucl-101917-021019

A General Survey on Attention Mechanisms in Deep Learning

Gianni Brauwers, Flavius Frăsincar • IEEE Transactions on Knowledge and Data Engineering (2021)

Attention is an important mechanism that can be employed for a variety of deep learning models across many different domains and tasks. This survey provides an overview of the most important attention mechanisms proposed in the literature. ...

OPENALEX Open Access DOI: https://doi.org/10.1109/tkde.2021.3126456

Deep Learning: The Good, the Bad, and the Ugly

Thomas Serre • Annual Review of Vision Science (2019)

Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains...

OPENALEX DOI: https://doi.org/10.1146/annurev-vision-091718-014951

Deep Learning for Population Genetic Inference

Sara Sheehan, Yun S. Song • PLoS Computational Biology (2016)

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep...

OPENALEX cc-by DOI: https://doi.org/10.1371/journal.pcbi.1004845 PMC: 4809617