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How structure determines correlations in neuronal networks.

Volker Pernice, Benjamin Staude, Stefano Cardanobile, Stefan Rotter • PLoS Computational Biology (2011)

Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, ...

DOAJ Open Access (DOAJ) DOI: 10.1371/journal.pcbi.1002059

Digital Local Return Services and Purchase Intention in Cross-Border E-Commerce: A Risk–Trust Perspective

Xianfa Shi, Miao Su, Keun-sik Park • Journal of Theoretical and Applied Electronic Commerce Research (2026)

Cross-border e-commerce offers consumers broader product access, yet uncertainty surrounding returns continues to suppress online purchase decisions. This study conceptualizes digital local return services as a digital assurance mechanism i...

DOAJ Open Access (DOAJ) DOI: 10.3390/jtaer21060165

A Revision of Empirical Models of Stirling Engine Performance Using Simple Artificial Neural Networks

Enrique González-Plaza, David García, Jesús-Ignacio Prieto • Inventions (2023)

Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models ...

DOAJ Open Access (DOAJ) DOI: 10.3390/inventions8040088

Evaluating artificial intelligence for a focal nodular hyperplasia diagnosis using magnetic resonance imaging: preliminary findings

Mecit Kantarcı, Volkan Kızılgöz, Ramazan Terzi, Ahmet Enes Kılıç et al. • Diagnostic and Interventional Radiology (2025)

PURPOSE: This study aimed to evaluate the effectiveness of artificial intelligence (AI) in diagnosing focal nodular hyperplasia (FNH) of the liver using magnetic resonance imaging (MRI) and compare its performance with that of radiologists....

DOAJ Open Access (DOAJ) DOI: 10.4274/dir.2025.243095

Evaluating the Accuracy of Deep Learning Models and Dental Postgraduate Students in Measuring Working Length on Intraoral Periapical X-rays: An In vitro Study

R. S. Basavanna, Ishaan Adhaulia, N. M. Dhanyakumar, Jyoti Joshi • Contemporary Clinical Dentistry (2025)

Background: The integration of artificial intelligence in dentistry has seen remarkable advancements, especially in diagnostic imaging. This study evaluates and compares the accuracy of deep learning models with that of dental postgraduate ...

DOAJ Open Access (DOAJ) DOI: 10.4103/ccd.ccd_274_24

Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing

Jianjing Zhang, Robert X. Gao • Chinese Journal of Mechanical Engineering (2021)

Abstract Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production b...

DOAJ Open Access (DOAJ) DOI: 10.1186/s10033-021-00587-y

Deep learning for cardiovascular medicine: a practical primer

Chayakrit Krittanawong, Kipp W. Johnson, Robert S. Rosenson, Zhen Wang et al. • European Heart Journal (2019)

Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via...

OPENALEX Open Access DOI: https://doi.org/10.1093/eurheartj/ehz056 PMC: 6600129

Understanding tourist engagement with traditional dwellings through multimodal deep learning: a case study of the Gao Family Compound, Xi’an

Liyue Wu, Qinchuan Zhan, Yanjun Li • Humanities & Social Sciences Communications (2026)

Abstract This study applies a multimodal deep learning approach to examine tourist engagement with traditional architectural heritage. The Gao Family Compound in Xi’an serves as a representative case. Social-media texts and images from Weib...

DOAJ Open Access (DOAJ) DOI: 10.1057/s41599-026-07879-5

Deep metabolome: Applications of deep learning in metabolomics

Yotsawat Pomyen, Kwanjeera Wanichthanarak, Patcha Poungsombat, Johannes Francois Fahrmann et al. • Computational and Structural Biotechnology Journal (2020)

In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using con...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.csbj.2020.09.033 PMC: 7575644

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

SPAF-SegNeXt: Prior-Guided Attention and Frequency-Spatial Hybrid Fusion for Multimodal Sugarcane Segmentation in Remote Sensing

Wei Zhang, Chao Ren, Haoming Bai, Huada Huang et al. • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2026)

Accurate monitoring of the spatial extent of sugarcane cultivation is critical for optimizing land-use efficiency and regulating the sugar industry. However, sugarcane cultivation areas are characterized by complex topography and fragmented...

DOAJ Open Access (DOAJ) DOI: 10.1109/JSTARS.2026.3716367

Image-based quantification of histological features as a function of spatial location using the Tissue Positioning SystemResearch in context

Ruichen Rong, Yonglong Wei, Lin Li, Tao Wang et al. • EBioMedicine (2023)

Summary: Background: Tissues such as the liver lobule, kidney nephron, and intestinal gland exhibit intricate patterns of zonated gene expression corresponding to distinct cell types and functions. To quantitatively understand zonation, it...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.ebiom.2023.104698

A Fast Inference Vision Transformer for Automatic Pavement Image Classification and Its Visual Interpretation Method

Yihan Chen, Xingyu Gu, Zhen Liu, Jia Liang • Remote Sensing (2022)

Traditional automatic pavement distress detection methods using convolutional neural networks (CNNs) require a great deal of time and resources for computing and are poor in terms of interpretability. Therefore, inspired by the successful a...

DOAJ Open Access (DOAJ) DOI: 10.3390/rs14081877

Survey on deep learning with class imbalance

Justin M. Johnson, Taghi M. Khoshgoftaar • Journal Of Big Data (2019)

The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in...

OPENALEX cc-by DOI: https://doi.org/10.1186/s40537-019-0192-5

Deep Learning for Health Informatics

Daniele Ravì, Charence Wong, Fani Deligianni, Melissa Berthelot et al. • IEEE Journal of Biomedical and Health Informatics (2016)

With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on mac...

OPENALEX cc-by DOI: https://doi.org/10.1109/jbhi.2016.2636665

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

Machine learning in breast MRI

Beatriu Reig, Laura C. Heacock, Krzysztof J. Geras, Linda P. Moy • Journal of Magnetic Resonance Imaging (2019)

Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmenta...

OPENALEX DOI: https://doi.org/10.1002/jmri.26852 PMC: 7085409