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Showing 20 of 813 academic articles

Deep learning applications and challenges in big data analytics

Maryam Mousaarab Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya et al. • Journal Of Big Data (2015)

Abstract Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can con...

OPENALEX cc-by DOI: https://doi.org/10.1186/s40537-014-0007-7

Deep learning for electroencephalogram (EEG) classification tasks: a review

Alexander Craik, Yongtian He, Jose Luis Contreras-Vidal • Journal of Neural Engineering (2019)

OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analyti...

OPENALEX cc-by DOI: https://doi.org/10.1088/1741-2552/ab0ab5

An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles

Prot Piyakawanich, Pattarapong Phasukkit • Drones (2024)

In the rapidly evolving domain of Unmanned Aerial Vehicles (UAVs), precise altitude estimation remains a significant challenge, particularly for lightweight UAVs. This research presents an innovative approach to enhance altitude estimation ...

DOAJ Open Access (DOAJ) DOI: 10.3390/drones8120718

Deep Learning for Identifying Metastatic Breast Cancer

Wang, Dayong, Aditya Khosla, Rishab Gargeya, Humayun Irshad et al. • arXiv (Cornell University) (2016)

The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems for the automated detection of metastatic breast cancer in whole slide images of sentinel lymph node biopsies. Our team won bo...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1606.05718

Combining the YOLOv4 Deep Learning Model with UAV Imagery Processing Technology in the Extraction and Quantization of Cracks in Bridges

Szu-Pyng Kao, Yung-Chen Chang, Feng-Liang Wang • Sensors (2023)

Bridges are often at risk due to the effects of natural disasters, such as earthquakes and typhoons. Bridge inspection assessments normally focus on cracks. However, numerous concrete structures with cracked surfaces are highly elevated or ...

DOAJ Open Access (DOAJ) DOI: 10.3390/s23052572

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

Research on high-precision recognition model for multi-scene asphalt pavement distresses based on deep learning

Sheng Zhang, Zhenghao Bei, Tonghua Ling, Qianqian Chen et al. • Scientific Reports (2024)

Abstract Accurate detection of asphalt pavement distress is crucial for road maintenance and traffic safety. However, traditional convolutional neural networks usually struggle with this task due to the varied distress patterns and complex ...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-024-77173-4

Deep Learning for the Earth Sciences

Gustau Camps‐Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein • Wiley eBooks (2021)

Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only re...

OPENALEX Open Access DOI: https://doi.org/10.1002/9781119646181

Deep Learning for Generic Object Detection: A Survey

Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth et al. • International Journal of Computer Vision (2019)

Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a ...

OPENALEX cc-by DOI: https://doi.org/10.1007/s11263-019-01247-4

Explainable and reliable kidney CT image classification using self-supervised DeiT-Tiny transformer

Sai Sri Hemantha Konala, Srinivas Koppu • Frontiers in Medicine (2026)

IntroductionKidney-related disorders are one of the global health concerns that require timely detection to prevent severe health complications. The use of computed tomography (CT) images for accurate classification of kidney diseases is im...

DOAJ Open Access (DOAJ) DOI: 10.3389/fmed.2026.1893419

An Integrated Method for River Water Level Recognition from Surveillance Images Using Convolution Neural Networks

Chen Chen, Rufei Fu, Xiaojian Ai, Chengbin Huang et al. • Remote Sensing (2022)

Water conservancy personnel usually need to know the water level by water gauge images in real-time and with an expected accuracy. However, accurately recognizing the water level from water gauge images is still a complex problem. This arti...

DOAJ Open Access (DOAJ) DOI: 10.3390/rs14236023

Deep Learning in Mammography

Anton S. Becker, Magda Marcon, Soleen Ghafoor, Moritz C. Wurnig et al. • Investigative Radiology (2017)

OBJECTIVES: The aim of this study was to evaluate the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mam...

OPENALEX DOI: https://doi.org/10.1097/rli.0000000000000358

Deep Learning for Anomaly Detection: A Survey

Raghavendra Chalapathy, Sanjay Chawla • arXiv (Cornell University) (2019)

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods i...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1901.03407

Application of Deep Learning for Object Detection

Ajeet Ram Pathak, Manjusha Pandey, Siddharth Swarup Rautaray • Procedia Computer Science (2018)

The ubiquitous and wide applications like scene understanding, video surveillance, robotics, and self-driving systems triggered vast research in the domain of computer vision in the most recent decade. Being the core of all these applicatio...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.procs.2018.05.144

Deep Learning for Computer Vision: A Brief Review

Athanasios S. Voulodimos, Nikolaos D. Doulamis, Anastasios D. Doulamis, Eftychios E. Protopapadakis • Computational Intelligence and Neuroscience (2018)

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief ...

OPENALEX cc-by DOI: https://doi.org/10.1155/2018/7068349 PMC: 5816885