Deep learning methods have shown significant performance in many research applications, such as computer vision [7], object tracking [8], gesture recognition [9], face recognition [10], and steganography [[11], [12], [13]]. Deep learning methods are widely used because of their improved ...
Deep learning is revolutionising the way that many industries operate, providing a powerful method to interpret large quantities of data automatically and relatively quickly. Deterioration is often multi-factorial and difficult to model deterministically
learning in power systems research.We review deep learning methodologies presented and applied in a wide range of supervised,unsupervised,and semi-supervised applications as well as reinforcement learning tasks.We discuss various settings of problems solved by discriminative deep models including stacked ...
ABSTRACT: Deep learning algorithms are a subset of the machine learning algorithms, which aim at discovering multiple levels of distributed representations. Recently, numerous deep learning algorithms have been proposed to solve traditional artificial intelligence problems. This work aims to review the stat...
Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions Human activity recognition is essential in many domains, including the medical and smart home sectors. Using deep learning, we conduct a comprehensive surv... P Kumar,S Chauhan,LK Awa...
Deep learning Medical image segmentation Multi-modality fusion Review 1. Introduction Segmentation using multi-modality has been widely studied with the development of medical image acquisition systems. Different strategies for image fusion, such as probability theory [1], [2], fuzzy concept [3], [...
A review of deep learning methods for semantic segmentation of remote sensing imagery 来自 Semantic Scholar 喜欢 0 阅读量: 1127 作者:X Yuan,J Shi,L Gu 摘要: Semantic segmentation of remote sensing imagery has been employed in many applications and is a key research topic for decades. With ...
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2. Deep learning techniques 3. Medical background 4. Databases 5. Performance measurements 6. Research methodology 7. Taxonomy of the review 8. Results of the review 9. Discussion 10. Conclusions Declaration of Competing Interest Acknowledgments Appendix A Appendix B Appendix C Appendix D Appendix ...
This review explores three foundational deep learning architectures—AlexNet, VGG16, and GoogleNet—that have significantly advanced the field of computer vision.