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Academic Articles & Research Papers
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Guangyu Zhu, Xueqi Luo, Tingting Yang, Li Cai et al. • Frontiers in Physiology
(2022)
The manual identification and segmentation of intracranial aneurysms (IAs) involved in the 3D reconstruction procedure are labor-intensive and prone to human errors. To meet the demands for routine clinical management and large cohort studi...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fphys.2022.1084202
Le Yang, Dongmei Jiang, Hichem Sahli • IEEE Access
(2020)
Depression disorder has become one of the major psychological diseases endangering human health. Researcher in the affective computing community is supporting the development of reliable depression severity estimation system, from multiple ...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2020.2970496
Zhen Li, Deqing Zou, Shouhuai Xu, Xinyu Ou et al. • Network and Distributed System Security Symposium
(2018)
The automatic detection of software vulnerabilities is an important research problem.However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negati...
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Open Access
DOI: https://doi.org/10.14722/ndss.2018.23158
Andre Esteva, Katherine Chou, Serena Y. Yeung, Nikhil Naik et al. • npj Digital Medicine
(2021)
A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields-including medicine-to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in th...
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DOI: https://doi.org/10.1038/s41746-020-00376-2
PMC: 7794558
Andrew J. Reader, Guillaume Corda-D’Incan, Abolfazl Mehranian, Casper O. da Costa-Luis et al. • IEEE Transactions on Radiation and Plasma Medical Sciences
(2020)
This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for the reconstruction of images in positron emission tomography (PET). Deep learning can be used either directly or as a component of conventio...
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Open Access
DOI: https://doi.org/10.1109/trpms.2020.3014786
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam et al. (2011)
Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We ...
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Licheng Jiao, Fan Zhang, Fang Liu, Shuyuan Yang et al. • IEEE Access
(2019)
Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people’s life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of ...
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DOI: https://doi.org/10.1109/access.2019.2939201
Toke T. Høye, Johanna Ärje, Kim Bjerge, Oskar Liset Pryds Hansen et al. • Proceedings of the National Academy of Sciences
(2021)
ABSTRACT Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phe...
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DOI: https://doi.org/10.1073/pnas.2002545117
PMC: 7812775
Mufti Mahmud, M. Shamim Kaiser, T. Martin McGinnity, Amir Hussain • Cognitive Computation
(2021)
Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data...
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DOI: https://doi.org/10.1007/s12559-020-09773-x
PMC: 7783296
Phillip M. Cheng, Emmanuel Montagnon, Rikiya Yamashita, Ian Pan et al. • Radiographics
(2021)
Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the...
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DOI: https://doi.org/10.1148/rg.2021200210
Yuying Zhang, Fei Xu, Yi Yang • Applied Sciences
(2025)
In recent years, gaze estimation has received a lot of interest in areas including human–computer interface, virtual reality, and user engagement analysis. Despite significant advances in convolutional neural network (CNN) techniques, direc...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/app152312475
Zijian Wang, Haimei Lu, Haixin Yan, Hongxing Kan et al. • Scientific Reports
(2023)
Abstract Diabetic Retinopathy (DR) is a major cause of blindness worldwide. Early detection and treatment are crucial to prevent vision loss, making accurate and timely diagnosis critical. Deep learning technology has shown promise in the a...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-023-38320-5
Xiaojiao Chen, Xiuqing Zhang, Mi Dong, Liansheng Huang et al. • Frontiers in Energy Research
(2021)
The prediction of wind power plays an indispensable role in maintaining the stability of the entire power grid. In this paper, a deep learning approach is proposed for the power prediction of multiple wind turbines. Starting from the time s...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fenrg.2021.723775
Mingyu Zhao, Shujia Zhang, Ke Li, Yanru Zhang et al. • Frontiers in Robotics and AI
(2026)
Densely populated enclosed environments, characterized by complex thermal stratification and overlapping breathing zones, represent high-risk clusters for the airborne transmission of respiratory pathogens. To address the dual challenges of...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/frobt.2026.1846010
Khoa Anh Tran, Olga Kondrashova, Andrew Peter Bradley, Elizabeth D. Williams et al. • Genome Medicine
(2021)
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning acr...
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DOI: https://doi.org/10.1186/s13073-021-00968-x
PMC: 8477474
Xing Su, Shan Xue, Fanzhen Liu, Jia Wu et al. • IEEE Transactions on Neural Networks and Learning Systems
(2022)
Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis...
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Open Access
DOI: https://doi.org/10.1109/tnnls.2021.3137396
Mingyu Kim, Jihye Yun, Yongwon Cho, Keewon Shin et al. • Neurospine
(2019)
The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by the human brain system, was developed by connecting layers with artificial neurons. However, due to the low computing power and insufficient learn...
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DOI: https://doi.org/10.14245/ns.1938396.198
PMC: 6945006
Alexander Selvikvåg Lundervold, Arvid Lundervold • Zeitschrift für Medizinische Physik
(2018)
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when...
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DOI: https://doi.org/10.1016/j.zemedi.2018.11.002
Xiaofei Yang, Yunming Ye, Xutao Li, Raymond Y.K. Lau et al. • IEEE Transactions on Geoscience and Remote Sensing
(2018)
Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision task...
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DOI: https://doi.org/10.1109/tgrs.2018.2815613
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...
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DOI: https://doi.org/10.1016/j.csbj.2020.09.033
PMC: 7575644