Results 41 to 50 of about 5,846,406 (312)
Semantic segmentation of human oocyte images using deep neural networks
BioMedical Engineering OnLine, 2021 Background Infertility is a significant problem of humanity. In vitro fertilisation is one of the most effective and frequently applied ART methods. The effectiveness IVF depends on the assessment and selection of gametes and embryo with the highest ...
Anna Targosz +3 more
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Adversarial Robustness of Deep Convolutional Neural Network-based Image Recognition Models: A Review
Leida xuebao, 2021 Deep convolutional neural networks have achieved great success in recent years. They have been widely used in various applications such as optical and SAR image scene classification, object detection and recognition, semantic segmentation, and change ...
Hao SUN +4 more
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Robust Sound Event Classification using Deep Neural Networks [PDF]
, 2015 The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding.
Song, Yan +4 more
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A Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Study
Sakarya University Journal of Computer and Information Sciences, 2020 Deep learning, a subfield of machine learning, has proved its efficacy on a wide range of applications including but not limited to computer vision, text analysis and natural language processing, algorithm enhancement, computational biology, physical ...
Abdullah Talha Kabakuş
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Exploiting more robust and efficacious deep learning techniques for modeling wind power with speed
Energy Reports, 2022 Sound analyses of the nonlinear relationship between wind speed and power generation are crucial for the advancement of wind energy optimization. As an emerging artificial intelligence technology, deep learning has received growing attention from energy ...
Hao Chen, Reidar Staupe-Delgado
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Learning Deep Belief Networks from Non-Stationary Streams [PDF]
, 2012 18.10.13 KB. Ok to add author version to spiral from LNCS; embargo period expired. SpringerDeep learning has proven to be beneficial for complex tasks such as classifying images. However, this approach has been mostly applied to static datasets.
Pouzols, Federico Montesino +11 more
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Compressed Superposition of Neural Networks for Deep Learning in Edge Computing
, 2021 This paper investigates a combination of the two recently proposed techniques: superposition of multiple neural networks into one and neural network compression.
Zoran Bosnic +5 more
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StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity
IEEE Access, 2016 Deep neural networks are a branch in machine learning that has seen a meteoric rise in popularity due to its powerful abilities to represent and model high-level abstractions in highly complex data.
Mohammad Javad Shafiee +2 more
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Stress detection using deep neural networks
BMC Medical Informatics and Decision Making, 2020 Background Over 70% of Americans regularly experience stress. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to physiological health and psychological wellbeing.
Russell Li, Zhandong Liu
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Synthesizing game audio using deep neural networks
, 2021 High quality audio plays an important role in gaming, contributing to player immersion during gameplay. Creating audio content which matches overall theme and aesthetic is essential, such that players can become fully engrossed in a game environment ...
Lemley, Joseph +3 more
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