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MDNN: memetic deep neural network for genomic prediction. [PDF]
Brief BioinformMao Y +9 more
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The impact of music visualization model by using internet of things techniques and deep neural network. [PDF]
Sci RepYu X.
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Corrigendum to "A Novel Network-Level Fused Self-Attention Deep Neural Network for Cervical Cancer Classification from Cervicography Images". [PDF]
Technol Cancer Res Treateuropepmc +1 more source
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Proceedings of the 29th ACM International Conference on Multimedia, 2021
With the rapid development of deep learning-based techniques, the general public can use a lot of "machine learning as a service" (MLaaS), which provides end-to-end machine learning solutions. Taking the image classification task as an example, users only need to update their dataset and labels to MLaaS without requiring the specific knowledge of ...
Nan Zhong +2 more
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With the rapid development of deep learning-based techniques, the general public can use a lot of "machine learning as a service" (MLaaS), which provides end-to-end machine learning solutions. Taking the image classification task as an example, users only need to update their dataset and labels to MLaaS without requiring the specific knowledge of ...
Nan Zhong +2 more
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Deep Morphological Neural Networks
International Journal of Pattern Recognition and Artificial Intelligence, 2022Mathematical morphology intends to extract object features such as geometric and topological structures in digital images. Given a set of target images and original images, it is cumbersome and time-consuming to determine the suitable morphological operations and structuring elements. In this paper, we propose deep morphological neural networks, which
Yucong Shen +3 more
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Tracking with deep neural networks
2013 47th Annual Conference on Information Sciences and Systems (CISS), 2013We present deep neural network models applied to tracking objects of interest. Deep neural networks trained for general-purpose use are introduced to conduct long-term tracking, which requires scale-invariant feature extraction even when the object dramatically changes shape as it moves in the scene.
Jonghoon Jin +4 more
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Deep neural network in QSAR studies using deep belief network
Applied Soft Computing Journal, 2018 There are two major challenges in the current high throughput screening drug design: the large number of descriptors which may also have autocorrelations and, proper parameter initialization in model prediction to avoid over-fitting problem.
Fahimeh Ghasemi +2 more
exaly +2 more sources
2021
Quantitative structure-activity relationship (QSAR) models are routinely applied computational tools in the drug discovery process. QSAR models are regression or classification models that predict the biological activities of molecules based on the features derived from their molecular structures.
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Quantitative structure-activity relationship (QSAR) models are routinely applied computational tools in the drug discovery process. QSAR models are regression or classification models that predict the biological activities of molecules based on the features derived from their molecular structures.
openaire +2 more sources