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Exploring convolutional neural networks and topic models for user profiling from drug reviews
Tutubalina E., Nikolenko S.
processing techniques, including extensions of topic models and convolutional neural networks (CNN). We apply
Human physical activity recognition algorithm based on smartphone data convolutional neural network and long short time memory
Yang, Z. X., Chen, Z. Y., H., Li
, convolutional neural network (CNN) and long short-term memory (LSTM) is proposed in the paper. The proposed
Human physical activity recognition algorithm based on smartphone data convolutional nerual network and long short time memory
Yang, Z. X., Chen, Z. Y.
, convolutional neural network (CNN) and long short-term memory (LSTM) is proposed in the paper. The proposed
Computing the stationary distribution of queueing systems with random resource requirements via fast fourier transform
Naumov V.A., Gaidamaka Y.V., Samouylov K.E.
in terms of truncated convolution powers of the cumulative distribution function of the resource
Developing a negative speech emotion recognition model for safety systems using deep learning
Jena S., Basak S., Agrawal H., Saini B., Gite S., Kotecha K., Alfarhood S.
the presence of the wake-up keyword 'ON' in the speech using a Convolutional Neural Network (CNN) model
Detection and Classification of Histopathological Breast Images Using a Fusion of CNN Frameworks
Rafiq A., Chursin A., Awad Alrefaei W., Rashed Alsenani T., Aldehim G., Abdel Samee N., Menzli L.J.
. In recent times, both convolutional neural networks (CNNs) and hybrid models of deep learning
DeepFrag: a deep convolutional neural network for fragment-based lead optimization
Green Harrison
that might be added to a known ligand to improve its binding affinity. We here describe a deep convolutional
Advancements in Gas Turbine Fault Detection: A Machine Learning Approach Based on the Temporal Convolutional Network–Autoencoder Model
Al-Tekreeti Khalid Fahmi Watban, Reza Kashyzadeh Kazem, Ghorbani Siamak
machine learning (ML) tools. For this purpose, an advanced Temporal Convolutional Network (TCN
On a class of multiplicative-convolution equations
Salekhova L., Chebotareva E.
multiplicative-convolution equations in space A'+, where A'+ is the space of distributions on ℝ, which
Effective Algorithm for Biomedical Image Segmentation
Di Zhao, Yi Tang, Gourinovitch, A. B.
to other methods in terms of Intersection over Union and F1-score. The network under consideration copes

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