A Novel Deep Learning Model Based on Convolutional Neural Networks for Employee Churn Prediction

2021 ◽  
Author(s):  
Ebru Pekel Ozmen ◽  
Tuncay Ozcan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Model ◽  
Churn Prediction ◽  
Model Based ◽  
Deep Learning Model

scholarly journals Development and Validation of a Deep Learning Model Using Convolutional Neural Networks to Identify Scaphoid Fractures in Radiographs

2021 ◽  
Vol 4 (5) ◽  
pp. e216096
Author(s):  
Alfred P. Yoon ◽  
Yi-Lun Lee ◽  
Robert L. Kane ◽  
Chang-Fu Kuo ◽  
Chihung Lin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Model ◽  
Scaphoid Fractures ◽  
Development And Validation ◽  
Deep Learning Model

scholarly journals A Training Method for Low Rank Convolutional Neural Networks Based on Alternating Tensor Compose-Decompose Method

2021 ◽  
Vol 11 (2) ◽  
pp. 643
Author(s):  
Sukho Lee ◽  
Hyein Kim ◽  
Byeongseon Jeong ◽  
Jungho Yoon
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Model ◽  
Low Rank ◽  
Training Method ◽  
Training Stage ◽  
Fixed Structure ◽  
Deep Learning Model

Over the past decade, deep learning-based computer vision methods have been shown to surpass previous state-of-the-art computer vision techniques in various fields, and have made great progress in various computer vision problems, including object detection, object segmentation, face recognition, etc. Nowadays, major IT companies are adding new deep-learning-based computer technologies to edge devices such as smartphones. However, since the computational cost of deep learning-based models is still high for edge devices, research is being actively carried out to compress deep learning-based models while not sacrificing high performance. Recently, many lightweight architectures have been proposed for deep learning-based models which are based on low-rank approximation. In this paper, we propose an alternating tensor compose-decompose (ATCD) method for the training of low-rank convolutional neural networks. The proposed training method can better train a compressed low-rank deep learning model than the conventional fixed-structure based training method, so that a compressed deep learning model with higher performance can be obtained in the end of the training. As a representative and exemplary model to which the proposed training method can be applied, we propose a rank-1 convolutional neural network (CNN) which has a structure alternatively containing 3-D rank-1 filters and 1-D filters in the training stage and a 1-D structure in the testing stage. After being trained, the 3-D rank-1 filters can be permanently decomposed into 1-D filters to achieve a fast inference in the test time. The reason that the 1-D filters are not being trained directly in 1-D form in the training stage is that the training of the 3-D rank-1 filters is easier due to the better gradient flow, which makes the training possible even in the case when the fixed structured network with fixed consecutive 1-D filters cannot be trained at all. We also show that the same training method can be applied to the well-known MobileNet architecture so that better parameters can be obtained than with the conventional fixed-structure training method. Furthermore, we show that the 1-D filters in a ResNet like structure can also be trained with the proposed method, which shows the fact that the proposed method can be applied to various structures of networks.

scholarly journals Cascaded one-shot deformable convolutional neural networks: Developing a deep learning model for respiratory motion estimation in ultrasound sequences

2020 ◽  
Vol 65 ◽  
pp. 101793 ◽  
Author(s):  
Fei Liu ◽  
Dan Liu ◽  
Jie Tian ◽  
Xiaoyan Xie ◽  
Xin Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Motion Estimation ◽  
Convolutional Neural Networks ◽  
Respiratory Motion ◽  
Learning Model ◽  
Deep Learning Model

Advanced deep learning model-based impact characterization method for composite laminates

2021 ◽  
Vol 207 ◽  
pp. 108713
Author(s):  
Kyung-Chae Jung ◽  
Seung-Hwan Chang
Keyword(s):  
Deep Learning ◽  
Composite Laminates ◽  
Learning Model ◽  
Model Based ◽  
Deep Learning Model

scholarly journals Transferable deep learning model based on the phenological matching principle for mapping crop extent

2021 ◽  
Vol 102 ◽  
pp. 102451
Author(s):  
Shuang Ge ◽  
Jinshui Zhang ◽  
Yaozhong Pan ◽  
Zhi Yang ◽  
Shuang Zhu
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Model Based ◽  
Deep Learning Model

scholarly journals A ConvBiLSTM Deep Learning Model Based Approach for Twitter Sentiment Classification

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sakirin Tam ◽  
Rachid Ben Said ◽  
Omer Ozgur Tanriover
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Sentiment Classification ◽  
Model Based ◽  
Deep Learning Model

Automated post-operative brain tumour segmentation: A deep learning model based on transfer learning from pre-operative images

2021 ◽  
Author(s):  
Mina Ghaffari ◽  
Gihan Samarasinghe ◽  
Michael Jameson ◽  
Farhannah Aly ◽  
Lois Holloway ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumour ◽  
Transfer Learning ◽  
Learning Model ◽  
Model Based ◽  
Tumour Segmentation ◽  
Deep Learning Model ◽  
Brain Tumour Segmentation
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