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You searched for subject:(Convolutional neural network). Showing records 1 – 30 of 477 total matches.

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California State University – Sacramento

1. Rawlani, Bhagyashree. Distracted driver detection using Capsule Network.

Degree: MS, Computer Science, 2019, California State University – Sacramento

Convolutional neural networks are generally assumed to be the best neural networks for classifying images. In November 2017, Geoffrey Hinton et al. introduced another neural(more)

Subjects/Keywords: Convolutional neural network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Rawlani, B. (2019). Distracted driver detection using Capsule Network. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/207659

Chicago Manual of Style (16th Edition):

Rawlani, Bhagyashree. “Distracted driver detection using Capsule Network.” 2019. Masters Thesis, California State University – Sacramento. Accessed January 20, 2021. http://hdl.handle.net/10211.3/207659.

MLA Handbook (7th Edition):

Rawlani, Bhagyashree. “Distracted driver detection using Capsule Network.” 2019. Web. 20 Jan 2021.

Vancouver:

Rawlani B. Distracted driver detection using Capsule Network. [Internet] [Masters thesis]. California State University – Sacramento; 2019. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/10211.3/207659.

Council of Science Editors:

Rawlani B. Distracted driver detection using Capsule Network. [Masters Thesis]. California State University – Sacramento; 2019. Available from: http://hdl.handle.net/10211.3/207659


University of Illinois – Urbana-Champaign

2. Yeh, Raymond Alexander. Stable and symmetric convolutional neural network.

Degree: MS, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 First we present a proof that convolutional neural networks (CNNs) with max-norm regularization, max-pooling, and Relu non-linearity are stable to additive noise. Second, we explore… (more)

Subjects/Keywords: convolutional neural network; deep learning

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APA (6th Edition):

Yeh, R. A. (2016). Stable and symmetric convolutional neural network. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/92687

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yeh, Raymond Alexander. “Stable and symmetric convolutional neural network.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed January 20, 2021. http://hdl.handle.net/2142/92687.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yeh, Raymond Alexander. “Stable and symmetric convolutional neural network.” 2016. Web. 20 Jan 2021.

Vancouver:

Yeh RA. Stable and symmetric convolutional neural network. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2142/92687.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yeh RA. Stable and symmetric convolutional neural network. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/92687

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Illinois – Urbana-Champaign

3. Zhu, Tianyilin. Lipreading with convolutional and recurrent neural network models.

Degree: MS, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

 Lip reading is the process of speech recognition from solely visual information. The goal of this thesis is to perform a silence vs. speech classification,… (more)

Subjects/Keywords: Lipreading; Convolutional neural network

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APA (6th Edition):

Zhu, T. (2017). Lipreading with convolutional and recurrent neural network models. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/97763

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zhu, Tianyilin. “Lipreading with convolutional and recurrent neural network models.” 2017. Thesis, University of Illinois – Urbana-Champaign. Accessed January 20, 2021. http://hdl.handle.net/2142/97763.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhu, Tianyilin. “Lipreading with convolutional and recurrent neural network models.” 2017. Web. 20 Jan 2021.

Vancouver:

Zhu T. Lipreading with convolutional and recurrent neural network models. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2142/97763.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhu T. Lipreading with convolutional and recurrent neural network models. [Thesis]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/97763

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Delft University of Technology

4. Snuverink, Iris (author). Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions.

Degree: 2017, Delft University of Technology

 In hyperspectral (HS) imaging, for every pixel a spectrum of wavelengths is captured. These spectra represent material properties, i.e. the spectral signatures. So, classification of… (more)

Subjects/Keywords: Deep Learning; Convolutional Neural Network; Fully Convolutional Neural network; Image segmentation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Snuverink, I. (. (2017). Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e6ce206b-09d1-4511-8349-b5e67f96c2d2

Chicago Manual of Style (16th Edition):

Snuverink, Iris (author). “Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions.” 2017. Masters Thesis, Delft University of Technology. Accessed January 20, 2021. http://resolver.tudelft.nl/uuid:e6ce206b-09d1-4511-8349-b5e67f96c2d2.

MLA Handbook (7th Edition):

Snuverink, Iris (author). “Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions.” 2017. Web. 20 Jan 2021.

Vancouver:

Snuverink I(. Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 20]. Available from: http://resolver.tudelft.nl/uuid:e6ce206b-09d1-4511-8349-b5e67f96c2d2.

Council of Science Editors:

Snuverink I(. Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:e6ce206b-09d1-4511-8349-b5e67f96c2d2


University of Ottawa

5. Zhang, Huizhen. Alpha Matting via Residual Convolutional Grid Network .

Degree: 2019, University of Ottawa

 Alpha matting is an important topic in areas of computer vision. It has various applications, such as virtual reality, digital image and video editing, and… (more)

Subjects/Keywords: Alpha matting; Convolutional Neural Network; Grid Network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhang, H. (2019). Alpha Matting via Residual Convolutional Grid Network . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/39467

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zhang, Huizhen. “Alpha Matting via Residual Convolutional Grid Network .” 2019. Thesis, University of Ottawa. Accessed January 20, 2021. http://hdl.handle.net/10393/39467.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhang, Huizhen. “Alpha Matting via Residual Convolutional Grid Network .” 2019. Web. 20 Jan 2021.

Vancouver:

Zhang H. Alpha Matting via Residual Convolutional Grid Network . [Internet] [Thesis]. University of Ottawa; 2019. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/10393/39467.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang H. Alpha Matting via Residual Convolutional Grid Network . [Thesis]. University of Ottawa; 2019. Available from: http://hdl.handle.net/10393/39467

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


NSYSU

6. WU, JHE-WEI. Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy.

Degree: Master, Information Management, 2017, NSYSU

 The rapid increase of computing power, along with the improvement of software capabilities has made artificial intelligence a new trend. Deep learning recently becomes the… (more)

Subjects/Keywords: Convolutional Neural Network; Artificial Intelligence; Stock investment

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APA (6th Edition):

WU, J. (2017). Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-145700

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

WU, JHE-WEI. “Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy.” 2017. Thesis, NSYSU. Accessed January 20, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-145700.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

WU, JHE-WEI. “Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy.” 2017. Web. 20 Jan 2021.

Vancouver:

WU J. Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy. [Internet] [Thesis]. NSYSU; 2017. [cited 2021 Jan 20]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-145700.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

WU J. Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-145700

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of North Texas

7. Sure, Venkata Leela. Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN.

Degree: 2019, University of North Texas

 Ulcerative colitis (UC) is a chronic inflammatory disease characterized by periods of relapses and remissions affecting more than 500,000 people in the United States. To… (more)

Subjects/Keywords: Convolutional Neural Network; Medical Image Classification

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APA (6th Edition):

Sure, V. L. (2019). Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc1538703/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sure, Venkata Leela. “Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN.” 2019. Thesis, University of North Texas. Accessed January 20, 2021. https://digital.library.unt.edu/ark:/67531/metadc1538703/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sure, Venkata Leela. “Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN.” 2019. Web. 20 Jan 2021.

Vancouver:

Sure VL. Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN. [Internet] [Thesis]. University of North Texas; 2019. [cited 2021 Jan 20]. Available from: https://digital.library.unt.edu/ark:/67531/metadc1538703/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sure VL. Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN. [Thesis]. University of North Texas; 2019. Available from: https://digital.library.unt.edu/ark:/67531/metadc1538703/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

8. Nordeng, Ian Edward. Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery.

Degree: MS, Mechanical Engineering, 2018, University of North Dakota

  This work presents a novel system utilizing previously developed convolutional neural network (CNN) architectures to aid in automating maintenance inspections of the dead-end body… (more)

Subjects/Keywords: CNN; Convolutional Neural Network; Inspections; Machine Learning

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APA (6th Edition):

Nordeng, I. E. (2018). Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery. (Masters Thesis). University of North Dakota. Retrieved from https://commons.und.edu/theses/2300

Chicago Manual of Style (16th Edition):

Nordeng, Ian Edward. “Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery.” 2018. Masters Thesis, University of North Dakota. Accessed January 20, 2021. https://commons.und.edu/theses/2300.

MLA Handbook (7th Edition):

Nordeng, Ian Edward. “Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery.” 2018. Web. 20 Jan 2021.

Vancouver:

Nordeng IE. Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery. [Internet] [Masters thesis]. University of North Dakota; 2018. [cited 2021 Jan 20]. Available from: https://commons.und.edu/theses/2300.

Council of Science Editors:

Nordeng IE. Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery. [Masters Thesis]. University of North Dakota; 2018. Available from: https://commons.und.edu/theses/2300


Delft University of Technology

9. Trommel, Kars (author). Wind Classification using Unsupervised Learning: In support of the Olympic Sailing Competition in Tokyo, Japan.

Degree: 2020, Delft University of Technology

During the preparation for the Olympic Sailing Competition, held in 2021 in Tokyo, Japan, the Dutch National Sailing Team encountered days with unpredicted wind behaviour.… (more)

Subjects/Keywords: Unsupervised; Wind; Classification; Convolutional; Neural; Network; Autoencoder

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Trommel, K. (. (2020). Wind Classification using Unsupervised Learning: In support of the Olympic Sailing Competition in Tokyo, Japan. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9ea87a34-0a19-4ed1-8c38-d43c94715ebb

Chicago Manual of Style (16th Edition):

Trommel, Kars (author). “Wind Classification using Unsupervised Learning: In support of the Olympic Sailing Competition in Tokyo, Japan.” 2020. Masters Thesis, Delft University of Technology. Accessed January 20, 2021. http://resolver.tudelft.nl/uuid:9ea87a34-0a19-4ed1-8c38-d43c94715ebb.

MLA Handbook (7th Edition):

Trommel, Kars (author). “Wind Classification using Unsupervised Learning: In support of the Olympic Sailing Competition in Tokyo, Japan.” 2020. Web. 20 Jan 2021.

Vancouver:

Trommel K(. Wind Classification using Unsupervised Learning: In support of the Olympic Sailing Competition in Tokyo, Japan. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 20]. Available from: http://resolver.tudelft.nl/uuid:9ea87a34-0a19-4ed1-8c38-d43c94715ebb.

Council of Science Editors:

Trommel K(. Wind Classification using Unsupervised Learning: In support of the Olympic Sailing Competition in Tokyo, Japan. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:9ea87a34-0a19-4ed1-8c38-d43c94715ebb


University of Illinois – Urbana-Champaign

10. Liu, Xianming. Feedback convolutional neural network in applications of computer vision.

Degree: PhD, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 With the development of deep neural networks, especially convolutional neural networks, computer vision tasks rely on training data to an unprecedented extent. As the network(more)

Subjects/Keywords: Convolutional Neural Network; Feedback; Computer Vision

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APA (6th Edition):

Liu, X. (2016). Feedback convolutional neural network in applications of computer vision. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/95471

Chicago Manual of Style (16th Edition):

Liu, Xianming. “Feedback convolutional neural network in applications of computer vision.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 20, 2021. http://hdl.handle.net/2142/95471.

MLA Handbook (7th Edition):

Liu, Xianming. “Feedback convolutional neural network in applications of computer vision.” 2016. Web. 20 Jan 2021.

Vancouver:

Liu X. Feedback convolutional neural network in applications of computer vision. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2142/95471.

Council of Science Editors:

Liu X. Feedback convolutional neural network in applications of computer vision. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/95471


University of Illinois – Urbana-Champaign

11. Qian, Kaizhi. Speech enhancement using deep dilated CNN.

Degree: MS, Electrical & Computer Engr, 2018, University of Illinois – Urbana-Champaign

 In recent years, deep learning has achieved great success in speech enhancement. However, there are two major limitations regarding existing works. First, the Bayesian framework… (more)

Subjects/Keywords: speech enhancement; convolutional neural network; beamforming

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APA (6th Edition):

Qian, K. (2018). Speech enhancement using deep dilated CNN. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/101644

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Qian, Kaizhi. “Speech enhancement using deep dilated CNN.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed January 20, 2021. http://hdl.handle.net/2142/101644.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Qian, Kaizhi. “Speech enhancement using deep dilated CNN.” 2018. Web. 20 Jan 2021.

Vancouver:

Qian K. Speech enhancement using deep dilated CNN. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2142/101644.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Qian K. Speech enhancement using deep dilated CNN. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/101644

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Iowa State University

12. Yu, Xianhua. Sequential neural network decoder for convolutional code with large block sizes.

Degree: 2020, Iowa State University

 Due to the curse of dimensionality, the training complexity of the neural network based channel-code decoder increases exponentially along with the code word’s length. Although… (more)

Subjects/Keywords: convolutional code; deep learning; neural network decoder

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yu, X. (2020). Sequential neural network decoder for convolutional code with large block sizes. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/18252

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yu, Xianhua. “Sequential neural network decoder for convolutional code with large block sizes.” 2020. Thesis, Iowa State University. Accessed January 20, 2021. https://lib.dr.iastate.edu/etd/18252.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yu, Xianhua. “Sequential neural network decoder for convolutional code with large block sizes.” 2020. Web. 20 Jan 2021.

Vancouver:

Yu X. Sequential neural network decoder for convolutional code with large block sizes. [Internet] [Thesis]. Iowa State University; 2020. [cited 2021 Jan 20]. Available from: https://lib.dr.iastate.edu/etd/18252.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yu X. Sequential neural network decoder for convolutional code with large block sizes. [Thesis]. Iowa State University; 2020. Available from: https://lib.dr.iastate.edu/etd/18252

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


NSYSU

13. Liu, Jia-Hong. Portfolio Investment Based on Neural Networks.

Degree: Master, Computer Science and Engineering, 2018, NSYSU

 In this thesis, we combine the trading signals generated by the gen expression programming (GEP) method of Lee et al. and the portfolio generated by… (more)

Subjects/Keywords: neural network; stock investment; gene expression programming; convolutional neural network; portfolio

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APA (6th Edition):

Liu, J. (2018). Portfolio Investment Based on Neural Networks. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621118-142449

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Liu, Jia-Hong. “Portfolio Investment Based on Neural Networks.” 2018. Thesis, NSYSU. Accessed January 20, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621118-142449.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Liu, Jia-Hong. “Portfolio Investment Based on Neural Networks.” 2018. Web. 20 Jan 2021.

Vancouver:

Liu J. Portfolio Investment Based on Neural Networks. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 Jan 20]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621118-142449.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu J. Portfolio Investment Based on Neural Networks. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621118-142449

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Mid Sweden University

14. Wang, Xutao. Chinese Text Classification Based On Deep Learning.

Degree: Information Systems and Technology, 2018, Mid Sweden University

  Text classification has always been a concern in area of natural language processing, especially nowadays the data are getting massive due to the development… (more)

Subjects/Keywords: Text classification; Recurrent neural network; Convolutional neural network; Computer Systems; Datorsystem

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APA (6th Edition):

Wang, X. (2018). Chinese Text Classification Based On Deep Learning. (Thesis). Mid Sweden University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Xutao. “Chinese Text Classification Based On Deep Learning.” 2018. Thesis, Mid Sweden University. Accessed January 20, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Xutao. “Chinese Text Classification Based On Deep Learning.” 2018. Web. 20 Jan 2021.

Vancouver:

Wang X. Chinese Text Classification Based On Deep Learning. [Internet] [Thesis]. Mid Sweden University; 2018. [cited 2021 Jan 20]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang X. Chinese Text Classification Based On Deep Learning. [Thesis]. Mid Sweden University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Notre Dame

15. Tianchen Wang. Statistical Neural Networks: Concepts, Frameworks and Applications</h1>.

Degree: Computer Science and Engineering, 2020, University of Notre Dame

  As one of the fundamental issues in the fields such as computer vision and deep learning, accelerating the inference speed of the convolutional neural(more)

Subjects/Keywords: convolutional neural network; independent component analysis; neural network acceleration

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, T. (2020). Statistical Neural Networks: Concepts, Frameworks and Applications</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/47429883t43

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Tianchen. “Statistical Neural Networks: Concepts, Frameworks and Applications</h1>.” 2020. Thesis, University of Notre Dame. Accessed January 20, 2021. https://curate.nd.edu/show/47429883t43.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Tianchen. “Statistical Neural Networks: Concepts, Frameworks and Applications</h1>.” 2020. Web. 20 Jan 2021.

Vancouver:

Wang T. Statistical Neural Networks: Concepts, Frameworks and Applications</h1>. [Internet] [Thesis]. University of Notre Dame; 2020. [cited 2021 Jan 20]. Available from: https://curate.nd.edu/show/47429883t43.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang T. Statistical Neural Networks: Concepts, Frameworks and Applications</h1>. [Thesis]. University of Notre Dame; 2020. Available from: https://curate.nd.edu/show/47429883t43

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Cambridge

16. Maji, Partha. Model-architecture co-design of deep neural networks for embedded systems.

Degree: PhD, 2020, University of Cambridge

 In deep learning, a convolutional neural network (ConvNet or CNN) is a powerful tool for building interesting embedded applications that use data to make predictions.… (more)

Subjects/Keywords: Neural Network; Convolutional Neural Network; Optimisation; SIMD; Low-rank; Compression; Accelerators

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Maji, P. (2020). Model-architecture co-design of deep neural networks for embedded systems. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.54581 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.810101

Chicago Manual of Style (16th Edition):

Maji, Partha. “Model-architecture co-design of deep neural networks for embedded systems.” 2020. Doctoral Dissertation, University of Cambridge. Accessed January 20, 2021. https://doi.org/10.17863/CAM.54581 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.810101.

MLA Handbook (7th Edition):

Maji, Partha. “Model-architecture co-design of deep neural networks for embedded systems.” 2020. Web. 20 Jan 2021.

Vancouver:

Maji P. Model-architecture co-design of deep neural networks for embedded systems. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Jan 20]. Available from: https://doi.org/10.17863/CAM.54581 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.810101.

Council of Science Editors:

Maji P. Model-architecture co-design of deep neural networks for embedded systems. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://doi.org/10.17863/CAM.54581 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.810101


University of Cambridge

17. Maji, Partha. Model-Architecture Co-design of Deep Neural Networks for Embedded Systems.

Degree: PhD, 2020, University of Cambridge

 In deep learning, a convolutional neural network (ConvNet or CNN) is a powerful tool for building interesting embedded applications that use data to make predictions.… (more)

Subjects/Keywords: Neural Network; Convolutional Neural Network; Optimisation; SIMD; Low-rank; Compression; Accelerators

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Maji, P. (2020). Model-Architecture Co-design of Deep Neural Networks for Embedded Systems. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/307488

Chicago Manual of Style (16th Edition):

Maji, Partha. “Model-Architecture Co-design of Deep Neural Networks for Embedded Systems.” 2020. Doctoral Dissertation, University of Cambridge. Accessed January 20, 2021. https://www.repository.cam.ac.uk/handle/1810/307488.

MLA Handbook (7th Edition):

Maji, Partha. “Model-Architecture Co-design of Deep Neural Networks for Embedded Systems.” 2020. Web. 20 Jan 2021.

Vancouver:

Maji P. Model-Architecture Co-design of Deep Neural Networks for Embedded Systems. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Jan 20]. Available from: https://www.repository.cam.ac.uk/handle/1810/307488.

Council of Science Editors:

Maji P. Model-Architecture Co-design of Deep Neural Networks for Embedded Systems. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://www.repository.cam.ac.uk/handle/1810/307488


Tampere University

18. Zhou, Yi. Sentiment classification with deep neural networks .

Degree: 2019, Tampere University

 Sentiment classification is an important task in Natural Language Processing (NLP) area. Deep neural networks become the mainstream method to perform the text sentiment classification… (more)

Subjects/Keywords: deep neural networks; convolutional neural network; recurrent neural network; sentiment classification; hotel reviews; TripAdvisor

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhou, Y. (2019). Sentiment classification with deep neural networks . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi//handle/10024/116148

Chicago Manual of Style (16th Edition):

Zhou, Yi. “Sentiment classification with deep neural networks .” 2019. Masters Thesis, Tampere University. Accessed January 20, 2021. https://trepo.tuni.fi//handle/10024/116148.

MLA Handbook (7th Edition):

Zhou, Yi. “Sentiment classification with deep neural networks .” 2019. Web. 20 Jan 2021.

Vancouver:

Zhou Y. Sentiment classification with deep neural networks . [Internet] [Masters thesis]. Tampere University; 2019. [cited 2021 Jan 20]. Available from: https://trepo.tuni.fi//handle/10024/116148.

Council of Science Editors:

Zhou Y. Sentiment classification with deep neural networks . [Masters Thesis]. Tampere University; 2019. Available from: https://trepo.tuni.fi//handle/10024/116148


Delft University of Technology

19. Hoogendoorn, Jasper (author). Sequential Monte Carlo method for training Neural Networks on non-stationary time series.

Degree: 2019, Delft University of Technology

In this thesis, we study the sequential Monte Carlo method for training neural networks in the context of time series forecasting. Sequential Monte Carlo can… (more)

Subjects/Keywords: sequential Monte Carlo; Neural Networks; Time Series Forecasting; Convolutional Neural Network

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APA (6th Edition):

Hoogendoorn, J. (. (2019). Sequential Monte Carlo method for training Neural Networks on non-stationary time series. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:659e9fd5-d251-46fe-8455-3a17bdd4f48c

Chicago Manual of Style (16th Edition):

Hoogendoorn, Jasper (author). “Sequential Monte Carlo method for training Neural Networks on non-stationary time series.” 2019. Masters Thesis, Delft University of Technology. Accessed January 20, 2021. http://resolver.tudelft.nl/uuid:659e9fd5-d251-46fe-8455-3a17bdd4f48c.

MLA Handbook (7th Edition):

Hoogendoorn, Jasper (author). “Sequential Monte Carlo method for training Neural Networks on non-stationary time series.” 2019. Web. 20 Jan 2021.

Vancouver:

Hoogendoorn J(. Sequential Monte Carlo method for training Neural Networks on non-stationary time series. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 20]. Available from: http://resolver.tudelft.nl/uuid:659e9fd5-d251-46fe-8455-3a17bdd4f48c.

Council of Science Editors:

Hoogendoorn J(. Sequential Monte Carlo method for training Neural Networks on non-stationary time series. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:659e9fd5-d251-46fe-8455-3a17bdd4f48c


University of Cincinnati

20. Sarpangala, Kishan. Semantic Segmentation Using Deep Learning Neural Architectures.

Degree: MS, Engineering and Applied Science: Computer Science, 2019, University of Cincinnati

 In many picture handling methods, the capacity to segment items of concern automatically is a helpful pre-processing phase. There are several algorithms segmenting pictures depending… (more)

Subjects/Keywords: Artificial Intelligence; Semantic Segmentation; Convolutional Neural Network; Computer Vision; Deep Learning Neural Network; Artificial Intelligence; Fully Convolutional Network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sarpangala, K. (2019). Semantic Segmentation Using Deep Learning Neural Architectures. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304

Chicago Manual of Style (16th Edition):

Sarpangala, Kishan. “Semantic Segmentation Using Deep Learning Neural Architectures.” 2019. Masters Thesis, University of Cincinnati. Accessed January 20, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304.

MLA Handbook (7th Edition):

Sarpangala, Kishan. “Semantic Segmentation Using Deep Learning Neural Architectures.” 2019. Web. 20 Jan 2021.

Vancouver:

Sarpangala K. Semantic Segmentation Using Deep Learning Neural Architectures. [Internet] [Masters thesis]. University of Cincinnati; 2019. [cited 2021 Jan 20]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304.

Council of Science Editors:

Sarpangala K. Semantic Segmentation Using Deep Learning Neural Architectures. [Masters Thesis]. University of Cincinnati; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304


University of Windsor

21. Ragunathan, Kumaran. Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks.

Degree: MS, Computer Science, 2020, University of Windsor

 Link Prediction (LP) in social networks (SN) is referred to as predicting the likelihood of a link formation in SNs in the near future. There… (more)

Subjects/Keywords: Convolutional Neural Network; Link Prediction; Machine Learning; PLACN; Social Network Analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ragunathan, K. (2020). Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks. (Masters Thesis). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/8308

Chicago Manual of Style (16th Edition):

Ragunathan, Kumaran. “Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks.” 2020. Masters Thesis, University of Windsor. Accessed January 20, 2021. https://scholar.uwindsor.ca/etd/8308.

MLA Handbook (7th Edition):

Ragunathan, Kumaran. “Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks.” 2020. Web. 20 Jan 2021.

Vancouver:

Ragunathan K. Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks. [Internet] [Masters thesis]. University of Windsor; 2020. [cited 2021 Jan 20]. Available from: https://scholar.uwindsor.ca/etd/8308.

Council of Science Editors:

Ragunathan K. Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks. [Masters Thesis]. University of Windsor; 2020. Available from: https://scholar.uwindsor.ca/etd/8308


University of Illinois – Urbana-Champaign

22. Yan, Zhicheng. Image recognition, semantic segmentation and photo adjustment using deep neural networks.

Degree: PhD, Computer Science, 2016, University of Illinois – Urbana-Champaign

 Deep Neural Networks (DNNs) have proven to be effective models for solving various problems in computer vision. Multi-Layer Perceptron Networks, Convolutional Neural Networks and Recurrent… (more)

Subjects/Keywords: Deep Neural Network; Image Recognition; Semantic Segmentation; Photo Adjustment; Convolutional Neural Network; Recurrent Neural Network; Multi-Layer Perceptron Network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yan, Z. (2016). Image recognition, semantic segmentation and photo adjustment using deep neural networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90724

Chicago Manual of Style (16th Edition):

Yan, Zhicheng. “Image recognition, semantic segmentation and photo adjustment using deep neural networks.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 20, 2021. http://hdl.handle.net/2142/90724.

MLA Handbook (7th Edition):

Yan, Zhicheng. “Image recognition, semantic segmentation and photo adjustment using deep neural networks.” 2016. Web. 20 Jan 2021.

Vancouver:

Yan Z. Image recognition, semantic segmentation and photo adjustment using deep neural networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2142/90724.

Council of Science Editors:

Yan Z. Image recognition, semantic segmentation and photo adjustment using deep neural networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90724


Brno University of Technology

23. Ustsinau, Usevalad. Segmentace nádorů mozku v MRI datech s využitím hloubkového učení: Segmentation of brain tumours in MRI images using deep learning.

Degree: 2020, Brno University of Technology

 The following master's thesis paper equipped with a short description of CT scans and MR images and the main differences between them, explanation of the… (more)

Subjects/Keywords: Medical Imaging; Brain Tumour; Convolutional Neural Network; Segmentation; U-Net; Medical Imaging; Brain Tumour; Convolutional Neural Network; Segmentation; U-Net

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ustsinau, U. (2020). Segmentace nádorů mozku v MRI datech s využitím hloubkového učení: Segmentation of brain tumours in MRI images using deep learning. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/189317

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ustsinau, Usevalad. “Segmentace nádorů mozku v MRI datech s využitím hloubkového učení: Segmentation of brain tumours in MRI images using deep learning.” 2020. Thesis, Brno University of Technology. Accessed January 20, 2021. http://hdl.handle.net/11012/189317.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ustsinau, Usevalad. “Segmentace nádorů mozku v MRI datech s využitím hloubkového učení: Segmentation of brain tumours in MRI images using deep learning.” 2020. Web. 20 Jan 2021.

Vancouver:

Ustsinau U. Segmentace nádorů mozku v MRI datech s využitím hloubkového učení: Segmentation of brain tumours in MRI images using deep learning. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/11012/189317.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ustsinau U. Segmentace nádorů mozku v MRI datech s využitím hloubkového učení: Segmentation of brain tumours in MRI images using deep learning. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/189317

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Bridgeport

24. Ali Albelwi, Saleh. Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition .

Degree: 2018, University of Bridgeport

 Recent advances in Convolutional Neural Networks (CNNs) have obtained promising results in difficult deep learning tasks. However, the success of a CNN depends on finding… (more)

Subjects/Keywords: Convolutional neural network; Convolutional neural networks architecture design; Correlation coefficient; Deconvolutional network; Deep learning; Objective function

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APA (6th Edition):

Ali Albelwi, S. (2018). Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition . (Thesis). University of Bridgeport. Retrieved from https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2040

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ali Albelwi, Saleh. “Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition .” 2018. Thesis, University of Bridgeport. Accessed January 20, 2021. https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2040.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ali Albelwi, Saleh. “Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition .” 2018. Web. 20 Jan 2021.

Vancouver:

Ali Albelwi S. Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition . [Internet] [Thesis]. University of Bridgeport; 2018. [cited 2021 Jan 20]. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2040.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ali Albelwi S. Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition . [Thesis]. University of Bridgeport; 2018. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2040

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Rochester Institute of Technology

25. Yin, Peng Nien. Characterizing bladder cancer cells by comparing general machine learning methods to convolutional neural network.

Degree: MS, Thomas H. Gosnell School of Life Sciences (COS), 2019, Rochester Institute of Technology

  Recently, deep learning techniques from the computer science field have dramatically improved the ability of computers to recognize objects in images. This raised the… (more)

Subjects/Keywords: Bladder cancer; Convolutional neural network; Histology; Invasive; Probailistic neural network; Tumor-node-metastasis

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APA (6th Edition):

Yin, P. N. (2019). Characterizing bladder cancer cells by comparing general machine learning methods to convolutional neural network. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10107

Chicago Manual of Style (16th Edition):

Yin, Peng Nien. “Characterizing bladder cancer cells by comparing general machine learning methods to convolutional neural network.” 2019. Masters Thesis, Rochester Institute of Technology. Accessed January 20, 2021. https://scholarworks.rit.edu/theses/10107.

MLA Handbook (7th Edition):

Yin, Peng Nien. “Characterizing bladder cancer cells by comparing general machine learning methods to convolutional neural network.” 2019. Web. 20 Jan 2021.

Vancouver:

Yin PN. Characterizing bladder cancer cells by comparing general machine learning methods to convolutional neural network. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2021 Jan 20]. Available from: https://scholarworks.rit.edu/theses/10107.

Council of Science Editors:

Yin PN. Characterizing bladder cancer cells by comparing general machine learning methods to convolutional neural network. [Masters Thesis]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10107


NSYSU

26. Wu, Tung-Han. Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition.

Degree: Master, Computer Science and Engineering, 2017, NSYSU

 In this paper, we combine the deep neural network De-noising Auto Encoder and Gaussian Mixture Model to implement an automatic speech recognition system in the… (more)

Subjects/Keywords: speech recognition; denoising auto encoder; convolutional neural network; deep learning; fully connected neural network

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APA (6th Edition):

Wu, T. (2017). Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wu, Tung-Han. “Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition.” 2017. Thesis, NSYSU. Accessed January 20, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wu, Tung-Han. “Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition.” 2017. Web. 20 Jan 2021.

Vancouver:

Wu T. Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition. [Internet] [Thesis]. NSYSU; 2017. [cited 2021 Jan 20]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wu T. Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of North Texas

27. Hesamifard, Ehsan. Privacy Preserving Machine Learning as a Service.

Degree: 2020, University of North Texas

 Machine learning algorithms based on neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires… (more)

Subjects/Keywords: Homomorphic encryption; machine learning; data privacy; deep learning; convolutional neural network; neural network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hesamifard, E. (2020). Privacy Preserving Machine Learning as a Service. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc1703277/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hesamifard, Ehsan. “Privacy Preserving Machine Learning as a Service.” 2020. Thesis, University of North Texas. Accessed January 20, 2021. https://digital.library.unt.edu/ark:/67531/metadc1703277/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hesamifard, Ehsan. “Privacy Preserving Machine Learning as a Service.” 2020. Web. 20 Jan 2021.

Vancouver:

Hesamifard E. Privacy Preserving Machine Learning as a Service. [Internet] [Thesis]. University of North Texas; 2020. [cited 2021 Jan 20]. Available from: https://digital.library.unt.edu/ark:/67531/metadc1703277/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hesamifard E. Privacy Preserving Machine Learning as a Service. [Thesis]. University of North Texas; 2020. Available from: https://digital.library.unt.edu/ark:/67531/metadc1703277/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


NSYSU

28. Wu, Pei-Hsuan. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.

Degree: Master, Computer Science and Engineering, 2018, NSYSU

 Deep Neural Networks (DNN) widely used in computer vision applications have superior performance in image classification and object detection. However, the huge amount of data… (more)

Subjects/Keywords: CNN hardware accelerator; deep neural network (DNN); convolutional neural network (CNN); machine learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wu, P. (2018). Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wu, Pei-Hsuan. “Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.” 2018. Thesis, NSYSU. Accessed January 20, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wu, Pei-Hsuan. “Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.” 2018. Web. 20 Jan 2021.

Vancouver:

Wu P. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 Jan 20]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wu P. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Georgia Southern University

29. Islam, Mohammad Anwarul. Reduced Dataset Neural Network Model for Manuscript Character Recognition.

Degree: MSin Mathematics (M.S.), Department of Mathematical Sciences, 2020, Georgia Southern University

  The automatic character recognition task has been of practical interest for a long time. Nowadays, there are well-established technologies and software to perform character… (more)

Subjects/Keywords: Neural network; Convolution; Convolutional neural network; Resampling.; Other Applied Mathematics; Other Mathematics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Islam, M. A. (2020). Reduced Dataset Neural Network Model for Manuscript Character Recognition. (Masters Thesis). Georgia Southern University. Retrieved from https://digitalcommons.georgiasouthern.edu/etd/2138

Chicago Manual of Style (16th Edition):

Islam, Mohammad Anwarul. “Reduced Dataset Neural Network Model for Manuscript Character Recognition.” 2020. Masters Thesis, Georgia Southern University. Accessed January 20, 2021. https://digitalcommons.georgiasouthern.edu/etd/2138.

MLA Handbook (7th Edition):

Islam, Mohammad Anwarul. “Reduced Dataset Neural Network Model for Manuscript Character Recognition.” 2020. Web. 20 Jan 2021.

Vancouver:

Islam MA. Reduced Dataset Neural Network Model for Manuscript Character Recognition. [Internet] [Masters thesis]. Georgia Southern University; 2020. [cited 2021 Jan 20]. Available from: https://digitalcommons.georgiasouthern.edu/etd/2138.

Council of Science Editors:

Islam MA. Reduced Dataset Neural Network Model for Manuscript Character Recognition. [Masters Thesis]. Georgia Southern University; 2020. Available from: https://digitalcommons.georgiasouthern.edu/etd/2138


University of Bridgeport

30. Hassan, Abdalraouf. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .

Degree: 2018, University of Bridgeport

 The evolution of the social media and the e-commerce sites produces a massive amount of unstructured text data on the internet. Thus, there is a… (more)

Subjects/Keywords: Convolutional neural network; Deep learning; Machine learning; Natural language processing; Recurrent neural network; Sentiment analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hassan, A. (2018). Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks . (Thesis). University of Bridgeport. Retrieved from https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hassan, Abdalraouf. “Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .” 2018. Thesis, University of Bridgeport. Accessed January 20, 2021. https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hassan, Abdalraouf. “Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .” 2018. Web. 20 Jan 2021.

Vancouver:

Hassan A. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks . [Internet] [Thesis]. University of Bridgeport; 2018. [cited 2021 Jan 20]. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hassan A. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks . [Thesis]. University of Bridgeport; 2018. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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