subject:(Recurrent Neural Network)

Texas A&M University

1. Fan, David Dawei. Backpropagation for Continuous Theta Neuron Networks.

Degree: MS, Electrical Engineering, 2015, Texas A&M University

URL: http://hdl.handle.net/1969.1/186998

► The Theta neuron model is a spiking neuron model which, unlike traditional Leaky-Integrate-and-Fire neurons, can model spike latencies, threshold adaptation, bistability of resting and tonic… (more)

Subjects/Keywords: Spiking Neural Network; Backpropagation; Recurrent Neural Network; Neural Network; Theta Neuron

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APA (6^th Edition):

Fan, D. D. (2015). Backpropagation for Continuous Theta Neuron Networks. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/186998

Chicago Manual of Style (16^th Edition):

Fan, David Dawei. “Backpropagation for Continuous Theta Neuron Networks.” 2015. Masters Thesis, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/186998.

MLA Handbook (7^th Edition):

Fan, David Dawei. “Backpropagation for Continuous Theta Neuron Networks.” 2015. Web. 07 Mar 2021.

Vancouver:

Fan DD. Backpropagation for Continuous Theta Neuron Networks. [Internet] [Masters thesis]. Texas A&M University; 2015. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/186998.

Council of Science Editors:

Fan DD. Backpropagation for Continuous Theta Neuron Networks. [Masters Thesis]. Texas A&M University; 2015. Available from: http://hdl.handle.net/1969.1/186998

Penn State University

2. Lin, Tao. A DATA TRIAGE RETRIEVAL SYSTEM FOR CYBER SECURITY OPERATIONS CENTER.

Degree: 2018, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/14787txl78

► Triage analysis is a fundamental stage in cyber operations in Security Operations Centers (SOCs). The massive data sources generate great demands on cyber security analysts'… (more)

Subjects/Keywords: Recurrent Neural Network; Machine Learning; Retrieval; Security

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APA (6^th Edition):

Lin, T. (2018). A DATA TRIAGE RETRIEVAL SYSTEM FOR CYBER SECURITY OPERATIONS CENTER. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14787txl78

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

Chicago Manual of Style (16^th Edition):

Lin, Tao. “A DATA TRIAGE RETRIEVAL SYSTEM FOR CYBER SECURITY OPERATIONS CENTER.” 2018. Thesis, Penn State University. Accessed March 07, 2021. https://submit-etda.libraries.psu.edu/catalog/14787txl78.

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

MLA Handbook (7^th Edition):

Lin, Tao. “A DATA TRIAGE RETRIEVAL SYSTEM FOR CYBER SECURITY OPERATIONS CENTER.” 2018. Web. 07 Mar 2021.

Vancouver:

Lin T. A DATA TRIAGE RETRIEVAL SYSTEM FOR CYBER SECURITY OPERATIONS CENTER. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Mar 07]. Available from: https://submit-etda.libraries.psu.edu/catalog/14787txl78.

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

Council of Science Editors:

Lin T. A DATA TRIAGE RETRIEVAL SYSTEM FOR CYBER SECURITY OPERATIONS CENTER. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/14787txl78

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

Mid Sweden University

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

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

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322

► 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 (6^th 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 (16^th Edition):

Wang, Xutao. “Chinese Text Classification Based On Deep Learning.” 2018. Thesis, Mid Sweden University. Accessed March 07, 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 (7^th Edition):

Wang, Xutao. “Chinese Text Classification Based On Deep Learning.” 2018. Web. 07 Mar 2021.

Vancouver:

Wang X. Chinese Text Classification Based On Deep Learning. [Internet] [Thesis]. Mid Sweden University; 2018. [cited 2021 Mar 07]. 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

York University

4. Bidgoli, Rohollah Soltani. Higher Order Recurrent Neural Network for Language Modeling.

Degree: MSc -MS, Computer Science, 2016, York University

URL: http://hdl.handle.net/10315/32337

► In this thesis, we study novel neural network structures to better model long term dependency in sequential data. We propose to use more memory units… (more)

Subjects/Keywords: Computer science; Machine Learning; Deep Learning; Neural Network; Recurrent Neural Network; Language Modeling; Higher Order Recurrent Neural Network

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APA (6^th Edition):

Bidgoli, R. S. (2016). Higher Order Recurrent Neural Network for Language Modeling. (Masters Thesis). York University. Retrieved from http://hdl.handle.net/10315/32337

Chicago Manual of Style (16^th Edition):

Bidgoli, Rohollah Soltani. “Higher Order Recurrent Neural Network for Language Modeling.” 2016. Masters Thesis, York University. Accessed March 07, 2021. http://hdl.handle.net/10315/32337.

MLA Handbook (7^th Edition):

Bidgoli, Rohollah Soltani. “Higher Order Recurrent Neural Network for Language Modeling.” 2016. Web. 07 Mar 2021.

Vancouver:

Bidgoli RS. Higher Order Recurrent Neural Network for Language Modeling. [Internet] [Masters thesis]. York University; 2016. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10315/32337.

Council of Science Editors:

Bidgoli RS. Higher Order Recurrent Neural Network for Language Modeling. [Masters Thesis]. York University; 2016. Available from: http://hdl.handle.net/10315/32337

Tampere University

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

Degree: 2019, Tampere University

URL: https://trepo.tuni.fi//handle/10024/116148

► 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)

▼ 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 nowadays. In this thesis two datasets are used. The first dataset is a hotel review dataset(TripAdvisor dataset) that collects the hotel reviews from the TripAdvisor website using Python Scrapy framework. The preprocessing steps are then applied to clean the dataset. A record in the TripAdvisor dataset consists of the text review and corresponding sentiment score. There are 5 sentimental labels: very negative, negative, neutral, positive, and very positive. The second dataset is the Stanford Sentiment Treebank (SST) dataset. It is a public and common dataset for sentiment classification. Text Convolutional Neural Network (Text-CNN), Very Deep Convolutional Neural Network (VDCNN), and Bidirectional Long Short Term Memory neural network (BiLSTM) were chosen as different methods for the evaluation in the experiments. The Text-CNN was the first work to apply convolutional neural network architecture for the text classification. The VD-CNN applied deep convolutional layers, with up to 29 layers, to perform the text classification. The BiLSTM exploited the bidirectional recurrent neural network with long short term memory cell mechanism. On the other hand, word embedding techniques are also considered as an important factor in sentiment classification. Thus, in this thesis, GloVe and FastText techniques were used to investigate the effect of word embedding initialization on the dataset. GloVe is a unsupervised word embedding learning algorithm. FastText uses shallow neural network to generate word vectors and it has fast convergence speed for training and high speed for inference. The experiment was implemented using PyTorch framework. It shows that the BiLSTM with GloVe as the word vector initialization achieved the highest accuracy 73.73% while the VD-CNN with FastText had the lowest accuracy 71.95% on the TripAdvisor dataset. The BiLSTM model achieved 0.68 F1-score while the VD-CNN model obtained 0.67 F1-score on the TripAdvisor dataset. On the SST dataset, BiLSTM with GloVe again achieved the highest accuracy 36.35% and 0.35 F1-score. The VD-CNN model with GloVe had the worst evaluation result in terms of accuracy and F1-score. The Text-CNN model performed better than the VD-CNN model even thought the VD-CNN model has more layers in most cases. By analyzing the misclassified reviews in the TripAdvisor dataset from the three deep neural networks, it is shown that the hotel reviews with more contradictory sentimental words were more prone to misclassification than other hotel reviews.

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

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APA (6^th 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 (16^th Edition):

Zhou, Yi. “Sentiment classification with deep neural networks .” 2019. Masters Thesis, Tampere University. Accessed March 07, 2021. https://trepo.tuni.fi//handle/10024/116148.

MLA Handbook (7^th Edition):

Zhou, Yi. “Sentiment classification with deep neural networks .” 2019. Web. 07 Mar 2021.

Vancouver:

Zhou Y. Sentiment classification with deep neural networks . [Internet] [Masters thesis]. Tampere University; 2019. [cited 2021 Mar 07]. 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

University of Illinois – Urbana-Champaign

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

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

URL: http://hdl.handle.net/2142/90724

► 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)

▼ 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 Neural Networks are representative examples of DNNs in the setting of supervised learning. The key ingredients in the successful development of DNN-based models include but not limited to task-specific designs of network architecture, discriminative feature representation learning and scalable training algorithms. In this thesis, we describe a collection of DNN-based models to address three challenging computer vision tasks, namely large-scale visual recognition, image semantic segmentation and automatic photo adjustment. For each task, the network architecture is carefully designed on the basis of the nature of the task. For large-scale visual recognition, we design a hierarchical Convolutional Neural Network to fully exploit a semantic hierarchy among visual categories. The resulting model can be deemed as an ensemble of specialized classifiers. We improve state-of-the-art results at an affordable increase of the computational cost. For image semantic segmentation, we integrate convolutional layers with novel spatially recurrent layers for incorporating global contexts into the prediction process. The resulting hybrid network is capable of learning improved feature representations, which lead to more accurate region recognition and boundary localization. Combined with a post-processing step involving a fully-connected conditional random field, our hybrid network achieves new state-of-the-art results on a large benchmark dataset. For automatic photo adjustment, we take a data-driven approach to learn the underlying color transforms from manually enhanced examples. We formulate the learning problem as a regression task, which can be approached with a Multi-Layer Perceptron network. We concatenate global contextual features, local contextual features as well as pixel-wise features and feed them into the deep network. State-of-the-art results are achieved on datasets with both global and local stylized adjustments. Advisors/Committee Members: Yu, Yizhou (advisor), Lazebnik, Svetlana (Committee Chair), Forsyth, David (committee member), Cao, Liangliang (committee member).

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 (6^th 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 (16^th Edition):

Yan, Zhicheng. “Image recognition, semantic segmentation and photo adjustment using deep neural networks.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed March 07, 2021. http://hdl.handle.net/2142/90724.

MLA Handbook (7^th Edition):

Yan, Zhicheng. “Image recognition, semantic segmentation and photo adjustment using deep neural networks.” 2016. Web. 07 Mar 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 Mar 07]. 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

Carnegie Mellon University

7. Le, Ngan Thi Hoang. Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling.

Degree: 2018, Carnegie Mellon University

URL: http://repository.cmu.edu/dissertations/1166

► Semantic labeling is becoming more and more popular among researchers in computer vision and machine learning. Many applications, such as autonomous driving, tracking, indoor navigation,… (more)

▼ Semantic labeling is becoming more and more popular among researchers in computer vision and machine learning. Many applications, such as autonomous driving, tracking, indoor navigation, augmented reality systems, semantic searching, medical imaging are on the rise, requiring more accurate and efficient segmentation mechanisms. In recent years, deep learning approaches based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have dramatically emerged as the dominant paradigm for solving many problems in computer vision and machine learning. The main focus of this thesis is to investigate robust approaches that can tackle the challenging semantic labeling tasks including semantic instance segmentation and scene understanding. In the first approach, we convert the classic variational Level Set method to a learnable deep framework by proposing a novel definition of contour evolution named Recurrent Level Set (RLS). The proposed RLS employs Gated Recurrent Units to solve the energy minimization of a variational Level Set functional. The curve deformation processes in RLS is formulated as a hidden state evolution procedure and is updated by minimizing an energy functional composed of fitting forces and contour length. We show that by sharing the convolutional features in a fully end-to-end trainable framework, RLS is able to be extended to Contextual Recurrent Level Set (CRLS) Networks to address semantic segmentation in the wild problem. The experimental results have shown that our proposed RLS improves both computational time and segmentation accuracy against the classic variational Level Set-based methods whereas the fully end-to-end system CRLS achieves competitive performance compared to the state-of-the-art semantic segmentation approaches on PAS CAL VOC 2012 and MS COCO 2014 databases. The second proposed approach, Contextual Recurrent Residual Networks (CRRN), inherits all the merits of sequence learning information and residual learning in order to simultaneously model long-range contextual infor- mation and learn powerful visual representation within a single deep network. Our proposed CRRN deep network consists of three parts corresponding to sequential input data, sequential output data and hidden state as in a recurrent network. Each unit in hidden state is designed as a combination of two components: a context-based component via sequence learning and a visualbased component via residual learning. That means, each hidden unit in our proposed CRRN simultaneously (1) learns long-range contextual dependencies via a context-based component. The relationship between the current unit and the previous units is performed as sequential information under an undirected cyclic graph (UCG) and (2) provides powerful encoded visual representation via residual component which contains blocks of convolution and/or batch normalization layers equipped with an identity skip connection. Furthermore, unlike previous scene labeling approaches [1, 2, 3], our method is not only able to exploit the…

Subjects/Keywords: Gated Recurrent Unit; Level Set; Recurrent Neural Networks; Residual Network; Scene Labeling; Semantic Instance Segmentation

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APA (6^th Edition):

Le, N. T. H. (2018). Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/1166

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

Chicago Manual of Style (16^th Edition):

Le, Ngan Thi Hoang. “Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling.” 2018. Thesis, Carnegie Mellon University. Accessed March 07, 2021. http://repository.cmu.edu/dissertations/1166.

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

MLA Handbook (7^th Edition):

Le, Ngan Thi Hoang. “Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling.” 2018. Web. 07 Mar 2021.

Vancouver:

Le NTH. Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling. [Internet] [Thesis]. Carnegie Mellon University; 2018. [cited 2021 Mar 07]. Available from: http://repository.cmu.edu/dissertations/1166.

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

Council of Science Editors:

Le NTH. Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling. [Thesis]. Carnegie Mellon University; 2018. Available from: http://repository.cmu.edu/dissertations/1166

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

8. Sarika, Pawan Kumar. Comparing LSTM and GRU for Multiclass Sentiment Analysis of Movie Reviews.

Degree: 2020, , Faculty of Computing

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20213

► Today, we are living in a data-driven world. Due to a surge in data generation, there is a need for efficient and accurate techniques… (more)

Subjects/Keywords: Gated recurrent unit; Multiclass classification; Movie reviews; Sentiment Analysis; Recurrent neural network; Computer Systems; Datorsystem

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APA (6^th Edition):

Sarika, P. K. (2020). Comparing LSTM and GRU for Multiclass Sentiment Analysis of Movie Reviews. (Thesis). , Faculty of Computing. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20213

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

Chicago Manual of Style (16^th Edition):

Sarika, Pawan Kumar. “Comparing LSTM and GRU for Multiclass Sentiment Analysis of Movie Reviews.” 2020. Thesis, , Faculty of Computing. Accessed March 07, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20213.

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

MLA Handbook (7^th Edition):

Sarika, Pawan Kumar. “Comparing LSTM and GRU for Multiclass Sentiment Analysis of Movie Reviews.” 2020. Web. 07 Mar 2021.

Vancouver:

Sarika PK. Comparing LSTM and GRU for Multiclass Sentiment Analysis of Movie Reviews. [Internet] [Thesis]. , Faculty of Computing; 2020. [cited 2021 Mar 07]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20213.

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

Council of Science Editors:

Sarika PK. Comparing LSTM and GRU for Multiclass Sentiment Analysis of Movie Reviews. [Thesis]. , Faculty of Computing; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20213

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

Addis Ababa University

9. Tewodros, Kibatu. ecurrent Neural Network-based Base Transceiver Station Power System Failure Prediction .

Degree: 2019, Addis Ababa University

URL: http://etd.aau.edu.et/handle/123456789/21111

► Global network infrastructures are increasing with the development of new technologies and growth in Internet traffic. As network infrastructures increases, maintaining and monitoring them will… (more)

▼ Global network infrastructures are increasing with the development of new technologies and growth in Internet traffic. As network infrastructures increases, maintaining and monitoring them will become very challenging since thousands of alarms are generated every day. Clearing those alarms by corrective maintenance activities require considerable effort and resources (car, labor, and budget). In mobile networks, a Base Transceiver Station (BTS) is one key infrastructure element performing the task of connecting customer equipment with the cellular network. BTS services may be interrupted due to transmission, optical fiber cut, power system failure, natural disaster or many more. In the case of Ethio Telecom (ET), the sole telecom service provider in Ethiopia, power system failure takes the biggest share for interruption of BTS services. Minimizing power system failure will reduce downtime of the BTS thereby, guarantee customer satisfaction and maximize revenue. Recently, machine learning algorithms are used to predict failure in various areas like power distribution, hydropower generation plants, solar power generation plants, high voltage transmission grid and many more. This thesis investigates predicting BTSs power system failure using a recurrent neural network (RNN) types namely, long short term memory (LSTM) and gated recurrent unit (GRU) with linear and sigmoid activation function applied for the output. In parallel, the prediction performance of LSTM and GRU has been compared. Data collected from five BTS sites for twenty weeks of observations are used to train and test the model. The data are prepared with two different data arrangements, which are a single site and multiple sites. The relevance of using different data size is, to check the impact of increasing data size with different arrangements on the prediction results. Mean squared error (MSE) and number of epoch are used to evaluate the performance of the models with different configurations. Based on the results found, GRU using sigmoid activation function with feature reduction achieves better performance than LSTM. In addition, both LSTM and GRU can be used for predicting BTS power system failure. Advisors/Committee Members: Dereje, Hailemariam (PhD) (advisor).

Subjects/Keywords: Base Transceiver Station; Gated Recurrent Unit; Long Short Term Memory; Recurrent Neural Network

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APA (6^th Edition):

Tewodros, K. (2019). ecurrent Neural Network-based Base Transceiver Station Power System Failure Prediction . (Thesis). Addis Ababa University. Retrieved from http://etd.aau.edu.et/handle/123456789/21111

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

Chicago Manual of Style (16^th Edition):

Tewodros, Kibatu. “ecurrent Neural Network-based Base Transceiver Station Power System Failure Prediction .” 2019. Thesis, Addis Ababa University. Accessed March 07, 2021. http://etd.aau.edu.et/handle/123456789/21111.

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

MLA Handbook (7^th Edition):

Tewodros, Kibatu. “ecurrent Neural Network-based Base Transceiver Station Power System Failure Prediction .” 2019. Web. 07 Mar 2021.

Vancouver:

Tewodros K. ecurrent Neural Network-based Base Transceiver Station Power System Failure Prediction . [Internet] [Thesis]. Addis Ababa University; 2019. [cited 2021 Mar 07]. Available from: http://etd.aau.edu.et/handle/123456789/21111.

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

Council of Science Editors:

Tewodros K. ecurrent Neural Network-based Base Transceiver Station Power System Failure Prediction . [Thesis]. Addis Ababa University; 2019. Available from: http://etd.aau.edu.et/handle/123456789/21111

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

University of California – Berkeley

10. Thanapirom, Chayut. Neural Representation Learning with Denoising Autoencoder Framework.

Degree: Physics, 2016, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/0hm6p6s5

► Understanding of how the brain works and how it can solve difficult problems like image recognition is very important, especially for the progress in developing… (more)

Subjects/Keywords: Biophysics; Neurosciences; Attractor Network; Denoising Autoencoder; Grid Cells; Neural Representation; Recurrent Neural Network; Sparse Coding

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APA (6^th Edition):

Thanapirom, C. (2016). Neural Representation Learning with Denoising Autoencoder Framework. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/0hm6p6s5

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

Chicago Manual of Style (16^th Edition):

Thanapirom, Chayut. “Neural Representation Learning with Denoising Autoencoder Framework.” 2016. Thesis, University of California – Berkeley. Accessed March 07, 2021. http://www.escholarship.org/uc/item/0hm6p6s5.

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

MLA Handbook (7^th Edition):

Thanapirom, Chayut. “Neural Representation Learning with Denoising Autoencoder Framework.” 2016. Web. 07 Mar 2021.

Vancouver:

Thanapirom C. Neural Representation Learning with Denoising Autoencoder Framework. [Internet] [Thesis]. University of California – Berkeley; 2016. [cited 2021 Mar 07]. Available from: http://www.escholarship.org/uc/item/0hm6p6s5.

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

Council of Science Editors:

Thanapirom C. Neural Representation Learning with Denoising Autoencoder Framework. [Thesis]. University of California – Berkeley; 2016. Available from: http://www.escholarship.org/uc/item/0hm6p6s5

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

University of Bridgeport

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

Degree: 2018, University of Bridgeport

URL: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274

► 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)

▼ 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 high demand to develop an intelligent model to process it and extract a useful information from it. Text classification plays an important task for many Natural Language Processing (NLP) applications such as, sentiment analysis, web search, spam filtering, and information retrieval, in which we need to assign single or multiple predefined categories to a sequence of text. In Neural Network Language Models learning long-term dependencies with gradient descent is difficult due to the vanishing gradient problem. Recently researchers started to increase the depth of the network in order to overcome the limitations of the existing techniques. However, increasing the depth of the network means increasing the number of the parameters, which makes the network computationally expensive, and more prone to overfitting. Furthermore, NLP systems traditionally treat words as discrete atomic symbols; the model can leverage small amounts of information regarding the relationship between the individual symbols. In recent years, deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been applied to language modeling with comparative, remarkable results. CNNs are a noble approach to extract higher-level features invariant to local translation. However, this method requires the stacking of multiple convolutional layers in order to capture long-term dependencies because of the locality of the convolutional and pooling layers. In this dissertation, we introduce a joint CNN-RNN framework to overcome the problems in the existing deep learning models. Briefly, we applied an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning network, then the pre-trained parameters of the network are used to initialize the model. At a final stage, the proposed framework combines former information with a set of feature maps learned by a convolutional layer with long-term dependencies learned via Long-Short-Term Memory (LSTM). Empirically, we show that our approach, with slight hyperparameter tuning and static vectors, achieves outstanding results on multiple sentiment analysis benchmarks. Our approach outperforms several existing approaches in term of accuracy; our results are also competitive with the state-of-the-art results on the Stanford Large Movie Review (IMDB) dataset, and the Stanford Sentiment Treebank (SSTb) dataset. Our approach has a significant role in reducing the number of parameters and constructing the convolutional layer followed by the recurrent layer with no pooling layers. Our results show that we were able to reduce the loss of detailed, local information and capture long-term dependencies with an efficient framework that has fewer parameters and a high level of performance.

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

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APA (6^th 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 (16^th Edition):

Hassan, Abdalraouf. “Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .” 2018. Thesis, University of Bridgeport. Accessed March 07, 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 (7^th Edition):

Hassan, Abdalraouf. “Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .” 2018. Web. 07 Mar 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 Mar 07]. 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

Victoria University of Wellington

12. Chandra, Rohitash. Problem Decomposition and Adaptation in Cooperative Neuro-Evolution.

Degree: 2012, Victoria University of Wellington

URL: http://hdl.handle.net/10063/2110

► One way to train neural networks is to use evolutionary algorithms such as cooperative coevolution - a method that decomposes the network's learnable parameters into… (more)

▼ One way to train neural networks is to use evolutionary algorithms such as cooperative coevolution - a method that decomposes the network's learnable parameters into subsets, called subcomponents. Cooperative coevolution gains advantage over other methods by evolving particular subcomponents independently from the rest of the network. Its success depends strongly on how the problem decomposition is carried out. This thesis suggests new forms of problem decomposition, based on a novel and intuitive choice of modularity, and examines in detail at what stage and to what extent the different decomposition methods should be used. The new methods are evaluated by training feedforward networks to solve pattern classification tasks, and by training recurrent networks to solve grammatical inference problems. Efficient problem decomposition methods group interacting variables into the same subcomponents. We examine the methods from the literature and provide an analysis of the nature of the neural network optimization problem in terms of interacting variables. We then present a novel problem decomposition method that groups interacting variables and that can be generalized to neural networks with more than a single hidden layer. We then incorporate local search into cooperative neuro-evolution. We present a memetic cooperative coevolution method that takes into account the cost of employing local search across several sub-populations. The optimisation process changes during evolution in terms of diversity and interacting variables. To address this, we examine the adaptation of the problem decomposition method during the evolutionary process. The results in this thesis show that the proposed methods improve performance in terms of optimization time, scalability and robustness. As a further test, we apply the problem decomposition and adaptive cooperative coevolution methods for training recurrent neural networks on chaotic time series problems. The proposed methods show better performance in terms of accuracy and robustness. Advisors/Committee Members: Frean, Marcus, Zhang, Mengjie.

Subjects/Keywords: Neural networks; Cooperative coevolution; Recurrent network; Co-operative co-evolution

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APA (6^th Edition):

Chandra, R. (2012). Problem Decomposition and Adaptation in Cooperative Neuro-Evolution. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/2110

Chicago Manual of Style (16^th Edition):

Chandra, Rohitash. “Problem Decomposition and Adaptation in Cooperative Neuro-Evolution.” 2012. Doctoral Dissertation, Victoria University of Wellington. Accessed March 07, 2021. http://hdl.handle.net/10063/2110.

MLA Handbook (7^th Edition):

Chandra, Rohitash. “Problem Decomposition and Adaptation in Cooperative Neuro-Evolution.” 2012. Web. 07 Mar 2021.

Vancouver:

Chandra R. Problem Decomposition and Adaptation in Cooperative Neuro-Evolution. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2012. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10063/2110.

Council of Science Editors:

Chandra R. Problem Decomposition and Adaptation in Cooperative Neuro-Evolution. [Doctoral Dissertation]. Victoria University of Wellington; 2012. Available from: http://hdl.handle.net/10063/2110

Georgia Tech

13. Chen, Hua. Single channel speech enhancement with residual learning and recurrent network.

Degree: MS, Electrical and Computer Engineering, 2020, Georgia Tech

URL: http://hdl.handle.net/1853/62839

► For speech enhancement tasks, non-stationary noise such as babble noise is much harder to suppress than stationary noise. In low SNR environment, it is even… (more)

Subjects/Keywords: Speech enhancement; Machine learning; ResNet; Convolutional recurrent neural network

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APA (6^th Edition):

Chen, H. (2020). Single channel speech enhancement with residual learning and recurrent network. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62839

Chicago Manual of Style (16^th Edition):

Chen, Hua. “Single channel speech enhancement with residual learning and recurrent network.” 2020. Masters Thesis, Georgia Tech. Accessed March 07, 2021. http://hdl.handle.net/1853/62839.

MLA Handbook (7^th Edition):

Chen, Hua. “Single channel speech enhancement with residual learning and recurrent network.” 2020. Web. 07 Mar 2021.

Vancouver:

Chen H. Single channel speech enhancement with residual learning and recurrent network. [Internet] [Masters thesis]. Georgia Tech; 2020. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1853/62839.

Council of Science Editors:

Chen H. Single channel speech enhancement with residual learning and recurrent network. [Masters Thesis]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62839

UCLA

14. Li, Siyuan. Application of Recurrent Neural Networks In Toxic Comment Classification.

Degree: Statistics, 2018, UCLA

URL: http://www.escholarship.org/uc/item/5f87h061

► Moderators of online discussion forums often struggle with controlling extremist comments on their platforms. To help provide an efficient and accurate tool to detect online… (more)

Subjects/Keywords: Statistics; classification; natural language processing; recurrent neural network; word2vec

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APA (6^th Edition):

Li, S. (2018). Application of Recurrent Neural Networks In Toxic Comment Classification. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/5f87h061

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

Chicago Manual of Style (16^th Edition):

Li, Siyuan. “Application of Recurrent Neural Networks In Toxic Comment Classification.” 2018. Thesis, UCLA. Accessed March 07, 2021. http://www.escholarship.org/uc/item/5f87h061.

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

MLA Handbook (7^th Edition):

Li, Siyuan. “Application of Recurrent Neural Networks In Toxic Comment Classification.” 2018. Web. 07 Mar 2021.

Vancouver:

Li S. Application of Recurrent Neural Networks In Toxic Comment Classification. [Internet] [Thesis]. UCLA; 2018. [cited 2021 Mar 07]. Available from: http://www.escholarship.org/uc/item/5f87h061.

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

Council of Science Editors:

Li S. Application of Recurrent Neural Networks In Toxic Comment Classification. [Thesis]. UCLA; 2018. Available from: http://www.escholarship.org/uc/item/5f87h061

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

Delft University of Technology

15. Mulder, Boris (author). Latent Space Modelling of Unsteady Flow Subdomains: Thesis Report.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:fbf93d6e-211f-4b92-a057-956d694db315

►

Very complex flows can be expensive to compute using current CFD techniques. In this thesis, models based on deep learning were used to replace certain… (more)

Subjects/Keywords: Aerodynamics; CFD; Deep Learning; Latent Space; Autoencoder; Recurrent Neural Network

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APA (6^th Edition):

Mulder, B. (. (2019). Latent Space Modelling of Unsteady Flow Subdomains: Thesis Report. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:fbf93d6e-211f-4b92-a057-956d694db315

Chicago Manual of Style (16^th Edition):

Mulder, Boris (author). “Latent Space Modelling of Unsteady Flow Subdomains: Thesis Report.” 2019. Masters Thesis, Delft University of Technology. Accessed March 07, 2021. http://resolver.tudelft.nl/uuid:fbf93d6e-211f-4b92-a057-956d694db315.

MLA Handbook (7^th Edition):

Mulder, Boris (author). “Latent Space Modelling of Unsteady Flow Subdomains: Thesis Report.” 2019. Web. 07 Mar 2021.

Vancouver:

Mulder B(. Latent Space Modelling of Unsteady Flow Subdomains: Thesis Report. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 07]. Available from: http://resolver.tudelft.nl/uuid:fbf93d6e-211f-4b92-a057-956d694db315.

Council of Science Editors:

Mulder B(. Latent Space Modelling of Unsteady Flow Subdomains: Thesis Report. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:fbf93d6e-211f-4b92-a057-956d694db315

Delft University of Technology

16. Samad, Azlaan Mustafa (author). Multi Agent Deep Recurrent Q-Learning for Different Traffic Demands.

Degree: 2020, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:84d20f53-3be7-4e85-8588-b92b962b32fe

► In today’s scenario due to rapid urbanisation there has been a shift of population from rural to urban areas especially in developing countries in search… (more)

Subjects/Keywords: Deep Reinforcement Learning; Deep Q-Network; Recurrent Neural Networks

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APA (6^th Edition):

Samad, A. M. (. (2020). Multi Agent Deep Recurrent Q-Learning for Different Traffic Demands. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:84d20f53-3be7-4e85-8588-b92b962b32fe

Chicago Manual of Style (16^th Edition):

Samad, Azlaan Mustafa (author). “Multi Agent Deep Recurrent Q-Learning for Different Traffic Demands.” 2020. Masters Thesis, Delft University of Technology. Accessed March 07, 2021. http://resolver.tudelft.nl/uuid:84d20f53-3be7-4e85-8588-b92b962b32fe.

MLA Handbook (7^th Edition):

Samad, Azlaan Mustafa (author). “Multi Agent Deep Recurrent Q-Learning for Different Traffic Demands.” 2020. Web. 07 Mar 2021.

Vancouver:

Samad AM(. Multi Agent Deep Recurrent Q-Learning for Different Traffic Demands. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Mar 07]. Available from: http://resolver.tudelft.nl/uuid:84d20f53-3be7-4e85-8588-b92b962b32fe.

Council of Science Editors:

Samad AM(. Multi Agent Deep Recurrent Q-Learning for Different Traffic Demands. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:84d20f53-3be7-4e85-8588-b92b962b32fe

University of Texas – Austin

17. Zhong, Shijing. A review on constrained recurrent sparse auto-encoder.

Degree: MSin Computational Science, Engineering, and Mathematics, Computational Science, Engineering, and Mathematics, 2020, University of Texas – Austin

URL: http://dx.doi.org/10.26153/tsw/10925

► Sparse Dictionary Learning generates a sparse representation for images and signals along with a generalized learned dictionary. We examine closely to the constrained recurrent sparse… (more)

Subjects/Keywords: Convolutional dictionary learning; FISTA; Recurrent neural network; Encoder-decoder

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APA (6^th Edition):

Zhong, S. (2020). A review on constrained recurrent sparse auto-encoder. (Masters Thesis). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/10925

Chicago Manual of Style (16^th Edition):

Zhong, Shijing. “A review on constrained recurrent sparse auto-encoder.” 2020. Masters Thesis, University of Texas – Austin. Accessed March 07, 2021. http://dx.doi.org/10.26153/tsw/10925.

MLA Handbook (7^th Edition):

Zhong, Shijing. “A review on constrained recurrent sparse auto-encoder.” 2020. Web. 07 Mar 2021.

Vancouver:

Zhong S. A review on constrained recurrent sparse auto-encoder. [Internet] [Masters thesis]. University of Texas – Austin; 2020. [cited 2021 Mar 07]. Available from: http://dx.doi.org/10.26153/tsw/10925.

Council of Science Editors:

Zhong S. A review on constrained recurrent sparse auto-encoder. [Masters Thesis]. University of Texas – Austin; 2020. Available from: http://dx.doi.org/10.26153/tsw/10925

University of New Mexico

18. Goudarzi, Alireza. Theory and Practice of Computing with Excitable Dynamics.

Degree: Department of Computer Science, 2016, University of New Mexico

URL: https://digitalrepository.unm.edu/cs_etds/81

► Reservoir computing (RC) is a promising paradigm for time series processing. In this paradigm, the desired output is computed by combining measurements of an… (more)

▼ Reservoir computing (RC) is a promising paradigm for time series processing. In this paradigm, the desired output is computed by combining measurements of an excitable system that responds to time-dependent exogenous stimuli. The excitable system is called a reservoir and measurements of its state are combined using a readout layer to produce a target output. The power of RC is attributed to an emergent short-term memory in dynamical systems and has been analyzed mathematically for both linear and nonlinear dynamical systems. The theory of RC treats only the macroscopic properties of the reservoir, without reference to the underlying medium it is made of. As a result, RC is particularly attractive for building computational devices using emerging technologies whose structure is not exactly controllable, such as self-assembled nanoscale circuits. RC has lacked a formal framework for performance analysis and prediction that goes beyond memory properties. To provide such a framework, here a mathematical theory of memory and information processing in ordered and disordered linear dynamical systems is developed. This theory analyzes the optimal readout layer for a given task. The focus of the theory is a standard model of RC, the echo state network (ESN). An ESN consists of a fixed recurrent neural network that is driven by an external signal. The dynamics of the network is then combined linearly with readout weights to produce the desired output. The readout weights are calculated using linear regression. Using an analysis of regression equations, the readout weights can be calculated using only the statistical properties of the reservoir dynamics, the input signal, and the desired output. The readout layer weights can be calculated from a priori knowledge of the desired function to be computed and the weight matrix of the reservoir. This formulation explicitly depends on the input weights, the reservoir weights, and the statistics of the target function. This formulation is used to bound the expected error of the system for a given target function. The effects of input-output correlation and complex network structure in the reservoir on the computational performance of the system have been mathematically characterized. Far from the chaotic regime, ordered linear networks exhibit a homogeneous decay of memory in different dimensions, which keeps the input history coherent. As disorder is introduced in the structure of the network, memory decay becomes inhomogeneous along different dimensions causing decoherence in the input history, and degradation in task-solving performance. Close to the chaotic regime, the ordered systems show loss of temporal information in the input history, and therefore inability to solve tasks. However, by introducing disorder and therefore heterogeneous decay of memory the temporal information of input history is preserved and the task-solving performance is recovered. Thus for systems at the edge of chaos, disordered structure may enhance temporal information processing. Although the… Advisors/Committee Members: Darko Stefanovic, Christof Teuscher, Lance Williams, Melanie Moses.

Subjects/Keywords: reservoir computing; recurrent neural network; excitable dynamics; dynamical systems; Computer Sciences

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APA (6^th Edition):

Goudarzi, A. (2016). Theory and Practice of Computing with Excitable Dynamics. (Doctoral Dissertation). University of New Mexico. Retrieved from https://digitalrepository.unm.edu/cs_etds/81

Chicago Manual of Style (16^th Edition):

Goudarzi, Alireza. “Theory and Practice of Computing with Excitable Dynamics.” 2016. Doctoral Dissertation, University of New Mexico. Accessed March 07, 2021. https://digitalrepository.unm.edu/cs_etds/81.

MLA Handbook (7^th Edition):

Goudarzi, Alireza. “Theory and Practice of Computing with Excitable Dynamics.” 2016. Web. 07 Mar 2021.

Vancouver:

Goudarzi A. Theory and Practice of Computing with Excitable Dynamics. [Internet] [Doctoral dissertation]. University of New Mexico; 2016. [cited 2021 Mar 07]. Available from: https://digitalrepository.unm.edu/cs_etds/81.

Council of Science Editors:

Goudarzi A. Theory and Practice of Computing with Excitable Dynamics. [Doctoral Dissertation]. University of New Mexico; 2016. Available from: https://digitalrepository.unm.edu/cs_etds/81

University of Waterloo

19. Ruvinov, Igor. Recurrent Neural Network Dual Resistance Control of Multiple Memory Shape Memory Alloys.

Degree: 2018, University of Waterloo

URL: http://hdl.handle.net/10012/13647

► Shape memory alloys (SMAs) are materials with extraordinary thermomechanical properties which have caused numerous engineering advances. NiTi SMAs in particular have been studied for decades… (more)

▼ Shape memory alloys (SMAs) are materials with extraordinary thermomechanical properties which have caused numerous engineering advances. NiTi SMAs in particular have been studied for decades revealing many useful characteristics relative to other SMA compositions. Their application has correspondingly been widespread, seeing use in the robotics, automotive, and aerospace industries, among others. Nevertheless, several limitations inherent to SMAs exist which inhibit their applicability, including their inherent single transformation temperature and their complex hysteretic actuation behaviour. To overcome the former challenge, one method utilizes high energy laser processing to perform localized vaporization of nickel and accurately adjust its transformation temperatures. This method can reliably produce NiTi SMAs with multiple monolithic transformation memories. There have also been attempts to overcome the latter of the aforementioned challenges by designing systems which model NiTi's hysteretic behaviour. When applied to actuators with a single transformation memory, these methods require the use of external sensors for modeling actuators with varying current and load, driving up the cost, weight, and complexity of the actuator. Embedding a second transformation memory with different phase into NiTi actuators can overcome this issue. By measuring electrical resistance across the two phases, sufficient information can be extracted for differentiating events caused by heating from those caused by applied load. The current study examines NiTi wires with two embedded transformation memories and utilizes recurrent neural networks for interpreting the sensed data. The knowledge gained through this study was used to create a recurrent neural network-based model which can accurately estimate the position and force applied to the NiTi actuator without the use of external sensors. The first part of the research focused on obtaining a comprehensive thermomechanical characterization of laser processed and thermomechanically post-processed NiTi wires with two embedded transformation memories, with one memory exhibiting full SME and the second partial PE at room temperature. A second objective of this section was to acquire cycling data from the processed wires which would be used for training the artificial neural networks in the following section of the study. The selected laser processing and post-processing parameters resulted in a transformation temperature increase of 61.5°C and 35.3°C for Af and Ms, respectively, relative to base metal. Furthermore, the post-processing was found to successfully restore the majority of the lost mechanical properties, with the ultimate tensile strength recovered to 84% of its corresponding base metal value. This research resulted in the fabrication of NiTi wires with two distinct embedded transformation memories, exhibiting sufficient mechanical and cyclic properties for the next phase of the research. Once an acceptable amount of NiTi actuation cycling data was acquired, the second…

Subjects/Keywords: Recurrent neural network; Shape memory alloys; Artificial intelligence

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APA (6^th Edition):

Ruvinov, I. (2018). Recurrent Neural Network Dual Resistance Control of Multiple Memory Shape Memory Alloys. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/13647

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

Chicago Manual of Style (16^th Edition):

Ruvinov, Igor. “Recurrent Neural Network Dual Resistance Control of Multiple Memory Shape Memory Alloys.” 2018. Thesis, University of Waterloo. Accessed March 07, 2021. http://hdl.handle.net/10012/13647.

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

MLA Handbook (7^th Edition):

Ruvinov, Igor. “Recurrent Neural Network Dual Resistance Control of Multiple Memory Shape Memory Alloys.” 2018. Web. 07 Mar 2021.

Vancouver:

Ruvinov I. Recurrent Neural Network Dual Resistance Control of Multiple Memory Shape Memory Alloys. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10012/13647.

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

Council of Science Editors:

Ruvinov I. Recurrent Neural Network Dual Resistance Control of Multiple Memory Shape Memory Alloys. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/13647

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

20. Herzfeld, David James. Modeling and Computational Framework for the Specification and Simulation of Large-scale Spiking Neural Networks.

Degree: 2011, Marquette University

URL: https://epublications.marquette.edu/theses_open/102

► Recurrently connected neural networks, in which synaptic connections between neurons can form directed cycles, have been used extensively in the literature to describe various neurophysiological… (more)

▼ Recurrently connected neural networks, in which synaptic connections between neurons can form directed cycles, have been used extensively in the literature to describe various neurophysiological phenomena, such as coordinate transformations during sensorimotor integration. Due to the directed cycles that can exist in recurrent networks, there is no well-known way to a priori specify synaptic weights to elicit neuron spiking responses to stimuli based on available neurophysiology. Using a common mean field assumption, that synaptic inputs are uncorrelated for sufficiently large populations of neurons, we show that the connection topology and a neuron's response characteristics can be decoupled. This assumption allows specification of neuron steady-state responses independent of the connection topology. Specification of neuron responses necessitates the creation of a novel simulator (computational framework) which allows modeling of large populations of connected spiking neurons. We describe the implementation of a spike-based computational framework, designed to take advantage of high performance computing architectures when available. We show that performance of the computational framework is improved using multiple message passing processes for large populations of neurons, resulting in a worst-case linear relationship between the number of neurons and the time required to complete a simulation. Using the computational framework and the ability to specify neuron response characteristics independent of synaptic weights, we systematically investigate the effects of Hebbian learning on the hemodynamic response. Changes in the magnitude of the hemodynamic responses of neural populations are assessed using a forward model that relates population synaptic currents to the blood oxygen dependant (BOLD) response via local field potentials. We show that the magnitude of the hemodynamic response is not a accurate indicator of underlying spiking activity for all network topologies. Instead, we note that large changes in the aggregate response of the population (>50%) can results in a decrease in the overall magnitude of the BOLD signal. We hypothesize that the hemodynamic response magnitude changed due to fluctuations in the balance of excitatory and inhibitory inputs in neural subpopulations. These results have important implications for mean-field models, suggesting that the underlying excitatory/inhibitory neural dynamics within a population may need to be taken into account to accurately predict hemodynamic responses. Advisors/Committee Members: Beardsley, Scott, Scheidt, Robert A., Struble, Craig A..

Subjects/Keywords: hemodynamic; neural network; recurrent; spiking; Biomedical Engineering and Bioengineering

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APA (6^th Edition):

Herzfeld, D. J. (2011). Modeling and Computational Framework for the Specification and Simulation of Large-scale Spiking Neural Networks. (Thesis). Marquette University. Retrieved from https://epublications.marquette.edu/theses_open/102

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

Chicago Manual of Style (16^th Edition):

Herzfeld, David James. “Modeling and Computational Framework for the Specification and Simulation of Large-scale Spiking Neural Networks.” 2011. Thesis, Marquette University. Accessed March 07, 2021. https://epublications.marquette.edu/theses_open/102.

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

MLA Handbook (7^th Edition):

Herzfeld, David James. “Modeling and Computational Framework for the Specification and Simulation of Large-scale Spiking Neural Networks.” 2011. Web. 07 Mar 2021.

Vancouver:

Herzfeld DJ. Modeling and Computational Framework for the Specification and Simulation of Large-scale Spiking Neural Networks. [Internet] [Thesis]. Marquette University; 2011. [cited 2021 Mar 07]. Available from: https://epublications.marquette.edu/theses_open/102.

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

Council of Science Editors:

Herzfeld DJ. Modeling and Computational Framework for the Specification and Simulation of Large-scale Spiking Neural Networks. [Thesis]. Marquette University; 2011. Available from: https://epublications.marquette.edu/theses_open/102

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

Texas A&M University

21. Wang, Han. Dynamic Analysis of Recurrent Neural Networks.

Degree: PhD, Computer Science, 2020, Texas A&M University

URL: http://hdl.handle.net/1969.1/191907

► With the advancement in deep learning research, neural networks have become one of the most powerful tools for artificial intelligence tasks. More specifically, recurrent neural… (more)

Subjects/Keywords: Recurrent Neural Network; machine learning; deep learning; dynamical system

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APA (6^th Edition):

Wang, H. (2020). Dynamic Analysis of Recurrent Neural Networks. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/191907

Chicago Manual of Style (16^th Edition):

Wang, Han. “Dynamic Analysis of Recurrent Neural Networks.” 2020. Doctoral Dissertation, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/191907.

MLA Handbook (7^th Edition):

Wang, Han. “Dynamic Analysis of Recurrent Neural Networks.” 2020. Web. 07 Mar 2021.

Vancouver:

Wang H. Dynamic Analysis of Recurrent Neural Networks. [Internet] [Doctoral dissertation]. Texas A&M University; 2020. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/191907.

Council of Science Editors:

Wang H. Dynamic Analysis of Recurrent Neural Networks. [Doctoral Dissertation]. Texas A&M University; 2020. Available from: http://hdl.handle.net/1969.1/191907

22. Ellis, Robert. Leveraging local and global word context for multi-label document classification.

Degree: 2020, Athabasca University

URL: http://hdl.handle.net/10791/334

►

With the increasing volume of text documents, it is crucial to identify the themes and topics contained within. Labelling documents with the identified topics is… (more)

Subjects/Keywords: Recurrent; Convolutional; Neural network; Classification; Attention; Hierarchy; Ensemble; Siamese

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APA (6^th Edition):

Ellis, R. (2020). Leveraging local and global word context for multi-label document classification. (Thesis). Athabasca University. Retrieved from http://hdl.handle.net/10791/334

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

Chicago Manual of Style (16^th Edition):

Ellis, Robert. “Leveraging local and global word context for multi-label document classification.” 2020. Thesis, Athabasca University. Accessed March 07, 2021. http://hdl.handle.net/10791/334.

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

MLA Handbook (7^th Edition):

Ellis, Robert. “Leveraging local and global word context for multi-label document classification.” 2020. Web. 07 Mar 2021.

Vancouver:

Ellis R. Leveraging local and global word context for multi-label document classification. [Internet] [Thesis]. Athabasca University; 2020. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10791/334.

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

Council of Science Editors:

Ellis R. Leveraging local and global word context for multi-label document classification. [Thesis]. Athabasca University; 2020. Available from: http://hdl.handle.net/10791/334

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

23. Lundström, Oscar. Learning a Better Attitude: A Recurrent Neural Filter for Orientation Estimation .

Degree: Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper, 2020, Chalmers University of Technology

URL: http://hdl.handle.net/20.500.12380/300918

► In the current paradigm of sensor fusion orientation estimation from inertial measurements unit sensor data is done using techniques derived with Bayesian statistics. These derivations… (more)

Subjects/Keywords: sensor-fusion; state estimation; absolute orientation estimation; recurrent neural filter; recurrent neural network; RNN; LSTM; IMU; MARG

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APA (6^th Edition):

Lundström, O. (2020). Learning a Better Attitude: A Recurrent Neural Filter for Orientation Estimation . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/300918

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

Chicago Manual of Style (16^th Edition):

Lundström, Oscar. “Learning a Better Attitude: A Recurrent Neural Filter for Orientation Estimation .” 2020. Thesis, Chalmers University of Technology. Accessed March 07, 2021. http://hdl.handle.net/20.500.12380/300918.

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

MLA Handbook (7^th Edition):

Lundström, Oscar. “Learning a Better Attitude: A Recurrent Neural Filter for Orientation Estimation .” 2020. Web. 07 Mar 2021.

Vancouver:

Lundström O. Learning a Better Attitude: A Recurrent Neural Filter for Orientation Estimation . [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/20.500.12380/300918.

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

Council of Science Editors:

Lundström O. Learning a Better Attitude: A Recurrent Neural Filter for Orientation Estimation . [Thesis]. Chalmers University of Technology; 2020. Available from: http://hdl.handle.net/20.500.12380/300918

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

24. Oskarsson, Gustav. Aktieprediktion med neurala nätverk : En jämförelse av statistiska modeller, neurala nätverk och kombinerade neurala nätverk.

Degree: 2019, , Department of Industrial Economics

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18214

►

This study is about prediction of the stockmarket through a comparison of neural networks and statistical models. The study aims to improve the accuracy… (more)

▼

This study is about prediction of the stockmarket through a comparison of neural networks and statistical models. The study aims to improve the accuracy of stock prediction. Much of the research made on predicting shares deals with statistical models, but also neural networks and then mainly the types RNN and CNN. No research has been done on how these neural networks can be combined, which is why this study aims for this. Tests are made on statistical models, neural networks and combined neural networks to predict stocks at minute level. The result shows that a combination of two neural networks of type RNN gives the best accuracy in the prediction of shares. The accuracy of the predictions increases further if these combined neural networks are trained to predict different time horizons. In addition to tests for accuracy, simulations have also been made which also confirm that there is some possibility to predict shares. Two combined RNNs gave best results, but in the simulations, even CNN made good predictions. One conclusion can be drawn that the stock market is not entirely effective as some opportunity to predict future values exists. Another conclusion is that neural networks are better than statistical models to predict stocks if the neural networks are combined and are of type RNN.

Denna studie behandlar prediktion av aktier genom en jämförelse av neurala nätverk och statistiska modeller. Studien syftar till att förbättra noggrannheten för aktieprediktion. Mycket av den forskning som gjorts om att förutspå aktier behandlar statistiska modeller, men även neurala nätverk och då främst typerna RNN och CNN. Ingen forskning har dock gjorts på hur dessa neurala nätverk kan kombineras, varför denna studie syftar till just detta. Tester är gjorda på statistiska modeller, neurala nätverk och kombinerade neurala nätverk för att förutspå aktier på minutnivå. Resultatet visar att en kombination av två neurala nätverk av typen RNN ger bäst noggrannhet vid prediktion av aktier. Noggrannheten i prediktionerna ökar ytterligare om dessa neurala nätverk tränas för att förutspå olika tidshorisont. Utöver tester för prediktionernas noggrannhet har även simuleringar genomförts som även de bekräftar att viss möjlighet finns att förutspå aktier. Två kombinerade RNN gav bra resultat, men här visade även CNN bra prediktioner. En slutsats kan dras om att aktiemarknaden inte är helt effektiv då viss möjlighet att förutspå framtida värden finns. Ytterligare en slutsats är att neurala nätverk är bättre än statistiska modeller till att förutspå aktier om de neurala nätverken kombineras och är av typen RNN.

Subjects/Keywords: neural network; stock market; recurrent neural network; neuralt nätverk; aktiemarknad; recurrent neural network; Other Engineering and Technologies not elsewhere specified; Övrig annan teknik

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APA (6^th Edition):

Oskarsson, G. (2019). Aktieprediktion med neurala nätverk : En jämförelse av statistiska modeller, neurala nätverk och kombinerade neurala nätverk. (Thesis). , Department of Industrial Economics. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18214

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

Chicago Manual of Style (16^th Edition):

Oskarsson, Gustav. “Aktieprediktion med neurala nätverk : En jämförelse av statistiska modeller, neurala nätverk och kombinerade neurala nätverk.” 2019. Thesis, , Department of Industrial Economics. Accessed March 07, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18214.

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

MLA Handbook (7^th Edition):

Oskarsson, Gustav. “Aktieprediktion med neurala nätverk : En jämförelse av statistiska modeller, neurala nätverk och kombinerade neurala nätverk.” 2019. Web. 07 Mar 2021.

Vancouver:

Oskarsson G. Aktieprediktion med neurala nätverk : En jämförelse av statistiska modeller, neurala nätverk och kombinerade neurala nätverk. [Internet] [Thesis]. , Department of Industrial Economics; 2019. [cited 2021 Mar 07]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18214.

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

Council of Science Editors:

Oskarsson G. Aktieprediktion med neurala nätverk : En jämförelse av statistiska modeller, neurala nätverk och kombinerade neurala nätverk. [Thesis]. , Department of Industrial Economics; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18214

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

Oklahoma State University

25. Phan, Manh Cong. Recurrent Neural Networks: Error Surface Analysis and Improved Training.

Degree: Electrical Engineering, 2014, Oklahoma State University

URL: http://hdl.handle.net/11244/15063

► Recurrent neural networks (RNNs) have powerful computational abilities and could be used in a variety of applications; however, training these networks is still a difficult… (more)

Subjects/Keywords: error surface; neural control; recurrent neural network; spurious valley; system identification; training

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

APA (6^th Edition):

Phan, M. C. (2014). Recurrent Neural Networks: Error Surface Analysis and Improved Training. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/15063

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

Chicago Manual of Style (16^th Edition):

Phan, Manh Cong. “Recurrent Neural Networks: Error Surface Analysis and Improved Training.” 2014. Thesis, Oklahoma State University. Accessed March 07, 2021. http://hdl.handle.net/11244/15063.

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

MLA Handbook (7^th Edition):

Phan, Manh Cong. “Recurrent Neural Networks: Error Surface Analysis and Improved Training.” 2014. Web. 07 Mar 2021.

Vancouver:

Phan MC. Recurrent Neural Networks: Error Surface Analysis and Improved Training. [Internet] [Thesis]. Oklahoma State University; 2014. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/11244/15063.

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

Council of Science Editors:

Phan MC. Recurrent Neural Networks: Error Surface Analysis and Improved Training. [Thesis]. Oklahoma State University; 2014. Available from: http://hdl.handle.net/11244/15063

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

Louisiana State University

26. Firth, Robert James. A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting.

Degree: PhD, Computer Sciences, 2016, Louisiana State University

URL: etd-04112016-151259 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2099

► Numerical weather prediction is a computationally expensive task that requires not only the numerical solution to a complex set of non-linear partial differential equations, but… (more)

Subjects/Keywords: time series; spatial; temporal; time step network; convolutional; recurrent; neural network; weather forecasting; ocean forecasting

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APA (6^th Edition):

Firth, R. J. (2016). A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting. (Doctoral Dissertation). Louisiana State University. Retrieved from etd-04112016-151259 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2099

Chicago Manual of Style (16^th Edition):

Firth, Robert James. “A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting.” 2016. Doctoral Dissertation, Louisiana State University. Accessed March 07, 2021. etd-04112016-151259 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2099.

MLA Handbook (7^th Edition):

Firth, Robert James. “A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting.” 2016. Web. 07 Mar 2021.

Vancouver:

Firth RJ. A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting. [Internet] [Doctoral dissertation]. Louisiana State University; 2016. [cited 2021 Mar 07]. Available from: etd-04112016-151259 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2099.

Council of Science Editors:

Firth RJ. A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting. [Doctoral Dissertation]. Louisiana State University; 2016. Available from: etd-04112016-151259 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2099

The Ohio State University

27. Zheng, Yilin. Text-Based Speech Video Synthesis from a Single Face Image.

Degree: MS, Electrical and Computer Engineering, 2019, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1572168353691788

► Speech video synthesis is a task to generate talking characters which look realistic to human evaluators. Previously, most of the studies used animation models and… (more)

Subjects/Keywords: Computer Science; Computer Engineering; Face Image Synthesis, Generative Adversarial Network, Recurrent Neural Network

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

APA (6^th Edition):

Zheng, Y. (2019). Text-Based Speech Video Synthesis from a Single Face Image. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1572168353691788

Chicago Manual of Style (16^th Edition):

Zheng, Yilin. “Text-Based Speech Video Synthesis from a Single Face Image.” 2019. Masters Thesis, The Ohio State University. Accessed March 07, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1572168353691788.

MLA Handbook (7^th Edition):

Zheng, Yilin. “Text-Based Speech Video Synthesis from a Single Face Image.” 2019. Web. 07 Mar 2021.

Vancouver:

Zheng Y. Text-Based Speech Video Synthesis from a Single Face Image. [Internet] [Masters thesis]. The Ohio State University; 2019. [cited 2021 Mar 07]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1572168353691788.

Council of Science Editors:

Zheng Y. Text-Based Speech Video Synthesis from a Single Face Image. [Masters Thesis]. The Ohio State University; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1572168353691788

Delft University of Technology

28. Voss, Sander (author). Application of Deep Learning for Spacecraft Fault Detection and Isolation.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:7c308a4b-f97b-4a83-b739-4019ad306853

►

Spacecraft require high availability, autonomous operation, and a high degree of mission success. Spacecraft use sensors, such as star trackers and GPS, and actuators, such… (more)

Subjects/Keywords: FDI; FDIR; Fault Detection; Fault Isolation; Deep Learning; Recurrent networks; Recurrent Neural Network; long short-term memory networks; LSTM

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

APA (6^th Edition):

Voss, S. (. (2019). Application of Deep Learning for Spacecraft Fault Detection and Isolation. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:7c308a4b-f97b-4a83-b739-4019ad306853

Chicago Manual of Style (16^th Edition):

Voss, Sander (author). “Application of Deep Learning for Spacecraft Fault Detection and Isolation.” 2019. Masters Thesis, Delft University of Technology. Accessed March 07, 2021. http://resolver.tudelft.nl/uuid:7c308a4b-f97b-4a83-b739-4019ad306853.

MLA Handbook (7^th Edition):

Voss, Sander (author). “Application of Deep Learning for Spacecraft Fault Detection and Isolation.” 2019. Web. 07 Mar 2021.

Vancouver:

Voss S(. Application of Deep Learning for Spacecraft Fault Detection and Isolation. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 07]. Available from: http://resolver.tudelft.nl/uuid:7c308a4b-f97b-4a83-b739-4019ad306853.

Council of Science Editors:

Voss S(. Application of Deep Learning for Spacecraft Fault Detection and Isolation. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:7c308a4b-f97b-4a83-b739-4019ad306853

Brno University of Technology

29. Huf, Petr. Machine Learning Strategies in Electronic Trading: Machine Learning Strategies in Electronic Trading.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/56492

► Successful stock trading is a dream of many people. Eletronic trading is an interesting branch of this business. The trading strategy runs on the computer… (more)

Subjects/Keywords: neuronová síť; rekurentní neuronová síť; burza; obchodování; trh; profit; model; neural network; recurrent neural network; stock exchange; trading; market; profit; model

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

APA (6^th Edition):

Huf, P. (2019). Machine Learning Strategies in Electronic Trading: Machine Learning Strategies in Electronic Trading. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/56492

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

Chicago Manual of Style (16^th Edition):

Huf, Petr. “Machine Learning Strategies in Electronic Trading: Machine Learning Strategies in Electronic Trading.” 2019. Thesis, Brno University of Technology. Accessed March 07, 2021. http://hdl.handle.net/11012/56492.

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

MLA Handbook (7^th Edition):

Huf, Petr. “Machine Learning Strategies in Electronic Trading: Machine Learning Strategies in Electronic Trading.” 2019. Web. 07 Mar 2021.

Vancouver:

Huf P. Machine Learning Strategies in Electronic Trading: Machine Learning Strategies in Electronic Trading. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/11012/56492.

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

Council of Science Editors:

Huf P. Machine Learning Strategies in Electronic Trading: Machine Learning Strategies in Electronic Trading. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/56492

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

30. 이, 유림. Chronic Kidney Disease Risk Prediction using Electronic Health Records Pattern Information based on Deep Learning.

Degree: 2019, Ajou University

URL: http://repository.ajou.ac.kr/handle/201003/17869 ; http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000028811

►

According to the National Health Insurance Service(NHIS) in 2013, 3.9% of adults aged 30 years or older have chronic kidney disease, and 16.5% are over… (more)

▼

According to the National Health Insurance Service(NHIS) in 2013, 3.9% of adults aged 30 years or older have chronic kidney disease, and 16.5% are over 65 years old. In particular, renal insufficiency causes many complications in the onset of the disease, which causes many patients to die as their diseases worsen. Nevertheless, the initial symptoms are not clear, so it is rare that patients feel a kidney abnormality in the patient 's position and want to enter the hospital. Studies using electronic health records (EHR) have been conducted to detect these chronic kidney diseases early. Recently, as the technology of deep learning has rapidly developed, it has been actively researched and utilized in the medical field and has shown good performance. In addition, various experiments have been made to modify the model structure of the deep learning, and there have been cases in which performance varies depending on the structure. However, there are relatively few cases of studies for the prediction of chronic kidney disease in the medical field by experimenting with various types of deep learning model structures. In this paper, we evaluated the risk of chronic kidney disease using diagnostic and prescription information of EHR data, and compared the performance of several structural deep learning models. We extracted the weights of the learned data and confirmed the information of the time points with high weight in the prediction of chronic kidney disease. The results of this study were as follows: National Health Insurance Corporation sample DB, 81.09% of Accuracy, 87.75% of the area under the Receiver Operating Characteristics Curve (AUROC), 52.72% of the Area under the Precision-Recall Curve (AUPRC) and 83.03% of weighted F1-score. The accuracy of the database of Ajou University Hospital was 82.07%, 88.24% of AUROC, 63.61% of AUPRC, 82.93% of weighted F1-score. Based on this, it is expected that the proposed model will effectively contribute to the early detection, delay, and reduction the prevalence of chronic kidney disease.

2013년도 질병관리본부의 국민건강통계에 따르면 30세 이상의 성인 중 3.9%가 만성 신질환(chronic kidney disease)을 가지고 있으며, 65세 이상에서는 16.5%로 유병률이 매우 높은 질환이다. 특히 발병 과정에서 신부전증으로 인해 여러 가지 합병증들이 유발되는데, 해당 질병들이 악화되면서 많은 환자들을 사망에 이르게 만든다. 그럼에도 불구하고 초기 증상이 뚜렷하지 않아 환자가 신장 이상을 느끼고 병원에 내원하는 경우는 드문 실정이다. 이러한 만성 신질환을 조기에 예측하고자 전자의무기록(electronic health record, EHR)을 이용한 연구들이 선행되고 있다. 최근 딥러닝의 기술이 급속하게 발전하면서 의료 분야에서도 활발한 연구가 진행되고 있으며 기존의 전통적인 기계학습(Machine Learning)보다 좋은 성능을 보이고 있다. 또한, 딥러닝 분야에서 다양한 모델 구조 실험들이 이루어지고 있으며 구조에 따라 성능이 달라지는 연구 사례들이 있었다. 그러나 의료분야에서 다양한 딥러닝 모델 구조를 실험하고 만성 신질환 예측을 위한 연구 사례는 비교적 적었다. 이에 본 논문에서는 이에이치알(EHR) 데이터 중 진단과 처방 정보를 활용한 만성 신질환 위험도를 예측하였으며 여러 가지 구조의 딥러닝 모델에 따른 성능을 비교 평가하였다. 학습된 데이터의 가중치를 추출하여 만성 신질환 예측에 높은 수치를 가진 시점들의 정보들을 확인하여 정성적인 평가를 하였다. 실험 결과 국민건강보험공단 표본연구 DB에 대하여 정확도(accuracy) 81.09%, 에이유알오씨(The Area under the Receiver Operating Characteristics Curve, AUROC) 87.75%, 에이유피알씨(The Area under the Precision-Recall Curve, AUPRC) 52.72%, 웨이티드 에프원 스코어(Weighted F1-score) 83.03%를 나타냈고, 아주대학교병원 데이터베이스에 대해서 정확도 82.07%,…

Advisors/Committee Members: 대학원 의학과, 201724667, 이, 유림.

Subjects/Keywords: Electronic Health Records; Convolutional Neural Network; Recurrent Neural Network; Embedding; Attention; 전자의무기록; 순환 신경망; 합성곱 신경망; 임베딩 기법; 어텐션 메커니즘

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

APA (6^th Edition):

이, �. (2019). Chronic Kidney Disease Risk Prediction using Electronic Health Records Pattern Information based on Deep Learning. (Thesis). Ajou University. Retrieved from http://repository.ajou.ac.kr/handle/201003/17869 ; http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000028811

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

Chicago Manual of Style (16^th Edition):

이, 유림. “Chronic Kidney Disease Risk Prediction using Electronic Health Records Pattern Information based on Deep Learning.” 2019. Thesis, Ajou University. Accessed March 07, 2021. http://repository.ajou.ac.kr/handle/201003/17869 ; http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000028811.

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

MLA Handbook (7^th Edition):

이, 유림. “Chronic Kidney Disease Risk Prediction using Electronic Health Records Pattern Information based on Deep Learning.” 2019. Web. 07 Mar 2021.

Vancouver:

이 �. Chronic Kidney Disease Risk Prediction using Electronic Health Records Pattern Information based on Deep Learning. [Internet] [Thesis]. Ajou University; 2019. [cited 2021 Mar 07]. Available from: http://repository.ajou.ac.kr/handle/201003/17869 ; http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000028811.

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

Council of Science Editors:

이 �. Chronic Kidney Disease Risk Prediction using Electronic Health Records Pattern Information based on Deep Learning. [Thesis]. Ajou University; 2019. Available from: http://repository.ajou.ac.kr/handle/201003/17869 ; http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000028811

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

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Open Access Theses and Dissertations