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You searched for +publisher:"Clemson University" +contributor:("Alexander Herzog"). Showing records 1 – 8 of 8 total matches.

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Clemson University

1. Du, Yuheng. Streaming Infrastructure and Natural Language Modeling with Application to Streaming Big Data.

Degree: PhD, School of Computing, 2019, Clemson University

  Streaming data are produced in great velocity and diverse variety. The vision of this research is to build an end-to-end system that handles the… (more)

Subjects/Keywords: Distributed Data Delivery Systems; Natural Language Understanding

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APA (6th Edition):

Du, Y. (2019). Streaming Infrastructure and Natural Language Modeling with Application to Streaming Big Data. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/2329

Chicago Manual of Style (16th Edition):

Du, Yuheng. “Streaming Infrastructure and Natural Language Modeling with Application to Streaming Big Data.” 2019. Doctoral Dissertation, Clemson University. Accessed August 04, 2020. https://tigerprints.clemson.edu/all_dissertations/2329.

MLA Handbook (7th Edition):

Du, Yuheng. “Streaming Infrastructure and Natural Language Modeling with Application to Streaming Big Data.” 2019. Web. 04 Aug 2020.

Vancouver:

Du Y. Streaming Infrastructure and Natural Language Modeling with Application to Streaming Big Data. [Internet] [Doctoral dissertation]. Clemson University; 2019. [cited 2020 Aug 04]. Available from: https://tigerprints.clemson.edu/all_dissertations/2329.

Council of Science Editors:

Du Y. Streaming Infrastructure and Natural Language Modeling with Application to Streaming Big Data. [Doctoral Dissertation]. Clemson University; 2019. Available from: https://tigerprints.clemson.edu/all_dissertations/2329


Clemson University

2. He, Yangyang. Recommending Privacy Settings for Internet-of-Things.

Degree: PhD, School of Computing, 2019, Clemson University

  Privacy concerns have been identified as an important barrier to the growth of IoT. These concerns are exacerbated by the complexity of manually setting… (more)

Subjects/Keywords: IoT; Machine Learning; Privacy; Smart Home; Statistics; User Interface

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APA (6th Edition):

He, Y. (2019). Recommending Privacy Settings for Internet-of-Things. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/2528

Chicago Manual of Style (16th Edition):

He, Yangyang. “Recommending Privacy Settings for Internet-of-Things.” 2019. Doctoral Dissertation, Clemson University. Accessed August 04, 2020. https://tigerprints.clemson.edu/all_dissertations/2528.

MLA Handbook (7th Edition):

He, Yangyang. “Recommending Privacy Settings for Internet-of-Things.” 2019. Web. 04 Aug 2020.

Vancouver:

He Y. Recommending Privacy Settings for Internet-of-Things. [Internet] [Doctoral dissertation]. Clemson University; 2019. [cited 2020 Aug 04]. Available from: https://tigerprints.clemson.edu/all_dissertations/2528.

Council of Science Editors:

He Y. Recommending Privacy Settings for Internet-of-Things. [Doctoral Dissertation]. Clemson University; 2019. Available from: https://tigerprints.clemson.edu/all_dissertations/2528


Clemson University

3. Sybrandt, Justin George. Exploiting Latent Features of Text and Graphs.

Degree: PhD, School of Computing, 2020, Clemson University

  As the size and scope of online data continues to grow, new machine learning techniques become necessary to best capitalize on the wealth of… (more)

Subjects/Keywords: Conditional Text Generation; Graph Embedding; Hypergraph Partitioning; Hypothesis Generation; Literature-based Discovery; Text Embedding

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APA (6th Edition):

Sybrandt, J. G. (2020). Exploiting Latent Features of Text and Graphs. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/2592

Chicago Manual of Style (16th Edition):

Sybrandt, Justin George. “Exploiting Latent Features of Text and Graphs.” 2020. Doctoral Dissertation, Clemson University. Accessed August 04, 2020. https://tigerprints.clemson.edu/all_dissertations/2592.

MLA Handbook (7th Edition):

Sybrandt, Justin George. “Exploiting Latent Features of Text and Graphs.” 2020. Web. 04 Aug 2020.

Vancouver:

Sybrandt JG. Exploiting Latent Features of Text and Graphs. [Internet] [Doctoral dissertation]. Clemson University; 2020. [cited 2020 Aug 04]. Available from: https://tigerprints.clemson.edu/all_dissertations/2592.

Council of Science Editors:

Sybrandt JG. Exploiting Latent Features of Text and Graphs. [Doctoral Dissertation]. Clemson University; 2020. Available from: https://tigerprints.clemson.edu/all_dissertations/2592


Clemson University

4. Chauhan, Varsha. Planar Graph Generation with Application to Water Distribution Networks.

Degree: MS, School of Computing, 2018, Clemson University

 The study of network representations of physical, biological, and social phenomena can help us better understand their structure and functional dynamics as well as formulate… (more)

Subjects/Keywords: Multiscale Graph generation; Planar Graphs; Water Distribution System Optimization; Water Networks

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APA (6th Edition):

Chauhan, V. (2018). Planar Graph Generation with Application to Water Distribution Networks. (Masters Thesis). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_theses/2975

Chicago Manual of Style (16th Edition):

Chauhan, Varsha. “Planar Graph Generation with Application to Water Distribution Networks.” 2018. Masters Thesis, Clemson University. Accessed August 04, 2020. https://tigerprints.clemson.edu/all_theses/2975.

MLA Handbook (7th Edition):

Chauhan, Varsha. “Planar Graph Generation with Application to Water Distribution Networks.” 2018. Web. 04 Aug 2020.

Vancouver:

Chauhan V. Planar Graph Generation with Application to Water Distribution Networks. [Internet] [Masters thesis]. Clemson University; 2018. [cited 2020 Aug 04]. Available from: https://tigerprints.clemson.edu/all_theses/2975.

Council of Science Editors:

Chauhan V. Planar Graph Generation with Application to Water Distribution Networks. [Masters Thesis]. Clemson University; 2018. Available from: https://tigerprints.clemson.edu/all_theses/2975


Clemson University

5. Feng, Haotian. Performance of Latent Dirichlet Allocation with Different Topic and Document Structures.

Degree: PhD, Mathematical Sciences, 2019, Clemson University

  Topic modeling has been used widely to extract the structures (topics) in a collection (corpus) of documents. One popular method is the Latent Dirichlet… (more)

Subjects/Keywords: Evaluation; Experimental Design; LDA; Pre-processing; Simulation; Topic Modeling

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APA (6th Edition):

Feng, H. (2019). Performance of Latent Dirichlet Allocation with Different Topic and Document Structures. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/2448

Chicago Manual of Style (16th Edition):

Feng, Haotian. “Performance of Latent Dirichlet Allocation with Different Topic and Document Structures.” 2019. Doctoral Dissertation, Clemson University. Accessed August 04, 2020. https://tigerprints.clemson.edu/all_dissertations/2448.

MLA Handbook (7th Edition):

Feng, Haotian. “Performance of Latent Dirichlet Allocation with Different Topic and Document Structures.” 2019. Web. 04 Aug 2020.

Vancouver:

Feng H. Performance of Latent Dirichlet Allocation with Different Topic and Document Structures. [Internet] [Doctoral dissertation]. Clemson University; 2019. [cited 2020 Aug 04]. Available from: https://tigerprints.clemson.edu/all_dissertations/2448.

Council of Science Editors:

Feng H. Performance of Latent Dirichlet Allocation with Different Topic and Document Structures. [Doctoral Dissertation]. Clemson University; 2019. Available from: https://tigerprints.clemson.edu/all_dissertations/2448


Clemson University

6. Stephens, Eric. Prisons, Genres, and Big Data: Understanding the Language of Corrections in America's Prisons.

Degree: PhD, Communication Studies, 2018, Clemson University

  This dissertation seeks to answer one fundamental question: How can I as a researcher conduct social justice research that is ethical, durable, and portable?… (more)

Subjects/Keywords: big data; genre; institutional genre analysis; prison; rhetoric; technical communication

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APA (6th Edition):

Stephens, E. (2018). Prisons, Genres, and Big Data: Understanding the Language of Corrections in America's Prisons. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/2103

Chicago Manual of Style (16th Edition):

Stephens, Eric. “Prisons, Genres, and Big Data: Understanding the Language of Corrections in America's Prisons.” 2018. Doctoral Dissertation, Clemson University. Accessed August 04, 2020. https://tigerprints.clemson.edu/all_dissertations/2103.

MLA Handbook (7th Edition):

Stephens, Eric. “Prisons, Genres, and Big Data: Understanding the Language of Corrections in America's Prisons.” 2018. Web. 04 Aug 2020.

Vancouver:

Stephens E. Prisons, Genres, and Big Data: Understanding the Language of Corrections in America's Prisons. [Internet] [Doctoral dissertation]. Clemson University; 2018. [cited 2020 Aug 04]. Available from: https://tigerprints.clemson.edu/all_dissertations/2103.

Council of Science Editors:

Stephens E. Prisons, Genres, and Big Data: Understanding the Language of Corrections in America's Prisons. [Doctoral Dissertation]. Clemson University; 2018. Available from: https://tigerprints.clemson.edu/all_dissertations/2103

7. Srivastava, Aishwarya. Automated Deployment of an End-to-End Pipeline on Amazon Web Services for Real-Time Visual Inspection using Fast Streaming High-Definition Images.

Degree: MS, School of Computing, 2019, Clemson University

  This thesis investigates various degrees of freedom and deployment challenges of building an end-to-end intelligent visual inspection system for use in automotive manufacturing. Current… (more)

Subjects/Keywords: Cloud; End-to-end pipeline; Latency; Performance; Real time system; Visual inspection

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APA (6th Edition):

Srivastava, A. (2019). Automated Deployment of an End-to-End Pipeline on Amazon Web Services for Real-Time Visual Inspection using Fast Streaming High-Definition Images. (Masters Thesis). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_theses/3156

Chicago Manual of Style (16th Edition):

Srivastava, Aishwarya. “Automated Deployment of an End-to-End Pipeline on Amazon Web Services for Real-Time Visual Inspection using Fast Streaming High-Definition Images.” 2019. Masters Thesis, Clemson University. Accessed August 04, 2020. https://tigerprints.clemson.edu/all_theses/3156.

MLA Handbook (7th Edition):

Srivastava, Aishwarya. “Automated Deployment of an End-to-End Pipeline on Amazon Web Services for Real-Time Visual Inspection using Fast Streaming High-Definition Images.” 2019. Web. 04 Aug 2020.

Vancouver:

Srivastava A. Automated Deployment of an End-to-End Pipeline on Amazon Web Services for Real-Time Visual Inspection using Fast Streaming High-Definition Images. [Internet] [Masters thesis]. Clemson University; 2019. [cited 2020 Aug 04]. Available from: https://tigerprints.clemson.edu/all_theses/3156.

Council of Science Editors:

Srivastava A. Automated Deployment of an End-to-End Pipeline on Amazon Web Services for Real-Time Visual Inspection using Fast Streaming High-Definition Images. [Masters Thesis]. Clemson University; 2019. Available from: https://tigerprints.clemson.edu/all_theses/3156

8. Avudaiappan, Neela Saranya. Data Mining in Large-Scale Clinical Visit Data for Rett Syndrome Patients.

Degree: MS, School of Computing, 2017, Clemson University

 Rett syndrome (RTT) is a rare neurological disorder that predominantly affects girls. Research on RTT has mostly centered around gene mutations and possibility of cure… (more)

Clemson University gave us access to 19 data files in the form of spreadsheets with medical… 

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

APA (6th Edition):

Avudaiappan, N. S. (2017). Data Mining in Large-Scale Clinical Visit Data for Rett Syndrome Patients. (Masters Thesis). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_theses/2728

Chicago Manual of Style (16th Edition):

Avudaiappan, Neela Saranya. “Data Mining in Large-Scale Clinical Visit Data for Rett Syndrome Patients.” 2017. Masters Thesis, Clemson University. Accessed August 04, 2020. https://tigerprints.clemson.edu/all_theses/2728.

MLA Handbook (7th Edition):

Avudaiappan, Neela Saranya. “Data Mining in Large-Scale Clinical Visit Data for Rett Syndrome Patients.” 2017. Web. 04 Aug 2020.

Vancouver:

Avudaiappan NS. Data Mining in Large-Scale Clinical Visit Data for Rett Syndrome Patients. [Internet] [Masters thesis]. Clemson University; 2017. [cited 2020 Aug 04]. Available from: https://tigerprints.clemson.edu/all_theses/2728.

Council of Science Editors:

Avudaiappan NS. Data Mining in Large-Scale Clinical Visit Data for Rett Syndrome Patients. [Masters Thesis]. Clemson University; 2017. Available from: https://tigerprints.clemson.edu/all_theses/2728

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