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You searched for +publisher:"University of Michigan" +contributor:("Zakarian, Armen"). Showing records 1 – 6 of 6 total matches.

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University of Michigan

1. Feng, Wen. Assessment of Social Preference in Automotive Market using Generalized Multinomial Logistic Regression.

Degree: MSin Engineering, Industrial and Systems Engineering, College of Engineering and Computer Science, 2016, University of Michigan

 Individual auto market share is always one of the major concerns of any auto manufacturing company. It indicates a lot of things about the company… (more)

Subjects/Keywords: Market share prediction; Social welfare function; Random utility theory; B-spline fitted logistic regression; G-test; Pseudo R-squared; Kendall rank correlation coefficient; Matthews correlation coefficient; Marketing

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APA (6th Edition):

Feng, W. (2016). Assessment of Social Preference in Automotive Market using Generalized Multinomial Logistic Regression. (Masters Thesis). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/136069

Chicago Manual of Style (16th Edition):

Feng, Wen. “Assessment of Social Preference in Automotive Market using Generalized Multinomial Logistic Regression.” 2016. Masters Thesis, University of Michigan. Accessed July 17, 2019. http://hdl.handle.net/2027.42/136069.

MLA Handbook (7th Edition):

Feng, Wen. “Assessment of Social Preference in Automotive Market using Generalized Multinomial Logistic Regression.” 2016. Web. 17 Jul 2019.

Vancouver:

Feng W. Assessment of Social Preference in Automotive Market using Generalized Multinomial Logistic Regression. [Internet] [Masters thesis]. University of Michigan; 2016. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2027.42/136069.

Council of Science Editors:

Feng W. Assessment of Social Preference in Automotive Market using Generalized Multinomial Logistic Regression. [Masters Thesis]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/136069


University of Michigan

2. Kaja, Nevrus. Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms.

Degree: PhD, College of Engineering & Computer Science, 2019, University of Michigan

 Automotive technology has continued to advance in many aspects. As an outcome of such advancements, autonomous vehicles are closer to commercialization and have brought to… (more)

Subjects/Keywords: Artificial intelligence; Cybersecurity; Machine learning; Fuzzy logic; Threat models; Automotive; Electrical and Computer Engineering

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APA (6th Edition):

Kaja, N. (2019). Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/149467

Chicago Manual of Style (16th Edition):

Kaja, Nevrus. “Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms.” 2019. Doctoral Dissertation, University of Michigan. Accessed July 17, 2019. http://hdl.handle.net/2027.42/149467.

MLA Handbook (7th Edition):

Kaja, Nevrus. “Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms.” 2019. Web. 17 Jul 2019.

Vancouver:

Kaja N. Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2027.42/149467.

Council of Science Editors:

Kaja N. Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/149467


University of Michigan

3. Ruas, Terry L. Semantic Feature Extraction Using Multi-Sense Embeddings and Lexical Chains.

Degree: PhD, College of Engineering & Computer Science, 2019, University of Michigan

 The relationship between words in a sentence often tell us more about the underlying semantic content of a document than its actual words individually. Natural… (more)

Subjects/Keywords: Synsets; WordNet; MSSA; Natural language processing; Semantics; Lexical chains; Computer and Information Science

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APA (6th Edition):

Ruas, T. L. (2019). Semantic Feature Extraction Using Multi-Sense Embeddings and Lexical Chains. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/149647

Chicago Manual of Style (16th Edition):

Ruas, Terry L. “Semantic Feature Extraction Using Multi-Sense Embeddings and Lexical Chains.” 2019. Doctoral Dissertation, University of Michigan. Accessed July 17, 2019. http://hdl.handle.net/2027.42/149647.

MLA Handbook (7th Edition):

Ruas, Terry L. “Semantic Feature Extraction Using Multi-Sense Embeddings and Lexical Chains.” 2019. Web. 17 Jul 2019.

Vancouver:

Ruas TL. Semantic Feature Extraction Using Multi-Sense Embeddings and Lexical Chains. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2027.42/149647.

Council of Science Editors:

Ruas TL. Semantic Feature Extraction Using Multi-Sense Embeddings and Lexical Chains. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/149647

4. Weiss, Brian Patrick. Examining the Relationship Between Age and Instrument Cluster Design Preference.

Degree: MSin Engineering, Industrial and Systems Engineering, College of Engineering and Computer Science, 2017, University of Michigan

 Previous research has indicated that elderly drivers, those aged 65 years and older, find current in-vehicle technology, specifically instrument cluster panel difficult to use. Existing… (more)

Subjects/Keywords: Cluster design; Elderly driver; Instrument cluster; Human factors; Driver safety; Vehicle technology; Industrial and Systems Engineering, College of Engineering and Computer Science

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APA (6th Edition):

Weiss, B. P. (2017). Examining the Relationship Between Age and Instrument Cluster Design Preference. (Masters Thesis). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/136194

Chicago Manual of Style (16th Edition):

Weiss, Brian Patrick. “Examining the Relationship Between Age and Instrument Cluster Design Preference.” 2017. Masters Thesis, University of Michigan. Accessed July 17, 2019. http://hdl.handle.net/2027.42/136194.

MLA Handbook (7th Edition):

Weiss, Brian Patrick. “Examining the Relationship Between Age and Instrument Cluster Design Preference.” 2017. Web. 17 Jul 2019.

Vancouver:

Weiss BP. Examining the Relationship Between Age and Instrument Cluster Design Preference. [Internet] [Masters thesis]. University of Michigan; 2017. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2027.42/136194.

Council of Science Editors:

Weiss BP. Examining the Relationship Between Age and Instrument Cluster Design Preference. [Masters Thesis]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/136194

5. Mali, Saurabh Mahadeo. Design Optimization of Latent Heat Thermal Energy Storage System Using Computational Fluid Dynamics, Response Surface Methodology and Genetic Algorithm.

Degree: MSin Engineering, Mechanical Engineering, College of Engineering & Computer Science, 2018, University of Michigan

 Product development processes involve computational methods like Computational Fluid Dynamics to simulate real world fluid flow and heat transfer phenomena numerically using computers. Also, traditional… (more)

Subjects/Keywords: Computational fluid dynamics; Genetic algorithm; Single and multi-objective optimization; Design optimization; Latent heat energy storage system; Response surface modelling; Phase change material; Industrial and operations engineering; Mechanical engineering; Optimization; Response surface modelling

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APA (6th Edition):

Mali, S. M. (2018). Design Optimization of Latent Heat Thermal Energy Storage System Using Computational Fluid Dynamics, Response Surface Methodology and Genetic Algorithm. (Masters Thesis). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/146774

Chicago Manual of Style (16th Edition):

Mali, Saurabh Mahadeo. “Design Optimization of Latent Heat Thermal Energy Storage System Using Computational Fluid Dynamics, Response Surface Methodology and Genetic Algorithm.” 2018. Masters Thesis, University of Michigan. Accessed July 17, 2019. http://hdl.handle.net/2027.42/146774.

MLA Handbook (7th Edition):

Mali, Saurabh Mahadeo. “Design Optimization of Latent Heat Thermal Energy Storage System Using Computational Fluid Dynamics, Response Surface Methodology and Genetic Algorithm.” 2018. Web. 17 Jul 2019.

Vancouver:

Mali SM. Design Optimization of Latent Heat Thermal Energy Storage System Using Computational Fluid Dynamics, Response Surface Methodology and Genetic Algorithm. [Internet] [Masters thesis]. University of Michigan; 2018. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2027.42/146774.

Council of Science Editors:

Mali SM. Design Optimization of Latent Heat Thermal Energy Storage System Using Computational Fluid Dynamics, Response Surface Methodology and Genetic Algorithm. [Masters Thesis]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/146774

6. Wang, Hanzhang. Intelligent Web Services Architecture Evolution Via An Automated Learning-Based Refactoring Framework.

Degree: PhD, College of Engineering & Computer Science, 2018, University of Michigan

 Architecture degradation can have fundamental impact on software quality and productivity, resulting in inability to support new features, increasing technical debt and leading to significant… (more)

Subjects/Keywords: Technical debt; Quality of services; Design defects; Architecture evolution; Web service refactoring; Search-based software engineering; Computer and information science

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

APA (6th Edition):

Wang, H. (2018). Intelligent Web Services Architecture Evolution Via An Automated Learning-Based Refactoring Framework. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/142810

Chicago Manual of Style (16th Edition):

Wang, Hanzhang. “Intelligent Web Services Architecture Evolution Via An Automated Learning-Based Refactoring Framework.” 2018. Doctoral Dissertation, University of Michigan. Accessed July 17, 2019. http://hdl.handle.net/2027.42/142810.

MLA Handbook (7th Edition):

Wang, Hanzhang. “Intelligent Web Services Architecture Evolution Via An Automated Learning-Based Refactoring Framework.” 2018. Web. 17 Jul 2019.

Vancouver:

Wang H. Intelligent Web Services Architecture Evolution Via An Automated Learning-Based Refactoring Framework. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2027.42/142810.

Council of Science Editors:

Wang H. Intelligent Web Services Architecture Evolution Via An Automated Learning-Based Refactoring Framework. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/142810

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