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You searched for +publisher:"Texas State University – San Marcos" +contributor:("Metsis, Vangelis"). Showing records 1 – 10 of 10 total matches.

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Texas State University – San Marcos

1. Jaiganesh, Jayadharini. An Efficient Connected Components Algorithm for Massively-Parallel Devices.

Degree: MS, Computer Science, 2017, Texas State University – San Marcos

 Massively-parallel devices such as GPUs are best suited for accelerating regular algorithms. Since the memory access patterns and control flow of irregular algorithms are data… (more)

Subjects/Keywords: GPU; Connected Components; Irregular algorithm; Parallel Processing; Computer science; Parallel processing (Electronic computers)

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APA (6th Edition):

Jaiganesh, J. (2017). An Efficient Connected Components Algorithm for Massively-Parallel Devices. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6570

Chicago Manual of Style (16th Edition):

Jaiganesh, Jayadharini. “An Efficient Connected Components Algorithm for Massively-Parallel Devices.” 2017. Masters Thesis, Texas State University – San Marcos. Accessed November 15, 2019. https://digital.library.txstate.edu/handle/10877/6570.

MLA Handbook (7th Edition):

Jaiganesh, Jayadharini. “An Efficient Connected Components Algorithm for Massively-Parallel Devices.” 2017. Web. 15 Nov 2019.

Vancouver:

Jaiganesh J. An Efficient Connected Components Algorithm for Massively-Parallel Devices. [Internet] [Masters thesis]. Texas State University – San Marcos; 2017. [cited 2019 Nov 15]. Available from: https://digital.library.txstate.edu/handle/10877/6570.

Council of Science Editors:

Jaiganesh J. An Efficient Connected Components Algorithm for Massively-Parallel Devices. [Masters Thesis]. Texas State University – San Marcos; 2017. Available from: https://digital.library.txstate.edu/handle/10877/6570


Texas State University – San Marcos

2. Alabandi, Ghadeer Ahmed. Combining Deep Learning with Traditional Machine Learning to Improve Classification Accuracy on Small Datasets.

Degree: MS, Computer Science, 2017, Texas State University – San Marcos

 Feature extraction and selection are essential phases in building machine learning classification models, and they have a great impact on the accuracy and the performance… (more)

Subjects/Keywords: Deep Learning; Machine learning; Feature extraction; Data mining

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APA (6th Edition):

Alabandi, G. A. (2017). Combining Deep Learning with Traditional Machine Learning to Improve Classification Accuracy on Small Datasets. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6923

Chicago Manual of Style (16th Edition):

Alabandi, Ghadeer Ahmed. “Combining Deep Learning with Traditional Machine Learning to Improve Classification Accuracy on Small Datasets.” 2017. Masters Thesis, Texas State University – San Marcos. Accessed November 15, 2019. https://digital.library.txstate.edu/handle/10877/6923.

MLA Handbook (7th Edition):

Alabandi, Ghadeer Ahmed. “Combining Deep Learning with Traditional Machine Learning to Improve Classification Accuracy on Small Datasets.” 2017. Web. 15 Nov 2019.

Vancouver:

Alabandi GA. Combining Deep Learning with Traditional Machine Learning to Improve Classification Accuracy on Small Datasets. [Internet] [Masters thesis]. Texas State University – San Marcos; 2017. [cited 2019 Nov 15]. Available from: https://digital.library.txstate.edu/handle/10877/6923.

Council of Science Editors:

Alabandi GA. Combining Deep Learning with Traditional Machine Learning to Improve Classification Accuracy on Small Datasets. [Masters Thesis]. Texas State University – San Marcos; 2017. Available from: https://digital.library.txstate.edu/handle/10877/6923


Texas State University – San Marcos

3. Saha, Biplab Kumar. Towards a Framework for Automating the Workflow for Building Machine Learning Based Performance Tuning.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 Recent interest in machine learning-based methods have produced many sophisticated models for performance modeling and optimi:,ation. These models tend to be sensitive to architectural parameters… (more)

Subjects/Keywords: High performance computing; Machine learning; High performance computing; Machine learning

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APA (6th Edition):

Saha, B. K. (2016). Towards a Framework for Automating the Workflow for Building Machine Learning Based Performance Tuning. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6343

Chicago Manual of Style (16th Edition):

Saha, Biplab Kumar. “Towards a Framework for Automating the Workflow for Building Machine Learning Based Performance Tuning.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed November 15, 2019. https://digital.library.txstate.edu/handle/10877/6343.

MLA Handbook (7th Edition):

Saha, Biplab Kumar. “Towards a Framework for Automating the Workflow for Building Machine Learning Based Performance Tuning.” 2016. Web. 15 Nov 2019.

Vancouver:

Saha BK. Towards a Framework for Automating the Workflow for Building Machine Learning Based Performance Tuning. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2019 Nov 15]. Available from: https://digital.library.txstate.edu/handle/10877/6343.

Council of Science Editors:

Saha BK. Towards a Framework for Automating the Workflow for Building Machine Learning Based Performance Tuning. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6343


Texas State University – San Marcos

4. Hinkle, Lee B. Determination of Emotional State through Physiological Measurement.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 The goal of this thesis is to develop and evaluate methods of emotional response classification using human physiological data. With the continued development of automated… (more)

Subjects/Keywords: Emotion Biosignal Machine Learning; Emotions; Psychophysiology

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APA (6th Edition):

Hinkle, L. B. (2016). Determination of Emotional State through Physiological Measurement. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6411

Chicago Manual of Style (16th Edition):

Hinkle, Lee B. “Determination of Emotional State through Physiological Measurement.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed November 15, 2019. https://digital.library.txstate.edu/handle/10877/6411.

MLA Handbook (7th Edition):

Hinkle, Lee B. “Determination of Emotional State through Physiological Measurement.” 2016. Web. 15 Nov 2019.

Vancouver:

Hinkle LB. Determination of Emotional State through Physiological Measurement. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2019 Nov 15]. Available from: https://digital.library.txstate.edu/handle/10877/6411.

Council of Science Editors:

Hinkle LB. Determination of Emotional State through Physiological Measurement. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6411


Texas State University – San Marcos

5. Biniwale, Alakh Sudhir. Analysis of human polysomnography (PSG) for automatic sleep event detection using Hidden Markov Model.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 This thesis work evaluates our proposed methodology for automated detection of sleep events from Polysomnographic (PSG) data. The sleep data was collected during real sleep… (more)

Subjects/Keywords: Sleep event; Polysomnograpy; HMM; Markov processes; Sleep disorders; Polysomnography

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APA (6th Edition):

Biniwale, A. S. (2016). Analysis of human polysomnography (PSG) for automatic sleep event detection using Hidden Markov Model. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6410

Chicago Manual of Style (16th Edition):

Biniwale, Alakh Sudhir. “Analysis of human polysomnography (PSG) for automatic sleep event detection using Hidden Markov Model.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed November 15, 2019. https://digital.library.txstate.edu/handle/10877/6410.

MLA Handbook (7th Edition):

Biniwale, Alakh Sudhir. “Analysis of human polysomnography (PSG) for automatic sleep event detection using Hidden Markov Model.” 2016. Web. 15 Nov 2019.

Vancouver:

Biniwale AS. Analysis of human polysomnography (PSG) for automatic sleep event detection using Hidden Markov Model. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2019 Nov 15]. Available from: https://digital.library.txstate.edu/handle/10877/6410.

Council of Science Editors:

Biniwale AS. Analysis of human polysomnography (PSG) for automatic sleep event detection using Hidden Markov Model. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6410


Texas State University – San Marcos

6. Mahant, Vaibhav. Improving Top-N Evaluation of Recommender Systems.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 Recommender systems are used to provide the user with a list of recommended items to help user find new items they might prefer. One of… (more)

Subjects/Keywords: Recommender Systems; Evaluation; Recommender systems (Information filtering); Management information systems; Artificial intelligence – Data processing

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APA (6th Edition):

Mahant, V. (2016). Improving Top-N Evaluation of Recommender Systems. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6309

Chicago Manual of Style (16th Edition):

Mahant, Vaibhav. “Improving Top-N Evaluation of Recommender Systems.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed November 15, 2019. https://digital.library.txstate.edu/handle/10877/6309.

MLA Handbook (7th Edition):

Mahant, Vaibhav. “Improving Top-N Evaluation of Recommender Systems.” 2016. Web. 15 Nov 2019.

Vancouver:

Mahant V. Improving Top-N Evaluation of Recommender Systems. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2019 Nov 15]. Available from: https://digital.library.txstate.edu/handle/10877/6309.

Council of Science Editors:

Mahant V. Improving Top-N Evaluation of Recommender Systems. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6309


Texas State University – San Marcos

7. Kazi, Mohammed Imran Rukmoddin. Exploring Potentially Discriminatory Biases In Book Recommendation.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 Recent issues which occurred in the field of artificial intelligence present disproportionality based on protected attributes such as sex, race, and ethnicity in their output… (more)

Subjects/Keywords: Recsys; Recommender systems (Information filtering); Management information systems; Artificial intelligence – Data processing

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APA (6th Edition):

Kazi, M. I. R. (2016). Exploring Potentially Discriminatory Biases In Book Recommendation. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6306

Chicago Manual of Style (16th Edition):

Kazi, Mohammed Imran Rukmoddin. “Exploring Potentially Discriminatory Biases In Book Recommendation.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed November 15, 2019. https://digital.library.txstate.edu/handle/10877/6306.

MLA Handbook (7th Edition):

Kazi, Mohammed Imran Rukmoddin. “Exploring Potentially Discriminatory Biases In Book Recommendation.” 2016. Web. 15 Nov 2019.

Vancouver:

Kazi MIR. Exploring Potentially Discriminatory Biases In Book Recommendation. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2019 Nov 15]. Available from: https://digital.library.txstate.edu/handle/10877/6306.

Council of Science Editors:

Kazi MIR. Exploring Potentially Discriminatory Biases In Book Recommendation. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6306


Texas State University – San Marcos

8. Ring, Patrick D. Movement Classification and Analysis from RGB – D Video Data.

Degree: MS, Computer Science, 2019, Texas State University – San Marcos

 The aim of this thesis is to develop and evaluate methods of human movement classification using motion tracking data captured using a RGB-D sensor. As… (more)

Subjects/Keywords: Kinect; Classification

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APA (6th Edition):

Ring, P. D. (2019). Movement Classification and Analysis from RGB – D Video Data. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/8325

Chicago Manual of Style (16th Edition):

Ring, Patrick D. “Movement Classification and Analysis from RGB – D Video Data.” 2019. Masters Thesis, Texas State University – San Marcos. Accessed November 15, 2019. https://digital.library.txstate.edu/handle/10877/8325.

MLA Handbook (7th Edition):

Ring, Patrick D. “Movement Classification and Analysis from RGB – D Video Data.” 2019. Web. 15 Nov 2019.

Vancouver:

Ring PD. Movement Classification and Analysis from RGB – D Video Data. [Internet] [Masters thesis]. Texas State University – San Marcos; 2019. [cited 2019 Nov 15]. Available from: https://digital.library.txstate.edu/handle/10877/8325.

Council of Science Editors:

Ring PD. Movement Classification and Analysis from RGB – D Video Data. [Masters Thesis]. Texas State University – San Marcos; 2019. Available from: https://digital.library.txstate.edu/handle/10877/8325


Texas State University – San Marcos

9. Krishnamoorthy, Vimal Moorthy. Implementation and evaluation of an android accessor-based IoT middleware.

Degree: MS, Computer Science, 2017, Texas State University – San Marcos

 This thesis proposes an IoT middleware for Android using V8 script engine. This middleware supports the accessor abstraction for IoT and composes IoT applications based… (more)

Subjects/Keywords: IoT; Accessors; Middleware; Android; Internet of things; Middleware; Software engineering; Mobile computing; Mobile communication systems

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APA (6th Edition):

Krishnamoorthy, V. M. (2017). Implementation and evaluation of an android accessor-based IoT middleware. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6814

Chicago Manual of Style (16th Edition):

Krishnamoorthy, Vimal Moorthy. “Implementation and evaluation of an android accessor-based IoT middleware.” 2017. Masters Thesis, Texas State University – San Marcos. Accessed November 15, 2019. https://digital.library.txstate.edu/handle/10877/6814.

MLA Handbook (7th Edition):

Krishnamoorthy, Vimal Moorthy. “Implementation and evaluation of an android accessor-based IoT middleware.” 2017. Web. 15 Nov 2019.

Vancouver:

Krishnamoorthy VM. Implementation and evaluation of an android accessor-based IoT middleware. [Internet] [Masters thesis]. Texas State University – San Marcos; 2017. [cited 2019 Nov 15]. Available from: https://digital.library.txstate.edu/handle/10877/6814.

Council of Science Editors:

Krishnamoorthy VM. Implementation and evaluation of an android accessor-based IoT middleware. [Masters Thesis]. Texas State University – San Marcos; 2017. Available from: https://digital.library.txstate.edu/handle/10877/6814


Texas State University – San Marcos

10. Channamsetty, Sushma. Recommender response to user profile diversity and popularity bias.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 Recommender systems are commonly evaluated to understand the effectiveness of their algorithms. Diversity and novelty of the recommender systems have been in consideration while evaluating… (more)

Subjects/Keywords: Recommender systems; Recommender; Recommender systems (Information filtering); Expert systems (Computer science)

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APA (6th Edition):

Channamsetty, S. (2016). Recommender response to user profile diversity and popularity bias. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6313

Chicago Manual of Style (16th Edition):

Channamsetty, Sushma. “Recommender response to user profile diversity and popularity bias.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed November 15, 2019. https://digital.library.txstate.edu/handle/10877/6313.

MLA Handbook (7th Edition):

Channamsetty, Sushma. “Recommender response to user profile diversity and popularity bias.” 2016. Web. 15 Nov 2019.

Vancouver:

Channamsetty S. Recommender response to user profile diversity and popularity bias. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2019 Nov 15]. Available from: https://digital.library.txstate.edu/handle/10877/6313.

Council of Science Editors:

Channamsetty S. Recommender response to user profile diversity and popularity bias. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6313

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