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You searched for +publisher:"Virginia Tech" +contributor:("Prakash, Bodicherla Aditya"). Showing records 1 – 21 of 21 total matches.

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1. Wagner, Mitchell James. Reconstructing Signaling Pathways Using Regular-Language Constrained Paths.

Degree: MS, Computer Science, 2018, Virginia Tech

 Signaling pathways are widely studied in systems biology. Several databases catalog our knowledge of these pathways, including the proteins and interactions that comprise them. However,… (more)

Subjects/Keywords: Regular Languages; Shortest Paths; Signaling Networks

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APA (6th Edition):

Wagner, M. J. (2018). Reconstructing Signaling Pathways Using Regular-Language Constrained Paths. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/85044

Chicago Manual of Style (16th Edition):

Wagner, Mitchell James. “Reconstructing Signaling Pathways Using Regular-Language Constrained Paths.” 2018. Masters Thesis, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/85044.

MLA Handbook (7th Edition):

Wagner, Mitchell James. “Reconstructing Signaling Pathways Using Regular-Language Constrained Paths.” 2018. Web. 21 Oct 2019.

Vancouver:

Wagner MJ. Reconstructing Signaling Pathways Using Regular-Language Constrained Paths. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/85044.

Council of Science Editors:

Wagner MJ. Reconstructing Signaling Pathways Using Regular-Language Constrained Paths. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/85044

2. Burch, Zachary Campbell. Credential Theft Powered Unauthorized Login Detection through Spatial Augmentation.

Degree: MS, Computer Science, 2018, Virginia Tech

 Credential theft is a network intrusion vector that subverts traditional defenses of a campus network, with a malicious login being the act of an attacker… (more)

Subjects/Keywords: Security; Machine Learning; Login Classification; Spatial Augmentation

…39 5.1 Chart of the amount of dynamic records collected each day from the Virginia Tech… …events that occurred at Virginia Tech in the period of November 13th to January 4th… …76 5.10 Result information for the GeoLogonalyzer tool over a week of Virginia Tech data… …the Virginia Tech campus 3 1.2. Proposed Solution Name Type Latitude Float Longitude… …this process for the Virginia Tech campus is in Chapter 4. Chapter 5 provides a dataset… 

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APA (6th Edition):

Burch, Z. C. (2018). Credential Theft Powered Unauthorized Login Detection through Spatial Augmentation. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/85583

Chicago Manual of Style (16th Edition):

Burch, Zachary Campbell. “Credential Theft Powered Unauthorized Login Detection through Spatial Augmentation.” 2018. Masters Thesis, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/85583.

MLA Handbook (7th Edition):

Burch, Zachary Campbell. “Credential Theft Powered Unauthorized Login Detection through Spatial Augmentation.” 2018. Web. 21 Oct 2019.

Vancouver:

Burch ZC. Credential Theft Powered Unauthorized Login Detection through Spatial Augmentation. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/85583.

Council of Science Editors:

Burch ZC. Credential Theft Powered Unauthorized Login Detection through Spatial Augmentation. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/85583


Virginia Tech

3. Sundareisan, Shashidhar. Making diffusion work for you: Classification sans text, finding culprits and filling missing values.

Degree: MS, Computer Science, 2014, Virginia Tech

 Can we find people infected with the flu virus even though they did not visit a doctor? Can the temporal features of a trending hashtag… (more)

Subjects/Keywords: Data Mining; Social Networks; Epidemiology; Culprits; Missing nodes; Diffusion; Protests; Classification

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APA (6th Edition):

Sundareisan, S. (2014). Making diffusion work for you: Classification sans text, finding culprits and filling missing values. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/49678

Chicago Manual of Style (16th Edition):

Sundareisan, Shashidhar. “Making diffusion work for you: Classification sans text, finding culprits and filling missing values.” 2014. Masters Thesis, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/49678.

MLA Handbook (7th Edition):

Sundareisan, Shashidhar. “Making diffusion work for you: Classification sans text, finding culprits and filling missing values.” 2014. Web. 21 Oct 2019.

Vancouver:

Sundareisan S. Making diffusion work for you: Classification sans text, finding culprits and filling missing values. [Internet] [Masters thesis]. Virginia Tech; 2014. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/49678.

Council of Science Editors:

Sundareisan S. Making diffusion work for you: Classification sans text, finding culprits and filling missing values. [Masters Thesis]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/49678


Virginia Tech

4. Senthil, Rathna. IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks.

Degree: MS, Computer Science, 2016, Virginia Tech

 Complex systems in areas such as biology, physics, social science, and technology are extensively modeled as networks due to the rich set of tools available… (more)

Subjects/Keywords: Overlapping Community Detection; Complex Networks; Local Expansion

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APA (6th Edition):

Senthil, R. (2016). IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/65160

Chicago Manual of Style (16th Edition):

Senthil, Rathna. “IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks.” 2016. Masters Thesis, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/65160.

MLA Handbook (7th Edition):

Senthil, Rathna. “IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks.” 2016. Web. 21 Oct 2019.

Vancouver:

Senthil R. IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/65160.

Council of Science Editors:

Senthil R. IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks. [Masters Thesis]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/65160


Virginia Tech

5. Mahendiran, Aravindan. Automated Vocabulary Building for Characterizing and Forecasting Elections using Social Media Analytics.

Degree: MS, Computer Science, 2014, Virginia Tech

 Twitter has become a popular data source in the recent decade and garnered a significant amount of attention as a surrogate data source for many… (more)

Subjects/Keywords: Election Forecasting; Twitter; Query Expansion; Social Group Modeling; Probabilistic Soft Logic

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APA (6th Edition):

Mahendiran, A. (2014). Automated Vocabulary Building for Characterizing and Forecasting Elections using Social Media Analytics. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/25430

Chicago Manual of Style (16th Edition):

Mahendiran, Aravindan. “Automated Vocabulary Building for Characterizing and Forecasting Elections using Social Media Analytics.” 2014. Masters Thesis, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/25430.

MLA Handbook (7th Edition):

Mahendiran, Aravindan. “Automated Vocabulary Building for Characterizing and Forecasting Elections using Social Media Analytics.” 2014. Web. 21 Oct 2019.

Vancouver:

Mahendiran A. Automated Vocabulary Building for Characterizing and Forecasting Elections using Social Media Analytics. [Internet] [Masters thesis]. Virginia Tech; 2014. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/25430.

Council of Science Editors:

Mahendiran A. Automated Vocabulary Building for Characterizing and Forecasting Elections using Social Media Analytics. [Masters Thesis]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/25430


Virginia Tech

6. Nachimuthu Nallasamy, Kanagaraj. Enhancing Fault Localization with Cost Awareness.

Degree: MS, Computer Science, 2019, Virginia Tech

 Debugging is a challenging and time-consuming process in software life-cycle. The focus of the thesis is to improve the accuracy of existing fault localization (FL)… (more)

Subjects/Keywords: fault localization; automated debugging; source code line features; cost-aware fault localization

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APA (6th Edition):

Nachimuthu Nallasamy, K. (2019). Enhancing Fault Localization with Cost Awareness. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/90575

Chicago Manual of Style (16th Edition):

Nachimuthu Nallasamy, Kanagaraj. “Enhancing Fault Localization with Cost Awareness.” 2019. Masters Thesis, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/90575.

MLA Handbook (7th Edition):

Nachimuthu Nallasamy, Kanagaraj. “Enhancing Fault Localization with Cost Awareness.” 2019. Web. 21 Oct 2019.

Vancouver:

Nachimuthu Nallasamy K. Enhancing Fault Localization with Cost Awareness. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/90575.

Council of Science Editors:

Nachimuthu Nallasamy K. Enhancing Fault Localization with Cost Awareness. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/90575


Virginia Tech

7. Patil, Supritha Basavaraj. Analysis of Moving Events Using Tweets.

Degree: MS, Computer Science, 2019, Virginia Tech

 The Digital Library Research Laboratory (DLRL) has collected over 3.5 billion tweets on different events for the Coordinated, Behaviorally-Aware Recovery for Transportation and Power Disruptions… (more)

Subjects/Keywords: Natural Language Processing; Twitter

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APA (6th Edition):

Patil, S. B. (2019). Analysis of Moving Events Using Tweets. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/90884

Chicago Manual of Style (16th Edition):

Patil, Supritha Basavaraj. “Analysis of Moving Events Using Tweets.” 2019. Masters Thesis, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/90884.

MLA Handbook (7th Edition):

Patil, Supritha Basavaraj. “Analysis of Moving Events Using Tweets.” 2019. Web. 21 Oct 2019.

Vancouver:

Patil SB. Analysis of Moving Events Using Tweets. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/90884.

Council of Science Editors:

Patil SB. Analysis of Moving Events Using Tweets. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/90884


Virginia Tech

8. Kaw, Rushi. Modeling and Computation of Complex Interventions in Large-scale Epidemiological Simulations using SQL and Distributed Database.

Degree: MS, Computer Science and Applications, 2014, Virginia Tech

 Scalability is an important problem in epidemiological applications that simulate complex intervention scenarios over large datasets. Indemics is one such interactive data intensive framework for… (more)

Subjects/Keywords: epidemic simulation; distributed system; database system

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APA (6th Edition):

Kaw, R. (2014). Modeling and Computation of Complex Interventions in Large-scale Epidemiological Simulations using SQL and Distributed Database. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/50434

Chicago Manual of Style (16th Edition):

Kaw, Rushi. “Modeling and Computation of Complex Interventions in Large-scale Epidemiological Simulations using SQL and Distributed Database.” 2014. Masters Thesis, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/50434.

MLA Handbook (7th Edition):

Kaw, Rushi. “Modeling and Computation of Complex Interventions in Large-scale Epidemiological Simulations using SQL and Distributed Database.” 2014. Web. 21 Oct 2019.

Vancouver:

Kaw R. Modeling and Computation of Complex Interventions in Large-scale Epidemiological Simulations using SQL and Distributed Database. [Internet] [Masters thesis]. Virginia Tech; 2014. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/50434.

Council of Science Editors:

Kaw R. Modeling and Computation of Complex Interventions in Large-scale Epidemiological Simulations using SQL and Distributed Database. [Masters Thesis]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/50434

9. Liu, Fang. Mining Security Risks from Massive Datasets.

Degree: PhD, Computer Science, 2017, Virginia Tech

 Cyber security risk has been a problem ever since the appearance of telecommunication and electronic computers. In the recent 30 years, researchers have developed various… (more)

Subjects/Keywords: Cyber Security; Big Data Security; Mobile Security; Data Leakage Detection

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APA (6th Edition):

Liu, F. (2017). Mining Security Risks from Massive Datasets. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78684

Chicago Manual of Style (16th Edition):

Liu, Fang. “Mining Security Risks from Massive Datasets.” 2017. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/78684.

MLA Handbook (7th Edition):

Liu, Fang. “Mining Security Risks from Massive Datasets.” 2017. Web. 21 Oct 2019.

Vancouver:

Liu F. Mining Security Risks from Massive Datasets. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/78684.

Council of Science Editors:

Liu F. Mining Security Risks from Massive Datasets. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/78684


Virginia Tech

10. Chen, Liangzhe. Segmenting, Summarizing and Predicting Data Sequences.

Degree: PhD, Computer Science, 2018, Virginia Tech

 Temporal data is ubiquitous nowadays and can be easily found in many applications. Consider the extensively studied social media website Twitter. All the information can… (more)

Subjects/Keywords: Sequence Mining; Segmentation; Topic Modeling

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APA (6th Edition):

Chen, L. (2018). Segmenting, Summarizing and Predicting Data Sequences. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83573

Chicago Manual of Style (16th Edition):

Chen, Liangzhe. “Segmenting, Summarizing and Predicting Data Sequences.” 2018. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/83573.

MLA Handbook (7th Edition):

Chen, Liangzhe. “Segmenting, Summarizing and Predicting Data Sequences.” 2018. Web. 21 Oct 2019.

Vancouver:

Chen L. Segmenting, Summarizing and Predicting Data Sequences. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/83573.

Council of Science Editors:

Chen L. Segmenting, Summarizing and Predicting Data Sequences. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83573


Virginia Tech

11. Shao, Huijuan. Temporal Mining Approaches for Smart Buildings Research.

Degree: PhD, Computer Science, 2017, Virginia Tech

 With the advent of modern sensor technologies, significant opportunities have opened up to help conserve energy in residential and commercial buildings. Moreover, the rapid urbanization… (more)

Subjects/Keywords: Data mining; Sustainability; Energy disaggregation; Occupancy prediction

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APA (6th Edition):

Shao, H. (2017). Temporal Mining Approaches for Smart Buildings Research. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/84349

Chicago Manual of Style (16th Edition):

Shao, Huijuan. “Temporal Mining Approaches for Smart Buildings Research.” 2017. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/84349.

MLA Handbook (7th Edition):

Shao, Huijuan. “Temporal Mining Approaches for Smart Buildings Research.” 2017. Web. 21 Oct 2019.

Vancouver:

Shao H. Temporal Mining Approaches for Smart Buildings Research. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/84349.

Council of Science Editors:

Shao H. Temporal Mining Approaches for Smart Buildings Research. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/84349


Virginia Tech

12. Hamouda, Sally Mohamed Fathy Mo. Enhancing Learning of Recursion.

Degree: PhD, Computer Science, 2015, Virginia Tech

 Recursion is one of the most important and hardest topics in lower division computer science courses. As it is an advanced programming skill, the best… (more)

Subjects/Keywords: Recursion; Online Learning; Automated Assessment; Concept Inventory; Binary Trees

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APA (6th Edition):

Hamouda, S. M. F. M. (2015). Enhancing Learning of Recursion. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64249

Chicago Manual of Style (16th Edition):

Hamouda, Sally Mohamed Fathy Mo. “Enhancing Learning of Recursion.” 2015. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/64249.

MLA Handbook (7th Edition):

Hamouda, Sally Mohamed Fathy Mo. “Enhancing Learning of Recursion.” 2015. Web. 21 Oct 2019.

Vancouver:

Hamouda SMFM. Enhancing Learning of Recursion. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/64249.

Council of Science Editors:

Hamouda SMFM. Enhancing Learning of Recursion. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/64249


Virginia Tech

13. Momtazpour, Marjan. Knowledge Discovery for Sustainable Urban Mobility.

Degree: PhD, Computer Science, 2016, Virginia Tech

 Due to the rapid growth of urban areas, sustainable urbanization is an inevitable task for city planners to address major challenges in resource management across… (more)

Subjects/Keywords: Data mining; Urban computing; Smart grids; Electric vehicles.

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APA (6th Edition):

Momtazpour, M. (2016). Knowledge Discovery for Sustainable Urban Mobility. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/65157

Chicago Manual of Style (16th Edition):

Momtazpour, Marjan. “Knowledge Discovery for Sustainable Urban Mobility.” 2016. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/65157.

MLA Handbook (7th Edition):

Momtazpour, Marjan. “Knowledge Discovery for Sustainable Urban Mobility.” 2016. Web. 21 Oct 2019.

Vancouver:

Momtazpour M. Knowledge Discovery for Sustainable Urban Mobility. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/65157.

Council of Science Editors:

Momtazpour M. Knowledge Discovery for Sustainable Urban Mobility. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/65157


Virginia Tech

14. Khan, Mohammed Saquib Akmal. Efficient Spatio-Temporal Network Analytics in Epidemiological Studies using Distributed Databases.

Degree: MS, Computer Science, 2015, Virginia Tech

 Real-time Spatio-Temporal Analytics has become an integral part of Epidemiological studies. The size of the spatio-temporal data has been increasing tremendously over the years, gradually… (more)

Subjects/Keywords: Data Analytics; Data Mining; Distributed Systems; Database Systems

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APA (6th Edition):

Khan, M. S. A. (2015). Efficient Spatio-Temporal Network Analytics in Epidemiological Studies using Distributed Databases. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/51223

Chicago Manual of Style (16th Edition):

Khan, Mohammed Saquib Akmal. “Efficient Spatio-Temporal Network Analytics in Epidemiological Studies using Distributed Databases.” 2015. Masters Thesis, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/51223.

MLA Handbook (7th Edition):

Khan, Mohammed Saquib Akmal. “Efficient Spatio-Temporal Network Analytics in Epidemiological Studies using Distributed Databases.” 2015. Web. 21 Oct 2019.

Vancouver:

Khan MSA. Efficient Spatio-Temporal Network Analytics in Epidemiological Studies using Distributed Databases. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/51223.

Council of Science Editors:

Khan MSA. Efficient Spatio-Temporal Network Analytics in Epidemiological Studies using Distributed Databases. [Masters Thesis]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/51223


Virginia Tech

15. Hossain, K.S.M. Tozammel. Modeling Evolutionary Constraints and Improving Multiple Sequence Alignments using Residue Couplings.

Degree: PhD, Computer Science, 2016, Virginia Tech

 Residue coupling in protein families has received much attention as an important indicator toward predicting protein structures and revealing functional insight into proteins. Existing coupling… (more)

Subjects/Keywords: residue coupling; multiple sequence alignment; graphical models; pattern set mining

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APA (6th Edition):

Hossain, K. S. M. T. (2016). Modeling Evolutionary Constraints and Improving Multiple Sequence Alignments using Residue Couplings. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83218

Chicago Manual of Style (16th Edition):

Hossain, K S M Tozammel. “Modeling Evolutionary Constraints and Improving Multiple Sequence Alignments using Residue Couplings.” 2016. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/83218.

MLA Handbook (7th Edition):

Hossain, K S M Tozammel. “Modeling Evolutionary Constraints and Improving Multiple Sequence Alignments using Residue Couplings.” 2016. Web. 21 Oct 2019.

Vancouver:

Hossain KSMT. Modeling Evolutionary Constraints and Improving Multiple Sequence Alignments using Residue Couplings. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/83218.

Council of Science Editors:

Hossain KSMT. Modeling Evolutionary Constraints and Improving Multiple Sequence Alignments using Residue Couplings. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/83218

16. Zhang, Yao. Optimizing and Understanding Network Structure for Diffusion.

Degree: PhD, Computer Science, 2017, Virginia Tech

 Given a population contact network and electronic medical records of patients, how to distribute vaccines to individuals to effectively control a flu epidemic? Similarly, given… (more)

Subjects/Keywords: Data Mining; Graph/Network; Diffusion

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APA (6th Edition):

Zhang, Y. (2017). Optimizing and Understanding Network Structure for Diffusion. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/79674

Chicago Manual of Style (16th Edition):

Zhang, Yao. “Optimizing and Understanding Network Structure for Diffusion.” 2017. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/79674.

MLA Handbook (7th Edition):

Zhang, Yao. “Optimizing and Understanding Network Structure for Diffusion.” 2017. Web. 21 Oct 2019.

Vancouver:

Zhang Y. Optimizing and Understanding Network Structure for Diffusion. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/79674.

Council of Science Editors:

Zhang Y. Optimizing and Understanding Network Structure for Diffusion. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/79674


Virginia Tech

17. Keneshloo, Yaser. Addressing Challenges of Modern News Agencies via Predictive Modeling, Deep Learning, and Transfer Learning.

Degree: PhD, Computer Science, 2019, Virginia Tech

 Today's news agencies are moving from traditional journalism, where publishing just a few news articles per day was sufficient, to modern content generation mechanisms, which… (more)

Subjects/Keywords: Text Summarization; Predictive Modeling; Deep Learning; Transfer Learning; Reinforcement Learning

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APA (6th Edition):

Keneshloo, Y. (2019). Addressing Challenges of Modern News Agencies via Predictive Modeling, Deep Learning, and Transfer Learning. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/91910

Chicago Manual of Style (16th Edition):

Keneshloo, Yaser. “Addressing Challenges of Modern News Agencies via Predictive Modeling, Deep Learning, and Transfer Learning.” 2019. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/91910.

MLA Handbook (7th Edition):

Keneshloo, Yaser. “Addressing Challenges of Modern News Agencies via Predictive Modeling, Deep Learning, and Transfer Learning.” 2019. Web. 21 Oct 2019.

Vancouver:

Keneshloo Y. Addressing Challenges of Modern News Agencies via Predictive Modeling, Deep Learning, and Transfer Learning. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/91910.

Council of Science Editors:

Keneshloo Y. Addressing Challenges of Modern News Agencies via Predictive Modeling, Deep Learning, and Transfer Learning. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/91910

18. Khadivi, Pejman. Online Denoising Solutions for Forecasting Applications.

Degree: PhD, Computer Science, 2016, Virginia Tech

 Dealing with noisy time series is a crucial task in many data-driven real-time applications. Due to the inaccuracies in data acquisition, time series suffer from… (more)

Subjects/Keywords: Data analytics; Denoising; Information theory; Time series; Forecasting

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APA (6th Edition):

Khadivi, P. (2016). Online Denoising Solutions for Forecasting Applications. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/72907

Chicago Manual of Style (16th Edition):

Khadivi, Pejman. “Online Denoising Solutions for Forecasting Applications.” 2016. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/72907.

MLA Handbook (7th Edition):

Khadivi, Pejman. “Online Denoising Solutions for Forecasting Applications.” 2016. Web. 21 Oct 2019.

Vancouver:

Khadivi P. Online Denoising Solutions for Forecasting Applications. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/72907.

Council of Science Editors:

Khadivi P. Online Denoising Solutions for Forecasting Applications. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/72907

19. Hafeez, Abdul. A Software Framework For the Detection and Classification of Biological Targets in Bio-Nano Sensing.

Degree: PhD, Computer Science, 2014, Virginia Tech

 Detection and identification of important biological targets, such as DNA, proteins, and diseased human cells are crucial for early diagnosis and prognosis. The key to… (more)

Subjects/Keywords: Accelerated Diagnosis; Solid-state micropores; Parallel and Distributed Computing

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

APA (6th Edition):

Hafeez, A. (2014). A Software Framework For the Detection and Classification of Biological Targets in Bio-Nano Sensing. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/50490

Chicago Manual of Style (16th Edition):

Hafeez, Abdul. “A Software Framework For the Detection and Classification of Biological Targets in Bio-Nano Sensing.” 2014. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/50490.

MLA Handbook (7th Edition):

Hafeez, Abdul. “A Software Framework For the Detection and Classification of Biological Targets in Bio-Nano Sensing.” 2014. Web. 21 Oct 2019.

Vancouver:

Hafeez A. A Software Framework For the Detection and Classification of Biological Targets in Bio-Nano Sensing. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/50490.

Council of Science Editors:

Hafeez A. A Software Framework For the Detection and Classification of Biological Targets in Bio-Nano Sensing. [Doctoral Dissertation]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/50490


Virginia Tech

20. Sun, Qing. Greedy Inference Algorithms for Structured and Neural Models.

Degree: PhD, Electrical Engineering, 2018, Virginia Tech

 A number of problems in Computer Vision, Natural Language Processing, and Machine Learning produce structured outputs in high-dimensional space, which makes searching for the global… (more)

Subjects/Keywords: greedy algorithm; natural language processing; graph models; recurrent neural networks; beam search

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

APA (6th Edition):

Sun, Q. (2018). Greedy Inference Algorithms for Structured and Neural Models. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/81860

Chicago Manual of Style (16th Edition):

Sun, Qing. “Greedy Inference Algorithms for Structured and Neural Models.” 2018. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/81860.

MLA Handbook (7th Edition):

Sun, Qing. “Greedy Inference Algorithms for Structured and Neural Models.” 2018. Web. 21 Oct 2019.

Vancouver:

Sun Q. Greedy Inference Algorithms for Structured and Neural Models. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/81860.

Council of Science Editors:

Sun Q. Greedy Inference Algorithms for Structured and Neural Models. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/81860


Virginia Tech

21. Rahman, Md Ahsanur. Unstable Communities in Network Ensembles.

Degree: PhD, Computer Science, 2016, Virginia Tech

 Ensembles of graphs arise naturally in many applications, for example, the temporal evolution of social contacts or computer communications, tissue-specific protein interaction networks, annual citation… (more)

Subjects/Keywords: Computational Biology; Graph Mining; Network Theory; Unstable Community; Hypergraph; Hyperedge

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

APA (6th Edition):

Rahman, M. A. (2016). Unstable Communities in Network Ensembles. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78290

Chicago Manual of Style (16th Edition):

Rahman, Md Ahsanur. “Unstable Communities in Network Ensembles.” 2016. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/78290.

MLA Handbook (7th Edition):

Rahman, Md Ahsanur. “Unstable Communities in Network Ensembles.” 2016. Web. 21 Oct 2019.

Vancouver:

Rahman MA. Unstable Communities in Network Ensembles. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/78290.

Council of Science Editors:

Rahman MA. Unstable Communities in Network Ensembles. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/78290

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