+publisher:"University of Houston" +contributor:("Eick, Christoph F.")

University of Houston

1. Budhavarapu, Aparna. Satpura: A Novel Framework for Density Estimation, Hotspot Discovery, Change Analysis, and Change-based Alerts.

Degree: MS, Computer Science, 2020, University of Houston

URL: http://hdl.handle.net/10657/6623

► Due to the technological advancement in remote sensors and sensor networks, different types of spatio-temporal data are increasingly available. Spatio-temporal data analysis has applications in… (more)

Subjects/Keywords: Satpura

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APA (6^th Edition):

Budhavarapu, A. (2020). Satpura: A Novel Framework for Density Estimation, Hotspot Discovery, Change Analysis, and Change-based Alerts. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/6623

Chicago Manual of Style (16^th Edition):

Budhavarapu, Aparna. “Satpura: A Novel Framework for Density Estimation, Hotspot Discovery, Change Analysis, and Change-based Alerts.” 2020. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/6623.

MLA Handbook (7^th Edition):

Budhavarapu, Aparna. “Satpura: A Novel Framework for Density Estimation, Hotspot Discovery, Change Analysis, and Change-based Alerts.” 2020. Web. 06 Mar 2021.

Vancouver:

Budhavarapu A. Satpura: A Novel Framework for Density Estimation, Hotspot Discovery, Change Analysis, and Change-based Alerts. [Internet] [Masters thesis]. University of Houston; 2020. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/6623.

Council of Science Editors:

Budhavarapu A. Satpura: A Novel Framework for Density Estimation, Hotspot Discovery, Change Analysis, and Change-based Alerts. [Masters Thesis]. University of Houston; 2020. Available from: http://hdl.handle.net/10657/6623

University of Houston

2. -7251-2238. Computational Methods for Tweet Summarization and Emotion Extraction.

Degree: MS, Computer Science, 2020, University of Houston

URL: http://hdl.handle.net/10657/6617

► The process of gathering insights from social media has gained significant importance in the last decade. Since social media data is growing larger and larger,… (more)

Subjects/Keywords: NLP; Twitter Analytics; Tweet Summarization; Sentiment Analysis

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APA (6^th Edition):

-7251-2238. (2020). Computational Methods for Tweet Summarization and Emotion Extraction. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/6617

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

-7251-2238. “Computational Methods for Tweet Summarization and Emotion Extraction.” 2020. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/6617.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

-7251-2238. “Computational Methods for Tweet Summarization and Emotion Extraction.” 2020. Web. 06 Mar 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-7251-2238. Computational Methods for Tweet Summarization and Emotion Extraction. [Internet] [Masters thesis]. University of Houston; 2020. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/6617.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-7251-2238. Computational Methods for Tweet Summarization and Emotion Extraction. [Masters Thesis]. University of Houston; 2020. Available from: http://hdl.handle.net/10657/6617

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

University of Houston

3. Biediger, Dan 1974-. Enhancing Multiple Sclerosis Lesion Segmentation in MRI Images.

Degree: MS, Computer Science, 2012, University of Houston

URL: http://hdl.handle.net/10657/560

► Multiple sclerosis (MS) is an autoimmune disease of the central nervous system that causes damage to the insulating myelin sheaths around the axons in the… (more)

▼ Multiple sclerosis (MS) is an autoimmune disease of the central nervous system that causes damage to the insulating myelin sheaths around the axons in the brain. It affects over 2.5 million people world-wide. The disease progresses at different rates in different people and can have periods of remission and relapse. A fast and accurate method for evaluating the number and size of MS lesions in the brain is a key component in evaluating the progress of the disease and the efficacy of treatments. MS lesion segmentation usually requires the expertise of a trained physician. Manual segmentation is slow and difficult and the results can be somewhat subjective. While many automated methods exist, they do not provide sufficiently accurate segmentation results. There exists a need for a robust, fast, and accurate method for automatically segmenting MS lesions. This thesis presents the results of an effort to improve the segmentation results of an existing system for lesion segmentation in MRI images. It includes two different strategies to improve the segmentation results by addressing opportunities missed in the existing approach. The first strategy leverages the current processing system at a granularity finer than the whole-brain to detect lesions at a local level. The existing system makes global estimates on the tissue intensities. Because these intensities vary across the brain, the global assumption provides inaccurate estimates in some cases. The first improvement combines a series of local results to produce a whole-brain lesion segmentation. This approach better captures the local lesion properties and produces encouraging results, with a general improvement in the detection rate of lesions. The second method looks at the individual voxel level and the local intensity neighborhood. As a post-processing method, it selects seed points from the results of the previous step. It uses a region growing method based on cellular automata to expand the lesion areas based on a local neighborhood similarity in intensity. While it provides some benefit, it is sensitive to initial conditions and the results depend on the implementation details. Advisors/Committee Members: Shah, Shishir Kirit (advisor), Eick, Christoph F. (committee member), Collet, Christophe (committee member).

Subjects/Keywords: Multiple Sclerosis; MRI; Lesion; Segmentation; Grow-cut; Cellular automata; Computer science

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APA (6^th Edition):

Biediger, D. 1. (2012). Enhancing Multiple Sclerosis Lesion Segmentation in MRI Images. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/560

Chicago Manual of Style (16^th Edition):

Biediger, Dan 1974-. “Enhancing Multiple Sclerosis Lesion Segmentation in MRI Images.” 2012. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/560.

MLA Handbook (7^th Edition):

Biediger, Dan 1974-. “Enhancing Multiple Sclerosis Lesion Segmentation in MRI Images.” 2012. Web. 06 Mar 2021.

Vancouver:

Biediger D1. Enhancing Multiple Sclerosis Lesion Segmentation in MRI Images. [Internet] [Masters thesis]. University of Houston; 2012. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/560.

Council of Science Editors:

Biediger D1. Enhancing Multiple Sclerosis Lesion Segmentation in MRI Images. [Masters Thesis]. University of Houston; 2012. Available from: http://hdl.handle.net/10657/560

University of Houston

4. -4085-1454. Design and Implementation of Real-time Student Performance Evaluation and Feedback System.

Degree: MS, Computer Science, 2017, University of Houston

URL: http://hdl.handle.net/10657/4565

► Undergraduate education is challenged by high dropout rates and by delayed student graduation due to dropping courses or having to repeat courses due to low… (more)

Subjects/Keywords: Educational data mining; Data analysis; Machine learning

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APA (6^th Edition):

-4085-1454. (2017). Design and Implementation of Real-time Student Performance Evaluation and Feedback System. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/4565

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

-4085-1454. “Design and Implementation of Real-time Student Performance Evaluation and Feedback System.” 2017. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/4565.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

-4085-1454. “Design and Implementation of Real-time Student Performance Evaluation and Feedback System.” 2017. Web. 06 Mar 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-4085-1454. Design and Implementation of Real-time Student Performance Evaluation and Feedback System. [Internet] [Masters thesis]. University of Houston; 2017. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/4565.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-4085-1454. Design and Implementation of Real-time Student Performance Evaluation and Feedback System. [Masters Thesis]. University of Houston; 2017. Available from: http://hdl.handle.net/10657/4565

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

University of Houston

5. Kotadia, Kinjal 1994-. Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering.

Degree: MS, Computer Science, 2018, University of Houston

URL: http://hdl.handle.net/10657/3117

► Detecting Social Network Groups from Video data acquired from surveillance cameras is a challenging problem currently being addressed by the Data Mining and Computer Vision… (more)

Subjects/Keywords: Social Network Groups; Motion Similarity; Graph Clustering; Elbow Method

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APA (6^th Edition):

Kotadia, K. 1. (2018). Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3117

Chicago Manual of Style (16^th Edition):

Kotadia, Kinjal 1994-. “Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering.” 2018. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/3117.

MLA Handbook (7^th Edition):

Kotadia, Kinjal 1994-. “Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering.” 2018. Web. 06 Mar 2021.

Vancouver:

Kotadia K1. Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering. [Internet] [Masters thesis]. University of Houston; 2018. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/3117.

Council of Science Editors:

Kotadia K1. Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering. [Masters Thesis]. University of Houston; 2018. Available from: http://hdl.handle.net/10657/3117

University of Houston

6. Man, Xiaoxi 1984-. DESIGN AND IMPLEMENTATION OF A CO-LOCATION ANALYSIS TOOL.

Degree: MS, Computer Science, 2014, University of Houston

URL: http://hdl.handle.net/10657/1613

► In recent years the widespread usage of scanning device, such as GPS-enabled devices, PDAs, and video cameras, has resulted in an abundance of spatial data.… (more)

Subjects/Keywords: Co-location; Ripley's k-function; K-nearest neighbor distance

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APA (6^th Edition):

Man, X. 1. (2014). DESIGN AND IMPLEMENTATION OF A CO-LOCATION ANALYSIS TOOL. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/1613

Chicago Manual of Style (16^th Edition):

Man, Xiaoxi 1984-. “DESIGN AND IMPLEMENTATION OF A CO-LOCATION ANALYSIS TOOL.” 2014. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/1613.

MLA Handbook (7^th Edition):

Man, Xiaoxi 1984-. “DESIGN AND IMPLEMENTATION OF A CO-LOCATION ANALYSIS TOOL.” 2014. Web. 06 Mar 2021.

Vancouver:

Man X1. DESIGN AND IMPLEMENTATION OF A CO-LOCATION ANALYSIS TOOL. [Internet] [Masters thesis]. University of Houston; 2014. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/1613.

Council of Science Editors:

Man X1. DESIGN AND IMPLEMENTATION OF A CO-LOCATION ANALYSIS TOOL. [Masters Thesis]. University of Houston; 2014. Available from: http://hdl.handle.net/10657/1613

University of Houston

7. Jidagam, Rohith 1990-. Design and Implementation of Faculty Support System to Reduce Course Dropout Rates.

Degree: MS, Computer Science, 2016, University of Houston

URL: http://hdl.handle.net/10657/3208

► The primary goal of educational systems is not only to provide quality of education but also to make sure that students graduate with a strong… (more)

▼ The primary goal of educational systems is not only to provide quality of education but also to make sure that students graduate with a strong academic standing. One specific challenge that universities face is high course drop rates. An early prediction of students’ failure may help to identify students who need special attention to reduce course drop rates by providing appropriate interventions, such as continuous mentoring and conducting review sessions. To address this problem, a new framework called Faculty Support System (FSS) is proposed that learns different classification models to predict student course performance based on his/her attendance, and performance in assignments, quizzes, in-class group projects, and exams. The investigated approaches for this task include Naïve Bayes, Multi-Layer Neural Networks, Decision Trees, and Random Forests. Next, using these models potentially low-performing students will be selected for interventions. Finally, data related to the performance of particular interventions and the employed classification models will be collected at the end of the semester. The proposed FSS framework is evaluated on two different real-world datasets that were obtained during two different semesters for two Computer Science courses at University of Houston, Texas. Our experimental results reveal that Multi-Layer Neural Networks performed the best, and the proposed modelling approach can efficiently identify students at risk, and recommend interventions to enhance their performance before the final exam of the semester. The evaluation of different classifiers on educational datasets gave some insights into how different data mining algorithms predict student performance and enhance student retention. Moreover, the experiments created valuable data about the performance of different interventions. Advisors/Committee Members: Eick, Christoph F. (advisor), Rizk, Nouhad (committee member), Tolar, Tammy (committee member), Shi, Weidong (committee member).

Subjects/Keywords: Educational data mining; Decision trees; Random forests; Naïve Bayes; Multiple Layer Neural Networks; Classification; Student performance

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APA (6^th Edition):

Jidagam, R. 1. (2016). Design and Implementation of Faculty Support System to Reduce Course Dropout Rates. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3208

Chicago Manual of Style (16^th Edition):

Jidagam, Rohith 1990-. “Design and Implementation of Faculty Support System to Reduce Course Dropout Rates.” 2016. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/3208.

MLA Handbook (7^th Edition):

Jidagam, Rohith 1990-. “Design and Implementation of Faculty Support System to Reduce Course Dropout Rates.” 2016. Web. 06 Mar 2021.

Vancouver:

Jidagam R1. Design and Implementation of Faculty Support System to Reduce Course Dropout Rates. [Internet] [Masters thesis]. University of Houston; 2016. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/3208.

Council of Science Editors:

Jidagam R1. Design and Implementation of Faculty Support System to Reduce Course Dropout Rates. [Masters Thesis]. University of Houston; 2016. Available from: http://hdl.handle.net/10657/3208

University of Houston

8. Kulkarni, Akshay Bhavani Kumar 1994-. Early Detection of Depression.

Degree: MS, Computer Science, 2018, University of Houston

URL: http://hdl.handle.net/10657/3089

► Depression is a mental disorder that affects more than 300 million people worldwide. An individual suffering from depression functions poorly in life, is prone to… (more)

▼ Depression is a mental disorder that affects more than 300 million people worldwide. An individual suffering from depression functions poorly in life, is prone to other diseases and in the worst-case, depression leads to suicide. There are many impediments that prevent expert care from reaching people suffering from depression in time. Impediments such as social stigma associated with mental disorders, lack of trained health-care professionals and ignorance of the signs of depression owing to a lack of awareness of the disease. Moreover, the World Health Organization (WHO) claims that individuals who are depressed are often not correctly diagnosed and others who are misdiagnosed are prescribed antidepressants. Thus, there is a strong need to automatically assess the risk of depression. Identification of depression from social media has been framed as a classification problem in the field of Natural Language Processing (NLP). In this work we study NLP approaches that can successfully extract information from textual data to enhance identification of depression. These NLP approaches perform feature extraction to build document representations. The issues of detecting depression in a social media environment is data scarcity for users with depression and the inherent noise associated with social media data. We attempt to address those issues by using representations that can naturally cope with a social media environment. Specifically, we propose the usage of Distributed Term Representations (DTRs) to capture information that can be used by supervised machine learning methods for learning and classifying users suffering from depression. Experimental evaluation provides evidence that DTRs are more effective for depression detection than traditional representations such as Bag of Words (BOW) and representations based on neural word embeddings. In fact, we have obtained state-of-the-art results with Document Occurrence Representation (DOR) for depression detection (F1-Score 0.66 on the depressed class). For early detection of depression, we have obtained the lowest reported Early Risk Detection Error (ERDE) using Pyramidal a newly adapted method that is used for computing document representations. Advisors/Committee Members: Solorio, Thamar (advisor), Gonzalez, Fabio A. (committee member), Eick, Christoph F. (committee member).

Subjects/Keywords: Natural Language Processing; Health care

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APA (6^th Edition):

Kulkarni, A. B. K. 1. (2018). Early Detection of Depression. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3089

Chicago Manual of Style (16^th Edition):

Kulkarni, Akshay Bhavani Kumar 1994-. “Early Detection of Depression.” 2018. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/3089.

MLA Handbook (7^th Edition):

Kulkarni, Akshay Bhavani Kumar 1994-. “Early Detection of Depression.” 2018. Web. 06 Mar 2021.

Vancouver:

Kulkarni ABK1. Early Detection of Depression. [Internet] [Masters thesis]. University of Houston; 2018. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/3089.

Council of Science Editors:

Kulkarni ABK1. Early Detection of Depression. [Masters Thesis]. University of Houston; 2018. Available from: http://hdl.handle.net/10657/3089

University of Houston

9. Zhang, Yongli 1990-. Density-Contour Based Framework for Spatio-Temporal Clustering and Event Tracking in Twitter.

Degree: PhD, Computer Science, 2018, University of Houston

URL: http://hdl.handle.net/10657/5707

► Due to the advances in remote sensors and sensor networks, different types of spatio-temporal datasets have become increasingly available. Revealing interesting spatio-temporal patterns from such… (more)

▼ Due to the advances in remote sensors and sensor networks, different types of spatio-temporal datasets have become increasingly available. Revealing interesting spatio-temporal patterns from such datasets is very important, as it has broad applications, such as understanding climate change, epidemics detection, and earthquake analysis. The main focus of this research is the development of spatio-temporal clustering frameworks. In this dissertation, we introduce a density-contour based framework for spatio-temporal clustering including several novel serial, density-contour based spatio-temporal clustering algorithms: ST-DCONTOUR, ST-DPOLY, and ST-COPOT. They all rely on a three-phase clustering approach, which takes the point cloud stream as input and divides it into batches based on fixed-size time windows. Next, a density estimation approach and contouring algorithms are employed to obtain spatial clusters as polygon models. Finally, spatio-temporal clusters are formed by identifying continuing relationships between spatial clusters in consecutive batches. The framework was successfully applied to New York City (NYC) taxi trips data. The experimental results show that all the algorithms can effectively discover interesting spatio-temporal patterns in taxi-pickup-location streams. Recently, Twitter, one of the fastest-growing microblogging services, induced lots of research; one hot topic was event detection from tweets. Since geo-tagged tweets can be viewed as location streams with time tags and the content of tweets, we propose a novel two-stage system to detect and track events from Twitter by integrating an LDA-based approach with the density-contour based spatio-temporal clustering approach we introduced earlier. In the proposed system, events were identified as topics in tweets using an LDA-based (Latent Dirichlet Allocation) topic discovery step. Next, each tweet was assigned an event label. After all locations were extracted from each event, the spatio-temporal approach was employed to obtain event clusters and track their temporal continuity. Through some case studies, we demonstrated the effectiveness of the proposed system. In summary, we aimed to acquire not only the semantic aspect of the events, but also the geographic distribution of the events and their continuity along time. Such information can be used to help individuals, corporations, or government organizations to stay informed of ``what is happening now" and to acquire actionable knowledge. Advisors/Committee Members: Eick, Christoph F. (advisor), Vilalta, Ricardo (committee member), Chen, Guoning (committee member), Choi, Yunsoo (committee member).

Subjects/Keywords: Spatio-temporal clustering; Event tracking

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APA (6^th Edition):

Zhang, Y. 1. (2018). Density-Contour Based Framework for Spatio-Temporal Clustering and Event Tracking in Twitter. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/5707

Chicago Manual of Style (16^th Edition):

Zhang, Yongli 1990-. “Density-Contour Based Framework for Spatio-Temporal Clustering and Event Tracking in Twitter.” 2018. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/5707.

MLA Handbook (7^th Edition):

Zhang, Yongli 1990-. “Density-Contour Based Framework for Spatio-Temporal Clustering and Event Tracking in Twitter.” 2018. Web. 06 Mar 2021.

Vancouver:

Zhang Y1. Density-Contour Based Framework for Spatio-Temporal Clustering and Event Tracking in Twitter. [Internet] [Doctoral dissertation]. University of Houston; 2018. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/5707.

Council of Science Editors:

Zhang Y1. Density-Contour Based Framework for Spatio-Temporal Clustering and Event Tracking in Twitter. [Doctoral Dissertation]. University of Houston; 2018. Available from: http://hdl.handle.net/10657/5707

University of Houston

10. Chebolu, Siva Uday Sampreeth 1995-. A General Summarization Matrix for Scalable Machine Learning Model Computation in the R Language.

Degree: MS, Computer Science, 2019, University of Houston

URL: http://hdl.handle.net/10657/4465

► Data analysis is an essential task for research. Modern large datasets indeed contain a high volume of data and may require a parallel DBMS, Hadoop… (more)

Subjects/Keywords: Summarization; Gamma; Machine learning; Linear regression; PCA; Naïve Bayes; K-Means; R machine learning

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APA (6^th Edition):

Chebolu, S. U. S. 1. (2019). A General Summarization Matrix for Scalable Machine Learning Model Computation in the R Language. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/4465

Chicago Manual of Style (16^th Edition):

Chebolu, Siva Uday Sampreeth 1995-. “A General Summarization Matrix for Scalable Machine Learning Model Computation in the R Language.” 2019. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/4465.

MLA Handbook (7^th Edition):

Chebolu, Siva Uday Sampreeth 1995-. “A General Summarization Matrix for Scalable Machine Learning Model Computation in the R Language.” 2019. Web. 06 Mar 2021.

Vancouver:

Chebolu SUS1. A General Summarization Matrix for Scalable Machine Learning Model Computation in the R Language. [Internet] [Masters thesis]. University of Houston; 2019. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/4465.

Council of Science Editors:

Chebolu SUS1. A General Summarization Matrix for Scalable Machine Learning Model Computation in the R Language. [Masters Thesis]. University of Houston; 2019. Available from: http://hdl.handle.net/10657/4465

University of Houston

11. -5450-1888. Cluster Validation of WHAN Galaxy Classification Using a Novel Approach To External Cluster Validation.

Degree: MS, Computer Science, 2016, University of Houston

URL: http://hdl.handle.net/10657/1568

► The classification of galaxies is traditionally carried out using human-eye analysis of morphology or through information provided by a large survey of galaxies. Clustering methods… (more)

▼ The classification of galaxies is traditionally carried out using human-eye analysis of morphology or through information provided by a large survey of galaxies. Clustering methods can reduce the effort of manual classification by automating this process. Out of all the different properties available for galaxy classification, classification based on emission-line spectra is among the easiest to carry out. Once we have clustering output, it is important to evaluate it. Cluster validation involves computing statistics over the clustering structure to derive an estimate of how good the clustering is. When performed using only clustered data points, cluster validation is said to be internal. When an independent external classification scheme is compared to the clustering result, it is called external cluster validation. The disadvantage with using traditional cluster validation metrics is the lack of a probabilistic model. Traditional cluster validation metrics output the average of the similarities obtained between clusters and classes. The novelty exhibited by the individual clusters with classes can be lost when an average is taken over all the similarity values. Our method for external cluster validation computes the separation between individual clusters and its estimated external class by projection of individual clusters and classes onto a dimension which preserves the discriminatory information in the original feature space. Our method uses a probabilistic approach to calculate the cluster separation. This method provides a better understanding of how individual clusters are similar or dissimilar to their external classification. The Sloan Digital Sky Survey Dataset (SDSS) was used to evaluate our algorithm. The external classification scheme used is the WHAN classification system. We can derive clusters similar to at least one of the external classes. The similarity between clusters and two external classes of galaxies can be explained by domain knowledge, which are classes which can have overlapping properties. The structure derived by the clustering algorithm is supported by the numerical experiments. Advisors/Committee Members: Vilalta, Ricardo (advisor), Eick, Christoph F. (committee member), de Souza, Rafael S. (committee member).

Subjects/Keywords: External cluster validation; Machine learning

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APA (6^th Edition):

-5450-1888. (2016). Cluster Validation of WHAN Galaxy Classification Using a Novel Approach To External Cluster Validation. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/1568

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Chicago Manual of Style (16^th Edition):

-5450-1888. “Cluster Validation of WHAN Galaxy Classification Using a Novel Approach To External Cluster Validation.” 2016. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/1568.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

-5450-1888. “Cluster Validation of WHAN Galaxy Classification Using a Novel Approach To External Cluster Validation.” 2016. Web. 06 Mar 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-5450-1888. Cluster Validation of WHAN Galaxy Classification Using a Novel Approach To External Cluster Validation. [Internet] [Masters thesis]. University of Houston; 2016. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/1568.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-5450-1888. Cluster Validation of WHAN Galaxy Classification Using a Novel Approach To External Cluster Validation. [Masters Thesis]. University of Houston; 2016. Available from: http://hdl.handle.net/10657/1568

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

University of Houston

12. -5450-0412. Flow Visualization and Analysis: From Geometry to Physics.

Degree: PhD, Computer Science, 2017, University of Houston

URL: http://hdl.handle.net/10657/4811

► As the size and complexity of flow data sets continuously increase, many vector field visualization techniques aim to generate an abstract representation of the geometric… (more)

▼ As the size and complexity of flow data sets continuously increase, many vector field visualization techniques aim to generate an abstract representation of the geometric characteristics of the flow to simplify its interpretation. However, most of the geometric-based visualization techniques lack the ability to reveal the physically important features. Additional efforts are needed to interpret the physical characteristics from the geometric representation of the flow. In this work, the Lagrangian accumulation framework is introduced first, which accumulates various local physical and geometric properties of individual particles along the associated integral curves. This accumulation process results in a number of attribute fields that encode the information of certain global behaviors of particles, which can be used to achieve an abstract representation of the flow data. This framework is utilized to aid the classification of integral curves, produce texture-based visualizations, study property transport structures, and identify discontinuous behaviors among neighboring integral curves. Although the accumulation framework is simple and effective, the detailed flow behavior at individual integration points (and times) along the integral curves is suppressed, leading to incomplete analysis and visualization of flow data. In order to achieve a more detailed exploration, a new flow-exploration framework is investigated based on the time-series data or Time Activity Curves (TAC) of local properties. In this framework, the physical behavior of the individual particles can be described via their respective TACs. An event detector based on TACs is proposed to capture the local and global similarity of any spatial point with its neighboring points with a new dissimilarity metric. A hierarchical clustering framework is then developed based on this metric, upon which a level-of-detail representation of the flow can be obtained. This new framework is applied to a number of 2D and 3D unsteady-flow data sets to demonstrate its effectiveness. Advisors/Committee Members: Chen, Guoning (advisor), Thompson, David S. (committee member), Deng, Zhigang (committee member), Eick, Christoph F. (committee member).

Subjects/Keywords: Flow visualization; Attributes; Integral Curves

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APA (6^th Edition):

-5450-0412. (2017). Flow Visualization and Analysis: From Geometry to Physics. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/4811

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

-5450-0412. “Flow Visualization and Analysis: From Geometry to Physics.” 2017. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/4811.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

-5450-0412. “Flow Visualization and Analysis: From Geometry to Physics.” 2017. Web. 06 Mar 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-5450-0412. Flow Visualization and Analysis: From Geometry to Physics. [Internet] [Doctoral dissertation]. University of Houston; 2017. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/4811.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-5450-0412. Flow Visualization and Analysis: From Geometry to Physics. [Doctoral Dissertation]. University of Houston; 2017. Available from: http://hdl.handle.net/10657/4811

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

University of Houston

13. Mantini, Pranav 1985-. Contextual Human Trajectory Forecasting within Indoor Environments and Its Applications.

Degree: PhD, Computer Science, 2015, University of Houston

URL: http://hdl.handle.net/10657/4880

► A human trajectory is the likely path a human subject would take to get to a destination. Human trajectory forecasting algorithms try to estimate or… (more)

Subjects/Keywords: Human trajectory forecasting; Human motion; Person re-identification; People tracking; Camera network; Camera placement; Human behavior

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APA (6^th Edition):

Mantini, P. 1. (2015). Contextual Human Trajectory Forecasting within Indoor Environments and Its Applications. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/4880

Chicago Manual of Style (16^th Edition):

Mantini, Pranav 1985-. “Contextual Human Trajectory Forecasting within Indoor Environments and Its Applications.” 2015. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/4880.

MLA Handbook (7^th Edition):

Mantini, Pranav 1985-. “Contextual Human Trajectory Forecasting within Indoor Environments and Its Applications.” 2015. Web. 06 Mar 2021.

Vancouver:

Mantini P1. Contextual Human Trajectory Forecasting within Indoor Environments and Its Applications. [Internet] [Doctoral dissertation]. University of Houston; 2015. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/4880.

Council of Science Editors:

Mantini P1. Contextual Human Trajectory Forecasting within Indoor Environments and Its Applications. [Doctoral Dissertation]. University of Houston; 2015. Available from: http://hdl.handle.net/10657/4880

University of Houston

14. Xu, Rengan 1987-. Optimizing the Performance of Directive-based Programming Model for GPGPUs.

Degree: PhD, Computer Science, 2016, University of Houston

URL: http://hdl.handle.net/10657/3211

► Accelerators have been deployed on most major HPC systems. They are considered to improve the performance of many applications. Accelerators such as GPUs have an… (more)

▼ Accelerators have been deployed on most major HPC systems. They are considered to improve the performance of many applications. Accelerators such as GPUs have an immense potential in terms of high compute capacity but programming these devices is a challenge. OpenCL, CUDA and other vendor-specific models for accelerator programming definitely offer high performance, but these are low-level models that demand excellent programming skills; moreover, they are time consuming to write and debug. In order to simplify GPU programming, several directive-based programming models have been proposed, including HMPP, PGI accelerator model and OpenACC. OpenACC has now become established as the de facto standard. We evaluate and compare these models involving several scientific applications. To study the implementation challenges and the principles and techniques of directive- based models, we built an open source OpenACC compiler on top of a main stream compiler framework (OpenUH as a branch of Open64). In this dissertation, we present the required techniques to parallelize and optimize the applications ported with OpenACC programming model. We apply both user-level optimizations in the applications and compiler and runtime-driven optimizations. The compiler optimization focuses on the parallelization of reduction operations inside nested parallel loops. To fully utilize all GPU resources, we also extend the OpenACC model to support multiple GPUs in a single node. Our application porting experience also revealed the challenge of choosing good loop schedules. The default loop schedule chosen by the compiler may not produce the best performance, so the user has to manually try different loop schedules to improve the performance. To solve this issue, we developed a locality-aware auto-tuning framework which is based on the proposed memory access cost model to help the compiler choose optimal loop schedules and guide the user to choose appropriate loop schedules. Advisors/Committee Members: Chapman, Barbara M. (advisor), Eick, Christoph F. (committee member), Shah, Shishir Kirit (committee member), Subhlok, Jaspal (committee member), Calandra, Henri (committee member).

Subjects/Keywords: GPU; OpenACC; OpenMP; Directives; Parallel programming; HPC; Programming models

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APA (6^th Edition):

Xu, R. 1. (2016). Optimizing the Performance of Directive-based Programming Model for GPGPUs. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/3211

Chicago Manual of Style (16^th Edition):

Xu, Rengan 1987-. “Optimizing the Performance of Directive-based Programming Model for GPGPUs.” 2016. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/3211.

MLA Handbook (7^th Edition):

Xu, Rengan 1987-. “Optimizing the Performance of Directive-based Programming Model for GPGPUs.” 2016. Web. 06 Mar 2021.

Vancouver:

Xu R1. Optimizing the Performance of Directive-based Programming Model for GPGPUs. [Internet] [Doctoral dissertation]. University of Houston; 2016. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/3211.

Council of Science Editors:

Xu R1. Optimizing the Performance of Directive-based Programming Model for GPGPUs. [Doctoral Dissertation]. University of Houston; 2016. Available from: http://hdl.handle.net/10657/3211

University of Houston

15. -3212-5345. Data Mining A PeopleSoft Database To Assist In Developing Student Retention Interventions.

Degree: EdD, Curriculum and Instruction, 2016, University of Houston

URL: http://hdl.handle.net/10657/1737

► Per a Bellwether Education Partners study (Aldeman, 2015, p. 8), "As of 2013, there were 29.1 million college dropouts versus 24.5 million Americans who dropped… (more)

Subjects/Keywords: Knowledge discovery process (KDP); Data mining; PeopleSoft; Student retention; Databases; Student intervention; Persistence; Attainment

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APA (6^th Edition):

-3212-5345. (2016). Data Mining A PeopleSoft Database To Assist In Developing Student Retention Interventions. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/1737

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

-3212-5345. “Data Mining A PeopleSoft Database To Assist In Developing Student Retention Interventions.” 2016. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/1737.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

-3212-5345. “Data Mining A PeopleSoft Database To Assist In Developing Student Retention Interventions.” 2016. Web. 06 Mar 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-3212-5345. Data Mining A PeopleSoft Database To Assist In Developing Student Retention Interventions. [Internet] [Doctoral dissertation]. University of Houston; 2016. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/1737.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-3212-5345. Data Mining A PeopleSoft Database To Assist In Developing Student Retention Interventions. [Doctoral Dissertation]. University of Houston; 2016. Available from: http://hdl.handle.net/10657/1737

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

University of Houston

16. Almogahed, Bassam A. 1980-. Toward Improved Classification of Imbalanced Data.

Degree: PhD, Computer Science, 2014, University of Houston

URL: http://hdl.handle.net/10657/1910

► There is an unprecedented amount of data available. This has caused knowledge discovery to garner attention in recent years. However, many real-world datasets are imbalanced.… (more)

▼ There is an unprecedented amount of data available. This has caused knowledge discovery to garner attention in recent years. However, many real-world datasets are imbalanced. Learning from imbalanced data poses major challenges and is recognized as needing significant research. The problem with imbalanced data is the performance of learning algorithms in the presence of underrepresented data and severely skewed class distributions. Models trained on imbalanced datasets strongly favor the majority class and largely ignore the minority class. Several approaches introduced to date present both data-based and algorithmic solutions. However, both types of approaches have been criticized for their lack of generalization, tendency to forfeit information, and likelihood of resulting in over-fitting difficulties. The goal of this thesis is to develop algorithms to balance imbalanced datasets to allow each classifier to reach optimal predictions. The specific objectives are to: (i) develop sampling methods for imbalanced data, (ii) develop a framework capable of determining which sampling method to use, (iii) evaluate performance of these methods on a variety of imbalanced datasets, and (iv) develop a new machine learning risk-prediction framework for cardiovascular events. We propose a method for filtering over-sampled data using non-cooperative game theory. It addresses the imbalanced data issue by formulating the problem as a non-cooperative game. The proposed algorithm does not require any prior assumptions and selects representative synthetic instances while generating only a very small amount of noise. We also propose a technique for addressing imbalanced data using semi-supervised learning. Our method integrates under-sampling and semi-supervised learning (US-SSL) to tackle the imbalance problem. The proposed algorithm, on average, significantly outperforms all other sampling algorithms in 67% of cases, across three different classifiers, and ranks second best for the remaining 33% of cases. Finally, we propose a novel framework based on the US-SSL algorithm to select the appropriate semi-supervised algorithm to balance and refine a given dataset in order to establish a well-defined training set. Advisors/Committee Members: Kakadiaris, Ioannis A. (advisor), Shah, Shishir Kirit (committee member), Eick, Christoph F. (committee member), Vilalta, Ricardo (committee member), Tsiamyrtzis, Panagiotis (committee member).

Subjects/Keywords: Classification; Imbalanced data

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APA (6^th Edition):

Almogahed, B. A. 1. (2014). Toward Improved Classification of Imbalanced Data. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/1910

Chicago Manual of Style (16^th Edition):

Almogahed, Bassam A 1980-. “Toward Improved Classification of Imbalanced Data.” 2014. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/1910.

MLA Handbook (7^th Edition):

Almogahed, Bassam A 1980-. “Toward Improved Classification of Imbalanced Data.” 2014. Web. 06 Mar 2021.

Vancouver:

Almogahed BA1. Toward Improved Classification of Imbalanced Data. [Internet] [Doctoral dissertation]. University of Houston; 2014. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/1910.

Council of Science Editors:

Almogahed BA1. Toward Improved Classification of Imbalanced Data. [Doctoral Dissertation]. University of Houston; 2014. Available from: http://hdl.handle.net/10657/1910

University of Houston

17. Amalaman, Paul K. 1966-. New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications.

Degree: PhD, Computer Science, 2015, University of Houston

URL: http://hdl.handle.net/10657/4888

► Obtaining hierarchical organizations of knowledge is important in many domains. To create such hierarchies, improved techniques for subdividing entities hierarchically ac-cording to similarities and differences… (more)

▼ Obtaining hierarchical organizations of knowledge is important in many domains. To create such hierarchies, improved techniques for subdividing entities hierarchically ac-cording to similarities and differences are needed. New techniques for organizing docu-ments in hierarchies, for automatic document retrieval and for hierarchical query cluster-ing are being made available at a fast pace. In this work, we investigate new methods to induce hierarchical models with the goal of obtaining better predictive models, to facili-tate the creation of background knowledge with respect to an underlining class distribu-tion, to obtain hierarchical groupings of a set of objects based on background knowledge they share, to detect sub-classes within existing class distribution, and to provide methods to evaluate hierarchical groupings. The results of this effort has led to the development of (1) TPRTI, a new regression tree induction approach which uses turning points, candi-dates split points computed before the recursive process takes place, to recursively split the node datasets; (2) PATHFINDER, a new classification tree induction capable of in-ducing very short trees with high accuracies for the price of not classifying examples deemed difficult to classify; (3) AVALANCHE, a new hierarchical divisive clustering approach which takes as input a distance matrix and forms clusters maximizing inter-cluster distances; (4) STAXAC, a new agglomerative clustering approach which creates supervised taxonomies that unlike traditional agglomerative clustering, which only uses proximity as the single criterion for merging, uses both proximity and class labels infor-mation to obtain hierarchical groupings of a set of objects. We applied the techniques we developed, (1) to molecular phylogenetic-based taxonomy generation and found that this new approach and the obtained supervised taxonomies can help biologists better charac-terize organisms according to some characteristics of interest such as diseases, growth rate, etc.; (2) to data editing; we were able to enhance the accuracy of the k-nearest neighbor classifier by removing minority class examples from clusters that were extracted from a supervised taxonomy; (3) to meta learning; we developed new algorithms that operate on supervised taxonomies and compute both the distribution of the classes within a dataset, and the difficulty of classifying examples belonging to a particular dataset. Advisors/Committee Members: Eick, Christoph F. (advisor), Vilalta, Ricardo (committee member), Shi, Weidong (committee member), Shah, Shishir Kirit (committee member), Cooper, Timothy F. (committee member).

Subjects/Keywords: Decision trees; Regression tree; Classification tree; Supervised taxonomy; Hierarchical clustering

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APA (6^th Edition):

Amalaman, P. K. 1. (2015). New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/4888

Chicago Manual of Style (16^th Edition):

Amalaman, Paul K 1966-. “New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications.” 2015. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/4888.

MLA Handbook (7^th Edition):

Amalaman, Paul K 1966-. “New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications.” 2015. Web. 06 Mar 2021.

Vancouver:

Amalaman PK1. New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications. [Internet] [Doctoral dissertation]. University of Houston; 2015. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/4888.

Council of Science Editors:

Amalaman PK1. New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications. [Doctoral Dissertation]. University of Houston; 2015. Available from: http://hdl.handle.net/10657/4888

University of Houston

18. Ugur, Muhsin Zahid 1984-. Integrating Mobile Psychometrics with Wearable Physiological Sensing in Longitudinal Studies: Design, Testing, and Clinical Benefits.

Degree: PhD, Computer Science, 2017, University of Houston

URL: http://hdl.handle.net/10657/4804

► Traditionally, psychometric questionnaires have been given in paper form. Increasingly, questionnaires are delivered through web interfaces. Although, this is an improvement, web interfaces are not… (more)

▼ Traditionally, psychometric questionnaires have been given in paper form. Increasingly, questionnaires are delivered through web interfaces. Although, this is an improvement, web interfaces are not as flexible as mobile interfaces. We focus on the latter, because their ubiquity, can potentially cure the adherence problem. Indeed, in longitudinal studies, participants may be asked to fill out questionnaires several times a day over a period of time. Several problems arise in such studies, with adherence being the number one problem. In this research, we chose circumplex, a powerful but complex psychometric instrument, as a case study for evaluating the effect of mobile interfacing. Circumplex is a 2D psychometric, requiring careful consideration of design issues, as one tries to make it fit in a small smartphone screen. Therefore, we first ran a user interface study where we measured the goodness of several designs in the lab. Having selected the best designs, we ran a field study to evaluate the goodness of these select designs in actual practice over a number of days. The next step was to use the winning design in a large longitudinal study with n=131 participants. This study is ongoing, and we have started analyzing the data, keeping a keen eye on the issue of adherence. Initial results are promising. We have also started collecting concomitantly with the psychometric scores, physiological markers. These markers are electrodermal activity signals recorded by a wristband on the subjects non-dominant hand. The ultimate goal is to mix mobile psychometrics with wearable sensing, thus delivering sustained multimodal responses that will paint a much more complete picture with respect to legacy approaches. These legacy approaches were not only suffering from adherence problems with regard to psychometric responses, but it was unthinkable to include complementary physiological responses, due to technical limitations. Advisors/Committee Members: Pavlidis, Ioannis T. (advisor), Tsekos, Nikolaos V. (committee member), Eick, Christoph F. (committee member), Sharp, Carla (committee member).

Subjects/Keywords: Online questionnaires; 2D questionnaires; Mobile devices; Mobile health care; User interface design; Adherence; Psychophysiology; Psychology

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APA (6^th Edition):

Ugur, M. Z. 1. (2017). Integrating Mobile Psychometrics with Wearable Physiological Sensing in Longitudinal Studies: Design, Testing, and Clinical Benefits. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/4804

Chicago Manual of Style (16^th Edition):

Ugur, Muhsin Zahid 1984-. “Integrating Mobile Psychometrics with Wearable Physiological Sensing in Longitudinal Studies: Design, Testing, and Clinical Benefits.” 2017. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/4804.

MLA Handbook (7^th Edition):

Ugur, Muhsin Zahid 1984-. “Integrating Mobile Psychometrics with Wearable Physiological Sensing in Longitudinal Studies: Design, Testing, and Clinical Benefits.” 2017. Web. 06 Mar 2021.

Vancouver:

Ugur MZ1. Integrating Mobile Psychometrics with Wearable Physiological Sensing in Longitudinal Studies: Design, Testing, and Clinical Benefits. [Internet] [Doctoral dissertation]. University of Houston; 2017. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/4804.

Council of Science Editors:

Ugur MZ1. Integrating Mobile Psychometrics with Wearable Physiological Sensing in Longitudinal Studies: Design, Testing, and Clinical Benefits. [Doctoral Dissertation]. University of Houston; 2017. Available from: http://hdl.handle.net/10657/4804

University of Houston

19. Yan, Xu 1986-. Modeling Local Behavior for Multi-Person Tracking.

Degree: PhD, Computer Science, 2014, University of Houston

URL: http://hdl.handle.net/10657/1895

► Multiple-pedestrian tracking in unconstrained environments is an important task that has received considerable attention from the computer vision community in the past two decades. Accurate… (more)

▼ Multiple-pedestrian tracking in unconstrained environments is an important task that has received considerable attention from the computer vision community in the past two decades. Accurate multiple-pedestrian tracking can greatly improve the performance of activity recognition and analysis of high level events through a surveillance system. Traditional approaches to pedestrian tracking build a motion prediction model to track the target. With improvements in object detection methods, recent approaches replace the motion prediction stage and track targets by selecting among the outputs of a detector. To incorporate the merit of traditional and recent approaches, we have developed a novel approach using an ensemble framework that optimally chooses target tracking results from that of independent trackers and a detector at each time step. The compound model is designed to select the best candidate scored by a function integrating detection confidence, appearance affinity, and smoothness constraints. To further improve the tracking performance we focus on the design of a novel motion prediction model. Human interaction behavior is known to play an important role in human motion. We present a novel tracking approach utilizing human collision avoidance behavior, which is motivated by the human vision system. The model predicts human motion based on modeling of perceived information. An attention map is designed to mimic human reasoning that integrates both spatial and temporal information. We also develop an enhanced tracker that models human group behavior using a hierarchical group structures. The groups are identified by a bottom-up social group discovery method. The inter- and intra-group structures are modeled as a two-layer graph and tracking is posed as optimization of the integrated structure. Finally, we propose another novel tracking method to unify multiple human behavior. To investigate the effects of potential multiple social behaviors, we present an algorithm that decomposes the combined social behaviors into multiple basic interaction modes, such as attraction, repulsion, and no interaction. We integrate these multiple social interaction modes into an interactive Markov Chain Monte Carlo tracker and demonstrate how the developed method translates into a more informed motion prediction, resulting in robust tracking performance. Advisors/Committee Members: Shah, Shishir Kirit (advisor), Gabriel, Edgar (committee member), Eick, Christoph F. (committee member), Prasad, Saurabh (committee member), Kakadiaris, Ioannis A. (committee member).

Subjects/Keywords: Multi-person Tracking; Local Behavior

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APA (6^th Edition):

Yan, X. 1. (2014). Modeling Local Behavior for Multi-Person Tracking. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/1895

Chicago Manual of Style (16^th Edition):

Yan, Xu 1986-. “Modeling Local Behavior for Multi-Person Tracking.” 2014. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/1895.

MLA Handbook (7^th Edition):

Yan, Xu 1986-. “Modeling Local Behavior for Multi-Person Tracking.” 2014. Web. 06 Mar 2021.

Vancouver:

Yan X1. Modeling Local Behavior for Multi-Person Tracking. [Internet] [Doctoral dissertation]. University of Houston; 2014. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/1895.

Council of Science Editors:

Yan X1. Modeling Local Behavior for Multi-Person Tracking. [Doctoral Dissertation]. University of Houston; 2014. Available from: http://hdl.handle.net/10657/1895

University of Houston

20. Wang, Sujing 1975-. Spatial and Spatio-Temporal Clustering.

Degree: PhD, Computer Science, 2014, University of Houston

URL: http://hdl.handle.net/10657/2022

► Due to the advances in technology, such as smart phones, general mobile devices, remote sensors, and sensor networks, different types of spatial data become increasingly… (more)

▼ Due to the advances in technology, such as smart phones, general mobile devices, remote sensors, and sensor networks, different types of spatial data become increasingly available. These data can also integrate multiple other types of information, such as temporal information, social information, and scientific measurements, which provide a tremendous potential for discovering new useful knowledge, as well as new research challenges. In this research, we focus on clustering and analyzing spatial and spatio-temporal data. We have addressed several important sub-problems in polygon-based spatial and spatio-temporal clustering and post-processing analysis techniques. We have developed (1) two distance functions that measure the distances between polygons, especially overlapping polygons; (2) a density-based spatial clustering algorithm for polygons; (3) two post-processing analysis techniques to extract interesting patterns and useful knowledge from spatial clusters; (4) two density-based spatio-temporal clustering algorithms for polygons; (5) a box plot based post-processing analysis technique to identify interesting spatio-temporal clusters of polygons; (6) a change-pattern-discovery algorithm to detect and analyze patterns of dynamic changes within spatio-temporal clusters of polygons; and (7) a formal definition of the task of finding uniform regions in spatial data and an algorithm to identify such uniform regions. Our algorithms and techniques are demonstrated and evaluated in challenging real-world case studies involving ozone pollution events in the Houston-Galveston-Brazoria area and the building data of Strasbourg, France. The results show that our algorithms are effective in finding compact clusters in spatial and spatio-temporal domains and in extracting interesting patterns and useful information from spatial and spatio-temporal data. Advisors/Committee Members: Eick, Christoph F. (advisor), Kaiser, Klaus (committee member), Chen, Guoning (committee member), Leiss, Ernst L. (committee member), Vilalta, Ricardo (committee member).

Subjects/Keywords: Spatial Clustering; Spatio-temporal clustering; Polygon; Post-processing Analysis; Uniform Regions; Popular Signatures

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

APA (6^th Edition):

Wang, S. 1. (2014). Spatial and Spatio-Temporal Clustering. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/2022

Chicago Manual of Style (16^th Edition):

Wang, Sujing 1975-. “Spatial and Spatio-Temporal Clustering.” 2014. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/2022.

MLA Handbook (7^th Edition):

Wang, Sujing 1975-. “Spatial and Spatio-Temporal Clustering.” 2014. Web. 06 Mar 2021.

Vancouver:

Wang S1. Spatial and Spatio-Temporal Clustering. [Internet] [Doctoral dissertation]. University of Houston; 2014. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/2022.

Council of Science Editors:

Wang S1. Spatial and Spatio-Temporal Clustering. [Doctoral Dissertation]. University of Houston; 2014. Available from: http://hdl.handle.net/10657/2022

University of Houston

21. Akdag, Fatih 1982-. A Computational Framework for Finding Interestingness Hotspots in Spatial Datasets.

Degree: PhD, Computer Science, 2016, University of Houston

URL: http://hdl.handle.net/10657/5401

► The significant growth of spatial data increased the need for automated discovery of spatial knowledge. An important task when analyzing spatial data is hotspot discovery.… (more)

▼ The significant growth of spatial data increased the need for automated discovery of spatial knowledge. An important task when analyzing spatial data is hotspot discovery. In this dissertation, we propose a novel methodology for discovering interestingness hotspots in spatial datasets. We define interestingness hotspots as contiguous regions in space which are interesting based on a domain expert’s notion of interestingness captured by an interestingness function. We propose computational methods for finding interestingness hotspots in point-based and polygonal spatial datasets, and gridded spatial-temporal datasets. The proposed framework identifies hotspots maximizing an externally given interestingness function defined on any number of spatial or non-spatial attributes using a five-step methodology, which consists of: (1) identifying neighboring objects in the dataset, (2) generating hotspot seeds, (3) growing hotspots from identified hotspot seeds, (4) post-processing to remove highly overlapping neighboring redundant hotspots, and (5) finding the scope of hotspots. In particular, we introduce novel hotspot growing algorithms that grow hotspots from hotspot seeds. A novel growing algorithm for point-based datasets is introduced that operates on Gabriel Graphs, capturing the neighboring relationships of objects in a spatial dataset. Moreover, we present a novel graph-based post-processing algorithm, which removes highly overlapping hotspots and employs a graph simplification step that significantly improves the runtime of finding maximum weight independent set in the overlap graph of hotspots. The proposed post-processing algorithm is quite generic and can be used with any methods to cope with overlapping hotspots or clusters. Additionally, the employed graph simplification step can be adapted as a preprocessing step by algorithms that find maximum weight clique and maximum weight independent sets in graphs. Furthermore, we propose a computational framework for finding the scope of two-dimensional point-based hotspots. We evaluate our framework in case studies using a gridded air-pollution dataset, and point-based crime and taxicab datasets in which we find hotspots based on different interestingness functions and we give a comparison of our framework with a state of the art hotspot discovery technique. Experiments show that our methodology succeeds in accurately discovering interestingness hotspots and does well in comparison to traditional hotspot detection methods. Advisors/Committee Members: Eick, Christoph F. (advisor), Gabriel, Edgar (committee member), Chen, Guoning (committee member), Solorio, Thamar (committee member), Choi, Yunsoo (committee member).

Subjects/Keywords: Hotspot discovery; Spatial data mining; Interestingness hotspot; Maximum weight clique; Polygon models; Polygon emptiness

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APA (6^th Edition):

Akdag, F. 1. (2016). A Computational Framework for Finding Interestingness Hotspots in Spatial Datasets. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/5401

Chicago Manual of Style (16^th Edition):

Akdag, Fatih 1982-. “A Computational Framework for Finding Interestingness Hotspots in Spatial Datasets.” 2016. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/5401.

MLA Handbook (7^th Edition):

Akdag, Fatih 1982-. “A Computational Framework for Finding Interestingness Hotspots in Spatial Datasets.” 2016. Web. 06 Mar 2021.

Vancouver:

Akdag F1. A Computational Framework for Finding Interestingness Hotspots in Spatial Datasets. [Internet] [Doctoral dissertation]. University of Houston; 2016. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/5401.

Council of Science Editors:

Akdag F1. A Computational Framework for Finding Interestingness Hotspots in Spatial Datasets. [Doctoral Dissertation]. University of Houston; 2016. Available from: http://hdl.handle.net/10657/5401

University of Houston

22. Gala, Apurva 1980-. Person Re-Identification in Distributed Wide-Area Surveillance.

Degree: PhD, Computer Vision, 2014, University of Houston

URL: http://hdl.handle.net/10657/5358

► Person re-identification (Re-ID) is a fundamental task in automated video surveillance and has been an area of intense research in the past few years. Given… (more)

Subjects/Keywords: Person re-identification; Open-set Re-ID; Closed-set Re-ID; Short-period Re-ID; Long-period Re-ID; Novelty detection

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APA (6^th Edition):

Gala, A. 1. (2014). Person Re-Identification in Distributed Wide-Area Surveillance. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/5358

Chicago Manual of Style (16^th Edition):

Gala, Apurva 1980-. “Person Re-Identification in Distributed Wide-Area Surveillance.” 2014. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/5358.

MLA Handbook (7^th Edition):

Gala, Apurva 1980-. “Person Re-Identification in Distributed Wide-Area Surveillance.” 2014. Web. 06 Mar 2021.

Vancouver:

Gala A1. Person Re-Identification in Distributed Wide-Area Surveillance. [Internet] [Doctoral dissertation]. University of Houston; 2014. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/5358.

Council of Science Editors:

Gala A1. Person Re-Identification in Distributed Wide-Area Surveillance. [Doctoral Dissertation]. University of Houston; 2014. Available from: http://hdl.handle.net/10657/5358

University of Houston

23. Dcosta, Malcolm 1986-. Sympathetic Loading in Critical Tasks.

Degree: PhD, Computer Science, 2015, University of Houston

URL: http://hdl.handle.net/10657/4869

► In this dissertation I developed or perfected unobtrusive methods to quantify sympathetic arousals. Furthermore, I used these methods to study the sympathetic system's role on… (more)

▼ In this dissertation I developed or perfected unobtrusive methods to quantify sympathetic arousals. Furthermore, I used these methods to study the sympathetic system's role on critical activities, arriving at intriguing conclusions. Sympathetic arousals occur during states of mental, emotional, and/or sensorimotor strain resulting from adverse or demanding circumstances. They are key elements of human physiology's coping mechanism, shoring up resources to a good effect. When the intensity and duration of these arousals are overwhelming, however, then they may block memory and disrupt rational thought or actions at the moment they are needed the most. Arousals abound in three types of critical activities: high-stakes situations, challenging tasks, and critical multitasking. Accordingly, my research was based on three studies representative of these three activity types: `Subject Screening', `Educational Exam', and `Distracted Driving'. In the first study I investigated the association of sympathetic arousals with deceptive behavior in interrogations. In the second study, I investigated the relationship between sympathetic arousals and exam performance. In the third study, I investigated the interaction between sympathetic arousals and driving performance under cognitive, emotional, and sensorimotor distractions. In the interrogation study, I used for the first time a contact-free electrodermal activity measurement method to quantify arousals. The method detected deceptive behavior based on differential sympathetic responses in well-structured interviews. In the exam study, I documented that sympathetic arousals positively correlate with students' exam performance, dispelling the myth of `easy going' super achievers. Finally, in the driving study, my results revealed that not only apparent sensorimotor stressors (texting while driving) but also hidden stressors (cognitive or emotional) could have a significant effect on driving performance. Advisors/Committee Members: Pavlidis, Ioannis T. (advisor), Eick, Christoph F. (committee member), Sharp, Carla (committee member), Tolar, Tammy (committee member), Tsekos, Nikolaos V. (committee member), Vilalta, Ricardo (committee member).

Subjects/Keywords: Perinasal perpiration; Stress; Critical tasks

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APA (6^th Edition):

Dcosta, M. 1. (2015). Sympathetic Loading in Critical Tasks. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/4869

Chicago Manual of Style (16^th Edition):

Dcosta, Malcolm 1986-. “Sympathetic Loading in Critical Tasks.” 2015. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/4869.

MLA Handbook (7^th Edition):

Dcosta, Malcolm 1986-. “Sympathetic Loading in Critical Tasks.” 2015. Web. 06 Mar 2021.

Vancouver:

Dcosta M1. Sympathetic Loading in Critical Tasks. [Internet] [Doctoral dissertation]. University of Houston; 2015. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/4869.

Council of Science Editors:

Dcosta M1. Sympathetic Loading in Critical Tasks. [Doctoral Dissertation]. University of Houston; 2015. Available from: http://hdl.handle.net/10657/4869

University of Houston

24. Le, Yen H. 1984-. Shape Priors for Segmentation of Mouse Brain in Gene Expression Images.

Degree: PhD, Computer Science, 2014, University of Houston

URL: http://hdl.handle.net/10657/1793

► The quantification and comparison of gene expression data across images plays a key role in understanding the functional network of various genes. To enable studying… (more)

▼ The quantification and comparison of gene expression data across images plays a key role in understanding the functional network of various genes. To enable studying of relationships between a large number of images, automated methods that can segment gene expression images into distinct anatomical regions/sub-regions are needed. Automated segmentation of mouse brain gene expression images is a challenging problem mainly due to the complexity of gene expression appearance: (i) the lack of visible edge cues for the anatomical regions, (ii) the inhomogeneous of intensity pattern inside each anatomical region, and (iii) the variation of intensity pattern of the same region across images. Therefore, the use of geometric priors and appearance cues can potentially help in accurate segmentation of gene expression images. The goal of this thesis is to develop segmentation methods that incorporate shape priors and appearance cues and apply them to gene expression image data. The specific objectives are: (i) to incorporate statistical shape information to segment gene expression images, (ii) to learn salient model points that will be selected to provide appearance cues for segmentation methods to handle images that complex appearance (e.g., gene expression images), (iii) to improve the representation ability of statistical shape models to represent a larger range of shapes, and (iv) to evaluate the proposed methods on the segmentation of gene expression images. Corresponding to each of the first three objectives, three methods have been proposed. They all outperform the state-of-the-art on a challenging problem of segmenting gene expression images of mouse brain. The best performance is achieved by PDM-ENLOR (Point Distribution Model-based ENsemble of Local Regressors): the overall mean and standard deviation (over all 14 anatomical regions and all test images) of Dice coefficient overlap were 88.1 % +/- 9.5 % and of Hausdorff distance were 0.235 mm +/- 0.100 mm. This dissertation contributes a method for determining a set of salient points for using in a given segmentation algorithm and a method that increases the flexibility of the statistical shape model and handles the detection errors simultaneously. These data-driven methods are generic and can be applied to other similar problems. Advisors/Committee Members: Kakadiaris, Ioannis A. (advisor), Carson, James P. (committee member), Deng, Zhigang (committee member), Eick, Christoph F. (committee member), Ju, Tao (committee member), Shah, Shishir Kirit (committee member).

Subjects/Keywords: Statistical shape model; PDM; ASM; PDM-ENLOR; Gene expression images

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

APA (6^th Edition):

Le, Y. H. 1. (2014). Shape Priors for Segmentation of Mouse Brain in Gene Expression Images. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/1793

Chicago Manual of Style (16^th Edition):

Le, Yen H 1984-. “Shape Priors for Segmentation of Mouse Brain in Gene Expression Images.” 2014. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/1793.

MLA Handbook (7^th Edition):

Le, Yen H 1984-. “Shape Priors for Segmentation of Mouse Brain in Gene Expression Images.” 2014. Web. 06 Mar 2021.

Vancouver:

Le YH1. Shape Priors for Segmentation of Mouse Brain in Gene Expression Images. [Internet] [Doctoral dissertation]. University of Houston; 2014. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/1793.

Council of Science Editors:

Le YH1. Shape Priors for Segmentation of Mouse Brain in Gene Expression Images. [Doctoral Dissertation]. University of Houston; 2014. Available from: http://hdl.handle.net/10657/1793

University of Houston

25. Uyanik, Ilyas 1984-. Monitoring Walking Behavior with Mobile Computing.

Degree: PhD, Computer Science, 2014, University of Houston

URL: http://hdl.handle.net/10657/1905

► The crux of this dissertation is to understand human walking behavior. Walking is one of the most fundamental human activities. Study of walking behavior is… (more)

▼ The crux of this dissertation is to understand human walking behavior. Walking is one of the most fundamental human activities. Study of walking behavior is difficult because it demands ubiquitous monitoring of a large subject pool for an extended period of time and consideration of multiple covariates. This research examines three factors that may significantly affect walking activity. The first factor is self-awareness. To this end, we use a smartphone app (iBurnCalorie) that transforms accelerometer readings to caloric values. The app keeps the user informed about her/his walking output. The original caloric transformation assumes that the users walk on a flat ground, which is not always true. In our research, we propose a novel calibration method that takes into account surface inclination, thus improving the accuracy of the initial mapping. The second factor that likely affects walking behavior is weather. The most common pollutant is ozone, and walking under unsafe ozone conditions is unhealthy. The current ozone alert technologies are neither highly dynamic nor highly specific in terms of locale and, hence, not very informative for walkers. In this dissertation, we have designed, developed, and evaluated an app called OzoneMap that delivers spatio-temporal ozone information. In a study that we performed, we documented the relevance and usefulness of such ozone information for walkers. Subsequently, we incorporated the OzoneMap app within the iBurnCalorie app. The third factor that likely affects walking behavior are the role models to which people compare themselves. In fact, the iBurnCalorie app supports the search and selection of role models from its user base; the users can monitor themselves and role models they freely choose. We analyzed entrainment effects among dominant and non-dominant nodes in the app's walker network. Identifying positive and negative reinforcement patterns that occur naturally will inform future interventions where struggling walkers, based on their characteristics, will be optimally matched to role models. The outcome of this work will be an updated iBurnCalorie app that computes caloric expenditure from walking more accurately, maximizes the opportunities for safe walking in polluted metropolitan centers, and virtually pairs walkers with role models that stand the best likelihood of behavioral inducing modification. Advisors/Committee Members: Pavlidis, Ioannis T. (advisor), Eick, Christoph F. (committee member), Tsekos, Nikolaos V. (committee member), Garbey, Marc (committee member), Akleman, Ergun (committee member), Tsiamyrtzis, Panagiotis (committee member).

Subjects/Keywords: Smartphone accelerometer; Energy expenditure; Physical activity monitoring; Inclination measurement; Walking app; Social networks; Role models; User study; Environmental effect; Ozone; Air pollution; Mobile application

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APA (6^th Edition):

Uyanik, I. 1. (2014). Monitoring Walking Behavior with Mobile Computing. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/1905

Chicago Manual of Style (16^th Edition):

Uyanik, Ilyas 1984-. “Monitoring Walking Behavior with Mobile Computing.” 2014. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/1905.

MLA Handbook (7^th Edition):

Uyanik, Ilyas 1984-. “Monitoring Walking Behavior with Mobile Computing.” 2014. Web. 06 Mar 2021.

Vancouver:

Uyanik I1. Monitoring Walking Behavior with Mobile Computing. [Internet] [Doctoral dissertation]. University of Houston; 2014. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/1905.

Council of Science Editors:

Uyanik I1. Monitoring Walking Behavior with Mobile Computing. [Doctoral Dissertation]. University of Houston; 2014. Available from: http://hdl.handle.net/10657/1905

26. Cao, Zechun 1986-. A Methodology for Finding Uniform Regions in Spatial Data and its Application to Analyzing the Composition of Cities.

Degree: MS, Computer Science, 2013, University of Houston

URL: http://hdl.handle.net/10657/470

► Cities all around the world are in constant evolution due to numerous factors, such as fast urbanization and new ways of communication and transportation. However,… (more)

Subjects/Keywords: Spatial data mining; Finding uniform regions in spatial datasets; Mining distribution signatures; Clustering with plug-in fitness functions; Algorithms; Spatial structures; Cities; Urban computing; Computer science

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APA (6^th Edition):

Cao, Z. 1. (2013). A Methodology for Finding Uniform Regions in Spatial Data and its Application to Analyzing the Composition of Cities. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/470

Chicago Manual of Style (16^th Edition):

Cao, Zechun 1986-. “A Methodology for Finding Uniform Regions in Spatial Data and its Application to Analyzing the Composition of Cities.” 2013. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/470.

MLA Handbook (7^th Edition):

Cao, Zechun 1986-. “A Methodology for Finding Uniform Regions in Spatial Data and its Application to Analyzing the Composition of Cities.” 2013. Web. 06 Mar 2021.

Vancouver:

Cao Z1. A Methodology for Finding Uniform Regions in Spatial Data and its Application to Analyzing the Composition of Cities. [Internet] [Masters thesis]. University of Houston; 2013. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/470.

Council of Science Editors:

Cao Z1. A Methodology for Finding Uniform Regions in Spatial Data and its Application to Analyzing the Composition of Cities. [Masters Thesis]. University of Houston; 2013. Available from: http://hdl.handle.net/10657/470

27. Sui, Bangsheng 1987-. INFORMATION GAIN FEATURE SELECTION BASED ON FEATURE INTERACTIONS.

Degree: MS, Computer Science, 2013, University of Houston

URL: http://hdl.handle.net/10657/523

► Analyzing high-dimensional data stands as a great challenge in machine learning. In order to deal with the curse of dimensionality, many effective and efficient feature-selection… (more)

Subjects/Keywords: Feature selection; Machine learning; Feature interaction; Information gain; Computer science

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APA (6^th Edition):

Sui, B. 1. (2013). INFORMATION GAIN FEATURE SELECTION BASED ON FEATURE INTERACTIONS. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/523

Chicago Manual of Style (16^th Edition):

Sui, Bangsheng 1987-. “INFORMATION GAIN FEATURE SELECTION BASED ON FEATURE INTERACTIONS.” 2013. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/523.

MLA Handbook (7^th Edition):

Sui, Bangsheng 1987-. “INFORMATION GAIN FEATURE SELECTION BASED ON FEATURE INTERACTIONS.” 2013. Web. 06 Mar 2021.

Vancouver:

Sui B1. INFORMATION GAIN FEATURE SELECTION BASED ON FEATURE INTERACTIONS. [Internet] [Masters thesis]. University of Houston; 2013. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/523.

Council of Science Editors:

Sui B1. INFORMATION GAIN FEATURE SELECTION BASED ON FEATURE INTERACTIONS. [Masters Thesis]. University of Houston; 2013. Available from: http://hdl.handle.net/10657/523

28. -7494-095X. A Fast Clustering Algorithm Merging The Expectation Maximization Algorithm and Markov Chain Monte Carlo.

Degree: MS, Computer Science, 2015, University of Houston

URL: http://hdl.handle.net/10657/1935

► Clustering is an important problem in Statistics and Machine Learning that is usually solved using Likelihood Maximization methods, of which the Expectation-Maximization algorithm (EM) is… (more)

Subjects/Keywords: Data mining; Clustering; Expectation maximization; Bayesian Methods; Markov Chain Monte Carlo

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APA (6^th Edition):

-7494-095X. (2015). A Fast Clustering Algorithm Merging The Expectation Maximization Algorithm and Markov Chain Monte Carlo. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/1935

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

-7494-095X. “A Fast Clustering Algorithm Merging The Expectation Maximization Algorithm and Markov Chain Monte Carlo.” 2015. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/1935.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

-7494-095X. “A Fast Clustering Algorithm Merging The Expectation Maximization Algorithm and Markov Chain Monte Carlo.” 2015. Web. 06 Mar 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-7494-095X. A Fast Clustering Algorithm Merging The Expectation Maximization Algorithm and Markov Chain Monte Carlo. [Internet] [Masters thesis]. University of Houston; 2015. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/1935.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-7494-095X. A Fast Clustering Algorithm Merging The Expectation Maximization Algorithm and Markov Chain Monte Carlo. [Masters Thesis]. University of Houston; 2015. Available from: http://hdl.handle.net/10657/1935

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

29. -4794-9876. Effects of Prescriptive Design on the Usage of a Walking App.

Degree: MS, Computer Science, 2015, University of Houston

URL: http://hdl.handle.net/10657/1746

► Walking is the most ubiquitous physical activity. Natural walking opportunities, however, have been declining in developed societies. This decline has been linked to the rise… (more)

Subjects/Keywords: Physical activity; Design; Usability study

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APA (6^th Edition):

-4794-9876. (2015). Effects of Prescriptive Design on the Usage of a Walking App. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/1746

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

-4794-9876. “Effects of Prescriptive Design on the Usage of a Walking App.” 2015. Masters Thesis, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/1746.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

-4794-9876. “Effects of Prescriptive Design on the Usage of a Walking App.” 2015. Web. 06 Mar 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-4794-9876. Effects of Prescriptive Design on the Usage of a Walking App. [Internet] [Masters thesis]. University of Houston; 2015. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/1746.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-4794-9876. Effects of Prescriptive Design on the Usage of a Walking App. [Masters Thesis]. University of Houston; 2015. Available from: http://hdl.handle.net/10657/1746

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

30. Tran, Khai Nguyen 1984-. Contextual Descriptors for Human Activity Recognition.

Degree: PhD, Computer Science, 2013, University of Houston

URL: http://hdl.handle.net/10657/475

► Human activity recognition is one of the most challenging problems that has received considerable attention from the computer vision community in recent years. Its applications… (more)

▼ Human activity recognition is one of the most challenging problems that has received considerable attention from the computer vision community in recent years. Its applications are diverse, spanning from its use in activity understanding for intelligent surveillance systems to improving human-computer interactions. The goal of human activity recognition is to automatically recognize ongoing activities from an unknown video (i.e. a sequence of image frames). The challenges in solving this problem are multi-fold due to the complexity of human motions, the spatial and temporal variations exhibited due to differences in duration of different activities performed, the changing spatial characteristics of the human form, and the contextual information in performing each activity. A number of approaches have been proposed to address these challenges over the past few years by trying to design effective, compact descriptors for human activity encoding activity characteristics with context; however the mechanisms for incorporating them are not unique. In this dissertation, I present efficient techniques to handle learning and recognizing human activities. The primary goal of this research is to design compact but rich descriptors along with effective algorithms that can generally accommodate useful activity representation in a way of recognizing a single human activity or a collective activity in a crowded scene. For single human activity recognition, I introduce the subject-centric descriptors incorporating of both local and global representations that provide robustness against noise, partial occlusion, and invariance to changes in image scales. For collective activity recognition, I present context-based descriptors that efficiently encode human activity characteristic with contextual information leading to improve methods for analyzing group activities in a crowded scene. My results focus on recognizing single human activity and collective activity in a crowded scene. I show how efficient of my proposed descriptors in encoding human activity to be made on several public datasets. Moreover, I show how to incorporate contextual information to human activity characteristic in analyzing human activities in a crowded scene. Advisors/Committee Members: Shah, Shishir Kirit (advisor), Kakadiaris, Ioannis A. (committee member), Eick, Christoph F. (committee member), Gabriel, Edgar (committee member), Eledath, Jayan (committee member).

Subjects/Keywords: Human action recognition; Contextual descriptors; Computer vision

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APA (6^th Edition):

Tran, K. N. 1. (2013). Contextual Descriptors for Human Activity Recognition. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/475

Chicago Manual of Style (16^th Edition):

Tran, Khai Nguyen 1984-. “Contextual Descriptors for Human Activity Recognition.” 2013. Doctoral Dissertation, University of Houston. Accessed March 06, 2021. http://hdl.handle.net/10657/475.

MLA Handbook (7^th Edition):

Tran, Khai Nguyen 1984-. “Contextual Descriptors for Human Activity Recognition.” 2013. Web. 06 Mar 2021.

Vancouver:

Tran KN1. Contextual Descriptors for Human Activity Recognition. [Internet] [Doctoral dissertation]. University of Houston; 2013. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10657/475.

Council of Science Editors:

Tran KN1. Contextual Descriptors for Human Activity Recognition. [Doctoral Dissertation]. University of Houston; 2013. Available from: http://hdl.handle.net/10657/475

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