Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 7 of 7
Filtering by
- All Subjects: Computer Science
Description
This research by studies the computational performance of four different mixed integer programming (MIP) formulations for single machine scheduling problems with varying complexity. These formulations are based on (1) start and completion time variables, (2) time index variables, (3) linear ordering variables and (4) assignment and positional date variables. The objective functions that are studied in this paper are total weighted completion time, maximum lateness, number of tardy jobs and total weighted tardiness. Based on the computational results, discussion and recommendations are made on which MIP formulation might work best for these problems. The performances of these formulations very much depend on the objective function, number of jobs and the sum of the processing times of all the jobs. Two sets of inequalities are presented that can be used to improve the performance of the formulation with assignment and positional date variables. Further, this research is extend to single machine bicriteria scheduling problems in which jobs belong to either of two different disjoint sets, each set having its own performance measure. These problems have been referred to as interfering job sets in the scheduling literature and also been called multi-agent scheduling where each agent's objective function is to be minimized. In the first single machine interfering problem (P1), the criteria of minimizing total completion time and number of tardy jobs for the two sets of jobs is studied. A Forward SPT-EDD heuristic is presented that attempts to generate set of non-dominated solutions. The complexity of this specific problem is NP-hard. The computational efficiency of the heuristic is compared against the pseudo-polynomial algorithm proposed by Ng et al. [2006]. In the second single machine interfering job sets problem (P2), the criteria of minimizing total weighted completion time and maximum lateness is studied. This is an established NP-hard problem for which a Forward WSPT-EDD heuristic is presented that attempts to generate set of supported points and the solution quality is compared with MIP formulations. For both of these problems, all jobs are available at time zero and the jobs are not allowed to be preempted.
ContributorsKhowala, Ketan (Author) / Fowler, John (Thesis advisor) / Keha, Ahmet (Thesis advisor) / Balasubramanian, Hari J (Committee member) / Wu, Teresa (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2012
Description
Beta-Amyloid(Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer’s disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. However, current methods to detect Aβ/tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (positron emission tomography (PET)). And one of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research projects focuses in the AD pathophysiological progress.
In this dissertation, I proposed three novel machine learning and statistical models to examine subtle aspects of the hippocampal morphometry from MRI that are associated with Aβ /tau burden in the brain, measured using PET images. The first model is a novel unsupervised feature reduction model to generate a low-dimensional representation of hippocampal morphometry for each individual subject, which has superior performance in predicting Aβ/tau burden in the brain. The second one is an efficient federated group lasso model to identify the hippocampal subregions where atrophy is strongly associated with abnormal Aβ/Tau. The last one is a federated model for imaging genetics, which can identify genetic and transcriptomic influences on hippocampal morphometry. Finally, I stated the results of these three models that have been published or submitted to peer-reviewed conferences and journals.
ContributorsWu, Jianfeng (Author) / Wang, Yalin (Thesis advisor) / Li, Baoxin (Committee member) / Liang, Jianming (Committee member) / Wang, Junwen (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2022
Description
High-dimensional data is omnipresent in modern industrial systems. An imaging sensor in a manufacturing plant a can take images of millions of pixels or a sensor may collect months of data at very granular time steps. Dimensionality reduction techniques are commonly used for dealing with such data. In addition, outliers typically exist in such data, which may be of direct or indirect interest given the nature of the problem that is being solved. Current research does not address the interdependent nature of dimensionality reduction and outliers. Some works ignore the existence of outliers altogether—which discredits the robustness of these methods in real life—while others provide suboptimal, often band-aid solutions. In this dissertation, I propose novel methods to achieve outlier-awareness in various dimensionality reduction methods. The problem is considered from many different angles depend- ing on the dimensionality reduction technique used (e.g., deep autoencoder, tensors), the nature of the application (e.g., manufacturing, transportation) and the outlier structure (e.g., sparse point anomalies, novelties).
ContributorsSergin, Nurettin Dorukhan (Author) / Yan, Hao (Thesis advisor) / Li, Jing (Committee member) / Wu, Teresa (Committee member) / Tsung, Fugee (Committee member) / Arizona State University (Publisher)
Created2021
Description
Recent advancements in computer vision models have largely been driven by supervised training on labeled data. However, the process of labeling datasets remains both costly and time-intensive. This dissertation delves into enhancing the performance of deep neural networks when faced with limited or no labeling information. I address this challenge through four primary methodologies: domain adaptation, self-supervision, input regularization, and label regularization. In situations where labeled data is unavailable but a similar dataset exists, domain adaptation emerges as a valuable strategy for transferring knowledge from the labeled dataset to the target dataset. This dissertation introduces three innovative domain adaptation methods that operate at pixel, feature, and output levels.Another approach to tackle the absence of labels involves a novel self-supervision technique tailored to train Vision Transformers in extracting rich features.
The third and fourth approaches focus on scenarios where only a limited amount of labeled data is available. In such cases, I present novel regularization techniques designed to mitigate overfitting by modifying the input data and the target labels, respectively.
ContributorsChhabra, Sachin (Author) / Li, Baoxin (Thesis advisor) / Venkateswara, Hemanth (Committee member) / Yang, Yezhou (Committee member) / Wu, Teresa (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2024
Description
With the exponential growth of multi-modal data in the field of computer vision, the ability to do inference effectively among multiple modalities—such as visual, textual, and auditory data—shows significant opportunities. The rapid development of cross-modal applications such as retrieval and association is primarily attributed to their ability to bridge the gap between different modalities of data. However, the current mainstream cross-modal methods always heavily rely on the availability of fully annotated paired data, presenting a significant challenge due to the scarcity of precisely matched datasets in real-world scenarios. In response to this bottleneck, several sophisticated deep learning algorithms are designed to substantially improve the inference capabilities across a broad spectrum of cross-modal applications. This dissertation introduces novel deep learning algorithms aimed at enhancing inference capabilities in cross-modal applications, which take four primary aspects. Firstly, it introduces the algorithm for image retrieval by learning hashing codes. This algorithm only utilizes the other modality data in weakly supervised tags format rather than the supervised label. Secondly, it designs a novel framework for learning the joint embeddings of images and texts for the cross-modal retrieval tasks. It efficiently learns the binary codes from the continuous CLIP feature space and can even deliver competitive performance compared with the results from non-hashing methods. Thirdly, it conducts a method to learn the fragment-level embeddings that capture fine-grained cross-modal association in images and texts. This method uses the fragment proposals in an unsupervised manner. Lastly, this dissertation also outlines the algorithm to enhance the mask-text association ability of pre-trained semantic segmentation models with zero examples provided. Extensive future plans to further improve this algorithm for semantic segmentation tasks will be discussed.
ContributorsZhuo, Yaoxin (Author) / Li, Baoxin (Thesis advisor) / Wu, Teresa (Committee member) / Davulcu, Hasan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2024
Description
Technology advancements in diagnostic imaging, smart sensing, and health information systems have resulted in a data-rich environment in health care, which offers a great opportunity for Precision Medicine. The objective of my research is to develop data fusion and system informatics approaches for quality and performance improvement of health care. In my dissertation, I focus on three emerging problems in health care and develop novel statistical models and machine learning algorithms to tackle these problems from diagnosis to care to system-level decision-making.
The first topic is diagnosis/subtyping of migraine to customize effective treatment to different subtypes of patients. Existing clinical definitions of subtypes use somewhat arbitrary boundaries primarily based on patient self-reported symptoms, which are subjective and error-prone. My research develops a novel Multimodality Factor Mixture Model that discovers subtypes of migraine from multimodality imaging MRI data, which provides complementary accurate measurements of the disease. Patients in the different subtypes show significantly different clinical characteristics of the disease. Treatment tailored and optimized for patients of the same subtype paves the road toward Precision Medicine.
The second topic focuses on coordinated patient care. Care coordination between nurses and with other health care team members is important for providing high-quality and efficient care to patients. The recently developed Nurse Care Coordination Instrument (NCCI) is the first of its kind that enables large-scale quantitative data to be collected. My research develops a novel Multi-response Multi-level Model (M3) that enables transfer learning in NCCI data fusion. M3 identifies key factors that contribute to improving care coordination, and facilitates the design and optimization of nurses’ training, workload assignment, and practice environment, which leads to improved patient outcomes.
The last topic is about system-level decision-making for Alzheimer’s disease early detection at the early stage of Mild Cognitive Impairment (MCI), by predicting each MCI patient’s risk of converting to AD using imaging and proteomic biomarkers. My research proposes a systems engineering approach that integrates the multi-perspectives, including prediction accuracy, biomarker cost/availability, patient heterogeneity and diagnostic efficiency, and allows for system-wide optimized decision regarding the biomarker testing process for prediction of MCI conversion.
The first topic is diagnosis/subtyping of migraine to customize effective treatment to different subtypes of patients. Existing clinical definitions of subtypes use somewhat arbitrary boundaries primarily based on patient self-reported symptoms, which are subjective and error-prone. My research develops a novel Multimodality Factor Mixture Model that discovers subtypes of migraine from multimodality imaging MRI data, which provides complementary accurate measurements of the disease. Patients in the different subtypes show significantly different clinical characteristics of the disease. Treatment tailored and optimized for patients of the same subtype paves the road toward Precision Medicine.
The second topic focuses on coordinated patient care. Care coordination between nurses and with other health care team members is important for providing high-quality and efficient care to patients. The recently developed Nurse Care Coordination Instrument (NCCI) is the first of its kind that enables large-scale quantitative data to be collected. My research develops a novel Multi-response Multi-level Model (M3) that enables transfer learning in NCCI data fusion. M3 identifies key factors that contribute to improving care coordination, and facilitates the design and optimization of nurses’ training, workload assignment, and practice environment, which leads to improved patient outcomes.
The last topic is about system-level decision-making for Alzheimer’s disease early detection at the early stage of Mild Cognitive Impairment (MCI), by predicting each MCI patient’s risk of converting to AD using imaging and proteomic biomarkers. My research proposes a systems engineering approach that integrates the multi-perspectives, including prediction accuracy, biomarker cost/availability, patient heterogeneity and diagnostic efficiency, and allows for system-wide optimized decision regarding the biomarker testing process for prediction of MCI conversion.
ContributorsSi, Bing (Author) / Li, Jing (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Schwedt, Todd (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2018
Description
Network traffic analysis by means of Quality of Service (QoS) is a popular research and development area among researchers for a long time. It is becoming even more relevant recently due to ever increasing use of the Internet and other public and private communication networks. Fast and precise QoS analysis is a vital task in mission-critical communication networks (MCCNs), where providing a certain level of QoS is essential for national security, safety or economic vitality. In this thesis, the details of all aspects of a comprehensive computational framework for QoS analysis in MCCNs are provided. There are three main QoS analysis tasks in MCCNs; QoS measurement, QoS visualization and QoS prediction. Definitions of these tasks are provided and for each of those, complete solutions are suggested either by referring to an existing work or providing novel methods.
A scalable and accurate passive one-way QoS measurement algorithm is proposed. It is shown that accurate QoS measurements are possible using network flow data.
Requirements of a good QoS visualization platform are listed. Implementations of the capabilities of a complete visualization platform are presented.
Steps of QoS prediction task in MCCNs are defined. The details of feature selection, class balancing through sampling and assessing classification algorithms for this task are outlined. Moreover, a novel tree based logistic regression method for knowledge discovery is introduced. Developed prediction framework is capable of making very accurate packet level QoS predictions and giving valuable insights to network administrators.
A scalable and accurate passive one-way QoS measurement algorithm is proposed. It is shown that accurate QoS measurements are possible using network flow data.
Requirements of a good QoS visualization platform are listed. Implementations of the capabilities of a complete visualization platform are presented.
Steps of QoS prediction task in MCCNs are defined. The details of feature selection, class balancing through sampling and assessing classification algorithms for this task are outlined. Moreover, a novel tree based logistic regression method for knowledge discovery is introduced. Developed prediction framework is capable of making very accurate packet level QoS predictions and giving valuable insights to network administrators.
ContributorsSenturk, Muhammet Burhan (Author) / Li, Jing (Thesis advisor) / Baydogan, Mustafa G (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2014