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Title: Ensemble Learning Algorithms for the Analysis of Bioinformatics Data.
Creator: Fazelpour, Alireza, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Developments in advanced technologies, such as DNA microarrays, have generated tremendous amounts of data available to researchers in the field of bioinformatics. These state-of-the-art technologies present not only unprecedented opportunities to study biological phenomena of interest, but significant challenges in terms of processing the data. Furthermore, these datasets inherently exhibit a number of challenging characteristics, such as class imbalance, high dimensionality, small dataset...
Show moreDevelopments in advanced technologies, such as DNA microarrays, have generated tremendous amounts of data available to researchers in the field of bioinformatics. These state-of-the-art technologies present not only unprecedented opportunities to study biological phenomena of interest, but significant challenges in terms of processing the data. Furthermore, these datasets inherently exhibit a number of challenging characteristics, such as class imbalance, high dimensionality, small dataset size, noisy data, and complexity of data in terms of hard to distinguish decision boundaries between classes within the data. In recognition of the aforementioned challenges, this dissertation utilizes a variety of machine-learning and data-mining techniques, such as ensemble classification algorithms in conjunction with data sampling and feature selection techniques to alleviate these problems, while improving the classification results of models built on these datasets. However, in building classification models researchers and practitioners encounter the challenge that there is not a single classifier that performs relatively well in all cases. Thus, numerous classification approaches, such as ensemble learning methods, have been developed to address this problem successfully in a majority of circumstances. Ensemble learning is a promising technique that generates multiple classification models and then combines their decisions into a single final result. Ensemble learning often performs better than single-base classifiers in performing classification tasks. This dissertation conducts thorough empirical research by implementing a series of case studies to evaluate how ensemble learning techniques can be utilized to enhance overall classification performance, as well as improve the generalization ability of ensemble models. This dissertation investigates ensemble learning techniques of the boosting, bagging, and random forest algorithms, and proposes a number of modifications to the existing ensemble techniques in order to improve further the classification results. This dissertation examines the effectiveness of ensemble learning techniques on accounting for challenging characteristics of class imbalance and difficult-to-learn class decision boundaries. Next, it looks into ensemble methods that are relatively tolerant to class noise, and not only can account for the problem of class noise, but improves classification performance. This dissertation also examines the joint effects of data sampling along with ensemble techniques on whether sampling techniques can further improve classification performance of built ensemble models.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004588
Subject Headings: Bioinformatics., Data mining -- Technological innovations., Machine learning.
Format: Document (PDF)

Title: Enhancing performance in publish/subscribe systems.
Creator: Kamdar, Akshay., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Publish/subscribe is a powerful paradigm for distributed applications based on decoupled clients of information. In pub/sub applications, there exist a large amount of publishers and subscribes ranging from hundreds to millions. Publish/subscribe systems need to disseminate numerous events through a network of brokers. Due to limited resources of brokers, there may be lots of events that cannot be handled in time which in turn causes overload problem. Here arises the need of admission control...
Show morePublish/subscribe is a powerful paradigm for distributed applications based on decoupled clients of information. In pub/sub applications, there exist a large amount of publishers and subscribes ranging from hundreds to millions. Publish/subscribe systems need to disseminate numerous events through a network of brokers. Due to limited resources of brokers, there may be lots of events that cannot be handled in time which in turn causes overload problem. Here arises the need of admission control mechanism to provide guaranteed services in publish/subscribe systems. Our approach gives the solution to this overload problem in the network of brokers by limiting the incoming subscriptions by certain criteria. The criteria are the factors like resources which include bandwidth, CPU, memory (in broker network), resource requirements by the subscription.
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Date Issued: 2009
PURL: http://purl.flvc.org/FAU/329842
Subject Headings: Electronic data processing, Distributed processing, Embedded computer systems, Text processing (Computer science)
Format: Document (PDF)

Title: Enabling access for mobile devices to the web services resource framework.
Creator: Mangs, Jan Christian., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The increasing availability of Web services and grid computing has made easier the access and reuse of different types of services. Web services provide network accessible interfaces to application functionality in a platform-independent manner. Developments in grid computing have led to the efficient distribution of computing resources and power through the use of stateful web services. At the same time, mobile devices as a platform of computing have become a ubiquitous, inexpensive, and...
Show moreThe increasing availability of Web services and grid computing has made easier the access and reuse of different types of services. Web services provide network accessible interfaces to application functionality in a platform-independent manner. Developments in grid computing have led to the efficient distribution of computing resources and power through the use of stateful web services. At the same time, mobile devices as a platform of computing have become a ubiquitous, inexpensive, and powerful computing resource. Concepts such as cloud computing has pushed the trend towards using grid concepts in the internet domain and are ideally suited for internet-supported mobile devices. Currently, there are a few complete implementations that leverage mobile devices as a member of a grid or virtual organization. This thesis presents a framework that enables the use of mobile devices to access stateful Web services on a Globus-based grid. To illustrate the presented framework, a user-friendly mobile application has been created that utilizes the framework libraries do to demonstrate the various functionalities that are accessible from any mobile device that supports Java ME.
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Date Issued: 2008
PURL: http://purl.flvc.org/FAU/186290
Subject Headings: User interfaces (Computer systems), Data structures (Computer science), Mobile computing, Security measures, Mobile communication systems, Computational grids (Computer systems)
Format: Document (PDF)

Title: Effects of gene selection and data sampling on prediction of breast cancer treatments.
Creator: Heredia, Brian, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In recent years more and more researchers have begun to use data mining and machine learning tools to analyze gene microarray data. In this thesis we have collected a selection of datasets revolving around prediction of patient response in the specific area of breast cancer treatment. The datasets collected in this paper are all obtained from gene chips, which have become the industry standard in measurement of gene expression. In this thesis we will discuss the methods and procedures used in...
Show moreIn recent years more and more researchers have begun to use data mining and machine learning tools to analyze gene microarray data. In this thesis we have collected a selection of datasets revolving around prediction of patient response in the specific area of breast cancer treatment. The datasets collected in this paper are all obtained from gene chips, which have become the industry standard in measurement of gene expression. In this thesis we will discuss the methods and procedures used in the studies to analyze the datasets and their effects on treatment prediction with a particular interest in the selection of genes for predicting patient response. We will also analyze the datasets on our own in a uniform manner to determine the validity of these datasets in terms of learning potential and provide strategies for future work which explore how to best identify gene signatures.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004292, http://purl.flvc.org/fau/fd/FA00004292
Subject Headings: Antineoplastic agents -- Development, Breast -- Cancer -- Treatment, Cancer -- Genetic aspects, DNA mircroarrays, Estimation theory, Gene expression
Format: Document (PDF)

Title: Fault tolerance and reliability patterns.
Creator: Buckley, Ingrid A., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The need to achieve dependability in critical infrastructures has become indispensable for government and commercial enterprises. This need has become more necessary with the proliferation of malicious attacks on critical systems, such as healthcare, aerospace and airline applications. Additionally, due to the widespread use of web services in critical systems, the need to ensure their reliability is paramount. We believe that patterns can be used to achieve dependability. We conducted a...
Show moreThe need to achieve dependability in critical infrastructures has become indispensable for government and commercial enterprises. This need has become more necessary with the proliferation of malicious attacks on critical systems, such as healthcare, aerospace and airline applications. Additionally, due to the widespread use of web services in critical systems, the need to ensure their reliability is paramount. We believe that patterns can be used to achieve dependability. We conducted a survey of fault tolerance, reliability and web service products and patterns to better understand them. One objective of our survey is to evaluate the state of these patterns, and to investigate which standards are being used in products and their tool support. Our survey found that these patterns are insufficient, and many web services products do not use them. In light of this, we wrote some fault tolerance and web services reliability patterns and present an analysis of them.
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Date Issued: 2008
PURL: http://purl.flvc.org/FAU/166447
Subject Headings: Fault-tolerant computing, Computer software, Reliability, Reliability (Engineering), Computer programs
Format: Document (PDF)

Title: Design and implementation of driver drowsiness detection system.
Creator: Colic, Aleksandar, Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: There is a substantial amount of evidence that suggests that driver drowsiness plays a significant role in road accidents. Alarming recent statistics are raising the interest in equipping vehicles with driver drowsiness detection systems. This dissertation describes the design and implementation of a driver drowsiness detection system that is based on the analysis of visual input consisting of the driver's face and eyes. The resulting system combines off-the-shelf software components for face...
Show moreThere is a substantial amount of evidence that suggests that driver drowsiness plays a significant role in road accidents. Alarming recent statistics are raising the interest in equipping vehicles with driver drowsiness detection systems. This dissertation describes the design and implementation of a driver drowsiness detection system that is based on the analysis of visual input consisting of the driver's face and eyes. The resulting system combines off-the-shelf software components for face detection, human skin color detection and eye state classification in a novel way. It follows a behavioral methodology by performing a non-invasive monitoring of external cues describing a driver's level of drowsiness. We look at this complex problem from a systems engineering point of view in order to go from a proof-of-concept prototype to a stable software framework. Our system utilizes two detection and analysis methods: (i) face detection with eye region extrapolation and (ii) eye state classification. Additionally, we use two confirmation processes - one based on custom skin color detection, the other based on nod detection - to make the system more robust and resilient while not sacrificing speed significantly. The system was designed to be dynamic and adaptable to conform to the current conditions and hardware capabilities.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004275, http://purl.flvc.org/fau/fd/FA00004275
Subject Headings: Circadian rhythms, Computer vision, Driver assistance systems, Drowsy driving, Fatigue -- Prevention
Format: Document (PDF)

Title: Design of a power management model for a solar/fuel cell hybrid energy system.
Creator: Melendez, Rosana., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This thesis proposes a Power Management Model (PMM) for optimization of several green power generation systems. A Photovoltaic/Fuel cell Hybrid Energy System (PFHES) consisting of solar cells, electrolyzer and fuel cell stack is utilized to meet a specific DC load bank for various applications. The Photovoltaic system is the primary power source to take advantage of renewable energy. The electrolyzer-fuel cell integration is used as a backup and as a hydrogen storage system with the different...
Show moreThis thesis proposes a Power Management Model (PMM) for optimization of several green power generation systems. A Photovoltaic/Fuel cell Hybrid Energy System (PFHES) consisting of solar cells, electrolyzer and fuel cell stack is utilized to meet a specific DC load bank for various applications. The Photovoltaic system is the primary power source to take advantage of renewable energy. The electrolyzer-fuel cell integration is used as a backup and as a hydrogen storage system with the different energy sources integrated through a DC link bus. An overall power management strategy is designed for the optimization of the power flows among the different energy sources. Extensive simulation experiments have been carried out to verify the system performance under PMM governing strategy. The simulation results indeed demonstrate the effectiveness of the proposed approach.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2705074
Subject Headings: Electric power systems, Building-integrated photovoltaic systems, Renewable energy sources, Hydrogen as fuel, Research
Format: Document (PDF)

Title: Determining the Effectiveness of Human Interaction in Human-in-the-Loop Systems by Using Mental States.
Creator: Lloyd, Eric, Huang, Shihong, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: A self-adaptive software is developed to predict the stock market. It’s Stock Prediction Engine functions autonomously when its skill-set suffices to achieve its goal, and it includes human-in-the-loop when it recognizes conditions benefiting from more complex, expert human intervention. Key to the system is a module that decides of human participation. It works by monitoring three mental states unobtrusively and in real time with Electroencephalography (EEG). The mental states are drawn from...
Show moreA self-adaptive software is developed to predict the stock market. It’s Stock Prediction Engine functions autonomously when its skill-set suffices to achieve its goal, and it includes human-in-the-loop when it recognizes conditions benefiting from more complex, expert human intervention. Key to the system is a module that decides of human participation. It works by monitoring three mental states unobtrusively and in real time with Electroencephalography (EEG). The mental states are drawn from the Opportunity-Willingness-Capability (OWC) model. This research demonstrates that the three mental states are predictive of whether the Human Computer Interaction System functions better autonomously (human with low scores on opportunity and/or willingness, capability) or with the human-in-the-loop, with willingness carrying the largest predictive power. This transdisciplinary software engineering research exemplifies the next step of self-adaptive systems in which human and computer benefit from optimized autonomous and cooperative interactions, and in which neural inputs allow for unobtrusive pre-interactions.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004764, http://purl.flvc.org/fau/fd/FA00004764
Subject Headings: Cognitive neuroscience., Neural networks (Computer science), Pattern recognition systems., Artificial intelligence., Self-organizing systems., Human-computer interaction., Human information processing.
Format: Document (PDF)

Title: Design and implementation of efficient routing protocols in delay tolerant networks.
Creator: Liu, Cong., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Delay tolerant networks (DTNs) are occasionally-connected networks that may suffer from frequent partitions. DTNs provide service despite long end to end delays or infrequent connectivity. One fundamental problem in DTNs is routing messages from their source to their destination. DTNs differ from the Internet in that disconnections are the norm instead of the exception. Representative DTNs include sensor-based networks using scheduled intermittent connectivity, terrestrial wireless networks...
Show moreDelay tolerant networks (DTNs) are occasionally-connected networks that may suffer from frequent partitions. DTNs provide service despite long end to end delays or infrequent connectivity. One fundamental problem in DTNs is routing messages from their source to their destination. DTNs differ from the Internet in that disconnections are the norm instead of the exception. Representative DTNs include sensor-based networks using scheduled intermittent connectivity, terrestrial wireless networks that cannot ordinarily maintain end-to-end connectivity, satellite networks with moderate delays and periodic connectivity, underwater acoustic networks with moderate delays and frequent interruptions due to environmental factors, and vehicular networks with cyclic but nondeterministic connectivity. The focus of this dissertation is on routing protocols that send messages in DTNs. When no connected path exists between the source and the destination of the message, other nodes may relay the message to the destination. This dissertation covers routing protocols in DTNs with both deterministic and non-deterministic mobility respectively. In DTNs with deterministic and cyclic mobility, we proposed the first routing protocol that is both scalable and delivery guaranteed. In DTNs with non-deterministic mobility, numerous heuristic protocols are proposed to improve the routing performance. However, none of those can provide a theoretical optimization on a particular performance measurement. In this dissertation, two routing protocols for non-deterministic DTNs are proposed, which minimizes delay and maximizes delivery rate on different scenarios respectively. First, in DTNs with non-deterministic and cyclic mobility, an optimal single-copy forwarding protocol which minimizes delay is proposed., In DTNs with non-deterministic mobility, an optimal multi-copy forwarding protocol is proposed. which maximizes delivery rate under the constraint that the number of copies per message is fixed . Simulation evaluations using both real and synthetic trace are conducted to compare the proposed protocols with the existing ones.
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Date Issued: 2009
PURL: http://purl.flvc.org/FAU/210522
Subject Headings: Computer network protocols, Computer networks, Reliability, Computer algorithms, Wireless communication systems, Technological innovations
Format: Document (PDF)

Title: Detection and classification of marine mammal sounds.
Creator: Esfahanian, Mahdi, Zhuang, Hanqi, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Ocean is home to a large population of marine mammals such as dolphins and whales and concerns over anthropogenic activities in the regions close to their habitants have been increased. Therefore the ability to detect the presence of these species in the field, to analyze and classify their vocalization patterns for signs of distress and distortion of their communication calls will prove to be invaluable in protecting these species. The objective of this research is to investigate methods...
Show moreOcean is home to a large population of marine mammals such as dolphins and whales and concerns over anthropogenic activities in the regions close to their habitants have been increased. Therefore the ability to detect the presence of these species in the field, to analyze and classify their vocalization patterns for signs of distress and distortion of their communication calls will prove to be invaluable in protecting these species. The objective of this research is to investigate methods that automatically detect and classify vocalization patterns of marine mammals. The first work performed is the classification of bottlenose dolphin calls by type. The extraction of salient and distinguishing features from recordings is a major part of this endeavor. To this end, two strategies are evaluated with real datasets provided by Woods Hole Oceanographic Institution: The first strategy is to use contour-based features such as Time-Frequency Parameters and Fourier Descriptors and the second is to employ texture-based features such as Local Binary Patterns (LBP) and Gabor Wavelets. Once dolphin whistle features are extracted for spectrograms, selection of classification procedures is crucial to the success of the process. For this purpose, the performances of classifiers such as K-Nearest Neighbor, Support Vector Machine, and Sparse Representation Classifier (SRC) are assessed thoroughly, together with those of the underlined feature extractors.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004282, http://purl.flvc.org/fau/fd/FA00004282
Subject Headings: Acoustic phenomena in nature, Marine mammals -- Effect of noise on, Marine mammals -- Vocalization, Signal processing -- Mathematics, Underwater acoustics, Wavelets (Mathematics)
Format: Document (PDF)

Title: Compliance Issues In Cloud Computing Systems.
Creator: Yimam, Dereje, Fernandez, Eduardo B., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Appealing features of cloud services such as elasticity, scalability, universal access, low entry cost, and flexible billing motivate consumers to migrate their core businesses into the cloud. However, there are challenges about security, privacy, and compliance. Building compliant systems is difficult because of the complex nature of regulations and cloud systems. In addition, the lack of complete, precise, vendor neutral, and platform independent software architectures makes compliance even...
Show moreAppealing features of cloud services such as elasticity, scalability, universal access, low entry cost, and flexible billing motivate consumers to migrate their core businesses into the cloud. However, there are challenges about security, privacy, and compliance. Building compliant systems is difficult because of the complex nature of regulations and cloud systems. In addition, the lack of complete, precise, vendor neutral, and platform independent software architectures makes compliance even harder. We have attempted to make regulations clearer and more precise with patterns and reference architectures (RAs). We have analyzed regulation policies, identified overlaps, and abstracted them as patterns to build compliant RAs. RAs should be complete, precise, abstract, vendor neutral, platform independent, and with no implementation details; however, their levels of detail and abstraction are still debatable and there is no commonly accepted definition about what an RA should contain. Existing approaches to build RAs lack structured templates and systematic procedures. In addition, most approaches do not take full advantage of patterns and best practices that promote architectural quality. We have developed a five-step approach by analyzing features from available approaches but refined and combined them in a new way. We consider an RA as a big compound pattern that can improve the quality of the concrete architectures derived from it and from which we can derive more specialized RAs for cloud systems. We have built an RA for HIPAA, a compliance RA (CRA), and a specialized compliance and security RA (CSRA) for cloud systems. These RAs take advantage of patterns and best practices that promote software quality. We evaluated the architecture by creating profiles. The proposed approach can be used to build RAs from scratch or to build new RAs by abstracting real RAs for a given context. We have also described an RA itself as a compound pattern by using a modified POSA template. Finally, we have built a concrete deployment and availability architecture derived from CSRA that can be used as a foundation to build compliance systems in the cloud.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004559, http://purl.flvc.org/fau/fd/FA00004559
Subject Headings: Biometric identification, Client/server computing -- Security measures, Cloud computing -- Security measures, Computational intelligence, Computer software -- Quality control, Electronic information resources -- Access control
Format: Document (PDF)

Title: Discovery and visualization of co-regulated genes relevant to target diseases.
Creator: Lad, Vaibhan., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In this thesis, we propose to discover co-regulated genes using microarray expression data, as well as providing visualization functionalities for domain experts to study relationships among discovered co-regulated genes. To discover co-regulated genes, we first use existing gene selection methods to select a small portion of genes which are relevant to the target diseases, on which we build an ordered similarity matrix by using nearest neighbor based similarity assessment criteria. We then...
Show moreIn this thesis, we propose to discover co-regulated genes using microarray expression data, as well as providing visualization functionalities for domain experts to study relationships among discovered co-regulated genes. To discover co-regulated genes, we first use existing gene selection methods to select a small portion of genes which are relevant to the target diseases, on which we build an ordered similarity matrix by using nearest neighbor based similarity assessment criteria. We then apply a threshold based clustering algorithm named Spectral Clustering to the matrix to obtain a number of clusters. The genes which are clustered together in one cluster represent a group of co-regulated genes and to visualize them, we use Java Swings as the tool and develop a visualization platform which provides functionalities for domain experts to study relationships between different groups of co-regulated genes; study internal structures within each group of genes, and investigate details of each individual gene and of course for gene function prediction. Results are analyzed based on microarray expression datasets collected from brain tumor, lung cancers and leukemia samples.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2976447
Subject Headings: Genomics, Gene mapping, Cell transformation, Cellular signal transduction
Format: Document (PDF)

Title: Effcient Implementation and Computational Analysis of Privacy-Preserving Protocols for Securing the Financial Markets.
Creator: Alvarez, Ramiro, Nojoumian, Mehrdad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Auctions are a key economic mechanism for establishing the value of goods that have an uncertain price. In recent years, as a consequence of the ubiquitous emergence of technology, auctions can reach consumers, and as a result drive market prices, on a global scale. Collection of private information such as past trades exposes more information than desired by market participants. The leaked information can be statistically analyzed to provide auctioneers, or competitors, an advantage on...
Show moreAuctions are a key economic mechanism for establishing the value of goods that have an uncertain price. In recent years, as a consequence of the ubiquitous emergence of technology, auctions can reach consumers, and as a result drive market prices, on a global scale. Collection of private information such as past trades exposes more information than desired by market participants. The leaked information can be statistically analyzed to provide auctioneers, or competitors, an advantage on future transactions. The need to preserve privacy has become a critical concern to reach an accepted level of fairness, and to provide market participants an environment in which they can bid a true valuation. This research is about possible mechanisms to carry out sealed-bid auctions in a distributed setting, and provides the reader with the challenges that currently persevere in the field. The first chapter offers an introduction to different kinds of auction, and to describe sealed-bid auction. The next chapter surveys the literature, and provides necessary theoretical background. Moving on to chapter 3, instead of solely focusing on theoretical aspects of sealed-bid auctions, this chapter dives into implementation details, and demonstrates through communication and computational analysis how different settings affect performance.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013051
Subject Headings: Auctions., Financial markets., Tender offers (Securities).
Format: Document (PDF)

Title: Generalized Feature Embedding Learning for Clustering and Classication.
Creator: Golinko, Eric David, Zhu, Xingquan, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Data comes in many di erent shapes and sizes. In real life applications it is common that data we are studying has features that are of varied data types. This may include, numerical, categorical, and text. In order to be able to model this data with machine learning algorithms, it is required that the data is typically in numeric form. Therefore, for data that is not originally numerical, it must be transformed to be able to be used as input into these algorithms. Along with this...
Show moreData comes in many di erent shapes and sizes. In real life applications it is common that data we are studying has features that are of varied data types. This may include, numerical, categorical, and text. In order to be able to model this data with machine learning algorithms, it is required that the data is typically in numeric form. Therefore, for data that is not originally numerical, it must be transformed to be able to be used as input into these algorithms. Along with this transformation it is common that data we study has many features relative to the number of samples in the data. It is often desirable to reduce the number of features that are being trained in a model to eliminate noise and reduce time in training. This problem of high dimensionality can be approached through feature selection, feature extraction, or feature embedding. Feature selection seeks to identify the most essential variables in a dataset that will lead to a parsimonious model and high performing results, while feature extraction and embedding are techniques that utilize a mathematical transformation of the data into a represented space. As a byproduct of using a new representation, we are able to reduce the dimension greatly without sacri cing performance. Oftentimes, by using embedded features we observe a gain in performance. Though extraction and embedding methods may be powerful for isolated machine learning problems, they do not always generalize well. Therefore, we are motivated to illustrate a methodology that can be applied to any data type with little pre-processing. The methods we develop can be applied in unsupervised, supervised, incremental, and deep learning contexts. Using 28 benchmark datasets as examples which include di erent data types, we construct a framework that can be applied for general machine learning tasks. The techniques we develop contribute to the eld of dimension reduction and feature embedding. Using this framework, we make additional contributions to eigendecomposition by creating an objective matrix that includes three main vital components. The rst being a class partitioned row and feature product representation of one-hot encoded data. Secondarily, the derivation of a weighted adjacency matrix based on class label relationships. Finally, by the inner product of these aforementioned values, we are able to condition the one-hot encoded data generated from the original data prior to eigenvector decomposition. The use of class partitioning and adjacency enable subsequent projections of the data to be trained more e ectively when compared side-to-side to baseline algorithm performance. Along with this improved performance, we can adjust the dimension of the subsequent data arbitrarily. In addition, we also show how these dense vectors may be used in applications to order the features of generic data for deep learning. In this dissertation, we examine a general approach to dimension reduction and feature embedding that utilizes a class partitioned row and feature representation, a weighted approach to instance similarity, and an adjacency representation. This general approach has application to unsupervised, supervised, online, and deep learning. In our experiments of 28 benchmark datasets, we show signi cant performance gains in clustering, classi cation, and training time.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013063
Subject Headings: Eigenvectors--Data processing., Algorithms., Cluster analysis.
Format: Document (PDF)

Title: Gene selection for sample sets with biased distribution.
Creator: Kamal, Abu Hena Mustafa., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Microarray expression data which contains the expression levels of a large number of simultaneously observed genes have been used in many scientific research and clinical studies. Due to its high dimensionalities, selecting a small number of genes has shown to be beneficial for many tasks such as building prediction models from the microarray expression data or gene regulatory network discovery. Traditional gene selection methods, however, fail to take the class distribution into the...
Show moreMicroarray expression data which contains the expression levels of a large number of simultaneously observed genes have been used in many scientific research and clinical studies. Due to its high dimensionalities, selecting a small number of genes has shown to be beneficial for many tasks such as building prediction models from the microarray expression data or gene regulatory network discovery. Traditional gene selection methods, however, fail to take the class distribution into the selection process. In biomedical science, it is very common to have microarray expression data which is severely biased with one class of examples (e.g., diseased samples) significantly less than other classes (e.g., normal samples). These sample sets with biased distributions require special attention from researchers for identification of genes responsible for a particular disease. In this thesis, we propose three filtering techniques, Higher Weight ReliefF, ReliefF with Differential Minority Repeat and ReliefF with Balanced Minority Repeat to identify genes responsible for fatal diseases from biased microarray expression data. Our solutions are evaluated on five well-known microarray datasets, Colon, Central Nervous System, DLBCL Tumor, Lymphoma and ECML Pancreas. Experimental comparisons with the traditional ReliefF filtering method demonstrate the effectiveness of the proposed methods in selecting informative genes from microarray expression data with biased sample distributions.
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Date Issued: 2009
PURL: http://purl.flvc.org/FAU/186330
Subject Headings: Gene expression, Research, Methodology, Medical informatics, Apoptosis, Molecular aspects, DNA microarrays, Research
Format: Document (PDF)

Title: Generating narratives: a pattern language.
Creator: Greene, Samuel., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In order to facilitate the development, discussion, and advancement of the relatively new subfield of Artificial Intelligence focused on generating narrative content, the author has developed a pattern language for generating narratives, along with a new categorization framework for narrative generation systems. An emphasis and focus is placed on generating the Fabula of the story (the ordered sequence of events that make up the plot). Approaches to narrative generation are classified into...
Show moreIn order to facilitate the development, discussion, and advancement of the relatively new subfield of Artificial Intelligence focused on generating narrative content, the author has developed a pattern language for generating narratives, along with a new categorization framework for narrative generation systems. An emphasis and focus is placed on generating the Fabula of the story (the ordered sequence of events that make up the plot). Approaches to narrative generation are classified into one of three categories, and a pattern is presented for each approach. Enhancement patterns that can be used in conjunction with one of the core patterns are also identified. In total, nine patterns are identified - three core narratology patterns, four Fabula patterns, and two extension patterns. These patterns will be very useful to software architects designing a new generation of narrative generation systems.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3355559
Subject Headings: Computational intelligence, Pattern recognition systems, Computer vision, Artificial intelligence, Image processing, Digital techiques
Format: Document (PDF)

Title: HEVC optimization in mobile environments.
Creator: Garcia, Ray, Kalva, Hari, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Recently, multimedia applications and their use have grown dramatically in popularity in strong part due to mobile device adoption by the consumer market. Applications, such as video conferencing, have gained popularity. These applications and others have a strong video component that uses the mobile device’s resources. These resources include processing time, network bandwidth, memory use, and battery life. The goal is to reduce the need of these resources by reducing the complexity of the...
Show moreRecently, multimedia applications and their use have grown dramatically in popularity in strong part due to mobile device adoption by the consumer market. Applications, such as video conferencing, have gained popularity. These applications and others have a strong video component that uses the mobile device’s resources. These resources include processing time, network bandwidth, memory use, and battery life. The goal is to reduce the need of these resources by reducing the complexity of the coding process. Mobile devices offer unique characteristics that can be exploited for optimizing video codecs. The combination of small display size, video resolution, and human vision factors, such as acuity, allow encoder optimizations that will not (or minimally) impact subjective quality. The focus of this dissertation is optimizing video services in mobile environments. Industry has begun migrating from H.264 video coding to a more resource intensive but compression efficient High Efficiency Video Coding (HEVC). However, there has been no proper evaluation and optimization of HEVC for mobile environments. Subjective quality evaluations were performed to assess relative quality between H.264 and HEVC. This will allow for better use of device resources and migration to new codecs where it is most useful. Complexity of HEVC is a significant barrier to adoption on mobile devices and complexity reduction methods are necessary. Optimal use of encoding options is needed to maximize quality and compression while minimizing encoding time. Methods for optimizing coding mode selection for HEVC were developed. Complexity of HEVC encoding can be further reduced by exploiting the mismatch between the resolution of the video, resolution of the mobile display, and the ability of the human eyes to acquire and process video under these conditions. The perceptual optimizations developed in this dissertation use the properties of spatial (visual acuity) and temporal information processing (motion perception) to reduce the complexity of HEVC encoding. A unique feature of the proposed methods is that they reduce encoding complexity and encoding time. The proposed HEVC encoder optimization methods reduced encoding time by 21.7% and bitrate by 13.4% with insignificant impact on subjective quality evaluations. These methods can easily be implemented today within HEVC.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004112
Subject Headings: Coding theory, Digital coding -- Data processing, Image processing -- Digital techniques, Multimedia systems, Video compression
Format: Document (PDF)

Title: Exploring the electromagnetics of millimeter-wave through terahertz spectrum: de novo studies vis-à-vis materials science, biomedical applications and wireless communication.
Creator: Sharma, Bharti, Neelakanta, Perambur S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The present research is a targeted endeavor to study the underlying characteristics and novel applications of millimeter (mm) wave through terahertz (THz) spectrum of electromagnetic (EM) energy. Focused thereof are the following specific tasks broadly considered pertinent to the said EM spectral range: (i) To elucidate the material characteristics vis-à-vis the interaction with EM energy at the test frequencies; (ii) to identify biomedical applications based on the material characteristics...
Show moreThe present research is a targeted endeavor to study the underlying characteristics and novel applications of millimeter (mm) wave through terahertz (THz) spectrum of electromagnetic (EM) energy. Focused thereof are the following specific tasks broadly considered pertinent to the said EM spectral range: (i) To elucidate the material characteristics vis-à-vis the interaction with EM energy at the test frequencies; (ii) to identify biomedical applications based on the material characteristics studied and applied to biomedia; and (iii) to model the wireless communication channels supporting EM waves at the test frequency bands of interest. Commensurate with the scope as above, the objectives of the research are as follows:
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004330, http://purl.flvc.org/fau/fd/FA00004330
Subject Headings: Electromagnetic waves -- Scattering, Pattern recognition systems, Scattering (Physics), Terahertz technology, Wireless communication systems
Format: Document (PDF)

Title: APIS: A SOFTWARE AND HARDWARE TOOLKIT FOR FEDERATED POWER MANAGEMENT IN ENERGY HARVESTING APPLICATIONS.
Creator: Prey, Adam, Hallstrom, Jason O., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Embedded systems and Internet of Things (IoT) devices have been limited in application by constraints posed by batteries. Batteries add size, weight, and upkeep costs, while also limiting the lifetime of devices that are preferred to be small, lightweight, and long-lasting. We present Apis, a software and hardware toolkit for federated power management in energy harvesting applications. By replacing batteries with rapid charging storage capacitors, circuitry to control federated energy...
Show moreEmbedded systems and Internet of Things (IoT) devices have been limited in application by constraints posed by batteries. Batteries add size, weight, and upkeep costs, while also limiting the lifetime of devices that are preferred to be small, lightweight, and long-lasting. We present Apis, a software and hardware toolkit for federated power management in energy harvesting applications. By replacing batteries with rapid charging storage capacitors, circuitry to control federated energy storage, and software support to make this architecture useful to developers, embedded devices can potentially run inde nitely with limited maintenance. We present the Apis hardware design for controlling federated energy storage, supporting software for controlling this hardware, and the results of experiments performed to validate the Apis model. The system is named after the taxonomy genus for the honey bee, a creature dedicated to the harvesting and federated storage of energy resources.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013405
Subject Headings: Internet of things, Energy harvesting, Embedded systems (Computer systems), Telecommunication--Power supply, Application program interfaces (Computer software)
Format: Document (PDF)

Title: 2D/3D face recognition.
Creator: Guan, Xin., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This dissertation introduces our work on face recognition using a novel approach based on creating 3D face model from 2D face images. Together with the pose angle estimation and illumination compensation, this method can be used successfully to recognize 2D faces with 3D recognition algorithms. The results reported here were obtained partially with our own face image database, which had 2D and 3D face images of 50 subjects, with 9 different pose angles. It is shown that by applying even the...
Show moreThis dissertation introduces our work on face recognition using a novel approach based on creating 3D face model from 2D face images. Together with the pose angle estimation and illumination compensation, this method can be used successfully to recognize 2D faces with 3D recognition algorithms. The results reported here were obtained partially with our own face image database, which had 2D and 3D face images of 50 subjects, with 9 different pose angles. It is shown that by applying even the simple PCA algorithm, this new approach can yield successful recognition rates using 2D probing images and 3D gallery images. The insight gained from the 2D/3D face recognition study was also extended to the case of involving 2D probing and 2D gallery images, which offers a more flexible approach since it is much easier and practical to acquire 2D photos for recognition. To test the effectiveness of the proposed approach, the public AT&T face database, which had 2D only face photos of 40 subjects, with 10 different images each, was utilized in the experimental study. The results from this investigation show that with our approach, the 3D recognition algorithm can be successfully applied to 2D only images. The performance of the proposed approach was further compared with some of the existing face recognition techniques. Studies on imperfect conditions such as domain and pose/illumination variations were also carried out. Additionally, the performance of the algorithms on noisy photos was evaluated. Pros and cons of the proposed face recognition technique along with suggestions for future studies are also given in the dissertation.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3342104
Subject Headings: Pattern recognition systems, Optical pattern recognition, Biometric identification, Face perception, Artificial intellingence
Format: Document (PDF)

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