View All Items
(1 - 20 of 21)
Pages
- Title
- A framework for prioritizing opportunities of improvement in the context of business excellence model in healthcare organization.
- Creator
-
Aldarmaki, Alia, Elshennawy, Ahmad, Lee, Gene, Rabelo, Luis, Darwish, Mohammed, University of Central Florida
- Abstract / Description
-
In today's world, the healthcare sector is facing challenges to improve the efficiency and effectiveness of its operations. More and more improvement projects are being adopted to enhance healthcare services, making it more patient-centric, and enabling better cost control. Healthcare organizations strive to identify and carry out such improvement initiatives to sustain their businesses and gain competitive advantage. Seeking to reach a higher operational level of excellence, healthcare...
Show moreIn today's world, the healthcare sector is facing challenges to improve the efficiency and effectiveness of its operations. More and more improvement projects are being adopted to enhance healthcare services, making it more patient-centric, and enabling better cost control. Healthcare organizations strive to identify and carry out such improvement initiatives to sustain their businesses and gain competitive advantage. Seeking to reach a higher operational level of excellence, healthcare organizations utilize business excellence criteria to conduct assessment and identify organizational strengths and weaknesses. However, while such assessments routinely identify numerous areas for potential improvement, it is not feasible to conduct all improvement projects simultaneously due to limitations in time, capital, and personnel, as well as conflict with other organization's projects or strategic objectives. An effective prioritization and selection approach is valuable in that it can assist the organization to optimize its available resources and outcomes. This study attempts to enable such an approach by developing a framework to prioritize improvement opportunities in healthcare in the context of the business excellence model through the integration of the Fuzzy Delphi Method and Fuzzy Interface System. To carry out the evaluation process, the framework consists of two phases. The first phase utilizes Fuzzy Delphi Method to identify the most significant factors that should be considered in healthcare for electing the improvement projects. The FDM is employed to handle the subjectivity of human assessment. The research identifies potential factors for evaluating projects, then utilizes FDM to capture expertise knowledge. The first round in FDM is intended to validate the identified list of factors from experts; which includes collecting additional factors from experts that the literature might have overlooked. When an acceptable level of consensus has been reached, a second round is conducted to obtain experts' and other related stakeholders' opinions on the appropriate weight of each factor's importance. Finally, FDM analyses eliminate or retain the criteria to produce a final list of critical factors to select improvement projects. The second phase in the framework attempts to prioritize improvement initiatives using the Hierarchical Fuzzy Interface System. The Fuzzy Interface System combines the experts' ratings for each improvement opportunity with respect to the factors deemed critical to compute the priority index. In the process of calculating the priority index, the framework allows the estimation of other intermediate indices including: social, financial impact, strategical, operational feasibility, and managerial indices. These indices bring an insight into the improvement opportunities with respect to each framework's dimensions. The framework allows for a reduction of the bias in the assessment by developing a knowledge based on the perspectives of multiple experts.
Show less - Date Issued
- 2018
- Identifier
- CFE0007304, ucf:52158
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007304
- Title
- THE DEVELOPMENT OF A HUMAN-CENTRIC FUZZY MATHEMATICAL MEASURE OF HUMAN ENGAGEMENT IN INTERACTIVE MULTIMEDIA SYSTEMS AND APPLICATIONS.
- Creator
-
Butler, Chandre, McCauley-Bush, Pamela, University of Central Florida
- Abstract / Description
-
The utilization of fuzzy mathematical modeling for the quantification of the Human Engagement is an innovative approach within Interactive Multimedia applications (mainly video-based games designed to entertain or train participants on intended topics of interest) that can result in measurable and repeatable results. These results can then be used to generate a cogent Human Engagement definition. This research is designed to apply proven quantification techniques and Industrial/Systems...
Show moreThe utilization of fuzzy mathematical modeling for the quantification of the Human Engagement is an innovative approach within Interactive Multimedia applications (mainly video-based games designed to entertain or train participants on intended topics of interest) that can result in measurable and repeatable results. These results can then be used to generate a cogent Human Engagement definition. This research is designed to apply proven quantification techniques and Industrial/Systems Engineering methodologies to nontraditional environments such as Interactive Multimedia. The outcomes of this research will provide the foundation, initial steps and preliminary validation for the development of a systematic fuzzy theoretical model to be applied for the quantification of Human Engagement. Why is there a need for Interactive Multimedia applications in commercial and educational environments including K-20 educational systems and industry? In the latter case, the debate over education reform has drawn from referenced areas within the Industrial Engineering community including quality, continuous improvement, benchmarking and metrics development, data analysis, and scientific/systemic justification requirements. In spite of these applications, the literature does not reflect a consistent and broad application of these techniques in addressing the evaluation and quantification of Human Engagement in Interactive Multimedia. It is strongly believed that until an administrative based Human Engagement definition is created and accepted, the benefits of Interactive Multimedia may not be fully realized. The influence of gaming on society is quite apparent. For example, the increased governmental appropriations for Simulations & Modeling development as well as the estimated multi-billion dollar consumer PC/console game market are evidence of Interactive Multimedia opportunity. This body of work will identify factors that address the actual and perceived levels of Human Engagement in Interactive Multimedia systems and Virtual Environments and factor degrees of existence necessary to quantify and measure Human Engagement. Finally, the research will quantify the inputs and produce a model that provides a numeric value that defines the level of Human Engagement as it is evaluated within the interactive multimedia application area. This Human Engagement definition can then be used as the basis of study within other application areas of interest.
Show less - Date Issued
- 2010
- Identifier
- CFE0003380, ucf:48459
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003380
- Title
- THE DEVELOPMENT OF A FUZZY MODEL TO QUANTIFY TRAINING ANDEDUCATIONAL FACTORS AND THE RESULTING IMPACT ON STUDENT SUCCESSAND LEARNING.
- Creator
-
Butler, Chandre, McCauley-Bell, Pamela, University of Central Florida
- Abstract / Description
-
The utilization of fuzzy mathematical modeling for quantification of the quality of training and educational delivery is an innovative application that can result in measurable and repeatable results. This research was designed to apply proven quantification techniques and Industrial Engineering methodologies to a nontraditional environment. The outcomes of this research provide the foundation, initial steps and preliminary validation for the development of a systematic fuzzy theoretical...
Show moreThe utilization of fuzzy mathematical modeling for quantification of the quality of training and educational delivery is an innovative application that can result in measurable and repeatable results. This research was designed to apply proven quantification techniques and Industrial Engineering methodologies to a nontraditional environment. The outcomes of this research provide the foundation, initial steps and preliminary validation for the development of a systematic fuzzy theoretical model to be applied for the quantification of various areas within training and education delivery. The test bed for this methodology is Orange County Public School system, the twelfth largest school district in the nation. The organizational and operational factors of a large school district are highly compatible with Systems Engineering concepts. The debate over education reform has drawn from referenced areas within the Industrial Engineering community including quality, continuous improvement, benchmarking and metrics development, data analysis, and scientific/systemic justification requirements. In spite of these applications, the literature does not reflect a consistent and broad application of these techniques in addressing the evaluation and quantification of educational delivery systems. This research draws on the previously listed areas within Industrial Engineering to apply these techniques to enhance the understanding and promote quantification of the multiple factors acting on the educational delivery system. The importance of addressing these issues is a national concern given the significant changes in the United States educational delivery system. For example, over the past 40 years there has been a more than three-hundred percent increase in per-pupil appropriations yet the academic performance gains have been limited and the quantification and measurement of those gains is even more limited. This body of work willidentify the systems, sub-systems, system factors, and factor degrees of existence necessary to quantify and measure these performance changes. Finally, the research will quantify the inputs and produce a model that provides a numeric value that represents the condition of the system and various subsystems of an educational system.
Show less - Date Issued
- 2005
- Identifier
- CFE0000890, ucf:46640
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000890
- Title
- Development of an Automated Method for Identification of Wet and Dry Channel Segments Using LiDAR Data and Fuzzy Logic Cluster Analysis.
- Creator
-
Rowney, Chris, Wang, Dingbao, Medeiros, Stephen, Kibler, Kelly, University of Central Florida
- Abstract / Description
-
Research into the use of LiDAR data for purposes other than simple topographic elevation determination, such as urban land cover classification and the identification of forest biomass, has become prominent in recent years. In many cases, alternative analysis methodologies conducted using airborne LiDAR data are possible because the raw data collected during a survey can include information other than the classically used elevation and coordinate points, the X, Y, and Z of the plane. In...
Show moreResearch into the use of LiDAR data for purposes other than simple topographic elevation determination, such as urban land cover classification and the identification of forest biomass, has become prominent in recent years. In many cases, alternative analysis methodologies conducted using airborne LiDAR data are possible because the raw data collected during a survey can include information other than the classically used elevation and coordinate points, the X, Y, and Z of the plane. In particular, intensity return values for each point in a LiDAR grid have been found to provide a useful data set for wet and dry channel classification. LiDAR intensity return data are, in essence, a numeric representation of the characteristic light reflectivity of the object being scanned; the more reflective the object is, the higher the intensity return will be. Intensity data points are collected along the course of the channel network and within the perceived banks of the channel. Intensity data do not crisply reflect a perfectly wet or dry condition, but instead vary over a range such that each location can be viewed as partially wet and partially dry. It is advantageous to assess problems of this type using the methods of fuzzy logic. Specifically, the variance in LiDAR intensity return data is such that the use of fuzzy logic to identify intensity cluster centers, and thereby assign wet and dry condition identifiers based on fuzzy memberships, is a possibility. Membership within a fuzzy data set is characterized by a value representing the degree of membership. Typically, membership values range from 0 (representing non-membership) through 1 (representing full membership), with many observations found to be not at either extreme but instead at some intermediate value representing partial membership. The ultimate goal of this research was to design and develop an automated algorithm to identify wet and dry channel sections, given a previously identified channel network based on topographic elevation, using a combination of intensity return values from LiDAR data and fuzzy logic clustering methods, and to implement that algorithm in such a way as to produce reliable multi-class channel segments in ArcGIS. To enable control of calculations, limiting parameters were defined, specifically including the maximum allowable bank slope, and a filtering percentage to more accurately accommodate the study area.Alteration of the maximum allowable bank slope has been shown to affect the comparative quantity of high and low intensity centroids, but only in extreme bank slope conditions are the centroids changed enough to hamper results. However, interference from thick vegetation has been shown to lower intensity values in dry channel sections into the range of a wet channel. The addition of a filtering algorithm alleviates some of the interference, but not all. Overall results of the tool show an effective methodology where basic channel conditions are identified, but refinement of the tool could produce more accurate results.
Show less - Date Issued
- 2015
- Identifier
- CFE0006053, ucf:50975
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006053
- Title
- Integration of Multidimensional Signal Detection Theory with Fuzzy Signal Detection Theory.
- Creator
-
O'Connell, Maureen, Szalma, James, Hancock, Peter, Bohil, Corey, Reinerman, Lauren, University of Central Florida
- Abstract / Description
-
Signal detection theory (SDT) has proven to be a robust and useful statistical model for analyzing human performance in detection and decision making tasks. As with many models extensions have been proposed in order capture and represent the real world to a greater degree. Multidimensional Signal Detection Theory (MSDT) has had success in describing and modeling complex signals, signals that are comprised by more than one identifiable component dimension. Fuzzy Signal Detection Theory (FSDT)...
Show moreSignal detection theory (SDT) has proven to be a robust and useful statistical model for analyzing human performance in detection and decision making tasks. As with many models extensions have been proposed in order capture and represent the real world to a greater degree. Multidimensional Signal Detection Theory (MSDT) has had success in describing and modeling complex signals, signals that are comprised by more than one identifiable component dimension. Fuzzy Signal Detection Theory (FSDT) has had success in modeling and measuring human performance in cases where there exist ambiguity in the signal or response dimension characteristics, through the application of fuzzy set theory to the definition of the performance outcome categories. Multidimensional Fuzzy Signal Detection Theory (MFSDT) was developed to accommodate simultaneously both the multidimensionality of a signal and the fuzzification of outcome categories in order to integrate the two extensions. A series of three studies were performed to develop and test the theory. One study's purpose was to develop and derive multidimensional mapping functions, the aspect of MFSDT where MSDT and FSDT were integrated. Two receiver operating characteristic (ROC) studies were performed, one simulated and one empirical. The results from both ROC analysis indicated that for perceptually separable and perceptually integral complex stimuli that MFDST is a viable methodological approach to analyzing performance of signal detection tasks where there are complex signals with ambiguous signal characteristics.
Show less - Date Issued
- 2015
- Identifier
- CFE0005983, ucf:50763
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005983
- Title
- A Framework for Quantifying and Managing Overcrowding in Healthcare Facilities.
- Creator
-
Albar, Abdulrahman, Elshennawy, Ahmad, Rabelo, Luis, Lee, Gene, Rahal, Ahmad, University of Central Florida
- Abstract / Description
-
Emergency Departments (EDs) represent a crucial component of any healthcare infrastructure. In today's world, healthcare systems face growing challenges in delivering efficient and time-sensitive emergency care services to communities. Overcrowding within EDs represents one of the most significant challenges for healthcare quality that adversely impacts clinical outcomes, patient safety, and overall satisfaction. Research in this area has resulted in creating several ED crowding indices, such...
Show moreEmergency Departments (EDs) represent a crucial component of any healthcare infrastructure. In today's world, healthcare systems face growing challenges in delivering efficient and time-sensitive emergency care services to communities. Overcrowding within EDs represents one of the most significant challenges for healthcare quality that adversely impacts clinical outcomes, patient safety, and overall satisfaction. Research in this area has resulted in creating several ED crowding indices, such as National Emergency Department Overcrowding Scale (NEDOCS) and Emergency Department Work Index (EDWIN) that have been developed to provide measures aimed at mitigating overcrowding. Recently, efforts made by researchers to examine the validity and reproducibility of these indices have shown that they are not reliable in accurately assessing overcrowding in regions beyond their original design settings. The shortcomings of such indices stem from their reliance upon the perspective and feedback of only clinical staff and the exclusion of other stakeholders. These limited perspectives introduce bias in the results of ED overcrowding indices. This study starts with confirming the inaccuracy of such crowding indices through examining their validity within a new healthcare system. To overcome the shortcomings of previous indices, the study presents a novel framework for quantifying and managing overcrowding based on emulating human reasoning in overcrowding perception. The framework of the proposed study takes into consideration emergency operational and clinical factors such as patient demand, patient complexity, staffing level, clinician workload, and boarding status when defining the crowding level. The hierarchical fuzzy logic approach is utilized to accomplish the goals of this framework by combining a diverse pool of healthcare expert perspectives while addressing the complexity of the overcrowding issue. The innovative design of the developed framework reduces bias in the assessment of ED crowding by developing a knowledge-base from the perspectives of multiple experts, and allows for its implementation in a variety of healthcare settings. Statistical analysis of results indicate that the developed index outperform previous indices in reflecting expert subjective assessments of overcrowding.
Show less - Date Issued
- 2016
- Identifier
- CFE0006521, ucf:51378
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006521
- Title
- MODIFICATIONS TO THE FUZZY-ARTMAP ALGORITHM FOR DISTRIBUTED LEARNING IN LARGE DATA SETS.
- Creator
-
Castro, Jose R, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
-
The Fuzzy-ARTMAP (FAM) algorithm is one of the premier neural network architectures for classification problems. FAM can learn on line and is usually faster than other neural network approaches. Nevertheless the learning time of FAM can slow down considerably when the size of the training set increases into the hundreds of thousands. We apply data partitioning and networkpartitioning to the FAM algorithm in a sequential and parallel settingto achieve better convergence time and to efficiently...
Show moreThe Fuzzy-ARTMAP (FAM) algorithm is one of the premier neural network architectures for classification problems. FAM can learn on line and is usually faster than other neural network approaches. Nevertheless the learning time of FAM can slow down considerably when the size of the training set increases into the hundreds of thousands. We apply data partitioning and networkpartitioning to the FAM algorithm in a sequential and parallel settingto achieve better convergence time and to efficiently train withlarge databases (hundreds of thousands of patterns).Our parallelization is implemented on a Beowulf clusters of workstations. Two data partitioning approaches and two networkpartitioning approaches are developed. Extensive testing of all the approaches is done on three large datasets (half a milliondata points). One of them is the Forest Covertype database from Blackard and the other two are artificially generated Gaussian data with different percentages of overlap between classes.Speedups in the data partitioning approach reached the order of the hundreds without having to invest in parallel computation. Speedups onthe network partitioning approach are close to linear on a cluster of workstations. Both methods allowed us to reduce the computation time of training the neural network in large databases from days to minutes. We prove formally that the workload balance of our network partitioning approaches will never be worse than an acceptable bound, and also demonstrate the correctness of these parallelization variants of FAM.
Show less - Date Issued
- 2004
- Identifier
- CFE0000065, ucf:46092
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000065
- Title
- NON-SILICON MICROFABRICATED NANOSTRUCTURED CHEMICAL SENSORS FOR ELECTRIC NOSE APPLICATION.
- Creator
-
Gong, Jianwei, Chen, Quanfang, University of Central Florida
- Abstract / Description
-
A systematic investigation has been performed for "Electric Nose", a system that can identify gas samples and detect their concentrations by combining sensor array and data processing technologies. Non-silicon based microfabricatition has been developed for micro-electro-mechanical-system (MEMS) based gas sensors. Novel sensors have been designed, fabricated and tested. Nanocrystalline semiconductor metal oxide (SMO) materials include SnO2, WO3 and In2O3 have been studied for gas sensing...
Show moreA systematic investigation has been performed for "Electric Nose", a system that can identify gas samples and detect their concentrations by combining sensor array and data processing technologies. Non-silicon based microfabricatition has been developed for micro-electro-mechanical-system (MEMS) based gas sensors. Novel sensors have been designed, fabricated and tested. Nanocrystalline semiconductor metal oxide (SMO) materials include SnO2, WO3 and In2O3 have been studied for gas sensing applications. Different doping material such as copper, silver, platinum and indium are studied in order to achieve better selectivity for different targeting toxic gases including hydrogen, carbon monoxide, hydrogen sulfide etc. Fundamental issues like sensitivity, selectivity, stability, temperature influence, humidity influence, thermal characterization, drifting problem etc. of SMO gas sensors have been intensively investigated. A novel approach to improve temperature stability of SMO (including tin oxide) gas sensors by applying a temperature feedback control circuit has been developed. The feedback temperature controller that is compatible with MEMS sensor fabrication has been invented and applied to gas sensor array system. Significant improvement of stability has been achieved compared to SMO gas sensors without temperature compensation under the same ambient conditions. Single walled carbon nanotube (SWNT) has been studied to improve SnO2 gas sensing property in terms of sensitivity, response time and recovery time. Three times of better sensitivity has been achieved experimentally. The feasibility of using TSK Fuzzy neural network algorithm for Electric Nose has been exploited during the research. A training process of using TSK Fuzzy neural network with input/output pairs from individual gas sensor cell has been developed. This will make electric nose smart enough to measure gas concentrations in a gas mixture. The model has been proven valid by gas experimental results conducted.
Show less - Date Issued
- 2005
- Identifier
- CFE0000377, ucf:46328
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000377
- Title
- HEURISTIC 3D RECONSTRUCTION OF IRREGULAR SPACED LIDAR.
- Creator
-
Shorter, Nicholas, Kasparis, Takis, University of Central Florida
- Abstract / Description
-
As more data sources have become abundantly available, an increased interest in 3D reconstruction has emerged in the image processing academic community. Applications for 3D reconstruction of urban and residential buildings consist of urban planning, network planning for mobile communication, tourism information systems, spatial analysis of air pollution and noise nuisance, microclimate investigations, and Geographical Information Systems (GISs). Previous, classical, 3D reconstruction...
Show moreAs more data sources have become abundantly available, an increased interest in 3D reconstruction has emerged in the image processing academic community. Applications for 3D reconstruction of urban and residential buildings consist of urban planning, network planning for mobile communication, tourism information systems, spatial analysis of air pollution and noise nuisance, microclimate investigations, and Geographical Information Systems (GISs). Previous, classical, 3D reconstruction algorithms solely utilized aerial photography. With the advent of LIDAR systems, current algorithms explore using captured LIDAR data as an additional feasible source of information for 3D reconstruction. Preprocessing techniques are proposed for the development of an autonomous 3D Reconstruction algorithm. The algorithm is designed for autonomously deriving three dimensional models of urban and residential buildings from raw LIDAR data. First, a greedy insertion triangulation algorithm, modified with a proposed noise filtering technique, triangulates the raw LIDAR data. The normal vectors of those triangles are then passed to an unsupervised clustering algorithm – Fuzzy Simplified Adaptive Resonance Theory (Fuzzy SART). Fuzzy SART returns a rough grouping of coplanar triangles. A proposed multiple regression algorithm then further refines the coplanar grouping by further removing outliers and deriving an improved planar segmentation of the raw LIDAR data. Finally, further refinement is achieved by calculating the intersection of the best fit roof planes and moving nearby points close to that intersection to exist at the intersection, resulting in straight roof ridges. The end result of the aforementioned techniques culminates in a well defined model approximating the considered building depicted by the LIDAR data.
Show less - Date Issued
- 2006
- Identifier
- CFE0001315, ucf:47017
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001315
- Title
- Investigating The Relationship Between Adverse Events and Infrastructure Development in an Active War Theater Using Soft Computing Techniques.
- Creator
-
Cakit, Erman, Karwowski, Waldemar, Lee, Gene, Thompson, William, Mikusinski, Piotr, University of Central Florida
- Abstract / Description
-
The military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the...
Show moreThe military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the capability to represent complex, ill-defined, and imprecise concepts, and soft computing modeling can deal with these concepts. There is currently no study on the use of any computational methodology for representing the relationship between adverse events and infrastructure development investments in an active war theater. This study investigates the relationship between adverse events and infrastructure development projects in an active war theater using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) that directly benefits from their accuracy in prediction applications. Fourteen developmental and economic improvement project types were selected based on allocated budget values and a number of projects at different time periods, urban and rural population density, and total adverse event numbers at previous month selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded, hijacked, and total number of adverse events has been estimated. For each model, the data was grouped for training and testing as follows: years between 2004 and 2009 (for training purpose) and year 2010 (for testing). Ninety-six different models were developed and investigated for Afghanistan and the country was divided into seven regions for analysis purposes. Performance of each model was investigated and compared to all other models with the calculated mean absolute error (MAE) values and the prediction accuracy within (&)#177;1 error range (difference between actual and predicted value). Furthermore, sensitivity analysis was performed to determine the effects of input values on dependent variables and to rank the top ten input parameters in order of importance.According to the the results obtained, it was concluded that the ANNs, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic projects' data. When the model accuracy was calculated based on the MAE for each of the models, the ANN had better predictive accuracy than FIS and ANFIS models in general as demonstrated by experimental results. The percentages of prediction accuracy with values found within (&)#177;1 error range around 90%. The sensitivity analysis results show that the importance of economic development projects varies based on the regions, population density, and occurrence of adverse events in Afghanistan. For the purpose of allocating resources and development of regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater; emphasis was on predicting the occurrence of events and assessing the potential impact of regional infrastructure development efforts on reducing number of such events.
Show less - Date Issued
- 2013
- Identifier
- CFE0004826, ucf:49757
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004826
- Title
- THE CREATION OF TOOLS AND MODELS TO CHARACTERIZE AND QUANTIFY USER-CENTERED DESIGN CONSIDERATIONS IN PRODUCT AND SYSTEM DEVELOPMENT.
- Creator
-
Meza, Katherine, Crumpton-Young, Lesia, University of Central Florida
- Abstract / Description
-
Ease of use differentiates products in a highly competitive market place. It also brings an added value that culminates in a higher degree of customer satisfaction, repeated business, increased sales, and higher revenue. User-centered design is a strategic asset that companies can use to improve their customer relationships by learning more about their customers, and increase their sales. In today's economy, the measurement of intangible assets such as user experience has become a major...
Show moreEase of use differentiates products in a highly competitive market place. It also brings an added value that culminates in a higher degree of customer satisfaction, repeated business, increased sales, and higher revenue. User-centered design is a strategic asset that companies can use to improve their customer relationships by learning more about their customers, and increase their sales. In today's economy, the measurement of intangible assets such as user experience has become a major need for industries because of the relationship between user-centered design and organizational benefits such as customer loyalty. As companies realize that the inclusion of user-centered design concepts in product or system design are a key component of attracting and maintaining customers, as well as increasing revenue, the need for quantitative methods to describe these benefits has become more urgent. The goal of this research is to develop a methodology to characterize user-centered design features, customer benefits and organizational benefits resulting from developing products using user-centered design principles through the use of an integrated framework of critical factors. Therefore, this research focuses on the identification of the most significant variables required to assess and measure the degree of user-centered design (UCD) characteristics included in the various aspects of product development such as physical design features, cognitive design attributes, industrial design aspects and user experience design considerations. Also this research focuses on the development of assessment tools for developers to use when evaluating the incorporation of user-centered design features in the creation of products and systems. In addition, a mathematical model to quantify the inclusion of UCD factors considered in the design of a product and systems is presented in this research. The results obtained using the assessment tools and the mathematical model can be employed to assess the customer benefits and organizational benefits resulting from including user-centered design features in the creation of products and systems. Overall, organizational benefits such as customer loyalty, company image, and profitability are expected to be impacted by the company's capability to meet or exceed stated design claims and performance consistency while maintaining aesthetic appeal, long product life, and product usefulness. The successful completion of this research has produced many beneficial research findings. For example, it has helped characterize and develop descriptors for estimating critical quantitative and qualitative components, sub-components, and factors influencing user-centered design that are related to customer and organizational benefits through the use of fuzzy set modeling. In addition, the development of specific tools, methods, and techniques for evaluating and quantifying UCD components resulted from this study.
Show less - Date Issued
- 2008
- Identifier
- CFE0002178, ucf:47524
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002178
- Title
- FAMTILE: AN ALGORITHM FOR LEARNING HIGH-LEVEL TACTICAL BEHAVIOR FROM OBSERVATION.
- Creator
-
Stensrud, Brian, Gonzalez, Avelino, University of Central Florida
- Abstract / Description
-
This research focuses on the learning of a class of behaviors defined as high-level behaviors. High-level behaviors are defined here as behaviors that can be executed using a sequence of identifiable behaviors. Represented by low-level contexts, these behaviors are known a priori to learning and can be modeled separately by a knowledge engineer. The learning task, which is achieved by observing an expert within simulation, then becomes the identification and representation of the low-level...
Show moreThis research focuses on the learning of a class of behaviors defined as high-level behaviors. High-level behaviors are defined here as behaviors that can be executed using a sequence of identifiable behaviors. Represented by low-level contexts, these behaviors are known a priori to learning and can be modeled separately by a knowledge engineer. The learning task, which is achieved by observing an expert within simulation, then becomes the identification and representation of the low-level context sequence executed by the expert. To learn this sequence, this research proposes FAMTILE - the Fuzzy ARTMAP / Template-Based Interpretation Learning Engine. This algorithm attempts to achieve this learning task by constructing rules that govern the low-level context transitions made by the expert. By combining these rules with models for these low-level context behaviors, it is hypothesized that an intelligent model for the expert can be created that can adequately model his behavior. To evaluate FAMTILE, four testing scenarios were developed that attempt to achieve three distinct evaluation goals: assessing the learning capabilities of Fuzzy ARTMAP, evaluating the ability of FAMTILE to correctly predict expert actions and context choices given an observation, and creating a model of the expert's behavior that can perform the high-level task at a comparable level of proficiency.
Show less - Date Issued
- 2005
- Identifier
- CFE0000503, ucf:46455
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000503
- Title
- HIGH PERFORMANCE DATA MINING TECHNIQUES FOR INTRUSION DETECTION.
- Creator
-
Siddiqui, Muazzam Ahmed, Lee, Joohan, University of Central Florida
- Abstract / Description
-
The rapid growth of computers transformed the way in which information and data was stored. With this new paradigm of data access, comes the threat of this information being exposed to unauthorized and unintended users. Many systems have been developed which scrutinize the data for a deviation from the normal behavior of a user or system, or search for a known signature within the data. These systems are termed as Intrusion Detection Systems (IDS). These systems employ different techniques...
Show moreThe rapid growth of computers transformed the way in which information and data was stored. With this new paradigm of data access, comes the threat of this information being exposed to unauthorized and unintended users. Many systems have been developed which scrutinize the data for a deviation from the normal behavior of a user or system, or search for a known signature within the data. These systems are termed as Intrusion Detection Systems (IDS). These systems employ different techniques varying from statistical methods to machine learning algorithms.Intrusion detection systems use audit data generated by operating systems, application softwares or network devices. These sources produce huge amount of datasets with tens of millions of records in them. To analyze this data, data mining is used which is a process to dig useful patterns from a large bulk of information. A major obstacle in the process is that the traditional data mining and learning algorithms are overwhelmed by the bulk volume and complexity of available data. This makes these algorithms impractical for time critical tasks like intrusion detection because of the large execution time.Our approach towards this issue makes use of high performance data mining techniques to expedite the process by exploiting the parallelism in the existing data mining algorithms and the underlying hardware. We will show that how high performance and parallel computing can be used to scale the data mining algorithms to handle large datasets, allowing the data mining component to search a much larger set of patterns and models than traditional computational platforms and algorithms would allow.We develop parallel data mining algorithms by parallelizing existing machine learning techniques using cluster computing. These algorithms include parallel backpropagation and parallel fuzzy ARTMAP neural networks. We evaluate the performances of the developed models in terms of speedup over traditional algorithms, prediction rate and false alarm rate. Our results showed that the traditional backpropagation and fuzzy ARTMAP algorithms can benefit from high performance computing techniques which make them well suited for time critical tasks like intrusion detection.
Show less - Date Issued
- 2004
- Identifier
- CFE0000056, ucf:46142
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000056
- Title
- GENETICALLY ENGINEERED ADAPTIVE RESONANCE THEORY (ART) NEURAL NETWORK ARCHITECTURES.
- Creator
-
Al-Daraiseh, Ahmad, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
-
Fuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data is of noisy and/or overlapping nature. To remedy this problem a number of researchers have...
Show moreFuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data is of noisy and/or overlapping nature. To remedy this problem a number of researchers have designed modifications to the training phase of Fuzzy ARTMAP that had the beneficial effect of reducing this phenomenon. In this thesis we propose a new approach to handle the category proliferation problem in Fuzzy ARTMAP by evolving trained FAM architectures. We refer to the resulting FAM architectures as GFAM. We demonstrate through extensive experimentation that an evolved FAM (GFAM) exhibits good (sometimes optimal) generalization, small size (sometimes optimal size), and requires reasonable computational effort to produce an optimal or sub-optimal network. Furthermore, comparisons of the GFAM with other approaches, proposed in the literature, which address the FAM category proliferation problem, illustrate that the GFAM has a number of advantages (i.e. produces smaller or equal size architectures, of better or as good generalization, with reduced computational complexity). Furthermore, in this dissertation we have extended the approach used with Fuzzy ARTMAP to other ART architectures, such as Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM) that also suffer from the ART category proliferation problem. Thus, we have designed and experimented with genetically engineered EAM and GAM architectures, named GEAM and GGAM. Comparisons of GEAM and GGAM with other ART architectures that were introduced in the ART literature, addressing the category proliferation problem, illustrate similar advantages observed by GFAM (i.e, GEAM and GGAM produce smaller size ART architectures, of better or improved generalization, with reduced computational complexity). Moverover, to optimally cover the input space of a problem, we proposed a genetically engineered ART architecture that combines the category structures of two different ART networks, FAM and EAM. We named this architecture UART (Universal ART). We analyzed the order of search in UART, that is the order according to which a FAM category or an EAM category is accessed in UART. This analysis allowed us to better understand UART's functionality. Experiments were also conducted to compare UART with other ART architectures, in a similar fashion as GFAM and GEAM were compared. Similar conclusions were drawn from this comparison, as in the comparison of GFAM and GEAM with other ART architectures. Finally, we analyzed the computational complexity of the genetically engineered ART architectures and we compared it with the computational complexity of other ART architectures, introduced into the literature. This analytical comparison verified our claim that the genetically engineered ART architectures produce better generalization and smaller sizes ART structures, at reduced computational complexity, compared to other ART approaches. In review, a methodology was introduced of how to combine the answers (categories) of ART architectures, using genetic algorithms. This methodology was successfully applied to FAM, EAM and FAM and EAM ART architectures, with success, resulting in ART neural networks which outperformed other ART architectures, previously introduced into the literature, and quite often produced ART architectures that attained optimal classification results, at reduced computational complexity.
Show less - Date Issued
- 2006
- Identifier
- CFE0000977, ucf:46696
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000977
- Title
- MODELING AND CHARACTERIZATION OF ACUTE STRESS UNDER DYNAMIC TASK CONDITIONS.
- Creator
-
Millan, Angel, Crumpton-Young, Lesia, University of Central Florida
- Abstract / Description
-
Stress can be defined as the mental, physical, and emotional response of humans to stressors encountered in their personal or professional environment. Stressors are introduced in various activities, especially those found in dynamic task conditions when multiple task requirements must be performed. Stress and stressors have been described as activators and inhibitors of human performance. The ability to manage high levels of acute stress is an important determinant of successful performance...
Show moreStress can be defined as the mental, physical, and emotional response of humans to stressors encountered in their personal or professional environment. Stressors are introduced in various activities, especially those found in dynamic task conditions when multiple task requirements must be performed. Stress and stressors have been described as activators and inhibitors of human performance. The ability to manage high levels of acute stress is an important determinant of successful performance in any occupation. In situations where performance is critical, personnel must be prepared to operate successfully under hostile or extreme stress conditions; therefore training programs and engineered systems must be tailored to assist humans in fulfilling these demands. To effectively design appropriate training programs for these conditions, it is necessary to quantitatively describe stress. A series of theoretical stress models have been developed in previous research studies; however, these do not provide quantification of stress levels nor the impact on human performance. By modeling acute stress under dynamic task conditions, quantitative values for stress and its impact on performance can be assessed. Thus, this research was designed to develop a predictive model for acute stress as a function of human performance and task demand. Initially, a four factor two level experimental design [2 (Noise) x 2 (Temperature) x 2 (Time Awareness) x 2 (Workload)] was performed to identify reliable physiological, cognitive and behavioral responses to stress. Next, multivariate analysis of variance (n=108) tests were performed, which showed statistically significant differences for physiological, cognitive and behavioral responses. Finally, fuzzy set theory techniques were used to develop a comprehensive stress index model. Thus, the resulting stress index model was constructed using input on physiological, cognitive and behavioral responses to stressors as well as characteristics inherent to the type of task performed and personal factors that interact as mediators (competitiveness, motivation, coping technique and proneness to boredom). Through using this stress index model to quantify and characterize the affects of acute stress on human performance, these research findings can inform proper training protocols and help to redesign tasks and working conditions that are prone to create levels of acute stress that adversely affect human performance.
Show less - Date Issued
- 2011
- Identifier
- CFE0004056, ucf:49151
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004056
- Title
- A SYSTEMATIC ANALYSIS TO IDENTIFY, MITIGATE, QUANTIFY, AND MEASURE RISK FACTORS CONTRIBUTING TO FALLS IN NASA GROUND SUPPORT OPERATIONS.
- Creator
-
Ware , Joylene, Bush , Pamela, University of Central Florida
- Abstract / Description
-
The objective of the research was to develop and validate a multifaceted model such as a fuzzy Analytical Hierarchy Process (AHP) model that considers both qualitative and quantitative elements with relative significance in assessing the likelihood of falls and aid in the design of NASA Ground Support Operations in aerospace environments. The model represented linguistic variables that quantified significant risk factor levels. Multiple risk factors that contribute to falls in NASA Ground...
Show moreThe objective of the research was to develop and validate a multifaceted model such as a fuzzy Analytical Hierarchy Process (AHP) model that considers both qualitative and quantitative elements with relative significance in assessing the likelihood of falls and aid in the design of NASA Ground Support Operations in aerospace environments. The model represented linguistic variables that quantified significant risk factor levels. Multiple risk factors that contribute to falls in NASA Ground Support Operations are task related, human/personal, environmental, and organizational. Six subject matter experts were asked to participate in a voting system involving a survey where they judge risk factors using the fundamental pairwise comparison scale. The results were analyzed and synthesize using Expert Choice Software, which produced the relative weights for the risk factors. The following are relative weights for these risk factors: Task Related (0.314), Human/Personal (0.307), Environmental (0.248), and Organizational (0.130). The overall inconsistency ratio for all risk factors was 0.07, which indicates the model results were acceptable. The results show that task related risk factors are the highest cause for falls and the organizational risk are the lowest cause for falls in NASA Ground Support Operations. The multiple risk factors weights were validated by having two teams of subject matter experts create priority vectors separately and confirm the weights are valid. The fuzzy AHP model usability was utilizing fifteen subjects in a repeated measures analysis. The subjects were asked to evaluate three scenarios in NASA KSC Ground Support Operations regarding various case studies and historical data. The three scenarios were Shuttle Landing Facility (SLF), Launch Complex Payloads (LCP), and Vehicle Assembly Building (VAB). The Kendall Coefficient of Concordance for assessment agreement between and within the subjects was 1.00. Therefore, the appraisers are applying essentially the same standard when evaluating the scenarios. In addition, a NASA subject matter expert was requested to evaluate the three scenarios also. The predicted value was compared to accepted value. The results from the subject matter expert for the model usability confirmed that the predicted value and accepted value for the likelihood rating were similar. The percentage error for the three scenarios was 0%, 33%, 0% respectively. Multiple descriptive statistics for a 95% confidence interval and t-test are the following: coefficient of variation (21.36), variance (0.251), mean (2.34), and standard deviation (0.501). Model validation was the guarantee of agreement with the NASA standard. Model validation process was partitioned into three components: reliability, objectivity, and consistency. The model was validated by comparing the fuzzy AHP model to NASA accepted model. The results indicate there was minimal variability with fuzzy AHP modeling. As a result, the fuzzy AHP model is confirmed valid. Future research includes developing fall protection guidelines.
Show less - Date Issued
- 2009
- Identifier
- CFE0002789, ucf:48094
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002789
- Title
- Detecting Threats from Constituent Parts: A Fuzzy Signal Detection Theory Analysis of Individual Differences.
- Creator
-
Van De Car, Ida, Szalma, James, Hancock, Peter, Mouloua, Mustapha, Kennedy, Robert, University of Central Florida
- Abstract / Description
-
Signal detection theory (SDT) provides a theoretical framework for describing performance on decision making tasks, and fuzzy signal detection theory (FSDT) extends this description to include tasks in which there are levels of uncertainty regarding the categorization of stimulus events. Specifically, FSDT can be used to quantify the degree to which an event is 'signal-like', i.e., the degree to which a stimulus event can be characterized by both signal and non-signal properties. For instance...
Show moreSignal detection theory (SDT) provides a theoretical framework for describing performance on decision making tasks, and fuzzy signal detection theory (FSDT) extends this description to include tasks in which there are levels of uncertainty regarding the categorization of stimulus events. Specifically, FSDT can be used to quantify the degree to which an event is 'signal-like', i.e., the degree to which a stimulus event can be characterized by both signal and non-signal properties. For instance, an improvised explosive device (IED) poses little threat when missing key elements of its assembly (a stimulus of low, but not zero, signal strength) whereas the threat is greater when all elements necessary to ignite the device are present (a stimulus of high signal strength). This research develops a link between key individual cognitive (i.e., spatial orientation and visualization) and personality (i.e., extroversion, conscientiousness, and neuroticism) differences among observers to performance on a fuzzy signal detection task, in which the items to be detected (IEDs) are presented in various states of assembly. That is, this research relates individual difference measures to task performance, uses FSDT in target detection, and provides application of the theory to vigilance tasks. In two experiments, participants viewed pictures of IEDs, not all of which are assembled or include key components, and categorize them using a fuzzy rating scale (no threat, low threat potential, moderate threat potential, or definite threat). In both experiments, there were significant interactions between the stimulus threat level category and the variability of images within each category. The results of the first experiment indicated that spatial and mechanical ability were stronger predictors of performance when the signal was ambiguous than when individuals viewed stimuli in which the signal was fully absent or fully present (and, thus, less ambiguous). The second study showed that the length of time a stimulus is viewed is greatest when the signal strength is low and there is ambiguity regarding the threat level of the stimulus. In addition, response times were substantially longer in study 2 than in study 1, although patterns of performance accuracy, as measured by the sensitivity index d', were similar across the two experiments. Together, the experiments indicate that individuals take longer to evaluate a potential threat as less critical, than to identify either an absence of threat or a high degree of threat and that spatial and mechanical ability assist decision making when the threat level is unclear. These results can be used to increase the efficiency of employees working in threat-detection positions, such as luggage screeners, provides an exemplar of use of FSDT, and contributes to the understanding of human decision making.
Show less - Date Issued
- 2015
- Identifier
- CFE0006016, ucf:51015
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006016
- Title
- A Human-Centric Approach to Data Fusion in Post-Disaster Managment: The Development of a Fuzzy Set Theory Based Model.
- Creator
-
Banisakher, Mubarak, McCauley, Pamular, Geiger, Christopher, Lee, Gene, Shi, Fuqian, Zou, Changchun, University of Central Florida
- Abstract / Description
-
It is critical to provide an efficient and accurate information system in the post-disaster phase for individuals' in order to access and obtain the necessary resources in a timely manner; but current map based post-disaster management systems provide all emergency resource lists without filtering them which usually leads to high levels of energy consumed in calculation. Also an effective post-disaster management system (PDMS) will result in distribution of all emergency resources such as,...
Show moreIt is critical to provide an efficient and accurate information system in the post-disaster phase for individuals' in order to access and obtain the necessary resources in a timely manner; but current map based post-disaster management systems provide all emergency resource lists without filtering them which usually leads to high levels of energy consumed in calculation. Also an effective post-disaster management system (PDMS) will result in distribution of all emergency resources such as, hospital, storage and transportation much more reasonably and be more beneficial to the individuals in the post disaster period. In this Dissertation, firstly, semi-supervised learning (SSL) based graph systems was constructed for PDMS. A Graph-based PDMS' resource map was converted to a directed graph that presented by adjacent matrix and then the decision information will be conducted from the PDMS by two ways, one is clustering operation, and another is graph-based semi-supervised optimization process. In this study, PDMS was applied for emergency resource distribution in post-disaster (responses phase), a path optimization algorithm based ant colony optimization (ACO) was used for minimizing the cost in post-disaster, simulation results show the effectiveness of the proposed methodology. This analysis was done by comparing it with clustering based algorithms under improvement ACO of tour improvement algorithm (TIA) and Min-Max Ant System (MMAS) and the results also show that the SSL based graph will be more effective for calculating the optimization path in PDMS. This research improved the map by combining the disaster map with the initial GIS based map which located the target area considering the influence of disaster. First, all initial map and disaster map will be under Gaussian transformation while we acquired the histogram of all map pictures. And then all pictures will be under discrete wavelet transform (DWT), a Gaussian fusion algorithm was applied in the DWT pictures. Second, inverse DWT (iDWT) was applied to generate a new map for a post-disaster management system. Finally, simulation works were proposed and the results showed the effectiveness of the proposed method by comparing it to other fusion algorithms, such as mean-mean fusion and max-UD fusion through the evaluation indices including entropy, spatial frequency (SF) and image quality index (IQI). Fuzzy set model were proposed to improve the presentation capacity of nodes in this GIS based PDMS.
Show less - Date Issued
- 2014
- Identifier
- CFE0005128, ucf:50702
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005128
- Title
- Lattice-Valued T-Filters and Induced Structures.
- Creator
-
Reid, Frederick, Richardson, Gary, Brennan, Joseph, Han, Deguang, Lang, Sheau-Dong, University of Central Florida
- Abstract / Description
-
A complete lattice is called a frame provided meets distribute over arbitrary joins. The implication operation in this context plays a central role. Intuitively, it measures the degree to which one element is less than or equal to another. In this setting, a category is defined by equipping each set with a T-convergence structure which is defined in terms of T-filters. This category is shown to be topological, strongly Cartesian closed, and extensional. It is well known that the category of...
Show moreA complete lattice is called a frame provided meets distribute over arbitrary joins. The implication operation in this context plays a central role. Intuitively, it measures the degree to which one element is less than or equal to another. In this setting, a category is defined by equipping each set with a T-convergence structure which is defined in terms of T-filters. This category is shown to be topological, strongly Cartesian closed, and extensional. It is well known that the category of topological spaces and continuous maps is neither Cartesian closed nor extensional.Subcategories of compact and of complete spaces are investigated. It is shown that each T-convergence space has a compactification with the extension property provided the frame is a Boolean algebra. T-Cauchy spaces are defined and sufficient conditions for the existence of a completion are given. T-uniform limit spaces are also defined and their completions are given in terms of the T-Cauchy spaces they induce. Categorical properties of these subcategories are also investigated. Further, for a fixed T-convergence space, under suitable conditions, it is shown that there exists an order preserving bijection between the set of all strict, regular, Hausdorff compactifications and the set of all totally bounded T-Cauchy spaces which induce the fixed space.
Show less - Date Issued
- 2019
- Identifier
- CFE0007520, ucf:52586
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007520
- Title
- CONTEXTUALIZING OBSERVATIONAL DATA FOR MODELING HUMAN PERFORMANCE.
- Creator
-
Trinh, Viet, Gonzalez, Avelino, University of Central Florida
- Abstract / Description
-
This research focuses on the ability to contextualize observed human behaviors in efforts to automate the process of tactical human performance modeling through learning from observations. This effort to contextualize human behavior is aimed at minimizing the role and involvement of the knowledge engineers required in building intelligent Context-based Reasoning (CxBR) agents. More specifically, the goal is to automatically discover the context in which a human actor is situated when...
Show moreThis research focuses on the ability to contextualize observed human behaviors in efforts to automate the process of tactical human performance modeling through learning from observations. This effort to contextualize human behavior is aimed at minimizing the role and involvement of the knowledge engineers required in building intelligent Context-based Reasoning (CxBR) agents. More specifically, the goal is to automatically discover the context in which a human actor is situated when performing a mission to facilitate the learning of such CxBR models. This research is derived from the contextualization problem left behind in Fernlund's research on using the Genetic Context Learner (GenCL) to model CxBR agents from observed human performance [Fernlund, 2004]. To accomplish the process of context discovery, this research proposes two contextualization algorithms: Contextualized Fuzzy ART (CFA) and Context Partitioning and Clustering (COPAC). The former is a more naive approach utilizing the well known Fuzzy ART strategy while the latter is a robust algorithm developed on the principles of CxBR. Using Fernlund's original five drivers, the CFA and COPAC algorithms were tested and evaluated on their ability to effectively contextualize each driver's individualized set of behaviors into well-formed and meaningful context bases as well as generating high-fidelity agents through the integration with Fernlund's GenCL algorithm. The resultant set of agents was able to capture and generalized each driver's individualized behaviors.
Show less - Date Issued
- 2009
- Identifier
- CFE0002563, ucf:48253
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002563
