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Title: Course scheduling support system.
Creator: Khan, Jawad Ahmed., Florida Atlantic University, Levow, Roy B., Hsu, Sam, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The Course Scheduling Support System is designed to facilitate manual generation of the faculty course scheduling process. It aids in assigning faculty to courses and assigning each course section to their time block. It captures historic and current scheduling information in an organized manner making information needed to create new schedules more readily and quickly available. The interaction between user and database is made as friendly as possible so that managing, manipulating,...
Show moreThe Course Scheduling Support System is designed to facilitate manual generation of the faculty course scheduling process. It aids in assigning faculty to courses and assigning each course section to their time block. It captures historic and current scheduling information in an organized manner making information needed to create new schedules more readily and quickly available. The interaction between user and database is made as friendly as possible so that managing, manipulating, populating and retrieving scheduling data is simple and efficient. We have implemented an open source web-based prototype of the proposed system using PHP, MySQL, and the Apache Web Server. It can be invoked with a standard Web browser and has an intuitive user interface. It provides tools for customizing web forms that can be easily used by non-technical users. Our department plans to deploy this system by Fall 2006.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/13343
Subject Headings: Scheduling--Data processing, Constraints (Artificial intelligence), Electronic data processing--Distributed processing
Format: Document (PDF)

Title: SCHEMATIC: AN EXPERIMENT IN MACHINE LEARNING USING CONCEPTUAL GRAPHS.
Creator: HALTERMAN, RICHARD L., Florida Atlantic University
Abstract/Description: Conceptual graphs form the basis of a powerful representation language for artificial intelligence research. SCHEMATIC is a system that uses a subset of conceptual graph theory in acquiring knowledge about a given domain. SCHEMATIC exhibits two types of learning. It will passively absorb information as imparted by the teacher, and it also has an active learning mode that, based on its current picture of the domain, aggressively queries the teacher for more information. The knowledge base,...
Show moreConceptual graphs form the basis of a powerful representation language for artificial intelligence research. SCHEMATIC is a system that uses a subset of conceptual graph theory in acquiring knowledge about a given domain. SCHEMATIC exhibits two types of learning. It will passively absorb information as imparted by the teacher, and it also has an active learning mode that, based on its current picture of the domain, aggressively queries the teacher for more information. The knowledge base, including the concept type hierarchy, the relation list, canonical forms, and the current domain, are dynamically maintained. Teacher interaction is handled exclusively with conceptual graphs. Action concepts are treated differently by SCHEMATIC, in that, once defined, they execute procedures that alter the domain.
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Date Issued: 1987
PURL: http://purl.flvc.org/fcla/dt/14421
Subject Headings: Machine learning, Artificial intelligence
Format: Document (PDF)

Title: Using a cerebellar model arithmetic computer (CMAC) neural network to control an autonomous underwater vehicle.
Creator: Comoglio, Rick F., Florida Atlantic University, Pandya, Abhijit S.
Abstract/Description: The design of an Autonomous Undersea Vehicle (AUV) control system is a significant challenge in-light of the highly uncertain nature of the ocean environment together with partially known nonlinear vehicle dynamics. This thesis describes a Neural Network architecture called Cerebellar Model Arithmetic Computer (CMAC). CMAC is used to control a model of an Autonomous Underwater Vehicle. The AUV model consists of two input parameters, the rudder and stern plane deflections, controlling six...
Show moreThe design of an Autonomous Undersea Vehicle (AUV) control system is a significant challenge in-light of the highly uncertain nature of the ocean environment together with partially known nonlinear vehicle dynamics. This thesis describes a Neural Network architecture called Cerebellar Model Arithmetic Computer (CMAC). CMAC is used to control a model of an Autonomous Underwater Vehicle. The AUV model consists of two input parameters, the rudder and stern plane deflections, controlling six output parameters; forward velocity, vertical velocity, pitch angle, side velocity, roll angle, and yaw angle. Properties of CMAC and results of computer simulations for identification and control of the AUV model are presented.
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Date Issued: 1991
PURL: http://purl.flvc.org/fcla/dt/14762
Subject Headings: Neural networks (Computer science), Artificial intelligence, Submersibles--Automatic control
Format: Document (PDF)

Title: Lifeline structures under earthquake excitations.
Creator: Reddy, Kondakrindhi Praveen., Florida Atlantic University, Yong, Yan, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: An analytical method is proposed for the response analysis of lifeline structures subjected to earthquake excitations. The main feature of the approach is to consider the vibrational motion as a result of the wave motion in a waveguide-like lifeline structure. Based on the theory of wave propagation, scattering matrices are derived to characterize the wave propagation in individual segments and wave reflections and transmissions at supports and boundaries. Response solution is derived in a...
Show moreAn analytical method is proposed for the response analysis of lifeline structures subjected to earthquake excitations. The main feature of the approach is to consider the vibrational motion as a result of the wave motion in a waveguide-like lifeline structure. Based on the theory of wave propagation, scattering matrices are derived to characterize the wave propagation in individual segments and wave reflections and transmissions at supports and boundaries. Response solution is derived in a closed form, suitable for stochastic analysis when the input is an earthquake excitation. A space-time earthquake ground motion model that accounts for both coherent decay and seismic wave propagation is used to specify motions at supports. The proposed technique can be used to obtain lifeline structural response accurately and determine the correlation between any two locations in an effective manner. The computational aspects of its implementation are also discussed. Numerical examples are presented to illustrate the application and efficiency of the proposed analytical scheme.
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Date Issued: 1993
PURL: http://purl.flvc.org/fcla/dt/14898
Subject Headings: Artificial intelligence, Fuzzy logic, Neural networks (Computer science), Pavements--Skid resistance
Format: Document (PDF)

Title: MAPLE: A knowledge-based system for computer-aided music composition.
Creator: Shaouy, William Philip., Florida Atlantic University, Hewett, Rattikorn, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Computer-aided music composition is a highly structured process. This thesis presents MAPLE, a prototype knowledge-based system that assists music composition by analyzing primordial lines in Western music. Unlike other music analysis systems, MAPLE uses an internal connective musical structure that provides a foundation for analysis. Thus our approach has a building block capability allowing easy extensions for analyzing more complex musical structures. We present a type hierarchy and...
Show moreComputer-aided music composition is a highly structured process. This thesis presents MAPLE, a prototype knowledge-based system that assists music composition by analyzing primordial lines in Western music. Unlike other music analysis systems, MAPLE uses an internal connective musical structure that provides a foundation for analysis. Thus our approach has a building block capability allowing easy extensions for analyzing more complex musical structures. We present a type hierarchy and hierarchical dependency of different musical concepts for controlling complex musical analysis processes. We provide comparisons of various knowledge representation structures and their corresponding inference mechanisms used for music analysis and conclude with a projection of an appropriate form of knowledge representation for musical knowledge.
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Date Issued: 1993
PURL: http://purl.flvc.org/fcla/dt/14986
Subject Headings: Music--Computer programs, Composition (Music)--Mechanical aids, Artificial intelligence--Musical applications, Musical analysis--Data processing
Format: Document (PDF)

Title: Artificial neural network prediction of alluvial river geometry.
Creator: Hoffman, David Carl., Florida Atlantic University, Scarlatos, Panagiotis (Pete) D.
Abstract/Description: An artificial neural network is used to predict the stable geometry of alluvial rivers. This knowledge is useful for the design of new channels or modification of natural rivers. Given inputs of river discharge, slope and mean particle size, an artificial neural network is trained to predict the corresponding stable channel width and depth. The network is trained using data from several alluvial canals and rivers. Various factors including training set size and composition, number of hidden...
Show moreAn artificial neural network is used to predict the stable geometry of alluvial rivers. This knowledge is useful for the design of new channels or modification of natural rivers. Given inputs of river discharge, slope and mean particle size, an artificial neural network is trained to predict the corresponding stable channel width and depth. The network is trained using data from several alluvial canals and rivers. Various factors including training set size and composition, number of hidden layer nodes, activation function type, and data scaling method are analyzed as variables affecting network performance. These factors are studied to determine impacts on network accuracy and generalizing ability.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15179
Subject Headings: Alluvial streams, Neural networks (Computer science), Back propagation (Artificial intelligence), Sediment transport--Computer programs
Format: Document (PDF)

Title: Software design using case based reasoning.
Creator: Smith, Nancy T., Florida Atlantic University, Ganesan, Krishnamurthy, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The project that was created for this thesis is a Case Based Reasoning application to be used in high level software design for Siemens' Telecommunications software. Currently, design engineers search for existing subtasks in the software that are similar to subtasks in their new designs by reading documentation and consulting with other engineers. The prototype for Software Design Using Case Based Reasoning (SDUCBR) stores these subtasks in a case library and enables the design engineer to...
Show moreThe project that was created for this thesis is a Case Based Reasoning application to be used in high level software design for Siemens' Telecommunications software. Currently, design engineers search for existing subtasks in the software that are similar to subtasks in their new designs by reading documentation and consulting with other engineers. The prototype for Software Design Using Case Based Reasoning (SDUCBR) stores these subtasks in a case library and enables the design engineer to locate relevant subtasks via three different indexing techniques. This thesis addresses knowledge representation and indexing mechanisms appropriate for this application. SDUCBR is domain-dependent. Cases are stored in a relational hierarchy to facilitate analyzing the existing implementation from various perspectives. The indexing mechanisms were designed to provide the software design engineer with the flexibility of describing a problem differently based on the objective, level of granularity, and special characteristics of the subtask.
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Date Issued: 1995
PURL: http://purl.flvc.org/fcla/dt/15198
Subject Headings: Computer software--Development, Case-based reasoning, Artificial intelligence--Data processing, System design
Format: Document (PDF)

Title: A connectionist approach to adaptive reasoning: An expert system to predict skid numbers.
Creator: Reddy, Mohan S., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This project illustrates the neural network approach to constructing a fuzzy logic decision system. This technique employs an artificial neural network (ANN) to recognize the relationships that exit between the various inputs and outputs. An ANN is constructed based on the variables present in the application. The network is trained and tested. Various training methods are explored, some of which include auxiliary input and output columns. After successful testing, the ANN is exposed to new...
Show moreThis project illustrates the neural network approach to constructing a fuzzy logic decision system. This technique employs an artificial neural network (ANN) to recognize the relationships that exit between the various inputs and outputs. An ANN is constructed based on the variables present in the application. The network is trained and tested. Various training methods are explored, some of which include auxiliary input and output columns. After successful testing, the ANN is exposed to new data and the results are grouped into fuzzy membership sets based membership evaluation rules. This data grouping forms the basis of a new ANN. The network is now trained and tested with the fuzzy membership data. New data is presented to the trained network and the results form the fuzzy implications. This approach is used to compute skid resistance values from G-analyst accelerometer readings on open grid bridge decks.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/15239
Subject Headings: Artificial intelligence, Fuzzy logic, Neural networks (Computer science), Pavements--Skid resistance
Format: Document (PDF)

Title: Design of analog building blocks useful for artificial neural networks.
Creator: Renavikar, Ajit Anand., Florida Atlantic University, Shankar, Ravi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Software simulations of a scaleable VLSI implementable architecture and algorithm for character recognition by a research group at Florida Atlantic University (FAU) have shown encouraging results. We address here hardware implementation issues pertinent to the classification phase of character recognition. Using the digit classification techniques developed at FAU as a foundation, we have designed and simulated general purpose building blocks useful for a possible implementation of a Digital ...
Show moreSoftware simulations of a scaleable VLSI implementable architecture and algorithm for character recognition by a research group at Florida Atlantic University (FAU) have shown encouraging results. We address here hardware implementation issues pertinent to the classification phase of character recognition. Using the digit classification techniques developed at FAU as a foundation, we have designed and simulated general purpose building blocks useful for a possible implementation of a Digital & Analog CMOS VLSI chip that is suitable for a variety of artificial neural network (ANN) architectures. HSPICE was used to perform circuit-level simulations of the building blocks. We present here the details of implementation of the recognition chip including the architecture, circuit design and the simulation results.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/15328
Subject Headings: Neural networks (Computer science), Artificial intelligence, Optical character recognition devices, Pattern recognition systems
Format: Document (PDF)

Title: Application of artificial neural networks to deduce robust forecast performance in technoeconomic contexts.
Creator: Dabbas, Mohammad A., Neelakanta, Perambur S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows...
Show moreThe focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows: A review on various methods adopted in technoeconomic forecasting and identified are econometric projections that can be used for forecasting via artificial neural network (ANN)-based simulations Developing and testing a compatible version of ANN designed to support a dynamic sigmoidal (squashing) function that morphs to the stochastical trends of the ANN input. As such, the network architecture gets pruned for reduced complexity across the span of iterative training schedule leading to the realization of a constructive artificial neural-network (CANN). Formulating a training schedule on an ANN with sparsely-sampled data via sparsity removal with cardinality enhancement procedure (through Nyquist sampling) and invoking statistical bootstrapping technique of resampling applied on the cardinality-improved subset so as to obtain an enhanced number of pseudoreplicates required as an adequate ensemble for robust training of the test ANN: The training and prediction exercises on the test ANN corresponds to optimally elucidating output predictions in the context of the technoeconomics framework of the power generation considered Prescribing a cone-of-error to alleviate over- or under-predictions toward prudently interpreting the results obtained; and, squeezing the cone-of-error to get a final cone-of-forecast rendering the forecast estimation/inference to be more precise Designing an ANN-based fuzzy inference engine (FIE) to ascertain the ex ante forecast details based on sparse sets of ex post data gathered in technoeconomic contexts - Involved thereof a novel method of .fusing fuzzy considerations and data sparsity.Lastly, summarizing the results with essential conclusions and identifying possible research items for future efforts identified as open-questions.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004097, http://purl.flvc.org/fau/fd/FA00004097
Subject Headings: Artificial intelligence, Fuzzy systems, Long waves (Economics), Multisensor data fusion, Neural networks (Computer science) -- Mathematical models
Format: Document (PDF)

Title: The future will be better tomorrow: a novel of apocalyptic sarcasm.
Creator: Irving, Christopher J., Bucak, Ayse Papatya, Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, Department of English
Abstract/Description: The Future Will Be Better Tomorrow is a satirical post-apocalyptic novel that examines the personal and social ironies that occur in a society that is unbalanced by an unexplained apocalyptic event. Working with a combination of dark humor and the terrifying realities of an apocalyptic event – in this case: a blackout – the novel aims to challenge the machinery established by this particular subset of the science fiction genre.
Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004124, http://purl.flvc.org/fau/fd/FA00004124
Subject Headings: Computer science., Computer communication systems., Data structures (Computer science)., Database management., Information storage and retrieval., Artificial intelligence., Computer Science., Data Structures, Cryptology and Information Theory., Database Management., Information Storage and Retrieval.
Format: Document (PDF)

Title: Dosimetric comparison of inverse planning by simulated annealing (IPSA) and dose points optimized treatment plans in high dose rate (HDR) brachytherapy of skin lesions using Freiburg flap applicator.
Creator: Ghebremichael, Bereket Tewolde, Ouhib, Zoubir, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description: A detailed dosimetric comparison between Inverse Planning by Simulated Annealing (IPSA) and Dose Points (DP) optimized treatment plans has been performed for High Dose Rate (HDR) brachytherapy of skin lesions using Freiburg Flap applicator in order to find out whether or not IPSA offers better clinical dosimetric outcomes for lesions categorized into four different curvatures. Without compromising target coverage, IPSA reduced the volume of Planning Target Volume (lesion) that received at...
Show moreA detailed dosimetric comparison between Inverse Planning by Simulated Annealing (IPSA) and Dose Points (DP) optimized treatment plans has been performed for High Dose Rate (HDR) brachytherapy of skin lesions using Freiburg Flap applicator in order to find out whether or not IPSA offers better clinical dosimetric outcomes for lesions categorized into four different curvatures. Without compromising target coverage, IPSA reduced the volume of Planning Target Volume (lesion) that received at least 125% of the prescription dose on average by 41%. It also reduced the volume of the healthy skin surrounding the lesion that receives at least 100% of the prescription dose on average by 42%. IPSA did not show any advantage over DP in sparing normal structures underlying the lesions treated. Although DP optimization algorithm has been regularly used at Lynn Cancer Institute for HDR brachytherapy of skin lesions, recent upgrades in IPSA software have made IPSA more amenable to rapid treatment planning and therefore IPSA can be used either in place of DP or as its alternative.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004285
Subject Headings: Artificial intelligence -- Medical applications, Cancer -- Treatment, Computational intellingence, Imaging systems in medicine, Medical physics
Format: Document (PDF)

Title: Analysis of machine learning algorithms on bioinformatics data of varying quality.
Creator: Shanab, Ahmad Abu, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: One of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One...
Show moreOne of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One particular challenge encountered when analyzing bioinformatics datasets is data noise, which refers to incorrect or missing values in datasets. Noise can be introduced as a result of experimental errors (e.g. faulty microarray chips, insufficient resolution, image corruption, and incorrect laboratory procedures), as well as other errors (errors during data processing, transfer, and/or mining). A special type of data noise called class noise, which occurs when an instance/example is mislabeled. Previous research showed that class noise has a detrimental impact on machine learning algorithms (e.g. worsened classification performance and unstable feature selection). In addition to data noise, gene expression datasets can suffer from the problems of high dimensionality (a very large feature space) and class imbalance (unequal distribution of instances between classes). As a result of these inherent problems, constructing accurate classification models becomes more challenging.
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Date Issued: 2015
PURL: http://purl.flvc.org./fau/fd/FA00004425, http://purl.flvc.org/fau/fd/FA00004425
Subject Headings: Artificial intelligence, Bioinformatics, Machine learning, System design, Theory of computation
Format: Document (PDF)

Title: Machine learning techniques for alleviating inherent difficulties in bioinformatics data.
Creator: Dittman, David, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In response to the massive amounts of data that make up a large number of bioinformatics datasets, it has become increasingly necessary for researchers to use computers to aid them in their endeavors. With difficulties such as high dimensionality, class imbalance, noisy data, and difficult to learn class boundaries, being present within the data, bioinformatics datasets are a challenge to work with. One potential source of assistance is the domain of data mining and machine learning, a field...
Show moreIn response to the massive amounts of data that make up a large number of bioinformatics datasets, it has become increasingly necessary for researchers to use computers to aid them in their endeavors. With difficulties such as high dimensionality, class imbalance, noisy data, and difficult to learn class boundaries, being present within the data, bioinformatics datasets are a challenge to work with. One potential source of assistance is the domain of data mining and machine learning, a field which focuses on working with these large amounts of data and develops techniques to discover new trends and patterns that are hidden within the data and to increases the capability of researchers and practitioners to work with this data. Within this domain there are techniques designed to eliminate irrelevant or redundant features, balance the membership of the classes, handle errors found in the data, and build predictive models for future data.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004362, http://purl.flvc.org/fau/fd/FA00004362
Subject Headings: Artificial intelligence, Bioinformatics, Machine learning, System design, Theory of computation
Format: Document (PDF)

Title: Science fiction girlfriends transgender politics and US science fiction television, 1990–present.
Creator: Cava, Peter, Scodari, Christine, Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, School of Communication and Multimedia Studies
Abstract/Description: The 1990s ushered in what historian Susan Stryker describes as “a tremendous burst of new transgender activism” in the United States. Concomitantly, the success of Star Trek: The Next Generation led to a renaissance of US science fiction television. This dissertation asks, what is the relation between transgender (trans) politics and US science fiction (sf) television from 1990 to the present? The theoretical framework is Trans/Elemental feminism, a new paradigm developed in the dissertation....
Show moreThe 1990s ushered in what historian Susan Stryker describes as “a tremendous burst of new transgender activism” in the United States. Concomitantly, the success of Star Trek: The Next Generation led to a renaissance of US science fiction television. This dissertation asks, what is the relation between transgender (trans) politics and US science fiction (sf) television from 1990 to the present? The theoretical framework is Trans/Elemental feminism, a new paradigm developed in the dissertation. The method is multiperspectival cultural studies, which considers how the production, content, and reception of media texts and their metatexts collectively determine the texts’ meaning. The data include trade articles about the television industry; published interviews with producers; 3,175 hours of televisual content; commercial advertisements for television programs; films, novels, and webisodes (Web episodes) in selected media franchises; professional reviews; online discussion boards; fan fiction; and fan videos.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004435, http://purl.flvc.org/fau/fd/FA00004435
Subject Headings: Computer science., Computers., Artificial intelligence., Applied mathematics., Engineering mathematics., Statistical physics., Dynamical systems., Vibration., Dynamics., Computer Science.
Format: Document (PDF)

Title: The importance of immobilization and localization of gynecological applicators in high dose rate brachytherapy treatments.
Creator: Hyvärinen, Mikko, Pella, Silvia, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description: Intracavitary high dose rate (HDR) brachytherapy is a form of radiation therapy generally in which a post-surgical tissue margin is treated. The dose gradient of HDR brachytherapy is very steep, and thus small displacements of the applicator, even as small as 1 mm, could potentially cause significant variations of dose which could result in undesired side effects such as overdose of a critical organ. In this retrospective dosimetric study, the variation of dose due to various small range...
Show moreIntracavitary high dose rate (HDR) brachytherapy is a form of radiation therapy generally in which a post-surgical tissue margin is treated. The dose gradient of HDR brachytherapy is very steep, and thus small displacements of the applicator, even as small as 1 mm, could potentially cause significant variations of dose which could result in undesired side effects such as overdose of a critical organ. In this retrospective dosimetric study, the variation of dose due to various small range motions of gynecological applicators is investigated. The results show that the implementation of additional immobilization and localization devices along with other safety measures needs to be further investigated.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004446
Subject Headings: Artificial intelligence -- Medical applications, Cancer -- Intraoperative radiotherapy, Generative organs, Female -- Cancer -- Treatment, Imaging systems in medicine, Medical physics, Radiosotope brachytherapy
Format: Document (PDF)

Title: An evaluation of machine learning algorithms for tweet sentiment analysis.
Creator: Prusa, Joseph D., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Sentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we...
Show moreSentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we discuss research that has been conducted on tweet sentiment classification, its accompanying data concerns, and methods of addressing these concerns. We test the impact of feature selection, data sampling and ensemble techniques in an effort to improve classifier performance. We also evaluate the combination of feature selection and ensemble techniques and examine the effects of high dimensionality when combining multiple types of features. Additionally, we provide strategies and insights for potential avenues of future work.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004460, http://purl.flvc.org/fau/fd/FA00004460
Subject Headings: Social media., Natural language processing (Computer science), Machine learning., Algorithms., Fuzzy expert systems., Artificial intelligence.
Format: Document (PDF)

Title: Kicks and Maps A different Approach to Modeling Biological Systems.
Creator: Ippolito, Stephen Anthony, Naudot, Vincent, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description: Modeling a biological systems, is a cyclic process which involves constructing a model from current theory and beliefs and then validating that model against the data. If the data does not match, qualitatively or quantitatively then there may be a problem with either our beliefs or the current theory. At the same time directly finding a model from the existing data would make generalizing results difficult. A considerable difficultly in this process is how to specify the model in the first...
Show moreModeling a biological systems, is a cyclic process which involves constructing a model from current theory and beliefs and then validating that model against the data. If the data does not match, qualitatively or quantitatively then there may be a problem with either our beliefs or the current theory. At the same time directly finding a model from the existing data would make generalizing results difficult. A considerable difficultly in this process is how to specify the model in the first place. There is a need to be practice which accounts for the growing use of mathematical and statistical methods. However, as a systems becomes more complex, standard mathematical approaches may not be sufficient. In the field of ecology, the standard techniques involve discrete maps, and continuous models such as ODE's. The intent of this work is to present the mathematics necessary to study hybrids of these two models, then consider two case studies. In first case we con sider a coral reef with continuous change, except in the presence of hurricanes. The results of the data are compared quantitatively and qualitatively with simulation results. For the second case we consider a model for rabies with a periodic birth pulse. Here the analysis is qualitative as we demonstrate the existence of a strange attractor by looking at the intersections of the stable and unstable manifold for the saddle point generating the attractor. For both cases studies the introduction of a discrete event into a continuous system is done via a Dirac Distribution or Measure.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004508, http://purl.flvc.org/fau/fd/FA00004508
Subject Headings: Artificial intellligence -- Biological applications, Biology -- Mathematical models, Computational intelligence, Differential dynamical systems, Nonliner mechanics -- Mathematical models
Format: Document (PDF)

Title: An Ant Inspired Dynamic Traffic Assignment for VANETs: Early Notification of Traffic Congestion and Traffic Incidents.
Creator: Arellano, Wilmer, Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Vehicular Ad hoc NETworks (VANETs) are a subclass of Mobile Ad hoc NETworks and represent a relatively new and very active field of research. VANETs will enable in the near future applications that will dramatically improve roadway safety and traffic efficiency. There is a need to increase traffic efficiency as the gap between the traveled and the physical lane miles keeps increasing. The Dynamic Traffic Assignment problem tries to dynamically distribute vehicles efficiently on the road...
Show moreVehicular Ad hoc NETworks (VANETs) are a subclass of Mobile Ad hoc NETworks and represent a relatively new and very active field of research. VANETs will enable in the near future applications that will dramatically improve roadway safety and traffic efficiency. There is a need to increase traffic efficiency as the gap between the traveled and the physical lane miles keeps increasing. The Dynamic Traffic Assignment problem tries to dynamically distribute vehicles efficiently on the road network and in accordance with their origins and destinations. We present a novel dynamic decentralized and infrastructure-less algorithm to alleviate traffic congestions on road networks and to fill the void left by current algorithms which are either static, centralized, or require infrastructure. The algorithm follows an online approach that seeks stochastic user equilibrium and assigns traffic as it evolves in real time, without prior knowledge of the traffic demand or the schedule of the cars that will enter the road network in the future. The Reverse Online Algorithm for the Dynamic Traffic Assignment inspired by Ant Colony Optimization for VANETs follows a metaheuristic approach that uses reports from other vehicles to update the vehicle’s perceived view of the road network and change route if necessary. To alleviate the broadcast storm spontaneous clusters are created around traffic incidents and a threshold system based on the level of congestion is used to limit the number of incidents to be reported. Simulation results for the algorithm show a great improvement on travel time over routing based on shortest distance. As the VANET transceivers have a limited range, that would limit messages to reach at most 1,000 meters, we present a modified version of this algorithm that uses a rebroadcasting scheme. This rebroadcasting scheme has been successfully tested on roadways with segments of up to 4,000 meters. This is accomplished for the case of traffic flowing in a single direction on the roads. It is anticipated that future simulations will show further improvement when traffic in the other direction is introduced and vehicles travelling in that direction are allowed to use a store carry and forward mechanism.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004566, http://purl.flvc.org/fau/fd/FA00004566
Subject Headings: Vehicular ad hoc networks (Computer networks)--Technological innovations., Routing protocols (Computer network protocols), Artificial intelligence., Intelligent transportation systems., Intelligent control systems., Mobile computing., Computer algorithms., Combinatorial optimization.
Format: Document (PDF)

Title: Self-Contained Soft Robotic Jellyfish with Water-Filled Bending Actuators and Positional Feedback Control.
Creator: Frame, Jennifer, Engeberg, Erik, Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: This thesis concerns the design, construction, control, and testing of a novel self-contained soft robotic vehicle; the JenniFish is a free-swimming jellyfish-like soft robot that could be adapted for a variety of uses, including: low frequency, low power sensing applications; swarm robotics; a STEM classroom learning resource; etc. The final vehicle design contains eight PneuNet-type actuators radially situated around a 3D printed electronics canister. These propel the vehicle when inflated...
Show moreThis thesis concerns the design, construction, control, and testing of a novel self-contained soft robotic vehicle; the JenniFish is a free-swimming jellyfish-like soft robot that could be adapted for a variety of uses, including: low frequency, low power sensing applications; swarm robotics; a STEM classroom learning resource; etc. The final vehicle design contains eight PneuNet-type actuators radially situated around a 3D printed electronics canister. These propel the vehicle when inflated with water from its surroundings by impeller pumps; since the actuators are connected in two neighboring groups of four, the JenniFish has bi-directional movement capabilities. Imbedded resistive flex sensors provide actuator position to the vehicle’s PD controller. Other onboard sensors include an IMU and an external temperature sensor. Quantitative constrained load cell tests, both in-line and bending, as well as qualitative free-swimming video tests were conducted to find baseline vehicle performance capabilities. Collected metrics compare well with existing robotic jellyfish.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004656, http://purl.flvc.org/fau/fd/FA00004656
Subject Headings: Adaptive control systems, Artificial intelligence, Autonomous robots, Computational intelligence, Robotics
Format: Document (PDF)

Artificial intelligence (32)	+ -
Neural networks (Computer science) (13)	+ -
Artificial intelligence. (11)	+ -
Machine learning (6)	+ -
Deep learning (Machine learning) (4)	+ -

Robotics (4)	+ -
Adaptive control systems (3)	+ -
Back propagation (Artificial intelligence) (3)	+ -
Computational intelligence (3)	+ -
Pattern recognition systems (3)	+ -
System design (3)	+ -
Artificial intelligence -- Medical applications (2)	+ -
Bioinformatics (2)	+ -
Bioinformatics. (2)	+ -
Computer Science. (2)	+ -
Computer science. (2)	+ -
Computer vision. (2)	+ -
Fuzzy logic (2)	+ -
Healthcare (2)	+ -
Machine learning. (2)	+ -
Medical physics (2)	+ -
Mobile computing. (2)	+ -
Optical pattern recognition. (2)	+ -
Pattern recognition systems. (2)	+ -
Pavements--Skid resistance (2)	+ -
Reinforcement learning (2)	+ -
Theory of computation (2)	+ -
Actuators -- Materials (1)	+ -
Affective Computing (1)	+ -
Algorithms. (1)	+ -

Florida Atlantic University (53)	+ -
College of Engineering and Computer Science (36)	+ -
Department of Computer and Electrical Engineering and Computer Science (29)	+ -
Charles E. Schmidt College of Science (10)	+ -
Department of Ocean and Mechanical Engineering (7)	+ -

Khoshgoftaar, Taghi M. (5)	+ -
Marques, Oge (5)	+ -
Barenholtz, Elan (4)	+ -
Dorothy F. Schmidt College of Arts and Letters (4)	+ -
Department of Psychology (3)	+ -
Pandya, Abhijit S. (3)	+ -
Center for Complex Systems and Brain Sciences (2)	+ -
Curet, Oscar M. (2)	+ -
Department of English (2)	+ -
Department of Mathematical Sciences (2)	+ -
Department of Physics (2)	+ -
School of Communication and Multimedia Studies (2)	+ -
Shankar, Ravi (2)	+ -
Zhong, Xiangnan (2)	+ -
Aalo, Valentine A. (1)	+ -
Abd, Moaed A. (1)	+ -
Abtahi, Homayoon (1)	+ -
Agarwal, Ankur (1)	+ -
Aguerrevere, Santiago Andres (1)	+ -
Amento, Brittanney Jaclyn (1)	+ -
Arellano, Wilmer (1)	+ -
Bagdasarian, Jennifer Ling (1)	+ -
Bertaska, Ivan Rodrigues (1)	+ -
Bucak, Ayse Papatya (1)	+ -
Calderon, Rodrigo (1)	+ -

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