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- Title
- Machine Learning Algorithms with Big Medicare Fraud Data.
- Creator
- Bauder, Richard Andrew, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Healthcare is an integral component in peoples lives, especially for the rising elderly population, and must be affordable. The United States Medicare program is vital in serving the needs of the elderly. The growing number of people enrolled in the Medicare program, along with the enormous volume of money involved, increases the appeal for, and risk of, fraudulent activities. For many real-world applications, including Medicare fraud, the interesting observations tend to be less frequent...
Show moreHealthcare is an integral component in peoples lives, especially for the rising elderly population, and must be affordable. The United States Medicare program is vital in serving the needs of the elderly. The growing number of people enrolled in the Medicare program, along with the enormous volume of money involved, increases the appeal for, and risk of, fraudulent activities. For many real-world applications, including Medicare fraud, the interesting observations tend to be less frequent than the normative observations. This difference between the normal observations and those observations of interest can create highly imbalanced datasets. The problem of class imbalance, to include the classification of rare cases indicating extreme class imbalance, is an important and well-studied area in machine learning. The effects of class imbalance with big data in the real-world Medicare fraud application domain, however, is limited. In particular, the impact of detecting fraud in Medicare claims is critical in lessening the financial and personal impacts of these transgressions. Fortunately, the healthcare domain is one such area where the successful detection of fraud can garner meaningful positive results. The application of machine learning techniques, plus methods to mitigate the adverse effects of class imbalance and rarity, can be used to detect fraud and lessen the impacts for all Medicare beneficiaries. This dissertation presents the application of machine learning approaches to detect Medicare provider claims fraud in the United States. We discuss novel techniques to process three big Medicare datasets and create a new, combined dataset, which includes mapping fraud labels associated with known excluded providers. We investigate the ability of machine learning techniques, unsupervised and supervised, to detect Medicare claims fraud and leverage data sampling methods to lessen the impact of class imbalance and increase fraud detection performance. Additionally, we extend the study of class imbalance to assess the impacts of rare cases in big data for Medicare fraud detection.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013108
- Subject Headings
- Medicare fraud, Big data, Machine learning, Algorithms
- Format
- Document (PDF)
- Title
- Machine learning algorithms for the analysis and detection of network attacks.
- Creator
- Najafabadi, Maryam Mousaarab, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The Internet and computer networks have become an important part of our organizations and everyday life. With the increase in our dependence on computers and communication networks, malicious activities have become increasingly prevalent. Network attacks are an important problem in today’s communication environments. The network traffic must be monitored and analyzed to detect malicious activities and attacks to ensure reliable functionality of the networks and security of users’ information....
Show moreThe Internet and computer networks have become an important part of our organizations and everyday life. With the increase in our dependence on computers and communication networks, malicious activities have become increasingly prevalent. Network attacks are an important problem in today’s communication environments. The network traffic must be monitored and analyzed to detect malicious activities and attacks to ensure reliable functionality of the networks and security of users’ information. Recently, machine learning techniques have been applied toward the detection of network attacks. Machine learning models are able to extract similarities and patterns in the network traffic. Unlike signature based methods, there is no need for manual analyses to extract attack patterns. Applying machine learning algorithms can automatically build predictive models for the detection of network attacks. This dissertation reports an empirical analysis of the usage of machine learning methods for the detection of network attacks. For this purpose, we study the detection of three common attacks in computer networks: SSH brute force, Man In The Middle (MITM) and application layer Distributed Denial of Service (DDoS) attacks. Using outdated and non-representative benchmark data, such as the DARPA dataset, in the intrusion detection domain, has caused a practical gap between building detection models and their actual deployment in a real computer network. To alleviate this limitation, we collect representative network data from a real production network for each attack type. Our analysis of each attack includes a detailed study of the usage of machine learning methods for its detection. This includes the motivation behind the proposed machine learning based detection approach, the data collection process, feature engineering, building predictive models and evaluating their performance. We also investigate the application of feature selection in building detection models for network attacks. Overall, this dissertation presents a thorough analysis on how machine learning techniques can be used to detect network attacks. We not only study a broad range of network attacks, but also study the application of different machine learning methods including classification, anomaly detection and feature selection for their detection at the host level and the network level.
Show less - Date Issued
- 2017
- PURL
- http://purl.flvc.org/fau/fd/FA00004882, http://purl.flvc.org/fau/fd/FA00004882
- Subject Headings
- Machine learning., Computer security., Data protection., Computer networks--Security measures.
- Format
- Document (PDF)
- Title
- Microarray deconvolution: a web application.
- Creator
- Canny, Stephanie, Petrie, Howard, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Microarray gene expression profiling is used in biology for a variety of purposes including identifying disease biomarkers or understanding cellular processes. Biological samples composed of multiple cell or tissue types pose a problem because different compositions of cell-types in samples affect the gene expression profile and also the expression profile of individual components of the sample may be of interest. Physical methods to separate mixed samples are time-consuming and expensive....
Show moreMicroarray gene expression profiling is used in biology for a variety of purposes including identifying disease biomarkers or understanding cellular processes. Biological samples composed of multiple cell or tissue types pose a problem because different compositions of cell-types in samples affect the gene expression profile and also the expression profile of individual components of the sample may be of interest. Physical methods to separate mixed samples are time-consuming and expensive. Consequently, several computational methods have been developed to deconvolute heterogeneous samples into individual components. Different software packages and applications are available to perform these calculations. Microarray Deconvolution is a web application that provides a simple-to-use interface that fills some gaps left by other packages in performing heterogeneous sample microarray deconvolution including microarray raw data processing and normalization, cell-type proportion estimation and simple linear deconvolution.
Show less - Date Issued
- 2013
- PURL
- http://purl.flvc.org/fau/fd/FA00004243
- Format
- Document (PDF)
- Title
- Spectral evaluation of motion compensated adv systems for ocean turbulence measurements.
- Creator
- Egeland, Matthew Nicklas, von Ellenrieder, Karl, VanZwieten, James H., Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
- Abstract/Description
-
A motion compensated ADV system was evaluated to determine its ability to make measurements necessary for characterizing the variability of the ambient current in the Gulf Stream. The impact of IMU error relative to predicted turbulence spectra was quantified, as well as and the ability of the motion compensation approach to remove sensor motion from the ADV measurements. The presented data processing techniques are shown to allow the evaluated ADV to be effectively utilized for quantifying...
Show moreA motion compensated ADV system was evaluated to determine its ability to make measurements necessary for characterizing the variability of the ambient current in the Gulf Stream. The impact of IMU error relative to predicted turbulence spectra was quantified, as well as and the ability of the motion compensation approach to remove sensor motion from the ADV measurements. The presented data processing techniques are shown to allow the evaluated ADV to be effectively utilized for quantifying ambient current fluctuations from 0.02 to 1 Hz (50 to 1 seconds) for dissipation rates as low as 3x10-7. This measurement range is limited on the low frequency end by IMU error, primarily by the calculated transformation matrix, and on the high end by Doppler noise. Inshore testing has revealed a 0.37 Hz oscillation inherent in the towfish designed and manufactured as part of this project, which can nearly be removed using the IMU.
Show less - Date Issued
- 2014
- PURL
- http://purl.flvc.org/fau/fd/FA00004191, http://purl.flvc.org/fau/fd/FA00004191
- Subject Headings
- Fluid dynamic measurements, Fluid mechanics -- Mathematical models, Motion control systems, Ocean atmosphere interaction, Ocean circulation, Turbulence, Wave motion, Theory of
- Format
- Document (PDF)
- Title
- Alleviating class imbalance using data sampling: Examining the effects on classification algorithms.
- Creator
- Napolitano, Amri E., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Imbalanced class distributions typically cause poor classifier performance on the minority class, which also tends to be the class with the highest cost of mis-classification. Data sampling is a common solution to this problem, and numerous sampling techniques have been proposed to address it. Prior research examining the performance of these techniques has been narrow and limited. This work uses thorough empirical experimentation to compare the performance of seven existing data sampling...
Show moreImbalanced class distributions typically cause poor classifier performance on the minority class, which also tends to be the class with the highest cost of mis-classification. Data sampling is a common solution to this problem, and numerous sampling techniques have been proposed to address it. Prior research examining the performance of these techniques has been narrow and limited. This work uses thorough empirical experimentation to compare the performance of seven existing data sampling techniques using five different classifiers and four different datasets. The work addresses which sampling techniques produce the best performance in the presence of class unbalance, which classifiers are most robust to the problem, as well as which sampling techniques perform better or worse with each classifier. Extensive statistical analysis of these results is provided, in addition to an examination of the qualitative effects of the sampling techniques on the types of predictions made by the C4.5 classifier.
Show less - Date Issued
- 2006
- PURL
- http://purl.flvc.org/fcla/dt/13413
- Subject Headings
- Combinatorial group theory, Data mining, Decision trees, Machine learning
- Format
- Document (PDF)
- Title
- Analysis and implementation issues of the Icon Generator, Manager and Repository for an icon-based software engineering environment.
- Creator
- Portnoy, Ilya., Florida Atlantic University, Larrondo-Petrie, Maria M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Computer icons, along with animation, sound and other multimedia features, can facilitate communications between clients, software engineers and managers. Icon-based Software Engineering Environment (IconSEE++), being developed at Florida Atlantic University, uses icons to convey concepts describing object-oriented software development projects, as well as to navigate to all technical and non-technical project documentation. IconSEE++ is composed of three layers. The Icon Generator, Manager...
Show moreComputer icons, along with animation, sound and other multimedia features, can facilitate communications between clients, software engineers and managers. Icon-based Software Engineering Environment (IconSEE++), being developed at Florida Atlantic University, uses icons to convey concepts describing object-oriented software development projects, as well as to navigate to all technical and non-technical project documentation. IconSEE++ is composed of three layers. The Icon Generator, Manager and Repository form a part of the middle layer. This thesis includes a survey of visual development tools and their comparison to IconSEE++. For the Icon Generator, Manager and Repository, the thesis presents object-oriented analysis models in UML notation, identifies implementation issues and provides strategies to solve them. Numerous code segments illustrating proposed solutions have been developed.
Show less - Date Issued
- 1999
- PURL
- http://purl.flvc.org/fcla/dt/15635
- Subject Headings
- Graphical user interfaces (Computer systems), Signs and symbols--Data processing
- Format
- Document (PDF)
- Title
- Analysis of a novel class of fault-tolerant multistage interconnection networks.
- Creator
- Huang, Chien-Jen, Florida Atlantic University, Mahgoub, Imad, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Multistage interconnection networks (MINs) have become an important subset of the interconnection networks which are used to communicate between processors and memory modules for large scale multiprocessor systems. Unfortunately, unique path MINs lack fault tolerance. In this dissertation, a novel scheme for constructing fault-tolerant MINs is presented. We first partition the given MINs into even sized partitions and show some fault-tolerant properties of the partitioned MINs. Using three...
Show moreMultistage interconnection networks (MINs) have become an important subset of the interconnection networks which are used to communicate between processors and memory modules for large scale multiprocessor systems. Unfortunately, unique path MINs lack fault tolerance. In this dissertation, a novel scheme for constructing fault-tolerant MINs is presented. We first partition the given MINs into even sized partitions and show some fault-tolerant properties of the partitioned MINs. Using three stages of multiplexers/demultiplexers, an augmenting scheme which takes advantage of locality in program execution is then proposed to further improve the fault-tolerant ability and performance of the partitioned MINs. The topological characteristics of augmented partitioned multistage interconnection networks (APMINs) are analyzed. Based on switch fault model, simulations have been carried out to evaluate the full access and dynamic full access capabilities of APMINs. The results show that the proposed scheme significantly improves the fault-tolerant capability of MINs. Cost effectiveness of this new scheme in terms of cost, full access, dynamic full access, locality, and average path length has also been evaluated. It has been shown that this new scheme is more cost effective for high switch failure rate and/or large size networks. Analytical modeling techniques have been developed to evaluate the performance of AP-Omega network and AP-Omega network-based multiprocessor systems. The performance of Omega, modified Omega, and AP-Omega networks in terms of processor utilization and processor waiting time have been compared and the results show that the new scheme indeed, improves the performance both in network level and in system level. Finally, based on the reliability of serial/parallel network components, models for evaluating the terminal reliability and the network reliability of AP-Omega network using upper and lower bound measures have also been proposed and the results show that applying locality improve APMINs' reliability.
Show less - Date Issued
- 1993
- PURL
- http://purl.flvc.org/fcla/dt/12345
- Subject Headings
- Integratged circuits--Very large scale integration, Fault-tolerant computing, Computer architecture, Parallel processing (Electronic computers)
- Format
- Document (PDF)
- Title
- CMOS VLSI Design of a Bluetooth™ Receiver Front-End: Performance Evaluation via ADS™-Based Simulations.
- Creator
- Talbot, Bethany J., Neelakanta, Perambur S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The research addressed and deliberated in this thesis refers to CMOS VLSI design approach of a Bluetooth™ receiver front-end. The performance outcome o f the design is verified with ADS™ RF simulation tool. Essentially the thesis offers an outline on the Bluetooth™ technology and its RF front-end component requirements. The relevant specifications of the designed front-end blocks are identified and are in concurrence with CMOS technology based topologies. For each block identified, both...
Show moreThe research addressed and deliberated in this thesis refers to CMOS VLSI design approach of a Bluetooth™ receiver front-end. The performance outcome o f the design is verified with ADS™ RF simulation tool. Essentially the thesis offers an outline on the Bluetooth™ technology and its RF front-end component requirements. The relevant specifications of the designed front-end blocks are identified and are in concurrence with CMOS technology based topologies. For each block identified, both circuit parameters and device characteristics are chosen as per available design formulations and empirical results in open literature. Specifically, the topology sections designed include antenna input matching, transmit/receive switch, necessary filters, low noise amplifier, mixer and phase lock loop units. The numerical TM, (designed) circuit parameters are duly addressed in appropriate ADS simulation tools and performance evaluations are conducted. Observed results including any deviations are identified and reported. The thesis concludes with a summary and indicates direction for future work.
Show less - Date Issued
- 2007
- PURL
- http://purl.flvc.org/fau/fd/FA00012559
- Subject Headings
- Integrated circuits--Very large scale integration--Design and construction, Metal oxide semiconductors, Complementary, Bluetooth technology, Network performance (Telecommunication)
- Format
- Document (PDF)
- Title
- Blind source separation using a spatial fourth-order cumulant matrix-pencil.
- Creator
- Dishman, John Fitzgerald., Florida Atlantic University, Aalo, Valentine A., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The research presented investigates the use of cumulants in conjunction with a spectral estimation technique of the signal subspace to perform the blind separation of statistically independent signals with low signal-to-noise ratios under a narrowband assumption. A new blind source separation (BSS) algorithm is developed that makes use of the generalized eigen analysis of a matrix pencil defined on two similar spatial fourth-order cumulant matrices. The algorithm works in the presence of...
Show moreThe research presented investigates the use of cumulants in conjunction with a spectral estimation technique of the signal subspace to perform the blind separation of statistically independent signals with low signal-to-noise ratios under a narrowband assumption. A new blind source separation (BSS) algorithm is developed that makes use of the generalized eigen analysis of a matrix pencil defined on two similar spatial fourth-order cumulant matrices. The algorithm works in the presence of spatially and/or temporally correlated noise and, unlike most existing higher-order BSS techniques, is based on a spectral estimation technique rather than a closed loop optimization of a contrast function, for which the convergence is often problematic. The dissertation makes several contributions to the area of blind source separation. These include: (1) Development of a robust blind source separation technique that is based on higher-order cumulant based principle component analysis that works at low signal-to-noise ratios in the presence of temporally and/or spatially correlated noise. (2) A novel definition of a spatial fourth-order cumulant matrix suited to blind source separation with non-equal gain and/or directional sensors. (3) The definition of a spatial fourth-order cumulant matrix-pencil using temporal information. (4) The concept of separation power efficiency (SPE) as a measure of the algorithm's performance. Two alternative definitions for the spatial fourth-order cumulant matrix that are found in the literature are also presented and used by the algorithm for comparison. Additionally, the research contributes the concept of wide sense equivalence between matrix-pencils to the field of matrix algebra. The algorithm's performance is verified by computer simulation using realistic digital communications signals in white noise. Random mixing matrices are generated to ensure the algorithm's performance is independent of array geometry. The computer results are promising and show that the algorithm works well down to input signal-to-noise ratios of -6 dB, and using as few as 250 x 103 samples.
Show less - Date Issued
- 2001
- PURL
- http://purl.flvc.org/fcla/dt/11963
- Subject Headings
- Matrix pencils, Adaptive signal processing, Matrices
- Format
- Document (PDF)
- Title
- Classification of software quality with tree modeling using C4.5 algorithm.
- Creator
- Ponnuswamy, Viswanathan Kolathupalayam., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Developing highly reliable software is a must in today's competitive environment. However quality control is a costly and time consuming process. If the quality of software modules being developed can be predicted early in their life cycle, resources can be effectively allocated improving quality, reducing cost and development time. This study examines the C4.5 algorithm as a tool for building classification trees, classifying software module either as fault-prone or not fault-prone. The...
Show moreDeveloping highly reliable software is a must in today's competitive environment. However quality control is a costly and time consuming process. If the quality of software modules being developed can be predicted early in their life cycle, resources can be effectively allocated improving quality, reducing cost and development time. This study examines the C4.5 algorithm as a tool for building classification trees, classifying software module either as fault-prone or not fault-prone. The classification tree models were developed based on four consecutive releases of a very large legacy telecommunication system. The first two releases were used as training data sets and the subsequent two releases were used as test data sets to evaluate the model. We found out that C4.5 was able to build compact classification trees models with balanced misclassification rates.
Show less - Date Issued
- 2001
- PURL
- http://purl.flvc.org/fcla/dt/12855
- Subject Headings
- Computer software--Quality control, Software measurement
- Format
- Document (PDF)
- Title
- Camera calibration techniques.
- Creator
- Xu, Xuan., Florida Atlantic University, Roth, Zvi S., Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
In order to facilitate the implementation of RAC-based camera calibration technique, several issues are addressed in this thesis. First, a straightforward extension of the RAC-based camera calibration technique to the case of unknown camera specifications is given. Second, to speed up the calibration process and reduce numerical difficulties, a simplified RAC-based method is presented. The simplified RAC-based method provides a closed-form solution to the camera calibration problem. Third,...
Show moreIn order to facilitate the implementation of RAC-based camera calibration technique, several issues are addressed in this thesis. First, a straightforward extension of the RAC-based camera calibration technique to the case of unknown camera specifications is given. Second, to speed up the calibration process and reduce numerical difficulties, a simplified RAC-based method is presented. The simplified RAC-based method provides a closed-form solution to the camera calibration problem. Third, the PTM-based camera calibration technique is presented to give an example of pre-1985 camera calibration technique. Fourth, a method is devised to compute the ratio of scale factors. Finally, an optimization scheme is suggested to estimate image center. These modifications preserve all the advantages possessed by the original RAC-based calibration technique. The experiment results are provided to illustrate the effectiveness of the present methods.
Show less - Date Issued
- 1991
- PURL
- http://purl.flvc.org/fcla/dt/14771
- Subject Headings
- Cameras--Calibration
- Format
- Document (PDF)
- Title
- Camera-aided SCARA arm calibration.
- Creator
- Wu, Wen-chiang., Florida Atlantic University, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The focus of this thesis is the kinematic calibration of a SCARA arm with a hand-mounted camera. Kinematic calibration can greatly improve the accuracy of SCARA arms, which are widely used in electronic assembly lines. Vision-based robot calibration has the potential of being a fast, nonintrusive, low-cost, and autonomous approach. In this thesis, we apply a vision-based technique to calibrate SCARA arms. The robot under investigation is modeled by the modified complete and parametrically...
Show moreThe focus of this thesis is the kinematic calibration of a SCARA arm with a hand-mounted camera. Kinematic calibration can greatly improve the accuracy of SCARA arms, which are widely used in electronic assembly lines. Vision-based robot calibration has the potential of being a fast, nonintrusive, low-cost, and autonomous approach. In this thesis, we apply a vision-based technique to calibrate SCARA arms. The robot under investigation is modeled by the modified complete and parametrically continuous model. By repeatedly calibrating the camera, the pose of the robot end-effector are collected at various robot measurement configurations. A least squares technique is then applied to estimate the geometric error parameters of the SCARA arm using the measured robot poses. In order to improve the robustness of the method, a new approach is proposed to calibrate the hand-mounted camera. The calibration algorithm is designed to deal with the case in which the camera sensor plane is nearly-parallel to the camera calibration board. Practical issues regarding robot calibration in general and SCARA arm calibration in particular are also addressed. Experiment studies reveal that the proposed camera-aided approach is a viable means for accuracy enhancement of SCARA arms.
Show less - Date Issued
- 1994
- PURL
- http://purl.flvc.org/fcla/dt/15075
- Subject Headings
- Robots--Calibration, Manipulators (Mechanism)--Calibration, Robots--Error detection and recovery, Image processing
- Format
- Document (PDF)
- Title
- Approaches to object/relational database systems.
- Creator
- Vijayanagaram, Hemanth Kumar., Florida Atlantic University, Solomon, Martin K., Larrondo-Petrie, Maria M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
In this thesis, we investigate the different proposed ways of combining object oriented and relational database systems (such combined systems are commonly called object-relational systems). This thesis is based on ideas presented in various papers about object and object relational databases. In this work, a discussion of standards such as ANSI's SQL3 (to be released) and ODMG-93 is given. In particular, the "Class = Relation" and "Class = Domain" approaches to object-relational systems are...
Show moreIn this thesis, we investigate the different proposed ways of combining object oriented and relational database systems (such combined systems are commonly called object-relational systems). This thesis is based on ideas presented in various papers about object and object relational databases. In this work, a discussion of standards such as ANSI's SQL3 (to be released) and ODMG-93 is given. In particular, the "Class = Relation" and "Class = Domain" approaches to object-relational systems are investigated. Arguments supporting the proposition that the latter approach is the correct approach are presented.
Show less - Date Issued
- 1996
- PURL
- http://purl.flvc.org/fcla/dt/15350
- Subject Headings
- Object-oriented databases, Relational databases
- Format
- Document (PDF)
- Title
- Application level intrusion detection using a sequence learning algorithm.
- Creator
- Dong, Yuhong., Florida Atlantic University, Hsu, Sam, Rajput, Saeed, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
An un-supervised learning algorithm on application level intrusion detection, named Graph Sequence Learning Algorithm (GSLA), is proposed in this dissertation. Experiments prove its effectiveness. Similar to most intrusion detection algorithms, in GSLA, the normal profile needs to be learned first. The normal profile is built using a session learning method, which is combined with the one-way Analysis of Variance method (ANOVA) to determine the value of an anomaly threshold. In the proposed...
Show moreAn un-supervised learning algorithm on application level intrusion detection, named Graph Sequence Learning Algorithm (GSLA), is proposed in this dissertation. Experiments prove its effectiveness. Similar to most intrusion detection algorithms, in GSLA, the normal profile needs to be learned first. The normal profile is built using a session learning method, which is combined with the one-way Analysis of Variance method (ANOVA) to determine the value of an anomaly threshold. In the proposed approach, a hash table is used to store a sparse data matrix in triple data format that is collected from a web transition log instead of an n-by-n dimension matrix. Furthermore, in GSLA, the sequence learning matrix can be dynamically changed according to a different volume of data sets. Therefore, this approach is more efficient, easy to manipulate, and saves memory space. To validate the effectiveness of the algorithm, extensive simulations have been conducted by applying the GSLA algorithm to the homework submission system at our computer science and engineering department. The performance of GSLA is evaluated and compared with traditional Markov Model (MM) and K-means algorithms. Specifically, three major experiments have been done: (1) A small data set is collected as a sample data, and is applied to GSLA, MM, and K-means algorithms to illustrate the operation of the proposed algorithm and demonstrate the detection of abnormal behaviors. (2) The Random Walk-Through sampling method is used to generate a larger sample data set, and the resultant anomaly score is classified into several clusters in order to visualize and demonstrate the normal and abnormal behaviors with K-means and GSLA algorithms. (3) Multiple professors' data sets are collected and used to build the normal profiles, and the ANOVA method is used to test the significant difference among professors' normal profiles. The GSLA algorithm can be made as a module and plugged into the IDS as an anomaly detection system.
Show less - Date Issued
- 2006
- PURL
- http://purl.flvc.org/fcla/dt/12220
- Subject Headings
- Data mining, Parallel processing (Electronic computers), Computer algorithms, Computer security, Pattern recognition systems
- Format
- Document (PDF)
- Title
- Analysis of nucleus reuniens cell behavior during hippocampal theta rhythm.
- Creator
- Morales, George J., Florida Atlantic University, Morgera, Salvatore D., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Coherence estimates have been used to determine the presence of functional coupling between two signals. While direct projections from the nucleus reuniens (RE) to the hippocampus formation in the rat have been discovered, little is known about the possible functional influence of the RE on the hippocampus. This investigation makes use of MATLAB to create a set of specialized algorithms to investigate coherence function estimates between RE cell activity and hippocampal EEG. In addition,...
Show moreCoherence estimates have been used to determine the presence of functional coupling between two signals. While direct projections from the nucleus reuniens (RE) to the hippocampus formation in the rat have been discovered, little is known about the possible functional influence of the RE on the hippocampus. This investigation makes use of MATLAB to create a set of specialized algorithms to investigate coherence function estimates between RE cell activity and hippocampal EEG. In addition, error prevention considerations as well as shortcomings in current data acquisition software that ultimately lead to the necessity for additional software analysis tools are also discussed. An investigation into RE cell behavior requires the calculation of cell activity spike rates as well as the identification of action potential bursting phenomena. Isolation of individual cell activity, from a population recording channel, is needed in order to prevent erroneous effects associated with using unresolved multi-neuron recordings. Changes in spike rate activity and frequency of bursting occurrences are calculated as a means of gauging RE unit response to the presence of a stimulus (e.g., tail pinch). The relationship of RE units on hippocampal EEG by analysis of coherence function estimates between RE units and hippocampal EEG, as well as evaluated RE unit behavior in terms of changes in unit spike rate and bursting activity are established.
Show less - Date Issued
- 2006
- PURL
- http://purl.flvc.org/fcla/dt/13383
- Subject Headings
- Hippocampus (Brain), Electroencephalography, Neurosciences, Theta rhythm, Memory
- Format
- Document (PDF)
- Title
- Analytical study of capability maturity model using content analysis.
- Creator
- Sheth, Dhaval Ranjitlal., Florida Atlantic University, Coulter, Neal S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Content analysis is used to investigate the essence of the Software Engineering Institute's Capability Maturity Model (CMM) through associated software process evaluation instruments. This study yields lexical maps of key terms from each questionnaire. The content analysis is studied in three possible ways for each of the questionnaires: By question, by key process area, and by maturity level. These maps are named suitably. Super network and distribution maps are used for finding relations...
Show moreContent analysis is used to investigate the essence of the Software Engineering Institute's Capability Maturity Model (CMM) through associated software process evaluation instruments. This study yields lexical maps of key terms from each questionnaire. The content analysis is studied in three possible ways for each of the questionnaires: By question, by key process area, and by maturity level. These maps are named suitably. Super network and distribution maps are used for finding relations among the maps. Analysis of the key terms from the maps are compared to extract the essence of CMM and the ability of the questionnaires to adequately assess an organization's process maturity.
Show less - Date Issued
- 1998
- PURL
- http://purl.flvc.org/fcla/dt/15554
- Subject Headings
- Software engineering--Management, Computer software--Development, Computer software--Evaluation
- Format
- Document (PDF)
- Title
- Data Quality in Data Mining and Machine Learning.
- Creator
- Van Hulse, Jason, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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With advances in data storage and data transmission technologies, and given the increasing use of computers by both individuals and corporations, organizations are accumulating an ever-increasing amount of information in data warehouses and databases. The huge surge in data, however, has made the process of extracting useful, actionable, and interesting knowled_qe from the data extremely difficult. In response to the challenges posed by operating in a data-intensive environment, the fields of...
Show moreWith advances in data storage and data transmission technologies, and given the increasing use of computers by both individuals and corporations, organizations are accumulating an ever-increasing amount of information in data warehouses and databases. The huge surge in data, however, has made the process of extracting useful, actionable, and interesting knowled_qe from the data extremely difficult. In response to the challenges posed by operating in a data-intensive environment, the fields of data mining and machine learning (DM/ML) have successfully provided solutions to help uncover knowledge buried within data. DM/ML techniques use automated (or semi-automated) procedures to process vast quantities of data in search of interesting patterns. DM/ML techniques do not create knowledge, instead the implicit assumption is that knowledge is present within the data, and these procedures are needed to uncover interesting, important, and previously unknown relationships. Therefore, the quality of the data is absolutely critical in ensuring successful analysis. Having high quality data, i.e., data which is (relatively) free from errors and suitable for use in data mining tasks, is a necessary precondition for extracting useful knowledge. In response to the important role played by data quality, this dissertation investigates data quality and its impact on DM/ML. First, we propose several innovative procedures for coping with low quality data. Another aspect of data quality, the occurrence of missing values, is also explored. Finally, a detailed experimental evaluation on learning from noisy and imbalanced datasets is provided, supplying valuable insight into how class noise in skewed datasets affects learning algorithms.
Show less - Date Issued
- 2007
- PURL
- http://purl.flvc.org/fau/fd/FA00000858
- Subject Headings
- Data mining--Quality control, Machine learning, Electronic data processing--Quality control
- Format
- Document (PDF)
- Title
- Detection of change-prone telecommunications software modules.
- Creator
- Weir, Ronald Eugene., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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Accurately classifying the quality of software is a major problem in any software development project. Software engineers develop models that provide early estimates of quality metrics which allow them to take actions against emerging quality problems. The use of a neural network as a tool to classify programs as a low, medium, or high risk for errors or change is explored using multiple software metrics as input. It is demonstrated that a neural network, trained using the back-propagation...
Show moreAccurately classifying the quality of software is a major problem in any software development project. Software engineers develop models that provide early estimates of quality metrics which allow them to take actions against emerging quality problems. The use of a neural network as a tool to classify programs as a low, medium, or high risk for errors or change is explored using multiple software metrics as input. It is demonstrated that a neural network, trained using the back-propagation supervised learning strategy, produced the desired mapping between the static software metrics and the software quality classes. The neural network classification methodology is compared to the discriminant analysis classification methodology in this experiment. The comparison is based on two and three class predictive models developed using variables resulting from principal component analysis of software metrics.
Show less - Date Issued
- 1995
- PURL
- http://purl.flvc.org/fcla/dt/15183
- Subject Headings
- Computer software--Evaluation, Software engineering, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- Design and implementation of a wireless ad hoc network.
- Creator
- Neelakanta, Mahesh., Florida Atlantic University, Hsu, Sam, Ilyas, Mohammad, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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This thesis addresses issues faced in the practical implementation of a wireless ad hoc network (WAHN) protocol for data transmission. This study focuses on: (1) Evaluating existing hardware and software options available for the WAHN implementation. (2) Appraising the issues faced while implementing a practical wireless ad hoc protocol. (3) Applying a set of MAC protocol specifications developed for a wireless ad hoc data network to a practical test network. Specific to the above topics of...
Show moreThis thesis addresses issues faced in the practical implementation of a wireless ad hoc network (WAHN) protocol for data transmission. This study focuses on: (1) Evaluating existing hardware and software options available for the WAHN implementation. (2) Appraising the issues faced while implementing a practical wireless ad hoc protocol. (3) Applying a set of MAC protocol specifications developed for a wireless ad hoc data network to a practical test network. Specific to the above topics of interest, the following research tasks are performed: (1) An elaborate survey and relevant discussions on wireless MAC protocols. (2) A comprehensive study comparing various wireless transceivers is performed. Range, data rate, frequency, interfacing method and cost are the factors compared. (3) A simple, low-cost and low baud-rate transceiver is modified with appropriate interface circuits to support wireless communications. A more advanced transceiver is also considered and used for the software foundation of a practical implementation of the ad hoc and MAC protocols. The studies enable assessing the problems faced during the implementation and suggest solutions to resolve these problems. Further areas for study are also discussed.
Show less - Date Issued
- 1998
- PURL
- http://purl.flvc.org/fcla/dt/15615
- Subject Headings
- Wireless communication systems, Data transmission systems, Computer networks
- Format
- Document (PDF)
- Title
- Design and modeling of hybrid software fault-tolerant systems.
- Creator
- Zhang, Man-xia Maria., Florida Atlantic University, Wu, Jie, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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Fault tolerant programming methods improve software reliability using the principles of design diversity and redundancy. Design diversity and redundancy, on the other hand, escalate the cost of the software design and development. In this thesis, we study the reliability of hybrid fault tolerant systems. Probability models based on fault trees are developed for the recovery block (RB), N-version programming (NVP) and hybrid schemes which are the combinations of RB and NVP. Two heuristic...
Show moreFault tolerant programming methods improve software reliability using the principles of design diversity and redundancy. Design diversity and redundancy, on the other hand, escalate the cost of the software design and development. In this thesis, we study the reliability of hybrid fault tolerant systems. Probability models based on fault trees are developed for the recovery block (RB), N-version programming (NVP) and hybrid schemes which are the combinations of RB and NVP. Two heuristic methods are developed to construct hybrid fault tolerant systems with total cost constraints. The algorithms provide a systematic approach to the design of hybrid fault tolerant systems.
Show less - Date Issued
- 1992
- PURL
- http://purl.flvc.org/fcla/dt/14783
- Subject Headings
- Computer software--Reliability, Fault-tolerant computing, Algorithms
- Format
- Document (PDF)
