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- Title
- An evaluation of Unsupervised Machine Learning Algorithms for Detecting Fraud and Abuse in the U.S. Medicare Insurance Program.
- Creator
- Da Rosa, Raquel C., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The population of people ages 65 and older has increased since the 1960s and current estimates indicate it will double by 2060. Medicare is a federal health insurance program for people 65 or older in the United States. Medicare claims fraud and abuse is an ongoing issue that wastes a large amount of money every year resulting in higher health care costs and taxes for everyone. In this study, an empirical evaluation of several unsupervised machine learning approaches is performed which...
Show moreThe population of people ages 65 and older has increased since the 1960s and current estimates indicate it will double by 2060. Medicare is a federal health insurance program for people 65 or older in the United States. Medicare claims fraud and abuse is an ongoing issue that wastes a large amount of money every year resulting in higher health care costs and taxes for everyone. In this study, an empirical evaluation of several unsupervised machine learning approaches is performed which indicates reasonable fraud detection results. We employ two unsupervised machine learning algorithms, Isolation Forest and Unsupervised Random Forest, which have not been previously used for the detection of fraud and abuse on Medicare data. Additionally, we implement three other machine learning methods previously applied on Medicare data which include: Local Outlier Factor, Autoencoder, and k-Nearest Neighbor. For our dataset, we combine the 2012 to 2015 Medicare provider utilization and payment data and add fraud labels from the List of Excluded Individuals/Entities (LEIE) database. Results show that Local Outlier Factor is the best model to use for Medicare fraud detection.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013042
- Subject Headings
- Machine learning, Medicare fraud, Algorithms
- Format
- Document (PDF)
- Title
- Automatic extraction and tracking of eye features from facial image sequences.
- Creator
- Xie, Xangdong., Florida Atlantic University, Sudhakar, Raghavan, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The dual issues of extracting and tracking eye features from video images are addressed in this dissertation. The proposed scheme is different from conventional intrusive eye movement measuring system and can be implemented using an inexpensive personal computer. The desirable features of such a measurement system are low cost, accuracy, automated operation, and non-intrusiveness. An overall scheme is presented for which a new algorithm is forwarded for each of the function blocks in the...
Show moreThe dual issues of extracting and tracking eye features from video images are addressed in this dissertation. The proposed scheme is different from conventional intrusive eye movement measuring system and can be implemented using an inexpensive personal computer. The desirable features of such a measurement system are low cost, accuracy, automated operation, and non-intrusiveness. An overall scheme is presented for which a new algorithm is forwarded for each of the function blocks in the processing system. A new corner detection algorithm is presented in which the problem of detecting corners is solved by minimizing a cost function. Each cost factor captures a desirable characteristic of the corner using both the gray level information and the geometrical structure of a corner. This approach additionally provides corner orientations and angles along with corner locations. The advantage of the new approach over the existing corner detectors is that it is able to improve the reliability of detection and localization by imposing criteria related to both the gray level data and the corner structure. The extraction of eye features is performed by using an improved method of deformable templates which are geometrically arranged to resemble the expected shape of the eye. The overall energy function is redefined to simplify the minimization process. The weights for the energy terms are selected based on the normalized value of the energy term. Thus the weighting schedule of the modified method does not demand any expert knowledge for the user. Rather than using a sequential procedure, all parameters of the template are changed simultaneously during the minimization process. This reduces not only the processing time but also the probability of the template being trapped in local minima. An efficient algorithm for real-time eye feature tracking from a sequence of eye images is developed in the dissertation. Based on a geometrical model which describes the characteristics of the eye, the measurement equations are formulated to relate suitably selected measurements to the tracking parameters. A discrete Kalman filter is then constructed for the recursive estimation of the eye features, while taking into account the measurement noise. The small processing time allows this tracking algorithm to be used in real-time applications. This tracking algorithm is suitable for an automated, non-intrusive and inexpensive system as the algorithm is capable of measuring the time profiles of the eye movements. The issue of compensating head movements during the tracking of eye movements is also discussed. An appropriate measurement model was established to describe the effects of head movements. Based on this model, a Kalman filter structure was formulated to carry out the compensation. The whole tracking scheme which cascades two Kalman filters is constructed to track the iris movement, while compensating the head movement. The presence of the eye blink is also taken into account and its detection is incorporated into the cascaded tracking scheme. The above algorithms have been integrated to design an automated, non-intrusive and inexpensive system which provides accurate time profile of eye movements tracking from video image frames.
Show less - Date Issued
- 1994
- PURL
- http://purl.flvc.org/fcla/dt/12377
- Subject Headings
- Kalman filtering, Eye--Movements, Algorithms, Image processing
- Format
- Document (PDF)
- Title
- COLLECTION AND ANALYSIS OF SLOW DENIAL OF SERVICE ATTACKS USING MACHINE LEARNING ALGORITHMS.
- Creator
- Kemp, Clifford, Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
Application-layer based attacks are becoming a more desirable target in computer networks for hackers. From complex rootkits to Denial of Service (DoS) attacks, hackers look to compromise computer networks. Web and application servers can get shut down by various application-layer DoS attacks, which exhaust CPU or memory resources. The HTTP protocol has become a popular target to launch application-layer DoS attacks. These exploits consume less bandwidth than traditional DoS attacks....
Show moreApplication-layer based attacks are becoming a more desirable target in computer networks for hackers. From complex rootkits to Denial of Service (DoS) attacks, hackers look to compromise computer networks. Web and application servers can get shut down by various application-layer DoS attacks, which exhaust CPU or memory resources. The HTTP protocol has become a popular target to launch application-layer DoS attacks. These exploits consume less bandwidth than traditional DoS attacks. Furthermore, this type of DoS attack is hard to detect because its network traffic resembles legitimate network requests. Being able to detect these DoS attacks effectively is a critical component of any robust cybersecurity system. Machine learning can help detect DoS attacks by identifying patterns in network traffic. With machine learning methods, predictive models can automatically detect network threats. This dissertation offers a novel framework for collecting several attack datasets on a live production network, where producing quality representative data is a requirement. Our approach builds datasets from collected Netflow and Full Packet Capture (FPC) data. We evaluate a wide range of machine learning classifiers which allows us to analyze slow DoS detection models more thoroughly. To identify attacks, we look at each dataset's unique traffic patterns and distinguishing properties. This research evaluates and investigates appropriate feature selection evaluators and search strategies. Features are assessed for their predictive value and degree of redundancy to build a subset of features. Feature subsets with high-class correlation but low intercorrelation are favored. Experimental results indicate Netflow and FPC features are discriminating enough to detect DoS attacks accurately. We conduct a comparative examination of performance metrics to determine the capability of several machine learning classifiers. Additionally, we improve upon our performance scores by investigating a variety of feature selection optimization strategies. Overall, this dissertation proposes a novel machine learning approach for detecting slow DoS attacks. Our machine learning results demonstrate that a single subset of features trained on Netflow data can effectively detect slow application-layer DoS attacks.
Show less - Date Issued
- 2021
- PURL
- http://purl.flvc.org/fau/fd/FA00013848
- Subject Headings
- Machine learning, Algorithms, Denial of service attacks
- Format
- Document (PDF)
- Title
- DATA COLLECTION FRAMEWORK AND MACHINE LEARNING ALGORITHMS FOR THE ANALYSIS OF CYBER SECURITY ATTACKS.
- Creator
- Calvert, Chad, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The integrity of network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. Also, many detection methods for popular network attacks have been developed using outdated or non-representative attack data. To effectively develop modern detection methodologies, there exists a need to acquire data that can fully encompass the behaviors...
Show moreThe integrity of network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. Also, many detection methods for popular network attacks have been developed using outdated or non-representative attack data. To effectively develop modern detection methodologies, there exists a need to acquire data that can fully encompass the behaviors of persistent and emerging threats. When collecting modern day network traffic for intrusion detection, substantial amounts of traffic can be collected, much of which consists of relatively few attack instances as compared to normal traffic. This skewed distribution between normal and attack data can lead to high levels of class imbalance. Machine learning techniques can be used to aid in attack detection, but large levels of imbalance between normal (majority) and attack (minority) instances can lead to inaccurate detection results.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013289
- Subject Headings
- Machine learning, Algorithms, Anomaly detection (Computer security), Intrusion detection systems (Computer security), Big data
- Format
- Document (PDF)
- Title
- Derivation and identification of linearly parametrized robot manipulator dynamic models.
- Creator
- Xu, Hua., Florida Atlantic University, Roth, Zvi S., Zilouchian, Ali, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The dissertation focuses on robot manipulator dynamic modeling, and inertial and kinematic parameters identification problem. An automatic dynamic parameters derivation symbolic algorithm is presented. This algorithm provides the linearly independent dynamic parameters set. It is shown that all the dynamic parameters are identifiable when the trajectory is persistently exciting. The parameters set satisfies the necessary condition of finding a persistently exciting trajectory. Since in...
Show moreThe dissertation focuses on robot manipulator dynamic modeling, and inertial and kinematic parameters identification problem. An automatic dynamic parameters derivation symbolic algorithm is presented. This algorithm provides the linearly independent dynamic parameters set. It is shown that all the dynamic parameters are identifiable when the trajectory is persistently exciting. The parameters set satisfies the necessary condition of finding a persistently exciting trajectory. Since in practice the system data matrix is corrupted with noise, conventional estimation methods do not converge to the true values. An error bound is given for Kalman filters. Total least squares method is introduced to obtain unbiased estimates. Simulations studies are presented for five particular identification methods. The simulations are performed under different noise levels. Observability problems for the inertial and kinematic parameters are investigated. U%wer certain conditions all L%wearly Independent Parameters derived from are observable. The inertial and kinematic parameters can be categorized into three parts according to their influences on the system dynamics. The dissertation gives an algorithm to classify these parameters.
Show less - Date Issued
- 1992
- PURL
- http://purl.flvc.org/fcla/dt/12291
- Subject Headings
- Algorithms, Manipulators (Mechanism), Robots--Control systems
- Format
- Document (PDF)
- Title
- Efficient localized broadcast algorithms in mobile ad hoc networks.
- Creator
- Lou, Wei., Florida Atlantic University, Wu, Jie, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The broadcast operation has a most fundamental role in mobile ad hoc networks because of the broadcasting nature of radio transmission, i.e., when a sender transmits a packet, all nodes within the sender's transmission range will be affected by this transmission. The benefit of this property is that one packet can be received by all neighbors while the negative effect is that it interferes with other transmissions. Flooding ensures that the entire network receives the packet but generates...
Show moreThe broadcast operation has a most fundamental role in mobile ad hoc networks because of the broadcasting nature of radio transmission, i.e., when a sender transmits a packet, all nodes within the sender's transmission range will be affected by this transmission. The benefit of this property is that one packet can be received by all neighbors while the negative effect is that it interferes with other transmissions. Flooding ensures that the entire network receives the packet but generates many redundant transmissions which may trigger a serious broadcast storm problem that may collapse the entire network. The broadcast storm problem can be avoided by providing efficient broadcast algorithms that aim to reduce the number of nodes that retransmit the broadcast packet while still guaranteeing that all nodes receive the packet. This dissertation focuses on providing several efficient localized broadcast algorithms to reduce the broadcast redundancy in mobile ad hoc networks. In my dissertation, the efficiency of a broadcast algorithm is measured by the number of forward nodes for relaying a broadcast packet. A classification of broadcast algorithms for mobile ad hoc networks has been provided at the beginning. Two neighbor-designating broadcast algorithms, called total dominant pruning and partial dominant pruning, have been proposed to reduce the number of the forward nodes. Several extensions based on the neighbor-designating approach have also been investigated. The cluster-based broadcast algorithm shows good performance in dense networks, and it also provides a constant upper bound approximation ratio to the optimum solution for the number of forward nodes in the worst case. A generic broadcast framework with K hop neighbor information has a trade-off between the number of the forward nodes and the size of the K-hop zone. A reliable broadcast algorithm, called double-covered broadcast, is proposed to improve the delivery ratio of a broadcast package when the transmission error rate of the network is high. The effectiveness of all these algorithms has been confirmed by simulations.
Show less - Date Issued
- 2004
- PURL
- http://purl.flvc.org/fau/fd/FADT12103
- Subject Headings
- Wireless LANS, Mobile communication systems, Wireless communication systems--Mathematics, Algorithms
- Format
- Document (PDF)
- Title
- Evolution and application of a parallel algorithm for explicit transient finite element analysis on SIMD/MIMD computers.
- Creator
- Das, Partha S., Florida Atlantic University, Case, Robert O., Tsai, Chi-Tay, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
- Abstract/Description
-
The development of a parallel data structure and an associated elemental decomposition algorithm for explicit finite element analysis for massively parallel SIMD computer, the DECmpp 12000 (MasPar MP-1) machine, is presented, and then extended to implementation on the MIMD computer, Cray-T3D. The new parallel data structure and elemental decomposition algorithm are discussed in detail and is used to parallelize a sequential Fortran code that deals with the application of isoparametric...
Show moreThe development of a parallel data structure and an associated elemental decomposition algorithm for explicit finite element analysis for massively parallel SIMD computer, the DECmpp 12000 (MasPar MP-1) machine, is presented, and then extended to implementation on the MIMD computer, Cray-T3D. The new parallel data structure and elemental decomposition algorithm are discussed in detail and is used to parallelize a sequential Fortran code that deals with the application of isoparametric elements for the nonlinear dynamic analysis of shells of revolution. The parallel algorithm required the development of a new procedure, called an 'exchange', which consists of an exchange of nodal forces at each time step to replace the standard gather-assembly operations in sequential code. In addition, the data was reconfigured so that all nodal variables associated with an element are stored in a processor along with other element data. The architectural and Fortran programming language features of the MasPar MP-1 and Cray-T3D computers which are pertinent to finite element computations are also summarized, and sample code segments are provided to illustrate programming in a data parallel environment. The governing equations, the finite element discretization and a comparison between their implementation on Von Neumann and SIMD-MIMD parallel computers are discussed to demonstrate their applicability and the important differences in the new algorithm. Various large scale transient problems are solved using the parallel data structure and elemental decomposition algorithm and measured performances are presented and analyzed in detail. Results show that Cray-T3D is a very promising parallel computer for finite element computation. The 32 processors of this machine shows an overall speedup of 27-28, i.e. an efficiency of 85% or more and 128 processors shows a speedup of 70-77, i.e. an efficiency of 55% or more. The Cray-T3D results demonstrated that this machine is capable of outperforming the Cray-YMP by a factor of about 10 for finite element problems with 4K elements, therefore, the method of developing the parallel data structure and its associated elemental decomposition algorithm is recommended for implementation on other finite element code in this machine. However, the results from MasPar MP-1 show that this new algorithm for explicit finite element computations do not produce very efficient parallel code on this computer and therefore, the new data structure is not recommended for further use on this MasPar machine.
Show less - Date Issued
- 1997
- PURL
- http://purl.flvc.org/fcla/dt/12500
- Subject Headings
- Finite element method, Algorithms, Parallel computers
- Format
- Document (PDF)
- Title
- Evolutionary algorithms for design and control of material handling and manufacturing systems.
- Creator
- Kanwar, Pankaj., Florida Atlantic University, Han, Chingping (Jim), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
- Abstract/Description
-
The crucial goal of enhancing industrial productivity has led researchers to look for robust and efficient solutions to problems in production systems. Evolving technologies has also, led to an immediate demand for algorithms which can exploit these developments. During the last three decades there has been a growing interest in algorithms which rely on analogies to natural processes. The best known algorithms in this class include evolutionary programming, genetic algorithms, evolution...
Show moreThe crucial goal of enhancing industrial productivity has led researchers to look for robust and efficient solutions to problems in production systems. Evolving technologies has also, led to an immediate demand for algorithms which can exploit these developments. During the last three decades there has been a growing interest in algorithms which rely on analogies to natural processes. The best known algorithms in this class include evolutionary programming, genetic algorithms, evolution strategies and neural networks. The emergence of massively parallel systems has made these inherently parallel algorithms of high practical interest. The advantages offered by these algorithms over other classical techniques has resulted in their wide acceptance. These algorithms have been applied for solving a large class of interesting problems, for which no efficient or reasonably fast algorithm exists. This thesis extends their usage to the domain of production research. Problems of high practical interest in the domain of production research are solved using a subclass of these algorithms i.e. those based on the principle of evolution. The problems include: the flowpath design of AGV systems and vehicle routing in a transportation system. Furthermore, a Genetic Based Machine Learning (GBML) system has been developed for optimal scheduling and control of a job shop.
Show less - Date Issued
- 1994
- PURL
- http://purl.flvc.org/fcla/dt/15025
- Subject Headings
- Industrial productivity--Data processing, Algorithms, Genetic algorithms, Motor vehicles--Automatic location systems, Materials handling--Computer simulation, Manufacturing processes--Computer simulation
- Format
- Document (PDF)
- Title
- A general pressure based Navier-Stokes solver in arbitrary configurations.
- Creator
- Ke, Zhao Ping., Florida Atlantic University, Chow, Wen L., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
- Abstract/Description
-
A pressure-based computer program for a general Navier-Stokes equations has been developed. Body-fitted coordinate system is employed to handle flows with complex geometry. Non-staggered grid is used while the pressure oscillation is eliminated by a special pressure interpolation scheme. The hybrid algorithm is adopted to discretize the equations and the finite-difference equations are solved by TDMA, while the whole solution is obtained through an under-relaxed iterative process. The...
Show moreA pressure-based computer program for a general Navier-Stokes equations has been developed. Body-fitted coordinate system is employed to handle flows with complex geometry. Non-staggered grid is used while the pressure oscillation is eliminated by a special pressure interpolation scheme. The hybrid algorithm is adopted to discretize the equations and the finite-difference equations are solved by TDMA, while the whole solution is obtained through an under-relaxed iterative process. The pressure field is evaluated using the compressible from of the SIMPLE algorithm., To test the accuracy and efficiency of the computer program, problems of incompressible and compressible flows are calculated. As examples of inviscid compressible flow problems, flows over a bump with 10% and 4% thickness are computed with the incoming Mach numbers of M[infinity] = 0.5 (subsonic flow), M[infinity] = 0.675 (transonic flow and M[infinity] = 1.65 (supersonic flow). One laminar subsonic flow over a bump with 5% thickness at M[infinity] = 0.5 is also calculated with the consideration of the full energy equation. With the help of the k-epsilon model incorporating the wall function, the computations of two turbulent incompressible flows are carried out. One is the flow past a flat plate and the other over a flame holder. As an application to the three-dimensional flow, a laminar flow in a driven cubic cavity is calculated. All the numerical results obtained here are compared with experimental data or other numerical results available in the literature.
Show less - Date Issued
- 1993
- PURL
- http://purl.flvc.org/fcla/dt/12330
- Subject Headings
- Navier-Stokes equations--Numerical solutions--Data processing, Algorithms, Flows (Differential dynamical systems)
- Format
- Document (PDF)
- Title
- GENERATIVE ADVERSARIAL NETWORK DATA GENERATION FOR THE USE OF REAL TIME IMAGE DETECTION IN SIDE-SCAN SONAR IMAGERY.
- Creator
- McGinley, James Patrick, Dhanak, Manhar, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
- Abstract/Description
-
Automatic target recognition of unexploded ordnances in side scan sonar imagery has been a struggling task, due to the lack of publicly available side-scan sonar data. Real time image detection and classification algorithms have been implemented to combat this task, however, machine learning algorithms require a substantial amount of training data to properly detect specific targets. Transfer learning methods are used to replace the need of large datasets, by using a pre trained network on...
Show moreAutomatic target recognition of unexploded ordnances in side scan sonar imagery has been a struggling task, due to the lack of publicly available side-scan sonar data. Real time image detection and classification algorithms have been implemented to combat this task, however, machine learning algorithms require a substantial amount of training data to properly detect specific targets. Transfer learning methods are used to replace the need of large datasets, by using a pre trained network on the side-scan sonar images. In the present study the implementation of a generative adversarial network is used to generate meaningful sonar imagery from a small dataset. The generated images are then added to the existing dataset to train an image detection and classification algorithm. The study looks to demonstrate that generative images can be used to aid in detecting objects of interest in side-scan sonar imagery.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013394
- Subject Headings
- Sidescan sonar, Algorithms, Machine learning
- Format
- Document (PDF)
- Title
- Information-theoretics based analysis of hard handoffs in mobile communications.
- Creator
- Bendett, Raymond Morris., Florida Atlantic University, Neelakanta, Perambur S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The research proposed and elaborated in this dissertation is concerned with the development of new decision algorithms for hard handoff strategies in mobile communication systems. Specifically, the research tasks envisaged include the following: (1) Use of information-theoretics based statistical distance measures as a metric for hard handoff decisions; (2) A study to evaluate the log-likelihood criterion towards decision considerations to perform the hard handoff; (3) Development of a...
Show moreThe research proposed and elaborated in this dissertation is concerned with the development of new decision algorithms for hard handoff strategies in mobile communication systems. Specifically, the research tasks envisaged include the following: (1) Use of information-theoretics based statistical distance measures as a metric for hard handoff decisions; (2) A study to evaluate the log-likelihood criterion towards decision considerations to perform the hard handoff; (3) Development of a statistical model to evaluate optimum instants of measurements of the metric used for hard handoff decision. The aforesaid objectives refer to a practical scenario in which a mobile station (MS) traveling away from a serving base station (BS-I) may suffer communications impairment due to interference and shadowing affects, especially in an urban environment. As a result, it will seek to switch over to another base station (BS-II) that facilitates a stronger signal level. This is called handoff procedure. (The hard handoff refers to the specific case in which only one base station serves the mobile at the instant of handover). Classically, the handoff decision is done on the basis of the difference between received signal strengths (RSS) from BS-I and BS-II. The algorithms developed here, in contrast, stipulate the decision criterion set by the statistical divergence and/or log-likelihood ratio that exists between the received signals. The purpose of the present study is to evaluate the relative efficacy of the conventional and proposed algorithms in reference to: (i) Minimization of unnecessary handoffs ("ping-pongs"); (ii) Minimization of delay in handing over; (iii) Ease of implementation and (iv) Minimization of possible call dropouts due to ineffective handover envisaged. Simulated results with data commensurate with practical considerations are furnished and discussed. Background literature is presented in the introductory chapter and scope for future work is identified via open questions in the concluding chapter.
Show less - Date Issued
- 2000
- PURL
- http://purl.flvc.org/fcla/dt/12639
- Subject Headings
- Mobile communication systems, Information theory, Algorithms
- Format
- Document (PDF)
- Title
- INVESTIGATING MACHINE LEARNING ALGORITHMS WITH IMBALANCED BIG DATA.
- Creator
- Hasanin, Tawfiq, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Recent technological developments have engendered an expeditious production of big data and also enabled machine learning algorithms to produce high-performance models from such data. Nonetheless, class imbalance (in binary classifications) between the majority and minority classes in big data can skew the predictive performance of the classification algorithms toward the majority (negative) class whereas the minority (positive) class usually holds greater value for the decision makers. Such...
Show moreRecent technological developments have engendered an expeditious production of big data and also enabled machine learning algorithms to produce high-performance models from such data. Nonetheless, class imbalance (in binary classifications) between the majority and minority classes in big data can skew the predictive performance of the classification algorithms toward the majority (negative) class whereas the minority (positive) class usually holds greater value for the decision makers. Such bias may lead to adverse consequences, some of them even life-threatening, when the existence of false negatives is generally costlier than false positives. The size of the minority class can vary from fair to extraordinary small, which can lead to different performance scores for machine learning algorithms. Class imbalance is a well-studied area for traditional data, i.e., not big data. However, there is limited research focusing on both rarity and severe class imbalance in big data.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013316
- Subject Headings
- Algorithms, Machine learning, Big data--Data processing, Big data
- Format
- Document (PDF)
- Title
- MACHINE LEARNING ALGORITHMS FOR THE DETECTION AND ANALYSIS OF WEB ATTACKS.
- Creator
- Zuech, Richard, Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
The Internet has provided humanity with many great benefits, but it has also introduced new risks and dangers. E-commerce and other web portals have become large industries with big data. Criminals and other bad actors constantly seek to exploit these web properties through web attacks. Being able to properly detect these web attacks is a crucial component in the overall cybersecurity landscape. Machine learning is one tool that can assist in detecting web attacks. However, properly using...
Show moreThe Internet has provided humanity with many great benefits, but it has also introduced new risks and dangers. E-commerce and other web portals have become large industries with big data. Criminals and other bad actors constantly seek to exploit these web properties through web attacks. Being able to properly detect these web attacks is a crucial component in the overall cybersecurity landscape. Machine learning is one tool that can assist in detecting web attacks. However, properly using machine learning to detect web attacks does not come without its challenges. Classification algorithms can have difficulty with severe levels of class imbalance. Class imbalance occurs when one class label disproportionately outnumbers another class label. For example, in cybersecurity, it is common for the negative (normal) label to severely outnumber the positive (attack) label. Another difficulty encountered in machine learning is models can be complex, thus making it difficult for even subject matter experts to truly understand a model’s detection process. Moreover, it is important for practitioners to determine which input features to include or exclude in their models for optimal detection performance. This dissertation studies machine learning algorithms in detecting web attacks with big data. Severe class imbalance is a common problem in cybersecurity, and mainstream machine learning research does not sufficiently consider this with web attacks. Our research first investigates the problems associated with severe class imbalance and rarity. Rarity is an extreme form of class imbalance where the positive class suffers extremely low positive class count, thus making it difficult for the classifiers to discriminate. In reducing imbalance, we demonstrate random undersampling can effectively mitigate the class imbalance and rarity problems associated with web attacks. Furthermore, our research introduces a novel feature popularity technique which produces easier to understand models by only including the fewer, most popular features. Feature popularity granted us new insights into the web attack detection process, even though we had already intensely studied it. Even so, we proceed cautiously in selecting the best input features, as we determined that the “most important” Destination Port feature might be contaminated by lopsided traffic distributions.
Show less - Date Issued
- 2021
- PURL
- http://purl.flvc.org/fau/fd/FA00013823
- Subject Headings
- Machine learning, Computer security, Algorithms, Cybersecurity
- Format
- Document (PDF)
- 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
- NEIGHBORING NEAR MINIMUM-TIME CONTROLS WITH DISCONTINUITIES AND THE APPLICATION TO THE CONTROL OF MANIPULATORS (PATH-PLANNING, TRACKING, FEEDBACK).
- Creator
- Zhuang, Hanqi, Florida Atlantic University, Hamano, Fumio, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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This thesis presents several algorithms to treat the problem of closed-loop near minimum-time controls with discontinuities. First, a neighboring control algorithm is developed to solve the problem in which controls are bounded by constant constraints. Secondly, the scheme is extended to account for state-dependent control constraints. And finally, a path tracking algorithm for robotic manipulators is presented, which is also a neighboring control algorithm. These algorithms are suitable for...
Show moreThis thesis presents several algorithms to treat the problem of closed-loop near minimum-time controls with discontinuities. First, a neighboring control algorithm is developed to solve the problem in which controls are bounded by constant constraints. Secondly, the scheme is extended to account for state-dependent control constraints. And finally, a path tracking algorithm for robotic manipulators is presented, which is also a neighboring control algorithm. These algorithms are suitable for real time controls because the on-line computations involved are relatively simple. Simulation results show that these algorithms work well despite the fact that the prescribed final points can not be reached exactly.
Show less - Date Issued
- 1986
- PURL
- http://purl.flvc.org/fcla/dt/14326
- Subject Headings
- Manipulators (Mechanism), Control theory, Algorithms
- Format
- Document (PDF)
- Title
- Optimal coordination of robotic systems with redundancy.
- Creator
- Varma, K. R. Hareendra., Florida Atlantic University, Huang, Ming Z., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
- Abstract/Description
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The research work described in this dissertation is primarily aimed at developing efficient algorithms for the rate allocation problem in redundant serial chain manipulators. While the problem of redundancy resolution in the context of robot manipulators, had been a well researched one, search for optimality in computational efficiency has caught the attention only recently. Further, the idea of modifying the already developed performance criteria to improve computational efficiency, had...
Show moreThe research work described in this dissertation is primarily aimed at developing efficient algorithms for the rate allocation problem in redundant serial chain manipulators. While the problem of redundancy resolution in the context of robot manipulators, had been a well researched one, search for optimality in computational efficiency has caught the attention only recently. Further, the idea of modifying the already developed performance criteria to improve computational efficiency, had rarely been treated with the importance it deserves. The present work in fact, provides many alternative formulations to the existing performance criteria. As a result of the present investigation, we developed a mathematical tool for the minimum norm solution for underdetermined systems of linear equations, using the orthogonal null space. Closed form equations were provided for cases with two or three degrees of redundancy. Detailed study of computational efficiency showed substantial reduction in the arithmetic operations necessary for such a solution. The above concept was later generalized to utilize the self motion characteristics of redundant manipulators, to provide alternate solutions. The duality concept between the Jacobian and the null space, established in this work, enabled the authors to develop a highly efficient formulation as an alternative to the commonly used pseudoinverse-based solution. In addition, by providing the example of a 7R anthropomorphic arm, the feasibility of obtaining analytical formulation of null space coefficient matrix and the transformed end effector velocity vector for any geometry has been demonstrated. By utilizing the duality between the Jacobian and its null space, different performance criteria commonly used in the redundancy resolution problem have been modified, increasing the computational efficiency. Various simulations performed as part of the present work, utilizing the analytical null space coefficient matrix and the transformed end effector velocity vector for 3R planar case and 7R spatial anthropomorphic arm corroborates the theories. Another practical application has been demonstrated by the example of a Titan 7F arm mounted on a mobile base. The work is consolidated by reiterating the insight obtained to the physical aspects of the redundancy resolution problem and providing a direction for future work. Suggestions are given for extending the work for high d.o.r. systems, with relevant mathematical foundations. Future work in the area of dynamic modelling, is delineated which also includes an example of modified dynamic manipulability measure.
Show less - Date Issued
- 1992
- PURL
- http://purl.flvc.org/fcla/dt/12292
- Subject Headings
- Algorithms, Redundancy (Engineering), Robotics, Robots--Motion
- Format
- Document (PDF)
- Title
- PALSAM INPUT DATA FILE GENERATOR.
- Creator
- ROBINSON, WILLIAM ROBERT, JR., Florida Atlantic University, Marcovitz, Alan B., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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The capabilities and limitations of Programmable Array Logic devices (PALs) are presented and compared to other logic devices. PALs are field programmable devices and a program called PALSAM exists to assist the designer in programming PALs. The attributes and limitations of PALSAM are discussed. The PALSAM Input Data File Generator program was written to eliminate many of the limitations of PALSAM. The need for an algorithmic method of reducing a general logic expression to a minimal sum-of...
Show moreThe capabilities and limitations of Programmable Array Logic devices (PALs) are presented and compared to other logic devices. PALs are field programmable devices and a program called PALSAM exists to assist the designer in programming PALs. The attributes and limitations of PALSAM are discussed. The PALSAM Input Data File Generator program was written to eliminate many of the limitations of PALSAM. The need for an algorithmic method of reducing a general logic expression to a minimal sum-of-products form is demonstrated. Several algorithms are discussed. The Zissos, Duncan and Jones Algorithm, which claims to produce a minimal sum-of-products expression but is presented without proof by its authors, is disproved by example. A modification of this algorithm is presented without proof. When tested in the 276 possible cases involving up to three variables, this new algorithm always produced a minimal sum-of-products expression, while the original algorithm failed in six of these cases. Finally, the PALSAM Input Data File Generator program which uses the modified algorithm is presented and documented.
Show less - Date Issued
- 1984
- PURL
- http://purl.flvc.org/fcla/dt/14199
- Subject Headings
- Programmable array logic, Microprocessors--Programming, Algorithms
- Format
- Document (PDF)
- Title
- PATH PLANNING ALGORITHMS FOR UNMANNED AIRCRAFT SYSTEMS WITH A SPACE-TIME GRAPH.
- Creator
- Steinberg, Andrew, Cardei, Mihaela, Cardei, Ionut, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
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Unmanned Aircraft Systems (UAS) have grown in popularity due to their widespread potential applications, including efficient package delivery, monitoring, surveillance, search and rescue operations, agricultural uses, along with many others. As UAS become more integrated into our society and airspace, it is anticipated that the development and maintenance of a path planning collision-free system will become imperative, as the safety and efficiency of the airspace represents a priority. The...
Show moreUnmanned Aircraft Systems (UAS) have grown in popularity due to their widespread potential applications, including efficient package delivery, monitoring, surveillance, search and rescue operations, agricultural uses, along with many others. As UAS become more integrated into our society and airspace, it is anticipated that the development and maintenance of a path planning collision-free system will become imperative, as the safety and efficiency of the airspace represents a priority. The dissertation defines this problem as the UAS Collision-free Path Planning Problem. The overall objective of the dissertation is to design an on-demand, efficient and scalable aerial highway path planning system for UAS. The dissertation explores two solutions to this problem. The first solution proposes a space-time algorithm that searches for shortest paths in a space-time graph. The solution maps the aerial traffic map to a space-time graph that is discretized on the inter-vehicle safety distance. This helps compute safe trajectories by design. The mechanism uses space-time edge pruning to maintain the dynamic availability of edges as vehicles move on a trajectory. Pruning edges is critical to protect active UAS from collisions and safety hazards. The dissertation compares the solution with another related work to evaluate improvements in delay, run time scalability, and admission success while observing up to 9000 flight requests in the network. The second solution to the path planning problem uses a batch planning algorithm. This is a new mechanism that processes a batch of flight requests with prioritization on the current slack time. This approach aims to improve the planning success ratio. The batch planning algorithm is compared with the space-time algorithm to ascertain improvements in admission ratio, delay ratio, and running time, in scenarios with up to 10000 flight requests.
Show less - Date Issued
- 2021
- PURL
- http://purl.flvc.org/fau/fd/FA00013696
- Subject Headings
- Unmanned aerial vehicles, Drone aircraft, Drone aircraft--Automatic control, Space and time, Algorithms
- Format
- Document (PDF)
- Title
- Perceptual methods for video coding.
- Creator
- Adzic, Velibor, Kalva, Hari, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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The main goal of video coding algorithms is to achieve high compression efficiency while maintaining quality of the compressed signal at the highest level. Human visual system is the ultimate receiver of compressed signal and final judge of its quality. This dissertation presents work towards optimal video compression algorithm that is based on the characteristics of our visual system. Modeling phenomena such as backward temporal masking and motion masking we developed algorithms that are...
Show moreThe main goal of video coding algorithms is to achieve high compression efficiency while maintaining quality of the compressed signal at the highest level. Human visual system is the ultimate receiver of compressed signal and final judge of its quality. This dissertation presents work towards optimal video compression algorithm that is based on the characteristics of our visual system. Modeling phenomena such as backward temporal masking and motion masking we developed algorithms that are implemented in the state-of- the-art video encoders. Result of using our algorithms is visually lossless compression with improved efficiency, as verified by standard subjective quality and psychophysical tests. Savings in bitrate compared to the High Efficiency Video Coding / H.265 reference implementation are up to 45%.
Show less - Date Issued
- 2014
- PURL
- http://purl.flvc.org/fau/fd/FA00004074, http://purl.flvc.org/fau/fd/FA00004074
- Subject Headings
- Algorithms, Coding theory, Digital coding -- Data processing, Imaging systems -- Image quality, Perception, Video processing -- Data processing
- Format
- Document (PDF)
- Title
- PIREN(copyright): A heuristic algorithm for standard cell placement.
- Creator
- Horvath, Elizabeth Iren., Florida Atlantic University, Shankar, Ravi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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The placement problem is an important part in the design process of VLSI chips. It is necessary to have a proper placement so that all connections between modules in a chip can be routed in a minimum area without violating any physical or electrical constraints. Current algorithms either do not give optimum solutions, are computationally slow, or are difficult to parallelize. PIREN(copyright) is a parallel implementation of a force directed algorithm which seeks to overcome the large amount...
Show moreThe placement problem is an important part in the design process of VLSI chips. It is necessary to have a proper placement so that all connections between modules in a chip can be routed in a minimum area without violating any physical or electrical constraints. Current algorithms either do not give optimum solutions, are computationally slow, or are difficult to parallelize. PIREN(copyright) is a parallel implementation of a force directed algorithm which seeks to overcome the large amount of computer time associated with solving the placement problem. Each active processor in the massively parallel SIMD machine, the MasPar MP-2.2, can perform in parallel the computation necessary to place cells in an optimum location relative to one another based upon the connectivity between cells. This is due to a salient feature of the serial algorithm which allows multiple permutations to be made simultaneously on all modules in order to minimize the objective function. The serial implementation of PIREN(copyright) compares favorably in both run time and layout quality to the simulated annealing based algorithm, TimberWolf3.2$\sp\copyright$. The parallel implementation on the MP-2.2 has a speedup of 4.5 to 58.0 over the serial version of PIREN$\sp\copyright$ running of the VAX 6320, while producing layouts for several MCNC benchmarks which are of the same quality as those produced by the serial implementation.
Show less - Date Issued
- 1992
- PURL
- http://purl.flvc.org/fcla/dt/12301
- Subject Headings
- Integrated circuits--Very large scale integration, Algorithms
- Format
- Document (PDF)
