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Pages
 Title
 Joint channel and data estimation: genetic algorithm based blind equalization.
 Creator
 Caimi, F. M., Wang, D., Harbor Branch Oceanographic Institute
 Date Issued
 1999
 PURL
 http://purl.flvc.org/FCLA/DT/3183717
 Subject Headings
 Genetic algorithms
 Format
 Document (PDF)
 Title
 Optimization algorithms for intensity modulated radiation treatment.
 Creator
 Doozan, Brian, Leventouri, Theodora, Graduate College
 Date Issued
 20130412
 PURL
 http://purl.flvc.org/fcla/dt/3361926
 Subject Headings
 Radiotherapy, Radiation dosimetry, Algorithms
 Format
 Document (PDF)
 Title
 Algorithms in Elliptic Curve Cryptography.
 Creator
 Hutchinson, Aaron, Karabina, Koray, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
 Abstract/Description

Elliptic curves have played a large role in modern cryptography. Most notably, the Elliptic Curve Digital Signature Algorithm (ECDSA) and the Elliptic Curve Di eHellman (ECDH) key exchange algorithm are widely used in practice today for their e ciency and small key sizes. More recently, the Supersingular Isogenybased Di eHellman (SIDH) algorithm provides a method of exchanging keys which is conjectured to be secure in the postquantum setting. For ECDSA and ECDH, e cient and secure...
Show moreElliptic curves have played a large role in modern cryptography. Most notably, the Elliptic Curve Digital Signature Algorithm (ECDSA) and the Elliptic Curve Di eHellman (ECDH) key exchange algorithm are widely used in practice today for their e ciency and small key sizes. More recently, the Supersingular Isogenybased Di eHellman (SIDH) algorithm provides a method of exchanging keys which is conjectured to be secure in the postquantum setting. For ECDSA and ECDH, e cient and secure algorithms for scalar multiplication of points are necessary for modern use of these protocols. Likewise, in SIDH it is necessary to be able to compute an isogeny from a given nite subgroup of an elliptic curve in a fast and secure fashion. We therefore nd strong motivation to study and improve the algorithms used in elliptic curve cryptography, and to develop new algorithms to be deployed within these protocols. In this thesis we design and develop dMUL, a multidimensional scalar multiplication algorithm which is uniform in its operations and generalizes the well known 1dimensional Montgomery ladder addition chain and the 2dimensional addition chain due to Dan J. Bernstein. We analyze the construction and derive many optimizations, implement the algorithm in software, and prove many theoretical and practical results. In the nal chapter of the thesis we analyze the operations carried out in the construction of an isogeny from a given subgroup, as performed in SIDH. We detail how to e ciently make use of parallel processing when constructing this isogeny.
Show less  Date Issued
 2018
 PURL
 http://purl.flvc.org/fau/fd/FA00013113
 Subject Headings
 Curves, Elliptic, Cryptography, Algorithms
 Format
 Document (PDF)
 Title
 Deterministic and nondeterministic basis reduction techniques for NTRU lattices.
 Creator
 Socek, Daniel, Florida Atlantic University, Magliveras, Spyros S.
 Abstract/Description

Finding the shortest or a "short enough" vector in an integral lattice of substantial dimension is a difficult problem. The problem is not known to be but most people believe it is [7]. The security of the newly proposed NTRU cryptosystem depends solely on this fact. However, by the definition NTRU lattices possess a certain symmetry. This suggests that there may be a way of taking advantage of this symmetry to enable a new cryptanalytical approach in combination with existing good lattice...
Show moreFinding the shortest or a "short enough" vector in an integral lattice of substantial dimension is a difficult problem. The problem is not known to be but most people believe it is [7]. The security of the newly proposed NTRU cryptosystem depends solely on this fact. However, by the definition NTRU lattices possess a certain symmetry. This suggests that there may be a way of taking advantage of this symmetry to enable a new cryptanalytical approach in combination with existing good lattice reduction algorithms. The aim of this work is to exploit the symmetry inherent in NTRU lattices to design a nondeterministic algorithm for improving basis reduction techniques for NTRU lattices. We show how the nontrivial cyclic automorphism of an NTRU lattice enables further reduction. Our approach combines the recently published versions of the famous LLL algorithm for lattice basis reduction with our automorphism utilization techniques.
Show less  Date Issued
 2002
 PURL
 http://purl.flvc.org/fcla/dt/12933
 Subject Headings
 Cryptography, Lattice theory, Algorithms
 Format
 Document (PDF)
 Title
 Performance analysis of the genetic algorithm and its applications.
 Creator
 Liu, Xinggang., Florida Atlantic University, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
 Abstract/Description

Research and applications of genetic algorithms have become increasingly important in a wide variety of scientific fields. In this thesis, we present an empirical analysis of genetic algorithms in the function optimization area. As a focus of our research, a novel empirical analysis approach to various genetic algorithms is provided. The research starts from the survey of current trends in genetic algorithms, followed by exploring the characteristics of the simple genetic algorithm, the...
Show moreResearch and applications of genetic algorithms have become increasingly important in a wide variety of scientific fields. In this thesis, we present an empirical analysis of genetic algorithms in the function optimization area. As a focus of our research, a novel empirical analysis approach to various genetic algorithms is provided. The research starts from the survey of current trends in genetic algorithms, followed by exploring the characteristics of the simple genetic algorithm, the modified genetic algorithm and hybridized genetic algorithm. A number of typical function optimization problems are solved by these genetic algorithms. Ample empirical data associated with various modifications to the simple genetic algorithm is also provided. Results from this research can be used to assist practitioners in their applications of genetic algorithms.
Show less  Date Issued
 1995
 PURL
 http://purl.flvc.org/fcla/dt/15210
 Subject Headings
 Genetic algorithms, Combinatorial optimization
 Format
 Document (PDF)
 Title
 THE MINIMUM KCENTER PROBLEM FOR GRID GRAPH.
 Creator
 HSUEH, CHIFU, Florida Atlantic University, Hadlock, Frank O., Hoffman, Frederick, Charles E. Schmidt College of Science, Department of Mathematical Sciences
 Abstract/Description

A study was made of the problem of locating M facilities on a connected grid graph, so that M is the minimum and so that every demand node on the graph is within given distance K of one of these M facilities. We call this problem briefly the G(N,K,M) problem, with N denoting the total number of demand nodes. An algorithm for solving this problem by using backtrack technique is presented in this thesis. A heuristic algorithm is also present; although the resulting M is not always minimum, it...
Show moreA study was made of the problem of locating M facilities on a connected grid graph, so that M is the minimum and so that every demand node on the graph is within given distance K of one of these M facilities. We call this problem briefly the G(N,K,M) problem, with N denoting the total number of demand nodes. An algorithm for solving this problem by using backtrack technique is presented in this thesis. A heuristic algorithm is also present; although the resulting M is not always minimum, it tends to be near minimum. The advantage over the backtrack algorithm is that the heuristic algorithm operates very quickly. Algorithms represented in this thesis are programmed in the Pascal language for the Univac 1100 computer at Florida Atlantic University, Boca Raton, Florida.
Show less  Date Issued
 1981
 PURL
 http://purl.flvc.org/fcla/dt/14077
 Subject Headings
 Graph theory, Algorithms
 Format
 Document (PDF)
 Title
 DECENTRALIZED SYSTEMS FOR INFORMATION SHARING IN DYNAMIC ENVIRONMENT USING LOCALIZED CONSENSUS.
 Creator
 Zamir, Linir, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
 Abstract/Description

Achieving a consensus among a large number of nodes has always been a challenge for any decentralized system. Consensus algorithms are the building blocks for any decentralized network that is susceptible to malicious activities from authorized and unauthorized nodes. ProofofWork is one of the first modern approaches to achieve at least a 51% consensus, and ever since many new consensus algorithms have been introduced with different approaches of consensus achievement. These decentralized...
Show moreAchieving a consensus among a large number of nodes has always been a challenge for any decentralized system. Consensus algorithms are the building blocks for any decentralized network that is susceptible to malicious activities from authorized and unauthorized nodes. ProofofWork is one of the first modern approaches to achieve at least a 51% consensus, and ever since many new consensus algorithms have been introduced with different approaches of consensus achievement. These decentralized systems, also called blockchain systems, have been implemented in many applications such as supply chains, medical industry, and authentication. However, it is mostly used as a cryptocurrency foundation for token exchange. For these systems to operate properly, they are required to be robust, scalable, and secure. This dissertation provides a different approach of using consensus algorithms for allowing information sharing among nodes in a secured fashion while maintaining the security and immutability of the consensus algorithm. The consensus algorithm proposed in this dissertation utilizes a trust parameter to enforce cooperation, i.e., a trust value is assigned to each node and it is monitored to prevent malicious activities over time. This dissertation also proposes a new solution, named localized consensus, as a method that allows nodes in small groups to achieve consensus on information that is only relevant to that small group of nodes, thus reducing the bandwidth of the system. The proposed models can be practical solutions for immense and highly dynamic environments with validation through trust and reputation values. Application for such localized consensus can be communication among autonomous vehicles where traffic data is relevant to only a small group of vehicles and not the entirety of the system.
Show less  Date Issued
 2022
 PURL
 http://purl.flvc.org/fau/fd/FA00014028
 Subject Headings
 Blockchain, Consensus algorithms
 Format
 Document (PDF)
 Title
 An Algorithm to Determine IMRT Optimization Parameters within the Elekta Monaco® Treatment Planning System that Increases Dose Homogeneity and Dose Conformity in the Planning Target Volume.
 Creator
 Gregorisch, David, Pella, Silvia, Kyriacou, Andreas, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
 Abstract/Description

An algorithm to determine IMRT optimization parameters within the Elekta Monaco® treatment planning system that increases dose homogeneity and dose conformity in the planning target volume was developed. This algorithm determines IMRT optimization parameters by calculating the difference between two pairs of dose points along the target volume’s dose volume histogram: Dmax – Dmin, and D2 – D98. The algorithm was tested on the Elekta Monaco® Treatment Planning System at GenesisCare of Coconut...
Show moreAn algorithm to determine IMRT optimization parameters within the Elekta Monaco® treatment planning system that increases dose homogeneity and dose conformity in the planning target volume was developed. This algorithm determines IMRT optimization parameters by calculating the difference between two pairs of dose points along the target volume’s dose volume histogram: Dmax – Dmin, and D2 – D98. The algorithm was tested on the Elekta Monaco® Treatment Planning System at GenesisCare of Coconut Creek, Florida using CT data from 10 anonymized patients with nonsmall cell lung cancer of various tumor sizes and locations. Nine iterations of parameters were tested on each patient. Once the ideal parameters were found, the results were evaluated using the ICRU report 83 homogeneity index as well as the Paddick conformity index. As an outcome of this research, it is recommended that at least three iterations of IMRT optimization parameters should be calculated to find the ideal parameters.
Show less  Date Issued
 2022
 PURL
 http://purl.flvc.org/fau/fd/FA00013990
 Subject Headings
 Radiotherapy, Algorithm, Medical physics
 Format
 Document (PDF)
 Title
 An algorithm for the integrated scheduling problem of a container handling system within a container terminal.
 Creator
 Zhao, Yueqiong, Kaisar, Evangelos I., Graduate College
 Date Issued
 20110408
 PURL
 http://purl.flvc.org/fcla/dt/3164731
 Subject Headings
 Algorithms, Cargo handling, Container terminals
 Format
 Document (PDF)
 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 kNearest 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
 GENERATIVE ADVERSARIAL NETWORK DATA GENERATION FOR THE USE OF REAL TIME IMAGE DETECTION IN SIDESCAN 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 sidescan 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 sidescan 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 sidescan 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 sidescan 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
 Bijections for partition identities.
 Creator
 Lai, JinMei Jeng, Florida Atlantic University, Meyerowitz, Aaron, Charles E. Schmidt College of Science, Department of Mathematical Sciences
 Abstract/Description

This paper surveys work of the last few years on construction of bijections for partition identities. We use the more general setting of sieveequivalent families. Suppose A1' ... ,An are subsets of a finite set A and B1' ... ,Bn are subsets of a finite set B. Define AS=∩(i∈S) Ai and BS = ∩ (i∈S) Bi for all S⊆N={1,...,n}. Given explicit bijections fS: AS>BS for each S⊆N, A∪Ai has the same size as B∪Bi. Several authors have given algorithms for producing an explicit bijection between these...
Show moreThis paper surveys work of the last few years on construction of bijections for partition identities. We use the more general setting of sieveequivalent families. Suppose A1' ... ,An are subsets of a finite set A and B1' ... ,Bn are subsets of a finite set B. Define AS=∩(i∈S) Ai and BS = ∩ (i∈S) Bi for all S⊆N={1,...,n}. Given explicit bijections fS: AS>BS for each S⊆N, A∪Ai has the same size as B∪Bi. Several authors have given algorithms for producing an explicit bijection between these two sets. In certain important cases they give the same result. We discuss and compare algorithms, use Graph Theory to illustrate them, and provide PAS CAL programs for them.
Show less  Date Issued
 1992
 PURL
 http://purl.flvc.org/fau/fd/FADT14826
 Subject Headings
 Algorithms, Partitions (Mathematics), Sieves (Mathematics)
 Format
 Document (PDF)
 Title
 NEIGHBORING NEAR MINIMUMTIME CONTROLS WITH DISCONTINUITIES AND THE APPLICATION TO THE CONTROL OF MANIPULATORS (PATHPLANNING, 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

This thesis presents several algorithms to treat the problem of closedloop near minimumtime 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 statedependent 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 closedloop near minimumtime 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 statedependent 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 online 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
 Parallel algorithms for domain load balance.
 Creator
 Huang, Hao., Florida Atlantic University, Wu, Jie
 Abstract/Description

To improve the performance of parallel/distributed systems, we propose four parallel load balance algorithms. The new partition algorithm achieves load balance among processors via domain partition. If we assume the problem domain is evenly load distributed, this algorithm will divide the whole domain into a required number of subdomains with the same area. If a problem domain has a dynamic load distribution, although the new partition algorithm is still suitable for the initial mapping, we...
Show moreTo improve the performance of parallel/distributed systems, we propose four parallel load balance algorithms. The new partition algorithm achieves load balance among processors via domain partition. If we assume the problem domain is evenly load distributed, this algorithm will divide the whole domain into a required number of subdomains with the same area. If a problem domain has a dynamic load distribution, although the new partition algorithm is still suitable for the initial mapping, we propose three dynamic load balance algorithms. These dynamic algorithms achieve load balance among processors by transferring load among processors. We applied the new partition algorithm to a specific domain and compared the method to some existing partition algorithms. We also simulated three dynamic load balance algorithms. Results of comparisons and simulations show that all the four algorithms have satisfactory performance.
Show less  Date Issued
 1997
 PURL
 http://purl.flvc.org/fcla/dt/15478
 Subject Headings
 Algorithms, Parallel processing (Electronic computers)
 Format
 Document (PDF)
 Title
 Comparison and development of compression algorithms for AUV telemetry: Recent advancements.
 Creator
 Caimi, F. M., Kocak, D. M., Ritter, G. X., Schmalz, Mark S., Harbor Branch Oceanographic Institute
 Date Issued
 1998
 PURL
 http://purl.flvc.org/FCLA/DT/3338524
 Subject Headings
 Algorithms, Telemetry, Image Quality, Image compression
 Format
 Document (PDF)
 Title
 CONTRIBUTIONS TO QUANTUMSAFE CRYPTOGRAPHY: HYBRID ENCRYPTION AND REDUCING THE T GATE COST OF AES.
 Creator
 Pham, Hai, Steinwandt, Rainer, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
 Abstract/Description

Quantum cryptography offers a wonderful source for current and future research. The idea started in the early 1970s, and it continues to inspire work and development toward a popular goal, largescale communication networks with strong security guarantees, based on quantummechanical properties. Quantum cryptography builds on the idea of exploiting physical properties to establish secure cryptographic operations. A particular quantumbased protocol has gathered interest in recent years for...
Show moreQuantum cryptography offers a wonderful source for current and future research. The idea started in the early 1970s, and it continues to inspire work and development toward a popular goal, largescale communication networks with strong security guarantees, based on quantummechanical properties. Quantum cryptography builds on the idea of exploiting physical properties to establish secure cryptographic operations. A particular quantumbased protocol has gathered interest in recent years for its use of mesoscopic coherent states. The AlphaEta protocol has been designed to exploit properties of coherent states of light to transmit data securely over an optical channel. AlphaEta aims to draw security from the uncertainty of any measurement of the transmitted coherent states due to intrinsic quantum noise. We propose a framework to combine this protocol with classical preprocessing, taking into account errorcorrection for the optical channel and establishing a strong provable security guarantee. Integrating a stateoftheart solution for fast authenticated encryption is straightforward, but in this case the security analysis requires heuristic reasoning.
Show less  Date Issued
 2019
 PURL
 http://purl.flvc.org/fau/fd/FA00013339
 Subject Headings
 Cryptography, Quantum computing, Algorithms, Mesoscopic coherent states
 Format
 Document (PDF)
 Title
 DEVELOPMENT OF AN ALGORITHM TO GUIDE A MULTIPOLE DIAGNOSTIC CATHETER FOR IDENTIFYING THE LOCATION OF ATRIAL FIBRILLATION SOURCES.
 Creator
 Ganesan, Prasanth, Ghoraani, Behnaz, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
 Abstract/Description

Atrial Fibrillation (AF) is a debilitating heart rhythm disorder affecting over 2.7 million people in the US and over 30 million people worldwide annually. It has a high correlation with causing a stroke and several other risk factors, resulting in increased mortality and morbidity rate. Currently, the nonpharmocological therapy followed to control AF is catheter ablation, in which the tissue surrounding the pulmonary veins (PVs) is cauterized (called the PV isolation  PVI procedure) aims...
Show moreAtrial Fibrillation (AF) is a debilitating heart rhythm disorder affecting over 2.7 million people in the US and over 30 million people worldwide annually. It has a high correlation with causing a stroke and several other risk factors, resulting in increased mortality and morbidity rate. Currently, the nonpharmocological therapy followed to control AF is catheter ablation, in which the tissue surrounding the pulmonary veins (PVs) is cauterized (called the PV isolation  PVI procedure) aims to block the ectopic triggers originating from the PVs from entering the atrium. However, the success rate of PVI with or without other anatomybased lesions is only 50%60%. A major reason for the suboptimal success rate is the failure to eliminate patientspecific nonPV sources present in the left atrium (LA), namely reentry source (a.k.a. rotor source) and focal source (a.k.a. point source). It has been shown from several animal and human studies that locating and ablating these sources significantly improves the longterm success rate of the ablation procedure. However, current technologies to locate these sources posses limitations with resolution, additional/special hardware requirements, etc. In this dissertation, the goal is to develop an efficient algorithm to locate AF reentry and focal sources using electrograms recorded from a conventionally used highresolution multipole diagnostic catheter.
Show less  Date Issued
 2019
 PURL
 http://purl.flvc.org/fau/fd/FA00013310
 Subject Headings
 Atrial Fibrillationdiagnosis, Algorithm, Catheter ablation
 Format
 Document (PDF)
 Title
 Generalized Feature Embedding Learning for Clustering and Classication.
 Creator
 Golinko, Eric David, Zhu, Xingquan, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
 Abstract/Description

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

An adversary armed with a quantum computer has algorithms[66, 33, 34] at their disposal, which are capable of breaking our current methods of encryption. Even with the birth of postquantum cryptography[52, 62, 61], some of best cryptanalytic algorithms are still quantum [45, 8]. This thesis contains several experiments on the efficacy of lattice reduction algorithms, BKZ and LLL. In particular, the difficulty of solving Learning With Errors is assessed by reducing the problem to an instance...
Show moreAn adversary armed with a quantum computer has algorithms[66, 33, 34] at their disposal, which are capable of breaking our current methods of encryption. Even with the birth of postquantum cryptography[52, 62, 61], some of best cryptanalytic algorithms are still quantum [45, 8]. This thesis contains several experiments on the efficacy of lattice reduction algorithms, BKZ and LLL. In particular, the difficulty of solving Learning With Errors is assessed by reducing the problem to an instance of the Unique Shortest Vector Problem. The results are used to predict the behavior these algorithms may have on actual cryptographic schemes with security based on hard lattice problems. Lattice reduction algorithms require several floatingpoint operations including multiplication. In this thesis, I consider the resource requirements of a quantum circuit designed to simulate floatingpoint multiplication with high precision.
Show less  Date Issued
 2020
 PURL
 http://purl.flvc.org/fau/fd/FA00013543
 Subject Headings
 Cryptanalysis, Cryptography, Algorithms, Lattices, Quantum computing
 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 realworld 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 realworld 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 wellstudied area in machine learning. The effects of class imbalance with big data in the realworld 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)