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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
- 2013-04-12
- 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 e-Hellman (ECDH) key exchange algorithm are widely used in practice today for their e ciency and small key sizes. More recently, the Supersingular Isogeny-based Di e-Hellman (SIDH) algorithm provides a method of exchanging keys which is conjectured to be secure in the post-quantum 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 e-Hellman (ECDH) key exchange algorithm are widely used in practice today for their e ciency and small key sizes. More recently, the Supersingular Isogeny-based Di e-Hellman (SIDH) algorithm provides a method of exchanging keys which is conjectured to be secure in the post-quantum 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 d-MUL, a multidimensional scalar multiplication algorithm which is uniform in its operations and generalizes the well known 1-dimensional Montgomery ladder addition chain and the 2-dimensional 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 non-deterministic 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 non-deterministic algorithm for improving basis reduction techniques for NTRU lattices. We show how the non-trivial 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 K-CENTER PROBLEM FOR GRID GRAPH.
- Creator
- HSUEH, CHI-FU, 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. Proof-of-Work 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. Proof-of-Work 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 non-small 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
- 2011-04-08
- 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 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
- 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
- Bijections for partition identities.
- Creator
- Lai, Jin-Mei 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 sieve--equivalent 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 sieve--equivalent 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 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
-
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
- 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 QUANTUM-SAFE 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, large-scale communication networks with strong security guarantees, based on quantum-mechanical properties. Quantum cryptography builds on the idea of exploiting physical properties to establish secure cryptographic operations. A particular quantum-based 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, large-scale communication networks with strong security guarantees, based on quantum-mechanical properties. Quantum cryptography builds on the idea of exploiting physical properties to establish secure cryptographic operations. A particular quantum-based 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 error-correction for the optical channel and establishing a strong provable security guarantee. Integrating a state-of-the-art 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 MULTI-POLE 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 non-pharmocological 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 non-pharmocological 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 anatomy-based lesions is only 50%-60%. A major reason for the suboptimal success rate is the failure to eliminate patientspecific non-PV 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 long-term 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 high-resolution multi-pole diagnostic catheter.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013310
- Subject Headings
- Atrial Fibrillation--diagnosis, 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 pre-processing. 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 one-hot 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 one-hot 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 side-to-side 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
- Eigenvectors--Data processing., Algorithms., Cluster analysis.
- Format
- Document (PDF)
- Title
- ALGORITHMS IN LATTICE-BASED 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 post-quantum 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 post-quantum 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 floating-point operations including multiplication. In this thesis, I consider the resource requirements of a quantum circuit designed to simulate floating-point 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
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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)
