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Title: EVALUATING ENVIRONMENTAL VARIABLES THAT INFLUENCE POND DISSOLVED OXYGEN TO INFORM PREDICTION MODEL DEVELOPMENT.
Creator: Weber, Ethan W., Wills, Paul S., Florida Atlantic University, Department of Marine Science and Oceanography, Charles E. Schmidt College of Science
Abstract/Description: Pond aquaculture accounts 65% of global finfish production. A major factor limiting pond aquaculture productivity is fluctuating oxygen levels, which are heavily influenced by atmospheric conditions and primary productivity. Being able to predict DO concentrations by measuring environmental parameters would be beneficial to improving the industry’s efficiencies. The data collected included pond DO, water temperature, air temperature, atmospheric pressure, wind speed/direction, solar...
Show morePond aquaculture accounts 65% of global finfish production. A major factor limiting pond aquaculture productivity is fluctuating oxygen levels, which are heavily influenced by atmospheric conditions and primary productivity. Being able to predict DO concentrations by measuring environmental parameters would be beneficial to improving the industry’s efficiencies. The data collected included pond DO, water temperature, air temperature, atmospheric pressure, wind speed/direction, solar irradiance, rainfall, pond Chl-a concentrations as well as water color images. Pearson’s correlations and stepwise regressions were used to determine the variables’ connection to DO and their potential usefulness for a prediction model. It was determined that sunlight levels play a crucial role in DO fluctuations and crashes because of its influence on pond heating, primary productivity, and pond stratification. It was also found that image data did have correlations to certain weather variables and helped improve prediction strength.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014012
Subject Headings: Pond aquaculture, Water--Dissolved oxygen, Algorithms
Format: Document (PDF)

Title: Efficient Machine Learning Algorithms for Identifying Risk Factors of Prostate and Breast Cancers among Males and Females.
Creator: Rikhtehgaran, Samaneh, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description: One of the most common types of cancer among women is breast cancer. It represents one of the diseases leading to a high number of mortalities among women. On the other hand, prostate cancer is the second most frequent malignancy in men worldwide. The early detection of prostate cancer is fundamental to reduce mortality and increase the survival rate. A comparison between six types of machine learning models as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, k Nearest...
Show moreOne of the most common types of cancer among women is breast cancer. It represents one of the diseases leading to a high number of mortalities among women. On the other hand, prostate cancer is the second most frequent malignancy in men worldwide. The early detection of prostate cancer is fundamental to reduce mortality and increase the survival rate. A comparison between six types of machine learning models as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, k Nearest Neighbors, and Naïve Bayes has been performed. This research aims to identify the most efficient machine learning algorithms for identifying the most significant risk factors of prostate and breast cancers. For this reason, National Health Interview Survey (NHIS) and Prostate, Lung, Colorectal, and Ovarian (PLCO) datasets are used. A comprehensive comparison of risk factors leading to these two crucial cancers can significantly impact early detection and progressive improvement in survival.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013755
Subject Headings: Machine learning, Algorithms, Cancer--Risk factors, Breast--Cancer, Prostate--Cancer
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: 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.
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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: 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.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013823
Subject Headings: Machine learning, Computer security, Algorithms, Cybersecurity
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.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013848
Subject Headings: Machine learning, Algorithms, Denial of service attacks
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.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013543
Subject Headings: Cryptanalysis, Cryptography, Algorithms, Lattices, Quantum computing
Format: Document (PDF)

Title: SUSTAINING CHAOS USING DEEP REINFORCEMENT LEARNING.
Creator: Vashishtha, Sumit, Verma, Siddhartha, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Numerous examples arise in fields ranging from mechanics to biology where disappearance of Chaos can be detrimental. Preventing such transient nature of chaos has been proven to be quite challenging. The utility of Reinforcement Learning (RL), which is a specific class of machine learning techniques, in discovering effective control mechanisms in this regard is shown. The autonomous control algorithm is able to prevent the disappearance of chaos in the Lorenz system exhibiting meta-stable...
Show moreNumerous examples arise in fields ranging from mechanics to biology where disappearance of Chaos can be detrimental. Preventing such transient nature of chaos has been proven to be quite challenging. The utility of Reinforcement Learning (RL), which is a specific class of machine learning techniques, in discovering effective control mechanisms in this regard is shown. The autonomous control algorithm is able to prevent the disappearance of chaos in the Lorenz system exhibiting meta-stable chaos, without requiring any a-priori knowledge about the underlying dynamics. The autonomous decisions taken by the RL algorithm are analyzed to understand how the system’s dynamics are impacted. Learning from this analysis, a simple control-law capable of restoring chaotic behavior is formulated. The reverse-engineering approach adopted in this work underlines the immense potential of the techniques used here to discover effective control strategies in complex dynamical systems. The autonomous nature of the learning algorithm makes it applicable to a diverse variety of non-linear systems, and highlights the potential of RLenabled control for regulating other transient-chaos like catastrophic events.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013498
Subject Headings: Machine learning--Technique, Reinforcement learning, Algorithms, Chaotic behavior in systems, Nonlinear systems
Format: Document (PDF)

Title: An Algorithmic Approach to Tran Van Trung's Basic Recursive Construction of t-Designs.
Creator: Lopez, Oscar A., Magliveras, Spyros S., Florida Atlantic University, Charles E. Schmidt College of Science, Department of Mathematical Sciences
Abstract/Description: It was not until the 20th century that combinatorial design theory was studied as a formal subject. This field has many applications, for example in statistical experimental design, coding theory, authentication codes, and cryptography. Major approaches to the problem of discovering new t-designs rely on (i) the construction of large sets of t designs, (ii) using prescribed automorphism groups, (iii) recursive construction methods. In 2017 and 2018, Tran Van Trung introduced new recursive...
Show moreIt was not until the 20th century that combinatorial design theory was studied as a formal subject. This field has many applications, for example in statistical experimental design, coding theory, authentication codes, and cryptography. Major approaches to the problem of discovering new t-designs rely on (i) the construction of large sets of t designs, (ii) using prescribed automorphism groups, (iii) recursive construction methods. In 2017 and 2018, Tran Van Trung introduced new recursive techniques to construct t – (v, k, λ) designs. These methods are of purely combinatorial nature and require using "ingredient" t-designs or resolutions whose parameters satisfy a system of non-linear equations. Even after restricting the range of parameters in this new method, the task is computationally intractable. In this work, we enhance Tran Van Trung's "Basic Construction" by a robust and efficient hybrid computational apparatus which enables us to construct hundreds of thousands of new t – (v, k, Λ) designs from previously known ingredient designs. Towards the end of the dissertation we also create a new family of 2-resolutions, which will be infinite if there are infinitely many Sophie Germain primes.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013233
Subject Headings: Combinatorial designs and configurations, Algorithms, t-designs
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.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013339
Subject Headings: Cryptography, Quantum computing, Algorithms, Mesoscopic coherent states
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.
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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: 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.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013394
Subject Headings: Sidescan sonar, Algorithms, Machine learning
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.
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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: 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.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013042
Subject Headings: Machine learning, Medicare fraud, 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.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013113
Subject Headings: Curves, Elliptic, Cryptography, Algorithms
Format: Document (PDF)

Title: Dosimetry comparison between treatment plans computed with Finite size pencil beam algorithm and Monte Carlo algorithm using InCise™ Multileaf collimator equipped CyberKnife® System.
Creator: Galpayage Dona, Kalpani Nisansala Udeni, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description: Since the release of the Cyberknife Multileaf Collimator (CK-MLC), it has been a constant concern on the realistic dose differences computed with its early-available Finite Size Pencil Beam algorithm (FSPB) from those computed by using industry well-accepted algorithms such as the Monte Carlo (MC) dose algorithm. In this study dose disparities between FSPB and MC dose calculation algorithms for selected CK-MLC treatment plans were quantified. The dosimetry for planning target volume (PTV) and...
Show moreSince the release of the Cyberknife Multileaf Collimator (CK-MLC), it has been a constant concern on the realistic dose differences computed with its early-available Finite Size Pencil Beam algorithm (FSPB) from those computed by using industry well-accepted algorithms such as the Monte Carlo (MC) dose algorithm. In this study dose disparities between FSPB and MC dose calculation algorithms for selected CK-MLC treatment plans were quantified. The dosimetry for planning target volume (PTV) and major organs at risks (OAR) was compared by calculating normalized percentage deviations (Ndev) between the two algorithms. It is found that the FSPB algorithm overestimates D95 of PTV when compared with the MC algorithm by averaging 24.0% in detached lung cases, and 15.0% in non-detached lung cases which is attributed to the absence of heterogeneity correction in the FSPB algorithm. Average dose differences are 0.3% in intracranial and 0.9% in pancreas cases. Ndev for the D95 of PTV range from 8.8% to 14.1% for the CK-MLC lung treatment plans with small field (SF ≤ 2x2cm2). Ndev is ranged from 0.5-7.0% for OARs.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013123
Subject Headings: Radiosurgery, Radiation dosimetry, Monte Carlo method, Algorithms, Lung Neoplasms--radiotherapy
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.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013108
Subject Headings: Medicare fraud, Big data, Machine learning, 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: 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%.
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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: 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.
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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: Statistical physics based heuristic clustering algorithms with an application to econophysics.
Creator: Baldwin, Lucia Liliana, Florida Atlantic University, Wille, Luc T.
Abstract/Description: Three new approaches to the clustering of data sets are presented. They are heuristic methods and represent forms of unsupervised (non-parametric) clustering. Applied to an unknown set of data these methods automatically determine the number of clusters and their location using no a priori assumptions. All are based on analogies with different physical phenomena. The first technique, named the Percolation Clustering Algorithm, embodies a novel variation on the nearest-neighbor algorithm...
Show moreThree new approaches to the clustering of data sets are presented. They are heuristic methods and represent forms of unsupervised (non-parametric) clustering. Applied to an unknown set of data these methods automatically determine the number of clusters and their location using no a priori assumptions. All are based on analogies with different physical phenomena. The first technique, named the Percolation Clustering Algorithm, embodies a novel variation on the nearest-neighbor algorithm focusing on the connectivity between sample points. Exploiting the equivalence with a percolation process, this algorithm considers data points to be surrounded by expanding hyperspheres, which bond when they touch each other. Once a sequence of joined spheres spans an entire cluster, percolation occurs and the cluster size remains constant until it merges with a neighboring cluster. The second procedure, named Nucleation and Growth Clustering, exploits the analogy with nucleation and growth which occurs in island formation during epitaxial growth of solids. The original data points are nucleation centers, around which aggregation will occur. Additional "ad-data" that are introduced into the sample space, interact with the data points and stick if located within a threshold distance. These "ad-data" are used as a tool to facilitate the detection of clusters. The third method, named Discrete Deposition Clustering Algorithm, constrains deposition to occur on a grid, which has the advantage of computational efficiency as opposed to the continuous deposition used in the previous method. The original data form the vertexes of a sparse graph and the deposition sites are defined to be the middle points of this graphs edges. Ad-data are introduced on the deposition site and the system is allowed to evolve in a self-organizing regime. This allows the simulation of a phase transition and by monitoring the specific heat capacity of the system one can mark out a "natural" criterion for validating the partition. All of these techniques are competitive with existing algorithms and offer possible advantages for certain types of data distributions. A practical application is presented using the Percolation Clustering Algorithm to determine the taxonomy of the Dow Jones Industrial Average portfolio. The statistical properties of the correlation coefficients between DJIA components are studied along with the eigenvalues of the correlation matrix between the DJIA components.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/12032
Subject Headings: Cluster analysis, Statistical physics, Percolation (Statistical physics), 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.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12933
Subject Headings: Cryptography, Lattice theory, Algorithms
Format: Document (PDF)

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