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Title: Rough Set-Based Software Quality Models and Quality of Data.
Creator: Bullard, Lofton A., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In this dissertation we address two significant issues of concern. These are software quality modeling and data quality assessment. Software quality can be measured by software reliability. Reliability is often measured in terms of the time between system failures. A failure is caused by a fault which is a defect in the executable software product. The time between system failures depends both on the presence and the usage pattern of the software. Finding faulty components in the development...
Show moreIn this dissertation we address two significant issues of concern. These are software quality modeling and data quality assessment. Software quality can be measured by software reliability. Reliability is often measured in terms of the time between system failures. A failure is caused by a fault which is a defect in the executable software product. The time between system failures depends both on the presence and the usage pattern of the software. Finding faulty components in the development cycle of a software system can lead to a more reliable final system and will reduce development and maintenance costs. The issue of software quality is investigated by proposing a new approach, rule-based classification model (RBCM) that uses rough set theory to generate decision rules to predict software quality. The new model minimizes over-fitting by balancing the Type I and Type II niisclassiflcation error rates. We also propose a model selection technique for rule-based models called rulebased model selection (RBMS). The proposed rule-based model selection technique utilizes the complete and partial matching rule sets of candidate RBCMs to determine the model with the least amount of over-fitting. In the experiments that were performed, the RBCMs were effective at identifying faulty software modules, and the RBMS technique was able to identify RBCMs that minimized over-fitting. Good data quality is a critical component for building effective software quality models. We address the significance of the quality of data on the classification performance of learners by conducting a comprehensive comparative study. Several trends were observed in the experiments. Class and attribute had the greatest impact on the performance of learners when it occurred simultaneously in the data. Class noise had a significant impact on the performance of learners, while attribute noise had no impact when it occurred in less than 40% of the most significant independent attributes. Random Forest (RF100), a group of 100 decision trees, was the most, accurate and robust learner in all the experiments with noisy data.
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Date Issued: 2008
PURL: http://purl.flvc.org/fau/fd/FA00012567
Subject Headings: Computer software--Quality control, Computer software--Reliability, Software engineering, Computer arithmetic
Format: Document (PDF)

Title: Using adaptive controllers in the realization of a position control scheme.
Creator: Samples, Robert Hyram, Jr., Florida Atlantic University, Pajunen, Grazyna, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Many positioning systems with varying loads or geometries, such as robotic systems, could take advantage of the class of non-linear controllers known as Adaptive Controls. Model Reference and Pole Placement Adaptive Controllers are usually the preferred techniques for position control systems. Pole Placement is the more universally applicable technique. Adaptive controllers must be able to change control parameters as the system's parameters change (i.e., as is the case with a load or...
Show moreMany positioning systems with varying loads or geometries, such as robotic systems, could take advantage of the class of non-linear controllers known as Adaptive Controls. Model Reference and Pole Placement Adaptive Controllers are usually the preferred techniques for position control systems. Pole Placement is the more universally applicable technique. Adaptive controllers must be able to change control parameters as the system's parameters change (i.e., as is the case with a load or geometry change). The most common and perhaps the fastest converging technique uses the Least Squares Identification Algorithm. Many positioning systems cannot tolerate overshoot. These systems should use an adaptive velocity controller in conjunction with a conventional position controller. This will minimize system overshoot during the learning period. Adaptive controllers tend to be very complex and require a great number of computations. With today's advances in computer technology, adaptive controllers can now be economically considered for many industrial, consumer and military positioning applications.
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Date Issued: 1988
PURL: http://purl.flvc.org/fcla/dt/14474
Subject Headings: Adaptive control systems
Format: Document (PDF)

Title: Synthesis of vision-based robot calibration using moving cameras.
Creator: Wang, Kuanchih., Florida Atlantic University, Roth, Zvi S., Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Robot calibration using a vision system and moving cameras is the focus of this dissertation. The dissertation contributes in the areas of robot modeling, kinematic identification and calibration measurement. The effects of perspective distortion of circular camera calibration points is analyzed. A new modified complete and parametrically continuous robot kinematic model, an evolution of the complete and parametrically continuous (CPC) model, is proposed. It is shown that the model's error...
Show moreRobot calibration using a vision system and moving cameras is the focus of this dissertation. The dissertation contributes in the areas of robot modeling, kinematic identification and calibration measurement. The effects of perspective distortion of circular camera calibration points is analyzed. A new modified complete and parametrically continuous robot kinematic model, an evolution of the complete and parametrically continuous (CPC) model, is proposed. It is shown that the model's error-model can be developed easily as the structure of this new model is very simple and similar to the Denavit-Hartenbert model. The derivation procedure of the error-model follows a systematic method that can be applied to any kind of robot arms. Pose measurement is the most crucial step in robot calibration. The use of stereo as well as mono mobile camera measurement system for collection of pose data of the robot end-effector is investigated. The Simulated Annealing technique is applied to the problem of optimal measurement configuration selection. Joint travel limits can be included in the cost function. It is shown that trapping into local minimum points can be effectively avoided by properly choosing an initial point and a temperature schedule. The concept of simultaneous calibration of camera and robot is developed and implemented as an automated process that determines the system model parameters using only the system's internal sensors. This process uses a unified mathematical model for the entire robot/camera system. The results of the kinematic identification, optimal configuration selection, and simultaneous calibration of robot and camera using the PUMA 560 robot arm have demonstrated that the modified complete and parametrically continuous model is a viable and simple modeling tool, which can achieve desired accuracy. The systematic way of modeling and performing of different kinds of vision-based robot applications demonstrated in this dissertation will pave the way for industrial standardizing of robot calibration done by the robot user on the manufacturing floor.
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Date Issued: 1993
PURL: http://purl.flvc.org/fcla/dt/12339
Subject Headings: Robot vision, Robot cameras--Calibration
Format: Document (PDF)

Title: DEEP LEARNING REGRESSION MODELS FOR LIMITED BIOMEDICAL TIME-SERIES DATA.
Creator: Hssayeni, Murtadha D., Behnaz Ghoraani, Behnaz, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Time-series data in biomedical applications are gaining an increased interest to detect and predict underlying diseases and estimate their severity, such as Parkinson’s disease (PD) and cardiovascular diseases. This interest is driven by advances in wearable sensors and deep learning models to a large extent. In the literature, less attention has been paid to regression models for continuous outcomes in these applications, especially when dealing with limited data. Training deep learning...
Show moreTime-series data in biomedical applications are gaining an increased interest to detect and predict underlying diseases and estimate their severity, such as Parkinson’s disease (PD) and cardiovascular diseases. This interest is driven by advances in wearable sensors and deep learning models to a large extent. In the literature, less attention has been paid to regression models for continuous outcomes in these applications, especially when dealing with limited data. Training deep learning models on raw limited data results in overfitted models, which is the main technical challenge we address in this dissertation. An example of limited and\or imbalanced time-series data is PD’s motion signals that are needed for the continuous severity estimation of Parkinson’s disease (PD). The significance of this continuous estimation is providing a tool for longitudinal monitoring of daily motor and non-motor fluctuations and managing PD medications. The dissertation objective is to train generalizable deep learning models for biomedical regression problems when dealing with limited training time-series data. The goal is designing, developing, and validating an automatic assessment system based on wearable sensors that can measure the severity of PD complications in the home-living environment while patients with PD perform their activities of daily living (ADL). We first propose using a combination of domain-specific feature engineering, transfer learning, and an ensemble of multiple modalities. Second, we utilize generative adversarial networks (GAN) and propose a new formulation of conditional GAN (cGAN) as a generative model for regression to handle an imbalanced training dataset. Next, we propose a dual-channel auxiliary regressor GAN (AR-GAN) trained using Wasserstein-MSE-correlation loss. The proposed AR-GAN is used as a data augmentation method in regression problems.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013992
Subject Headings: Deep learning (Machine learning), Regression analysis--Mathematical models, Biomedical engineering
Format: Document (PDF)

Title: SELECTED APPLICATIONS OF MPC.
Creator: Ghaseminejad, Mohammad Raeini, Liu, Feng-Hao, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Secure multiparty computation (secure MPC) is a computational paradigm that enables a group of parties to evaluate a public function on their private data without revealing the data (i.e., by preserving the privacy of their data). This computational approach, sometimes also referred to as secure function evaluation (SFE) and privacy-preserving computation, has attracted significant attention in the last couple of decades. It has been studied in different application domains, including in...
Show moreSecure multiparty computation (secure MPC) is a computational paradigm that enables a group of parties to evaluate a public function on their private data without revealing the data (i.e., by preserving the privacy of their data). This computational approach, sometimes also referred to as secure function evaluation (SFE) and privacy-preserving computation, has attracted significant attention in the last couple of decades. It has been studied in different application domains, including in privacy-preserving data mining and machine learning, secure signal processing, secure genome analysis, sealed-bid auctions, etc. There are different approaches for realizing secure MPC. Some commonly used approaches include secret sharing schemes, Yao's garbled circuits, and homomorphic encryption techniques. The main focus of this dissertation is to further investigate secure multiparty computation as an appealing area of research and to study its applications in different domains. We specifically focus on secure multiparty computation based on secret sharing and fully homomorphic encryption (FHE) schemes. We review the important theoretical foundations of these approaches and provide some novel applications for each of them. For the fully homomorphic encryption (FHE) part, we mainly focus on FHE schemes based on the LWE problem [142] or RLWE problem [109]. Particularly, we provide a C++ implementation for the ring variant of a third generation FHE scheme called the approximate eigenvector method (a.k.a., the GSW scheme) [67]. We then propose some novel approaches for homomorphic evaluation of common functionalities based on the implemented (R)LWE [142] and [109] and RGSW [38,58] schemes. We specifically present some constructions for homomorphic computation of pseudorandom functions (PRFs). For secure computation based on secret sharing [150], we provide some novel protocols for secure trust evaluation (STE). Our proposed STE techniques [137] enable the parties in trust and reputation systems (TRS) to securely assess their trust values in each other while they keep their input trust values private. It is worth mentioning that trust and reputation are social mechanisms which can be considered as soft security measures that complement hard security measures (e.g., cryptographic and secure multiparty computation techniques) [138, 171].
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014018
Subject Headings: Data encryption (Computer science), Computers, privacy and data protection, Computer security
Format: Document (PDF)

Title: MACHINE LEARNING METHODS FOR IMAGE ENHANCEMENT IN DEGRADED VISUAL ENVIRONMENTS.
Creator: Estrada, Dennis, Tang, Yufei, Ouyang, Bing, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Significant reduction in space, weight, power, and cost (SWAP-C) of imaging hardware has induced a paradigm shift in remote sensing where unmanned platforms have become the mainstay. However, mitigating the degraded visual environment (DVE) remains an issue. DVEs can cause a loss of contrast and image detail due to particle scattering and distortion due to turbulence-induced effects. The problem is especially challenging when imaging from unmanned platforms such as autonomous underwater...
Show moreSignificant reduction in space, weight, power, and cost (SWAP-C) of imaging hardware has induced a paradigm shift in remote sensing where unmanned platforms have become the mainstay. However, mitigating the degraded visual environment (DVE) remains an issue. DVEs can cause a loss of contrast and image detail due to particle scattering and distortion due to turbulence-induced effects. The problem is especially challenging when imaging from unmanned platforms such as autonomous underwater vehicles (AUV) and unmanned ariel vehicles (UAV). While single-frame image restoration techniques have been studied extensively in recent years, single image capture is not adequate to address the effects of DVEs due to under-sampling, low dynamic range, and chromatic aberration. Significant development has been made to employ multi-frame image fusion techniques to take advantage of spatial and temporal information to aid in the recovery of corrupted image detail and high-frequency content and increasing dynamic range.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013987
Subject Headings: Image Enhancement, Machine learning, Remote sensing
Format: Document (PDF)

Title: MODELING, PATH PLANNING, AND CONTROL CO-DESIGN OF MARINE CURRENT TURBINES.
Creator: Hasankhani, Arezoo, Tang, Yufei, VanZwieten, James, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Marine and hydrokinetic (MHK) energy systems, including marine current turbines and wave energy converters, could contribute significantly to reducing reliance on fossil fuels and improving energy security while accelerating progress in the blue economy. However, technologies to capture them are nascent in development due to several technical and economic challenges. For example, for capturing ocean flows, the fluid velocity is low but density is high, resulting in early boundary layer...
Show moreMarine and hydrokinetic (MHK) energy systems, including marine current turbines and wave energy converters, could contribute significantly to reducing reliance on fossil fuels and improving energy security while accelerating progress in the blue economy. However, technologies to capture them are nascent in development due to several technical and economic challenges. For example, for capturing ocean flows, the fluid velocity is low but density is high, resulting in early boundary layer separation and high torque. This dissertation addresses critical challenges in modeling, optimization, and control co-design of MHK energy systems, with specific case studies of a variable buoyancy-controlled marine current turbine (MCT). Specifically, this dissertation presents (a) comprehensive dynamic modeling of the MCT, where data recorded by an acoustic Doppler current profiler will be used as the real ocean environment; (b) vertical path planning of the MCT, where the problem is formulated as a novel spatial-temporal optimization problem to maximize the total harvested power of the system in an uncertain oceanic environment; (c) control co-design of the MCT, where the physical device geometry and turbine path control are optimized simultaneously. In a nutshell, the contributions are summarized as follows:
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013991
Subject Headings: Marine turbines, Modeling dynamic systems, Ocean wave power
Format: Document (PDF)

Title: ADDRESSING HIGHLY IMBALANCED BIG DATA CHALLENGES FOR MEDICARE FRAUD DETECTION.
Creator: Johnson, Justin M., Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Access to affordable healthcare is a nationwide concern that impacts most of the United States population. Medicare is a federal government healthcare program that aims to provide affordable health insurance to the elderly population and individuals with select disabilities. Unfortunately, there is a significant amount of fraud, waste, and abuse within the Medicare system that inevitably raises premiums and costs taxpayers billions of dollars each year. Dedicated task forces investigate the...
Show moreAccess to affordable healthcare is a nationwide concern that impacts most of the United States population. Medicare is a federal government healthcare program that aims to provide affordable health insurance to the elderly population and individuals with select disabilities. Unfortunately, there is a significant amount of fraud, waste, and abuse within the Medicare system that inevitably raises premiums and costs taxpayers billions of dollars each year. Dedicated task forces investigate the most severe fraudulent cases, but with millions of healthcare providers and more than 60 million active Medicare beneficiaries, manual fraud detection efforts are not able to make widespread, meaningful impact. Through the proliferation of electronic health records and continuous breakthroughs in data mining and machine learning, there is a great opportunity to develop and leverage advanced machine learning systems for automating healthcare fraud detection. This dissertation identifies key challenges associated with predictive modeling for large-scale Medicare fraud detection and presents innovative solutions to address these challenges in order to provide state-of-the-art results on multiple real-world Medicare fraud data sets. Our methodology for curating nine distinct Medicare fraud classification data sets is presented with comprehensive details describing data accumulation, data pre-processing, data aggregation techniques, data enrichment strategies, and improved fraud labeling. Data-level and algorithm-level methods for treating severe class imbalance, including a flexible output thresholding method and a cost-sensitive framework, are evaluated using deep neural network and ensemble learners. Novel encoding techniques and representation learning methods for high-dimensional categorical features are proposed to create expressive representations of provider attributes and billing procedure codes.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014057
Subject Headings: Medicare fraud, Big data, Machine learning
Format: Document (PDF)

Title: A body area network as a pre-screening surrogate to the polysomnography.
Creator: LaFleur, Sheryl, Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Out of 60 million Americans suffering from sleep disorder, an estimated 18 million have sleep apnea. According to the U.S. Department of Health & Human Services, sleep apnea is a chronic condition that disrupts a patient’s sleep. While the annual cost of treating sleep apnea patients in the United States is approximately $3.18 billion (including screening costs) it is estimated that untreated sleep apnea may cause $3.4 billion in additional medical costs. A polysomnography (PSG) is an all...
Show moreOut of 60 million Americans suffering from sleep disorder, an estimated 18 million have sleep apnea. According to the U.S. Department of Health & Human Services, sleep apnea is a chronic condition that disrupts a patient’s sleep. While the annual cost of treating sleep apnea patients in the United States is approximately $3.18 billion (including screening costs) it is estimated that untreated sleep apnea may cause $3.4 billion in additional medical costs. A polysomnography (PSG) is an all-night sleep study which monitors various physical functions during sleep including electrical activity of the heart, brain wave patterns, eye movement, muscle tone, body movements, and breathing. It is currently, the most accurate and sophisticated test for the diagnosis of sleep-disordered breathing (SDB), but also, the most expensive. The cost of an overnight sleep study is estimated between $900 and $3,000. In addition, the PSG is not mobile and has to be administered outside a patient’s home. The Long QT Syndrome (LQTS) is a rhythm disorder that causes erratic (unpredictable) heartbeats. The LQTS has been linked to patients with the most severe form of sleep apnea. If LQTS is left untreated, sudden cardiac death may occur.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004300
Subject Headings: Neurophysiology, Nevous system -- Diseases -- Diagnosis, Polysomnography, Sleep -- Physiological aspects, Sleep apnea syndromes -- Diagnosis, Sleep disorders -- Diagnosis
Format: Document (PDF)

Title: COLLISION FREE NAVIGATION IN 3D UNSTRUCTURED ENVIRONMENTS USING VISUAL LOOMING.
Creator: Yepes, Juan David Arango, Raviv, Daniel, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Vision is a critical sense for many species, with the perception of motion being a fundamental aspect. This aspect often provides richer information than static images for understanding the environment. Motion recognition is a relatively simple computation compared to shape recognition. Many creatures can discriminate moving objects quite well while having virtually no capacity for recognizing stationary objects. Traditional methods for collision-free navigation require the reconstruction of...
Show moreVision is a critical sense for many species, with the perception of motion being a fundamental aspect. This aspect often provides richer information than static images for understanding the environment. Motion recognition is a relatively simple computation compared to shape recognition. Many creatures can discriminate moving objects quite well while having virtually no capacity for recognizing stationary objects. Traditional methods for collision-free navigation require the reconstruction of a 3D model of the environment before planning an action. These methods face numerous limitations as they are computationally expensive and struggle to scale in unstructured and dynamic environments with a multitude of moving objects. This thesis proposes a more scalable and efficient alternative approach without 3D reconstruction. We focus on visual motion cues, specifically ’visual looming’, the relative expansion of objects on an image sensor. This concept allows for the perception of collision threats and facilitates collision-free navigation in any environment, structured or unstructured, regardless of the vehicle’s movement or the number of moving objects present.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014239
Subject Headings: Motion perception (Vision), Collision avoidance systems, Visual perception
Format: Document (PDF)

Title: OCR2SEQ: A NOVEL MULTI-MODAL DATA AUGMENTATION PIPELINE FOR WEAK SUPERVISION.
Creator: Lowe, Michael A., Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: With the recent large-scale adoption of Large Language Models in multidisciplinary research and commercial space, the need for large amounts of labeled data has become more crucial than ever to evaluate potential use cases for opportunities in applied intelligence. Most domain specific fields require a substantial shift that involves extremely large amounts of heterogeneous data to have meaningful impact on the pre-computed weights of most large language models. We explore extending the...
Show moreWith the recent large-scale adoption of Large Language Models in multidisciplinary research and commercial space, the need for large amounts of labeled data has become more crucial than ever to evaluate potential use cases for opportunities in applied intelligence. Most domain specific fields require a substantial shift that involves extremely large amounts of heterogeneous data to have meaningful impact on the pre-computed weights of most large language models. We explore extending the capabilities a state-of-the-art unsupervised pre-training method; Transformers and Sequential Denoising Auto-Encoder (TSDAE). In this study we show various opportunities for using OCR2Seq a multi-modal generative augmentation strategy to further enhance and measure the quality of noise samples used when using TSDAE as a pretraining task. This study is a first of its kind work that leverages converting both generalized and sparse domains of relational data into multi-modal sources. Our primary objective is measuring the quality of augmentation in relation to the current implementation of the sentence transformers library. Further work includes the effect on ranking, language understanding, and corrective quality.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014367
Subject Headings: Natural language processing (Computer science), Deep learning (Machine learning)
Format: Document (PDF)

Title: DEVELOPMENT OF A WEARABLE DEVICE FOR MONITORING PHYSIOLOGICAL PARAMETERS RELATED TO HEART FAILURE.
Creator: Iqbal, Sheikh Muhammad Asher, Asghar, Waseem, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Heart failure is a chronic cardiovascular disease that is caused due to the lack of blood supply from heart. This lack of blood supply leads to accumulation of the fluid in the thoracic region. Traditionally, implantable cardioverter defibrillators (ICDs) are used to treat HF and to monitor its parameters. Healthcare wearable devices (HWDs) are healthcare devices that can be worn or attached to the skin. HWD are non-invasive and low-cost means of providing healthcare at the point-of-care (POC...
Show moreHeart failure is a chronic cardiovascular disease that is caused due to the lack of blood supply from heart. This lack of blood supply leads to accumulation of the fluid in the thoracic region. Traditionally, implantable cardioverter defibrillators (ICDs) are used to treat HF and to monitor its parameters. Healthcare wearable devices (HWDs) are healthcare devices that can be worn or attached to the skin. HWD are non-invasive and low-cost means of providing healthcare at the point-of-care (POC). Herein, this dissertation discusses the development of a HWD for the monitoring of the parameters of heart failure (HF). These parameters include thoracic impedance, electrocardiogram (ECG), heart rate, oxygen saturation in blood and activity status of the subject. These are similar parameters as monitored using ICD. The dissertation will discuss the development, design, and results of the HWD.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014349
Subject Headings: Wearable technology--Design and construction, Wearable devices, Heart failure
Format: Document (PDF)

Title: INCORPORATING EMOTION RECOGNITION IN CO-ADAPTIVE SYSTEMS.
Creator: Al-Omair, Osamah M., Huang, Shihong, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The collaboration between human and computer systems has grown astronomically over the past few years. The ability of software systems adapting to human's input is critical in the symbiosis of human-system co-adaptation, where human and software-based systems work together in a close partnership to achieve synergetic goals. However, it is not always clear what kinds of human’s input should be considered to enhance the effectiveness of human and system co-adaptation. To address this issue,...
Show moreThe collaboration between human and computer systems has grown astronomically over the past few years. The ability of software systems adapting to human's input is critical in the symbiosis of human-system co-adaptation, where human and software-based systems work together in a close partnership to achieve synergetic goals. However, it is not always clear what kinds of human’s input should be considered to enhance the effectiveness of human and system co-adaptation. To address this issue, this research describes an approach that focuses on incorporating human emotion to improve human-computer co-adaption. The key idea is to provide a formal framework that incorporates human emotions as a foundation for explainability into co-adaptive systems, especially, how software systems recognize human emotions and adapt the system’s behaviors accordingly. Detecting and recognizing optimum human emotion is a first step towards human and computer symbiosis. As the first step of this research, we conduct a comparative review for a number of technologies and methods for emotion recognition. Specifically, testing the detection accuracy of facial expression recognition of different cloud-services, algorithms, and methods. Secondly, we study the application of emotion recognition within the areas of e-learning, robotics, and explainable artificial intelligence (XAI). We propose a formal framework that incorporates human emotions into an adaptive e-learning system, to create a more personalized learning experience for higher quality of learning outcomes. In addition, we propose a framework for a co-adaptive Emotional Support Robot. This human-centric framework adopts a reinforced learning approach where the system assesses its own emotional re-actions.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013926
Subject Headings: Emotion recognition, Human-computer interaction, Affective Computing
Format: Document (PDF)

Title: ADVANCING ONE-CLASS CLASSIFICATION: A COMPREHENSIVE ANALYSIS FROM THEORY TO NOVEL APPLICATIONS.
Creator: Abdollah, Zadeh Azadeh, Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: This dissertation explores one-class classification (OCC) in the context of big data and fraud detection, addressing challenges posed by imbalanced datasets. A detailed survey of OCC-related literature forms a core part of the study, categorizing works into outlier detection, novelty detection, and deep learning applications. This survey reveals a gap in the application of OCC to the inherent problems of big data, such as class rarity and noisy data. Building upon the foundational insights...
Show moreThis dissertation explores one-class classification (OCC) in the context of big data and fraud detection, addressing challenges posed by imbalanced datasets. A detailed survey of OCC-related literature forms a core part of the study, categorizing works into outlier detection, novelty detection, and deep learning applications. This survey reveals a gap in the application of OCC to the inherent problems of big data, such as class rarity and noisy data. Building upon the foundational insights gained from the comprehensive literature review on OCC, the dissertation progresses to a detailed comparative analysis between OCC and binary classification methods. This comparison is pivotal in understanding their respective strengths and limitations across various applications, emphasizing their roles in addressing imbalanced datasets. The research then specifically evaluates binary and OCC using credit card fraud data. This practical application highlights the nuances and effectiveness of these classification methods in real-world scenarios, offering insights into their performance in detecting fraudulent activities. After the evaluation of binary and OCC using credit card fraud data, the dissertation extends this inquiry with a detailed investigation into the effectiveness of both methodologies in fraud detection. This extended analysis involves utilizing not only the Credit Card Fraud Detection Dataset but also the Medicare Part D dataset. The findings show the comparative performance and suitability of these classification methods in practical fraud detection scenarios. Finally, the dissertation examines the impact of training OCC algorithms on majority versus minority classes, using the two previously mentioned datasets in addition to Medicare Part B and Durable Medical Equipment, Prosthetics, Orthotics and Supplies (DMEPOS) datasets. This exploration offers critical insights into model training strategies and their implications, suggesting that training on the majority class can often lead to more robust classification results. In summary, this dissertation provides a deep understanding of OCC, effectively bridging theoretical concepts with novel applications in big data and fraud detection. It contributes to the field by offering a comprehensive analysis of OCC methodologies, their practical implications, and their effectiveness in addressing class imbalance in big data.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014387
Subject Headings: Classification, Big data, Deep learning (Machine learning), Computer engineering
Format: Document (PDF)

Title: FACILITATING PEER-TO-PEER ENERGY TRADING THROUGH COOPERATIVE GAMES AND FUZZY INFERENCE SYSTEMS.
Creator: Lopez, Hector, Zilouchian, Ali, Abtahi, Amir, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: This dissertation proposes a utility-centric peer-to-peer (P2P) energy trading framework as an alternative to traditional net metering, aiming to resolve conflicts between distributed energy resource owners and utilities. It advocates for practical software services and dynamic payment mechanisms tailored to prosumer needs, offering an alternative to reducing net metering incentives. Additionally, it explores game theory principles to ensure equitable compensation for prosumer cooperation,...
Show moreThis dissertation proposes a utility-centric peer-to-peer (P2P) energy trading framework as an alternative to traditional net metering, aiming to resolve conflicts between distributed energy resource owners and utilities. It advocates for practical software services and dynamic payment mechanisms tailored to prosumer needs, offering an alternative to reducing net metering incentives. Additionally, it explores game theory principles to ensure equitable compensation for prosumer cooperation, driving the adoption of P2P energy markets. It also builds on demand-side payment mechanisms like NRG-X-Change by adapting it to provide fair payment distribution to prosumer coalitions. The interoperable energy storage systems with P2P trading also presented battery chemistry detection using neural network models. A fuzzy inference system is also designed to facilitate prosumers' choice in participating in P2P markets, providing flexibility for energy trading preferences. The simulation results demonstrated the effectiveness of the proposed design schemes.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014425
Subject Headings: Energy, Fuzzy systems, Cooperative game theory, Electrical engineering
Format: Document (PDF)

Title: FRAUD DETECTION IN HIGHLY IMBALANCED BIG DATA WITH NOVEL AND EFFICIENT DATA REDUCTION TECHNIQUES.
Creator: Hancock III, John T., Taghi M. Khoshgoftaar, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The rapid growth of digital transactions and the increasing sophistication of fraudulent activities have necessitated the development of robust and efficient fraud detection techniques, particularly in the financial and healthcare sectors. This dissertation focuses on the use of novel data reduction techniques for addressing the unique challenges associated with detecting fraud in highly imbalanced Big Data, with a specific emphasis on credit card transactions and Medicare claims. The highly...
Show moreThe rapid growth of digital transactions and the increasing sophistication of fraudulent activities have necessitated the development of robust and efficient fraud detection techniques, particularly in the financial and healthcare sectors. This dissertation focuses on the use of novel data reduction techniques for addressing the unique challenges associated with detecting fraud in highly imbalanced Big Data, with a specific emphasis on credit card transactions and Medicare claims. The highly imbalanced nature of these datasets, where fraudulent instances constitute less than one percent of the data, poses significant challenges for traditional machine learning algorithms. This dissertation explores novel data reduction techniques tailored for fraud detection in highly imbalanced Big Data. The primary objectives include developing efficient data preprocessing and feature selection methods to reduce data dimensionality while preserving the most informative features, investigating various machine learning algorithms for their effectiveness in handling imbalanced data, and evaluating the proposed techniques on real-world credit card and Medicare fraud datasets. This dissertation covers a comprehensive examination of datasets, learners, experimental methodology, sampling techniques, feature selection techniques, and hybrid techniques. Key contributions include the analysis of performance metrics in the context of newly available Big Medicare Data, experiments using Big Medicare data, application of a novel ensemble supervised feature selection technique, and the combined application of data sampling and feature selection. The research demonstrates that, across both domains, the combined application of random undersampling and ensemble feature selection significantly improves classification performance.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014424
Subject Headings: Fraud, Big data, Data reduction, Credit card fraud, Medicare fraud
Format: Document (PDF)

Title: Big data driven co-occurring evidence discovery in chronic obstructive pulmonary disease patients.
Creator: Baechle, Christopher, Agarwal, Ankur, Zhu, Xingquan
Date Issued: 2017-12-04
PURL: http://purl.flvc.org/fau/flvc_fau_islandoraimporter_10.1186_s40537-017-0067-6_1629211082
Format: Citation

Title: Counting manatee aggregations using deep neural networks and Anisotropic Gaussian Kernel.
Creator: Zhiqiang Wang, Yiran Pang, Cihan Ulus, Xingquan Zhu
Abstract/Description: Manatees are aquatic mammals with voracious appetites. They rely on sea grass as the main food source, and often spend up to eight hours a day grazing. They move slow and frequently stay in groups (i.e. aggregations) in shallow water to search for food, making them vulnerable to environment change and other risks. Accurate counting manatee aggregations within a region is not only biologically meaningful in observing their habit, but also crucial for designing safety rules for boaters, divers,...
Show moreManatees are aquatic mammals with voracious appetites. They rely on sea grass as the main food source, and often spend up to eight hours a day grazing. They move slow and frequently stay in groups (i.e. aggregations) in shallow water to search for food, making them vulnerable to environment change and other risks. Accurate counting manatee aggregations within a region is not only biologically meaningful in observing their habit, but also crucial for designing safety rules for boaters, divers, etc., as well as scheduling nursing, intervention, and other plans. In this paper, we propose a deep learning based crowd counting approach to automatically count number of manatees within a region, by using low quality images as input. Because manatees have unique shape and they often stay in shallow water in groups, water surface reflection, occlusion, camouflage etc. making it difficult to accurately count manatee numbers. To address the challenges, we propose to use Anisotropic Gaussian Kernel (AGK), with tunable rotation and variances, to ensure that density functions can maximally capture shapes of individual manatees in different aggregations. After that, we apply AGK kernel to different types of deep neural networks primarily designed for crowd counting, including VGG, SANet, Congested Scene Recognition network (CSRNet), MARUNet etc. to learn manatee densities and calculate number of manatees in the scene. By using generic low quality images extracted from surveillance videos, our experiment results and comparison show that AGK kernel based manatee counting achieves minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The proposed method works particularly well for counting manatee aggregations in environments with complex background.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FAUIR000535
Format: Document (PDF)

Title: Engineering cancer microenvironments for in vitro 3-D tumor models.
Creator: Asghar, Waseem, El Assal, Rami, Shafiee, Hadi, Pitteri, Sharon, Paulmurugan, Ramasamy, Demirci, Utkan
Date Issued: 2015-12
PURL: http://purl.flvc.org/fau/flvc_fau_islandoraimporter_10.1016_j.mattod.2015.05.002_1630594119
Format: Citation

Title: A Collaborative Geospatial Shoreline Inventory Tool to Guide Coastal Development and Habitat Conservation.
Creator: Mitsova, Diana, Wissinger, Frank, Esnard, Ann-Margaret, Shankar, Ravi, Gies, Peter
Date Issued: 2013-05-13
PURL: http://purl.flvc.org/fau/fd/FAUIR000175
Format: Citation

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