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Title: SCHEMATIC: AN EXPERIMENT IN MACHINE LEARNING USING CONCEPTUAL GRAPHS.
Creator: HALTERMAN, RICHARD L., Florida Atlantic University
Abstract/Description: Conceptual graphs form the basis of a powerful representation language for artificial intelligence research. SCHEMATIC is a system that uses a subset of conceptual graph theory in acquiring knowledge about a given domain. SCHEMATIC exhibits two types of learning. It will passively absorb information as imparted by the teacher, and it also has an active learning mode that, based on its current picture of the domain, aggressively queries the teacher for more information. The knowledge base,...
Show moreConceptual graphs form the basis of a powerful representation language for artificial intelligence research. SCHEMATIC is a system that uses a subset of conceptual graph theory in acquiring knowledge about a given domain. SCHEMATIC exhibits two types of learning. It will passively absorb information as imparted by the teacher, and it also has an active learning mode that, based on its current picture of the domain, aggressively queries the teacher for more information. The knowledge base, including the concept type hierarchy, the relation list, canonical forms, and the current domain, are dynamically maintained. Teacher interaction is handled exclusively with conceptual graphs. Action concepts are treated differently by SCHEMATIC, in that, once defined, they execute procedures that alter the domain.
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Date Issued: 1987
PURL: http://purl.flvc.org/fcla/dt/14421
Subject Headings: Machine learning, Artificial intelligence
Format: Document (PDF)

Title: THE ROBOT WILL SEE YOU NOW: ARTIFICIAL INTELLIGENCE AND THE MICROFOUNDATIONS OF INSTITUTIONAL CHANGE WITHIN MEDICINE.
Creator: Bagdasarian, Jennifer Ling, Goodrick, Elizabeth, Florida Atlantic University, Department of Management Programs, College of Business
Abstract/Description: The presence of artificial intelligence (AI) has incrementally increased in our lives since its introduction in the 1950s and has exponentially increased in the last decade. In medicine, AI holds the promise of providing complete panoramic views of a patient’s medical history, improving medical decision making, avoiding errors such as misdiagnosis and unnecessary procedures, interpretating tests and making treatment recommendations. In this study, I examine the influence of AI on decision...
Show moreThe presence of artificial intelligence (AI) has incrementally increased in our lives since its introduction in the 1950s and has exponentially increased in the last decade. In medicine, AI holds the promise of providing complete panoramic views of a patient’s medical history, improving medical decision making, avoiding errors such as misdiagnosis and unnecessary procedures, interpretating tests and making treatment recommendations. In this study, I examine the influence of AI on decision-making behaviors and the changes to the professional institution of medicine. This paper links theories of institutional change and professions to further our understanding of the processes of change in response to emergent technology. Recognizing that the autonomy of decision making is central to the model of professional work, this study (1) shows how changes in decision-making processes are a driver of change in the institution of professions and (2) highlights how this impacts the professional role identity of health care providers which has implications for how medicine is taught and how diagnoses are made.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014125
Subject Headings: Artificial intelligence, Medicine
Format: Document (PDF)

Title: ESTABLISHMENT AND APPLICATION OF WORKFLOWS FOR STRUCTURE-FUNCTION ANALYSIS OF SYNAPTIC COMPONENTS.
Creator: Thomas, Connon I., Kamasawa, Naomi, Murphey, Rodney, Florida Atlantic University, Department of Biological Sciences, Charles E. Schmidt College of Science
Abstract/Description: At the site of neuronal communication, multiple interacting components drive synapse structure and function. Synaptic vesicle pools, membrane proteins, mitochondria, and perisynaptic astrocyte processes (PAPs) are all structures that can be altered through naturally occurring plasticity mechanisms to modulate neurotransmission, and disruption of these structures can result in synapse dysfunction and disease. Due to the minute size of the synapse, electron microscopy (EM) remains the gold...
Show moreAt the site of neuronal communication, multiple interacting components drive synapse structure and function. Synaptic vesicle pools, membrane proteins, mitochondria, and perisynaptic astrocyte processes (PAPs) are all structures that can be altered through naturally occurring plasticity mechanisms to modulate neurotransmission, and disruption of these structures can result in synapse dysfunction and disease. Due to the minute size of the synapse, electron microscopy (EM) remains the gold standard for ultrastructural characterization; however, due to the complexity of EM datasets, extraction of information has become a bottleneck which places limits on the amount of data that can be collected and analyzed. A need exists for easy-to-use workflows that automate and enhance analysis throughput, to keep up with the streams of image data that are able to be produced. Here, I develop the use of AI algorithms, correlative microscopy techniques, and novel structural analysis methods to characterize postsynaptic mitochondria, PAPs, synaptic vesicles, and integral membrane proteins and their impact on synapse structure and function. I show that both postsynaptic mitochondria and PAPs in the visual cortex are positioned to support synapse structure and function; cleavage of a synaptic adhesion molecule affects synaptic vesicle accumulation in the amygdala; and presynaptic voltage gated calcium channels aggregate near active zone machinery in the brainstem. In addition, I highlight the use of virtual reality as a fast and intuitive tool for the identification and isolation of individual neurites in 3D EM. Thus, my work establishes novel technical approaches for EM and advances our understanding of neuronal communication through original research of several synaptic components.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014315
Subject Headings: Synapses, Artificial intelligence, Astrocytes
Format: Document (PDF)

Title: ASSESSING METHODS AND TOOLS TO IMPROVE REPORTING, INCREASE TRANSPARENCY, AND REDUCE FAILURES IN MACHINE LEARNING APPLICATIONS IN HEALTHCARE.
Creator: Garbin, Christian, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Artificial intelligence (AI) had a few false starts – the AI winters of the 1970s and 1980s. We are now in what looks like an AI summer. There are many useful applications of AI in the field. But there are still unfulfilled promises and outright failures. From self-driving cars that work only in constrained cases, to medical image analysis products that would replace radiologists but never did, we still struggle to translate successful research into successful real-world applications. The...
Show moreArtificial intelligence (AI) had a few false starts – the AI winters of the 1970s and 1980s. We are now in what looks like an AI summer. There are many useful applications of AI in the field. But there are still unfulfilled promises and outright failures. From self-driving cars that work only in constrained cases, to medical image analysis products that would replace radiologists but never did, we still struggle to translate successful research into successful real-world applications. The software engineering community has accumulated a large body of knowledge over the decades on how to develop, release, and maintain products. AI products, being software products, benefit from some of that accumulated knowledge, but not all of it. AI products diverge from traditional software products in fundamental ways: their main component is not a specific piece of code, written for a specific purpose, but a generic piece of code, a model, customized by a training process driven by hyperparameters and a dataset. Datasets are usually large and models are opaque. We cannot directly inspect them as we can inspect the code of traditional software products. We need other methods to detect failures in AI products.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013580
Subject Headings: Machine learning, Artificial intelligence, Healthcare
Format: Document (PDF)

Title: KINOVA ROBOTIC ARM MANIPULATION WITH PYTHON PROGRAMMING.
Creator: Veit, Cameron, Zhong, Xiangnan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: As artificial intelligence (AI), such as reinforcement learning (RL), has continued to grow, the introduction of AI for use in robotic arms in order to have them autonomously complete tasks has become an increasingly popular topic. Robotic arms have recently had a drastic spike in innovation, with new robotic arms being developed for a variety of tasks both menial and complicated. One robotic arm recently developed for everyday use in close proximity to the user is the Kinova Gen 3 Lite, but...
Show moreAs artificial intelligence (AI), such as reinforcement learning (RL), has continued to grow, the introduction of AI for use in robotic arms in order to have them autonomously complete tasks has become an increasingly popular topic. Robotic arms have recently had a drastic spike in innovation, with new robotic arms being developed for a variety of tasks both menial and complicated. One robotic arm recently developed for everyday use in close proximity to the user is the Kinova Gen 3 Lite, but limited formal research has been conducted about controlling this robotic arm both with an AI and in general. Therefore, this thesis covers the implementation of Python programs in controlling the robotic arm physically as well as the use of a simulation to train an RL based AI compatible with the Kinova Gen 3 Lite. Additionally, the purpose of this research is to identify and solve the difficulties in the physical instance and the simulation as well as the impact of the learning parameters on the robotic arm AI. Similarly, the issues in connecting two Kinova Gen 3 Lites to one computer at once are also examined. This thesis goes into detail about the goal of the Python programs created to move the physical robotic arm as well as the overall setup and goal of the robotic arm simulation for the RL method. In particular, the Python programs for the physical robotic arm pick up the object and place it at a different location, identifying a method to prevent the gripper from crushing an object without a tactile sensor in the process. The thesis also covers the effect of various learning parameters on the accuracy and steps to goal curves of an RL method designed to make a Kinova Gen 3 Lite grab an object in a simulation. In particular, a neural network implementation of RL method with one of the learning parameters changed in comparison to the optimal learning parameters. The neural network is trained using Python Anaconda to control a Kinova Gen 3 Lite robotic arm model for a simulation made in the Unity compiler.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014022
Subject Headings: Robotics, Artificial intelligence, Reinforcement learning
Format: Document (PDF)

Title: INTELLIGENT OPERATION OF ROBOTIC ARMS BASED ON TURTLEBOT3 MOBILE ROBOTS.
Creator: Veit, Connor, Zhong, Xiangnan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: As technology progresses, tasks involving object manipulation that were once conducted by humans are now being accomplished through robots. Specifically, robots carry out these goals through the utilization of different forms of artificial intelligence, including deep learning via a convolutional neural network. One robot made to accomplish this purpose is the ROS controlled TurtleBot3 Waffle Pi with an OpenMANIPULATOR-X robotic arm. This type of TurtleBot3 was developed with the express...
Show moreAs technology progresses, tasks involving object manipulation that were once conducted by humans are now being accomplished through robots. Specifically, robots carry out these goals through the utilization of different forms of artificial intelligence, including deep learning via a convolutional neural network. One robot made to accomplish this purpose is the ROS controlled TurtleBot3 Waffle Pi with an OpenMANIPULATOR-X robotic arm. This type of TurtleBot3 was developed with the express purpose of education and research but may not be limited to those two usages. Based on the current design of this classification of TurtleBot3, it may have multiple applications outside the testing environment, granting it further uses in a variety of tasks. The TurtleBot3 is easy to setup to fulfill the purposes for which the TurtleBot3 Waffle Pi was designed, and the exploration into further uses would allow for the discovery of alternatives to some tasks that normally require more work. For that reason, this thesis was conducted to determine the various uses of the TurtleBot3 with a robotic arm and if this robot can be used outside of a testing environment for various real-world tasks.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014026
Subject Headings: Robotics, Artificial intelligence, Mobile robots
Format: Document (PDF)

Title: ARTIFICIAL INTELLIGENCE (AI) ENABLES SENSORIMOTOR INTEGRATION FOR PROSTHETIC HAND DEXTERITY.
Creator: Abd, Moaed A., Engeberg, Erik D., Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Hand amputation is a devastating feeling for amputees, and it is lifestyle changing since it is challenging to perform the basic life activities with amputation. Hand amputation means interrupting the closed loop between sensory feedback and motor control. The absence of sensory feedback requires a significant cognitive effort from the amputee to perform basic daily activities with prosthetic hand. Loss of tactile sensations is a major roadblock preventing amputees from multitasking or using...
Show moreHand amputation is a devastating feeling for amputees, and it is lifestyle changing since it is challenging to perform the basic life activities with amputation. Hand amputation means interrupting the closed loop between sensory feedback and motor control. The absence of sensory feedback requires a significant cognitive effort from the amputee to perform basic daily activities with prosthetic hand. Loss of tactile sensations is a major roadblock preventing amputees from multitasking or using the full dexterity of their prosthetic hands. One of the most significant features lacking from commercial prosthetic hands is sensory feedback, according to amputees. Many amputees abandoned their prosthetic devices due to the lack of tactile feedback. In the field of prosthetics, restoring sensory feedback is the most challenging task due to the complexity of integration between the prosthetic and the peripheral nervous system. A prosthetic hand with sensory feedback that imitates the intact hand would improve the lives of millions of amputees worldwide by inducing the prosthetic hand to be a part of the body image and significant impact the control of the prosthetic. To restore the sensory feedback and improve the dexterity for upper limb amputee, multiple components needed to be integrated together to provide the sensory feedback. Tactile sensors are the first components that needed to be integrated into the sensorimotor loop. In this research two tactile sensors were integrated in the sensory feedback loop. The first tactile sensor is BioTac which is a commercially available sensor. The first novel contribution with BioTac is the development of an ANN classifier to detect the direction a grasped object slips in a dexterous robotic hand in real time, and the second novel aspect of this study is the use of slip direction detection for adaptive robotic grasp reflexes. The second tactile sensor is the liquid metal sensor (LMS), this sensor was developed entirely in our lab (BioRobotics lab). The novel contribution for LMS is to detect and prevent slip in real time application, and to recognize different surface features and different sliding speeds.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013875
Subject Headings: Artificial intelligence, Haptic devices, Tactile sensors, Sensorimotor integration, Artificial hands
Format: Document (PDF)

Title: CONNECTED MULTI-DOMAIN AUTONOMY AND ARTIFICIAL INTELLIGENCE: AUTONOMOUS LOCALIZATION, NETWORKING, AND DATA CONFORMITY EVALUATION.
Creator: Tountas, Konstantinos, Pados, Dimitris, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The objective of this dissertation work is the development of a solid theoretical and algorithmic framework for three of the most important aspects of autonomous/artificialintelligence (AI) systems, namely data quality assurance, localization, and communications. In the era of AI and machine learning (ML), data reign supreme. During learning tasks, we need to ensure that the training data set is correct and complete. During operation, faulty data need to be discovered and dealt with to...
Show moreThe objective of this dissertation work is the development of a solid theoretical and algorithmic framework for three of the most important aspects of autonomous/artificialintelligence (AI) systems, namely data quality assurance, localization, and communications. In the era of AI and machine learning (ML), data reign supreme. During learning tasks, we need to ensure that the training data set is correct and complete. During operation, faulty data need to be discovered and dealt with to protect from -potentially catastrophic- system failures. With our research in data quality assurance, we develop new mathematical theory and algorithms for outlier-resistant decomposition of high-dimensional matrices (tensors) based on L1-norm principal-component analysis (PCA). L1-norm PCA has been proven to be resistant to irregular data-points and will drive critical real-world AI learning and autonomous systems operations in the future. At the same time, one of the most important tasks of autonomous systems is self-localization. In GPS-deprived environments, localization becomes a fundamental technical problem. State-of-the-art solutions frequently utilize power-hungry or expensive architectures, making them difficult to deploy. In this dissertation work, we develop and implement a robust, variable-precision localization technique for autonomous systems based on the direction-of-arrival (DoA) estimation theory, which is cost and power-efficient. Finally, communication between autonomous systems is paramount for mission success in many applications. In the era of 5G and beyond, smart spectrum utilization is key.. In this work, we develop physical (PHY) and medium-access-control (MAC) layer techniques that autonomously optimize spectrum usage and minimizes intra and internetwork interference.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013617
Subject Headings: Artificial intelligence, Machine learning, Tensor algebra
Format: Document (PDF)

Title: AN APPROACH USING AFFECTIVE COMPUTING TO PREDICT INTERACTION QUALITY FROM CONVERSATIONS.
Creator: Matic, Richard N., Maniaci, Michael, Florida Atlantic University, Department of Psychology, Charles E. Schmidt College of Science
Abstract/Description: John Gottman’s mathematical models have been shown to accurately predict a couple’s style of interaction using only the sentiments found in the couple’s conversations. I derived speaker sentiment slopes from 151 recorded dyadic audio conversations from the IEMOCAP dataset through an IBM Watson emotion recognition pipeline and assessed its accuracy as input for a Gottman model by comparing the cumulative speaker sentiment slope for each conversation produced from predicted emotion codes to...
Show moreJohn Gottman’s mathematical models have been shown to accurately predict a couple’s style of interaction using only the sentiments found in the couple’s conversations. I derived speaker sentiment slopes from 151 recorded dyadic audio conversations from the IEMOCAP dataset through an IBM Watson emotion recognition pipeline and assessed its accuracy as input for a Gottman model by comparing the cumulative speaker sentiment slope for each conversation produced from predicted emotion codes to that produced from groundtruth codes provided by IEMOCAP. Watson produced sentiment slopes strongly correlated with those produced by groundtruth emotion codes. An abbreviated pipeline was also assessed consisting just of the Watson textual emotion recognition model using IEMOCAP’s human transcriptions as input. It produced predicted sentiment slopes very strongly correlated with those produced by groundtruth. The research demonstrated that artificial intelligence has potential to be used to predict interaction quality from short samples of conversational data.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014023
Subject Headings: Affective Computing, Emotion recognition, Artificial intelligence
Format: Document (PDF)

Title: INVESTIGATING AND IMPROVING FAIRNESS AND BIAS IN MACHINE LEARNING MODELS FOR DERMATOLOGY.
Creator: Corbin, Adam, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Advancements in Artificial Intelligence (AI) and Machine Learning (ML) have significantly improved their application in dermatology. However, bias issues in AI systems can result in missed diagnoses and disparities in healthcare, especially for individuals with different skin types. This dissertation aims to investigate and improve the fairness and bias in machine learning models for dermatology by evaluating and enhancing their performance across different Fitzpatrick skin types. The...
Show moreAdvancements in Artificial Intelligence (AI) and Machine Learning (ML) have significantly improved their application in dermatology. However, bias issues in AI systems can result in missed diagnoses and disparities in healthcare, especially for individuals with different skin types. This dissertation aims to investigate and improve the fairness and bias in machine learning models for dermatology by evaluating and enhancing their performance across different Fitzpatrick skin types. The technical contributions of the dissertation include generating metadata for Fitzpatrick Skin Type using Individual Typology Angle; outlining best practices for Explainable AI (XAI) and the use of colormaps; developing and enhancing ML models through skin color transformation and extending the models to include XAI methods for better interpretation and improvement of fairness and bias; and providing a list of steps for successful application of deep learning in medical image analysis. The research findings of this dissertation have the potential to contribute to the development of fair and unbiased AI/ML models in dermatology. This can ultimately lead to better health outcomes and reduced healthcare costs, particularly for individuals with different skin types.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014131
Subject Headings: Diagnostic Imaging, Machine learning, Dermatology, Artificial intelligence
Format: Document (PDF)

Title: READING TRANSNESS IN AI NARRATIVES: HOW ARTIFICIALITY CONSTRUCTS TRANSGENDER IDENTITY.
Creator: Sheridan, Tristan, Miller, Timothy, Florida Atlantic University, Department of English, Dorothy F. Schmidt College of Arts and Letters
Abstract/Description: Transgender identity and the concept of artificial intelligence are constructed and understood through dichotomies such as natural/unnatural and real/artificial, with each dichotomy informing the other; what is “unnatural” is often deemed to be a mimicry of the “natural,” therefore a false representation of what is “real.” By surveying various classic SF texts and their portrayal of AI characters through the lens of transgender studies—drawing upon scholars including Susan Stryker, Sandy...
Show moreTransgender identity and the concept of artificial intelligence are constructed and understood through dichotomies such as natural/unnatural and real/artificial, with each dichotomy informing the other; what is “unnatural” is often deemed to be a mimicry of the “natural,” therefore a false representation of what is “real.” By surveying various classic SF texts and their portrayal of AI characters through the lens of transgender studies—drawing upon scholars including Susan Stryker, Sandy Stone, and Florence Ashley—I assert that artificiality itself is a construction formed by cisnormative ideals and standards to exclude certain others (namely, transgender people) that requires reframing. I examine how these representations in works such as Richard Powers’s Galatea 2.2 reveal and articulate the constructed dichotomies and cultural narratives which surround transgender identity, as well as how contemporary, trans-authored works such as Annalee Newitz’s Autonomous can offer tools for responding to and reconfiguring those dichotomies.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014196
Subject Headings: Transgender fiction, Artificial intelligence in literature
Format: Document (PDF)

Title: Self-Contained Soft Robotic Jellyfish with Water-Filled Bending Actuators and Positional Feedback Control.
Creator: Frame, Jennifer, Engeberg, Erik, Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: This thesis concerns the design, construction, control, and testing of a novel self-contained soft robotic vehicle; the JenniFish is a free-swimming jellyfish-like soft robot that could be adapted for a variety of uses, including: low frequency, low power sensing applications; swarm robotics; a STEM classroom learning resource; etc. The final vehicle design contains eight PneuNet-type actuators radially situated around a 3D printed electronics canister. These propel the vehicle when inflated...
Show moreThis thesis concerns the design, construction, control, and testing of a novel self-contained soft robotic vehicle; the JenniFish is a free-swimming jellyfish-like soft robot that could be adapted for a variety of uses, including: low frequency, low power sensing applications; swarm robotics; a STEM classroom learning resource; etc. The final vehicle design contains eight PneuNet-type actuators radially situated around a 3D printed electronics canister. These propel the vehicle when inflated with water from its surroundings by impeller pumps; since the actuators are connected in two neighboring groups of four, the JenniFish has bi-directional movement capabilities. Imbedded resistive flex sensors provide actuator position to the vehicle’s PD controller. Other onboard sensors include an IMU and an external temperature sensor. Quantitative constrained load cell tests, both in-line and bending, as well as qualitative free-swimming video tests were conducted to find baseline vehicle performance capabilities. Collected metrics compare well with existing robotic jellyfish.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004656, http://purl.flvc.org/fau/fd/FA00004656
Subject Headings: Adaptive control systems, Artificial intelligence, Autonomous robots, Computational intelligence, Robotics
Format: Document (PDF)

Title: Analysis of machine learning algorithms on bioinformatics data of varying quality.
Creator: Shanab, Ahmad Abu, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: One of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One...
Show moreOne of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One particular challenge encountered when analyzing bioinformatics datasets is data noise, which refers to incorrect or missing values in datasets. Noise can be introduced as a result of experimental errors (e.g. faulty microarray chips, insufficient resolution, image corruption, and incorrect laboratory procedures), as well as other errors (errors during data processing, transfer, and/or mining). A special type of data noise called class noise, which occurs when an instance/example is mislabeled. Previous research showed that class noise has a detrimental impact on machine learning algorithms (e.g. worsened classification performance and unstable feature selection). In addition to data noise, gene expression datasets can suffer from the problems of high dimensionality (a very large feature space) and class imbalance (unequal distribution of instances between classes). As a result of these inherent problems, constructing accurate classification models becomes more challenging.
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Date Issued: 2015
PURL: http://purl.flvc.org./fau/fd/FA00004425, http://purl.flvc.org/fau/fd/FA00004425
Subject Headings: Artificial intelligence, Bioinformatics, Machine learning, System design, Theory of computation
Format: Document (PDF)

Title: Parallel Distributed Deep Learning on Cluster Computers.
Creator: Kennedy, Robert Kwan Lee, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Deep Learning is an increasingly important subdomain of arti cial intelligence. Deep Learning architectures, arti cial neural networks characterized by having both a large breadth of neurons and a large depth of layers, bene ts from training on Big Data. The size and complexity of the model combined with the size of the training data makes the training procedure very computationally and temporally expensive. Accelerating the training procedure of Deep Learning using cluster computers faces...
Show moreDeep Learning is an increasingly important subdomain of arti cial intelligence. Deep Learning architectures, arti cial neural networks characterized by having both a large breadth of neurons and a large depth of layers, bene ts from training on Big Data. The size and complexity of the model combined with the size of the training data makes the training procedure very computationally and temporally expensive. Accelerating the training procedure of Deep Learning using cluster computers faces many challenges ranging from distributed optimizers to the large communication overhead speci c to a system with o the shelf networking components. In this thesis, we present a novel synchronous data parallel distributed Deep Learning implementation on HPCC Systems, a cluster computer system. We discuss research that has been conducted on the distribution and parallelization of Deep Learning, as well as the concerns relating to cluster environments. Additionally, we provide case studies that evaluate and validate our implementation.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013080
Subject Headings: Deep learning., Neural networks (Computer science)., Artificial intelligence., Machine learning.
Format: Document (PDF)

Title: Machine learning techniques for alleviating inherent difficulties in bioinformatics data.
Creator: Dittman, David, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In response to the massive amounts of data that make up a large number of bioinformatics datasets, it has become increasingly necessary for researchers to use computers to aid them in their endeavors. With difficulties such as high dimensionality, class imbalance, noisy data, and difficult to learn class boundaries, being present within the data, bioinformatics datasets are a challenge to work with. One potential source of assistance is the domain of data mining and machine learning, a field...
Show moreIn response to the massive amounts of data that make up a large number of bioinformatics datasets, it has become increasingly necessary for researchers to use computers to aid them in their endeavors. With difficulties such as high dimensionality, class imbalance, noisy data, and difficult to learn class boundaries, being present within the data, bioinformatics datasets are a challenge to work with. One potential source of assistance is the domain of data mining and machine learning, a field which focuses on working with these large amounts of data and develops techniques to discover new trends and patterns that are hidden within the data and to increases the capability of researchers and practitioners to work with this data. Within this domain there are techniques designed to eliminate irrelevant or redundant features, balance the membership of the classes, handle errors found in the data, and build predictive models for future data.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004362, http://purl.flvc.org/fau/fd/FA00004362
Subject Headings: Artificial intelligence, Bioinformatics, Machine learning, System design, Theory of computation
Format: Document (PDF)

Title: Performance analysis of back propagation algorithm using artificial neural networks.
Creator: Malladi, Sasikanth., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Backpropagation is a standard algorithm that is widely employed in many neural networks. Due to its wide acceptance and implementation, a standard benchmark for evaluating the performance of the algorithm is a handy tool for software design and development. The object of this thesis is to propose the use of the classic XOR problem for the performance evaluation of the backpropagation algorithm, with some variations on the input data sets. This thesis covers background work in this area and...
Show moreBackpropagation is a standard algorithm that is widely employed in many neural networks. Due to its wide acceptance and implementation, a standard benchmark for evaluating the performance of the algorithm is a handy tool for software design and development. The object of this thesis is to propose the use of the classic XOR problem for the performance evaluation of the backpropagation algorithm, with some variations on the input data sets. This thesis covers background work in this area and discusses the results obtained by other researchers. A series of test cases are then developed and run to perform the performance analysis of the backpropagation algorithm. As the performance of the networks depends strongly on the inputs, the effect of variation of the design parameters for the networks are evaluated and discussed.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12961
Subject Headings: Back propagation (Artificial intelligence), Neural networks (Computer science)
Format: Document (PDF)

Title: OPTIMIZED DEEP LEARNING ARCHITECTURES AND TECHNIQUES FOR EDGE AI.
Creator: Zaniolo, Luiz, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The recent rise of artificial intelligence (AI) using deep learning networks allowed the development of automatic solutions for many tasks that, in the past, were seen as impossible to be performed by a machine. However, deep learning models are getting larger, need significant processing power to train, and powerful machines to use it. As deep learning applications become ubiquitous, another trend is taking place: the growing use of edge devices. This dissertation addresses selected...
Show moreThe recent rise of artificial intelligence (AI) using deep learning networks allowed the development of automatic solutions for many tasks that, in the past, were seen as impossible to be performed by a machine. However, deep learning models are getting larger, need significant processing power to train, and powerful machines to use it. As deep learning applications become ubiquitous, another trend is taking place: the growing use of edge devices. This dissertation addresses selected technical issues associated with edge AI, proposes novel solutions to them, and demonstrates the effectiveness of the proposed approaches. The technical contributions of this dissertation include: (i) architectural optimizations to deep neural networks, particularly the use of patterned stride in convolutional neural networks used for image classification; (ii) use of weight quantization to reduce model size without hurting its accuracy; (iii) systematic evaluation of the impact of image imperfections on skin lesion classifiers' performance in the context of teledermatology; and (iv) a new approach for code prediction using natural language processing techniques, targeted at edge devices.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013822
Subject Headings: Artificial intelligence, Deep learning (Machine learning), Neural networks (Computer science)
Format: Document (PDF)

Title: Using a cerebellar model arithmetic computer (CMAC) neural network to control an autonomous underwater vehicle.
Creator: Comoglio, Rick F., Florida Atlantic University, Pandya, Abhijit S.
Abstract/Description: The design of an Autonomous Undersea Vehicle (AUV) control system is a significant challenge in-light of the highly uncertain nature of the ocean environment together with partially known nonlinear vehicle dynamics. This thesis describes a Neural Network architecture called Cerebellar Model Arithmetic Computer (CMAC). CMAC is used to control a model of an Autonomous Underwater Vehicle. The AUV model consists of two input parameters, the rudder and stern plane deflections, controlling six...
Show moreThe design of an Autonomous Undersea Vehicle (AUV) control system is a significant challenge in-light of the highly uncertain nature of the ocean environment together with partially known nonlinear vehicle dynamics. This thesis describes a Neural Network architecture called Cerebellar Model Arithmetic Computer (CMAC). CMAC is used to control a model of an Autonomous Underwater Vehicle. The AUV model consists of two input parameters, the rudder and stern plane deflections, controlling six output parameters; forward velocity, vertical velocity, pitch angle, side velocity, roll angle, and yaw angle. Properties of CMAC and results of computer simulations for identification and control of the AUV model are presented.
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Date Issued: 1991
PURL: http://purl.flvc.org/fcla/dt/14762
Subject Headings: Neural networks (Computer science), Artificial intelligence, Submersibles--Automatic control
Format: Document (PDF)

Title: DIGITAL TRANSFORMATION OF HEALTHCARE USING ARTIFICIAL INTELLIGENCE.
Creator: Gogova, Jennifer, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Digital transformation is rapidly changing the healthcare industry, and artificial intelligence (AI) is a critical component in this evolution. This thesis investigates three selected challenges that might delay the adoption of AI in healthcare and proposes ways to address them successfully. Challenge #1 states that healthcare professionals may not feel sufficiently knowledgeable about AI. This is addressed by Contribution #1 which is a guide for self-actualization in AI for healthcare...
Show moreDigital transformation is rapidly changing the healthcare industry, and artificial intelligence (AI) is a critical component in this evolution. This thesis investigates three selected challenges that might delay the adoption of AI in healthcare and proposes ways to address them successfully. Challenge #1 states that healthcare professionals may not feel sufficiently knowledgeable about AI. This is addressed by Contribution #1 which is a guide for self-actualization in AI for healthcare professionals. Challenge #2 explores the concept of transdisciplinary teams needing a work protocol to deliver successful results. This is addressed by Contribution #2 which is a step-by-step protocol for medical and AI researchers working on data-intensive projects. Challenge #3 states that the NIH All of Us Research Hub has a steep learning curve, and this is addressed by Contribution #3 which is a pilot project involving transdisciplinary teams using All of Us datasets.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014179
Subject Headings: Healthcare, Medical care, Artificial intelligence—Medical applications
Format: Document (PDF)

Title: COMPARISON OF CLASSIFYING HUMAN ACTIONS FROM BIOLOGICAL MOTION WITH ARTIFICIAL NEURAL NETWORKS.
Creator: Wong, Rachel, Barenholtz, Elan, Florida Atlantic University, Department of Psychology, Charles E. Schmidt College of Science
Abstract/Description: The ability to recognize human actions is essential for individuals to navigate through their daily life. Biological motion is the primary mechanism people use to recognize actions quickly and efficiently, but their precision can vary. The development of Artificial Neural Networks (ANNs) has the potential to enhance the efficiency and effectiveness of accomplishing common human tasks, including action recognition. However, the performance of ANNs in action recognition depends on the type of...
Show moreThe ability to recognize human actions is essential for individuals to navigate through their daily life. Biological motion is the primary mechanism people use to recognize actions quickly and efficiently, but their precision can vary. The development of Artificial Neural Networks (ANNs) has the potential to enhance the efficiency and effectiveness of accomplishing common human tasks, including action recognition. However, the performance of ANNs in action recognition depends on the type of model used. This study aimed to improve the accuracy of ANNs in action classification by incorporating biological motion information into the input conditions. The study used the UCF Crime dataset, a dataset containing surveillance videos of normal and criminal activity, and extracted biological motion information with OpenPose, a pose estimation ANN. OpenPose adjusted to create four condition types using the biological motion information (image-only, image with biological motion, only biological motion, and coordinates only) and used either a 3-Dimensional Convolutional Neural Network (3D CNN) or a Gated Recurrent Unit (GRU) to classify the actions. Overall, the study found that including biological motion information in the input conditions led to higher accuracy regardless of the number of action categories in the dataset. Moreover, the GRU model using the 'coordinates only' condition had the best accuracy out of all the action classification models. These findings suggest that incorporating biological motion into input conditions and using numerical format input data can benefit the development of accurate action classification models using ANNs.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014164
Subject Headings: Neural networks (Computer science), Human activity recognition, Artificial intelligence
Format: Document (PDF)

Artificial intelligence (32)	+ -
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Barenholtz, Elan (4)	+ -
Dorothy F. Schmidt College of Arts and Letters (4)	+ -
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