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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: 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: QUANTIFICATION OF PERMAFROST THAW DEPTH AND SNOW DEPTH IN INTERIOR ALASKA AT MULTIPLE SCALES USING FIELD, AIRBORNE, AND SPACEBORNE DATA.
Creator: Brodylo, David, Zhang, Caiyun, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
Abstract/Description: Much of Interior Alaska contains permafrost, which is a permanently frozen layer found within or at the surface of the Earth. Historically, this permafrost has experienced relative stability, with limited thaw during warmer summer months and fire events. However, largely due to the impact of a warming climate, among other factors, permafrost that would typically experience limited thawing during the summer season has recently been thawing at an unprecedented rate. Trapped by this layer of...
Show moreMuch of Interior Alaska contains permafrost, which is a permanently frozen layer found within or at the surface of the Earth. Historically, this permafrost has experienced relative stability, with limited thaw during warmer summer months and fire events. However, largely due to the impact of a warming climate, among other factors, permafrost that would typically experience limited thawing during the summer season has recently been thawing at an unprecedented rate. Trapped by this layer of permafrost is a large quantity of carbon (C), which could be released into the atmosphere as greenhouse gases such as carbon dioxide (CO2) and methane (CH4). Due to the remoteness of the Arctic, there is a lack of yearly recorded permafrost thaw depth and snow depth values across much of the region. As such, the focus of this research was to establish a framework to identify how permafrost thaw depth and snow depth can be predicted across both a 1 km2 local scale and a 100 km2 regional scale in Interior Alaska by a combination of 1 m2 field data, airborne and spaceborne remote sensing products, and object-based machine learning techniques from 2014 – 2022. Machine learning techniques Random Forest, Support Vector Machine, k-Nearest Neighbor, Multiple Linear Regression, and Ensemble Analysis were applied to predict the permafrost thaw depth and snow depth. Results indicated that this methodology was able to successfully upscale both the 1 m2 field permafrost thaw depth and snow depth data to a 1 km2 local scale before successfully further upscaling the estimated results to a 100 km2 regional scale, while also linking the estimated values with ecotypes. The best results were produced by Ensemble Analysis, which tended to have the highest Pearson’s Correlation Coefficient, alongside the lowest Mean Absolute Error and Root Mean Square Error. Both Random Forest and k-Nearest Neighbor also provided encouraging results. The presence or absence of a thick canopy cover was strongly connected with thaw depth and snow depth estimates. Image resolution was an important factor when upscaling field data to the local scale, however it was overall less critical for further upscaling to the regional scale.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014229
Subject Headings: Permafrost--Alaska, Remote sensing, Machine learning
Format: Document (PDF)

Title: DATA-DRIVEN ATTRACTOR IDENTIFICATION VIA COMPUTATIONAL HOMOLOGY AND MACHINE LEARNING.
Creator: Tatasciore, Paul, Wille, Luc T., Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description: Dynamical systems play a pivotal role across various scientific domains, encompassing disciplines from physics to biology and engineering. The long-term behavior of these systems hinges on the structure of their attractors, with many exhibiting multistability characterized by multiple minimal attractors. Understanding the structure of these attractors and their corresponding basins is a central theme in dynamical systems theory. In recent years, machine learning algorithms have emerged as...
Show moreDynamical systems play a pivotal role across various scientific domains, encompassing disciplines from physics to biology and engineering. The long-term behavior of these systems hinges on the structure of their attractors, with many exhibiting multistability characterized by multiple minimal attractors. Understanding the structure of these attractors and their corresponding basins is a central theme in dynamical systems theory. In recent years, machine learning algorithms have emerged as potent tools for clustering, prediction, and modeling complex data. By harnessing the capabilities of neural networks along with techniques from topological data analysis, in particular persistence homology, we can construct surrogate models of system asymptotics. This approach also allows for the decomposition of phase space into polygonal regions and the identification of plausible attracting neighborhoods, facilitating homological Conley index computation at reduced computational expense compared to current methods. Through various illustrative examples, we demonstrate that sufficiently low training loss yields constructed neighborhoods whose homological Conley indices aligns with a priori knowledge of the dynamics.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014475
Subject Headings: Dynamical systems, Computational physics, Machine learning
Format: Document (PDF)

Title: MACHINE LEARNING TO PREDICT BUSINESS SUCCESS: THEORIES, FEATURES, AND MODELS.
Creator: Gangwani, Divya, Zhu, Xingquan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Businesses are the driving force behind economic systems and are the lifeline of the community as they help in the prosperity and growth of the nation. Hence it is important for the business to succeed in the market. The business’s success provides economic stability and sustainability that helps preserve resources for future generations. The success of a business is not only important to the owners but is also critical to the regional/domestic economic system, or even the global economy....
Show moreBusinesses are the driving force behind economic systems and are the lifeline of the community as they help in the prosperity and growth of the nation. Hence it is important for the business to succeed in the market. The business’s success provides economic stability and sustainability that helps preserve resources for future generations. The success of a business is not only important to the owners but is also critical to the regional/domestic economic system, or even the global economy. Recent years have witnessed many new emerging businesses with tremendous success, such as Google, Apple, Facebook etc.. Yet, millions of businesses also fail or fade out within a rather short period of time. Finding patterns/factors connected to the business rise and fall remains a long-lasting question that puzzles many economists, entrepreneurs, and government officials. Recent advancements in artificial intelligence, especially machine learning, has lent researchers the powers to use data to model and predict business success. However, due to the data-driven nature of all machine learning methods, existing approaches are rather domain-driven and ad-hoc in their design and validations, particularly in the field of business prediction. The main challenge of business success prediction is twofold: (1) Identifying variables for defining business success; (2) Feature selection and feature engineering based on three main categories Investment, Business, and Market, each of which is focused on modeling a business from a particular perspective, such as sales, management, innovation etc.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014506
Subject Headings: Machine learning, Business enterprises, Business education
Format: Document (PDF)

Title: NOVEL TECHNIQUES FOR HANDLING IMBALANCED DATA WITH UNSUPERVISED METHODS.
Creator: Kennedy, Robert Kwan Lee, Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: In the modern data landscape, vast amounts of unlabeled data are continuously generated, necessitating development of robust unsupervised techniques for handling unlabeled data. This is the case for fraud detection and healthcare sectors analyses, where data is often significantly imbalanced. This dissertation focuses on novel techniques for handling imbalanced data, with specific emphasis on a novel unsupervised class labeling technique for unlabeled fraud detection datasets and unlabeled...
Show moreIn the modern data landscape, vast amounts of unlabeled data are continuously generated, necessitating development of robust unsupervised techniques for handling unlabeled data. This is the case for fraud detection and healthcare sectors analyses, where data is often significantly imbalanced. This dissertation focuses on novel techniques for handling imbalanced data, with specific emphasis on a novel unsupervised class labeling technique for unlabeled fraud detection datasets and unlabeled cognitive datasets. Traditional supervised machine learning relies on labeled data, which is often expensive and difficult to create, particularly in domains requiring expert input. Additionally, such datasets suffer from challenges associated with class imbalance, where one class has significantly fewer examples than another, complicating model training and significantly reducing performance. The primary objectives of this dissertation include developing a novel unsupervised cleaning method, and an innovative unsupervised class labeling method. We validate and evaluate our methods across various datasets, which include two Medicare fraud detection datasets, a credit card fraud detection dataset, and three datasets used for detecting cognitive decline. Our unique approach involves using an unsupervised autoencoder to learn from dataset features and synthesize labels. Primarily targeting imbalanced datasets, but still effective for balanced datasets, our method calculates an error metric for each instance. This metric is used to distinguish between fraudulent and legitimate cases, allowing us to assign a binary class label. To further improve label generation, we integrate an unsupervised feature selection method that ranks and identifies the most important features without using class labels.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014547
Subject Headings: Machine learning, Big data, Computer science
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: 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: IMAGE QUALITY AND BEAUTY CLASSIFICATION USING DEEP LEARNING.
Creator: Golchubian, Arash, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding...
Show moreThe field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding artifacts, saturation, and lighting, as well as for its’ aesthetic appeal. The purpose of such a mechanism could be detecting and discarding noisy, blurry, dark, or over exposed images, as well as detecting images that would be considered beautiful by a majority of viewers. In this dissertation, the detection of various quality and aesthetic aspects of an image using CNNs is explored. This research produced two datasets that are manually labeled for quality issues such as blur, poor lighting, and digital noise, and for their aesthetic qualities, and Convolutional Neural Networks were designed and trained using these datasets. Lastly, two case studies were performed to show the real-world impact of this research to traffic sign detection and medical image diagnosis.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014029
Subject Headings: Deep learning (Machine learning), Computer vision, Aesthetics, Image Quality
Format: Document (PDF)

Title: COMPUTATION IN SELF-ATTENTION NETWORKS.
Creator: Morris, Paul, Barenholtz, Elan, Florida Atlantic University, Center for Complex Systems and Brain Sciences, Charles E. Schmidt College of Science
Abstract/Description: Neural network models with many tunable parameters can be trained to approximate functions that transform a source distribution, or dataset, into a target distribution of interest. In contrast to low-parameter models with simple governing equations, the dynamics of transformations learned in deep neural network models are abstract and the correspondence of dynamical structure to predictive function is opaque. Despite their “black box” nature, neural networks converge to functions that...
Show moreNeural network models with many tunable parameters can be trained to approximate functions that transform a source distribution, or dataset, into a target distribution of interest. In contrast to low-parameter models with simple governing equations, the dynamics of transformations learned in deep neural network models are abstract and the correspondence of dynamical structure to predictive function is opaque. Despite their “black box” nature, neural networks converge to functions that implement complex tasks in computer vision, Natural Language Processing (NLP), and the sciences when trained on large quantities of data. Where traditional machine learning approaches rely on clean datasets with appropriate features, sample densities, and label distributions to mitigate unwanted bias, modern Transformer neural networks with self-attention mechanisms use Self-Supervised Learning (SSL) to pretrain on large, unlabeled datasets scraped from the internet without concern for data quality. SSL tasks have been shown to learn functions that match or outperform their supervised learning counterparts in many fields, even without task-specific finetuning. The recent paradigm shift to pretraining large models with massive amounts of unlabeled data has given credibility to the hypothesis that SSL pretraining can produce functions that implement generally intelligent computations.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014061
Subject Headings: Neural networks (Computer science), Machine learning, Self-supervised learning
Format: Document (PDF)

Title: NETWORK INTRUSION DETECTION AND DEEP LEARNING MECHANISMS.
Creator: Chatterjee, Suvosree, Cardei, Ionut, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Cyber attack is a strong threat to the digital world. So, it’s very essential to keep the network safe. Network Intrusion Detection system is the system to address this problem. Network Intrusion Detection system functions like a firewall, and monitors incoming and outgoing traffic like ingress and egress filtering fire wall. Network Intrusion Detection System does anomaly and hybrid detection for detecting known and unknown attacks. My thesis discusses about the several network cyber attacks...
Show moreCyber attack is a strong threat to the digital world. So, it’s very essential to keep the network safe. Network Intrusion Detection system is the system to address this problem. Network Intrusion Detection system functions like a firewall, and monitors incoming and outgoing traffic like ingress and egress filtering fire wall. Network Intrusion Detection System does anomaly and hybrid detection for detecting known and unknown attacks. My thesis discusses about the several network cyber attacks we face nowadays and I created several Deep learning models to detect accurately, I used NSL-KDD dataset which is a popular dataset, that contains several network attacks. After experimenting with different deep learning models I found some disparities in the training accuracy and validation accuracy, which is a clear indication of overfitting. To reduce the overfitting I introduced regularization and dropout in the models and experimented with different hyperparameters.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014128
Subject Headings: Deep learning (Machine learning), Cyberterrorism, Intrusion detection systems (Computer security)
Format: Document (PDF)

Title: A UNIFIED SOFT SENSING FRAMEWORK FOR COMPLEX DYNAMICAL SYSTEMS.
Creator: Huang, Yu, Tang, Yufei, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: In the past few years, the development of complex dynamical networks or systems has stimulated great interest in the study of the principles and mechanisms underlying the Internet of things (IoT). IoT is envisioned as an intelligent network infrastructure with a vast number of ubiquitous smart devices present in diverse application domains and have already improved many aspects of daily life. Many overtly futuristic IoT applications acquire data gathered via distributed sensors that can be...
Show moreIn the past few years, the development of complex dynamical networks or systems has stimulated great interest in the study of the principles and mechanisms underlying the Internet of things (IoT). IoT is envisioned as an intelligent network infrastructure with a vast number of ubiquitous smart devices present in diverse application domains and have already improved many aspects of daily life. Many overtly futuristic IoT applications acquire data gathered via distributed sensors that can be uniquely identified, localized, and communicated with, i.e., the support of sensor networks. Soft-sensing models are in demand to support IoT applications to achieve the maximal exploitation of transforming the information of measurements into more useful knowledge, which plays essential roles in condition monitoring, quality prediction, smooth control, and many other essential aspects of complex dynamical systems. This in turn calls for innovative soft-sensing models that account for scalability, heterogeneity, adaptivity, and robustness to unpredictable uncertainties. The advent of big data, the advantages of ever-evolving deep learning (DL) techniques (where models use multiple layers to extract multi-levels of feature representations progressively), as well as ever-increasing processing power in hardware, has triggered a proliferation of research that applies DL to soft-sensing models. However, many critical questions need to be further investigated in the deep learning-based soft-sensing.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013993
Subject Headings: Dynamical systems, Dynamics, Sensor networks, Deep learning (Machine learning)
Format: Document (PDF)

Title: AN ARTIFICIAL INTELLIGENCE DRIVEN FRAMEWORK FOR MEDICAL IMAGING.
Creator: Sanghvi, Harshal A., Agarwal, Ankur, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The major objective of this dissertation was to create a framework which is used for medical image diagnosis. In this diagnosis, we brought classification and diagnosing of diseases through an Artificial Intelligence based framework, including COVID, Pneumonia, and Melanoma cancer through medical images. The algorithm ran on multiple datasets. A model was developed which detected the medical images through changing hyper-parameters. The aim of this work was to apply the new transfer learning...
Show moreThe major objective of this dissertation was to create a framework which is used for medical image diagnosis. In this diagnosis, we brought classification and diagnosing of diseases through an Artificial Intelligence based framework, including COVID, Pneumonia, and Melanoma cancer through medical images. The algorithm ran on multiple datasets. A model was developed which detected the medical images through changing hyper-parameters. The aim of this work was to apply the new transfer learning framework DenseNet-201 for the diagnosis of the diseases and compare the results with the other deep learning models. The novelty in the proposed work was modifying the Dense Net 201 Algorithm, changing hyper parameters (source weights, Batch Size, Epochs, Architecture (number of neurons in hidden layer), learning rate and optimizer) to quantify the results. The novelty also included the training of the model by quantifying weights and in order to get more accuracy. During the data selection process, the data were cleaned, removing all the outliers. Data augmentation was used for the novel architecture to overcome overfitting and hence not producing false absurd results the computational performance was also observed. The proposed model results were also compared with the existing deep learning models and the algorithm was also tested on multiple datasets.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014274
Subject Headings: Diagnostic imaging, Artificial intelligence, Deep learning (Machine learning)
Format: Document (PDF)

Title: FEATURE REPRESENTATION LEARNING FOR ONLINE ADVERTISING AND RECOMMENDATIONS.
Creator: Gharibshah, Zhabiz, Zhu, Xingquan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Online advertising [100], as a multi-billion dollar business, provides a common marketing experience when people access online services using electronic devices, such as desktop computers, tablets, smartphones, and so on. Using the Internet as a means of advertising, different stakeholders take actions in the background to provide and deliver advertisements to users through numerous platforms, such as search engines, news sites, and social networks, where dedicated spots of areas are used to...
Show moreOnline advertising [100], as a multi-billion dollar business, provides a common marketing experience when people access online services using electronic devices, such as desktop computers, tablets, smartphones, and so on. Using the Internet as a means of advertising, different stakeholders take actions in the background to provide and deliver advertisements to users through numerous platforms, such as search engines, news sites, and social networks, where dedicated spots of areas are used to display advertisements (ads) along with search results, posts, or page content. Online advertising is mainly based on dynamically selecting ads through a real-time bidding (or auction) mechanism. Predicting user responses like clicking ads in e-commerce platforms and internet-based advertising systems, as the first measurable user response, is an essential step for many digital advertising and recommendation systems to capture the user’s propensity to follow up actions, such as purchasing a product or subscribing to a service. To maximize revenue and user satisfaction, online advertising platforms must predict the expected user behavior of each displayed advertisement and maximize the user’s expectations of clicking [28]. Based on this observed feedback, these systems are tailored to user preferences to decide the order in that ads or any promoted content should be served to them. This objective provides an incentive to develop new research by using ideas derived from different domains like machine learning and data mining combined with models for information retrieval and mathematical optimization. They introduce different machine learning and data mining methods that employ deep learning-based predictive models to learn the representation of input features with the aim of user response prediction. Feature representation learning is known as a fundamental task on how to input information is going to be represented in machine learning models. A good feature representation learning method that seeks to learn low-dimensional embedding vectors is a key factor for the success of many downstream analytics tasks, such as click-through prediction and conversion prediction in recommendation systems and online advertising platforms.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014269
Subject Headings: Internet advertising, Deep learning (Machine learning), Internet marketing
Format: Document (PDF)

Title: DATA AUGMENTATION IN DEEP LEARNING.
Creator: Shorten, Connor, Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Recent successes of Deep Learning-powered AI are largely due to the trio of: algorithms, GPU computing, and big data. Data could take the shape of hospital records, satellite images, or the text in this paragraph. Deep Learning algorithms typically need massive collections of data before they can make reliable predictions. This limitation inspired investigation into a class of techniques referred to as Data Augmentation. Data Augmentation was originally developed as a set of label-preserving...
Show moreRecent successes of Deep Learning-powered AI are largely due to the trio of: algorithms, GPU computing, and big data. Data could take the shape of hospital records, satellite images, or the text in this paragraph. Deep Learning algorithms typically need massive collections of data before they can make reliable predictions. This limitation inspired investigation into a class of techniques referred to as Data Augmentation. Data Augmentation was originally developed as a set of label-preserving transformations used in order to simulate large datasets from small ones. For example, imagine developing a classifier that categorizes images as either a “cat” or a “dog”. After initial collection and labeling, there may only be 500 of these images, which are not enough data points to train a Deep Learning model. By transforming these images with Data Augmentations such as rotations and brightness modifications, more labeled images are available for model training and classification! In addition to applications for learning from limited labeled data, Data Augmentation can also be used for generalization testing. For example, we can augment the test set to set the visual style of images to “winter” and see how that impacts the performance of a stop sign detector. The dissertation begins with an overview of Deep Learning methods such as neural network architectures, gradient descent optimization, and generalization testing. Following an initial description of this technology, the dissertation explains overfitting. Overfitting is the crux of Deep Learning methods in which improvements to the training set do not lead to improvements on the testing set. To the rescue are Data Augmentation techniques, of which the Dissertation presents an overview of the augmentations used for both image and text data, as well as the promising potential of generative data augmentation with models such as ChatGPT. The dissertation then describes three major experimental works revolving around CIFAR-10 image classification, language modeling a novel dataset of Keras information, and patient survival classification from COVID-19 Electronic Health Records. The dissertation concludes with a reflection on the evolution of limitations of Deep Learning and directions for future work.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014228
Subject Headings: Deep learning (Machine learning), Artificial intelligence, Data augmentation
Format: Document (PDF)

Title: Computer-aided diagnosis of skin cancers using dermatology images.
Creator: Gilani, Syed Qasim, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Skin cancer is a prevalent cancer that significantly contributes to global mortality rates. Early detection is crucial for a high survival rate. Dermatologists primarily rely on visual inspection to diagnose skin cancers, but this method is inaccurate. Deep learning algorithms can enhance the diagnostic accuracy of skin cancers. However, these algorithms require substantial labeled data for effective training. Acquiring annotated data for skin cancer classification is time-consuming,...
Show moreSkin cancer is a prevalent cancer that significantly contributes to global mortality rates. Early detection is crucial for a high survival rate. Dermatologists primarily rely on visual inspection to diagnose skin cancers, but this method is inaccurate. Deep learning algorithms can enhance the diagnostic accuracy of skin cancers. However, these algorithms require substantial labeled data for effective training. Acquiring annotated data for skin cancer classification is time-consuming, expensive, and necessitates expert annotation. Moreover, skin cancer datasets often suffer from imbalanced data distribution. Generative Adversarial Networks (GANs) can be used to overcome the challenges of data scarcity and lack of labels by automatically generating skin cancer images. However, training and testing data from different distributions can introduce domain shift and bias, impacting the model’s performance. This dissertation addresses this issue by developing deep learning-based domain adaptation models. Additionally, this research emphasizes deploying deep learning models on hardware to enable real-time skin cancer detection, facilitating accurate diagnoses by dermatologists. Deploying conventional deep learning algorithms on hardware is not preferred due to the problem of high resource consumption. Therefore, this dissertation presents spiking neural network-based (SNN) models designed specifically for hardware implementation. SNNs are preferred for their power-efficient behavior and suitability for hardware deployment.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014233
Subject Headings: Deep learning (Machine learning), Diagnostic imaging, Skin--Cancer--Diagnosis
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: 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: AN ADAPTIVE DEEP LEARNING FRAMEWORK TO ENHANCE THE PERFORMANCE OF MONITORING SYSTEMS FOR BIOMEDICAL APPLICATIONS.
Creator: Shuqair, Mustafa, Ghoraani, Behnaz, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Deep learning strategies combined with wearable sensors have advanced the capabilities of monitoring systems in biomedical applications, offering precise and efficient solutions for diagnosing and managing diseases. However, applying these systems faces several challenges. One of the challenges is the diminishing performance when these systems encounter new data with more complex patterns than those seen before. Another challenge is the limited availability of labeled data, on which deep...
Show moreDeep learning strategies combined with wearable sensors have advanced the capabilities of monitoring systems in biomedical applications, offering precise and efficient solutions for diagnosing and managing diseases. However, applying these systems faces several challenges. One of the challenges is the diminishing performance when these systems encounter new data with more complex patterns than those seen before. Another challenge is the limited availability of labeled data, on which deep learning-based systems depend highly. Additionally, obtaining high-quality labeled data to train deep learning models is often expensive, requiring significant time and resources. Another significant challenge is ensuring the practicality, accessibility, and convenience of the monitoring systems. This dissertation proposes an innovative deep learning framework to overcome these challenges and improve system generalization performance in classification and regression tasks, specifically monitoring patients with neurological disorders like Parkinson’s.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014542
Subject Headings: Deep learning (Machine learning), Monitoring systems, Biomedical engineering
Format: Document (PDF)

Title: ARIAL PHOTOGRAMMETRY AND LIDAR POINT CLOUD REGISTRATION USING DEEP LEARNING.
Creator: Mandal, Anil Kumar, Yong, Yan, Su, Hongbo, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: This research develops a new pipeline for large-scale point cloud registration by integrating chunked-based data processing within feature-based deep learning models to align aerial LiDAR and UAV photogrammetric data. By processing data in manageable chunks, this approach optimizes memory usage while retaining the spatial continuity essential for precise alignment across expansive datasets. Three models—DeepGMR, FMR, and PointNetLK—were evaluated within this framework, demonstrating the...
Show moreThis research develops a new pipeline for large-scale point cloud registration by integrating chunked-based data processing within feature-based deep learning models to align aerial LiDAR and UAV photogrammetric data. By processing data in manageable chunks, this approach optimizes memory usage while retaining the spatial continuity essential for precise alignment across expansive datasets. Three models—DeepGMR, FMR, and PointNetLK—were evaluated within this framework, demonstrating the pipeline’s robustness in handling datasets with up to 49.73 million points. The models achieved average epoch times of 35 seconds for DeepGMR, 112 seconds for FMR, and 333 seconds for PointNetLK. Accuracy in alignment was also reliable, with rotation errors averaging 2.955, 1.966, and 1.918 degrees, and translation errors at 0.174, 0.191, and 0.175 meters, respectively. This scalable, high-performance pipeline offers a practical solution for spatial data processing, making it suitable for applications that require precise alignment in large, cross-source datasets, such as mapping, urban planning, and environmental analysis.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014538
Subject Headings: Deep learning (Machine learning), Photogrammetry, Three-dimensional modeling
Format: Document (PDF)

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