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Title: A COMPARATIVE STUDY OF STRUCTURED VERSUS UNSTRUCTURED TEXT DATA.
Creator: Cardenas, Erika, Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: In today’s world, data is generated at an unprecedented rate, and a significant portion of it is unstructured text data. The recent advancements in Natural Language Processing have enabled computers to understand and interpret human language. Data mining techniques were once unable to use text data due to the high dimensionality of text processing models. This limitation was overcome with the ability to represent data as text. This thesis aims to compare the predictive performance of...
Show moreIn today’s world, data is generated at an unprecedented rate, and a significant portion of it is unstructured text data. The recent advancements in Natural Language Processing have enabled computers to understand and interpret human language. Data mining techniques were once unable to use text data due to the high dimensionality of text processing models. This limitation was overcome with the ability to represent data as text. This thesis aims to compare the predictive performance of structured versus unstructured text data in two different applications. The first application is in the field of real estate. We compare the performance of tabular real-estate data and unstructured text descriptions of homes to predict the house price. The second application is in translating Electronic Health Records (EHR) tabular data to text data for survival classification of COVID-19 patients. Lastly, we present a range of strategies and perspectives for future research.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014220
Subject Headings: Natural language processing (Computer science), Text data mining
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: FIELD EXPERIMENT ON THE CAPACITY IMPACT OF VEHICLE AUTOMATION ON ELECTRIC VEHICLES (EVS) – CASE STUDY OF ADAPTIVE CRUISE CONTROL (ACC).
Creator: Majumder, Tasnim Anika, Kan, David, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: Today’s mainstream vehicles are partially automated via an Advanced Driver Assistance Feature (ADAS) known as Adaptive Cruise Control (ACC). ACC relies on data from onboard sensors to automatically adjust speed to maintain a safe following distance with the preceding vehicle. Contrary to expectations for automated vehicles, ACC may reduce capacity at bottlenecks because its delayed response and limited initial acceleration during queue discharge could increase the average headway. Fortunately...
Show moreToday’s mainstream vehicles are partially automated via an Advanced Driver Assistance Feature (ADAS) known as Adaptive Cruise Control (ACC). ACC relies on data from onboard sensors to automatically adjust speed to maintain a safe following distance with the preceding vehicle. Contrary to expectations for automated vehicles, ACC may reduce capacity at bottlenecks because its delayed response and limited initial acceleration during queue discharge could increase the average headway. Fortunately, when ACC is paired with fully electric vehicles (EVs), EV’s unique powertrain characteristics such as instantaneous torque and aggressive regenerative braking could allow ACC to adopt shorter headways and accelerate more swiftly to maintain shorter headways during queue discharge, therefore reverse the negative impact on capacity. This has been verified in a series of car following field experiments. Field experiments demonstrate that EVs with ACC can achieve a capacity as high as 3333 veh/hr/lane when cruising in steady state conditions at typical freeway speeds (60 mph and 55 mph) and arterial speeds (45 mph and 35 mph). Furthermore, speed fluctuations and disturbances that may come from queues forming at or near the bottleneck do not reduce the capacity, unlike ACC-equipped internal combustion engine (ICE) vehicles, making ACC-equipped EVs outperform ICE vehicles with ACC, as well as human drivers.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014283
Subject Headings: Automated vehicles, Electric vehicles, Adaptive control systems
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: DESIGN AND FAILURE ANALYSIS OF MULTI-COMPONENT MOORING LINES WITH NON-LINEAR POLYMER SPRINGS FOR FLOATING OFFSHORE WIND TURBINES.
Creator: McFadden, Jared, Mahfuz, Hassan, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: This research studied the effects of mooring line pretension, spring safe working load, and spring response curve on peak loads and platform surge. The maximum tension load from the optimized assembly was applied to a modelled section of 8-strand multiplait rope to study stress concentrations. The analyses yielded a mooring line pretensioned at 1250 kN with a 4500 kN safe working load degressive spring was optimal. Fatigue damage from 12-hour duration of 50-year storm conditions was 8.04 × 10...
Show moreThis research studied the effects of mooring line pretension, spring safe working load, and spring response curve on peak loads and platform surge. The maximum tension load from the optimized assembly was applied to a modelled section of 8-strand multiplait rope to study stress concentrations. The analyses yielded a mooring line pretensioned at 1250 kN with a 4500 kN safe working load degressive spring was optimal. Fatigue damage from 12-hour duration of 50-year storm conditions was 8.04 × 10−6. Infinite life is predicted at annual average conditions. The peak tension from 50-year storm conditions of 3671 kN and annual average conditions of 1388 kN was applied to the section model, yielding a maximum stress of 3.70 × 108 Pa and 2.01 × 108 Pa, respectively, from friction and longitudinal compression of the rope’s cross section. The maximum stress from the static structural analysis was 33.5% of polyester’s ultimate strength, based on the maximum stress failure criterion.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014245
Subject Headings: Wind turbines--Design and construction, Wind turbines--Testing, Deep-sea moorings
Format: Document (PDF)

Title: CORROSION MONITORING AND ANALYSIS OF REINFORCED CONCRETE: CORROSION RESISTANT ALLOYS AFTER LONG TERM EXPOSURE TO CHLORIDES.
Creator: Taylor, Redmayne, Presuel-Moreno, Francisco, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Reinforced concrete (RC) is the building block of modern architecture and industry. The failure of which is costly and dangerous. Typically made with carbon steel rebars, corrosion resistant alloys provide an alternative method of delaying failure. Stainless steels, while more expensive than carbon steels, provide excellent corrosion resistance, but less is known about the long term monitoring of corrosion activity for stainless steel than for carbon steel. This study looks at samples...
Show moreReinforced concrete (RC) is the building block of modern architecture and industry. The failure of which is costly and dangerous. Typically made with carbon steel rebars, corrosion resistant alloys provide an alternative method of delaying failure. Stainless steels, while more expensive than carbon steels, provide excellent corrosion resistance, but less is known about the long term monitoring of corrosion activity for stainless steel than for carbon steel. This study looks at samples prepared between 2005 and 2009 using 304SS, 316SS, and 2304SS rebars, as well as SMI and Stelax stainless steel clad carbon steel reinforcements embedded in three different concrete mixes. These selected samples are split into two exposure environments, inside humidity chambers within the laboratory and outdoor exposure. Measurements reported here were made monthly over the course of 250 plus days using the Galvanostatic Pulse method, Electrochemical Impedance Spectroscopy, and a Gecor 8 device. These methods were used to determine corrosion current, isolated corrosion current density, and solution resistance. Corrosion current density values calculated from measurements by the Galvanostatic Pulse and Electrochemical Impedance Spectroscopy method are too small to indicate corrosion, based on value ranges provided by Andrade. However, Gecor 8 corrosion current density values indicate low levels or moderate levels of corrosion for all samples compared to the Andrade’s value ranges. The area used by the Gecor is unknown, so it’s possible this is driving up the measured values.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014258
Subject Headings: Reinforced concrete, Corrosion resistant alloys, Carbon steel, Corrosion
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: FIELD EXPERIMENTS ON ADAPTIVE CRUISE CONTROL (ACC) CAR FOLLOWING BEHAVIOR – IMPACT OF LANE CHANGES ON CAPACITY.
Creator: Khan, Md Mahede Hasan, Kan, David, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: Today’s mainstream vehicles are partially automated via an advanced driver assistance feature (ADAS) known as Adaptive Cruise Control (ACC). ACC uses data from on-board sensors to automatically adjust speed to maintain a safe following distance with the preceding vehicle. Contrary to expectations, ICE vehicles equipped with ACC may reduce capacity at bottlenecks because its delayed response and limited initial acceleration during queue discharge could increase the average headway. On the...
Show moreToday’s mainstream vehicles are partially automated via an advanced driver assistance feature (ADAS) known as Adaptive Cruise Control (ACC). ACC uses data from on-board sensors to automatically adjust speed to maintain a safe following distance with the preceding vehicle. Contrary to expectations, ICE vehicles equipped with ACC may reduce capacity at bottlenecks because its delayed response and limited initial acceleration during queue discharge could increase the average headway. On the other hand, ACC equipped EVs can potentially mitigate this effect for having ready torque and quicker acceleration. However, this has not been investigated for cases when lane changers enter from the adjacent lane. ACC could respond differently under these conditions, and this car following behavior is often referred as receiving lane change car following. Carefully planned field experiments on lane change car following demonstrate that lane changes and the subsequent receiving lane change car following from ICE vehicles equipped with ACC increases the gap unless the lane changer and the target lane traffic have identical or similar speeds for internal combustion engine (ICE) vehicles and ACC in the EVs doesn’t increase the gap after lane change increasing capacity for merging compared to ICE vehicles. For ICE, this trend also correlates with the selected ACC gap, with larger gap selection resulting in longer gap following the lane change maneuver and the receiving lane change car following in response. Larger gap setting shows better results after lane change for EVs.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014276
Subject Headings: Automated vehicles, Automobile driving--Lane changing
Format: Document (PDF)

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

Title: BIOLUMINESCENCE OF THE CTENOPHORE MNEMIOPSIS LEIDYI: FIRST FLASH KINETICS.
Creator: Blackburn, Abigail, Twardowski, Michael, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: The ctenophore Mnemiopsis leidyi is an opportunistic species that can be extremely abundant and invasive in many parts of the world. It is well known for its bright bioluminescence, but its light emission response to flow stimulation has not been rigorously quantified. The objective of this study is to determine the luminescent response of M. leidyi to several types of mechanical stimuli, an impeller pump with the Underwater Bioluminescence Assessment Tool (UBAT) bathyphotometer and stirring...
Show moreThe ctenophore Mnemiopsis leidyi is an opportunistic species that can be extremely abundant and invasive in many parts of the world. It is well known for its bright bioluminescence, but its light emission response to flow stimulation has not been rigorously quantified. The objective of this study is to determine the luminescent response of M. leidyi to several types of mechanical stimuli, an impeller pump with the Underwater Bioluminescence Assessment Tool (UBAT) bathyphotometer and stirring as the stimulus within an integrating sphere. Tests were conducted with three day old cydippid larvae, analyzing flash parameters of rise time, peak intensity, decay slope, decay time, total integrated emission, total mechanically stimulable luminescence (TMSL), integrated flash emission, and flash duration. There were four patterns of bioluminescent responses measured with the UBAT, but they did not have statistically different flash kinetics. For the integrating sphere, the average peak intensity and TMSL were much greater than for the UBAT due to the different forms of stimulation. This study provides a well-defined baseline of cydippid larvae flash responses which may be used for identifying this species at this life stage in situ.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014341
Subject Headings: Mnemiopsis leidyi, Bioluminescence, Ctenophores
Format: Document (PDF)

Title: AN EFFECTIVE ENSEMBLE LEARNING-BASED REAL-TIME INTRUSION DETECTION SCHEME FOR IN-VEHICLE NETWORK.
Creator: Alalwany, Easa, Mahgoub, Imadeldin, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Connectivity and automation have expanded with the development of autonomous vehicle technology. One of several automotive serial protocols that can be used in a wide range of vehicles is the controller area network (CAN). The growing functionality and connectivity of modern vehicles make them more vulnerable to cyberattacks aimed at vehicular networks. The CAN bus protocol is vulnerable to numerous attacks as it lacks security mechanisms by design. It is crucial to design intrusion detection...
Show moreConnectivity and automation have expanded with the development of autonomous vehicle technology. One of several automotive serial protocols that can be used in a wide range of vehicles is the controller area network (CAN). The growing functionality and connectivity of modern vehicles make them more vulnerable to cyberattacks aimed at vehicular networks. The CAN bus protocol is vulnerable to numerous attacks as it lacks security mechanisms by design. It is crucial to design intrusion detection systems (IDS) with high accuracy to detect attacks on the CAN bus. In this dissertation, to address all these concerns, we design an effective machine learning-based IDS scheme for binary classification that utilizes eight supervised ML algorithms, along with ensemble classifiers, to detect normal and abnormal activities in the CAN bus. Moreover, we design an effective ensemble learning-based IDS scheme for detecting and classifying DoS, fuzzing, replay, and spoofing attacks. These are common CAN bus attacks that can threaten the safety of a vehicle’s driver, passengers, and pedestrians. For this purpose, we utilize supervised machine learning in combination with ensemble methods. Ensemble learning aims to achieve better classification results through the use of different classifiers that are combined into a single classifier. Furthermore, in the pursuit of real-time attack detection and classification, we use the Kappa architecture for efficient data processing, enhancing the IDS’s accuracy and effectiveness. We build this system using the most recent CAN intrusion dataset provided by the IEEE DataPort. We carried out the performance evaluation of the proposed system in terms of accuracy, precision, recall, F1-score, and area under curve receiver operator characteristic (ROC-AUC). For the binary classification, the ensemble classifiers outperformed the individual supervised ML classifiers and improved the effectiveness of the classifier. For detecting and classifying CAN bus attacks, the ensemble learning methods resulted in a robust and accurate multiclassification IDS for common CAN bus attacks. The stacking ensemble method outperformed other recently proposed methods, achieving the highest performance. For the real-time attack detection and classification, the ensemble methods significantly enhance the accuracy the real-time CAN bus attack detection and classification. By combining the strengths of multiple models, the stacking ensemble technique outperformed individual supervised models and other ensembles.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014298
Subject Headings: Automated vehicles, Controller Area Network (Computer network), Intrusion detection systems (Computer security)
Format: Document (PDF)

Title: ENHANCING IOT DEVICES SECURITY: ENSEMBLE LEARNING WITH CLASSICAL APPROACHES FOR INTRUSION DETECTION SYSTEM.
Creator: Alotaibi, Yazeed, Ilyas, Mohammad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: The Internet of Things (IoT) refers to a network of interconnected nodes constantly engaged in communication, data exchange, and the utilization of various network protocols. Previous research has demonstrated that IoT devices are highly susceptible to cyber-attacks, posing a significant threat to data security. This vulnerability is primarily attributed to their susceptibility to exploitation and their resource constraints. To counter these threats, Intrusion Detection Systems (IDS) are...
Show moreThe Internet of Things (IoT) refers to a network of interconnected nodes constantly engaged in communication, data exchange, and the utilization of various network protocols. Previous research has demonstrated that IoT devices are highly susceptible to cyber-attacks, posing a significant threat to data security. This vulnerability is primarily attributed to their susceptibility to exploitation and their resource constraints. To counter these threats, Intrusion Detection Systems (IDS) are employed. This study aims to contribute to the field by enhancing IDS detection efficiency through the integration of Ensemble Learning (EL) methods with traditional Machine Learning (ML) and deep learning (DL) models. To bolster IDS performance, we initially utilize a binary ML classification approach to classify IoT network traffic as either normal or abnormal, employing EL methods such as Stacking and Voting. Once this binary ML model exhibits high detection rates, we extend our approach by incorporating a ML multi-class framework to classify attack types. This further enhances IDS performance by implementing the same Ensemble Learning methods. Additionally, for further enhancement and evaluation of the intrusion detection system, we employ DL methods, leveraging deep learning techniques, ensemble feature selections, and ensemble methods. Our DL approach is designed to classify IoT network traffic. This comprehensive approach encompasses various supervised ML, and DL algorithms with ensemble methods. The proposed models are trained on TON-IoT network traffic datasets. The ensemble approaches are evaluated using a comprehensive metrics and compared for their effectiveness in addressing this classification tasks. The ensemble classifiers achieved higher accuracy rates compared to individual models, a result attributed to the diversity of learning mechanisms and strengths harnessed through ensemble learning. By combining these strategies, we successfully improved prediction accuracy while minimizing classification errors. The outcomes of these methodologies underscore their potential to significantly enhance the effectiveness of the Intrusion Detection System.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014304
Subject Headings: Internet of things, Intrusion detection systems (Computer security), Machine learning
Format: Document (PDF)

Title: RSSI-BASED PASSIVE LOCALIZATION IN COMPLEX OUTDOOR ENVIRONMENTS USING WI-FI PROBE REQUESTS.
Creator: Bao, Fanchen, Hallstrom, Jason O., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Capturing pedestrian mobility patterns with high fidelity provides a foundation for data-driven decision-making in support of city planning, emergency response, and more. Due to scalability requirements and the sensitive nature of studying pedestrian movements in public spaces, the methods involved must be passive, low-cost, and privacy-centric. Pedestrian localization based on Received Signal Strength Indicator (RSSI) measurements from Wi-Fi probe requests is a promising approach. Probe...
Show moreCapturing pedestrian mobility patterns with high fidelity provides a foundation for data-driven decision-making in support of city planning, emergency response, and more. Due to scalability requirements and the sensitive nature of studying pedestrian movements in public spaces, the methods involved must be passive, low-cost, and privacy-centric. Pedestrian localization based on Received Signal Strength Indicator (RSSI) measurements from Wi-Fi probe requests is a promising approach. Probe requests are spontaneously emitted by Wi-Fi-enabled devices, are readily captured by of-the-shelf components, and offer the potential for anonymous RSSI measurement. Given the ubiquity of Wi-Fi-enabled devices carried by pedestrians (e.g., smartphones), RSSI-based passive localization in outdoor environments holds promise for mobility monitoring at scale. To this end, we developed the Mobility Intelligence System (MobIntel), comprising inexpensive sensor hardware to collect RSSI data, a cloud backend for data collection and storage, and web-based visualization tools. The system is deployed along Clematis Street in the heart of downtown West Palm Beach, FL, and over the past three years, over 50 sensors have been installed. Our research first confirms that RSSI-based passive localization is feasible in a controlled outdoor environment (i.e., no obstructions and little signal interference), achieving ≤ 4 m localization error in more than 90% of the cases. When significant time-varying signal fluctuations are introduced as a result of long-term deployment, performance can be maintained with an overhaul of the problem formulation and an updated localization model. However, when the outdoor environment is fully uncontrolled (e.g., along Clematis Street), the performance decreases to ≤ 4 m error in fewer than 70% of the cases. However, the drop in performance may be addressed through improved sensor maintenance, additional data collection, and appropriate domain knowledge.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014299
Subject Headings: Pedestrian traffic flow, Information technology, Computer Science
Format: Document (PDF)

Title: NOVEL KIRIGAMI-INSPIRED FLEXIBLE ROBOTIC EXTENSION FOR MOBILE PLATFORMS.
Creator: Den Ouden, Casey, Su, Tsung-Chow, Ouyang, Bing, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
Abstract/Description: Since 2010, aquaculture practices have produced 70% of global seafood consumption. However, this fast-growing sector of agriculture has yet to see the adoption of advanced technologies to improve farm operations. The Hybrid Aerial Underwater robotiCs System (HAUCS) is an Internet of Things (IoT) framework that aims to bring transformative changes to pond aquaculture. This project focuses on the latest developments in the HAUCS mobile sensing platform and field deployment. A novel rigid...
Show moreSince 2010, aquaculture practices have produced 70% of global seafood consumption. However, this fast-growing sector of agriculture has yet to see the adoption of advanced technologies to improve farm operations. The Hybrid Aerial Underwater robotiCs System (HAUCS) is an Internet of Things (IoT) framework that aims to bring transformative changes to pond aquaculture. This project focuses on the latest developments in the HAUCS mobile sensing platform and field deployment. A novel rigid Kirigami-based robotic extension subsystem was created to expand the functionality of the HAUCS platform. The primary objective of this design was to limit the surface area of an extender arm on the drone during flight operations and minimize the in-flight drag. By utilizing a novel combination of shape memory polymer (SMP) and nitinol to extend and retrieve the sensing arm, the structure was able to conserve energy while operating under varying environmental conditions.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014324
Subject Headings: Aquaculture, Sensors, Robotics
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: OPTIMIZATION OF DATA ACQUISITION IN OPTICAL TOMOGRAPHY BASED ON ESTIMATION THEORY.
Creator: Javidan, Mahshad, Pashaie, Ramin, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: In any experimental platform, data acquisition is the first and essential step, and occasionally the most time-consuming and costly operation. During the process of data acquisition, we conduct experiments to measure the response of the system to a set of inputs. Methods of optimal design of experiment can be used to determine the most informative measurements and avoid numerous traps that trial-and-error experimentation might cause. In this research, we have developed a general approach for...
Show moreIn any experimental platform, data acquisition is the first and essential step, and occasionally the most time-consuming and costly operation. During the process of data acquisition, we conduct experiments to measure the response of the system to a set of inputs. Methods of optimal design of experiment can be used to determine the most informative measurements and avoid numerous traps that trial-and-error experimentation might cause. In this research, we have developed a general approach for designing optimal experiments, subsequently applying it to the domain of optical tomography. Optical tomography is a vital technology that enables three-dimensional imaging by reconstructing images from two-dimensional projections. This technology has applications across various fields, including medicine and material science. The process involves two main phases: data acquisition and image reconstruction. The traditional raster scanning method has been the standard approach for data acquisition, but it presents challenges in terms of scanning speed, quality, and exposure to harmful radiations in some cases. This has prompted researchers to explore ways to optimize the scanning process.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014350
Subject Headings: Optical tomography, Data Collection, Estimation theory
Format: Document (PDF)

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

Title: DEVELOPMENT OF AN AUTOMATED DEVICE FOR THE OPTIMIZED REGULATION OF CEREBROSPINAL FLUID (CSF).
Creator: Anjum, Muhammad Waleed, Asghar, Waseem, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Cerebrospinal fluid (CSF) has a role, in keeping the brain and spinal cord safe and nourished within the nervous system (CNS). This clear and colorless fluid is produced in the ventricles of the brain. Surrounds these structures acting as a protective cushion. CSF plays a role in maintaining nervous system health and ensuring optimal functioning. CSF accomplishes four objectives. Protection: The brain and spinal cord are shielded from harm due to CSFs natural shock absorbing properties. This...
Show moreCerebrospinal fluid (CSF) has a role, in keeping the brain and spinal cord safe and nourished within the nervous system (CNS). This clear and colorless fluid is produced in the ventricles of the brain. Surrounds these structures acting as a protective cushion. CSF plays a role in maintaining nervous system health and ensuring optimal functioning. CSF accomplishes four objectives. Protection: The brain and spinal cord are shielded from harm due to CSFs natural shock absorbing properties. This effectively safeguards these structures, from injuries caused by impacts or collisions. Nutrition It ensures a favorable environment for neural cells to perform at their peak by supplying essential nutrients and removing waste products from the brain and spinal cord.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014342
Subject Headings: Cerebrospinal fluid, Biomedical devices, Biomedical engineering
Format: Document (PDF)

Title: TACKLING BIAS, PRIVACY, AND SCARCITY CHALLENGES IN HEALTH DATA ANALYTICS.
Creator: Wang, Shuwen, Zhu, Xingquan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Health data analysis has emerged as a critical domain with immense potential to revolutionize healthcare delivery, disease management, and medical research. However, it is confronted by formidable challenges, including sample bias, data privacy concerns, and the cost and scarcity of labeled data. These challenges collectively impede the development of accurate and robust machine learning models for various healthcare applications, from disease diagnosis to treatment recommendations. Sample...
Show moreHealth data analysis has emerged as a critical domain with immense potential to revolutionize healthcare delivery, disease management, and medical research. However, it is confronted by formidable challenges, including sample bias, data privacy concerns, and the cost and scarcity of labeled data. These challenges collectively impede the development of accurate and robust machine learning models for various healthcare applications, from disease diagnosis to treatment recommendations. Sample bias and specificity refer to the inherent challenges in working with health datasets that may not be representative of the broader population or may exhibit disparities in their distributions. These biases can significantly impact the generalizability and effectiveness of machine learning models in healthcare, potentially leading to suboptimal outcomes for certain patient groups. Data privacy and locality are paramount concerns in the era of digital health records and wearable devices. The need to protect sensitive patient information while still extracting valuable insights from these data sources poses a delicate balancing act. Moreover, the geographic and jurisdictional differences in data regulations further complicate the use of health data in a global context. Label cost and scarcity pertain to the often labor-intensive and expensive process of obtaining ground-truth labels for supervised learning tasks in healthcare. The limited availability of labeled data can hinder the development and deployment of machine learning models, particularly in specialized medical domains.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014336
Subject Headings: Data analytics, Data mining, Ensemble learning (Machine learning), Machine learning, Health
Format: Document (PDF)

Title: TOWARDS DEPLOYABLE QUANTUM-SAFE CRYPTOSYSTEMS.
Creator: Koziel, Brian, Azarderakhsh, Reza, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: It is well known that in the near future, a large-scale quantum computer will be unveiled, one that could be used to break the cryptography that underlies our digital infrastructure. Quantum computers operate on quantum mechanics, enabling exponential speedups to certain computational problems, including hard problems at the cornerstone of our deployed cryptographic algorithms. With a vulnerability in this security foundation, our online identities, banking information, and precious data is...
Show moreIt is well known that in the near future, a large-scale quantum computer will be unveiled, one that could be used to break the cryptography that underlies our digital infrastructure. Quantum computers operate on quantum mechanics, enabling exponential speedups to certain computational problems, including hard problems at the cornerstone of our deployed cryptographic algorithms. With a vulnerability in this security foundation, our online identities, banking information, and precious data is now vulnerable. To address this, we must prepare for a transition to post-quantum cryptography, or cryptosystems that are protected from attacks by both classical and quantum computers. This is a dissertation proposal targeting cryptographic engineering that is necessary to deploy isogeny-based cryptosystems, one known family of problems that are thought to be difficult to break, even for quantum computers. Isogeny-based cryptography utilizes mappings between elliptic curves to achieve public-key encryption, digital signatures, and other cryptographic objectives necessary to support our digital infrastructure's security. This proposal focuses on three aspects of isogeny-based cryptography: 1) cryptographic engineering of isogeny-based cryptosystems; 2) developing and optimizing security-enabling isogeny applications; and 3) improving the security from known and emerging implementation attacks. By improving each of these aspects, we are providing confidence in the deployability of isogeny-based cryptography and helping to prepare for a post-quantum transition.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013998
Subject Headings: Cryptography, Quantum computers
Format: Document (PDF)

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