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Building A Deep Model For Multi-Class Coral Species Discrimination, Hyeong Gyu Jang 2022 Grand Valley State University

Building A Deep Model For Multi-Class Coral Species Discrimination, Hyeong Gyu Jang

Culminating Experience Projects

The goal of this qualitative research project is to develop and optimize a multi-class discrimination model to identify different species of coral based on their digital images. Currently, there are artificial intelligence (AI) models that can distinguish between coral and other undersea objects such as sand or rocks, but to our knowledge the problem of multi-species classification has not yet been addressed. Given that coral reefs are a good indicator of overall ocean health, it is important to develop models that can classify the presence of different species in underwater images as a way to monitor the effects of climate …


Travel Dashboard, Naveen Kumar Lalam 2022 Grand Valley State University

Travel Dashboard, Naveen Kumar Lalam

Culminating Experience Projects

Travel Dashboard is a one stop solution for all the travel needs of travelers and tourists visiting a new place. In today’s world travel has become a part of everyone’s life and we love to travel whenever there is a holiday or long a weekend. Earlier, the travel industry was mostly dictated by tour operators who used to plan and organize tours with standard itinerary, while tourists had very limited choices and needed to pick one of the itineraries given by operator as there was no other option left for them. Time have changed now as travelers love to plan …


College Job Portal, Harikrishna Gonuguntla 2022 Grand Valley State University

College Job Portal, Harikrishna Gonuguntla

Culminating Experience Projects

Through this project, I am producing a portal called "College Job Portal" that will make life easier for students, colleges, and the companies who hire the students by handling the hiring process. On-campus job placements are a crucial component in contemporary educational institutions. By entering information about their educational history, grades, technological abilities, and CV, students would register with the portal. Like students, companies would sign up with the site by supplying basic details like their address and contact information for human resources. The college would be the portal's administrator. Companies can advertise job openings using this site by including …


Fairness And Privacy In Machine Learning Algorithms, Neha Bhargava 2022 Kennesaw State University

Fairness And Privacy In Machine Learning Algorithms, Neha Bhargava

Master of Science in Computer Science Theses

Roughly 2.5 quintillion bytes of data is generated daily in this digital era. Manual processing of such huge amounts of data to extract useful information is nearly impossible but with the widespread use of machine learning algorithms and their ability to process enormous data in a fast, cost-effective, and scalable way has proven to be a preferred choice to glean useful insights and solve business problems in many domains. With this widespread use of machine learning algorithms there has always been concerns about the ethical issues that may arise from the use of this modern technology. While achieving high accuracies, …


Assessing Wood Failure In Plywood By Deep Learning/Semantic Segmentation, Ramon Ferreira Oliveira 2022 Mississippi State University

Assessing Wood Failure In Plywood By Deep Learning/Semantic Segmentation, Ramon Ferreira Oliveira

Theses and Dissertations

The current method for estimating wood failure is highly subjective. Various techniques have been proposed to improve the current protocol, but none have succeeded. This research aims to use deep learning/semantic segmentation using SegNet architecture to estimate wood failure in four types of three-ply plywood from mechanical shear strength specimens. We trained and tested our approach on custom and commercial plywood with bio-based and phenol-formaldehyde adhesives. Shear specimens were prepared and tested. Photographs of 255 shear bonded areas were taken. Forty photographs were used to solicit visual estimates from five human evaluators, and the remaining photographs were used to train …


Augmented Reality Fonts With Enhanced Out-Of-Focus Text Legibility, Mohammed Safayet Arefin 2022 Mississippi State University

Augmented Reality Fonts With Enhanced Out-Of-Focus Text Legibility, Mohammed Safayet Arefin

Theses and Dissertations

In augmented reality, information is often distributed between real and virtual contexts, and often appears at different distances from the viewer. This raises the issues of (1) context switching, when attention is switched between real and virtual contexts, (2) focal distance switching, when the eye accommodates to see information in sharp focus at a new distance, and (3) transient focal blur, when information is seen out of focus, during the time interval of focal distance switching. This dissertation research has quantified the impact of context switching, focal distance switching, and transient focal blur on human performance and eye fatigue in …


Examining The Relationship Between Stomiiform Fish Morphology And Their Ecological Traits, Mikayla L. Twiss 2022 Nova Southeastern University

Examining The Relationship Between Stomiiform Fish Morphology And Their Ecological Traits, Mikayla L. Twiss

All HCAS Student Capstones, Theses, and Dissertations

Trait-based ecology characterizes individuals’ functional attributes to better understand and predict their interactions with other species and their environments. Utilizing morphological traits to describe functional groups has helped group species with similar ecological niches that are not necessarily taxonomically related. Within the deep-pelagic fishes, the Order Stomiiformes exhibits high morphological and species diversity, and many species undertake diel vertical migration (DVM). While the morphology and behavior of stomiiform fishes have been extensively studied and described through taxonomic assessments, the connection between their form and function regarding their DVM types, morphotypes, and daytime depth distributions is not well known. Here, three …


Explainable Ai Helps Bridge The Ai Skills Gap: Evidence From A Large Bank, Selina Carter, Jonathan Hersh 2022 Carnegie Mellon University

Explainable Ai Helps Bridge The Ai Skills Gap: Evidence From A Large Bank, Selina Carter, Jonathan Hersh

Economics Faculty Articles and Research

Advances in machine learning have created an “AI skills gap” both across and within firms. As AI becomes embedded in firm processes, it is unknown how this will impact the digital divide between workers with and without AI skills. In this paper we ask whether managers trust AI to predict consequential events, what manager characteristics are associated with increasing trust in AI predictions, and whether explainable AI (XAI) affects users’ trust in AI predictions. Partnering with a large bank, we generated AI predictions for whether a loan will be late in its final disbursement. We embedded these predictions into a …


Guaranteed Conformance Of Neurosymbolic Models To Natural Constraints, Kaustubh Sridhar, Souradeep Dutta, James Weimer, Insup Lee 2022 University of Pennsylvania

Guaranteed Conformance Of Neurosymbolic Models To Natural Constraints, Kaustubh Sridhar, Souradeep Dutta, James Weimer, Insup Lee

Departmental Papers (CIS)

Deep neural networks have emerged as the workhorse for a large section of robotics and control applications, especially as models for dynamical systems. Such data-driven models are in turn used for designing and verifying autonomous systems. This is particularly useful in modeling medical systems where data can be leveraged to individualize treatment. In safety-critical applications, it is important that the data-driven model is conformant to established knowledge from the natural sciences. Such knowledge is often available or can often be distilled into a (possibly black-box) model M. For instance, the unicycle model for an F1 racing car. In this light, …


A Maturity Model Of Data Modeling In Self-Service Business Intelligence Software, Anna Kurenkov 2022 Kennesaw State University

A Maturity Model Of Data Modeling In Self-Service Business Intelligence Software, Anna Kurenkov

Master of Science in Information Technology Theses

Although Self-Service Business Intelligence (SSBI) is continually being adopted in various industries, there is a lack of research focused on data modeling in SSBI. This research aims to fill that research gap and propose a maturity model for SSBI data modeling which is generalizeable between different software and applicable for users of all technical backgrounds. Through extensive literature review, a five-tier maturity model was proposed, explained, and instantiated in PowerBI and Tableau. The testing of the model was found to be simple and intuitive, and the research concludes that the model is applicable to enterprise SSBI environments. This research is …


Payload-Byte: A Tool For Extracting And Labeling Packet Capture Files Of Modern Network Intrusion Detection Datasets, Yasir Farrukh, Irfan Khan, Syed Wali, David A. Bierbrauer, John Pavlik, Nathaniel D. Bastian 2022 Army Cyber Institute, United States Military Academy

Payload-Byte: A Tool For Extracting And Labeling Packet Capture Files Of Modern Network Intrusion Detection Datasets, Yasir Farrukh, Irfan Khan, Syed Wali, David A. Bierbrauer, John Pavlik, Nathaniel D. Bastian

ACI Journal Articles

Adapting modern approaches for network intrusion detection is becoming critical, given the rapid technological advancement and adversarial attack rates. Therefore, packet-based methods utilizing payload data are gaining much popularity due to their effectiveness in detecting certain attacks. However, packet-based approaches suffer from a lack of standardization, resulting in incomparability and reproducibility issues. Unlike flow-based datasets, no standard labeled dataset exists, forcing researchers to follow bespoke labeling pipelines for individual approaches. Without a standardized baseline, proposed approaches cannot be compared and evaluated with each other. One cannot gauge whether the proposed approach is a methodological advancement or is just being benefited …


Actively Guided Cansats For Assisting Localization And Mapping In Unstructured And Unknown Environments, Cary Chun, M. Hassan Tanveer 2022 Kennesaw State University

Actively Guided Cansats For Assisting Localization And Mapping In Unstructured And Unknown Environments, Cary Chun, M. Hassan Tanveer

Symposium of Student Scholars

When navigating in unknown and unstructured environments, Unmanned Arial Vehicles (UAVs) can struggle when attempting to preform Simultaneous Localization and Mapping (SLAM) operations. Particularly challenging circumstance arise when an UAV may need to land or otherwise navigate through treacherous environments. As the primary UAV may be too large and unwieldly to safely investigate in these types of situations, this research effort proposes the use of actively guided CanSats for assisting in localization and mapping of unstructured environments. A complex UAV could carry multiple of these SLAM capable CanSats, and when additional mapping and localization capabilities where required, the CanSat would …


Hydrogen Bonding In Small Model Peptides; The Dft And Mp2 Study, Gracie Smith, Martina Kaledin 2022 Kennesaw State University

Hydrogen Bonding In Small Model Peptides; The Dft And Mp2 Study, Gracie Smith, Martina Kaledin

Symposium of Student Scholars

Formamide is a small model compound for the study of the peptide bond. The peptide bond links amino acids together, specifies rigidity to the protein backbone, and includes the essential docking sites for hydrogen-bond-mediated protein folding and protein aggregation, namely, the C=O acceptor and the N-H donor parts. Therefore, the infrared C=O (amide-I) and N-H (amide-A) vibrations provide sensitive and widely used probes into the structure of peptides. This computational chemistry work, we study hydrogen bonds in formamide dimer isomers. We evaluate the accuracy of the density functional theory (DFT) and many-body perturbation theory to the 2nd order (MP2) …


Software Supply Chain Security Attacks And Analysis Of Defense, Juanjose Rodriguez-Cardenas, Jobair Hossain Faruk, Masura Tansim, Asia Shavers, Corey Brookins, Shamar Lake, Ava Norouzi, Marie Nassif, Kenneth Burke, Miranda Dominguez 2022 Kennesaw State University

Software Supply Chain Security Attacks And Analysis Of Defense, Juanjose Rodriguez-Cardenas, Jobair Hossain Faruk, Masura Tansim, Asia Shavers, Corey Brookins, Shamar Lake, Ava Norouzi, Marie Nassif, Kenneth Burke, Miranda Dominguez

Symposium of Student Scholars

The Software Supply chain or SSC is the backbone of the logistics industry and is crucial to a business's success and operation. The surge of attacks and risks for the SSC has grown in coming years with each attack's impact becoming more significant. These attacks have led to the leaking of both client and company sensitive information, corruption of the data, and having it subject to malware and ransomware installation, despite new practices implemented and investments into SSC security and its branches that have not stopped attackers from developing new vulnerabilities and exploits. In our research, we have investigated Software …


Secure Cloud-Based Iot Water Quality Gathering For Analysis And Visualization, Soin Abdoul Kassif Baba M Traore 2022 Kennesaw State University

Secure Cloud-Based Iot Water Quality Gathering For Analysis And Visualization, Soin Abdoul Kassif Baba M Traore

Symposium of Student Scholars

Water quality refers to measurable water characteristics, including chemical, biological, physical, and radiological characteristics usually relative to human needs. Dumping waste and untreated sewage are the reasons for water pollution and several diseases to the living hood. The quality of water can also have a significant impact on animals and plant ecosystems. Therefore, keeping track of water quality is a substantial national interest. Much research has been done for measuring water quality using sensors to prevent water pollution. In summary, those systems are built based on online and reagent-free water monitoring SCADA systems in wired networks. However, centralized servers, transmission …


The Interaction Of Normalisation And Clustering In Sub-Domain Definition For Multi-Source Transfer Learning Based Time Series Anomaly Detection, Matthew Nicholson, Rahul Agrahari, Clare Conran, Haythem Assem, John D. Kelleher 2022 ADAPT Centre, Trinity College Dublin

The Interaction Of Normalisation And Clustering In Sub-Domain Definition For Multi-Source Transfer Learning Based Time Series Anomaly Detection, Matthew Nicholson, Rahul Agrahari, Clare Conran, Haythem Assem, John D. Kelleher

Articles

This paper examines how data normalisation and clustering interact in the definition of sub-domains within multi-source transfer learning systems for time series anomaly detection. The paper introduces a distinction between (i) clustering as a primary/direct method for anomaly detection, and (ii) clustering as a method for identifying sub-domains within the source or target datasets. Reporting the results of three sets of experiments, we find that normalisation after feature extraction and before clustering results in the best performance for anomaly detection. Interestingly, we find that in the multi-source transfer learning scenario clustering on the target dataset and identifying subdomains in the …


Artificial Intelligence In The Medical Field: Medical Review Sentiment Analysis, Nicholas Podlesak 2022 Northern Illinois University

Artificial Intelligence In The Medical Field: Medical Review Sentiment Analysis, Nicholas Podlesak

Honors Capstones

In this research project, natural language processing techniques’ ability to accurately classify medical text was measured to reinforce the relevance of artificial intelligence in the medical field. Sentiment analyses (analyses to determine whether the text was positive or negative) were performed on the prescription drug reviews in an open-source dataset using four different models: lexical, a neural network, a support vector machine, and a logistic regression model. Each model’s effectiveness was gauged by its ability to correctly classify unlabeled drug reviews (i.e., a percentage representing accuracy). The machine learning models were able to accurately classify the text, while the lexical …


Algorithms For Compression Of Electrocardiogram Signals, Yuliyan Velchev 2022 Technical University of Sofia

Algorithms For Compression Of Electrocardiogram Signals, Yuliyan Velchev

Books

The study is dedicated to modern methods and algorithms for compression of electrocardiogram (ECG) signals. In its original part, two lossy compression algorithms based on a combination of linear transforms are proposed. These algorithms are with relatively low computational complexity, making them applicable for implementation in low power designs such as mobile devices or embedded systems. Since the algorithms do not provide perfect signal reconstruction, they would find application in ECG monitoring systems rather than those intended for precision medical diagnosis.

This monograph consists of abstract, preface, five chapters and conclusion. The chapters are as follows: Chapter 1 — Introduction …


Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things, Tarek Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance Kaplan, Mani Srivastava, Venugopal Veeravalli 2022 Army Cyber Institute, U.S. Military Academy

Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things, Tarek Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance Kaplan, Mani Srivastava, Venugopal Veeravalli

ACI Journal Articles

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features …


An Empirical Study On The Classification Of Python Language Features Using Eye-Tracking, Jigyasa Chauhan 2022 University of Nebraska-Lincoln

An Empirical Study On The Classification Of Python Language Features Using Eye-Tracking, Jigyasa Chauhan

Computer Science and Engineering: Theses, Dissertations, and Student Research

Python, currently one of the most popular programming languages, is an object-
oriented language that also provides language feature support for other programming
paradigms, such as functional and procedural. It is not currently understood how
support for multiple paradigms affects the ability of developers to comprehend that
code. Understanding the predominant paradigm in code, and how developers classify
the predominant paradigm, can benefit future research in program comprehension as
the paradigm may factor into how people comprehend that code. Other researchers
may want to look at how the paradigms in the code interact with various code smells.
To investigate how …


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