Computer Sciences Commons^™

Big Data Analytics Of Medical Data, Ashwin Rajasankar

Culminating Experience Projects

Data has become a huge part of modern decision making. With the improvements in computing performance and storage in the past two decades, storing large amounts of data has become much easier. Analyzing large amounts of data and creating data models with them can help organizations obtain insights and information which helps their decision making. Big data analytics has become an integral part of many fields such as retail, real estate, education, and medicine. In the project, the goal is to understand the working of Apache Spark and its different storage methods and create a data warehouse to analyze data. …

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 …

Muse: A Genetic Algorithm For Musical Chord Progression Generation, Griffin Going

Culminating Experience Projects

Foundational to our understanding and enjoyment of music is the intersection of harmony and movement. This intersection manifests as chord progressions which themselves underscore the rhythm and melody of a piece. In musical compositions, these progressions often follow a set of rules and patterns which are themselves frequently broken for the sake of novelty. In this work, we developed a genetic algorithm which learns these rules and patterns (and how to break them) from a dataset of 890 songs from various periods of the Billboard Top 100 rankings. The algorithm learned to generate increasingly valid, yet interesting chord progressions via …

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, …

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

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, …

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

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

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 …

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

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 …

Computer Simulation Of The Light Absorption Band Of The Jumping Spider Isorhodopsin, Noah Zoldak

Honors Projects

In order to simulate the photoisomerization of the 9-cis Jumping Spider Isorhodopsin (JSiR-1) it is necessary to first simulate its light-absorption band. Here we report on the absorption band simulated using protein models constructed using the advanced Automatic Rhodopsin Modeling (a-ARM) program. A population of S₀ models was created and the corresponding S₀ to S₁ transitions were determined for each member of the resulting population. The calculation resulted in a Gaussian plot showing that the wavelength of the absorption maximum of 560 nm (a violet color) that is consistent, but red-shifted, with respect the experimentally observed value.

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

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 2^nd 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

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

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 …

A High-Accuracy Detection System: Based On Transfer Learning For Apical Lesions On Periapical Radiograph, Yueh Chuo, Wen-Ming Lin, Tsung-Yi Chen, Mei-Ling Chan, Yu-Sung Chang, Yan-Ru Lin, Yuan-Jin Lin, Yu-Han Shao, Chiung-An Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold …

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 …

Behaviors For Which Deinonychosaurs Used Their Feet, Alexander King

Honors Projects

This paper seeks to show for what purpose deinonychosaurs used their feet. Fowler et al., (2011) showed that D. antirrhopus’s feet were closest in function to accipitrids, as they found it was more built for grasping prey than running.

I answered this question by using 2D images of the feet of three modern birds (Buteo jamaicensis, Phasianus colchicus, and Gallus gallus domesticus), one eudromaeosaur (Deinonychus antirrhopus), and one troodontid (Borogovia gracilicrus). I used ImageJ to apply 73 landmarks to each foot, capturing the variation between species in the metatarsals and pedal phalanges. These data were then uploaded to the software …

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 …