Clayton State Digital Repository

The Clayton State Digital Repository (CSDR) collects, preserves, and shares scholarly research with its community of users. These contributions demonstrate the value placed on instruction and research as well as illustrate how the University’s mission is met across our campus community. The uploaded documents will be freely accessible online and will include faculty and student scholarship, electronic theses, open access journals, campus documents and publications, and more.

Recent Submissions

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    Skin Cancer Awareness and Detection
    (Clayton State University) Nnoroum, Godstime; Akhtar, Shakil; Nyguen , Ken; Rahman , Muhammad; Department of Computer Science and Information Technology
    The rapid increase in digital health data has led to new areas of research in healthcare and data sciences. Traditional methods of handling health data have struggled because they can't manage the huge, fast-moving, and diverse amounts of data that are constantly changing. Skin cancer, particularly melanoma, represents a significant public health challenge due to factors such as increased ultraviolet (UV) radiation exposure and evolving lifestyle patterns. The application of big data and machine learning technologies offers promising advancements in the early detection of skin cancer by processing and analyzing extensive datasets, which include patient histories, environmental exposures, and genetic predispositions. Machine learning algorithms, particularly those focused on dermatological image recognition, enable the identification of skin lesions with high precision, thus facilitating the timely diagnosis of melanoma. Furthermore, predictive analytics models can identify individuals at heightened risk, potentially enabling early interventions and more personalized preventive strategies. The integration of big data and advanced computational techniques into skin cancer detection holds the potential to significantly enhance early diagnosis, treatment outcomes, and overall prevention efforts. The software using the latest machine learning techniques is under development at the CS/IT Department at Clayton State University as a funded NSF project. The purpose of this thesis is to collect, tabulate, and analyze skin cancer detection data among various segments of population. In addition to the initial testing and detection is carried out by using the software smartphone application software are developed for Android and Mac platforms. The apps allow for secure and private upload of images predicting various types of skin cancer with a percentage likelihood.
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    Efficient Subgraph Search in Time-Dependent Networks
    (Clayton State University) Sullivan, Everett; Qu, Junfeng; Krop, Elliot; Bai, Shuju; College of Information and Mathematical Sciences
    Subgraph search is a problem that has long been studied in computer science and a number of algorithms have been designed to preform this task completely. Variations on this problem have included multi-graphs, colored edges and vertices, and hypergraphs among others. A network can be described as a graph, and as that network changes over time, these changes can also be modeled as a graph. In this paper we show how existing algorithms can be made more efficient when preforming subgraph search on a time dependent network.
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    Applying LEAN to the Hospital Emergency Department: A Case Study
    Kathy Brown; Michiel S. Stegall; Peter G. Fitzpatrick; Thomas McIlwain
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    The Adventures of Johan & Peewit #4: The Moonstone
    Edward Joseph Johnson
    Hefty Smurf and Brainy Smurf discover an ancient amphora at the bottom of a pond. Upon opening it, they unleash a powerful genie. Unfortunately, the genie has lost his memory and does everything half-heartedly! The Smurfs will soon understand why…

    Other tales include another comedy adventure from Johan and Peewit and short stories showcasing the whole Smurfs Village. A perfect tie-in opportunity with the all-new Smurfs animated series on Nickelodeon!

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