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Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Computational Approaches To Facilitate Automated Interchange Between Music And Art, Rao Hamza Ali
Computational Approaches To Facilitate Automated Interchange Between Music And Art, Rao Hamza Ali
Computational and Data Sciences (PhD) Dissertations
Recently, there has been a tremendous increase in generating and synthesizing music and art using various computational techniques. An area that is still under-researched, however, is how one medium can be converted into the other, while maintaining the overall aesthetics. Over the last few centuries, artists, composers, and scholars, have attempted to use substitute one form of art for the other: by proposing techniques where music notes are synonymous to colors, by inventing instruments that combine the aesthetics of music and visual art, and by incorporating the two media in live performances. A widely accepted computational approach, for the conversion, …
Enhancing Microbiome Host Disease Prediction With Variational Autoencoders, Celeste Manughian-Peter
Enhancing Microbiome Host Disease Prediction With Variational Autoencoders, Celeste Manughian-Peter
Computational and Data Sciences (MS) Theses
Advancements in genetic sequencing methods for microbiomes in recent decades have permitted the collection of taxonomic and functional profiles of microbial communities, accelerating the discovery of the functional aspects of the microbiome and generating an increased interest among clinicians in applying these techniques with patients. This advancement has coincided with software and hardware improvements in the field of machine learning and deep learning. Combined, these advancements implicate further potential for progress in disease diagnosis and treatment in humans. The ability to classify a human microbiome profile into a disease category, and additionally identify the differentiating factors within the profile between …
Automated Parsing Of Flexible Molecular Systems Using Principal Component Analysis And K-Means Clustering Techniques, Matthew J. Nwerem
Automated Parsing Of Flexible Molecular Systems Using Principal Component Analysis And K-Means Clustering Techniques, Matthew J. Nwerem
Computational and Data Sciences (MS) Theses
Computational investigation of molecular structures and reactions of biological and pharmaceutical interests remains a grand scientific challenge due to the size and conformational flexibility of these systems. The work requires parsing and analyzing thousands of conformations in each molecular state for meaningful chemical information and subjecting the ensemble to costly quantum chemical calculations. The current status quo typically involves a manual process where the investigator must look at each conformation, separating each into structural families. This process is time-intensive and tedious, making this process infeasible in some cases, and limiting the ability of theoreticians to study these systems. However, the …
Integrated Machine Learning And Bioinformatics Approaches For Prediction Of Cancer-Driving Gene Mutations, Oluyemi Odeyemi
Integrated Machine Learning And Bioinformatics Approaches For Prediction Of Cancer-Driving Gene Mutations, Oluyemi Odeyemi
Computational and Data Sciences (PhD) Dissertations
Cancer arises from the accumulation of somatic mutations and genetic alterations in cell division checkpoints and apoptosis, this often leads to abnormal tumor proliferation. Proper classification of cancer-linked driver mutations will considerably help our understanding of the molecular dynamics of cancer. In this study, we compared several cancer-specific predictive models for prediction of driver mutations in cancer-linked genes that were validated on canonical data sets of functionally validated mutations and applied to a raw cancer genomics data. By analyzing pathogenicity prediction and conservation scores, we have shown that evolutionary conservation scores play a pivotal role in the classification of cancer …
Exploring The Employment Landscape For Individuals With Autism Spectrum Disorders Using Supervised And Unsupervised Machine Learning, Kayleigh Hyde
Exploring The Employment Landscape For Individuals With Autism Spectrum Disorders Using Supervised And Unsupervised Machine Learning, Kayleigh Hyde
Computational and Data Sciences (PhD) Dissertations
Autism Spectrum Disorders (ASD) are a class of neurodevelopmental disorders which usually present with difficulties in social interactions, verbal and nonverbal forms of communication, repetitive behaviors, and restricted interests. Employment rates of young adults with ASD is a national concern, and research suggests that young adults with “high functioning” ASD experience significant difficulty in transitioning to work. One of the goals of this study was to identify the barriers associated with these individuals’ transition into the world of work. A classification tree analysis was used with a sample of 236 caregivers of individuals with ASD or the individuals themselves, who …
Player-Response: On The Nature Of Interactive Narratives As Literature, Lee Feldman
Player-Response: On The Nature Of Interactive Narratives As Literature, Lee Feldman
English (MA) Theses
In recent years, having evolved beyond solely play-based interactions, it is now possible to analyze video games alongside other narrative forms, such as novels and films. Video games now involve rich stories that require input and interaction on behalf of the player. This level of agency likens video games to a kind of modern hypertext, networking and weaving various narrative threads together, something which traditional modes of media lack. When examined from the lens of reader-response criticism, this interaction deepens even further, acknowledging the player’s experience as a valid interpretation of a video game’s plot. The wide freedom of choice …
Optimized Forecasting Of Dominant U.S. Stock Market Equities Using Univariate And Multivariate Time Series Analysis Methods, Michael Schwartz
Optimized Forecasting Of Dominant U.S. Stock Market Equities Using Univariate And Multivariate Time Series Analysis Methods, Michael Schwartz
Computational and Data Sciences Theses
This dissertation documents an investigation into forecasting U.S. stock market equities via two very different time series analysis techniques: 1) autoregressive integrated moving average (ARIMA), and 2) singular spectrum analysis (SSA). Approximately 40% of the S&P 500 stocks are analyzed. Forecasts are generated for one and five days ahead using daily closing prices. Univariate and multivariate structures are applied and results are compared. One objective is to explore the hypothesis that a multivariate model produces superior performance over a univariate configuration. Another objective is to compare the forecasting performance of ARIMA to SSA, as SSA is a relatively recent development …
An Application Of The Autism Management Platform To Tracking Student Progress In The Special Education Environment, Ryan Thomas Burns
An Application Of The Autism Management Platform To Tracking Student Progress In The Special Education Environment, Ryan Thomas Burns
Computational and Data Sciences Theses
In the age of online courses and digital textbooks, several areas of academia, such as special education, are far behind in the technological revolution. Some teachers use long unstructured digital documents, while others maintain large physical files for students containing every piece of information or coursework they have ever received. Could these extremely unstructured approaches to data collection and aggregation be streamlined with a software platform built specifically for this purpose? Could this platform also be built to accommodate multiple integrations and practical new features? Most importantly, in terms of usability, would this software be enjoyable to use? The Autism …