Browsing OSU Dissertations by Subject "machine learning"
Now showing items 1-14 of 14
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Application of Raman and infrared microscopy coupled with chemometrics for the forensic examination of automotive clear coats and paint smears
(2022-07)Modern automotive paints typically use thinner undercoat and color coat layers protected by a thicker clear coat layer. All too often, a clear coat is the only layer of automotive paint left at the crime scene. Current ... -
Context-aware quality assessment of structured and unstructured data
(2020-07)Data analysis is a crucial process in the field of data science that extracts useful information from any form of data. The ease of access and maintenance makes structured data the most popular choice among many organizations ... -
Critical assessment of CEO succession on organizational performance through descriptive and predictive research methods
(2021-07)This research offers a comprehensive analysis of extant CEO succession literature to discover and illuminate previously unanalyzed variable relationships and potential areas for future research. An analysis is performed ... -
Data-driven sub-grid model development for large eddy simulations of turbulence
(2019-05)Turbulence modeling remains an active area of research due to its significant impact on a diverse set of challenges such as those pertaining to the aerospace and geophysical communities. Researchers continue to search for ... -
Deep Autoencoders for Cross-Modal Retrieval
(2019-05-01)Increased accuracy and affordability of depth sensors such as Kinect has created a great depth-data source for 3D processing. Specifically, 3D model retrieval is attracting attention in the field of computer vision and ... -
Developing Clinical Decision Support Systems for Sepsis Prediction Using Temporal and Non-Temporal Machine Learning Methods
(2019-07)In healthcare, diagnostic errors represent the biggest challenge to synthesize accurate treatments. In the United States, patient deaths due to misdiagnoses are estimated at 40,000 to 80,000 per year. It was also found ... -
Digital Transformation: How to Beat the High Failure Rate
(2019-05-01)Firms every year spend $1.3 trillion on digital transformation programs to improve efficiency because digital leaders outperform their peers in nearly every industry. However, digital transformations that are intended to ... -
Interpreting natural language processing (NLP) models and lifting their limitations
(2021-07)There have been many advances in the artificial intelligence field due to the emergence of deep learning and big data. In almost all sub-fields, artificial neural networks have reached or exceeded human-level performance. ... -
Leveraging atomistic simulations and machine learning for the design of ionic liquids as electrolytes for battery application
(2022-05)Ionic liquids are classes of salts that are often found in a liquid state composed entirely of ions. They have gained widespread interest in the research community because of several unique and desirable features, such as ... -
Machine learning and personality traits: A disturbing contribution from the algorithmic culture to behavioral science
(2018-12)The time has come for behavioral scholars to benefit from the superior prediction accuracy of modern data mining practices over traditional data modeling. The current investigative study uses machine learning techniques ... -
Model-data fusion in digital twins of large scale dynamical systems
(2022-07)Digital twins (DTs) are virtual entities that serve as the real-time digital counterparts of actual physical systems across their life-cycle. In a typical application of DTs, the physical system provides sensor measurements ... -
New stochastic pore-scale simulation and machine learning approach to predicting permeability and tortuosity of heterogeneous porous media
(2023-05)A new 3D stochastic pore-scale simulation approach was introduced in this study to investigate how stochastic pore connectivity impacts the permeability and hydraulic tortuosity of heterogeneous porous media. Multiple ... -
Self-tuned, block-coordinate, and incremental mirror descent methods with applications in machine learning and wireless communications
(2020-07)Uncertainty, high-dimensionality, and matrix structure of the decision variables are among the main challenges that may arise in addressing a wide range of stochastic optimization and equilibrium problems in machine learning ... -
Studying employee absenteeism due to health-related factors: A data-science approach
(2022-07)United States employers are spending approximately $950 billion on healthcare benefits, and these costs are impeding their ability to compete in their respective markets. Furthermore, these costs do not include employee ...