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Now showing items 1-9 of 9
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 ...
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 ...
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. ...
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 ...
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 ...
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 ...
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 ...
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 ...