Digital Commons Network^™

(R1239) A New Type Ii Half Logistic-G Family Of Distributions With Properties, Regression Models, System Reliability And Applications, Emrah Altun, Morad Alizadeh, Haitham M. Yousof, Mahdi Rasekhi, G. G. Hamedani

Applications and Applied Mathematics: An International Journal (AAM)

This study proposes a new family of distributions based on the half logistic distribution. With the new family, the baseline distributions gain flexibility through additional shape parameters. The important statistical properties of the proposed family are derived. A new generalization of the Weibull distribution is used to introduce a location-scale regression model for the censored response variable. The utility of the introduced models is demonstrated in survival analysis and estimation of the system reliability. Three data sets are analyzed. According to the empirical results, it is observed that the proposed family gives better results than other existing models.

Comparison Of Statistical Methods For Modeling Count Data With An Application To Length Of Hospital Stay, Gustavo A. Fernandez

Theses and Dissertations

Hospital length of stay (LOS) is a key indicator of hospital care management efficiency, cost of care, and hospital planning. Therefore, understanding hospital LOS variability is always an important healthcare focus. Hospital LOS data are count data, with discrete and nonnegative values, typically right-skewed, and often exhibiting excessive zeros. Numerous studies have been conducted to model hospital LOS to identify significant predictors contributing to its variability. Many researchers have used linear regression with or without logarithmic transformation of the outcome variable LOS, or logistic regression on a dichotomized LOS. These regression methods usually violate models’ assumptions and are subject …

Aggregating Twitter Text Through Generalized Linear Regression Models For Tweet Popularity Prediction And Automatic Topic Classification, Chen Mo, Jingjing Yin, Isaac Chun-Hai Fung, Zion Tse

Department of Biostatistics, Epidemiology, and Environmental Health Sciences Faculty Publications

Social media platforms have become accessible resources for health data analysis. However, the advanced computational techniques involved in big data text mining and analysis are challenging for public health data analysts to apply. This study proposes and explores the feasibility of a novel yet straightforward method by regressing the outcome of interest on the aggregated influence scores for association and/or classification analyses based on generalized linear models. The method reduces the document term matrix by transforming text data into a continuous summary score, thereby reducing the data dimension substantially and easing the data sparsity issue of the term matrix. To …

Empirical Modeling Of Tilt-Rotor Aerodynamic Performance, Michael C. Stratton

Mechanical & Aerospace Engineering Theses & Dissertations

There has been increasing interest into the performance of electric vertical takeoff and landing (eVTOL) aircraft. The propellers used for the eVTOL propulsion systems experience a broad range of aerodynamic conditions, not typically experienced by propellers in forward flight, that includes large incidence angles relative to the oncoming airflow. Formal experiment design and analysis techniques featuring response surface methods were applied to a subscale, tilt-rotor wind tunnel test for three, four, five, and six blade, 16-inch diameter, propeller configurations in support of development of the NASA LA-8 aircraft. Investigation of low-speed performance included a maximum speed of 12 m/s and …

Seasonal Patterns Of Growth And Senescence In Cynodon Spp. Cv Tifton 85 Grazed Swards, L. F. M. Pinto, L. G. Barioni, S. C. Da Silva

IGC Proceedings (1997-2023)

Growth and senescence are very important determinants of grassland productivity and correspond to key features to be considered for grazing management purposes. The present study was carried out at ESALQ, Piracicaba, S.P., Brazil and evaluated growth and senescence as a function of sward surface height. Mathematical models generated revealed that a similar pattern of response occurred as described in the literature for temperate grass species. However, a seasonal variation in response curves for growth and senescence was also observed and could be related to climatic events. Highest herbage accumulation rates were observed within the range of 15 to 20 cm …

Investigating Model Solution Correctness For Parameter Uncertainty In Both Objective Function And Constraints, Shangyao Yan, Sin-Siang Wang, Chun-Yi Wang

Journal of Marine Science and Technology

To resolve engineering management problems encountered in the real world, optimization models are usually formulated with some parameter assumptions. Parameter uncertainty, which may arise due to changes in the environment or human error, may thus be incorporated into the objective function and the constraints. However, to simplify the modeling, the values of these parameters are usually set or projected as deterministic values. It is no wonder that the modelling results based on these inaccurate parameters are neither correct nor reliable. Thus, it is important to examine the correctness of the model results in relation to parameter uncertainty. This study aims …

Extension To Multidimensional Problems Of A Fuzzy-Based Explainable & Noise-Resilient Algorithm, Javier Viana, Stephan Ralescu, Kelly Cohen, Anca Ralescu, Vladik Kreinovich

Departmental Technical Reports (CS)

While Deep Neural Networks (DNNs) have shown incredible performance in a variety of data, they are brittle and opaque: easily fooled by the presence of noise, and difficult to understand the underlying reasoning for their predictions or choices. This focus on accuracy at the expense of interpretability and robustness caused little concern since, until recently, DNNs were employed primarily for scientific and limited commercial work. An increasing, widespread use of artificial intelligence and growing emphasis on user data protections, however, motivates the need for robust solutions with explainable methods and results. In this work, we extend a novel fuzzy based …

Association Between Stream Impairment By Mercury And Superfund Sites In The Conterminous Usa, Karessa L. Manning

Masters Theses

Mercury is a natural element that can cause harm to the brain, heart, kidneys, lungs, and immune system, especially to fetuses developing in the womb. Many natural and anthropogenic factors contribute to mercury in the environment, such as geologic deposits, landfills, gold and silver mining operations, cement production, and atmospheric deposition. Mercury has been identified as a contaminant of concern at many National Priority List (NPL) sites, however, studies on contamination at NPL sites are often only conducted on a local level. This study was to analyze the potential connection between mercury-contaminated NPL sites and the presence of mercury impaired …

Discovering Kepler’S Third Law From Planetary Data, Boyan Kostadinov, Satyanand Singh

Publications and Research

In this data-inspired project, we illustrate how Kepler’s Third Law of Planetary Motion can be discovered from fitting a power model to real planetary data obtained from NASA, using regression modeling. The power model can be linearized, thus we can use linear regression to fit the model parameters to the data, but we also show how a non-linear regression can be implemented, using the R programming language. Our work also illustrates how the linear least squares used for fitting the power model can be implemented in Desmos, which could serve as the computational foundation for this project at a lower …

Bibliometric Review Of Monsoon Rainfall Prediction Models: With Special Reference To Use Of Artificial Intelligence In Rainfall Prediction, Shilpa Manoj Hudnurkar, Neela Rayavarapu Dr.

Library Philosophy and Practice (e-journal)

Rainfall is a result of several complex atmospheric processes making it challenging to predict. For countries whose economy is dominated by agricultural sector, accurate rainfall prediction is highly essential. A huge network of weather stations is spread across the globe for the observation of meteorological parameters. These generate vast amounts of data which can be used to accurately predict the weather. This necessitates the use better tools such as various artificially intelligent algorithms. This study aims to explore global research trends in monsoon rainfall prediction techniques using Artificial Intelligence (AI) and Artificial Neural Networks (ANN). Scopus database has been used …

Regression Analysis: Graduation Rate In Kentucky Public High Schools, Rebecca Price

Mahurin Honors College Capstone Experience/Thesis Projects

Kentucky’s Public High School graduation rates vary widely across the rural and urban regions in the state. In addition to their graduation rates, each of these schools have their own unique demographics, funding, teacher-student ratio, etc. that define said school’s identity. This research aims to analyze the aforementioned variables, as well as other variables listed on each school state report card, in order to create a model to predict any school’s graduation rate.

In order to create this model, data was taken on all public high schools in Kentucky from the Kentucky Department of Education’s School Report Card. Data were …

Family Communication: Examining The Differing Perceptions Of Parents And Teens Regarding Online Safety Communication, Tara Rutkowski

Honors Undergraduate Theses

The opportunity for online engagement increases possible exposure to potentially risky behaviors for teens, which may have significant negative consequences (Hair et al., 2009). Effective family communication about online safety can help reduce the risky adolescent behavior and limit the consequences after it occurs. This paper contributes a theory of communication factors that positively influence teen and parent perception of communication about online safety and provides design implications based on those findings. Previous work identified gaps in family communication, however, this study seeks to empirically identify factors that would close the communication gap from the perspective of both teens and …

Stock Trend Prediction Using Candlestick Charting And Ensemble Machine Learning Techniques With A Novelty Feature Engineering Scheme, Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu

Information Technology & Decision Sciences Faculty Publications

Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, complex and chaotic nature of the stock price time series. With a simple eight-trigram feature engineering scheme of the inter-day candlestick patterns, we construct a novel ensemble machine learning framework for daily stock pattern prediction, combining traditional candlestick charting with the latest artificial intelligence methods. Several machine learning techniques, including deep learning methods, are applied to stock data to predict the direction of the closing price. This framework can give a suitable machine learning prediction method for each pattern based on the trained results. The investment …

An Evaluation Of Knot Placement Strategies For Spline Regression, William Klein

CMC Senior Theses

Regression splines have an established value for producing quality fit at a relatively low-degree polynomial. This paper explores the implications of adopting new methods for knot selection in tandem with established methodology from the current literature. Structural features of generated datasets, as well as residuals collected from sequential iterative models are used to augment the equidistant knot selection process. From analyzing a simulated dataset and an application onto the Racial Animus dataset, I find that a B-spline basis paired with equally-spaced knots remains the best choice when data are evenly distributed, even when structural features of a dataset are known …

Explainable Feature- And Decision-Level Fusion, Siva Krishna Kakula

Dissertations, Master's Theses and Master's Reports

Information fusion is the process of aggregating knowledge from multiple data sources to produce more consistent, accurate, and useful information than any one individual source can provide. In general, there are three primary sources of data/information: humans, algorithms, and sensors. Typically, objective data---e.g., measurements---arise from sensors. Using these data sources, applications such as computer vision and remote sensing have long been applying fusion at different "levels" (signal, feature, decision, etc.). Furthermore, the daily advancement in engineering technologies like smart cars, which operate in complex and dynamic environments using multiple sensors, are raising both the demand for and complexity of fusion. …

Full Interpretable Machine Learning Method With In-Line Coordinates, Hoang Phan

All Master's Theses

This thesis explores a new approach for machine learning classification task in 2-dimensional space (2-D ML) with In-line Coordinates. This is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. In-line coordinates method allows discovering n-D patterns in 2-D space without loss of n-D information using graph representation of n-D data in 2-D. Specifically, this thesis shows that it can be done with In-line Based Coordinates in different modifications, which are defined, including static and dynamic ones. Some classification and regression algorithms based on these In-line Coordinates were explored. Two successful cases …