Quinton J. Nottingham

A hybrid grouping genetic algorithm for the registration area planning problem

With the growing use of mobile communication devices, the management of such technologies is of increasing importance. The registration area planning (RAP) problem examines the grouping of cells comprising a personal communication services (PCS) network into contiguous blocks in an effort to reduce the cost of managing the location of the devices operating on the network, in terms of bandwidth. This study introduces a hybridized grouping genetic algorithm (HGGA) to obtain cell formations for the RAP problem. The hybridization is accomplished by adding a tabu search-based improvement operator to a traditional grouping genetic algorithm (GGA). Results indicate that significant performance gains can be realized by hybridizing the algorithm, especially for larger problem instances. The HGGA is shown to consistently outperform the traditional GGA on problems of size greater than 19 cells.

Utilization of neural networks for the recognition of variance shifts in correlated manufacturing process parameters

Traditional statistical process control (SPC) charting techniques were developed for use in discrete industries where independence exists between process parameters over time. Process parameters from many manufacturing industries are not independent, however, but they are serially correlated. Consequently, the power of traditional SPC charts was greatly weakened. The paper discusses the development of neural network models to identify successfully shifts in the variance of correlated process parameters. These neural network models can be used to monitor manufacturing process parameters and signal when process adjustments are needed.

An augmented neural network classification approach to detecting mean shifts in correlated manufacturing process parameters

Statistical process control (SPC) techniques have traditionally been used to identify when the mean of a manufacturing process has shifted out of control. In situations where there is correlation among the observed outputs of the process, however, the underlying assumptions of SPC are violated and alternative approaches such as neural networks become necessary in order to characterize the process behaviour. This paper discusses the development of a neural network technique that provides a significantly improved capability for recognizing these process shifts as compared to the current techniques in the literature. The procedure in question is an augmented neural-network based approach, which incorporates a data preprocessing classification algorithm that provides information to facilitate early detection of out of control operating conditions. This approach is shown to improve significantly upon the performance of previous neural network techniques for identifying process shifts in the presence of correlation.

Visualization of multivariate data with radial plots using SAS

Data visualization tools can provide very powerful information and insight when performing data analysis. In many situations, a set of data can be adequately analyzed through data visualization methods alone. In other situations, data visualization can be used for preliminary data analysis. In this paper, radial plots are developed as a SAS-based data visualization tool that can improve ones ability to monitor, analyze and control a process. Using the program developed in this research, we present two examples of data analysis using radial plots; the first example is based on data from a particle board manufacturing process and the second example is a business process for monitoring the time-varying level of stock return data.

Local linear regression for estimating time series data

Predicting future performance based on past performance history is a task often undertaken by business process managers. Various statistical and analytical techniques, such as time series and neural network modeling, are available. However, these techniques require the availability of a long time series for the development of a predictive model. Local linear regression (LLR) is an additional nonparametric statistical method that can be used to estimate a time series response variable. The LLR technique does not require a long time series for the development of a predictive model. In fact, the LLR technique can be utilized for prediction once three data points have been collected from the business process. In this work, LLR was evaluated as a tool for predicting future values of process parameters based on historical values. If successful, the LLR technique could be applied in start-up conditions or used as an alternative in some situations to time series modeling. The LLR procedure outperformed traditional time series techniques for the example stationary data sets and had comparable results to the ARIMA model for the example seasonal data set. In addition the LLR technique uses the data that is currently available from a process as its basis for prediction, thus providing a dynamic predictive technique that can continue to function in the presence of process changes.

Determining the antecedents of digital security practices in the general public dimension

Our increased reliance on digital information and our expansive use of the Internet for a steadily rising number of tasks requires that more emphasis be placed on digital information security. The importance of securing digital information is apparent but the success in persuading individual users to adopt and utilize tools to improve security has been arguably more difficult. In this study, we propose a number of factors that may influence individual security practices. These constructs are developed by adapting existing theory from information security and privacy research to examine information security behaviors in the general public dimension. The influence of these factors on perceived need and actual behavior is then examined. The resulting model is shown to fit well and support is found for many of the proposed relationships. The determination of the antecedents of individual digital security practices may provide useful insight to tailoring programs for adoption and utilization of security tools by individuals in the general public dimension.

A note on the small sample behavior of logistic regression in a bioassay setting

The logistic regression procedure is a popular statistical method used when analyzing quantal dose-response data. However, logistic regression results based on a poorly designed experiment can be seriously compromised. Our results indicate that depending on the spacing of the doses, the number of doses, and the number of replications at each dose, the user can get very misleading results, including ineffective lack-of-fit tests and severely biased coefficient estimates along with biased estimates of response. In addition, variance formulas based on asymptotic theory may be completely inappropriate. Simulation results are used to support these statements.

A multi-year SEM model predicting the impact of behavior attributes on overall patient satisfaction

Purpose Pressure from competition; inflexible third-party reimbursements; greater demand from government, regulatory, and certifying agencies; discerning patients; and the quest of healthcare entities for greater profitably place demands and high expectations for service quality impacting overall patient experience. Extending a prior multivariate, single-period model of varied medical practices predicting patient experience to a three-year time period to understand whether there was a change in overall assessment using data analytics. Design/methodology/approach SEM was employed on a per year and aggregated, three-year basis to gain insights into qualitative psychometric constructs predicting overall patient experience and strength of the relationships. Findings Statistically significant differences were uncovered between years indicating the strength of the relationships of latent variables on overall performance. Research limitations/implications Study focused on data gathered from a questionnaire maile...