Steven Walczak

Improving opening book performance through modeling of chess opponents

Opening books play a vital part in the performance of current chess programs. Use of the opening book places the chess program in a position that is already several moves into the game and can be positionally inferior. A method for acquiring opening knowledge about a specific opponent is presented. The method analyzes the historic performance of the opponent to find the opening sequences that are known to the opponent. Knowledge about the opening preferences of an opponent affords a strategic advantage to a chess program. The performance of this method is demonstrated and analyzed. Current chess programs that utilize knowledge about the opening repertoire of an opponent will be able to decrease the size of their opening books and can develop a game strategy from the start of the chess game instead of the beginning of the middle game.

Universal versus contextual effects on TQM: a triangulation study using neural networks

Abstract The objective of this study is to extend previous research on total quality management (TQM)-context-performance relationships and ‘fit’ using multiple methods. We combine artificial neural networks (ANNs) with structural equation modelling (SEM) to analyse several hypotheses and propositions. This is the first study in this area of research that utilises ANNs and a triangulation technique in the presence of several contextual factors. The SEM analyses suggest that company size and industry type may have contingency effects on some of the TQM practices and/or TQM-performance relationships. However, the ANN models have shown that these two contingency factors do not moderate TQM outcomes, implying that all organisations can benefit from TQM regardless of size and type. As well, these models show that formal TQM implementation and/or ISO certifications do not add any predictive power to the ANN models except in one case: TQM implementation and/or ISO certification added to organisational effectiveness and customer results to predict financial and market (F&M) results. The results further indicate that even though implementing TQM alone has a bigger impact on F&M results than obtaining ISO certification alone, combining the two will have an even greater impact on these results. Joint implementation leads to greater improvements in organisational effectiveness, which, in turn, has a positive effect on customer results and consequently F&M results. This is a unique finding within the context of moderator effects on TQM-performance relationships.

Improving prognosis and reducing decision regret for pancreatic cancer treatment using artificial neural networks

Abstract Cancer is a worldwide health problem with extremely high morbidity and mortality. Pancreatic cancer specifically is the fourth leading cause of death by cancer in the United States and is a leading cause of cancer deaths worldwide. The optimal treatment for pancreatic cancer is resection surgery, but even with surgery many patients suffer high morbidity and mortality, leading to regret in physicians over whether or not the optimal course of treatment with regard to the patients quality of life was made. Patients also suffer regret concerning the morbidity associated with treatment. An artificial neural network is developed to predict 7-month survival of pancreatic cancer patients that achieves over a 91% sensitivity and an overall accuracy above 70%. The artificial neural network outcome predictions may be used as an additional source of information to assist physicians and patients in selecting the treatment that provides the best quality of life for the patient and reduces treatment decision regret.

Geography learning in primary school: Comparing face-to-face versus tablet-based instruction methods

Abstract Touchscreen tablet technology is being widely adopted in primary and secondary schools throughout the world. Current research largely explores how to use this technology to teach reading and writing, mathematics, and to a lesser extent science. However a research gap exists in exploring tablet technology to teach geography. The research in this article examines if any differences in learning outcomes exist between a more traditional teaching method and one that is centered on using touchscreen tablet technology when teaching USA states’ shapes and locations to second-graders. The results indicate that there is no statistically significant difference between the two teaching methods, but that combining the two methods may lead to significant improvements in learning outcomes.

An Evaluation of Artificial Neural Networks in Predicting Pancreatic Cancer Survival

ObjectiveThis study aims to evaluate the development of an artificial neural network (ANN) method for predicting the survival likelihood of pancreatic adenocarcinoma patients. The ANN predictive model should produce results with a 90% sensitivity.MethodsA prospective examination of the records for 283 consecutive pancreatic adenocarcinoma patients is used to identify 219 records with complete data. These records are then used to create two unique samples which are then used to train and validate an ANN predictive model. Numerous network architectures are evaluated, following recommended ANN development protocols.ResultsSeveral backpropagation-trained ANNs were produced that satisfied the 90% sensitivity requirement. An ANN model with over a 91% sensitivity is selected because even though it did not have the highest sensitivity, it was able to achieve over 38% specificity.ConclusionsANN models can accurately predict the 7-month survival of pancreatic adenocarcinoma patients, both with and without resection, at a 91% sensitivity and 38% specificity. This implies that ANN models may be useful objective decision tools in complex treatment decisions. This information may be used by patients and surgeons in determining optimal treatment plans that minimize regret and improve the quality of life for these patients.

An Artificial Neural Network Classification of Prescription Nonadherence

This study investigates the use of artificial neural networks ANNs to classify reasons for medication nonadherence. A survey method is used to collect individual reasons for nonadherence to treatment plans. Seven reasons for nonadherence are identified from the survey. ANNs using backpropagation learning are trained and validated to produce a nonadherence classification model. Most patients identified multiple reasons for nonadherence. The ANN models were able to accurately predict almost 63 percent of the reasons identified for each patient. After removal of two highly common nonadherence reasons, new ANN models are able to identify 73 percent of the remaining nonadherence reasons. ANN models of nonadherence are validated as a reliable medical informatics tool for assisting healthcare providers in identifying the most likely reasons for treatment nonadherence. Physicians may use the identified nonadherence reasons to help overcome the causes of nonadherence for each patient.

Personality type effects on perceptions of online credit card payment services

Credit cards and the subsequent payment of credit card debt play a crucial role in e-commerce transactions. While website design effects on trust and e-commerce have been studied, these are usually coarse grained models. A more individualized approach to utilization of online credit card payment services is examined that utilizes personality as measured by the Myers-Briggs personality type assessment to determine variances in perception of online payment service features. The results indicate that certain overriding principles appear to be largely universal, namely security and efficiency (or timeliness) of the payment system. However there are differences in the perceived benefit of these features and other features between personality types, which may be capitalized upon by payment service providers to attract a broader base of consumers and maintain continuance of existing users.

Artificial Neural Networks and other AI Applications for Business Management Decision Support

Artificial intelligence AI in general and artificial neural networks ANN in particular provide a tremendous amount of knowledge to improve managerial decision making. Additionally, these same ANN and AI techniques also serve as knowledge repositories and distribution schema for organizations that facilitate managerial leadership responsibilities. This article examines how various ANN and other AI applications may be adapted to facilitate managerial leadership, improve manager performance and in some cases perform management activities. Further research that classifies leadership styles and the desired qualities of leaders is reviewed.