Engineering Commons^™

Predicting Cross-Gaming Propensity Using E-Chaid Analysis, Eunju Suh, Matt Alhaery

UNLV Gaming Research & Review Journal

Cross-selling different types of games could provide an opportunity for casino operators to generate additional time and money spent on gaming from existing patrons. One way to identify the patrons who are likely to cross-play is mining individual players’ gaming data using predictive analytics. Hence, this study aims to predict casino patrons’ propensity to play both slots and table games, also known as cross-gaming, by applying a data-mining algorithm to patrons’ gaming data. The Exhaustive Chi-squared Automatic Interaction Detector (E-CHAID) method was employed to predict cross-gaming propensity. The E-CHAID models based on the gaming-related behavioral data produced actionable model accuracy …

An Evaluation Of The Use Of Diversity To Improve The Accuracy Of Predicted Ratings In Recommender Systems, Gillian Browne

Dissertations

The diversity; versus accuracy trade off, has become an important area of research within recommender systems as online retailers attempt to better serve their customers and gain a competitive advantage through an improved customer experience. This dissertation attempted to evaluate the use of diversity measures in predictive models as a means of improving predicted ratings. Research literature outlines a number of influencing factors such as personality, taste, mood and social networks in addition to approaches to the diversity challenge post recommendation. A number of models were applied included DecisionStump, Linear Regression, J48 Decision Tree and Naive Bayes. Various evaluation metrics …

Towards Closed-Loop Deep Brain Stimulation: Behavior Recognition From Human Stn, Soroush Niketeghad

Electronic Theses and Dissertations

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the …

A Comparative Study Of Two Different Fpga-Based Arrhythmia Classifier Architectures, Ahmet Turan Özdemi̇r, Kenan Danişman

Turkish Journal of Electrical Engineering and Computer Sciences

Early diagnosis of dangerous heart conditions is very important for the treatment of heart diseases and for the prevention of sudden cardiac death. Automatic electrocardiogram (ECG) arrhythmia classifiers are essential to timely diagnosis. However, most of the medical diagnosis systems proposed in the literature are software-based. This work focused on the hardware implementation of a mobile artificial neural network (ANN)-based arrhythmia classifier that is implemented on a field programmable gate array (FPGA) as a single chip solution, as an alternative to various software models of ANNs. Due to the parallel nature of ANNs, hardware implementation of ANNs needs a large …

Contrast Pattern Aided Regression And Classification, Vahid Taslimitehrani

Browse all Theses and Dissertations

Regression and classification techniques play an essential role in many data mining tasks and have broad applications. However, most of the state-of-the-art regression and classification techniques are often unable to adequately model the interactions among predictor variables in highly heterogeneous datasets. New techniques that can effectively model such complex and heterogeneous structures are needed to significantly improve prediction accuracy. In this dissertation, we propose a novel type of accurate and interpretable regression and classification models, named as Pattern Aided Regression (PXR) and Pattern Aided Classification (PXC) respectively. Both PXR and PXC rely on identifying regions in the data space where …

Intelligent Network Intrusion Detection Using An Evolutionary Computation Approach, Samaneh Rastegari

Theses: Doctorates and Masters

With the enormous growth of users' reliance on the Internet, the need for secure and reliable computer networks also increases. Availability of effective automatic tools for carrying out different types of network attacks raises the need for effective intrusion detection systems.

Generally, a comprehensive defence mechanism consists of three phases, namely, preparation, detection and reaction. In the preparation phase, network administrators aim to find and fix security vulnerabilities (e.g., insecure protocol and vulnerable computer systems or firewalls), that can be exploited to launch attacks. Although the preparation phase increases the level of security in a network, this will never completely …