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There is a perception in the United States that campaign contributions equate with vote buying. Outright vote buying is illegal, but many citizens believe that loopholes in campaign contribution laws allow some to buy votes while perpetuating a façade of legitimacy. Both federal and state laws attempt to regulate campaign contributions, but many of those have been limited by the Supreme Court’s ruling that campaign spending is considered free speech (Buckley vs. Valeo, 1976). Without the ability to limit campaign spending, the amount of money it takes to run a campaign, particularly a presidential campaign, has increased substantially. This had led to an increase in the use of bundling by presidential campaigns, with the winners often rewarding their bundlers. It has also led to an increase in outside independent organizations, known as Super PACs, with an unlimited ability to raise and spend money. This creates an additional problem as a small percentage of wealthy individuals constitute the vast majority of campaign contributors, leading to the perception that politicians cater to the elite. Whether a politician is affected by these factors or not is hard to prove, but it still leaves a perception by voters that their votes are less influential than large campaign contributors and there is always a risk that a vote has been bought.

This mixed methods multiple case study examines the knowledge, understanding, and awareness of 25 health board/facility oversight managers and 20 health professional association directors about privacy and security issues important to achieving health information exchange (HIE) in the state of Nebraska. Within case analyses revealed that health board/facility oversight managers were unaware of key elements of the federal agenda; their concerns about privacy encompassed broad definitions both of what constituted a “health record” and “regulations centeredness.” Alternatively, health professional association leaders were keenly aware of national initiatives. Despite concerns about HIE, they supported information exchange believing that patient care quality and safety would improve. Cross-case analyses revealed a perceptual disconnect between board/facility oversight managers and professional association leaders; however, both favored HIE. Understanding state-level stakeholder perceptions helps us further understand our progress toward achieving the national health information interoperability goal. There is an ongoing need to assure adequate patient privacy protection. Licensure and facility boards at the state level are likely to have a major role in the assurance of patient protections through facility oversight and provider behavior. The need for these boards to take an active role in oversight of patient rights and protections is imminent. Similarly, professional associations are the major vehicles for post-graduate education of practicing health professionals. Their engagement is essential to maintaining health professions knowledge. States will need to understand and engage both of these key stakeholders to make substantial progress in moving the HIE agenda forward.

Results of a previous study showed that use of health information technology (HIT) significantly reduced potential medication prescribing errors. However, the results also revealed a less than 100% rate of HIT adoption by primary care physicians. The current study reports on personal interviews with participating physicians that explored the barriers they faced when attempting to fully adopt a particular HIT. Content analysis of qualitative interviews revealed three barrier themes: time, technology, and environment. Interviews also revealed two other areas of concern; specifically, the compatibility of the HIT with the physician's patient mix and the physician's own attitude toward the use of HIT. A theoretical model of technology acceptance and use is used to discuss and further explain the data derived from the physician interviews. With a better understanding of these issues, health care administrators can develop successful strategies for adoption of HIT across their health care organizations.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Recently, the modelling and simulation of switched systems containing new nonlinear components in electronics and power electronics industry have gained importance. In this paper, both feed‐forward artificial neural networks (ANN) and adaptive network‐based fuzzy inference systems (ANFIS) have been applied to switched circuits and systems. Then their performances have been compared in this contribution by developed simulation programs. It has been shown that ANFIS require less training time and offer better performance than those of ANN. In addition, ANFIS using “clustering algorithm” to generate the rules and the numbers of membership functions gives a smaller number of parameters, better performance and less training time than those of ANFIS using “grid partition” to generate the rules. The work not only demonstrates the advantage of the ANFIS architecture using clustering algorithm but also highlights the advantages of the architecture for hardware realizations.

The aim of this study is to propose an efficient rule extraction and integration approach for identifying phishing websites. The proposed approach can elucidate patterns of phishing websites and identify them accurately.

Hyperlink indicators along with URL-based features are used to build the identification model. In the proposed approach, very simple rules are first extracted based on individual features to provide meaningful and easy-to-understand rules. Then, the F-measure score is used to select high-quality rules for identifying phishing websites. To construct a reliable and promising phishing website identification model, the selected rules are integrated using a simple neural network model.

Experiments conducted using self-collected and benchmark data sets show that the proposed approach outperforms 16 commonly used classifiers (including seven non–rule-based and four rule-based classifiers as well as five deep learning models) in terms of interpretability and identification performance.

Investigating patterns of phishing websites based on hyperlink indicators using the efficient rule-based approach is innovative. It is not only helpful for identifying phishing websites, but also beneficial for extracting simple and understandable rules.

This study demonstrates two visual methodologies to support analysts using artificial neural networks (ANNs) in data mining operations. The first part of the paper illustrates the differences and similarities between various learning rules that might be employed by ANN data miners. Since different learning rules lead to different connection weights and stability coefficients, a graphical representation of the data that provides a novel visual means of discerning these similarities and differences is demonstrated. The second part of this research demonstrates a methodology for ANN model variable interpretation that uses network connection weights. It uses empirical marketing data to optimize an ANN and response elasticity graphs are built for each ANN model variable by plotting the derivative of the network output with respect to each variable, while changing network input in equal increments across the range of inputs for each variable. Finally, this paper concludes that such an approach to ANN model interpretation can provide data miners with a rich interpretation of variable importance.

Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study, GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk, despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data.

Auditors are required to perform analytical procedures during the planning and concluding phases of the audit. Such procedures typically use data aggregated at a high level. The authors investigate whether artificial neural networks, a more sophisticated technique for analytical review than typically used by auditors, may be effective when using high level data.

Data from companies operating in the dairy industry were used to train an artificial neural network. Data with and without material seeded errors were used to test alternative techniques.

Results suggest that the artificial neural network approach was not significantly more effective (taking into account both Type I and II errors) than traditional ratio and regression analysis, and none of the three approaches provided more overall effectiveness than a purely random procedure. However, the artificial neural network approach did yield considerably fewer Type II errors than the other methods, which suggests artificial neural networks could be a candidate to improve the performance of analytical procedures in circumstances where Type II error rates are the primary concern of the auditor.

The authors extend the work of Coakley and Brown (1983) by investigating the application of artificial neural networks as an analytical procedure using aggregated data. Furthermore, the authors examine multiple companies from one industry and supplement financial information with both exogenous industry and macro-economic data.