Recently people pay more and more attention on how to effectively and efficiently analyze the result of regular physical examinations to provide the most helpful information for individual health management. In this paper, we design and develop an interactive system of virtual healthcare assistant to help people, especially for those who suffer from chronic diseases (e.g., metabolic syndrome) to easily understand their health conditions and then well manage it. This system analyzes the result of regular physical examination to evaluate the health risk and provide personalized healthcare services for users in terms of diet and exercise guideline recommendations. We developed some interactive ways for users to easily feedback their vital signs to the system and quickly get the suggestions for health management from the system. Besides the browser-based system, we also developed a mobile App that can regularly remind users to carry out the recommendations, which are provided by the system. To prove the system is feasible in the real-world clinical environment, we also applied the Institutional Review Board (IRB) for a human subject research to validate this system. Other than the functional features, there are also several important non-functional features of the extensibility and the convenience for use. First, we use the physical examination result as the raw data to be analyzed. It's very convenient for users with very low cost. Second, the system design is extendable, so it can be easily adjusted to work for any chronic ills, even other kinds of diseases. Moreover, it can be extended to provide other kinds of healthcare guideline recommendations as well. These features constitute the main contributions of this work.
In this paper, we present a study of genetic-based stock selection models using the data of fundamentals of initial public offerings (IPOs). The stock selection model intends to derive the relative quality of the IPOs in order to obtain their relative rankings. Top-ranked IPOs can be selected to form a portfolio.In this study, we also employ Genetic Algorithms (GA) for optimization of model parameters and feature selection for input variables to the stock selection model. We will show that our proposed models deliver above-average first-day returns.Based upon the promising results obtained, we expect our GA-based methodology to advance the research in soft computing for computational ("mance and provide an effective solution to stock selection for IPOs in practice.Stock selection has long been recognized as a challenging and important research area in finance. Its main application consists of selecting promising stocks out of a universe of regular stocks or initial public offerings (IPOs). The success of this task is highly contingent on reliable models that utilize relevant information to pick stocks to deliver above-average returns in the future (for regular stocks) or first-day returns (for IPOs).In contrast to traditional approaches, such as regression-based methods, recent advances in computational intelligence (CI) are leading to promising opportunities to solve the problems of stock selection more effectively [1] . In the past, interesting CI methods developed for tackling this task include fuzzy inference models, artificial neural networks (ANNs), support vector machines (SV Ms), as well as evolutionary algorithms (EAs). For instance, in the area of fuzzy model applications, earlier work includes Chu et al. 's 978-1-4673-1487-9/12/$31.00 ©2012 IEEE fuzzy multiple attribute decision analysis to select stocks for portfolio construction [2] . Analogously, Zargham and Sayeh [3] employed a fuzzy rule-based system to evaluate a set of stocks for the same task. These fuzzy approaches denote early efforts in employing CI for the problems of stock selection, but they usually lack sufficient learning ability.Quah and Srinivasan [4] studied an ANN stock selection system to choose stocks that are top-ranked performers. They showed their proposed model outperformed the benchmark model in terms of compounded actual returns. Chapados and Bengio [5] also trained neural networks for estimation and prediction of asset behavior in order to facilitate decision-making in asset allocation. Although these models have been shown to work in some applications, they often suffer from overfitting problems.Caplan and Becker [6] , and Becker, Fei and Lester [7] employed genetic programming (GP) to develop stock ranking models for the U.S. market. Although these methods seemed to work in some applications, it is often difficult for human experts to use the resultant complicated models for straightforward decision making. In contrast to these complicated models, simpler and more intuitive models were developed. For example, Kim and Han ...
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