A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Fundamentals, Misvaluation, and Investment: The Real Story

Is real investment fully determined by fundamentals or is it sometimes affected by stock market misvaluation? We introduce three new tests that: measure the reaction of investment to sales shocks for firms that may be overvalued; use Fama-MacBeth regressions to determine whether "overinvestment" affects subsequent returns; and analyze the time path of the marginal product of capital in reaction to fundamental and misvaluation shocks. Besides these qualitative tests, we introduce a measure of misvaluation into standard investment equations to estimate the quantitative effect of misvaluation on investment. Overall, the evidence suggests that both fundamental and misvaluation shocks affect investment.investment, stock market, fundamentals, misvaluation, bubbles, real effects of financial markets

Fundamentals, Misvaluation, and Investment. The Real Story

Is real investment fully determined by fundamentals or is it sometimes affected by stockmarket misvaluation? We introduce three new tests that: measure the reaction of investment to sales shocks for firms that may be overvalued; use Fama-MacBeth regressions to determine whether "overinvestment" affects subsequent returns; and analyze the time path of the marginal product of capital in reaction to fundamental and misvaluation shocks. Besides these qualitative tests, we introduce a measure of misvaluation into standard investment equations to estimate the quantitative effect of misvaluation on investment. Overall, the evidence suggests that both fundamental and misvaluation shocks affect investment.Investment, Stock market, Fundamentals, Misvaluation, Bubbles, Real effects of financial markets

Macro Factors in UK Excess Bond Returns: Principal Components and Factor-Model Approach

We use factor augmented predictive regressions to investigate the relationship between excess bond returns and the macro economy. Our application is for the case of United Kingdom. The dimension of the large data set with 127 variables is reduced by the method of principal components and the Onatski (2009) procedure is used to determine the number factors. Our data covers the period 1983:09 - 2006:10. We find that variation in the one year ahead excess returns on 2 to 5-year UK government bonds can be modeled by macroeconomic fundamentals with R-square values varying from 34 percent to 44 percent. Specifically, three macro factors "unemployment" factor, "inflation" factor and "stock market" factor have significant predictive power in explaining the variation in the excess bond returns. Our results provide new evidence against the expectations hypothesis for the case of UK. We contribute to the literature by analyzing the direct link between macroeconomic variables and excess bond returns for a European market rather than the US. Unpredictability of excess bond returns is not the case in the UK either.Principal Components Analysis (PCA); Expectations Hypothesis; Excess Bond Returns; Factor Models.

Business Intelligence and Big Data in Higher Education: Status of a Multi-Year Model Curriculum Development Effort for Business School Undergraduates, MS Graduates, and MBAs

Business intelligence (BI), “big data”, and analytics solutions are being deployed in an increasing number of organizations, yet recent predictions point to severe shortages in the number of graduates prepared to work in the area. New model curriculum is needed that can properly introduce BI and analytics topics into existing curriculum. That curriculum needs to incorporate current big data developments even as new dedicated analytics programs are becoming more prominent throughout the world. This paper contributes to the BI field by providing the first BI model curriculum guidelines. It focuses on adding appropriate elective courses to existing curriculum in order to foster the development of BI skills, knowledge, and experience for undergraduate majors, master of science in business information systems degree students, and MBAs. New curricula must achieve a delicate balance between a topic’s level of coverage that is appropriate to students’ level of expertise and background, and it must reflect industry workforce needs. Our approach to model curriculum development for business intelligence courses follows the structure of Krathwohl’s (2002) revised taxonomy, and we incorporated multi-level feedback from faculty and industry experts. Overall, this was a long-term effort that resulted in model curriculum guidelines