"Can the neuro fuzzy model predict stock indexes better than its rivals?"

This paper develops a model of a trading system by using neuro fuzzy framework in order to better predict the stock index. Thirty well-known stock indexes are analyzed with the help of the model developed here. The empirical results show strong evidence of nonlinearity in the stock index by using KD technical indexes. The trading point analysis and the sensitivity analysis of trading costs show the robustness and opportunity for making further profits through using the proposed nonlinear neuro fuzzy system. The scenario analysis also shows that the proposed neuro fuzzy system performs consistently over time.

Asthma Exacerbation in Children: A Practical Review

Asthma is the most common chronic lower respiratory tract disease in childhood throughout the world. Despite advances in asthma management, acute exacerbations continue to be a major problem in patients and they result in a considerable burden on direct/indirect health care providers. A severe exacerbation occurring within 1 year is an independent risk factor. Respiratory tract viruses have emerged as the most frequent triggers of exacerbations in children. It is becoming increasingly clear that interactions may exist between viruses and other triggers, increasing the likelihood of an exacerbation. In this study, we provide an overview of current knowledge about asthma exacerbations, including its definition, impact on health care providers, and associated factors. Prevention management in intermittent asthma as well as intermittent wheeze in pre-school children and those with persistent asthma are discussed. Our review findings support the importance of controlling persistent asthma, as indicated in current guidelines. In addition, we found that early episodic intervention appeared to be crucial in preventing severe attacks and future exacerbations. Besides the use of medication, timely education after an exacerbation along with a comprehensive plan in follow up is also vitally important

A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.

"A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?"

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.

An Integrated Model to Explain How Corporate Social Responsibility Affects Corporate Financial Performance

The effect of corporate social responsibility (CSR) on financial performance has important implications for enterprises, communities, and countries, and the significance of this issue cannot be ignored. Therefore, this paper proposes an integrated model to explain the influence of CSR on financial performance with intellectual capital as a mediator and industry type as a moderator. Empirical results indicate that intellectual capital mediates the relationship between CSR and financial performance, and industry type moderates the direct influence of CSR on financial performance. Such results have critical implications for both academia and practice

Can the Neuro Fuzzy Model Predict Stock Indexes Better than its Rivals?

This paper develops a model of a trading system by using neuro fuzzy framework in order to better predict the stock index. Thirty well-known stock indexes are analyzed with the help of the model developed here. The empirical results show strong evidence of nonlinearity in the stock index by using KD technical indexes. The trading point analysis and the sensitivity analysis of trading costs show the robustness and opportunity for making further profits through using the proposed nonlinear neuro fuzzy system. The scenario analysis also shows that the proposed neuro fuzzy system performs consistently over time.本文フィルはリンク先を参照のこ

Can the Neuro Fuzzy Model Predict Stock Indexes Better than its Rivals?

This paper develops a model of a trading system by using neuro fuzzy framework in order to better predict the stock index. Thirty well-known stock indexes are analyzed with the help of the model developed here. The empirical results show strong evidence of nonlinearity in the stock index by using KD technical indexes. The trading point analysis and the sensitivity analysis of trading costs show the robustness and opportunity for making further profits through using the proposed nonlinear neuro fuzzy system. The scenario analysis also shows that the proposed neuro fuzzy system performs consistently over time

Allergic children with extremely high total IgE but no allergen identified in the initial screening panel

Background: High serum IgE level in atopic children usually implies a highly sensitized condition. However, there is a subgroup of atopic children for whom a specific allergen cannot be identified. In this study, we analyzed follow-up data from these children. Methods: From March 2014 to July 2017, we recruited 14 atopic children with serum total IgE level higher than 500 Ku/L, but with no specific allergen identified by repeated MAST tests initially. Follow-up studies of specific IgE were conducted by the OPTIGEN MAST Allergy test and ImmunoCAP assays (Thermo Fisher Scientific/Phadia), while total IgE and specific IgG were measured by ImmunoCAP. Results: The patients were aged from 2 to 17 y/o. The follow-up MAST tests showed significantly positive results in 10 patients. There were no significant differences in any of the clinical characteristics between the MAST-positive and MAST-negative groups. In the MAST-negative group, five allergen-specific IgE antibodies, including those for cockroach, Euroglyphus maynei, Blomia tropicalis, shrimp, and crab, were strongly predictive of negative ImmunoCAP results, according to ROC (Receiver operating characteristic curve) analysis of the AUC (Area under the Curve of ROC) (0.70–0.95), with significance set at p < 0.05. Conclusion: In two thirds of atopic children with a high serum IgE whose specific allergen had yet to be identified, it was possible to identify the specific MAST allergen(s) after an average follow-up of 33.2 months. For patients who still had negative results in follow-up MAST, mite DP, DF, and DM may be suitable choices for further allergen identification by ImmunoCAP