Improving trading saystems using the RSI financial indicator and neural networks.

Proceedings of: 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services (PKAW 2010), 20 August-3 September 2010, Daegu (Korea)Trading and Stock Behavioral Analysis Systems require efficient Artificial Intelligence techniques for analyzing Large Financial Datasets (LFD) and have become in the current economic landscape a significant challenge for multi-disciplinary research. Particularly, Trading-oriented Decision Support Systems based on the Chartist or Technical Analysis Relative Strength Indicator (RSI) have been published and used worldwide. However, its combination with Neural Networks as a branch of computational intelligence which can outperform previous results remain a relevant approach which has not deserved enough attention. In this paper, we present the Chartist Analysis Platform for Trading (CAST, in short) platform, a proof-of-concept architecture and implementation of a Trading Decision Support System based on the RSI and Feed-Forward Neural Networks (FFNN). CAST provides a set of relatively more accurate financial decisions yielded by the combination of Artificial Intelligence techniques to the RSI calculation and a more precise and improved upshot obtained from feed-forward algorithms application to stock value datasets.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the EUREKA project SITIO (TSI-020400-2009-148), SONAR2 (TSI-020100-2008-665 and GO2 (TSI-020400-2009-127). Furthermore, this work is supported by the General Council of Superior Technological Education of Mexico (DGEST). Additionally, this work is sponsored by the National Council of Science and Technology (CONACYT) and the Public Education Secretary (SEP) through PROMEP.Publicad

Microleakage of four different restorative glass ionomer formulations in Class V cavities: Er:YAG laser versus conventional preparation

Objective: To investigate microleakage in class V cavities following restoration with conventional glass-ionomer cements (CGICs) or resin-modified glass-ionomer cements (RMGICs), following Er:YAG laser or conventional preparation. Background Data: The sealing ability of GICs in Er:YAG-lased cavities has not been studied extensively. Methods: Three hundred and twenty class V cavities were assigned to four groups: those in groups A and B were prepared using an Er: YAG laser, and those in groups C and D were conventionally prepared. In groups B and D the surface was additionally conditioned with cavity conditioner. Each group was subdivided according to the GIC used: groups 1 (Fuji II), 2 (Fuji IX), 3 (Fuji II LC) and 4 ( Fuji VIII). After thermocycling, the specimens were immersed in a 2% methylene blue solution, sectioned oro-facially, and analyzed for leakage. The effect of the conditioner was analyzed using a scanning electron microscope (SEM). Results: Significant differences between occlusal and gingival margins were found in all groups (p < 0.05) except B4, D3, and D4. Comparison of preparation methods (groups A-D) revealed significant differences at the occlusal margin in groups 1 and 3, but in all groups at the gingival margin (p < 0.05). Laser preparation without conditioning allowed more leakage (p < 0.05). Comparison of filling materials (groups 1-4) revealed significant differences in groups B and C at the occlusal margin, and in all groups at the gingival margin (p < 0.05). In these groups, laser-prepared cavities (with or without conditioning) restored with Fuji II LC and Fuji VIII showed the least leakage at both margins. Conclusion: RMGICs allowed less microleakage than CGICs. Complete marginal sealing was not achieved and conditioning is recommended