Genetic Evaluation of the Class III Dentofacial in Rural and Urban Spanish Population by AI Techniques

The etiology of skeletal class III malocclusion is multifactorial, complex and likely results from mutations in numerous genes. In this study, we sought to understand genotype correlation of the class III dentofacial deformity in rural and urban spanish population of more than one generation. The genetic analyze was made using a Genome-wide scan. It will hold a novel classification using Artificial Intelligence techniques highlighting the difference between the two groups at the level of polymorphism. Our phenotypic and genetic analysis highlights that each group is unique

Real-time CBR-agent with a mixture of experts in the reuse stage to classify and detect DoS attacks

Security is a major concern when service environments are implemented. This has led to the proposal of a variety of specifications and proposals based on soft computing methods to provide the necessary security for these environments. However, most proposed approaches focus only on ensuring confidentiality and integrity, without putting forward mechanisms that ensure the availability of services and resources offered. A considerable number of attack mechanisms can lead to a web service system crash. As a result, the web service cannot allow access to authorized users. This type of attack is a so-called denial of service attack (DoS) which affects the availability of the services and recourses available. This article presents a novel soft computing-based approach to cope with DoS attacks, but unlike existing solutions, our proposal takes into account the different soft computing mechanisms that can lead to a DoS attack. Our approach is based on a real time classifier agent that incorporates a mixture of experts to choose a specific classification technique depending on the feature of the attack and the time available to solve the classification. With this scheme it is possible to divide the problem into subproblems, solving the classification of the web service requests in a more simple and effective way and always within a time bound interval. This research presents a case study to evaluate the effectiveness of the approach and also presents the preliminary results obtained with an initial prototype.Security is a major concern when service environments are implemented. This has led to the proposal of a variety of specifications and proposals based on soft computing methods to provide the necessary security for these environments. However, most proposed approaches focus only on ensuring confidentiality and integrity, without putting forward mechanisms that ensure the availability of services and resources offered. A considerable number of attack mechanisms can lead to a web service system crash. As a result, the web service cannot allow access to authorized users. This type of attack is a so-called denial of service attack (DoS) which affects the availability of the services and recourses available. This article presents a novel soft computing-based approach to cope with DoS attacks, but unlike existing solutions, our proposal takes into account the different soft computing mechanisms that can lead to a DoS attack. Our approach is based on a real time classifier agent that incorporates a mixture of experts to choose a specific classification technique depending on the feature of the attack and the time available to solve the classification. With this scheme it is possible to divide the problem into subproblems, solving the classification of the web service requests in a more simple and effective way and always within a time bound interval. This research presents a case study to evaluate the effectiveness of the approach and also presents the preliminary results obtained with an initial prototype

Real-time CBR-agent with a mixture of experts in the reuse stage to classify and detect DoS attacks

Security is a major concern when service environments are implemented. This has led to the proposal of a variety of speci¿cations and proposals based on soft computing methods to provide the necessary security for these environments. However, most proposed approaches focus only on ensuring con¿dentiality and integrity, without putting forward mechanisms that ensure the availability of services and resources offered. A considerable number of attack mechanisms can lead to a web service system crash. As a result, the web service cannot allow access to authorized users. This type of attack is a so-called denial of service attack (DoS) which affects the availability of the services and recourses available. This article presents a novel soft computing-based approach to cope with DoS attacks, but unlike existing solutions, our proposal takes into account the different soft computing mechanisms that can lead to a DoS attack. Our approach is based on a real time classi¿er agent that incorporates a mixture of experts to choose a speci¿c classi¿cation technique depending on the feature of the attack and the time available to solve the classi¿cation. With this scheme it is possible to divide the problem into subproblems, solving the classi¿cation of the web service requests in a more simple and effective way and always within a time bound interval. This research presents a case study to evaluate the effectiveness of the approach and also presents the preliminary results obtained with an initial prototype. © 2010 Elsevier B.V. All rights reserved.This work has been partially supported by the JCYL-2002-05 Project, SA071A08 Spanish Project, the Spanish government (TIN2009-13839-C03-01), FEDER and CONSOLIDER-INGENIO (2010 CSD2007-00022), the Generalitat Valenciana (PROME-TEO/2008/051) and the Professional Excellence Program 2006-2010 IFARHU-SENACYT-Panama.Pinzón, CI.; Paz, JFD.; Navarro Llácer, M.; Bajo, J.; Julian Inglada, VJ.; Corchado, JM. (2011). Real-time CBR-agent with a mixture of experts in the reuse stage to classify and detect DoS attacks. Applied Soft Computing. 11(7):4384-4398. https://doi.org/10.1016/j.asoc.2010.12.003S4384439811