A Nonlinear Support Vector Machine Analysis Using Kernel Functions for Nature and Medicine

After the emergence of Artificial Intelligence (AI), great developments have taken place in the fields of science, economics, medicine and all other fields that use computer science. Along with the resulting developments in these fields, artificial intelligence has also solved many intractable problems, such as predicting specific serious diseases, determining future product sales, as well as analyzing and studying big data in the shortest possible time … SVM is one of the most important technologies in this field of artificial intelligence that goes into supervised methods, and which every machine learning expert should have in his/her arena. For this reason, in this article, we studied this technique and determined its advantages and disadvantages as well as its fields of application. Next, we applied this technique to three different databases, using four basis change functions, and we compared the results obtained to determine the best way to use the basis change functions.

Efficient Basis Change for Sparse-Grid Interpolating Polynomials with Application to T-Cell Sensitivity Analysis

Sparse-grid interpolation provides good approximations to smooth functions in high dimensions based on relatively few function evaluations, but in standard form it is expressed in Lagrange polynomials. Here, we give a block-diagonal factorization of the basis-change matrix to give an efficient conversion of a sparse-grid interpolant to a tensored orthogonal polynomial (or gPC) representation. We describe how to use this representation to give an efficient method for estimating Sobol' sensitivity coefficients and apply this method to analyze and efficiently approximate a complex model of T-cell signaling events.

3D_Mec: An Application to Teach Mechanics

3D_Mec is a GPL-ed application for the solution of problems related to Mechanics. It is specially well suited for the direct kinematic and dynamic simulation of general systems. Is has been used during the last 10 years as a practical complement of the “Mechanics” Bachelor Course in Mechanical Engineering at the Public University of Nabarre-Spain. The main focus of the application is teaching Classical Mechanical principles and their applications. To that end 3D_Mec is constructed over an underlying Symbolic Algebra Kernel that allows the user to express itself in a language that closely resembles that of the mechanics and its level of abstraction. Classical kinematical primitives: Point, Basis and Reference, and set of operators acting upon them; support for vector and tensors, automatic basis change, ... relieve the user from the tedious algebraic manipulations directing his focus at the concept. A set of OpenGL based graphical primitives allow to represent the system and its simulation, and to create clips from them, it also represents a valuable way to check for the validity of the defined vectors and equations. 3D_Mec can also be used as a preprocessor or postprocessor, this is accomplished exporting the defined variables and different sets of equations in the popular language MatLab [3], where the integration or a different problem can be readily made based on well formed tested equations, results can be easily imported back from files containing a temporal series of the different variables. The program is also being used successfully in this way in “Dynamics of MultiBody Systems” PhD. course.