Approximate ab initio calculations and the method of molecular fragments

A two stage approach to performing ab initio calculations on medium and large sized molecules is described. The first step is to perform SCF calculations on small molecules or molecular fragments using the OPIT Program. This employs a small basis set of spherical and p-type Gaussian functions. The Gaussian functions can be identified very closely with atomic cores, bond pairs, lone pairs, etc. The position and exponent of any of the Gaussian functions can be varied by OPIT to produce a small but fully optimised basis set. The second stage is the molecular fragments method. As an example of this, Gaussian exponents and distances are taken from an OPIT calculation on ethylene and used unchanged in a single SCF calculation on benzene. Approximate ab initio calculations of this type give much useful information and are often preferable to semi-empirical approaches, since the nature of the approximations involved is much better defined.

PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm

As one of the newest members in the field of articial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training data, instead domain or expert knowledge is required to predetermine the mapping between input signals from a particular instance to the three categories used by the DCA. This data preprocessing phase has received the criticism of having manually over-fitted the data to the algorithm, which is undesirable. Therefore, in this paper we have attempted to ascertain if it is possible to use principal component analysis (PCA) techniques to automatically categorise input data while still generating useful and accurate classication results. The integrated system is tested with a biometrics dataset for the stress recognition of automobile drivers. The experimental results have shown the application of PCA to the DCA for the purpose of automated data preprocessing is successful.