Search Using N-gram Technique Based Statistical Analysis for Knowledge Extraction in Case Based Reasoning Systems

Searching techniques for Case Based Reasoning systems involve extensive methods of elimination. In this paper, we look at a new method of arriving at the right solution by performing a series of transformations upon the data. These involve N-gram based comparison and deduction of the input data with the case data, using Morphemes and Phonemes as the deciding parameters. A similar technique for eliminating possible errors using a noise removal function is performed. The error tracking and elimination is performed through a statistical analysis of obtained data, where the entire data set is analyzed as sub-categories of various etymological derivatives. A probability analysis for the closest match is then performed, which yields the final expression. This final expression is referred to the Case Base. The output is redirected through an Expert System based on best possible match. The threshold for the match is customizable, and could be set by the Knowledge-Architect.

Autogenic Training With Natural Language Processing Modules: A Recent Tool For Certain Neuro Cognitive Studies

Learning to respond to voice-text input involves the subject's ability in understanding the phonetic and text based contents and his/her ability to communicate based on his/her experience. The neuro-cognitive facility of the subject has to support two important domains in order to make the learning process complete. In many cases, though the understanding is complete, the response is partial. This is one valid reason why we need to support the information from the subject with scalable techniques such as Natural Language Processing (NLP) for abstraction of the contents from the output. This paper explores the feasibility of using NLP modules interlaced with Neural Networks to perform the required task in autogenic training related to medical applications.