The LH5 model for data mining

In the age of E-Business many companies faced with massive data sets that must be analysed for gaining a competitive edge. these data sets are in many instances incomplete and quite often not of very high quality. Although statistical analysis can be used to pre-process these data sets, this technique has its own limitations. In this paper we are presenting a system - and its underlying model - that can be used to test the integrity of existing data and pre-process the data into clearer data sets to be mined. LH5 is a rule-based system, capable of self-learning and is illustrated using a medical data set.

A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiology