This paper deals with automatic classification of Arabic web documents. Such a classification is very useful for affording directory search functionality, which has been used by many web portals and search engines to cope with an ever-increasing number of documents on the web. In this paper, Naive Bayes (NB) which is a statistical machine learning algorithm, is used to classify non-vocalized Arabic web documents (after their words have been transformed to the corresponding canonical form, i.e., roots) to one of five pre-defined categories. Cross validation experiments are used to evaluate the NB categorizer. The data set used during these experiments consists of 300 web documents per category. The results of cross validation in the leave-one-out experiment show that, using 2,000 terms/roots, the categorization accuracy varies from one category to another with an average accuracy over all categories of 68.78 %. Furthermore, the best categorization performance by category during cross validation experiments goes up to 92.8%. Further tests carried out on a manually collected evaluation set which consists of 10 documents from each of the 5 categories, show that the overall classification accuracy achieved over all categories is 62%, and that the best result by category reaches 90%.
Abstract:One of the major problems confronted in precision agriculture is uncertainty about how exactly would yield in a certain area respond to decreased application of certain nutrients. One way to deal with this type of uncertainty is the use of scenarios as a method to explore future projections from current objectives and constraints. In the absence of data, soft computing techniques can be used as effective semi-quantitative methods to produce scenario simulations, based on a consistent set of conditions. In this work, we propose a dynamic rule-based Fuzzy Cognitive Map variant to perform simulations, where the novelty resides in an enhanced forward inference algorithm with reasoning that is characterized by magnitudes of change and effects. The proposed method leverages expert knowledge to provide an estimation of crop yield, and hence it can enable farmers to gain insights about how yield varies across a field, so they can determine how to adapt fertilizer application accordingly. It allows also producing simulations that can be used by managers to identify effects of increasing or decreasing fertilizers on yield, and hence it can facilitate the adoption of precision agriculture regulations by farmers. We present an illustrative example to predict cotton yield change, as a response to stimulated management options using proactive scenarios, based on decreasing Phosphorus, Potassium and Nitrogen. The results of the case study revealed that decreasing the three nutrients by half does not decrease yield by more than 10%.
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