Mohamed Baddar

We consider, for the first time, utilising the
mobile big data for ‘microscopic’ level traffic
analysis. The project develops a HMM
formulation, and uses Viterbi decoding to
discover actual road segments of trips. This
facilitates road-level analysis without the
need for high cost traffic on-road traditional
sensors. We then generate Dakar traffic
intensity maps for main roads, for every hour
in the covered 50 weeks, of the year 2013.
Moreover, we develop and apply… Mehr anzeigen

We consider, for the first time, utilising the
mobile big data for ‘microscopic’ level traffic
analysis. The project develops a HMM
formulation, and uses Viterbi decoding to
discover actual road segments of trips. This
facilitates road-level analysis without the
need for high cost traffic on-road traditional
sensors. We then generate Dakar traffic
intensity maps for main roads, for every hour
in the covered 50 weeks, of the year 2013.
Moreover, we develop and apply a traffic
prediction model to the data and identify
significant traffic seasonality patterns. Weniger anzeigen

Predictive analytics software helps to find non-obvious, hidden patterns in large data sets. With the rapid growth of text information, text classification has become one of the key techniques for organizing text data. Compared to the state-of-art method, Support Vector Machines (SVM) classifiers with Bag-of-Words (BoW) representation of text data show substantial classification performance with respect to accuracy and generalization. The main challenge is extracting features and selecting the… Mehr anzeigen

Predictive analytics software helps to find non-obvious, hidden patterns in large data sets. With the rapid growth of text information, text classification has become one of the key techniques for organizing text data. Compared to the state-of-art method, Support Vector Machines (SVM) classifiers with Bag-of-Words (BoW) representation of text data show substantial classification performance with respect to accuracy and generalization. The main challenge is extracting features and selecting the SVM parameters to get the best possible performance. We propose a generic framework for building text classifiers using IBM SPSS Modeler and Java, with no need to domain specific dictionaries.
This article explores building SVM-based classification framework for text classification. We show the complete design and implementation details for this framework in IBM SPSS Modeler and Java. In addition, we show a case study of applying this framework for the classification of sample software defect data for smart software engineering. We illustrate experimentation steps along with obtained results. Weniger anzeigen