A very challenging issue in real world data is that in many domains like medicine, finance, marketing, web, telecommunication, management etc., the distribution of data among classes is inherently imbalanced. A widely accepted researched issue is that the traditional classifier algorithms assume a balanced distribution among the classes. Data imbalance is evident when the number of instances representing the class of concern is much lesser than other classes. Hence, the classifiers tend to bias towards the well-represented class. This leads to a higher misclassification rate among the lesser represented class. Hence, there is a need of efficient learners to classify imbalanced data. This chapter aims to address the need, challenges, existing methods and evaluation metrics identified when learning from imbalanced data sets. Future research challenges and directions are highlighted.

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Abstract-The education system in rural and semi-rural areas of developing and underdeveloped countries are facing many challenges. The limited accessibility and challenges to the education are attributed mainly to political, economic and social issues of these underdeveloped countries. We propose a "Feasible Rural Education System (FRES)" based on Ontology and supported by Cloud to enhance the accessibility to education in rural areas. The system has been proposed incorporating the FOSS approach.

The present educational systems are trying to incorporate the semantic web technologies, so as to provide an adaptable, personalized and an intelligent learning environment. The long term vision of Education is to provide AAAL: Anytime, Anywhere, Anybody Learning. This paper discusses the architecture of semantic web for the purpose education system which satisfies AAAL .It contains an overview of how ontology can represent the knowledge base which forms the backbone of the Semantic Web based Educational system, thus making the system useful to the learners. It also contains methods on how ontology construction can take place for a particular domain.

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