The concept of personalization in its many forms has gained traction driven by the demands of computer-mediated interactions generally implemented in large-scale distributed systems and ad hoc wireless networks. Personalization requires the identification and selection of entities based on a defined profile (a context); an entity has been defined as a person, place, or physical or computational object. Context employs contextual information that combines to describe an entities current state. Historically, the range of contextual information utilized (in context-aware systems) has been limited to identity, location, and proximate data; there has, however, been advances in the range of data and information addressed. As such, context can be highly dynamic with inherent complexity. In addition, context-aware systems must accommodate constraint satisfaction and preference compliance.This article addresses personalization and context with consideration of the domains and systems to which context has been applied and the nature of the contextual data. The developments in computing and service provision are addressed with consideration of the relationship between the evolving computing landscape and context. There is a discussion around rule strategies and conditional relationships in decision support. Logic systems are addressed with an overview of the open world assumption versus the closed world assumption and the relationship with the Semantic Web. The event-driven rule-based approach, which forms the basis upon which intelligent context processing can be realized, is presented with an evaluation and proof-of-concept. The issues and challenges identified in the research are considered with potential solutions and research directions; alternative approaches to context processing are discussed. The article closes with conclusions and open research questions.

Context is inherently complex requiring intelligent context processing to address the broad and diverse range of available data that when processed can be viewed as contextual information useful in intelligent context-aware systems in a broad range of domains and systems. The function of contextaware systems is to target service provision based on an entities context; an entity has been defined as "a person, place, or physical or computational object". Given the diverse range of available data and entities a primary requirement for an intelligent context-aware system is decision support under uncertainty. This paper provides an overview of context and intelligent context processing. The novel 'extended' context matching algorithm is presented with an overview of fuzzy systems design as it relates to context processing and matching. The design of the membership function is discussed and it is shown that context matching using a membership function based on semantic representation provides a basis upon which the granularity of the context matching result can be improved. Anomalies where the context matching result lies close to a decision boundary are discussed with context processing under uncertainty. The paper concludes with a discussion conclusions, and open research questions.

High dimensional biomedical datasets contain thousands of features which can be used in molecular diagnosis of disease, however, such datasets contain many irrelevant or weak correlation features which influence the predictive accuracy of diagnosis. Without a feature selection algorithm, it is difficult for the existing classification techniques to accurately identify patterns in the features. The purpose of feature selection is to not only identify a feature subset from an original set of features [without reducing the predictive accuracy of classification algorithm] but also reduce the computation overhead in data mining. In this paper, we present our improved shuffled frog leaping algorithm which introduces a chaos memory weight factor, an absolute balance group strategy and an adaptive transfer factor. Our proposed approach explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant features in high-dimensional biomedical data. To evaluate the effectiveness of our proposed method we have employed the K-nearest neighbor method with a comparative analysis in which we compare our proposed approach with genetic algorithms, particle swarm optimization, and the shuffled frog leaping algorithm. Experimental results show that our improved algorithm achieves improvements in the identification of relevant subsets and in classification accuracy.

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There have been significant developments in higher education resulting in interest in personalised educational provision. Concomitant with these changes is the evolving capability and ubiquity of mobile technologies. To facilitate personalisation and leverage the power of mobile technologies in mobile pedagogic systems identification of individuals is a prerequisite; this can be achieved using an individual’s profile (termed context). This chapter considers the background to context with related research. Context modelling, the processing of contextual information, context matching and the context matching algorithm, ontology, and the Semantic Web technologies are introduced. Context reasoning and inference in rule-based systems is considered and the context reasoning ontology is presented with scenario-based evaluation. The chapter concludes with a discussion, consideration of future research, and open research questions.

Brain networks can be divided into two categories: structural and functional networks. Many studies of neuroscience have reported that the complex brain networks are characterized by small-world or scale-free properties. The identification of nodes is the key factor in studying the properties of networks on the macro-, micro- or mesoscale in both structural and functional networks. In the study of brain networks, nodes are always determined by atlases. Therefore, the selection of atlases is critical, and appropriate atlases are helpful to combine the analyses of structural and functional networks. Currently, some problems still exist in the establishment or usage of atlases, which are often caused by the segmentation or the parcellation of the brain. We suggest that quantification of brain networks might be affected by the selection of atlases to a large extent. In the process of building atlases, the influences of single subjects and groups should be balanced. In this article, we focused on the effects of atlases on the analysis of brain networks and the improved divisions based on the tractography or connectivity in the parcellation of atlases.