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This book introduces the concept of context-aware computing and its applications in various areas. It is designed particularly for the beginners who would like to design and develop the smart environment with context-aware computing. The user-friendly content is offered not only for the readers from Information Communication Technology related areas but also other professional domains such as Humanity, Public Health, Social Science, etc. The foundation of context-aware computing is described in this book such as definitions, categories, characteristics, context awareness, etc. Here, the elements of context-aware applications including context acquisition, context modeling, context reasoning, context distribution, and context adaptation are also emphasized. Communication and security are introduced so that the readers understand how all components work together with the security awareness. Additionally, some existing middleware and applications are presented so that the readers get the idea for selecting the right tool for their requirements and developing their applications appropriately. More importantly, the author's perspectives accordingly to context definitions, its awareness, and future context applications are suggested in this book. The ultimate goal of this book is to expand the contribution of context-aware computing to new professional areas where the utilization of personalized and rationalized applications as smart environments are required.

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The compatibility of members and groups requires the appropriate matching method which depends on the level of attitudes toward the components of the mentoring system. Generally, the mentoring system consists of two roles, including the mentor and the mentee. Identifying these compatible roles in an online community is challenging. This paper proposes a matching model for classifying the compatibility of mentors and mentees in a large group of members by using compatible different attributes, which consist of 24 attributes of attitude towards similarities and dissimilarities attributes between those two roles. The data was collected from 32 mentors and 205 mentees at four universities including

Early detection of Type 2 diabetes is necessary for its prevention. The prediction models for detection systems normally employ common factors that may not properly fit all persons having different health conditions. Therefore, this study proposes a method for type 2 diabetes prediction with factors representing personal health conditions. More specifically, this study proposes a novel prediction method named Average Weighted Objective Distance (AWOD) based on the assumption that the individual has diverse health conditions resulting from different individual factors, a requirement for an effective prediction model. AWOD is a modification of Weighted Objective Distance (WOD) by applying information gain to reveal significant and insignificant individual factors having different priorities, which are represented by different weights. For AWOD, the data set is divided into a training set used to determine all relevant thresholds and constant values required for AWOD calculation and the testing set. In particular, AWOD is designed for binary classification problems with a relatively small dataset. To validate the proposed method, two datasets from open sources, Pima Indians Diabetes (Dataset 1) and Mendeley Data for Diabetes (Dataset 2) each containing 392 records, were studied. The prediction performance for both datasets is compared with the machine learning-based prediction methods, including K-Nearest Neighbors, Support Vector Machines, Random Forest, and Deep Learning. The comparison results showed that the proposed method provided 93.22% and 98.95% accuracy for Dataset 1 and Dataset 2, respectively, which are higher than those provided by other machine learning-based methods.

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