Text with limited context is referred as short text. Short text understanding means detecting the concepts mentioned in a short text. Understanding short texts is important to many applications, but challenges abound. The reasons are, firstly, short texts do not always observe the syntax of a written language. Therefore, traditional natural language processing tools cannot be easily implemented. Secondly, short texts usually do not contain sufficient statistical signals to support many state-of-the-art approaches for text mining such as topic modeling. Thirdly, short texts are generated in a huge volume, which increases the difficulty to handle them. The main goal of the paper is to explore the semantics from short text and utilize it for proper decision making. A community blog is implemented in which the registered users in the community can text and the concept of the short text makes it possible to cluster the users. Online and Offline processing is performed. The instance ambiguity scoring and locating substrings in a text which are similar to terms contained in a predefined vocabulary in the offline processing increase the accuracy of the proposed system.

Abstract-Recent developments and advances in the field of wireless communication has led to the discovery of Wireless Body Area Networks (WBANs). The wearable computing devices made it easy to monitor the health issues of patients. The WBANs are widely used taking into account its numerous advantages. Many publications are available mentioning the various challenges of WBANs. In this paper, a survey is performed on the current state-of-art of WBANs based on the latest standards and publications. Open issues and challenges within each area are also explored as a source of inspiration towards future developments in WBANs.

The texts with limited context are referred as short text. Understanding and Interpreting short texts are important to many applications, but challenges appear. The problem when handling with short text is that firstly, short texts do not always observe the syntax of a written language. As a result, traditional natural language processing tools, ranging from part-of-speech tagging to dependency parsing, cannot be easily applied. Secondly, short texts usually do not contain sufficient statistical signals to support many state-of-the-art approaches for text mining such as topic modelling. Thirdly, short texts are more ambiguous and noisy, and are therefore generated in a huge volume, which further increases the problem to handle them. Various methods for short text understanding and interpretation along with the limitations faced and, how the interpretation can be used in an effective way is discussed.