Innovation in latest technologies have provided means to gather data using various ways. Almost all the domains generate, store, and analyze the data for the improvement of the services they provide. The healthcare industry too generates a significant data which is used for improving public healthcare. While dealing with health data, it is necessary to follow appropriate data ethics as health data is considered the most sensitive and it needs to be properly collected, stored, processed and shared with different domains. This research paper discusses about the various data ethics to be followed to handle individual's health data, suggests a framework to deal with this data and a use case is suggested to understand the data ethics tenets.

No abstract

Design patterns are useful Software Engineering tools that enable the reuse of expert solutions to recurring problems. There are a large number of patterns, spread in multiple catalogs and in heterogeneous formats. Selecting and applying the right design pattern requires an in-depth understanding of patterns and their classification. The solution architects must either rely on the advice of experts or laboriously go through the available literature to find the relevant patterns. Pattern applicability will improve if the entire pattern knowledge is available in one place and in a standard format. If the pattern data is augmented with additional knowledge to guide the architect on choosing the right patterns for a particular requirement, it will be immensely useful and productive. The objective of the knowledge discovery process on the design pattern landscape is to extract useful relations and groups of patterns to enable users to select and apply patterns effectively. The present work discusses a model for analyzing existing pattern data, extracting knowledge thereof, and representing this knowledge in a format to enable pattern search and its application.

The recognition of Mathematical Expressions (ME) constitutes a challenging problem in character recognition research. A very few studies of offline Mathematical expressions have been so far reported in the literature. This paper focuses on offline handwritten and printed mathematical logical expressions recognition using Support Vector Machine classifier (SVM). In the work of expression recognition, the expressions were segmented into individual characters. The feature extraction method with combination of Normalized chain code and zone based density was used to get the features of a character. The present work considers logical expressions with subscripts for recognition. The experimental results for recognition rates of handwritten and printed expressions are reported. The result shows that the recognition rate of handwritten expression is 84.1% and that for printed expression is 90.3%.