Clinical Decision Support systems link health observations with health knowledge to determine health option by clinicians for improved health care. The main idea of Clinical Decision Support System is a set of rules derived from medical professionals applied on a dynamic knowledge. Data mining is well suited to give decision support for the healthcare. There are several classification techniques available that can be used for clinical decision support system. Different techniques are used for different diagnosis. In this paper, various classification techniques for clinical decision support system are discussed with example. .

Falling is often accepted as a natural part of the aging process Elderly people are typically, more unsteady and frailer. Thus are more likely to fall and be injured than younger individuals. Falls can have a serious affect on both the quality of life of elder people and on health as well as social care costs. In this paper, we give a comprehensive survey of different wearable sensors for fall detection and their underlying algorithms and comparing their strengths and weaknesses. Conclusion is derived with some discussions on techniques of fall detection and pros and cons to wear wearable device for fall detection.

Quality control is imperative in any industry. Inspection of ceramic tile is done in conditions like noise, extreme temperature, and humidity. There are several stages through which we can maintain quality of a tile. These stages include inspection of color variation in a tile, chip offs in a tile, surface defects in a tile. This paper provides analysis of techniques which are useful to find surface defects like crack, blob, hole, variation in color, defect in corners, and pattern mismatch in tile which has pattern on it.

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