International Journal of Innovative Research and Development

Abstract

Health communities offer huge variety of Information regarding Medical sector which is useful for Drug dealers, Doctors and patients. This work includes the collection of Real time health posts from trusted websites, these websites contain patients experiences and side effects on drugs used by them. By collecting these information from trusted websites, this paper perform summarization of user posts per drug and come out with useful conclusions for Drug dealers, Doctors, Patients.

Further, It classify the users based on their ‘emotional state of mind’ In this paper it perform the knowledge discovery from user post, which gives useful ‘Patterns’, ‘Keyword extraction’, by using Association rule Method.

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Abstract

Photographs have been used to document space-time events and they have often served as evidence in courts. Powerful digital image editing software makes image modifications straight forward and this undermines the trust in photographs. Forgery detection method is proposed which exploits subtle inconsistencies in the color of the illumination of images. It is machine-learning based approach and it requires minimal user interaction. Physics and statistical based illuminant estimators include the generalized gray world estimates and inverse intensity chromaticity estimates on image regions of similar material are incorporated. From these illuminant estimates, texture- and edge-based features are extracted by SASI algorithm and HOG edge algorithm which are then provided to a machine-learning approach for automatic decision-making. The extracted features are then paired using the same descriptors which are then classified by Adaboost classifier.

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