Biblio

Advertising channels have evolved from conventional print media, billboards and radio-advertising to online digital advertising (ad), where the users are exposed to a sequence of ad campaigns via social networks, display ads, search etc. While advertisers revisit the design of ad campaigns to concurrently serve the requirements emerging out of new ad channels, it is also critical for advertisers to estimate the contribution from touch-points (view, clicks, converts) on different channels, based on the sequence of customer actions. This process of contribution measurement is often referred to as multi-touch attribution (MTA). In this work, we propose CAMTA, a novel deep recurrent neural network architecture which is a causal attribution mechanism for user-personalised MTA in the context of observational data. CAMTA minimizes the selection bias in channel assignment across time-steps and touchpoints. Furthermore, it utilizes the users' pre-conversion actions in a principled way in order to predict per-channel attribution. To quantitatively benchmark the proposed MTA model, we employ the real-world Criteo dataset and demonstrate the superior performance of CAMTA with respect to prediction accuracy as compared to several baselines. In addition, we provide results for budget allocation and user-behaviour modeling on the predicted channel attribution.

In this paper, a novel DNA based computing method is proposed for encryption of biometric color(face)and gray fingerprint images. In many applications of present scenario, gray and color images are exhibited major role for authenticating identity of an individual. The values of aforementioned images have considered as two separate matrices. The key generation process two level mathematical operations have applied on fingerprint image for generating encryption key. For enhancing security to biometric image, DNA computing has done on the above matrices generating DNA sequence. Further, DNA sequences have scrambled to add complexity to biometric image. Results of blending images, image of DNA computing has shown in experimental section. It is observed that the proposed substitution DNA computing algorithm has shown good resistant against statistical and differential attacks.