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The consumer services market greatly depends on the consumers feedbacks. The best provided services will be increasing the rating of those services subsequently annotated with their good feedback. To give feedback one platform is social media like twitter is very suitable one. To attain consumers interest on their services, consumer markets utilizes advertisements via search engine marketing and social media platforms. The advertisements are very attractive and mind catching, people will be informed, motivated, influenced. All advertisers give advertises in form of text, picture, and video, audio and by mixing them with the aid of professional ad-makers. The search engine is a search program for finding particular sites on World Wide Web, which discovers the stuff related to keywords or characters specified by the user. In an increasingly competitive marketplace to expand and grow the business the Search engine marketing (SEM) is the effective approach. The advertisers also select video sharing platforms like YouTube-a video sharing channel, and also the search engine marketing platform to launch their advertisements to be available for consumers publicly. The public can view and share their opinion via likes/dislikes count and also comments for every video. This paper focus on attaining stock predictions from different sources and also discuss about gathering text analysis for the required stock from digital media like search engines, video channels, news feeds. The aim of this study is to consider the stock price prediction from major E-commerce consumer services companies namely Just Dial and Info edge that are publicly traded in NSE/BSE by considering web advertising and their influence on consumer services markets like Just Dial and Info edge, by adopting ensemble machine learning algorithms like Random forest, Gradient boost, XG-boost and it is observed that XG Boost outperforms the other algorithms as it exhibits least RMSE,MAE and MAPE providing the accuracy of 71.78%.

Visual Cryptography encrypts the secret or key into 'n' shares or portions and distributes them to a group of 'n' participants. The secret is recovered only when shares of all the participants in the group are stacked upon one another depending on the method used. This technique eliminates complex computations during decryption. It uses simple OR or XOR Boolean operations. Once the secret is revealed, it is no more a secret. So, a new secret acts as a key, that is to be again shared confidentially. The same process is performed and new shares are generated and distributed. The generation and distribution of shares must be done every time when a new secret or a combinational key is shared. In this paper, a trusted third party generates the shares and distributes it to the group of participants. It also generates an extra share for itself. To reveal the secret, the third party's share is also used along with other participants shares. Every time when the secret is to be changed, the third party regenerates its share only, instead of generating shares for all participants. This method reduces the overhead of regeneration and redistribution of shares to all participants with every change of the secret or key. This method of key management also retains the perfect contrast and security. The OR based method leads to noise during recovery. XOR based operations during recovery recovers lossless image. So, XOR operation is more preferable during recovery of the secret.