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Cloud computing turns into the cutting edge engineering of IT Enterprise. Cloud essential key thought is to move the whole information models into the server farms where the portability and administration of information isn't completely reliable. The principle challenge in the cloud is the client information and framework application information is gotten to from the cloud supplier's premises; even security arrangements sent in the premises don't take care of the demand of the clients where the need differs. The on-request security arrangement is alluring, yet now days all the cloud suppliers are utilizing encryption systems to exchange the information and information ask for and reaction. Consequently in this proposition, another security show is proposed for the cloud model to give security highlights. The new thought is to apply elliptic curve cryptography to give the security highlights to on request information handling. This proposition explores the essential issue of cloud computing information security. At last the security demonstrate is conveyed in the cloud OS "open stack" and "cloud stack".

Nowadays thousands of drivers and passengers were losing their lives every year on road accident, due to deadly crashes between more than one vehicle. There are number of many research focuses were dedicated to the development of intellectual driver assistance systems and autonomous vehicles over the past decade, which reduces the danger by monitoring the on-road environment. In particular, researchers attracted towards the on-road detection of vehicles in recent years. Different parameters have been analyzed in this paper which includes camera placement and the various applications of monocular vehicle detection, common features and common classification methods, motion- based approaches and nighttime vehicle detection and monocular pose estimation. Previous works on the vehicle detection listed based on camera poisons, feature based detection and motion based detection works and night time detection.

According to recent studies, young adults in India faced mental health issues due to closures of universities and loss of income, low self-esteem, distress, and reported symptoms of anxiety and/or depressive disorder (43%). This makes it a high time to come up with a solution. A new classifier proposed to find those individuals who might be having depression based on their tweets from the social media platform Twitter. The proposed model is based on linguistic analysis and text classification by calculating probability using the TF ∗ IDF (term frequency-inverse document frequency). Indians tend to tweet predominantly using English, Hindi, or a mix of these two languages (colloquially known as Hinglish). In this proposed approach, data has been collected from Twitter and screened via passing them through a classifier built using the multinomial Naive Bayes algorithm and grid search, the latter being used for hyperparameter optimization. Each tweet is classified as depressed or not depressed. The entire architecture works over English and Hindi languages, which shall help in implementation globally and across multiple platforms and help in putting a stop to the ever-increasing depression rates in a methodical and automated manner. In the proposed model pipeline, composed techniques are used to get the better results, as 96.15% accuracy and 0.914 as the F1 score have been attained.