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Clients have restricted access on the stored information at the far off area oversaw at various service providers as privacy and protection are the significant doubts in cloud computing. The data delivered from the wearable devices become more testing as it has more delicate and heterogeneous in nature. The vast majority of the current strategies in the literature is having high calculation and communication costs and is attackable to different known attacks, which decrease their significance for relevance in real world. Henceforth, in this paper, we proposed another cloud based validation technique for giving security to Health Data. A secret session key that is utilized for secure interchanges after effective shared verification between a client and wearable sensor node. The Real-Or-Random model based proper security examination and regularly acknowledged Automated Validation of Internet Security Protocols and Applications device based conventional security check. it gives the session key security and ensures dynamic attacks for proposed technique. The proposed technique is likewise casually examined to show its flexibility against other known attacks. Additionally, we have done a point by point relative assessment for the correspondence and count costs close by security and convenience remembers that gives its profitability for diverge from the other existing methods. [ABSTRACT FROM AUTHOR]

The aim of the proposed work is to predict the performance of the Artificial Neural Network (ANN) algorithm in detection of novel car quality performance by comparing it with the Decision tree algorithm. Materials and Methods: Proposed work uses a total sample size of 1556 dataset collected from UCI repository with accurate quality 778 samples and inaccurate 778 samples. The collected samples are divided into two groups, group 1 with training data (n = 1167 [75%]) and group 2 with testing data (n = 389 [25%]). Calculation of samples is done by using G power analysis with clincalc which contains two different groups, alpha (0.05), power (80%) and environment ratio. Results: In the proposed model ANN algorithm achieved accuracy, recall and precision of 94.09%, 97.95%, and 95.21% respectively compared to 92.25%, 96.81% and 93.34% by Decision tree algorithm. The obtained significance value of this analysis is 0.000. Conclusion: For the given dataset, the ANN shows significantly better results than the Decision tree algorithm in the car acceptability evaluation. [ABSTRACT FROM AUTHOR]

The world is fully engaged in the usage of plastics, where foremost use comes from the food industry. All the food industries use plastic packaging for most of food. The restaurants uses maximum amount of plastic spoon, where most of them are reused again and again. By consuming food with these reused spoons the human health has been effected. Like wise plastics spoons are also used in ice cream, many confectionery and in sweets too. By consuming more plastics lead to circumstances to dump them to the soil, which causes soil pollution and even they affect the health of the humankind. The dumping give rise to major problems like landfills and environmental pollution. In order to replace all these harmful factors, the edible spoon provides the solution to these problems. [ABSTRACT FROM AUTHOR]