While cloud computing offers several benefits, it has also raised privacy and security problems, which have piqued the attention of researchers in the field. One of the main issues in this field is the storage and accessibility of sensitive records. Research on methods to decipher encrypted files kept on distant cloud servers has attracted a lot of attention from academics. There have been a lot of proposals for methods to do conjunctive keyword searches, but fewer for methods that are more targeted. In this study, we provide a Bloom filterbased phrase search method that outperforms state-of-the-art methods while maintaining or improving upon storage and transmission costs. To back up the functionality, our method employs a sequence of n-gram filters. The technique is flexible enough to withstand inclusion-relation assaults and displays a trade-off between storage and false positive rate. It also details a design method that takes into account the desired false positive

Recognising plant diseases using deep convolutional networks to analyse leaf photos is the main focus of this article. We train a deep convolutional neural network to detect crop illnesses using a large publicly available dataset made up of photos of healthy and sick plant leaves taken in controlled environments. The convolution method involves applying filters to an image in order to produce a feature map. The input picture or feature map is first passed through a linear filter with a bias added, then a nonlinear filter is applied. Following this, Max Pooling is used, which simplifies calculations for higher layers, removes the minimum value, and offers translational invariance. There are three methods in which our accuracy in determining whether tomato and potato leaves are healthy was proven: Early blight or the bacterial spot is contained inside. Our approach achieves an accuracy of 91% thanks to the given outcome.