Marine species recognition is the process of identifying various species that help in population estimation and identifying the endangered types for taking further remedies and actions. The superior performance of deep learning for classification is due to the property of estimating millions of parameters that have to be extracted from many annotated datasets. However, many types of fish species are becoming extinct, which may reduce the number of samples. The unavailability of a large dataset is a significant hurdle for applying a deep neural network that can be overcome using transfer learning techniques. To overcome this problem, we propose a transfer learning technique using a pre-trained model that uses underwater fish images as input and applies a transfer learning technique to detect the fish species using a pre-trained Google Inception-v3 model. We have evaluated our proposed method on the Fish4knowledge(F4K) dataset and obtained an accuracy of 95.37%. The research would be helpful to identify fish existence and quantity for marine biologists to understand the underwater environment to encourage its preservation and study the behavior and interactions of marine animals.

The numerous technologies and procedures used to safeguard private cloud infrastructure are private cloud security. Despite multitenant public cloud environments, resources are dedicated to individual companies in a private cloud. Cloud computing uses several data security mechanisms, including authentication and identification, access control, encryption, secure deletion, integrity checking, and data masking. Cloud computing relies heavily on security and traffic management. Because data must be transferred from a local computer to a remote computer when using a cloud computing service, data protection is becoming an increasingly important security problem. Research on cloud computing-based secure dynamic bit standard (CC-SDBS) technology has increased data security. This technique's cryptographic algorithms (CA) improve cloud computing security. As a result of the private cloud security level algorithm (PCSLA), the client's private data can be easily accessed in the cloud. For the client's private cloud data, PCSLA provides dynamic security measures. Cloud computing applications can use the tests' better data security in the future. The proposed algorithm's testing results have shown a noticeable improvement in cipher size and execution time compared with other commonly used cloud computing. Numerical results revealed that CC-SDBS improved average energy consumption by 20%, average network latency by 25%, average key generation time analysis by 4.3%, and average network use by 15%.

In this paper, we proposed a plant leaf disease identification model based on a Pretrained deep convolutional neural network (Deep CNN). The Deep CNN model is trained using an open dataset with 10 different classes of tomato leaves We observed that overall architectures which can increase the best performance of the model. The proposed model was trained using different training epochs, batch sizes and dropouts. The Xception has attained maximum accuracy compare with all other approaches. After an extensive simulation, the proposed model achieves classification accuracy better. This accuracy of the proposed work is greater than the accuracy of all other Pretrained approaches. The proposed model is also tested with respect to its consistency and reliability. The set of data used for this work was collected from the plant village dataset, including sick and healthy images. Models for detection of plant disease should predict the disease quickly and accurately in the early stage itself so that a proper precautionary measures can be applied to avoid further spread of the diseases. So, to reduce the main issue about the leaf diseases, we can analyze distinct kinds of deep neural network architectures in this research. From the outcomes, Xception has a constantly improving more to enhance the accuracy by increasing the number of epochs, without any indications of overfitting and decreasein quality. And Xception also generated a fine 99.45% precision in less computing time.