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Privacy preservation in recommendations has been increasingly garnering huge interest from the research community owing to the rapid rise in data security and privacy concerns among users. The computation overhead and attaining high recommendation accuracy remain the key issues in the existing methods. In this research, a course recommendation method using Federated Learning (FL) based on Deep Learning is presented. The course recommendation technique is carried out in the local nodes using multiple phases, like agglomerative matrix generation, course grouping, bi-level matching, retrieval of learner-preferred courses, and course recommendation. Here, course grouping is accomplished using Deep Fuzzy Clustering (DFC), and Deep Convolutional Neural Networks (DCNN) performs recommendation. The DFC-DCNN-FL is examined based on accuracy, False Positive Rate (FPR), loss function, Mean Square Error (MSE), Root MSE (RMSE), and Mean Average Precision (MAP) and is found to have attained values of 0.909, 0.116, 0.126, 0.291, 0.539, and 0.925. [ABSTRACT FROM AUTHOR]

Attacks are the major problems in the Internet of Things (IoT) applications and communication networks. The undetected intruders affect the availability of the system for end-users, increase identity theft and data breaches. Hence, it is required to detect the attacks in the IoT systems to ensure effective defense and security. In this research, the Border Collie Cat Optimization-based Deep Recurrent Neural Network is proposed to detect intrusion in the IoT networks. Here, the proposed Border Collie Cat Optimization algorithm is derived by the integration of Border Collie Optimization and Cat Swarm Optimization. At first, the messages are authenticated at the authentication phase using the hashing and encryption function. After authenticating the device, the communication between the server and user is carried out at the communication phase to make the IoT device eligible for data transfer within the network. Then, the Deep Recurrent Neural Network classifier is employed to detect the intruders in the IoT network in such a way that the training process is carried out using the proposed Border Collie Optimization algorithm. The proposed approach obtained higher performance with the metrics, like detection rate, sensitivity, specificity and accuracy with the values of 0.9375, 0.9539, 0.8791 and 0.9263, respectively. [ABSTRACT FROM AUTHOR]

The Crop yield-based prediction technique is a significant field of research that helps to ensure the food security and safe guard the food all over the nation. The projected system assists and support to predict enhanced and improved yield of crop and as well suggest the preeminent crop in dissimilar types of climatic conditions based on various parameter associated to the climates. This calculation will assist the farmer in the direction of choosing, whether the selected and particular crop is appropriate for that region and during the demanding season. In accumulation this website-based application gives information about details of productivity rate, irrigation, and other crop related details. Therefore, with the help of this website along with the different features help out the farmers to increase the crop production rate and crop quality improvement. [ABSTRACT FROM AUTHOR]