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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]

In this research, a novel metaheuristic-based approach for enhancing the image quality of Magnetic resonance imaging (MRI) scans for Alzheimer's disease (AD) classification and segmentation is proposed. The proposed approach involves four phases: data collection, preprocessing, segmentation, and classification. The dataset used in this study is collected from an open-source internet platform and contains four classes of AD. To improve the quality of the collected images, various enhancement techniques are employed, such as local entropy-weighted histogram equalization using an adaptive wind-driven optimization algorithm and guided filter-based image denoising using an adaptive Salp Swarm Optimization algorithm. In addition to this, geometric transformations, image augmentation, noise reduction, and grayscale conversion are also utilized. The proposed segmentation model employs a drop-block in Segmentation Network (SegNet) for regularized feature learning, resulting in highly efficient pixel-wise segmentation. A modified loss function is also introduced for fine-tuning the performance of SegNet. The classification layer of SegNet utilizes a Restricted boltzmann machine (RBM) model for classifying AD. Performance evaluation of the proposed approach is done by considering performance metrics such as accuracy, precision, recall, F1-measure, Root means square error (RMSE), and mean absolute error (MAE). The proposed approach outperforms existing techniques such as Convolutional neural network (CNN), Recurrent neural network (RNN), Long short-term memory (LSTM), Understanding networks (UNET), and SegNet, demonstrating the effectiveness of the proposed metaheuristic-based image quality enhancement approach. The implementation of the proposed approach is carried out using Matlab. [ABSTRACT FROM AUTHOR]