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With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific journals points to around 40,000 with about four million articles published each year. Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research. In this current, we propose an optimized interface for bibliographic information retrieval as a running example, which allows different kind of researchers to find their needs following some relevant criteria through natural language understanding. Papers indexed in Web of Science and Scopus are in high demand. Natural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries. Our Interface uses association rules to find more related papers for recommendation. Spanning trees are challenged to optimize the search process of the system.

Business intelligence is based on the existence of main components including : data warehouses .The data warehouse is a specialized database which main task is to provide quick access to data in analysis objective. But in some cases it is necessary to use a set of data warehouses to provide a complete information. This structure is what we call federation, and even if the components are physically separated, they are logically seen as a single component. Generally, these items are heterogeneous which make it difficult to create the logical federation schema ,and the execution of user queries a complicated mission. In this paper, we will fill this gap by proposing a model for logical federation schema creation based on ontology, in order to treat different schema types (star , snow flack) including the treatment of hierarchies dimension too.

Deep Neural networks algorithms are recently used to solve problems in medical imaging like no time ever. However, one of the main challenges for training robust and accurate machine learning algorithms, such as Convolutional neural networks (CNNs) is to find a large dataset, which is, unfortunately, not available for public usage, or it is not available when it comes to a rare disease. Federated Learning (FL) could be a solution to data lack. It can make training and validation through multicenter datasets possible, without compromising the privacy and data protection. In this paper we summarize, discuss, and present an UpToDate overview of FL for medical image analysis solutions and related approaches.