Seyedamin Pouriyeh

TL;DR: Several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering are described, which briefly explain text mining in biomedical and health care domains.

Abstract: In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. Text summarization is the task of shortening a text document into a condensed version keeping all the important information and content of the original document. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.

TL;DR: The experimental results demonstrate that the federated-learning (FL)-based anomaly detection approach outperforms the classic/centralized machine learning (non-FL) versions in securing the privacy of user data and provides an optimal accuracy rate in attack detection.

TL;DR: The main approaches to automatic text summarization are described and the effectiveness and shortcomings of the different methods are described.