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Time‐series clustering typically entails clustering similar patterns across various time scales or comparing various point trajectories. However, this study emphasizes to group data points based on their motions and forecast how the clusters will evolve across a number of immediate time frames. To achieve this, we propose a DYNamic Aggregation of Mutually‐connected poInts clusTEring (DYNAMITE) based clustering algorithm for time series. DYNAMITE is based on the interaction between points in a time series and it majorly consists of three components: (1) cluster initialization; (2) calculation of mutually connected points; and (3) cluster updating. [ABSTRACT FROM AUTHOR]

The world is affected by COVID‐19, an infectious disease caused by the SARS‐CoV‐2 virus. Tests are necessary for everyone as the number of COVID‐19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID‐19 in a few seconds by uploading a single chest X‐ray image. A deep learning‐aided architecture that can handle client and server sides efficiently has been proposed in this work. The front‐end part has been developed using StreamLit, and the back‐end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID‐19 pandemic and will help to push the envelope of this work to a different extent. [ABSTRACT FROM AUTHOR]