"DCA-IoMT Dataset"

Citation Author(s):
Nasrullah
Khan
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Zongmin
Ma
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Aman
Ullah
School of Computer Science and Engineering, Central South University, Changsha, 410083, China
Kemal
Polat
Department of Electrical and Electronics Engineering, Bolu Abant Izzet Baysal University, Bolu 14280, Turkey
Submitted by:
Nasrullah Khan
Last updated:
Sat, 05/07/2022 - 03:25
DOI:
10.21227/ypcj-3h88
Data Format:
License:
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Abstract 

“DCA-IoMT Dataset” belongs to the research article entitled “DCA-IoMT: Knowledge Graph Embedding-enhanced Deep Collaborative Alerts-recommendation against COVID19 (DOI: 10.1109/TII.2022.3159710)” accepted for publication in the Journal of IEEE Transactions on Industrial Informatics.

Instructions: 

For Instructions, Please Check "Section IV. A. Datasets and Pre-Processing" of the concerned paper, available at DOI: 10.1109/TII.2022.3159710

Declaration:

 The information in these datasets do not belong to any individual person or organization; any resemblance or co-incident will be accidental. We declare that we used these datasets for the experimental work only.

 Copyright:

 As we declared that the provided data do not belong to any individual's or organization's official or private records, rather collected (i.e., crawled from the web) and pre-processed for the experimental purpose of our proposed approach i.e., DCA-IoMT only; and therefore, totally based on the raw facts and figures. Formally, we can't endorse any claim or surety about its perfectness; however, pragmatically it is effective and feasible in the experimental environment of KG-based RecSyss from the perspectives of IoMT-based applications. Authors expect the following ethical norms from the potential users of the data (i.e., datasets).

 1.They will not divide / distribute without permission.

2.They will not de-anonymize.

3.They will use it for educational purpose only.

Funding Agency: 
The work was supported in part by the Basic Research Program of Jiangsu Province (BK20191274) and the National Natural Science Foundation of China (61772269 and 62176121).

Comments

very good

Submitted by Timothy Ezeokoli on Mon, 07/17/2023 - 09:53

good

Submitted by john smith on Fri, 09/06/2024 - 20:21