An Improved Imputation Method for Accurate Prediction of Imputed Dataset Based Radon Time Series

Author:

Mir Adil Aslam1,Celebi Fatih Vehbi1ORCID,Rafique Muhammad2ORCID,Hussain Lal3ORCID,Almasoud Ahmed S.4,Alajmi Masoud5,Al-Wesabi Fahd N.6ORCID,Hilal Anwer Mustafa7

Affiliation:

1. Department of Computer Engineering, Ankara Yıldırım Beyazıt University, Ayvalı, Keçiören, Turkey

2. Department of Physics, The University of Azad Jammu and Kashmir, King Abdullah Campus, Chatter Kalas, Muzaffarabad, Azad Kashmir, Pakistan

3. Department of Computer Science and Information Technology, The University of Azad Jammu and Kashmir, King Abdullah Campus, Chatter Kalas, Muzaffarabad, Azad Kashmir, Pakistan

4. Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

5. Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia

6. Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Abha, Saudi Arabia

7. Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

Funder

Taif University

King Khalid University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Reference52 articles.

1. Multiple Imputation for Missing Data: Making the most of What you Know

2. Multiple imputation for missing data: Concepts and new development;yuan;Proc 25th Annu SAS Users Group Int Conf,2000

3. The influence of meteorological parameters on soil gas radon;singh;J Assoc Explor Geophys,1988

4. Soil-gas radon as seismotectonic indicator in Garhwal Himalaya

5. Interrelationship of indoor radon concentrations, soil-gas flux, and meteorological parameters

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