PMID- 36207091
OWN - NLM
STAT- MEDLINE
DCOM- 20221011
LR  - 20221221
IS  - 1873-2860 (Electronic)
IS  - 0933-3657 (Print)
IS  - 0933-3657 (Linking)
VI  - 132
DP  - 2022 Oct
TI  - Mathematical modeling and AI based decision making for COVID-19 suspects backed 
      by novel distance and similarity measures on plithogenic hypersoft sets.
PG  - 102390
LID - S0933-3657(22)00143-9 [pii]
LID - 10.1016/j.artmed.2022.102390 [doi]
AB  - It goes without saying that coronavirus (COVID-19) is an infectious disease and 
      many countries are coping with its different variants. Owing to the limited 
      medical facilities, vaccine and medical experts, need of the hour is to 
      intelligently tackle its spread by making artificial intelligence (AI) based 
      smart decisions for COVID-19 suspects who develop different symptoms and they are 
      kept under observation and monitored to see the severity of the symptoms. The 
      target of this study is to analyze COVID-19 suspects data and detect whether a 
      suspect is a COVID-19 patient or not, and if yes, then to what extent, so that a 
      suitable decision can be made. The decision can be categorized such that an 
      infected person can be isolated or quarantined at home or at a facilitation 
      center or the person can be sent to the hospital for the treatment. This target 
      is achieved by designing a mathematical model of COVID-19 suspects in the form of 
      a multi-criteria decision making (MCDM) model and a novel AI based technique is 
      devised and implemented with the help of newly developed plithogenic distance and 
      similarity measures in fuzzy environment. All findings are depicted graphically 
      for a clear understanding and to provide an insight of the necessity and 
      effectiveness of the proposed method. The concept and results of the proposed 
      technique make it suitable for implementation in machine learning, deep learning, 
      pattern recognition etc.
CI  - Copyright (c) 2022 Elsevier B.V. All rights reserved.
FAU - Ahmad, Muhammad Rayees
AU  - Ahmad MR
AD  - Department of Mathematics, University of Management and Technology, Lahore 54770, 
      Pakistan.
FAU - Afzal, Usman
AU  - Afzal U
AD  - Department of Mathematics, University of Management and Technology, Lahore 54770, 
      Pakistan. Electronic address: usman.afzal@umt.edu.pk.
LA  - eng
PT  - Journal Article
DEP - 20220902
PL  - Netherlands
TA  - Artif Intell Med
JT  - Artificial intelligence in medicine
JID - 8915031
RN  - 0 (Vaccines)
SB  - IM
MH  - Artificial Intelligence
MH  - *COVID-19/epidemiology
MH  - Decision Making
MH  - Humans
MH  - Models, Theoretical
MH  - *Vaccines
PMC - PMC9436789
OTO - NOTNLM
OT  - COVID-19
OT  - Multi-criteria decision making (MCDM)
OT  - Plithogenic distance measure (PDM)
OT  - Plithogenic hypersoft set (PHSS)
OT  - Plithogenic similarity measure (PSM)
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2022/10/08 06:00
MHDA- 2022/10/12 06:00
PMCR- 2022/09/02
CRDT- 2022/10/07 21:03
PHST- 2021/09/14 00:00 [received]
PHST- 2022/08/08 00:00 [revised]
PHST- 2022/08/29 00:00 [accepted]
PHST- 2022/10/07 21:03 [entrez]
PHST- 2022/10/08 06:00 [pubmed]
PHST- 2022/10/12 06:00 [medline]
PHST- 2022/09/02 00:00 [pmc-release]
AID - S0933-3657(22)00143-9 [pii]
AID - 102390 [pii]
AID - 10.1016/j.artmed.2022.102390 [doi]
PST - ppublish
SO  - Artif Intell Med. 2022 Oct;132:102390. doi: 10.1016/j.artmed.2022.102390. Epub 
      2022 Sep 2.