OBJECTIVE: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers.

RESULTS: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging.

METHODS: The panel consists of sixteen experts from the Occupational and Environmental Health Unit of Ministry of Health, Vector-borne Disease Control Unit of Ministry of Health and Occupational and Safety Health Unit of both public and private universities. A set of questionnaires related to noise and chemical exposure were compiled based on the literature search. There was a total of six constructs with 60 items in which three constructs for knowledge, attitude, and practice of noise exposure and three constructs for knowledge, attitude, and practice of chemical exposure. The validation process replicated recent Fuzzy Delphi method that using a concept of Triangular Fuzzy Numbers and Defuzzification process.

RESULTS: A 100% response rate was obtained from all the sixteen experts with an average Likert scoring of four to five. Post FDM analysis, the first prerequisite was fulfilled with a threshold value (d) ≤ 0.2, hence all the six constructs were accepted. For the second prerequisite, three items (21%) from noise-attitude construct and four items (40%) from chemical-practice construct had expert consensus lesser than 75%, which giving rise to about 12% from the total items in the questionnaire. The third prerequisite was used to rank the items within the constructs by calculating the average fuzzy numbers. The seven items which did not fulfill the second prerequisite similarly had lower ranks during the analysis, therefore those items were discarded from the final draft.

OBJECTIVES: This research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods.

METHODS: The methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM.

RESULTS: (1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values.