A soft computing approach for obesity assessment

Ahmed, Jawed; Alam, M. Afshar; Mobin, Abdul; Tarannum, Shahla

doi:10.1109/icrito.2016.7784946

2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) 2016

DOI: 10.1109/icrito.2016.7784946

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A soft computing approach for obesity assessment

Jawed Ahmed

M. Afshar Alam

Abdul Mobin³

et al.

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2022

2024

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Cited by 2 publications

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Prioritizing severity level of COVID-19 using correlation coefficient and intuitionistic fuzzy logic

Tarannum

Jabin

2022

Int. j. inf. tecnol.

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During the peak of COVID-19 pandemic crisis in 2020 and 2021, with limited medical resources and surge in Covid cases in every hospital and clinic, identifying the most vulnerable patient requiring immediate critical treatment was a great challenge for the medical practitioners. And if such a patient suffers from multiple ailments, his/her condition may deteriorate rapidly if proper treatment is delayed any further. In this paper, we used a novel method which supports medical care units in identifying the patients who need urgent medical treatment. We used Gerstenkorn and Manko correlation coefficient and the intuitionistic fuzzy sets to classify such patients, who should be given the highest priority to start the treatment first. The role of this correlation measurement is very vital in any decision-making process. An intuitionistic fuzzy set (IFS) handles uncertainty, vagueness, ambiguity etc. present in the data and helps in making decision process more realistic. Combining the correlation coefficient with the Intuitionistic fuzzy set makes the decision making process more easy, accurate and reliable. We used COVID-19 dataset which maintains early-stage symptoms of COVID-19 patients, and is publicly available. We applied correlation coefficient and IFS to predict the severity level of the COVID-19 cases by establishing the relationship between the patient and the ailments a COVID-19 patient is suffering from.

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Prioritizing severity level of COVID-19 using correlation coefficient and intuitionistic fuzzy logic

Tarannum

Jabin

2022

Int. j. inf. tecnol.

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Obesity treatment applying effective fuzzy soft multiset-based decision-making process

Sakr

2024

MATH

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<p>Nowadays, obesity is recognized as a worldwide epidemic that has become a major cause of death and comorbidities. Recommending appropriate treatment is critical in the global health environment. For obesity treatment to be effective, the person must be able to follow a specific diet that meets his needs so that he can follow it for a long time or forever to maintain fitness. This research aims to determine the best diet among the trusted diets for every person based on his needs and circumstances. This occurs when applying a decision-making technique based on the effective fuzzy soft multiset concept. For this purpose, the definition of the effective fuzzy soft multiset as well as its types, operations, and properties are introduced. Furthermore, a decision-making method is proposed based on the effective fuzzy soft multiset environment. Using matrices operations, one can easily apply the decision-making process based on this new extension of sets to choose the optimal diet for everyone. Finally, an extensive comparative analysis of the previous methods is undertaken and also summarized in a chart to attract focus on the benefits of the suggested algorithm and to demonstrate how they differ from the current one.</p>

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A soft computing approach for obesity assessment

Cited by 2 publications

References 4 publications

Prioritizing severity level of COVID-19 using correlation coefficient and intuitionistic fuzzy logic

Prioritizing severity level of COVID-19 using correlation coefficient and intuitionistic fuzzy logic

Obesity treatment applying effective fuzzy soft multiset-based decision-making process

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