A Design of mRNA-Based COVID-19 Vaccine Through Fuzzy Neural Network.

The worldwide effort to develop a vaccine against SARS-CoV-2 has led to a revolution in vaccinology by introducing a completely new class of vaccines messenger RNA (mRNA) vaccine. The mRNA-based vaccine is a singular molecule made in the lab that teaches the cells to produce an antigen to trigger the immune response against the fake infection. However, new variants of SARS-CoV-2 may consist of an unprecedented set of genetic mutations including a sampling of earlier variants in addition to the other unknown mutations on the spike protein that may bind a part of the virus to human cells like a grappling hook.

FLeAC: A Human-Centered Associative Classifier Using the Validity Concept.

Fuzzy associative classifiers (FACs) have recently received considerable attention in the data mining community due to their ability to address the imprecision and graduality of truth. Similar to their more traditional statistical peers, these classifiers, however, have remained largely data driven, not leveraging human knowledge to their advantage. This is while human expert opinion and intuition should be a unique vantage point for such systems.

Bimodal fuzzy analytic hierarchy process (BFAHP) for coronary heart disease risk assessment.

Rooted deeply in medical multiple criteria decision-making (MCDM), risk assessment is very important especially when applied to the risk of being affected by deadly diseases such as coronary heart disease (CHD). CHD risk assessment is a stochastic, uncertain, and highly dynamic process influenced by various known and unknown variables. In recent years, there has been a great interest in fuzzy analytic hierarchy process (FAHP), a popular methodology for dealing with uncertainty in MCDM.

A novel generalized belief structure comprising unprecisiated uncertainty applied to aphasia diagnosis.

Our basic understanding of evidential reasoning, in its vast and intricate complexity, has been guided by different methods including probabilistic and possibilistic models. However, restrictions surrounding evidence must be fully precisiated/validated before discussing their probability or possibility which is rarely achieved, especially in uncertain or imprecise environments. In this paper, a new generalization of the Dempster-Shafer Theory (DST) is presented that accounts for this issue.