Fuzzy-Evolutionary Synergism in an Intelligent Medical Diagnosis System

Social Sustainability and Resilience in Supply Chains of Latin America on COVID-19 Times: Classification Using Evolutionary Fuzzy Knowledge

The number of research papers interested in studying the social dimension of supply chain sustainability and resilience is increasing in the literature. However, the social dimension is complex, with several uncertainty variables that cannot be expressed with a traditional Boolean logic of totally true or false. To cope with uncertainty, Fuzzy Logic allows the development of models to obtain crisp values from the concept of fuzzy linguistic variables. Using the Structural Equation Model by Partial Least Squares (SEM-PLS) and Evolutionary Fuzzy Knowledge, this research aims to analyze the predictive power of social sustainability characteristics on supply chain resilience performance in the context of the COVID-19 pandemic with representative cases from Mexico and Chile. We validate our approach using the Chile database for training our model and the Mexico database for testing. The fuzzy knowledge database has a predictive power of more than 80%, using social sustainability features as inputs regarding supply chain resilience in the context of the COVID-19 pandemic disruption. To our knowledge, no works in the literature use fuzzy evolutionary knowledge to study social sustainability in correlation with resilience. Moreover, our proposed approach is the only one that does not require a priori expert knowledge or a systematic mathematical setup.

Managing urinary incontinence through hand-held real-time decision support aid

In this paper, we present an intelligent system for the diagnosis and treatment of urinary incontinence (UI) for males as well as females, called e-URIN. e-URIN is an intelligent system for diagnosis and treatment of urinary incontinence according to symptoms that are realized in one patient and usually recorded through his clinical examination as well as specific test results. The user-friendly proposed intelligent system is accommodated on a hospital server supporting e-health tools, for use through pocket PCs under wireless connection as a decision support system for resident doctors, as well as an educational tool for medical students. It is based on expert system knowledge representation provided from urology experts in combination with rich bibliographic search and study ratified with statistical results from clinical practice. Preliminary experimental results on a real patient hospital database provide acceptable performance that can be improved using more than one computational intelligence approaches in the future.