Novel 3-phenyl/pyridinyl-trans-2-(aryl/heteryl)vinyl-3H-quinazolin-4-ones 3a,b, 4a, 5a, 7a and their 6,7-difluoro de- rivatives 3c,d, 4b, 5b, 7b have been obtained by condensation of the correspondent 2-methylquinazol...Novel 3-phenyl/pyridinyl-trans-2-(aryl/heteryl)vinyl-3H-quinazolin-4-ones 3a,b, 4a, 5a, 7a and their 6,7-difluoro de- rivatives 3c,d, 4b, 5b, 7b have been obtained by condensation of the correspondent 2-methylquinazolin-4-ones 1, 6 with aromatic (heterocyclic) aldehydes in the presence of ZnCl2 (AcONa) or by the reaction of 2-methyl-3,1-benzoxazin-4- ones 2 with the Shiff bases. Effects of aryl(heteryl) substituents on photophysical properties of (aryl/heteryl) quinazolinylethenes have been studied.展开更多
Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from li...Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.展开更多
文摘Novel 3-phenyl/pyridinyl-trans-2-(aryl/heteryl)vinyl-3H-quinazolin-4-ones 3a,b, 4a, 5a, 7a and their 6,7-difluoro de- rivatives 3c,d, 4b, 5b, 7b have been obtained by condensation of the correspondent 2-methylquinazolin-4-ones 1, 6 with aromatic (heterocyclic) aldehydes in the presence of ZnCl2 (AcONa) or by the reaction of 2-methyl-3,1-benzoxazin-4- ones 2 with the Shiff bases. Effects of aryl(heteryl) substituents on photophysical properties of (aryl/heteryl) quinazolinylethenes have been studied.
文摘Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.
