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
A set of measurements have been conducted, using gamma spectrometry technique, in order to determine the activity-level in some carbonated soft drinks. The obtained activity is about 0.18 ± 0.07 Bq/l for <sup&...A set of measurements have been conducted, using gamma spectrometry technique, in order to determine the activity-level in some carbonated soft drinks. The obtained activity is about 0.18 ± 0.07 Bq/l for <sup>137</sup>Cs, whereas it is less than 0.13, 0.18 and 4.51 Bq/l respectively for <sup>212</sup>Pb, <sup>214</sup>Pb and <sup>40</sup>K. The total average annual dose is about 3.49, 1.69 and 1.68 μSv/y respectively for 7 - 12, 12 - 17 and >17 years old person leading to a radiological risk about 0.142 for adolescent and adults. The obtained results show no significant radiation dose and radiation hazard on human health due to the consumption of these carbonated soft drinks.展开更多
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.
文摘A set of measurements have been conducted, using gamma spectrometry technique, in order to determine the activity-level in some carbonated soft drinks. The obtained activity is about 0.18 ± 0.07 Bq/l for <sup>137</sup>Cs, whereas it is less than 0.13, 0.18 and 4.51 Bq/l respectively for <sup>212</sup>Pb, <sup>214</sup>Pb and <sup>40</sup>K. The total average annual dose is about 3.49, 1.69 and 1.68 μSv/y respectively for 7 - 12, 12 - 17 and >17 years old person leading to a radiological risk about 0.142 for adolescent and adults. The obtained results show no significant radiation dose and radiation hazard on human health due to the consumption of these carbonated soft drinks.
