de Macedo et al[187] proposed analyzing binary criteria such as presence vs absence of mosaic-like pattern, red punctate lesions, and cherry-red spots, without subdivisions or classification systems. These binary criteria were associated with high inter-observer reliability and accuracy (94%, 81% and 83% respectively). Mosaic-like pattern was associated with high sensitivity (100%). The previously used classifications and subdivisions showed unsatisfactory reliability and low inter-observer agreement.
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PRC will soon administer the first nursing licensure examination this June 2 and 3, 2013 in the cities of: Manila, Baguio, Cagayan de Oro, Cebu, Davao, Iloilo, Legazpi, Lucena, Pagadian, Tacloban, Tuguegarao, Zamboanga, Angeles, Cabanatuan, Dagupan & Laoag. The results of the June 2013 Nursing Board Exam (NLE) will be posted here together with the top 10, top schools and school performance.
Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon) were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network with two hidden layers was optimized using artificial bee colony algorithm (ABC), which is known to be successful in exploration. Test data were given to two different neural networks, each of which were trained separately with backpropagation (BP) and ABC, and average test performances were measured as 60% for the artificial neural network trained with BP and 76.39% for the artificial neural network trained with ABC. Training and test phases were repeated 30 times to obtain these average performance measurements. This level of performance shows that the artificial neural network trained with ABC is successful in classifying aroma data. PMID:26927124
Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon) were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network with two hidden layers was optimized using artificial bee colony algorithm (ABC), which is known to be successful in exploration. Test data were given to two different neural networks, each of which were trained separately with backpropagation (BP) and ABC, and average test performances were measured as 60% for the artificial neural network trained with BP and 76.39% for the artificial neural network trained with ABC. Training and test phases were repeated 30 times to obtain these average performance measurements. This level of performance shows that the artificial neural network trained with ABC is successful in classifying aroma data.
The implementation of costs management systems has been extremely helpful to healthcare area owing to their efficacy in cutting expenditures as well as improving service quality. The ABC classification is an applied strategy to stocktaking and control. The research, which consists of an exploratory/descriptive quantitative analysis, has been carried out in order to identify, in a year time period, the demand for supplies at Universidade de Sao Paulo's Hospital. Of 1938 classified materials, 67 itens had been classified that they correspond to the materials with bigger costs for the hospital. 31.3% of these A-Class supplies catalogued items are the nursing materials, more used for the nursing team.
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