{"title":"Evaluating the Deep Learning accuracy in data extraction from synthetic image sequences","authors":"André Franceschi de Angelis, Thaı́s Rocha","doi":"10.6062/jcis.2019.10.03.0168","DOIUrl":"https://doi.org/10.6062/jcis.2019.10.03.0168","url":null,"abstract":"We have investigating the use of Deep Learning (DL) to process sequences of images captured by satellites aiming to improve the quality of river flow forecasting methods, because current ones are still not accurate enough to support efficient management of large national electrical systems. Towards this goal, we are assessing the accuracy of DL networks in extracting information from image sequences by means of classification processes. We have set a test environment composed by an image sequence generator, some generating models, the Nvidia DIGITS tool, two DL preset networks, and the needed hardware. Each model produced one image sequence and one data series corresponding to a selected measure in the images. We have trained the DL networks and evaluated its accuracy in extracting the measured data. In this paper, we show that the performance of DL is extremely sensible to the image type, the measure taken into account, and the DL network applied. Our process presented better performance recognizing coverage area rates in images that resemble clouds and linear distances, but had poor accuracy with angles.","PeriodicalId":90209,"journal":{"name":"Journal of computational interdisciplinary sciences","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71175845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Recovery from stress - a cell cycle perspective.","authors":"Elahe Radmaneshfar, Marco Thiel","doi":"10.6062/jcis.2012.03.01.0049","DOIUrl":"10.6062/jcis.2012.03.01.0049","url":null,"abstract":"<p><p>We develop a Boolean model to explore the dynamical behaviour of budding yeast in response to osmotic and pheromone stress. Our model predicts that osmotic stress halts the cell cycle progression in either of four possible arrest points. The state of the cell at the onset of the stress dictates which arrest point is finally reached. According to our study and consistent with biological data, these cells can return to the cell cycle after removal of the stress. Moreover, the Boolean model illustrates how osmotic stress alters the state transitions of the cell. Furthermore, we investigate the influence of a particular pheromone based method for the synchronisation of the cell cycles in a population of cells. We show this technique is not a suitable method to study one of the arrest points under osmotic stress. Finally, we discuss how an osmotic stress can cause some of the so called <i>frozen</i> cells to divide. In this case the stress can move these cells to the cell cycle trajectory, such that they will replicate again.</p>","PeriodicalId":90209,"journal":{"name":"Journal of computational interdisciplinary sciences","volume":"3 1-2","pages":"33-44"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3982136/pdf/emss-54547.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32260155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}