Developing examination scheduling automation system by using evolutionary computing technique

In this study, manually (paperwork) and periodically prepared exam scheduling applications at universities have been taken into a computer automation system by developing a software solution. In the software developed by the authors, Evolutionary Algorithm method has been applied and university administration's specific improvement requests have also been taken into consideration while developing the software. The developed software has two parts: First part is about collecting data through the web application, and the second part is the application project, which calculates the final scheduling results. By utilizing the software, a considerable amount of time lost by manually preparing exam schedules will be saved. In addition, with the real-time connection to the student automation system database, numerous problems will be vanished, i.e. students/classes exam scheduling conflicts, etc. In the study, tables added to the current student automation system database have been explained. Additionally, by applying the genetic algorithm methods to the various parts of data have been examined along with the constraints used in the application, which are essential parts of the software. User interfaces have been designed with their sample instances. Finally, exam-scheduling table has been created and an example output of the schedule has been generated in the study

DEVELOPING AN IMAGE PROCESSING APPLICATION THAT SUPPORTS NEW FEATURES OF JPEG2000 STANDARD

In recent years, developing technologies in multimedia brought the importance of image processing and compression. Images that are reduced in size using lossless and lossy compression techniques without degrading the quality of the image to an unacceptable level take up much less space in memory. This enables them to be sent and received over the Internet or mobile devices in much shorter time. The wavelet-based image compression standard JPEG2000 has been created by the Joint Photographic Experts Group (JPEG) committee to superseding the former JPEG standard. Works on various additions to this standard are still under development. In this study, an Application has been developed in Visual C# 2005 which implies important image processing techniques such as edge detection and noise reduction. The important feature of this Application is to support JPEG2000 standard as well as supporting other image types, and the implementation does not only apply to two-dimensional images, but also to multi-dimensional images. Modern software development platforms that support image processing have also been compared and several features of the developed software have been identified

Predicting academically at-risk engineering students: A soft computing application

This paper presents a study on predicting academically at-risk engineering students at the early stage of their education. For this purpose, some soft computing tools namely support vectors machines and artificial neural networks have been employed. The study population included all students enrolled in Pamukkale University, Faculty of Engineering at 2008-2009 and 2009-2010 academic years as freshmen. The data are retrieved from various institutions and questionnaires conducted on the students. Each input data point is of 38-dimension, which includes demographic and academic information about the students, while the output based on the first-year GPA of the students falls into either at-risk or not. The results of the study have shown that either support vector machine or artificial neural network methods can be used to predict first-year performance of a student in a priori manner. Thus, a proper course load and graduation schedule can be transcribed for the student to manage their graduation in a way that potential dropout risks are reduced. Moreover, an input sensitivity analysis has been conducted to determine the importance of each input used in the study

Effects of fluoxetine on human embryo development

The use of antidepressant treatment during pregnancy is increasing, and selective serotonin reuptake inhibitors (SSRIs) are the most widely prescribed antidepressants in pregnant women. Serotonin plays a role in embryogenesis, and serotonin transporters are expressed in two-cell mouse embryos. Thus, the aim of the present study was to evaluate whether fluoxetine, one of the most prescribed SSRI antidepressant world-wide, exposure influences the timing of different embryo developmental stages, and furthermore, to analyze what protein, and protein networks, are affected by fluoxetine in the early embryo development. Human embryos (17 = 48) were randomly assigned to treatment with 0.25 or 0.5 IiM fluoxetine in culture medium. Embryo development was evaluated by time-lapse monitoring. The fluoxetine-induced human embryo proteome was analyzed by shotgun mass spectrometry. Protein secretion from fluoxetine-exposed human embryos was analyzed by use of high-multiplex immunoassay. The lower dose of fluoxetine had no influence on embryo development. A trend toward reduced time between thawing and start of cavitation was noted in embryos treated with 0.5 it M fluoxetine (p = 0.065). Protein analysis by shotgun mass spectrometry detected 45 proteins that were uniquely expressed in fluoxetine-treated embryos. These proteins are involved in cell growth, survival, proliferation, and inflammatory response. Culturing with 0.5 p M, but not 0.25 p M fluoxetine, caused a significant increase in urokinase-type plasminogen activator (uPA) in the culture medium. In conclusion, fluoxetine has marginal effects on the timing of developmental stages in embryos, but induces expression and secretion of several proteins in a manner that depends on dose. For these reasons, and in line with current guidelines, the lowest possible dose of SSRI should be used in pregnant women who need to continue treatment