SEARCH RESULT

Data collection, data retention and analysis are becoming more and more important every day because of the fact that technology is involved almost all our life. The processing and analysis of the data can become more difficult with increasing precision. In three-dimensional surface modeling, due to the increase in sensitivity and data size depending on the surface state and extent of the surface, collecting and processing the data may become difficult. As a solution to this situation, we can see that Computational Geometry is used extensively. Computational Geometry derives intermediate interpolations by taking the start and end data as references instead of keeping each data separately. In this way, it is possible to model by determining intermediate values based on the mentioned reference points. There are various methods in Computational Geometry such as intersection detection, point position and triangulation. According to the needs, a solution way can be produced by various geometric computations. In our study, "Delaunay Triangulation" which is the most used type of triangulation methods will be examined.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

Mustafa Aksin Emrullah Demiral Ismail Rakıp Karas

Worldwide, vegetation cover functioning as the secure region for wild life, and natural water, air filter from pollution. Forecasting the vegetation dynamics assist the governments and managements to decrease the negative influence of vegetation dynamic fluctuations. In recent years, forecasting of precise vegetation dynamics become and highly important issue, due to rapid vegetation changings and the needs to protect this natural resource. The aim of this article is to forecasting the vegetation dynamics by applying neural networks (NN). Düzce region utilized as case study, which situated in the north west region of Turkey. Normalized difference vegetation index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) were employed to create vegetation time series. From United States Geological Survey website, 300 NDVI interval data acquired and processed in ArcGIS software. The dataset of vegetation time series built based on required neural networks data structure. Spatiotemporal pixel based sampling strategy performed to forecast the vegetation dynamics. A number of geospatial data handling steps employed using Python and Matlab programing languages. Forecasting data separated to two subdivisions (training set, and testing set). Mean squared error (MSE) utilized as performance accuracy assessment metric. Neural networks effectively implemented using spatiotemporal data and achieve high testing accuracy. Consequences reveals the fitness of neural networks to forecast vegetation dynamics maps.

International Science and Engineering Application Symposium on Hazards
ISESH

S.K.M. ABUJAYYAB Ismail Rakıp Karas Emrullah Demiral

Retina is one of the important layers of the eyes, which includes sensitive cells to colour and light and nerve fibers. Retina can be displayed by using some medical devices such as fundus camera, ophthalmoscope. Hence, some lesions like microaneurysm, haemorrhage, exudate with many diseases of the eye can be detected by looking at the images taken by devices. In computer vision and biomedical areas, studies to detect lesions of the eyes automatically have been done for a long time. In order to make automated detections, the concept of ROI may be utilized. ROI which stands for region of interest generally serves the purpose of focusing on particular targets. The main concentration of this paper is the algorithm to automatically detect retinal region of interest belonging to different retinal images on a software application. The algorithm consists of three stages such as pre-processing stage, detecting ROI on processed images and overlapping between input image and obtained ROI of the image.

International Workshop on GeoInformation Science
GEOADVANCES

E. Şehirli M. K. Turan Emrullah Demiral