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Öğe Automatic generation of 3D networks in cityGML and design of an intelligent individual evacuation model for building fires within the scope of 3D GIS(Kluwer Academic Publishers, 2014) Atila, U.; Karas, I.R.; Turan, M.K.; Rahman, A.A.Designing 3D navigation systems requires addressing solution methods for complex topologies, 3D modelling, visualization, topological network analysis and so on. 3D navigation within 3D-GIS environment is increasingly growing and spreading to various fields. One of those fields is evacuation through the shortest path with safety in case of disasters such as fire, massive terrorist attacks happening in complex and tall buildings of today’s world. Especially fire with no doubt is one of the most dangerous disaster threatening these buildings including thousands of occupants inside. This chapter presents entire solution methods for designing an intelligent individual evacuation model starting from data generation process. The model is based on Multilayer Perceptron (MLP) which is one of the most preferred artificial neural network architecture in classification and prediction problems. We focus on integration of this model with a 3D-GIS based simulation for demonstrating an individual evacuation process. © Springer International Publishing Switzerland 2014.Öğe Design of an intelligent individual evacuation model for high rise building fires based on neural network within the scope of 3D GIS(Copernicus GmbH, 2013) Atila, U.; Karas, I.R.; Turan, M.K.; Rahman, A.A.One of the most dangerous disaster threatening the high rise and complex buildings of today's world including thousands of occupants inside is fire with no doubt. When we consider high population and the complexity of such buildings it is clear to see that performing a rapid and safe evacuation seems hard and human being does not have good memories in case of such disasters like world trade center 9/11. Therefore, it is very important to design knowledge based realtime interactive evacuation methods instead of classical strategies which lack of flexibility. This paper presents a 3D-GIS implementation which simulates the behaviour of an intelligent indoor pedestrian navigation model proposed for a self -evacuation of a person in case of fire. The model is based on Multilayer Perceptron(MLP) which is one of the most preferred artificial neural network architecture in classification and prediction problems. A sample fire scenario following through predefined instructions has been performed on 3D model of the Corporation Complex in Putrajaya (Malaysia) and the intelligent evacuation process has been realized within a proposed 3D-GIS based simulation. © 2013 Copernicus. All Rights Reserved.Öğe Filter development for automatic detection of analyzable metaphases(Institute of Electrical and Electronics Engineers Inc., 2018) Yilmaz, H.; Turan, M.K.Abnormalities in the structure of chromosomes cause fetal deaths or developmental disorders. Chromosome analysis is a method used to diagnose many chromosomal disorders such as Down syndrome. Metaphase images are needed for chromosome analysis. Objective selections must be made during the acquisition of these images. Selecting of non-analyzable images could directly affect the results of chromosome analysis. In this study, a filter was developed that automatically detects analyzable metaphase images. The developed filter was used with the motorized microscope table and the analyzable metaphase images were detected. After expert evaluation on the results obtained, the average success rate of the filter was calculated as 98.9%. The filter performed an average run time of 76 milliseconds per square. © 2018 IEEE.Öğe A novel method for segmentation of QRS complex on ecg signals and classification of cardiovascular diseases via a hybrid model based on machine learning(Ismail Saritas, 2021) Sehirli, E.; Turan, M.K.Automated-detecting intelligent programs and methods are developing to find out diseases in medicine in recent years. Developing new methods and improving existing ones are currently ongoing research. One of the most important health problems is heart diseases for all people in the world. Electrocardiography (ECG) is a diagnosis tool that gives substantially functional information about heart and cardiac system. In this work, it is primarily aimed at developing an intelligent system based on ECG signal processing, analysis, and classification via a hybrid machine learning model. This work uses 837 ECG signal fragments that includes 7 different classes shared in MIT-BIH Arrhythmia database for one lead. The ECG signals are applied on a preprocessing to smooth signals and correct baselines. Q, R and S waves (QRS) complex on ECG signals are segmented based on k-means clustering and tracking local extrema points. Feature extraction and selection are then performed, and a dataset is created by calculating measurement parameters for each QRS points separately. Training sets and test sets based on 8-fold cross validation are generated. A hybrid model based on machine learning models including decision tree (DT), k-nearest neighbor (KNN), random forest (RF), naïve bayes (NB), linear discriminant analysis (LDA), support vector machines (SVM) and quadratic discriminant analysis (QDA) is developed to classify cardiovascular diseases (CVD) into 7 different classes such as normal sinus rhythm (NSR), atrial premature beat (APB), atrial fibrillation (AFIB), premature ventricular contraction (PVC), ventricular bigeminy (VB), left bundle branch block beat (LBBBB) and right bundle branch block beat (RBBBB). Sensitivity, specificity, accuracy, and Matthews correlation coefficient (MCC) of detection of QRS complex are obtained respectively as 94.75%, 95.96%, 95.57% and 0.90. Sensitivity, specificity, accuracy and MCC of classification of CVD classes are obtained respectively as 92.33%, 92.50%, 92.41%, 0.85. © 2021, Ismail Saritas. All rights reserved.Öğe RFID-based mobile positioning system design for 3D indoor environment(Conference Chairs of 3DGeoInfo 2014 in Karlsruhe, 2014) Demiral, E.; Karas, I.R.; Turan, M.K.; Atila, U.In this study, an RFID based indoor positioning system has been proposed. In the system, while RFID readers have been considered to be mobile, RFID tags have been attached on fixed positions inside building. Performance of various types of readers and tags on indoor positioning has been investigated and most appropriate tag/reader couple has been used. In the experiments of this study, geographical proximity approach has been used. As the results of tests performed on three different model proposed for indoor positioning, it has been shown that best rate for position estimations without error have been obtained from third model with the rate of approximately 76% and in the worst case, position estimation error has been obtained 2 meters. Copyright © (2014) by the corresponding authors of the papers.
