Information systems plays an important role in medicine because it helps process more data more e... more Information systems plays an important role in medicine because it helps process more data more efficiently while providing access to more people in different parts of the world. In this research we analyzed the data of legionella pneumophila and other legionella species collected by the public hygiene center (PHC). PHC collected 7,211 water samples from different sources of different locations in different cities in Turkey from year 1995 to 2008. The main goal of this research is to develop a conceptual framework for preventing disease and to design a medical decision support system to help administration assessing the risk of Legionnaires' disease and preventing the outbreaks of the disease. The DSS involves SOM software which was programmed with C# to search for patterns and similarities in data sets by producing SOM risk maps. Thus administrators can decide where to monitor cautiously to prevent the disease.
2007 IEEE 15th Signal Processing and Communications Applications, 2007
... olan gürültünün ağırlık merkezleri çakış ise ye mik ık r değiştirme tarı sıfır olacaktır. Böy... more ... olan gürültünün ağırlık merkezleri çakış ise ye mik ık r değiştirme tarı sıfır olacaktır. Böylesi bir duruma bulanıklaşma örnek gösterilebilir. Bulanıklaşma sonucunda ağırlık merkezi konumunda oluşan yer değişim miktarı sıfır yada sıfıra çok yakındır. Bu durum Oral ve Deniz'in yakın ...
This paper describes a novel method, centre of mass model, to detect moving objects in a dynamic ... more This paper describes a novel method, centre of mass model, to detect moving objects in a dynamic scene based on background subtraction. Any displacement of the position of centre of mass (CoMs) in two consecutive frames is the indicator of a moving object in a scene. Dividing a scene into subregions and modelling them as individual masses allow segmentation of the moving object(s). In the proposed scheme, an image is divided into blocks that are called super-pixels and each super-pixel is represented with the x and y components of CoM of a block. The segmentation is achieved by taking the absolute difference between CoM of current super-pixel and the mean of CoMs of previous corresponding super-pixels, and thresholding the difference with a dynamically updated value. A comparative work has been carried out to evaluate the performance of the proposed model and the previously reported seven different methods. The model produced consistent outputs for the images taken in different environmental conditions. The moving objects were successfully segmented with no post-processing operations. Centre of mass model demonstrated better overall performance than the methods previously reported. Its output was superior for auto-focused video images.
Complex variability is a significant problem in predicting construction crew productivity. Neural... more Complex variability is a significant problem in predicting construction crew productivity. Neural Networks using supervised learning methods like Feed Forward Back Propagation (FFBP) and General Regression Neural Networks (GRNN) have proved to be more advantageous than statistical methods like multiple regression, considering factors like the modelling ease and the prediction accuracy. Neural Networks using unsupervised learning like Self Organizing Maps (SOM) have additionally been introduced as methods that overcome some of the weaknesses of supervised learning methods through their clustering ability. The objective of this article is thus to compare the performances of FFBP, GRNN and SOM in predicting construction crew productivity. Related data has been collected from 117 plastering crews through a systematized time study and comparison of prediction performances of the three methods showed that SOM have a superior performance in predicting plastering crew productivity.► We compare prediction of supervised and unsupervised learning methods for crew productivity. ► We use FFBP, GRNN and SOM. ► SOM have a superior performance in predicting plastering crew productivity.
A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data i... more A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data in low-dimensional form without losing topological relations of the data. After an unsupervised learning process, it organizes the data on the basis on similarity. In the current study, a SOM based algorithm has been developed which not only produces 2-D maps to analyze the relationship between various factors and crew productivity, but also predicts productivity under given conditions. Validation of the model has been achieved both by using artificial data set and data from 144 concrete pouring, 101 formwork and 101 reinforcement crews. The results show that maps which are produced by the model are satisfactory in clustering the data and prediction performance of the model is superior to similar artificial neural network models.
Complex variability is a significant problem in predicting construction crew productivity. Neural... more Complex variability is a significant problem in predicting construction crew productivity. Neural Networks using supervised learning methods like Feed Forward Back Propagation (FFBP) and General Regression Neural Networks (GRNN) have proved to be more advantageous than statistical methods like multiple regression, considering factors like the modelling ease and the prediction accuracy. Neural Networks using unsupervised learning like Self Organizing Maps (SOM) have additionally been introduced as methods that overcome some of the weaknesses of supervised learning methods through their clustering ability. The objective of this article is thus to compare the performances of FFBP, GRNN and SOM in predicting construction crew productivity. Related data has been collected from 117 plastering crews through a systematized time study and comparison of prediction performances of the three methods showed that SOM have a superior performance in predicting plastering crew productivity.► We compare prediction of supervised and unsupervised learning methods for crew productivity. ► We use FFBP, GRNN and SOM. ► SOM have a superior performance in predicting plastering crew productivity.
Mühendislik ve ekonomik problemlerin çözümünde sıklıkla kullanılan Yapay Sinir Ağları (YSA)'nın b... more Mühendislik ve ekonomik problemlerin çözümünde sıklıkla kullanılan Yapay Sinir Ağları (YSA)'nın bir alt kolu olan Öz Örgütlenmeli Haritalar (ÖÖH) yöntemi; çok parametreli durumları iki boyutlu (2D) haritalara indirgeyerek, duruma etki eden parametrelerin birbirleri ile olan ilişkilerini görsel olarak sunmaktadır. Bu çalışma; daha önce İskenderun Körfezi'nden elde edilen Orfoz (Ephinephelus marginatus)'ların; mevsimsel olarak boy, ağırlık ve parazitlenme (Nematod, Cestod, İsopod) ilişkilerinin; ÖÖH kullanılarak (2D haritalara indirgenmesi), yeniden değerlendirilmesine dayanmaktadır.
A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data i... more A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data in lowdimensional form without losing topological relations of the data. After an unsupervised learning process, it organizes the data on the basis on similarity. In the current study, a SOM based algorithm has been developed which not only produces 2-D maps to analyze the relationship between various factors and crew productivity, but also predicts productivity under given conditions. Validation of the model has been achieved both by using artificial data set and data from 144 concrete pouring, 101 formwork and 101 reinforcement crews. The results show that maps which are produced by the model are satisfactory in clustering the data and prediction performance of the model is superior to similar artificial neural network models.
Information systems plays an important role in medicine because it helps process more data more e... more Information systems plays an important role in medicine because it helps process more data more efficiently while providing access to more people in different parts of the world. In this research we analyzed the data of legionella pneumophila and other legionella species collected by the public hygiene center (PHC). PHC collected 7,211 water samples from different sources of different locations in different cities in Turkey from year 1995 to 2008. The main goal of this research is to develop a conceptual framework for preventing disease and to design a medical decision support system to help administration assessing the risk of Legionnaires' disease and preventing the outbreaks of the disease. The DSS involves SOM software which was programmed with C# to search for patterns and similarities in data sets by producing SOM risk maps. Thus administrators can decide where to monitor cautiously to prevent the disease.
2007 IEEE 15th Signal Processing and Communications Applications, 2007
... olan gürültünün ağırlık merkezleri çakış ise ye mik ık r değiştirme tarı sıfır olacaktır. Böy... more ... olan gürültünün ağırlık merkezleri çakış ise ye mik ık r değiştirme tarı sıfır olacaktır. Böylesi bir duruma bulanıklaşma örnek gösterilebilir. Bulanıklaşma sonucunda ağırlık merkezi konumunda oluşan yer değişim miktarı sıfır yada sıfıra çok yakındır. Bu durum Oral ve Deniz'in yakın ...
This paper describes a novel method, centre of mass model, to detect moving objects in a dynamic ... more This paper describes a novel method, centre of mass model, to detect moving objects in a dynamic scene based on background subtraction. Any displacement of the position of centre of mass (CoMs) in two consecutive frames is the indicator of a moving object in a scene. Dividing a scene into subregions and modelling them as individual masses allow segmentation of the moving object(s). In the proposed scheme, an image is divided into blocks that are called super-pixels and each super-pixel is represented with the x and y components of CoM of a block. The segmentation is achieved by taking the absolute difference between CoM of current super-pixel and the mean of CoMs of previous corresponding super-pixels, and thresholding the difference with a dynamically updated value. A comparative work has been carried out to evaluate the performance of the proposed model and the previously reported seven different methods. The model produced consistent outputs for the images taken in different environmental conditions. The moving objects were successfully segmented with no post-processing operations. Centre of mass model demonstrated better overall performance than the methods previously reported. Its output was superior for auto-focused video images.
Complex variability is a significant problem in predicting construction crew productivity. Neural... more Complex variability is a significant problem in predicting construction crew productivity. Neural Networks using supervised learning methods like Feed Forward Back Propagation (FFBP) and General Regression Neural Networks (GRNN) have proved to be more advantageous than statistical methods like multiple regression, considering factors like the modelling ease and the prediction accuracy. Neural Networks using unsupervised learning like Self Organizing Maps (SOM) have additionally been introduced as methods that overcome some of the weaknesses of supervised learning methods through their clustering ability. The objective of this article is thus to compare the performances of FFBP, GRNN and SOM in predicting construction crew productivity. Related data has been collected from 117 plastering crews through a systematized time study and comparison of prediction performances of the three methods showed that SOM have a superior performance in predicting plastering crew productivity.► We compare prediction of supervised and unsupervised learning methods for crew productivity. ► We use FFBP, GRNN and SOM. ► SOM have a superior performance in predicting plastering crew productivity.
A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data i... more A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data in low-dimensional form without losing topological relations of the data. After an unsupervised learning process, it organizes the data on the basis on similarity. In the current study, a SOM based algorithm has been developed which not only produces 2-D maps to analyze the relationship between various factors and crew productivity, but also predicts productivity under given conditions. Validation of the model has been achieved both by using artificial data set and data from 144 concrete pouring, 101 formwork and 101 reinforcement crews. The results show that maps which are produced by the model are satisfactory in clustering the data and prediction performance of the model is superior to similar artificial neural network models.
Complex variability is a significant problem in predicting construction crew productivity. Neural... more Complex variability is a significant problem in predicting construction crew productivity. Neural Networks using supervised learning methods like Feed Forward Back Propagation (FFBP) and General Regression Neural Networks (GRNN) have proved to be more advantageous than statistical methods like multiple regression, considering factors like the modelling ease and the prediction accuracy. Neural Networks using unsupervised learning like Self Organizing Maps (SOM) have additionally been introduced as methods that overcome some of the weaknesses of supervised learning methods through their clustering ability. The objective of this article is thus to compare the performances of FFBP, GRNN and SOM in predicting construction crew productivity. Related data has been collected from 117 plastering crews through a systematized time study and comparison of prediction performances of the three methods showed that SOM have a superior performance in predicting plastering crew productivity.► We compare prediction of supervised and unsupervised learning methods for crew productivity. ► We use FFBP, GRNN and SOM. ► SOM have a superior performance in predicting plastering crew productivity.
Mühendislik ve ekonomik problemlerin çözümünde sıklıkla kullanılan Yapay Sinir Ağları (YSA)'nın b... more Mühendislik ve ekonomik problemlerin çözümünde sıklıkla kullanılan Yapay Sinir Ağları (YSA)'nın bir alt kolu olan Öz Örgütlenmeli Haritalar (ÖÖH) yöntemi; çok parametreli durumları iki boyutlu (2D) haritalara indirgeyerek, duruma etki eden parametrelerin birbirleri ile olan ilişkilerini görsel olarak sunmaktadır. Bu çalışma; daha önce İskenderun Körfezi'nden elde edilen Orfoz (Ephinephelus marginatus)'ların; mevsimsel olarak boy, ağırlık ve parazitlenme (Nematod, Cestod, İsopod) ilişkilerinin; ÖÖH kullanılarak (2D haritalara indirgenmesi), yeniden değerlendirilmesine dayanmaktadır.
A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data i... more A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data in lowdimensional form without losing topological relations of the data. After an unsupervised learning process, it organizes the data on the basis on similarity. In the current study, a SOM based algorithm has been developed which not only produces 2-D maps to analyze the relationship between various factors and crew productivity, but also predicts productivity under given conditions. Validation of the model has been achieved both by using artificial data set and data from 144 concrete pouring, 101 formwork and 101 reinforcement crews. The results show that maps which are produced by the model are satisfactory in clustering the data and prediction performance of the model is superior to similar artificial neural network models.
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