Current Issue : July - September Volume : 2012 Issue Number : 3 Articles : 6 Articles
Identification of patients requiring intensive care is a critical issue in clinical treatment. The objective of this study is to develop a\r\nnovel methodology using hemodynamic features for distinguishing such patients requiring intensive care from a group of healthy\r\nsubjects. In this study, based on the hemodynamic features, subjects are divided into three groups: healthy, risky and patient.\r\nFor each of the healthy and patient subjects, the evaluated features are based on the analysis of existing differences between\r\nhemodynamic variables: Blood Pressure and Heart Rate. Further, four criteria from the hemodynamic variables are introduced:\r\ncircle criterion, estimation error criterion, Poincare plot deviation, and autonomic response delay criterion. For each of these\r\ncriteria, three fuzzy membership functions are defined to distinguish patients from healthy subjects. Furthermore, based on the\r\nevaluated criteria, a scoring method is developed. In this scoring method membership degree of each subject is evaluated for the\r\nthree classifying groups. Then, for each subject, the cumulative sum of membership degree of all four criteria is calculated. Finally,\r\na given subject is classified with the group which has the largest cumulative sum. In summary, the scoring method results in 86%\r\nsensitivity, 94.8% positive predictive accuracy and 82.2% total accuracy....
The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical\r\noncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid\r\nthe clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for\r\noncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural\r\nnetworks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second\r\none is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used\r\nin this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis\r\nby providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC\r\nimage quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall\r\ncell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET\r\ndata has shown promising results and can successfully classify and quantify malignant lesions....
In this research , we combine the Fazzy Analytic Hierarchy Process(FAHP) and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS) methods to eliminate the restriction of TOPSIS method based on financial ratios.at first we evaluate the experts ideas in industry by FAHP.Companies relative efficiency is calculated by TOPSIS method based on financial ratios.Statistical universe was all 28 cement companies in the Tehran Stock Exchange and 15 Turkish cement companies in the Istanbul Stock Exchange.Hypothesis is evaluated based on spearmans correlation coefficient.Ranking based on (FAHP-TOPSIS) and TOPSIS methods are compared.A significant difference isn''t observed in (FAHP-TOPSIS) and TOPSIS ranking....
Supplier selection is a fundamental issue of supply chain area that heavily contributes to the overall supply chain performance, and,\r\nalso, it is a hard problem since supplier selection is typically a multicriteria group decision problem. In many practical situations,\r\nthere usually exists incomplete and uncertain, and the decision makers cannot easily express their judgments on the candidates with\r\nexact and crisp values. Therefore, in this paper an extended technique for order preference by similarity to ideal solution (TOPSIS)\r\nmethod for group decision making with Atanassov�s interval-valued intuitionistic fuzzy numbers is proposed to solve the supplier\r\nselection problem under incomplete and uncertain information environment. In other researches in this area, the weights of each\r\ndecision maker and in many of them the weights of criteria are predetermined, but these weights have been calculated in this paper\r\nby using the decision matrix of each decision maker. Also, the normalized Hamming distance is proposed to calculate the distance\r\nbetween Atanassov�s interval-valued intuitionistic fuzzy numbers. Finally, a numerical example for supplier selection is given to\r\nclarify the main results developed in this paper....
The aim of this paper is to introduce some new weaker forms of fuzzy sp continuity, namely fuzzy almost sp-continuous mappings; fuzzy weakly sp-continuous mappings and fuzzy faintly sp-continuous mappings by using the notion of fuzzy sp-open sets. Certain fundamental properties, some new results related to these new concepts are obtained, fuzzy sp-compact is introduced and the relations and inverse relations between these new fuzzy mappings are investigated....
We implement an algorithm that uses a system of fuzzy relation equations (SFRE) with the max-min composition for solving a\r\nproblem of spatial analysis. We integrate this algorithm in a Geographical Information System (GIS) tool, and the geographical\r\narea under study is divided in homogeneous subzones (with respect to the parameters involved) to which we apply our process\r\nto determine the symptoms after that an expert sets the SFRE with the values of the impact coefficients. We find that the best\r\nsolutions and the related results are associated to each subzone. Among others, we define an index to evaluate the reliability of the\r\nresults....
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