Our purpose is to develop a clinical decision support system to classify the patients' diagnostics based on features gathered from Magnetic Resonance Imaging (MRI) and Expanded Disability Status Scale (EDSS). We studied 120 patients and 19 healthy individuals (not afflicted with MS) have been studied for this study. Healthy individuals in the control group do not have any complaint or drug use history. For the kernel trick, efficient performance in non-linear classification, the Convex Combination of Infinite Kernels model was developed to measure the health status of patients based on features gathered from MRI and EDSS. Our calculations show that our proposed model classifies the multiple sclerosis (MS) diagnosis level with better accuracy than single kernel, artificial neural network and other machine learning methods, and it can also be used as a decision support system for identifying MS health status of patients.
Due to copyright restrictions, the access to the full text of this article is only available via subscription.This research is focused on the cooperative multi-task assignment problem for heterogeneous UAVs, where a set of multiple tasks, each requiring a predetermined number of UAVs, have to be completed at specific locations. We modeled this as an optimization problem to minimize the number of uncompleted tasks while also minimizing total airtime and total distance traveled by all the UAVs. By taking into account the UAV flight capacities. For the solution of the problem, we adopted a multi-Traveling Salesman Problem (mTSP) method [1] and designed a new genetic structure for it so that it can be applied to cooperative multi-task assignment problems. Furthermore, we developed two domain specific mutation operators to improve the quality of the solutions in terms of number of uncompleted tasks, total airtime and total distance traveled by all the UAVs. The simulation experiments showed that these operators significantly improve the solution quality. Our main contributions are the application of the Multi Structure Genetic Algorithm (MSGA) to cooperative multi-task assignment problem and the development of two novel mutation operators to improve the solution of MSGA.TÜBİTA
Abstract-This study aims to publish a novel similarity metric to increase the speed of comparison operations. Also the new metric is suitable for distance-based operations among strings.Most of the simple calculation methods, such as string length are fast to calculate but doesn't represent the string correctly. On the other hand the methods like keeping the histogram over all characters in the string are slower but good to represent the string characteristics in some areas, like natural language.We propose a new metric, easy to calculate and satisfactory for string comparison.Method is built on a hash function, which gets a string at any size and outputs the most frequent K characters with their frequencies.The outputs are open for comparison and our studies showed that the success rate is quite satisfactory for the text mining operations.
ÖZETÇEBu çalışma, haber ve yazılar için yapılan yorumların otomatik filtrelemesi için yapılacak olan bir projenin ön çalışmasıdır. Veri tabanımızda 1 milyonun üzerinde haber ve yorum bulunmaktadır. Elimizdeki verilerin yoğunluğundan dolayı deney seti olarak 44 farklı konuda yazılmış 15.064 adet gazete haberi ve makalesine yapılan 30.677 adet yorum kullanılmıştır. Literatürde yapılan sınıflandırma tabanlı yaklaşımlardan farklı olarak önerilen düzensizlik tabanlı yöntem de, yüksek hafıza gerekliliği ve yüksek hesaplama karmaşıklığına gerek kalmadan hızlı ve yüksek başarımda sonuçlar elde edilmiştir. ABSTRACT This is the preliminary work for a project which will be filtering comments made on news and papers automatically. Our database has over 1 million news and comments. Due to the intensity of our data, 30.677 comments made on 15.064 articles on 44 different categories are used as experimental data. Proposed anomaly based method have been obtained fast and high accuracy results without the high storage requirement and high computational complexity with respect to other classification based methods on literature.
Özet�e-Bu �ah�mada, otonom insanslz hava ara�lari (iHA) i�in ger�ek zamanh olarak u�u� öncesi yada u�u� esnasmda tespit edilen radarlara yakalanmadan güzergah planlamasl yapabilen bir metodoloji geli�tirilmi�tir. Önerilen method ile uygulanan genetik algoritmalar, paralel ger�eklenmi� ve süreler büyük oranda indirgenmi�tir. Geli�tirdigimiz metodoloji tek yada birden fazla iHA'mn otonom veya operatör yardlmh u�u�lar i�in hlzh ve güvenli güzergahlar planlanmasml saglayabilmektedir.Anahtar Kelimeier -insanslz hava araCl; güzergah planlama; genetik algoritma; gezgin sattcl problemi Abstract-In this study, a methodology detecting the radar make caught in real time during the flight or pre-flight route planning has been developed for an autonomous unmanned aerial vehicles (UAVs). The proposed method and genetic algorithms are implemented in parallel and duration is reduced to a large extent. The developed methodology can provide fast and safe routes for autonomous single or multiple UA V or operator-assisted flight. Keywords Unmanned air vehicle; route plannig; genetic algorithm; travelling salesman problem I. GiRiS insanslz Hava Araylan (iHA) ilk olarak 1900'lü yJllarda uzaktan kumanda edilebilen uyan sistemler olarak ortaya ytkml� ve 0 zamanlardan günümüze biryok farkh askeri ve sivil alanda kullamlrnasl, farkh bilim dallan ile ili�kisi olrnasl ve hala üzerinde yapllacak biryok yah�ma konusu olrnasmdan dolaYI ara�trrmacIlarm ilgi odagl olrnu�tur. iHA'lar aslmda kontrol kumanda sistemleri aylsmdan iki ana sllllfa aynlabilir. ilki ve ilk zamanlardan bu yana kullamlrnaya devam eden; uzaktan kumanda edilerek uyan sistemler digeri ise belli bir UyU� plam üzerinden otomatik olarak hareket edebilen insanslz uyan sistemleridir. iHA'lar sivil olarak arama kurtarma, yangm söndünne, izleme ve gözetleme, hava metoroloji ara�trrmasl, ta�lillacJltk, sulama, gübreleme, gibi alanlarda kullamlabildigi gibi askeri olarak da hedef ve yem olrna, saldlfl, gözetleme, yatl�ma, lojistik gibi biryok farkh alanda kullamlmaktadlf. Üzerine takIlan yararh yükler, kamera, sensor ve radarlar da 978-1-4673-5563-6/13/$31.00 ©20131EEE Ugur Ayan Bili�im ve Bilgi Güvenligi i1eri Teknolojiler Ara�tlfma Merkezi, TUBIT AK istanbul, Türkiye ugur.ayan@tubitak.gov.tr ara�trrmacIlarm üzerinde yah�tlgl diger alanlar olarak ön plana Ylkmaktadlf. iHA'larm sonra yIllarda daha da önem kazanmasmm belli ba�h sebepleri bulunmaktadlr. Bunlardan en önemlileri daha az maliyet ve slflf insan kaybldlf. Tehlike arz eden kimyasal ve nükleer kullamlacak görevIerde, ölüm riski ta�lyan yarpl�ma ve saldlfl görevlerinde rahatltkla kullamlabilmektedir. ir. insan hayatml riske atan taarruz ve hava savunma gii durumlarda ilIalarm kullamlrnasl ba�anh sonuylar verir ve riski ortadan kaldlflf. iHA'larm stkya kullamlmaya ba�lamasl iHA'larm özellikle otonom görevierde yer alrnasma ve bu görevlere uygun güzergah planlamasl konusunda da ara�tlrmalarm artmasma neden olrnu�tur. Bu konuda yapIlan yah�malarm �ogu tek iHA üzerinde ve herhangi iki nokta arasmda en klsa ve uygun güzergahl bulma ...
Depending on the market strength and structure, it is a known fact that there is a correlation between the stock market values and the content in newspapers. The correlation increases in weak and speculative markets, while they never get reduced to zero in the strongest markets. This research focuses on the correlation between the economic news published in a highly circulating newspaper in Turkey and the stock market closing values in Turkey. In the research several feature extraction methodologies are implemented on both of the data sources, which are the stock market values and economic news. Since the economic news is in natural language format, the text mining technique, term frequency – inverse document frequency is implemented. On the other hand, the time series analysis methods like random walk, Bollinger band, moving average or difference are applied over the stock market values. After the feature extraction step, the classification methods are built on the well-known classifiers support vector machine, k-nearest neighborhood and decision tree. Moreover, an ensemble classifier based on majority voting is implemented on top of these classifiers. The success rates show that the results are satisfactory to claim the methods implemented in this study can be spread to future research with similar data sets from other countries.
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