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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.