scholarly journals Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy C Means and K-Means Algorithms

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Yeliz Karaca ◽  
Carlo Cattani ◽  
Majaz Moonis ◽  
Şengül Bayrak
Keyword(s):  
Clustering Algorithms ◽  
Feature Descriptor ◽  
Stroke Subtype ◽  
Stroke Subtypes ◽  
Time In Literature ◽  
Fcm Clustering ◽  
Clustering Comparison ◽  
First Time ◽  
Accuracy Rates ◽  
Accuracy Performance

Multifractal denoising techniques capture interest in biomedicine, economy, and signal and image processing. Regarding stroke data there are subtle details not easily detectable by eye physicians. For the stroke subtypes diagnosis, details are important due to including hidden information concerning the possible existence of medical history, laboratory results, and treatment details. Recently, K-means and fuzzy C means (FCM) algorithms have been applied in literature with many datasets. We present efficient clustering algorithms to eliminate irregularities for a given set of stroke dataset using 2D multifractal denoising techniques (Bayesian (mBd), Nonlinear (mNold), and Pumping (mPumpD)). Contrary to previous methods, our method embraces the following assets: (a) not applying the reduction of the stroke datasets’ attributes, leading to an efficient clustering comparison of stroke subtypes with the resulting attributes; (b) detecting attributes that eliminate “insignificant” irregularities while keeping “meaningful” singularities; (c) yielding successful clustering accuracy performance for enhancing stroke data qualities. Therefore, our study is a comprehensive comparative study with stroke datasets obtained from 2D multifractal denoised techniques applied for K-means and FCM clustering algorithms. Having been done for the first time in literature, 2D mBd technique, as revealed by results, is the most successful feature descriptor in each stroke subtype dataset regarding the mentioned algorithms’ accuracy rates.

scholarly journals Mineralogy and Geochemistry of Ultramafic Rocks from Rachoni Magnesite Mine, Gerakini (Chalkidiki, Northern Greece)

Minerals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 934
Author(s):  
Evangelos Tzamos ◽  
Micol Bussolesi ◽  
Giovanni Grieco ◽  
Pietro Marescotti ◽  
Laura Crispini ◽  
...  
Keyword(s):  
Raw Materials ◽  
Mining Area ◽  
Ultramafic Rocks ◽  
Northern Greece ◽  
Icp Ms ◽  
Time In Literature ◽  
History Of ◽  
First Time ◽  
The Eu

The importance of magnesite for the EU economy and industry is very high, making the understanding of their genesis for the exploration for new deposits a priority for the raw materials scientific community. In this direction, the study of the magnesite-hosting ultramafic rocks can be proved very useful. For the present study, ultramafic rock samples were collected from the magnesite ore-hosting ophiolite of the Gerakini mining area (Chalkidiki, Greece) to investigate the consecutive alteration events of the rocks which led to the metallogenesis of the significant magnesite ores of the area. All samples were subjected to a series of analytical methods for the determination of their mineralogical and geochemical characteristics: optical microscopy, XRD, SEM, EMPA, ICP–MS/OES and CIPW normalization. The results of these analyses revealed that the ultramafic rocks of the area have not only all been subjected to serpentinization, but these rocks have also undergone carbonation, silification and clay alteration. The latter events are attributed to the circulation of CO2-rich fluids responsible for the formation of the magnesite ores and locally, the further alteration of the serpentinites into listvenites. The current mineralogy of these rocks was found to be linked to one or more alteration event that took place, thus a significant contribution to the metallo- and petrogenetic history of the Gerakini ophiolite has been made. Furthermore, for the first time in literature, Fe inclusions in olivines from Greece were reported.

scholarly journals A novel content based image retrieval system using K-means/KNN with feature extraction

2012 ◽  
Vol 9 (4) ◽  
pp. 1645-1661 ◽  
Author(s):  
Ray-I Chang ◽  
Shu-Yu Lin ◽  
Jan-Ming Ho ◽  
Chi-Wen Fann ◽  
Yu-Chun Wang
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor ◽  
Color Analysis ◽  
Image Retrieval System ◽  
First Time ◽  
System Designs

Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature extraction module, K-means and k-nearest neighbor clustering algorithms and bring in the neighborhood module to build the CBIR system. Concept of neighborhood color analysis module which also recognizes the side of every grids of image is first contributed in this paper. The results show the CBIR systems performs well in the training and it also indicates there contains many interested issue to be optimized in the query stage of image retrieval.

scholarly journals A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chunhua Ren ◽  
Linfu Sun
Keyword(s):  
Clustering Algorithms ◽  
Real Data ◽  
Local Information ◽  
Data Sets ◽  
Clustering Ensemble ◽  
K Nearest Neighbors ◽  
Fuzzy C Means ◽  
Clustering Quality ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

AbstractThe classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

A novel MADM approach to the ground-handling agent selection problem in B2B markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahmut Bakır ◽  
Emircan Özdemir ◽  
Şahap Akan
Keyword(s):  
Airline Industry ◽  
Multiple Attribute Decision Making ◽  
Content Type ◽  
B2b Marketing ◽  
Multiple Attribute ◽  
Time In Literature ◽  
Agent Selection ◽  
Ground Handling ◽  
First Time

PurposeGround-handling services are important for effective aircraft operations in the air transportation system. Airlines often outsource these services to ground-handling agents through business-to-business (B2B) marketing decisions. Therefore, this paper aims to address the problem of ground-handling agent selection in the airline industry.Design/methodology/approachA real-world case study was carried out to demonstrate the applicability of the integrated best worst method and fuzzy multi-attribute ideal real comparative analysis (F-MAIRCA) approach to solve ground-handling agent selection problems under uncertainty and imprecision. A two-stage sensitivity analysis was also conducted to ensure the credibility and validity of the application.FindingsIn the weighting stage, “Quality” was determined as the most important criterion in terms of supplier performance. With regard to the performance of the ground-handling agents, A2 was found as the optimal supplier in terms of both credibility and validity.Practical implicationsThis study enumerated several criteria that ground-handling agents must meet in order to effectively supply services for the airlines. In addition, this study provides a novel framework from which managers can gain additional benefits from their businesses. Finally, it is concluded that this approach will help airline managers quantitatively in choosing the most appropriate ground-handling agent.Originality/valueThe contributions of this study to the existing literature are twofold. First, we propose a novel multiple attribute decision-making approach to address the problem of supplier selection for airlines under uncertainty and imprecision. Second, the selection of ground-handling agents from the B2B perspective is addressed for the first time in literature.

Abstract 3884: Stroke Subtypes in Hispanic Patients in the NINDS Stroke Genetics Network (SiGN): A Comparison of Two Classification Systems

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Michael Katsnelson ◽  
Tatjana Rundek ◽  
Ralph Sacco ◽  
Hannah Gardener ◽  
Shaneela Malik ◽  
...  
Keyword(s):  
Risk Factors ◽  
Ischemic Stroke ◽  
Classification Systems ◽  
Blood Collection ◽  
Large Artery ◽  
Stroke Subtype ◽  
Reliable Determination ◽  
Stroke Treatment ◽  
Stroke Subtypes ◽  
Small Artery Occlusion

Objectives: Identification of gene variants of stroke subtypes is important for the development of tailored ischemic stroke therapies among various ethnic groups. Valid and reliable determination of ischemic stroke subtype is essential for achieving this goal and to standardize a classification scheme across multi-center studies and different populations. Causative Classification System for Ischemic Stroke (CCS) is a novel computerized subclassification tool developed to improve reliability and accuracy of classifying stroke types. The CCS algorithm relies on both phenotypic and causative stroke variables. A Hispanic subset of the SiGN, an important and distinct target population with greater risk of certain stroke subtypes, was evaluated with Trial of Org 10172 in Acute Stroke Treatment (TOAST) and CCS and the agreement between the two classification systems was analyzed. Methods: Over 6000 subjects at 15 sites across US and Europe were enrolled, with TOAST and CCS locally adjudicated. Blood collection and central data quality control (10% central readjudication) were performed on all participants. A subset of Hispanics was analyzed for the purpose of this study and the agreement between the TOAST and CCS were assessed by kappa statistic. Findings: Hispanics (n=595, 10.9%) compared to non-Hispanics (n=5457) were more likely to be younger (63.7 vs. 64.0), male (55% vs. 46%) and have fewer of the traditional stroke risk factors HTN (54% vs. 64%), Afib (11% vs. 14%), DM(23% vs. 25%), CAD(16% vs. 20%) and smoking(19% vs. 22%). While the TOAST showed no differences between stroke subtypes for Hispanic vs. non-Hispanics, in CCS, Hispanics were classified with more of large vessel (22% vs. 20%), cardioembolic (37% vs. 30%) and small vessel strokes (13% vs. 9%) and fewer with undetermined etiology (28% vs. 40%) as compared to non-Hispanics. TOAST and CCS offered moderate correlation across all stroke types in Hispanics: kappa of 0.66 for large artery atherosclerosis, 0.58 for cardioembolic, and 0.58 for small artery occlusion. Conclusion: CCS offers a more sensitive and accurate system for subphenotyping of strokes in Hispanics who tended to have relatively fewer risk factors and unclassified strokes. Further studies correlating the two classification systems and their relation to genotyping data are warranted.

scholarly journals Pulmonary multiple cystic lesions in a patient with Behçet’s syndrome

Reumatismo ◽  
2019 ◽  
Vol 71 (2) ◽  
pp. 103-104
Author(s):  
M.E. Tezcan ◽  
B. Isci
Keyword(s):  
Pulmonary Involvement ◽  
Behçet’S Syndrome ◽  
Cystic Lesions ◽  
Computerised Tomography ◽  
Behçet's Syndrome ◽  
Time In Literature ◽  
Behcet’S Syndrome ◽  
First Time ◽  
Behcet's Syndrome

Pulmonary involvement, mainly originating from vasculitis, is one of the features of Behçet’s syndrome (BS). We describe, for the first time in literature, computerised tomography images of a male BS patient with multiple pulmonary cystic lesions possibly originated from vasculitis or bronchiolar stenosis.

Multiobjective Collaborative Robust Optimization With Interval Uncertainty and Interdisciplinary Uncertainty Propagation

2008 ◽  
Vol 130 (8) ◽  
Author(s):  
M. Li ◽  
S. Azarm
Keyword(s):  
Robust Optimization ◽  
Probability Distributions ◽  
Uncertainty Propagation ◽  
Multiple Objectives ◽  
Interval Uncertainty ◽  
Optimization Approach ◽  
Solution Approach ◽  
Time In Literature ◽  
Collaborative Robust Optimization ◽  
First Time

We present a new solution approach for multidisciplinary design optimization (MDO) problems that, for the first time in literature, has all of the following characteristics: Each discipline has multiple objectives and constraints with mixed continuous-discrete variables; uncertainty exists in parameters and as a result, uncertainty propagation exists within and across disciplines; probability distributions of uncertain parameters are not available but their interval of uncertainty is known; and disciplines can be fully (two-way) coupled. The proposed multiobjective collaborative robust optimization (McRO) approach uses a multiobjective genetic algorithm as an optimizer. McRO obtains solutions that are as best as possible in a multiobjective and multidisciplinary sense. Moreover, for McRO solutions, the variation of objective and/or constraint functions can be kept within an acceptable range. McRO includes a technique for interdisciplinary uncertainty propagation. The approach can be used for robust optimization of MDO problems with multiple objectives, or constraints, or both together at system and subsystem levels. Results from an application of McRO to a numerical and an engineering example are presented. It is concluded that McRO can solve fully coupled MDO problems with interval uncertainty and obtain solutions that are comparable to a single-disciplinary robust optimization approach.

Bilateral morgagni hernia in a case of Weill-Marchesani syndrome-a rare association

2017 ◽  
Vol 5 (6) ◽  
pp. 2790
Author(s):  
Ravindran Chetambath ◽  
Jabeed Parengal ◽  
Mohammed Aslam ◽  
Sanjeev Shivashankaran
Keyword(s):  
Female Patient ◽  
Morgagni Hernia ◽  
Rare Association ◽  
Morphometric Features ◽  
Time In Literature ◽  
First Time ◽  
Diaphragmatic Hernias

Morgagni hernia constitutes only about 2% of all diaphragmatic hernias and bilateral Morgagni hernia is extremely rare. Here we present a 75 year old female patient with morphometric features of Weill-Marchesani syndrome who has bilateral Morgagni hernia. This association is reported for the first time in literature.

Harnessing low energy photons (635 nm) for the production of H2O2 using upconversion nanohybrid photocatalysts

2016 ◽  
Vol 9 (3) ◽  
pp. 1063-1073 ◽  
Author(s):  
Hyoung-il Kim ◽  
Oh Seok Kwon ◽  
Sujeong Kim ◽  
Wonyong Choi ◽  
Jae-Hong Kim
Keyword(s):  
Hydrogen Peroxide ◽  
Low Energy ◽  
Time In Literature ◽  
First Time ◽  
Photocatalytic Synthesis ◽  
Triplet Triplet Annihilation

This study demonstrates, for the first time in literature, in situ photocatalytic synthesis of hydrogen peroxide (H2O2) through sensitized triplet–triplet annihilation (TTA) upconversion (UC) of low-energy, sub-bandgap photons.

A Study of FCM Clustering Algorithm Based on Interval Multiple Attribute Information

2013 ◽  
Vol 444-445 ◽  
pp. 676-680
Author(s):  
Li Guo ◽  
Guo Feng Liu ◽  
Yu E Bao
Keyword(s):  
Clustering Algorithm ◽  
Distance Measure ◽  
Clustering Algorithms ◽  
Interval Numbers ◽  
Fuzzy Partition ◽  
Multiple Attribute ◽  
Attribute Information ◽  
Fcm Clustering ◽  
Attribute Clustering ◽  
Interval Valued

In multiple attribute clustering algorithms with uncertain interval numbers, most of the distances between the interval-valued vectors only consider the differences of each interval endpoint ignoring a lot of information. To solve this problem, according to the differences between corresponding points in each interval number, this paper gives a distance formula between interval-valued vectors, extends a FCM clustering algorithm based on interval multiple attribute information. Through an example, we prove the validity and rationality of the algorithm. Keywords: interval-valued vector; FCM clustering algorithm; distance measure; fuzzy partition

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