The Impact of Distance Measures in K-Means Clustering Algorithm for Natural Color Images

2020 ◽  
pp. 947-963
Author(s):  
P. Ganesan ◽  
B. S. Sathish ◽  
L. M. I. Leo Joseph ◽  
K. M. Subramanian ◽  
R. Murugesan
Keyword(s):  
Clustering Algorithm ◽  
Color Images ◽  
Distance Measures ◽  
Natural Color ◽  
The Impact

scholarly journals Superpixels extraction by an Intuitionistic fuzzy clustering algorithm

2021 ◽  
Vol 19 (2) ◽  
pp. 140-152
Author(s):  
Dante Mújica Vargas
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Experimental Tests ◽  
Color Images ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Clustering Algorithm ◽  
Natural Color ◽  
Intuitionistic Fuzzy Clustering ◽  
Segmentation Task

A scheme to develop the image over-segmentation task is introduced in this paper, it considers the pixels of an image as intuitive fuzzy sets and develops an intuitionistic clustering process of them. In this regard, the main contribution is to provide a method for extracting superpixels with greater adherence to the edges of the regions. Experimental tests were developed considering biomedical grayscale and natural color images. The robustness and effectiveness of this proposal was verified by quantitative and qualitative results.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

scholarly journals Top-induced contributions to H → $$ b\overline{b} $$ and H → $$ c\overline{c} $$ at $$ \mathcal{O}\left({\alpha}_s^3\right) $$

2020 ◽  
Vol 2020 (12) ◽  
Author(s):  
Roberto Mondini ◽  
Ulrich Schubert ◽  
Ciaran Williams
Keyword(s):  
Top Quark ◽  
Clustering Algorithm ◽  
Percent Level ◽  
Effective Field ◽  
Total Width ◽  
Monte Carlo Code ◽  
Final State ◽  
Fully Differential ◽  
State Selection ◽  
The Impact

Abstract In this paper we present a fully-differential calculation for the contributions to the partial widths H →$$ b\overline{b} $$ b b ¯ and H →$$ c\overline{c} $$ c c ¯ that are sensitive to the top quark Yukawa coupling yt to order $$ {\alpha}_s^3 $$ α s 3 . These contributions first enter at order $$ {\alpha}_s^2 $$ α s 2 through terms proportional to ytyq (q = b, c). At order $$ {\alpha}_s^3 $$ α s 3 corrections to the mixed terms are present as well as a new contribution proportional to $$ {y}_t^2 $$ y t 2 . Our results retain the mass of the final-state quarks throughout, while the top quark is integrated out resulting in an effective field theory (EFT). Our results are implemented into a Monte Carlo code allowing for the application of arbitrary final-state selection cuts. As an example we present differential distributions for observables in the Higgs boson rest frame using the Durham jet clustering algorithm. We find that the total impact of the top-induced (i.e. EFT) pieces is sensitive to the nature of the final-state cuts, particularly b-tagging and c-tagging requirements. For bottom quarks, the EFT pieces contribute to the total width (and differential distributions) at around the percent level. The impact is much bigger for the H →$$ c\overline{c} $$ c c ¯ channel, with effects as large as 15%. We show however that their impact can be significantly reduced by the application of jet-tagging selection cuts.

Linkage analysis using geographical proximity: a test of the efficacy of distance measures

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shumpei Haginoya ◽  
Aiko Hanayama ◽  
Tamae Koike
Keyword(s):  
Environmental Factors ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Distance Measures ◽  
Manhattan Distance ◽  
Geographical Proximity ◽  
Discrimination Accuracy ◽  
Content Type ◽  
Shortest Route ◽  
The Impact

Purpose The purpose of this paper was to compare the accuracy of linking crimes using geographical proximity between three distance measures: Euclidean (distance measured by the length of a straight line between two locations), Manhattan (distance obtained by summing north-south distance and east-west distance) and the shortest route distances. Design/methodology/approach A total of 194 cases committed by 97 serial residential burglars in Aomori Prefecture in Japan between 2004 and 2015 were used in the present study. The Mann–Whitney U test was used to compare linked (two offenses committed by the same offender) and unlinked (two offenses committed by different offenders) pairs for each distance measure. Discrimination accuracy between linked and unlinked crime pairs was evaluated using area under the receiver operating characteristic curve (AUC). Findings The Mann–Whitney U test showed that the distances of the linked pairs were significantly shorter than those of the unlinked pairs for all distance measures. Comparison of the AUCs showed that the shortest route distance achieved significantly higher accuracy compared with the Euclidean distance, whereas there was no significant difference between the Euclidean and the Manhattan distance or between the Manhattan and the shortest route distance. These findings give partial support to the idea that distance measures taking the impact of environmental factors into consideration might be able to identify a crime series more accurately than Euclidean distances. Research limitations/implications Although the results suggested a difference between the Euclidean and the shortest route distance, it was small, and all distance measures resulted in outstanding AUC values, probably because of the ceiling effects. Further investigation that makes the same comparison in a narrower area is needed to avoid this potential inflation of discrimination accuracy. Practical implications The shortest route distance might contribute to improving the accuracy of crime linkage based on geographical proximity. However, further investigation is needed to recommend using the shortest route distance in practice. Given that the targeted area in the present study was relatively large, the findings may contribute especially to improve the accuracy of proactive comparative case analysis for estimating the whole picture of the distribution of serial crimes in the region by selecting more effective distance measure. Social implications Implications to improve the accuracy in linking crimes may contribute to assisting crime investigations and the earlier arrest of offenders. Originality/value The results of the present study provide an initial indication of the efficacy of using distance measures taking environmental factors into account.

scholarly journals Maternal Distress/Coping and Children's Adaptive Behaviors During the COVID-19 Lockdown: Mediation Through Children's Emotional Experience

2020 ◽  
Vol 8 ◽  
Author(s):  
Serena Petrocchi ◽  
Annalisa Levante ◽  
Federica Bianco ◽  
Ilaria Castelli ◽  
Flavia Lecciso
Keyword(s):  
Structural Equation ◽  
Positive Emotions ◽  
Online Survey ◽  
Emotional Experience ◽  
Psychological Impact ◽  
Mediation Effect ◽  
Distance Measures ◽  
Maternal Distress ◽  
Equation Modeling ◽  
The Impact

The present study focused on the psychological impact that the lockdown due to coronavirus disease-19 (COVID-19) had on families in Italy. During the COVID-19 pandemic, the Italian government imposed a strict lockdown for all citizens. People were forced to stay at home, and the length of the lockdown was uncertain. Previous studies analyzed the impact of social distance measures on individuals' mental health, whereas few studies have examined the interplay between the adults' functioning, as parents, during this period and the association with the child's adjustment. The present study tested if maternal distress/coping predicts children's behaviors during the COVID-19 lockdown, hypothesizing a mediation effect via children's emotional experience. Participants were 144 mothers (Mage = 39.3, 25–52, SD = 5.6) with children aged 5–10 years (Mage = 7.54, SD = 1.6, 82 boys); mothers answered to an online survey. Results indicated that mothers with higher exposure to COVID-19 showed higher levels of distress and higher display of coping attitudes, even if in the structural equation modeling model, the COVID-19 exposure was not a predictor of mothers' distress. Compared with mothers with good coping skills, mothers with higher stress levels were more likely to attribute negative emotions to their children at the expense of their positive emotions. Moreover, children's emotions acted as mediators between maternal distress/coping and children's adaptive/maladaptive behaviors. In conclusion, it is important to support parents during pandemic emergence, by providing them with adequate information to manage the relationship with their children, to reduce their level of distress and to enhance their coping abilities.

scholarly journals Social Frontiers: Estimating the Spatial Boundaries Between Residential Groups and Their Impacts on Crime

2021 ◽  
pp. 285-304
Author(s):  
Ivana Křížková ◽  
Meng Le Zhang ◽  
Dan Olner ◽  
Gwilym Pryce
Keyword(s):  
Cultural Distance ◽  
Free Market ◽  
Poisson Model ◽  
Distance Measures ◽  
The Usa ◽  
Short Run ◽  
History Of ◽  
Conditional Autoregressive ◽  
The Uk ◽  
The Impact

AbstractInthischapter, we highlight the importance of social frontiers—sharp spatial divisions in the residential make-up of adjacent communities—as a potentially important form of segregation. The handful of studies estimating the impacts of social frontiers have been based in the USA and the UK, both of which are free-market democracies with a long history of immigration, ethnic mix and segregation. There are currently no studies of social frontiers in former socialist countries, for example, or in countries where immigration and ethnic mix are only a recent phenomenon or non-existent. This chapter aims to address this research gap by estimating the impacts of social frontiers on crime rates in a post-socialistcountry, Czechia. We demonstrate how a Bayesianspatial conditional autoregressive estimation can be used to detect social frontiers in this setting, and we use a fixed effect quasi-Poisson model to investigate the impact on crime. Our results suggest that in new immigration destinations, social frontiers may not be associated with higher rates of crime, at least in the short run. Moreover, our use of cultural distance measures helps to promote a more nuanced approach to studying the impact of segregation and highlights the role of cultural diversity in understanding the link between immigrant segregation and crime. We reflect on how this approach could contribute to the study of segregation and inequality in the Chinese context.

scholarly journals AN ALGORITHM FOR GENERATING NATURAL COLOR IMAGES FROM FALSE COLOR USING SPECTRAL TRANSFORMATION TECHNIQUE WITH HIGHER POLYNOMIAL ORDER

2018 ◽  
Vol XLII-5 ◽  
pp. 643-648
Author(s):  
S. Kala ◽  
A. Kumar ◽  
A. K. Joshi ◽  
V. M. Bothale ◽  
B. G. Krishna
Keyword(s):  
Second Order ◽  
Color Images ◽  
Water Bodies ◽  
Blue Band ◽  
Spectral Transformation ◽  
False Color ◽  
Natural Color ◽  
Polynomial Transformation ◽  
Order Polynomial ◽  
Color Composite

<p><strong>Abstract.</strong> Satellite imageries in True color composite or Natural Color composite (NCC) serves the best combination for visual interpretation. Red, Green and Infrared channels form false color composite which might not be as useful as NCC to a non-remote sensing professional. As blue band is affected by large atmospheric scattering, satellites like IRS-LISS IV, SPOT do not have blue band. To generate NCC from such satellite data blue band must be simulated. Existing algorithms of spectral transformation do not provide robust coefficients leading to wrong NCC colors especially in water bodies. To achieve more robust coefficients, we have proposed new algorithm to generate NCC for IRS-LISS IV data using second order polynomial regression technique. Second order polynomial transformation functions consider even minor variability present in the image as compared to 1st order so that the derived coefficients are adjustable to accommodate spatial and temporal variability while generating NCC. In this study, Sentinel-2 image was used for deriving coefficients with blue band as dependent and green, red and infrared as independent variables. Simulated Sentinel band showed high accuracy with correlation of 0.93 and 0.97 for two test sites. Using the same coefficients, blue band was simulated for LISS-IV which also showed good correlation of 0.90 with sentinel original blue band. On comparing LISS-IV simulated NCC with simulated NCC from other algorithms, it was observed that higher order polynomial transformation was able to achieve higher accuracy especially for water bodies where expected color is green. Thus, proposed algorithms can be used for transforming false color image to natural color images.</p>

Empirical analysis of the impact of financial development on the income gap between urban and rural residents in the context of large data using fuzzy Kmeans clustering algorithm

2020 ◽  
pp. 002072092093683
Author(s):  
Yao Li
Keyword(s):  
Empirical Analysis ◽  
Financial Development ◽  
Rural Areas ◽  
Clustering Algorithm ◽  
Financial Industry ◽  
Rural Residents ◽  
Income Gap ◽  
Urban And Rural Residents ◽  
Urban And Rural Areas ◽  
The Impact

With the rise of the tertiary industry, the financial industry has achieved unprecedented development, which is mainly reflected in the rapid growth of economic aggregate, the increasingly balanced financial structure system and the increasingly diversified financial products. However, with the rapid development of financial industry, the income of urban and rural residents is increasingly unbalanced. The increasing income gap between urban and rural areas has caused a large number of adverse phenomena in the process of economic development, seriously affecting the income distribution of the people and even causing social instability. Therefore, in today’s big data era, it is necessary to systematically study and analyze the impact of financial industry development on the national income gap between urban and rural areas. At the same time, it is of great significance to improve the problem of excessive income gap between urban and rural areas. This paper mainly analyses the relationship between the three effects of the development of financial industry and the income gap between urban and rural residents. In the empirical aspect, the paper creatively uses the fuzzy Kmeans clustering algorithm to regression analysis the panel data of a certain area from 2010 to 2018. At the same time, in the empirical data analysis, this paper creatively replaces the European norm measure of the Kmeans clustering algorithm with the AE measure, and puts forward a proposal. The index of financial development level is based on the proportion of loans from financial institutions. Through theoretical and empirical analysis, this paper draws the following conclusions: the financial scale in the financial industry will have a huge impact on the income gap between urban and rural areas. Finally, based on the above problems and current situation, this paper puts forward relevant improvement suggestions.

scholarly journals Analysis of Minimum Cost of Supply Chain Emergencies Based on Diffusion Path Processing

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yan Cao ◽  
Tian Tian ◽  
Wanyu Wei ◽  
Liang Huang ◽  
Yujia Wu
Keyword(s):  
Supply Chain ◽  
Clustering Algorithm ◽  
Minimum Cost ◽  
Diffusion Path ◽  
Critical Event ◽  
Design Structure ◽  
Perform Cluster Analysis ◽  
The Impact ◽  
The Relationship ◽  
Case Data

In view of the complexity and severity of the impact of supply chain emergencies on enterprise economy, this paper proposes modular processing to improve the design structure matrix (DMS), and the designed clustering algorithm is used to perform cluster analysis of the improved DMS, to predict the possible diffusion path of emergencies, and to establish the critical event diffusion path planning model by designing the critical event diffusion path storage method. As in the case data of a certain type of servo motor of the H Company, after data screening, the diffusion path is classified and stored by analyzing the relationship between each member of the supply chain network. Secondly, the same group of data is put into the method of this paper and other scholars’ to calculate the minimum cost of emergency response in time.

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