Sørensen-Dice Similarity Indexing based Weighted Iterative Clustering for Big Data Analytics

Big data is a collection of large volume of data and extract similar data points from large dataset. Clustering is an essential data mining technique for examining large volume of data. Several techniques have been developed for handling big dataset. However, with much time consumption and space complexity, accuracy is said to be compromised. In order to improve clustering accuracy with less complexity, Sørensen-Dice Indexing based Weighted Iterative X-means Clustering (SDI-WIXC) technique is introduced. SDI-WIXC technique is used for grouping the similar data points with higher clustering accuracy and minimal time. First, number of data points is collected from big dataset. Then, along with the weight value, the given dataset is partitioned into ‘X’ number of clusters. Next, based on the similarity measure, Weighted Iterated X-means Clustering (WIXC) is applied for clustering data points. Sørensen-Dice Indexing Process is used for measuring similarity between cluster weight value and data points. Upon similarity found between weight value of cluster and data point, data points are grouped into a specific cluster. Besides, the WIXC method also improves the cluster assignments through repeated subdivision using Bayesian probability criterion. This in turn helps to group all data points and hence, improving the clustering accuracy. Experimental evaluation is carried out with number of factors such as clustering accuracy, clustering time and space complexity with respect to the number of data points. The experimental results reported that the proposed SDI-WIXC technique obtains high clustering accuracy with minimum time as well as space complexity.

Investigation on fatigue damage equivalence probability for the structure with similar fatigue hazardous detail parts

Fatigue damage of a whole structure with multiple similar fatigue hazardous detail parts is unclear. This paper focuses on the concept of quantified fatigue damage for the structure with similar fatigue hazardous detail parts by using the probability method and fatigue failure probability of the severe load spectrum. The probability criterion and calculation method of equivalent damage with different load spectra were proposed. The fatigue life probability distribution of the severe load spectrum was analyzed, and the acceleration ratio was defined by the similar details number of fatigue cracking in combination with the fatigue failure probability characteristics of the severe load spectrum. The results show that there is good agreement between the similar details number range of fatigue cracking in two load spectra, which means they are considered to be equivalent. The ratio of the sum of two similar details number ranges is used as acceleration ratio to evaluate the severe load spectrum. The application of this study in the statistical sense of engineering structure fatigue failure is more convincing.

Exploring Geostatistical Modeling and VisualizationTechniques of Uncertainties for Categorical Spatial Data

This article presents and analyzes the indicator geostatistical modeling and some visualization techniques of uncertainty models for categorical spatial attributes. A set of sample points of some categorical attribute is used as input information. The indicator approach requires a transformation of sample points on fields of indicator samples according to the classes of interest. Experimental and theoretical semivariograms of the indicator fields are defined representing the spatial variation of the indicator information. The indicator fields, along with their semivariograms, are used to determine the uncertainty model, the conditioned probability distribution function, of the attribute at any location inside the geographic region delimited by the samples. The probability functions are considered for producing prediction and probability maps based on the maximum class probability criterion. These maps can be visualized using different techniques. In this work, it is considered individual visualization of the predicted and probability maps and a combination of them. The predicted maps can also be visualized with or without constraints related to the uncertainty probabilities. The combined visualizations are based on three-dimensional (3D) planar projection and on the Red-Green-Blue to Intensity-Hue-Saturation (RGB-IHS) fusion transformation techniques. The methodology of this article is illustrated by a case study with real data, a sample set of soil textures observed in an experimental farm located in the region of São Carlos city in São Paulo State, Brazil. The resulting maps of this case study are presented and the advantages and the drawbacks of the visualization options are analyzed and discussed.

MAKING A CASE ON SOCIAL INTERACTION OF PARENTS OF CHILDREN WITH AUTISTIC SPECTRUM DISORDERS: A STUDY OF SELECTED COMPOUNDS OF LUSAKA, ZAMBIA

<p>Parenting a child is a challenging experience let alone parenting a child with a disability. The nature of the disability in a child goes with its complexity and it often puts parenting skills to the test. This study explores the experiences on social interactions of parents with Autistic Spectrum Disorder (ASD) in selected compounds of Lusaka, Zambia. The study used an interpretative phenomenological framework supported by qualitative approach. In-depth interviews were used to collect data. Non-probability criterion purposive sampling procedure was used to select participants. The sample size comprised of 10 parents with children with ASD. Qualitative data was analyzed thematically. The study revealed that parents had unique experiences in raising their children with ASD. These included; inadequate information on ASD; lack of support services; miscommunications between parents and children with ASD; stigmatization; lack of adequate sleeping time and negative attitude towards parents and their children with ASD. It was evident from the study that parents require supportive services including counselling to enable them to deal with the complexities that emanate from parenting children with ASD. In view of these the study recommended increased awareness on ASD; development, promotion and regular evaluation of intervention strategies parents use aimed at improving the daily lives of parents and their children with ASD in the study sites.</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0875/a.php" alt="Hit counter" /></p>

Cloud Computing Virtual Machine Workload Prediction Method Based on Variational Autoencoder

The paper proposes a method for predicting the workload of virtual machines in the cloud infrastructure. Reconstruction probabilities of variational autoencoders were used to provide the prediction. Reconstruction probability is a probability criterion that considers the variability in the distribution of variables. In the proposed approach, the values of the reconstruction probabilities of the variational autoencoder show the workload level of the virtual machines. The results of the experiments showed that variational autoencoders gave better results in predicting the workload of virtual machines compared to simple deep neural networks. The generative characteristics of the variational autoencoders determine the workload level by the data reconstruction.

A Satisficing Framework for Environmental Policy Under Model Uncertainty

We propose a novel framework for the economic assessment of environmental policy. Our main point of departure from existing work is the adoption of a satisficing, as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-a-vis some intertemporal objective function. Consistent to the nature of environmental policymaking, our model takes explicit account of model uncertainty. To this end, the decision criterion we propose is an analog of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply our criterion to the climate-change context and the probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. Insights from computational geometry facilitate computations considerably and allow for the efficient application of the model in high-dimensional settings.