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Zeitschrift

Annals of Data Science

Annals of Data Science OnlineFirst articles

24.07.2017

On a Weibull-Inverse Exponential Distribution

In this paper we study various reliability properties of a Weibull inverse exponential distribution. The maximum likelihood and Bayes estimates of unknown parameters and reliability characteristics are obtained. Bayes estimates are obtained with …

22.07.2017

Approximate Shortest Distance Computing Using k-Medoids Clustering

Shortest distance query is widely used aspect in large scale networks. Numerous approaches are present in the literature to approximate the distance between two query nodes. Most popular distance approximation approach is landmark embedding …

21.07.2017

Hardware Implementation of Bone Fracture Detector Using Fuzzy Method Along with Local Normalization Technique

Bone fracture detection from the digital image segmentation is a well-known image processing application which is frequently used to process biomedical images. Hardware realization of different image processing algorithm specially utilizing Field …

21.07.2017

Logarithmic Transformed Family of Distributions with Application

In this paper, a new three-parameter distribution, called $$\alpha $$ α logarithmic transformed generalized exponential distribution ( $$\alpha LTGE$$ α L T G E ) is proposed. Various properties of the proposed distribution, including explicit …

21.07.2017

Evaluation and Comparison of Estimators in the Gompertz Distribution

This article addresses the different methods of estimation of the probability density function and the cumulative distribution function for the Gompertz distribution. Following estimation methods are considered: maximum likelihood estimators …

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Über diese Zeitschrift

Annals of Data Science (AODS) is a new academic journal focusing on Big Data analytics and applications. It not only promotes how to use interdisciplinary techniques, including statistics, artificial intelligence and optimization, to process Big Data and conduct data mining, but also how to use the knowledge gleaned from Big Data for real-life applications. AODS accepts high-quality contributions on the foundations of data science, technical papers on various challenging problems in Big Data and meaningful case studies concerning business analytics in the context of Big Data.

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