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Annals of Data Science OnlineFirst articles

06.09.2017

Equalization and Carrier Frequency Offset Compensation for Underwater Acoustic OFDM Systems

Due to noise enhancement, conventional Zero Forcing (ZF) equalizers are not suitable for wireless Underwater Acoustic (UWA) Orthogonal Frequency Division Multiplexing (OFDM) communication systems. Furthermore, these systems suffer from increasing …

17.08.2017

Exponentiated Generalized Kumaraswamy Distribution with Applications

In this article, we introduced and studied exponentiated generalized Kumaraswamy distribution. We derived mathematical properties including quantile function, moment generating function, ordinary moments, probability weighted moments, incomplete …

01.08.2017

Big Data and Causality

Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has …

29.07.2017

A Feature Selection Method Based on Ranked Vector Scores of Features for Classification

One of the major aspects of any classification process is selecting the relevant set of features to be used in a classification algorithm. This initial step in data analysis is called the feature selection process. Disposing of the irrelevant …

29.07.2017

Parallel String Matching with Linear Array, Butterfly and Divide and Conquer Models

String Matching is a technique of searching a pattern in a text. It is the basic concept to extract the fruitful information from large volume of text, which is used in different applications like text processing, information retrieval, text …

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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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