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Annals of Data Science

Annals of Data Science OnlineFirst articles


Measuring Variability and Factors Affecting the Agricultural Production: A Ridge Regression Approach

The present investigation deals with the use of ridge regression in the presence of high degree multicollinearity, to determine the major factors influencing the agricultural production in Haryana state, India. Time series data for the period …


Monitoring Novel Corona Virus (COVID-19) Infections in India by Cluster Analysis

It is a great challenge of identification as well as formation of groups of infectious disease data set. Data mining, a process of uncovering silent characteristics of big data is one of such techniques which have nowadays become more popular for …

18.05.2020 Open Access

Sparse Principal Component Analysis for Natural Language Processing

High dimensional data are rapidly growing in many different disciplines, particularly in natural language processing. The analysis of natural language processing requires working with high dimensional matrices of word embeddings obtained from text …

18.05.2020 Open Access

A Six Parameters Beta Distribution with Application for Modeling Waiting Time of Muslim Early Morning Prayer

Beta distribution is a well-known and widely used distribution for modeling and analyzing lifetime data, due to its interesting characteristics. In this paper, a six parameters beta distribution is introduced as a generalization of the two …


Demand Prediction in the Automobile Industry Independent of Big Data

In recent years, various kinds of big data have been handled, and many variables are used in prediction model research. However, a gap between research and practice is thought to exist. As a result of adding variables that cannot be obtained at …

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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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