Who Runs the World: Data

Gülseçen, Sevinç; Sharma, Sushil; Akadal, Emre

doi:10.26650/b/et06.2020.011

Cited by 5 publications

(3 citation statements)

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“…Astronomers data statisticians urgently need multivariate data to analyze survival in the universe (Gülseçen & ESENOĞLU, 2020). In the object data population we can obtain a data sample that is unbiased in the construction of an astronomical survey (Kremer et al, 2017).…”

Section: Survive Analysis Methodsmentioning

confidence: 99%

Statistical Data Retrieval Technique in Astronomy Computational Physics

Siagian

Pribadi²,

Sinaga

et al. 2023

JATISI

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Computational astronomy is a very important branch in today's era, where physicists or researchers can use computers to process statistics in astronomical physics. researchers can process abstract data from raw data and can convert data into data visualizations. Computational physics astronomy is a sophisticated and well-established method, this branch of science can provide and process data, solve complex problems, and is very helpful for statisticians and computer scientists. Astronomical physicists have many problems, among others; there is a problem that is hierarchical, and complex, so that this paper will provide a basis for methods for optimizing methods in processing statistical data on physics. The author's hope is that astronomical physicists can perform an important and effective processing of astronomical data optimally and effectively.

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Section: Survive Analysis Methodsmentioning

confidence: 99%

Statistical Data Retrieval Technique in Astronomy Computational Physics

Siagian

Pribadi²,

Sinaga

et al. 2023

JATISI

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show abstract

“…In fact, while exclusive clustering establishes that a data point can occur only in one cluster, overlapping clustering enables data points to be part of multiple clusters with different degrees of membership. Exclusive and overlapping clustering are hard or k-means clustering and soft or fuzzy k-means clustering, respectively [42][43][44]. In hard clustering, every element in a database might be a part of a single and precise cluster, whereas in soft clustering, there is a probability of having each data point into a different cluster [44].…”

Section: Clusteringmentioning

confidence: 99%

“…Exclusive and overlapping clustering are hard or k-means clustering and soft or fuzzy k-means clustering, respectively [42][43][44]. In hard clustering, every element in a database might be a part of a single and precise cluster, whereas in soft clustering, there is a probability of having each data point into a different cluster [44]. Generally speaking, k-means clustering is a "distance-based" method in which each "clustered set" is linked with a centroid that is considered to minimize the sum of the distances between data points in the cluster.…”

Section: Clusteringmentioning

confidence: 99%

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

et al. 2022

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NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

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Effective Data Utilization in the Context of Industry 4.0 Technology Integration

2022

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We are part of the digital transformation of society and industry. The digital transformation of industry is based on new technologies brought about by the fourth industrial revolution. The Internet of Things (IoT), Cloud Computing, Cyber–Physical Systems (CPS) and Big Data provide the digital link between machines and individuals in processes. This completely new system is based on efficient data collection, data analysis and immediate interventions in organizational processes based on the results of the analysis. Smart organizations are driven by data and not by models. By working efficiently with the vast amounts of data available, the smart organizations of the future can ensure business sustainability, increase competitiveness through process optimization and reduce costs. In general, the aim of this paper was to identify the means to achieve a paradigm shift from traditional organizations to smart organizations through the use of data in the context of integrating Industry 4.0 technologies. The aim of the research was to determine the extent to which different Industry 4.0 technologies are applied in the effective use of data from specific activities/processes in industrial organizations to bring about a paradigm shift from traditional organizations to smart organizations. The first part of the paper describes the theoretical background of the transition from traditional to smart organizations using selected Industry 4.0 technologies. The second part of the paper characterizes the research objective, the methods used in the paper and the basic statistics used to determine the research questions and hypotheses. The next section evaluates the research questions and hypotheses that were used to meet the research objective. The last part of the paper is a summary of the obtained results, based on which we conclude that the primary challenge for organizations in the Slovak Republic is to learn how to work with the collected data, the need for their appropriate structuring and subsequent archiving, which is manifested by the need for training and application of data analysts in a broader context.

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Who Runs the World: Data

Cited by 5 publications

References 0 publications

Statistical Data Retrieval Technique in Astronomy Computational Physics

Statistical Data Retrieval Technique in Astronomy Computational Physics

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

Effective Data Utilization in the Context of Industry 4.0 Technology Integration

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