Annals of Data Science

Aims & Scope

Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science.

Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data.

ADS encourages contributors to address such challenging problems at this exchange platform.

At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.

ADS is a series of volumes edited by either the editorial office or guest editors.

Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.

View Aims & Scope

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Metrics & Ranking

Journal Rank

Year	Value
2024	6406

Journal Citation Indicator

Year	Value
2024	1006

SJR (SCImago Journal Rank)

Year	Value
2024	0.832

Quartile

Year	Value
2024	Q1

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Business, Management and Accounting, Computer Science and Decision Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• A Comprehensive Survey of Clustering Algorithms

  Citation: 1456

  Authors: Dongkuan, Yingjie

• A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting

  Citation: 467

  Authors: Akshit, Pavan, Aarya, Manan

• A Comprehensive Survey of Loss Functions in Machine Learning

  Citation: 415

  Authors: Qi, Yue, Kun, Yingjie

• Internet of Things, Real-Time Decision Making, and Artificial Intelligence

  Citation: 391

  Authors: James M.

• Genetic Algorithms in the Fields of Artificial Intelligence and Data Sciences

  Citation: 184

  Authors: Ayesha

• How Many Judges Should There Be in a Group ?

  Citation: 141

  Authors: Thomas L., Mujgan Sağır

• Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

  Citation: 134

  Authors: Iqbal H.

• Forecasting with Big Data: A Review

  Citation: 112

  Authors: Hossein, Emmanuel Sirimal

• Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning

  Citation: 110

  Authors: Shah, Muhammad Qasim

• A Review on Applications of Chaotic Maps in Pseudo-Random Number Generators and Encryption

  Citation: 105

  Authors: Rasika B., Udayprakash

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