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Innovations in Data Science Research for Future Computing Era

Edited by:
Mohammad Nishat Akhtar, Universiti Sains Malaysia, Malaysia
Muhammad Rafiq Khan Kakar, Bern University of Applied Sciences, Switzerland
Supavadee Aramvith, Chulalongkorn University, Thailand
Michael Ng, Hong Kong Baptist University, China

Submission Status: Closed 


Advances in Continuous and Discrete Models is calling for submissions to our Collection on 'Innovations in Data Science Research for Future Computing Era.' This Collection aims to bring out the innovations in data science research for the future computing era.

About the Collection

Advances in Continuous and Discrete Models is calling for submissions to our Collection on 'Innovations in Data Science Research for Future Computing Era.'

Today's technological landscape is rapidly changing, and data science makes a significant impact on every technological transformation. As human interference with technology continues to grow every day, the amount of data generated from technological applications also grows significantly. The amount of data generated every day is immeasurable. It is more determined that data will be at the core of transformative technology across every sector, with new possibilities and opportunities in the coming decade. Some of the applications where data science will play a significant role in the future computing era include healthcare, manufacturing, education, etc. Further, businesses today are exploring transformative techniques to enhance productivity and improve customer experiences. Data-driven business intelligence adds immense value to the business. The future of the computing era is completely dependent on the data, and the businesses that do take advantage of it will grow significantly.

As a widely developing area of research, data science has enormous scope for the future computing era. The latest trends and innovations in data science have made this technique a part of the future world. Data science research usually deals with collecting, processing, analysing, and representing the data in a visual format that enables businesses to make critical applications. Some of the emerging trends in data science include artificial intelligence, IoT, big data, augmented reality, virtual reality, quantum computing, automated machine learning, digital twin, and so on. Thus, we can conclude that data science is one of the disruptive techniques, and it takes the future generation of computing systems to the next level of advancements through the realisation of important business data insights. Further, it changes the way in which people interact with the technology and offers a competitive edge to the business that adapts it. However, the volume and velocity of the data are accelerating every day. As a result, there is a crucial need for data science algorithms that are secure, efficient, improve performance, and reduce downtime. The system should operate autonomously with higher flexibility and availability of operations. Also, there is a critical need for integrating intelligent solutions that have higher visibility and accessibility measures. 

This Collection aims to bring out the innovations in data science research for the future computing era. We welcome researchers and practitioners working in this discipline to present their novel and unpublished research findings.

Possible topics include, but are not limited to:

 - Innovations in data science for future computing applications
 - Big data and data science for emerging digital era
 - Role of artificial intelligence in future generation healthcare systems
 - Data science innovation in the era of transformation
 - Synergy of IoT and data science for emerging applications
 - Reinventing the big data analytics with new innovative data science approaches
 - Innovation and opportunities of data science in future digital era
 - Emerging trends in data science and its applications
 - Cloud assisted big data intelligence for future era
 - Emerging advances in artificial intelligence for modern computing systems
 - Trends in data science for big data analytics

  1. In the context of rapid urbanization, accurately identifying the visual factors that influence environmental safety perception is crucial for improving urban transportation environments and enhancing pedestria...

    Authors: Chen Pan, Haibo Li, Lu Wang, Jiawei Wu, Jiaming Guo, Nengjie Qiu and Xiaodong Liu
    Citation: Advances in Continuous and Discrete Models 2024 2024:44
    Content type: Research Published on:

  2. 2D magnetotelluric (MT) imaging detects underground structures by measuring electromagnetic fields. This study tackles two issues in the field: traditional methods’ limitations due to insufficient forward mode...

    Authors: Yaohua Luo, Jiachen Li, Xuben Wang, Junjie Zong and Haoyu Tang
    Citation: Advances in Continuous and Discrete Models 2024 2024:43
    Content type: Research Published on:

  3. On rainy days the uncertainty of the shape and distribution of rain streaks can cause the images captured by RGB image-based measurement tools to be blurred and distorted. Thanks to the wavelet transform abili...

    Authors: Wenyin Tao, Xuefeng Yan, Yongzhen Wang and Mingqiang Wei
    Citation: Advances in Continuous and Discrete Models 2024 2024:42
    Content type: Research Published on:

  4. This study introduces a cutting-edge profit health assessment framework that merges signaling theory and agency theory within a data science context, leveraging both financial and nonfinancial indicators to pr...

    Authors: Wen Zhu, Chengcheng Wu, Meiling Li and Fengmin Yao
    Citation: Advances in Continuous and Discrete Models 2024 2024:41
    Content type: Research Published on:

  6. Under the current big topic of enterprise management, how to cultivate, use, and maintain talents is the critical problem of modern human resource management. Based on big data mining, cloud computing, and oth...

    Authors: Shichao Wang
    Citation: Advances in Continuous and Discrete Models 2024 2024:38
    Content type: Research Published on:

Submission Guidelines

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This Collection welcomes submission of Research articles and Reviews. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. 

Articles for this Collection should be submitted via our submission system, Editorial Manager. Please select the appropriate Collection title “Innovations in Data Science Research for Future Computing Era" from the dropdown menu. Articles will undergo the journal’s standard peer-review process and are subject to all the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer-review process. The peer-review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.