Deep Learning-Based Advanced Research Trends in Scalable Computing
https://scpe.org/index.php/scpe/CFP_SI_MLB_IOT
Introduction:
Deep learning has revolutionized the field of artificial intelligence, and
its applications are widespread across various industries, including
healthcare, finance, and e-commerce. With the emergence of big data and the
need for high-performance computing resources, deep learning has become an
essential technology for scalable computing. Scalable computing, which
refers to the ability of computer systems to handle an increasing amount of
workloads and data, is crucial for organizations looking to scale their
operations and meet growing demands. The intersection of deep learning and
scalable computing has opened up new avenues for research and development.
Deep learning-based scalable computing systems have the potential to
provide faster and more accurate results, handle larger datasets, and
enhance the performance of applications in various industries. However,
there are several challenges to be addressed, such as the complexity of
deep learning models, the need for massive computational resources, and the
increasing demand for data storage.
This special issue aims to explore the latest research trends, challenges,
and best practices in the area of deep learning-based scalable computing.
The objective is to provide readers with insights into the latest advances
in deep learning and how they can be applied to solve real-world problems.
The scope of this special issue includes but is not limited to, deep
learning-based architectures and frameworks for scalable applications,
distributed computing, resource allocation and scheduling, security,
privacy, and data management in the cloud.
The articles included in this special issue will present the latest
research findings, insights, and perspectives on the potential applications
of deep learning in scalable computing. We welcome contributions from
researchers, academics, and practitioners in the field of deep
learning-based scalable computing who have expertise in the above topics.
This special issue aims to provide a comprehensive understanding of the
latest research trends and challenges in the field of deep learning-based
scalable computing and to highlight the potential impact of deep learning
in scalable computing and its practical applications in various industries.
Recommended topics (but not limited to):
The following are the recommended topics for this special issue:
• Deep learning-based architectures and frameworks for scalable
applications
• Distributed computing and resource management in deep learning
• Security and privacy in deep learning-based scalable computing
• Data management and analysis in deep learning-based scalable computing
• Deep learning-based edge computing and its applications in scalable
computing
• Internet of Things (IoT) and deep learning-based scalable computing
• Performance optimization and evaluation of deep learning-based scalable
computing systems
• Case studies and real-world applications of deep learning-based scalable
computing
• Transfer learning and federated learning for scalable computing
• Future research directions and open challenges in deep learning-based
scalable computing
Important dates:
Submission deadline: 31 December, 2023
Authors notification: 30 January, 2024
Revised version deadline: 29 February, 2024
Final decision: 31 March, 2024
Completion of Special Issue: June, 2024
We welcome contributions from researchers, academics, and practitioners in
the field of deep learning-based scalable computing who have expertise in
the above topics. The articles included in this special issue will present
the latest research findings, insights, and perspectives on the potential
applications of deep learning in scalable computing.
Submission guidelines:
Original and unpublished works on any of the topics aforementioned or
related are welcome. The SCPE journal has a rigorous peer-reviewing process
and papers will be reviewed by at least two referees. All submitted papers
must be formatted according to the journal's instructions, which can be
found at the journal WWW site (only manuscripts prepared in LaTeX will be
considered).
During submission please select a Special Issue that you want to submit to
and provide this information in the Comments for the Editor field.
Guest Editors:
Lead: Dr. B. Nagaraj M.E., Ph.D., MIEEE, Dean - Innovation Centre, Rathinam
Group of Institutions, Coimbatore, Tamilnadu, India, email:
dean.sa@rathinam.in
Dr. Danilo Pelusi, Dept. of Communication Engineering, University of
Teramo, Italy, email: dpelusi@unite.it
Prof. Raffaele Mascella, Dept. of Communication Engineering, University of
Teramo, Italy, email: rmascella@unite.it
Dr. Hayath Thameem Basha, Department of Mathematical science, Ulsan
National Institute of Science & Technology (UNIST), Ulsan, Republic of
Korea, email: basha.thameem666@gmail.com
Prof. David Al-Dabass (BSc(Eng), ACGI, PhD, Ceng, CMath, FIMA, FIET, FBCS),
School of Computing & Informatics, Nottingham Trent University, England,
email: david.al-dabass@ntu.ac.uk
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