IEEE International Conference on E-Business Engineering (ICEBE) 2023

Service Oriented Computing and Applications

Special Issue on "Service Computing meets AI Deep Neural Networks"

Service computing is a critical paradigm that provides cross-disciplinary computational abstractions, architecture, and technologies to facilitate business services. Web services, service-oriented architecture (SOA), cloud computing, business consulting methodologies and utilities, business process modelling, transformation and integration are all components of the underlying technological suite. Services computing seeks to enhance the effectiveness and efficiency of business services by taking full advantage of IT services and computer technology.

Artificial intelligence (AI) has been gaining tremendous attention in the past decade due to its vital role in critical application domains. With the increasing availability of data and more powerful computers, deep learning has become increasingly popular. Many fields and research areas now leverage neural network-based deep learning technology to analyze problems and reliably infer the necessary information.

The application of services computing has been facing numerous challenges in intelligently integrating with deep learning. However, as AI deep learning technologies continue to advance, the research focus of services computing has also been shifting.

This special issue seeks to showcase the latest progress in the application of artificial intelligence deep neural networks to service computing, highlighting the associated challenges, presenting solutions and introducing new approaches. Submissions exploring various aspects of AI deep learning-based service computing is welcomed, including (but not limited to):

• AI deep neural networks for service-oriented architectures/computing/decision making/security
• Service system, service model and web services
• Service application, service network, service framework, service analysis and dynamic services
• Service modeling, management, composition
• Services related technologies, like scheduling, prediction, clustering
• Service performance

Important Dates

• Submissions deadline: Jan 31, 2024
• Notification of the first-round review: March 31 2024
• Deadline for revision submissions: April 30, 2024
• Notification of final decisions: May 31, 2024

Guest Editors

• Prof. Qingyao Wu, School of Software Engineering, South China University of Technology, China. (qyw@scut.edu.cn)
• Prof. Xutao Li, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China. (lixutao@hit.edu.cn)
• Prof. Guanbin Li, School of computer science and engineering, Sun Yat-sen University, China. (liguanbin@mail.sysu.edu.cn)

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Journal of Future Generation Computer Systems

Special Issue on "Advanced Technologies in E-Business Engineering and Applications"

The area of E-business engineering and applications is continuously changing due to the rapid development in state-of-the-art technologies such as big data, cloud, Internet of Things (IoT) and Artificial Intelligence (AI). Big data has a wider application in successful provisioning of E-business services, such as, data analytics for customer engagement, increase in sale, and personalization of customer experience. IoT has been increasingly used in modern E-business for various purposes such as improving logistics and tracking process, automating shipping and delivery and maximizing time and profit. However, given the large volume, variety and velocity of big data, it needs to be processed first before the intended benefits from it can be achieved. This ranges from the sufficient storage of the data for processing to making sure that the processed analysis is understood and free from bias. Businesses and organisations therefore need to adopt cloud-enabled elastic resources and sophisticated tools that will assist the AI models in the decision making process. This is to also ensure that businesses comply with the General Data Protection Regulation (GDPR) that require them to explain to their consumers how their AI-based decision models have led them to the decision being reached.

The objective of this special issue is to explore new research in the advanced technologies of big data, cloud, Internet, IoT and their AI applications in order to further advance the area of E-business systems and applications. This special issue invite papers which include (but not limited to) the following topics:

• Big data models and technologies
• Big data analytics and visualisation
• Cloud computing and big data in E-business
• Big data and NoSQL databases
• Big data and knowledge engineering
• E-business data mining and data extraction
• Machine learning and big data in E-business
• Security, privacy and trust in E-business
• Web services and SOA in E-business
• AI-based models for decision making in business
• Reliability and trustworthy of the AI-based decision making models
• AI and big data in business applications

Paper Submission

Papers must be original and must not be under consideration for publication in other peer-reviewed outlets. The special issue will consider high quality papers from open call as well as extended versions of selected papers from the IEEE International Conference on e-Business Engineering (ICEBE), 3-5 November 2023, Sydney, Australia. All papers submitted to the special issue will go through peer review. Acceptance of the papers will be decided on the outcome of peer reviews.

Important Dates

• Submission deadline: 30 Dec 2023
• Notification of 1st round of review: 1 Mar 2024
• Revised manuscript due: 15 Apr 2024
• Notification of 2nd round of review: 15 May 2024
• Final version due: 15 Jun 2024

Guest Editors

• Prof. Omar Hussain, School of Business, University of New South Wales, Australia. (o.hussain@adfa.edu.au)
• Prof. Muhammad Younas, School of Engineering, Computing and Mathematics, Oxford Brookes University, UK.
• Prof. Junchi Yan, Department of Computer Science and Engineering, Shanghai Jiao Tong University, China.
• Prof. Yu-Sheng Su, Department of Computer Science and Engineering, National Taiwan Ocean University, Taiwan.