Calls for Papers (special): International Journal of Distributed Systems and Technologies (IJDST)


Special Issue On: Hybridization on Deep Architectures for Big Data Classification

Submission Due Date
9/30/2020

Guest Editors
R. Sudhakar, Anna University, India
Satish Chander, Waljat College of Applied Sciences, Sultanate of Oman;
Rajakumar B.R., Resbee Info Technologies (P) Ltd, India

Introduction
This proposal is designed as a forum for high standard publications on research and applications concerning the extraction of knowns aspects of big data classification. Accordingly, it publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research.

Objective
Recent developments in the field, hybridization of deep architectures, offer powerful tools for intelligent big data classification. We believe that a cognitive formalism such as deep architecture that combines artificial intelligence and machine learning will leapfrog current perception of information processing and management.

It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advancements in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. The issue also publishes survey papers that outline, and illuminate the special deep approaches for classifying big data.

Recommended Topics
• Big Data Science and Foundations, Analytics, Visualization And Semantics
• Software And Tools For Big Data Management
• Security, Privacy and Legal Issues Specific To Big Data
• Big Data Economy, Qos and Business Models
• Machine Learning Algorithms for Big Data
• New deep learning algorithms
• New neural network architectures for deep learning
• Hierarchical deep learning
• Multi-dimensional deep learning
• Deep learning of spatio-temporal data
• On-line deep learning neural networks
• Neuromorphic deep learning architectures
• Better combinations of existing algorithms and techniques for deep learning
• Data-driven deep learning and control
• Optimization by deep neural networks
• Scalable Storage Systems
• Data Capture and Storage
• Search, Sharing, and Analytics
• Architectures for Massively Parallel Processing

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Hybridization of Deep Architectures for Big Data Classification on or before September 30, 2020. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
R. Sudhakar
Guest Editor
International Journal of Distributed Systems and Technologies (IJDST)
E-mail: sudhakarvk2002@gmail.com

Special Issue On: AI-Powered Anomaly Detection for Distributed Systems and the Pervasive Edge Computing

Submission Due Date
12/31/2020

Guest Editors
Fa Zhu, Nanjing Forestry University, China
Bin Zhao, Nanjing Normal University, China
Ziyang Guo, Huazhong University of Science and Technology, China

Introduction
In some real applications, it is impractical to collect all classes from limited samples, such as distributed system. For supervised learning, we need to deduce whether a test sample is from a known class or an unknown class by learning a model from a training set which consists of several known classes. The samples from unknown class are termed as anomalies or novelties. The anomalies or novelties widely exist in many real applications, such as illegal intrusion in internet of services, irregular visiting in edge computing, and abnormal events in Internet of Things to name just a few. Detecting anomalies or novelties is a challenge task in the community of machine learning. AI-Power makes identify anomalies or novelties automatically. Additionally, the massive data derived from lots of IoT devices and applications (e.g., smart grid, healthcare, smart building, and smart distributed systems) would be inevitably corrupted by noises which can be seen as the anomalies for normal data. The corrupted data would make the performance of edge computing degrade. Therefore, anomaly detection is crucial for edge computing and distributed system, which can reduce the impact of noises.

Objective
This special issue is expected to spur further research and development of anomaly detection and to provide a unique opportunity to allow researchers in several domains of computing area to contribute to anomaly detection and its applications in the Internet of Things and distributed systems.

Recommended Topics
• Anomaly Detection in the fields of Edge and Fog Computing
• Anomaly Detection in Edge Computing
• Anomaly Detection for Mobile Edge Network Security
• Anomaly Detection in Distributed Systems
• Anomaly Detection in Internet of Things (IoT) and Internet of Medical Things (IoMT)
• Anomaly Detection in Smart Healthcare
• Anomaly Detection in Smart Exercise Monitoring
• Anomaly Detection in Smart Agriculture
• Anomaly Detection for Intelligent Computing System
• Anomaly Detection for Decision Support and Therapy Improvement of AI-based Monitoring System

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on AI-Powered Anomaly Detection for Distributed Systems and the Pervasive Edge Computing on or before December 31, 2020. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Fa Zhu
Guest Editor
International Journal of Distributed Systems and Technologies (IJDST)
E-mail: fazhu@njfu.edu.cn

Special Issue On: AI and Edge Computing Driven Technologies for Knowledge Defined Networking

Submission Due Date
5/31/2021

Guest Editors
Jianhui Lv (Leader GE), Tsinghua University, China
Yuhui Shi, Southern University of Science and Technology, China, IEEE Fellow
Xingwei Wang, Northeastern University, China
Qing Li, Southern University of Science and Technology, China
Lianbo Ma, Northeastern University, China
Tian Pan, Beijing University of Posts and Telecommunications, China

Introduction
Software Defined Networking (SDN) and Network Function Virtualization (NFV) are the two main driving forces to transform the network architecture from rigid and ossified to flexible and programmable. Now, with the widespread use of Artificial Intelligence (AI) and edge computing, the new concept of Knowledge Defined Networking (KDN) emerges, which has the potential in addressing the new challenges in the current programmable networks by providing mobile edge computing and edge caching capabilities together with AI to the proximity of end users. In the AI and edge computing integrated networks, edge resources are managed by AI systems for offering powerful computational processing and massive data acquisition locally at edge networks. AI helps to obtain efficient resource scheduling strategies in a complex environment with heterogeneous resources and a massive number of devices, while meeting the ultra-low latency and ultra-high reliability requirements of novel applications, e.g., self-driving cars, remote operation, intelligent transport systems, Industry 4.0, smart energy, e-health, and AR/VR services. By integrating AI functions and edge computing technologies into KDN, the network system become evolvable by forming a closed network loop that consists of data collection, learning, deciding and forwarding, which will finally have a full insight into the operating environment and can adapt resource allocation or orchestration in a dynamic manner.

Objective
Despite the benefits introduced by the AI and edge computing driven KDN, many challenges are still faced in this new paradigm. Until now, limited research efforts have been made for applying big data, AI and edge computing in KDN. The aim of this Special Issue is to promote the integration among the technologies of big data, AI and edge computing to speed up the development of KDN on the basis of SDN and NFV. The Special Issue will also present and highlight the advances and latest implementations and applications in the field of KDN such that the theoretical and practical frontiers can be moved forward for a deeper understanding from both the academic and industrial viewpoints.

Recommended Topics
• Architectures, frameworks, and models for the integration of big data, AI and edge computing
• AI driven edge-cloud architecture for KDN
• New concept, theory, and protocols for big data, AI and edge computing enabled KDN
• Edge computing, communication, caching, and control in KDN
• Deep reinforcement learning approaches for KDN
• Networking between edge nodes for resource scheduling and orchestration in KDN
• Resource optimization for edge computing empowered KDN
• Machine learning approaches for resource synergy in KDN
• Big data and learning fusion in AI empowered KDN
• Network traffic prediction and control in AI empowered KDN
• Energy efficiency and greenness-performance tradeoff for edge computing driven KDN
• Security and privacy in KDN

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on AI and Edge Computing Driven Technologies for Knowledge Defined Networking on or before May 31, 2021. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Jianhui Lv,
Yuhui Shi,
Xingwei Wang,
Qing Li,
Lianbo Ma,
Tian Pan

Guest Editors
International Journal of Distributed Systems and Technologies (IJDST)
E-mail: lvjianhui2012@sz.tsinghua.edu.cn,
shiyh@sustech.edu.cn,
wangxw@mail.neu.edu.cn,
liq@pcl.ac.cn,
malb@swc.neu.edu.cn,
platinum127@gmail.com