Discrete Dynamics in Nature and Society

Cognitive Modeling of Multimodal Data Intensive Systems for Applications in Nature and Society (COMDICS)


Publishing date
01 Nov 2020
Status
Closed
Submission deadline
03 Jul 2020

Lead Editor

1University of Strathclyde, Glasgow, UK

2Texas A&M University, Corpus Christi, USA

3Beijing University of Technology, Beijing, China

4Tianjin University, Tianjin, China

This issue is now closed for submissions.
More articles will be published in the near future.

Cognitive Modeling of Multimodal Data Intensive Systems for Applications in Nature and Society (COMDICS)

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Internet technologies, in particular the availability of multimodality data from various sensors including Internet of Things and Sensor Networks, have developed rapidly. The integration and combination of these with conventional complex systems (CS), especially addressing the problems in nature and society, have dramatically expanded in acquiring more diverse observations in terms of condition monitoring, fault diagnosis, and improved accuracy and efficiency. This, along with the explosion of intelligent data analytics, especially deep learning-based artificial intelligence, has made it feasible to model the links, dynamics, and feedback loops within such systems. Typical examples can be widely found in many applications, for example, smart transportation, advanced manufacturing, logistics, natural resource and disaster management, and network traffic and load optimization.

To implement such complex systems, particularly those linked to real nature and society challenges, cognitive modeling-based intelligent computing has become a trend for advanced modeling and analysis. This is particularly the case when dealing with huge volumes of multimodality data from different nodes of the system and more effectively modeling the chaotic and nonlinear nature of such systems. Over the past two decades, various models and approaches have been proposed to address the underlying challenges within this interdisciplinary topic, and due to the nature of these rather complicated systems, how to effectively and efficiently tackle the different aspects of the associated challenges has become crucial.

This special issue aims to collate cross-disciplinary original research and review articles with a main focus on integrated concepts and technologies, adaptation to the complex nature of the application, and how to deal with these challenges under resources-tight environments. These will include not only new models, algorithms, and innovative applications, but also practical solutions that particularly focus on how to tailor generic techniques to specific applications. Of particular interest is the application of cognitive models, due to their close links to AI-enabled machine learning, and their direct applications to nature and society.

Discrete mathematical models in addressing challenges in nature and society are particularly emphasized.

Potential topics include but are not limited to the following:

  • AI machine learning-based integrated concepts and solutions
  • Deep learning-based fusion and mining of multimodality data
  • Performance modeling and risk assessment of multimodal data intensive systems
  • Genetic modeling and evolutionary computing for multimodality data
  • Sparse representation and compressive sensing for smart data sampling and effective dimensionality reduction
  • Integrated solutions and emerging applications in multimodality data
  • Benchmark data/methods, performance assessment, and surveying in such systems

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 9587353
  • - Research Article

The Interval Parameter Optimization Model Based on Three-Way Decision Space and Its Application on “Green Products Recommendation”

Mingxia Li | Kebing Chen | ... | Baoxiang Liu
  • Special Issue
  • - Volume 2020
  • - Article ID 6452536
  • - Research Article

Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks

Linyan Li | Yu Sun | ... | Jinchang Ren
  • Special Issue
  • - Volume 2020
  • - Article ID 5916205
  • - Research Article

3D Semantic VSLAM of Indoor Environment Based on Mask Scoring RCNN

Chongben Tao | Yufeng Jin | ... | Hanwen Gao
  • Special Issue
  • - Volume 2020
  • - Article ID 2479172
  • - Research Article

Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming

Yan Guo | Jin Zhang | ... | Wei Wang
  • Special Issue
  • - Volume 2020
  • - Article ID 4013185
  • - Research Article

A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis

Chen Zhang | Jie He | ... | Bojian Zhou
  • Special Issue
  • - Volume 2020
  • - Article ID 8215389
  • - Research Article

RGBD Scene Flow Estimation with Global Nonrigid and Local Rigid Assumption

Xiuxiu Li | Yanjuan Liu | ... | Jiangbin Zheng
  • Special Issue
  • - Volume 2020
  • - Article ID 9475782
  • - Research Article

Fuzzy Matching Template Attacks on Multivariate Cryptography: A Case Study

Weijian Li | Xian Huang | ... | Fuxiang Lu
  • Special Issue
  • - Volume 2020
  • - Article ID 3838547
  • - Research Article

Efficient Coded-Block Delivery and Caching in Information-Centric Networking

Yan Liu | Jun Cai | ... | Hua Lu
  • Special Issue
  • - Volume 2020
  • - Article ID 5476896
  • - Research Article

Chinese Tone Recognition Based on 3D Dynamic Muscle Information

JianRong Wang | Li Wan | ... | Jing Hu
  • Special Issue
  • - Volume 2020
  • - Article ID 4705982
  • - Research Article

CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network

Guojie Liu | Jianbiao Zhang
Discrete Dynamics in Nature and Society
 Journal metrics
Acceptance rate26%
Submission to final decision52 days
Acceptance to publication33 days
CiteScore1.800
Impact Factor1.348
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