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Complex Systems and Artificial Intelligence
Topic Information
Dear Colleagues,
Due to the rapid development of algorithms, frameworks, hardware, networks, and the increased volume of data, artificial intelligence (AI) algorithms (e.g., deep learning, neural computing, biological computing, etc.) have been widely employed to solve problems in many complex systems. AI-based techniques and emerging new computing paradigms could extract sophisticated features and help us to address the practical problems that occur in complex systems more easily. For example, DNA computing—an emerging branch of biological computing—uses DNA, biochemistry, and molecular biology hardware to overcome the limitations of traditional electronic computing architecture in storing technologies, synthetic controllers and reaction networks, etc.
This Topic aims to highlight and present the latest developments in complex systems and artificial intelligence, addressing the challenge of how to apply advanced artificial intelligence algorithms, frameworks, and technologies to complex systems for a better world. Research fields could include industry, traffic, biology, agriculture, economy, environment, management, etc.
Contributions will focus on challenging issues in the field of complex systems and artificial intelligence technologies, frameworks, architectures, algorithms, and applications. Both theoretical and experimental contributions containing novel applications with new insights and findings in the field of complex systems and artificial intelligence are welcome. Review articles detailing the current state of the art are also encouraged. Topics of interest include, but are not limited to, the following:
- Complex evolutionary and adaptive systems;
- Self-organizing collective systems;
- AI-driven problem solving for complex systems;
- Machine learning for complex systems;
- Deep learning for complex systems;
- Neural computing for complex systems;
- Multi-agent for complex systems;
- Knowledge graph for complex systems;
- Data-driven AI for complex systems;
- Feature extraction and optimization;
- Multimodal feature fusion for complex systems;
- Biological computing for complex systems;
- AI in networks;
- AI and optimization problems;
- AI-based sensing, decision, and control for complex systems…
Prof. Dr. Qiang Zhang
Prof. Dr. Yifeng Zeng
Topic Editors
Keywords
- artificial intelligence
- complex systems
- computational intelligence
- data-driven artificial intelligence
- cognitive computing
- evolutionary computation
- bio-inspired algorithms
- reinforcement learning
- human–machine shared control
- multi-modal human-robot interaction
- Metaverse
- knowledge acquisition
- information fusion
- computational biology
- molecular biology
- genomics
- DNA Computing
- bioinformatics
- health informatics
- feature extraction
- neuroscience and cognitive science
- multi-objective game theory
- data-driven AI
- machine learning
- complex evolutionary
- brain–computer interface
- brain–machine interface
- human-machine interaction
- human–computer interaction
- system modelling
- deep learning
- signal processing
- computer vision
- image processing
- speech processing
- vedio processing
- audio processing
- neural computing
- random systems
- hybrid AI models
- collaborative AI systems
- large-scale system
Participating Journals
Journal Name | Impact Factor | CiteScore | Launched Year | First Decision (median) | APC |
---|---|---|---|---|---|
Applied Sciences
|
2.5 | 5.3 | 2011 | 17.8 Days | CHF 2400 |
Entropy
|
2.1 | 4.9 | 1999 | 22.4 Days | CHF 2600 |
Sensors
|
3.4 | 7.3 | 2001 | 16.8 Days | CHF 2600 |
Genes
|
2.8 | 5.2 | 2010 | 16.3 Days | CHF 2600 |
Journal of Personalized Medicine
|
3.0 | 4.1 | 2011 | 16.7 Days | CHF 2600 |
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Published Papers (49 papers)
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: KTAT:A Complex Embedding Model of Knowledge Graph Integrating Type Information and Attention Mechanism
Authors: Ying Liu; Peng Wang
Affiliation: Ying Liu: School of Computer Science and Technology; Changchun University of Science and Technology; Changchun; China Peng Wang: School of Computer Science and Technology; Changchun University of Science and Technology; Changchun; China
Abstract: Knowledge graph embedding learning aims to represent the entities and relationships of real-world knowledge as low-dimensional dense vectors, existing knowledge representation learning methods mostly only aggregates the internal information of triples and graph structure information. Recent research has proved that multi-source information of entities is conducive to more accurate knowledge embedding tasks. In this paper, we proposed a model based attention mechanism and integrating type information of entities, KTAT, which embedding different representations for entities under different types information by maps the entities to type-specific hyperplanes and integrates text description information to supplement embedding to improve model performance. The experimental results show that KTAT outperformed to the previous advanced methods, and the performance of link prediction is effectively improved by combining type information.