雜志介紹

Evolving Systems雜志介紹

《Evolving Systems》是一本以English為主的未開放獲取國際優秀期刊，中文名稱不斷發展的系統，本刊主要出版、報道計算機科學-COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE領域的研究動態以及在該領域取得的各方面的經驗和科研成果，介紹該領域有關本專業的最新進展，探討行業發展的思路和方法，以促進學術信息交流，提高行業發展。該刊已被國際權威數據庫SCIE收錄，為該領域相關學科的發展起到了良好的推動作用，也得到了本專業人員的廣泛認可。該刊最新影響因子為2.7，最新CiteScore 指數為7.8。

本刊近期中國學者發表的論文主要有：

• Very deep fully convolutional encoder-decoder network based on wavelet transform for art image fusion in cloud computing environment

  Author: Chen, Tong; Yang, Juan

• A human activity recognition method using wearable sensors based on convtransformer model

  Author: Zhang, Zhanpeng; Wang, Wenting; An, Aimin; Qin, Yuwei; Yang, Fazhi

• PDRF-Net: a progressive dense residual fusion network for COVID-19 lung CT image segmentation

  Author: Lu, Xiaoyan; Xu, Yang; Yuan, Wenhao

• Temperature and humidity prediction of mountain highway tunnel entrance road surface based on improved Bi-LSTM neural network

  Author: Tao, Rui; Peng, Rui; Wang, Hao; Wang, Jie; Qiao, Jiangang

英文介紹

Evolving Systems雜志英文介紹

Evolving Systems covers surveys, methodological, and application-oriented papers in the area of dynamically evolving systems. ‘Evolving systems’ are inspired by the idea of system model evolution in a dynamically changing and evolving environment. In contrast to the standard approach in machine learning, mathematical modelling and related disciplines where the model structure is assumed and fixed a priori and the problem is focused on parametric optimisation, evolving systems allow the model structure to gradually change/evolve. The aim of such continuous or life-long learning and domain adaptation is self-organization. It can adapt to new data patterns, is more suitable for streaming data, transfer learning and can recognise and learn from unknown and unpredictable data patterns. Such properties are critically important for autonomous, robotic systems that continue to learn and adapt after they are being designed (at run time).

Evolving Systems solicits publications that address the problems of all aspects of system modelling, clustering, classification, prediction and control in non-stationary, unpredictable environments and describe new methods and approaches for their design.

The journal is devoted to the topic of self-developing, self-organised, and evolving systems in its entirety — from systematic methods to case studies and real industrial applications. It covers all aspects of the methodology such as

Evolving Systems methodology

Evolving Neural Networks and Neuro-fuzzy Systems

Evolving Classifiers and Clustering

Evolving Controllers and Predictive models

Evolving Explainable AI systems

Evolving Systems applications

but also looking at new paradigms and applications, including medicine, robotics, business, industrial automation, control systems, transportation, communications, environmental monitoring, biomedical systems, security, and electronic services, finance and economics. The common features for all submitted methods and systems are the evolving nature of the systems and the environments.

The journal is encompassing contributions related to:

1) Methods of machine learning, AI, computational intelligence and mathematical modelling

2) Inspiration from Nature and Biology, including Neuroscience, Bioinformatics and Molecular biology, Quantum physics

3) Applications in engineering, business, social sciences.