Artificial intelligence brings innovation to traditional management systems. This innovation is based on personal knowledge and experience. Substantial amounts of data and information attract the attention of managers in various fields, such as the environment, energy, traffic, e-commerce, and import and export trade sectors. The potentially causative and correlative knowledge is supposed to be mined from the historical and real-time data. In data mining, machine learning and artificial intelligence have been powerful tools in feature extraction, relation regressive, trend prediction, and decision support. In management, data mining covers the procedures of description, diagnosis, prediction, decision-making, and control. There is continuous research discussing how to apply intelligent techniques to data mining in management.

Machine learning and artificial intelligence require improvements in terms of their application in a complex management environment. Classical intelligent methods can meet the requirements of an individual task, such as image classification, target tracking, and speech recognition. Nevertheless, data and information in management are of various categories, and a management problem consists of multiple tasks and processes. Therefore, intelligent methods should be integrated with other techniques and domain knowledge dependent on specific applications. In previous research and surveys, integrated intelligence has been proved feasible in complex systems with multiple data sources and tasks. For instance, intelligent methods are developed based on deep learning, neuromorphic computing, and neural symbolic methods. Meanwhile, they are integrated with others for signal processing, system modelling, time series analysis, etc.

The aim of this Special Issue is to bring together original research and review articles highlighting innovative ideas and solutions addressing management issues based on integrated intelligence. Submissions can include management issues in terms of monitoring, prediction, decision-making, and control in various fields. Both innovative emerging intelligent techniques and integrated methods are welcome.

Potential topics include but are not limited to the following:

• Intelligent sensing and monitoring in management systems
• Comprehensive assessment with multivariate information management systems
• Time-series prediction in management systems
• Autonomous decision-making in management systems
• Intelligent decision support system in management systems
• Data-driven modelling in management systems
• Integration of sub-symbolic and symbolic methods in management systems
• Neuro-symbolic integration in management systems
• Correlation analysis in a dynamic and complex system
• Feature extraction of multitype data in management systems
• Optimization of neural networks in management systems
• Integrated machine learning methods in management systems
• Fusion theory and methods in management systems
• Intelligent control in management systems