Deep and Multi-view Learning-Driven Computational Intelligence Models for Smart Cities

Dear Colleagues,

Smart city realization adopts the new generation of Internet, big data, Internet of Things, artificial intelligence, cloud computing and other information technologies to realize the intelligence of urban construction, planning, management and service, forming an innovative and sustainable intelligent city. It integrates a variety of new generation information technologies to complete the automatic perception, collection, integration, analysis and sharing of urban information resources and realizes intelligent medical care, emergency response, environmental protection, education, transportation, etc., thus bringing convenience, high efficiency and intelligence to people's lives and responds to their personal needs. The construction of a smart city involves many aspects, from urban road traffic to urban spatial layout and management, which require high technical support and a complete information-based decision-making mechanism to provide a reliable guarantee for urban development.

In recent years, advanced computational intelligence models such as deep learning, active learning, transfer learning and information fusion have brought opportunities for smart cities. Computational intelligence models have been successfully applied in many areas of smart city construction, such as urban traffic flow prediction, health monitoring and early warning, mobile intelligent question answering system, intelligent environmental resource deployment, etc. Although the existing computational intelligence models based on single-view data have achieved certain results, their practical application performance still cannot meet the needs of smart city construction. Compared with single-view data, multi-view data can provide more abundant and comprehensive information for the computational intelligence models, thereby further improving the performance of the model. Therefore, it is necessary to study the deep multi-view learning-driven computational intelligence model to overcome the defects existing in the construction of smart cities.

In this Special Issue, we hope to build a platform for researchers and engineers to explore this field and contribute their experience and wisdom to the development of computational intelligence models for smart cities. Topics of intended papers include, but are not limited to, the following:

• Advanced computational intelligence models for smart cities, such as deep learning, sparse learning, transfer learning, active learning and multi-task learning;
• Smart city information management platforms involving artificial intelligence;
• Smart city information decision-making systems based on multi-view data;
• Prediction models combined with multi-view features, such as intelligent traffic flow prediction, intelligent medical disease prediction, intelligent weather prediction and signal light warning;
• Visualized human–computer interaction platforms for smart cities;
• Smart city monitoring systems driven by deep multi-view learning;
• Deployment and management for smart cities with unsupervised methods, such as self-training models, clustering algorithms and principal component analysis;
• Data automated management and analysis for smart city;
• Screening and fusion of multi-modal heterogeneous clinical data for smart medical care with supervision methods, such as random forest, decision tree and naive bayes;
• Design of smart city resource allocation system based on big data analysis.

We look forward to receiving your interesting submissions.

Dr. Chenxi Huang
Guest Editor

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• computational intelligence
• transfer learning
• multi-view learning
• human–computer interaction
• smart city