Computational Intelligence covers a number of nature-inspired computational methodologies, mainly artificial neural networks (ANNs), fuzzy sets, genetic algorithms (GAs), swarm intelligence, and their hybridisation for addressing real-world problems to which conventional modelling cannot be used due to reasons such as complexity, existence of uncertainties, and the stochastic nature of the processes. Computational intelligence is a powerful methodology for a wide range of pattern recognition and data analysis problems such as financial forecasting, as well as industrial, scientific, and social media applications. The recent advances in computational intelligence have shown very promising results in industry, business, and social media studies. These techniques have been particularly successful in the fields of pattern recognition and data analytics.

Given the success of computational intelligence methods and techniques in big data analysis applications, it is expected that they can also be applied successfully in pattern recognition. Recent improvements in artificial intelligence, big data, and machine learning have enhanced the importance of biomedical signal and image processing research. Biomedical image processing is similar in concept to biomedical signal processing in multiple dimensions. It includes the analysis, enhancement, and presentation of images captured via X-Ray, ultrasound, MRI, nuclear medicine, and visual imaging technologies. Machine Learning is now quickly extending in all science and engineering research fields, including biomedical sciences. In this context, computational intelligence paradigms comprising of numerous branches including neural networks, swarm intelligence, expert systems, evolutionary computing, fuzzy systems, and artificial immune systems can play a vital role in handling the different aspects of pattern recognition and data analytics.

The aim of this Special Issue is to gather and present recent work where computational intelligence algorithms are specifically designed for, or applied to, solving complex real-world problems in data analytics and pattern recognition, by means of state-of-the-art methods having general applicability, domain-specific solutions, or hybrid algorithms that integrate computational intelligence with traditional numerical and mathematical methods. Review articles discussing the current state of the art are also welcomed.

Potential topics include but are not limited to the following:

• Pattern recognition/data mining/optimisation methods
• Biomedical engineering
• Soft computing approaches
• Health care informatics
• Artificial intelligence techniques
• Medical imaging and pattern recognition
• Biomedical imaging and image processing
• Pattern mining algorithms on biological problems
• Computational intelligence for data analysis and pattern recognition
• Artificial intelligence and pattern recognition technologies for recommendation in healthcare
• Deep learning and machine learning algorithms for efficient indexing and retrieval in medical imaging