Pattern Recognition is one of the most significant abilities in human beings and intelligent machines and part of the broader area of Artificial Intelligence (AI). Pattern recognition deals with the ability of intelligence of machines for environmental perception while other branches of AI deals with cognition (including reasoning, knowledge engineering, and language understanding). Both pattern Recognition and AI are used to design intelligent systems like the perceptron process exploiting knowledge to solve ambiguities in recognition. Therefore, a growing interest has been observed in the application of mathematical methods and tools for pattern recognition problems. These mathematical models and tools for pattern recognition are also heavily used to solve engineering problems nowadays. The scope of pattern recognition includes the statistical and structural pattern classification, Artificial Neural Networks, Kernel Methods, ensemble methods, unsupervised learning, feature extraction and selection, data preprocessing methods, applications in information retrieval, social media analysis, medical imaging, video surveillance, intelligent transportation.

The mathematical models for pattern recognition and its applications have seen tremendous growth in recent years. Moreover, advancements in neural networks and deep learning have brought improvements in many fields like image processing, decision-making, speech processing and text classification, and so on. This special issue aims at bringing together researchers, engineers, and interested pioneers from both academia and industry to report on, review, and exchange the latest progress, particularly new ideas, methods, and innovative applications of pattern recognition. We welcome papers focused on theoretical studies, practical applications, solutions to engineering problems, and experimental prototypes.

Potential topics include but are not limited to the following:

• Mathematical methods for pattern classification
• Deep machine learning for pattern recognition
• Mathematical methods for feature extraction and selection
• Pattern recognition algorithms/mathematical formulation for decision-making, predictions, and recommender systems for big data
• Intelligent pattern recognition algorithms for Image and video detection
• Applications in text categorization, document analysis, social media analysis, data mining, intelligent transportation, and robotics for engineering problems
• Heuristics algorithms of pattern recognition to solve complex NP hard or soft optimization problems, for example, multiagent systems, coalition formation, and coalition structure generation in multiagent systems