Aligning Learning Materials and Assessment with Course Learning Outcomes in MOOCs using Data Mining Techniques -- Edge-Centric Queries Stream Management based on an Ensemble Model -- Bitcoin Price Prediction Combining Data and Text Mining -- Towards New Evaluation Metrics for Relational Learning -- Color Models for Skin Lesions Classification from Dermatoscopic Images -- Methods of Statistical Analysis and Machine Learning for the Evaluation of Generated Hardware and Firmware Designs -- Genetic Algorithms for Creating Large Job Shop Dispatching Rules.

This book presents a number of research efforts in combining AI methods or techniques to solve complex problems in various areas. The combination of different intelligent methods is an active research area in artificial intelligence (AI), since it is believed that complex problems can be more easily solved with integrated or hybrid methods, such as combinations of different soft computing methods (fuzzy logic, neural networks, and evolutionary algorithms) among themselves or with hard AI technologies like logic and rules; machine learning with soft computing and classical AI methods; and agent-based approaches with logic and non-symbolic approaches. Some of the combinations are already extensively used, including neuro-symbolic methods, neuro-fuzzy methods, and methods combining rule-based and case-based reasoning. However, other combinations are still being investigated, such as those related to the semantic web, deep learning and swarm intelligence algorithms. Most are connected with specific applications, while the rest are based on principles.