Artificial Intelligence Applications and Innovations

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Intro -- Preface -- Organization -- Table of Contents -- Keynote -- Conjoint Mining of Data and Content with Applications in Business, Bio-medicine, Transport Logistics and Electrical Power Systems -- 1 Introduction -- 2 Aims and Underlying Issues -- 3 Significance of Work -- 4 Approach and Methodology -- 4.1 Unstructured Data Cleansing -- 4.2 Unstructured Data Annotation -- 4.3 Fuzzy Matching and Data and Content Correlation -- 4.4 Techniques for Conjoint Data and Content Mining -- 5 Applications to the Electric Power Industry -- 6 Applications to Business Problems -- 7 Application in Transport Logistics Industry -- 8 Applications to Biomedical Applications -- 9 Conclusions -- References -- Learning-Ensemble Learning -- Breaking Ties of Plurality Voting in Ensembles of Distributed Neural Network Classifiers Using Soft Max Accumulations -- 1 Introduction -- 2 The Ensemble of Distributed Regularization Networks -- 3 Combining Predictions Using Majority Voting -- 4 Experimental Simulations -- 5 Conclusions -- References -- Predicting Firms' Credit Ratings Using Ensembles of Artificial Immune Systems and Machine Learning - An Over-Sampling Approach -- 1 Introduction -- 2 Problem Formulation and Dataset -- 3 Modelling Financial and Sentiment Indicators -- 3.1 Artificial Immune Classification Algorithms -- 3.2 Machine Learning and Neural Networks -- 3.3 Comparison across Classifiers -- 4 Conclusion -- References -- Automating Transition Functions: A Way To Improve Trading Profits with Recurrent Reinforcement Learning -- 1 Introduction -- 2 RRL-Type Trading Systems -- 2.1 Recurrent Reinforcement Learning -- 2.2 Regime-Switching RRL -- 2.3 Regime-Switching RRL 2.0 -- 3 Results of the Experiment -- 3.1 The Context of the Experiment -- 3.2 Out-of-Sample Trading Performance -- 4 Conclusions -- References

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