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International Joint Conference SOCO'16-CISIS'16-ICEUTE'16
San Sebastián, Spain, October 19th-21st, 2016 Proceedings
SOCO (Conference) 2016 : San Sebastián, Spain), jointly held conference.
International Conference on Complex, Intelligent, and Software Intensive Systems 2016 : San Sebastián, Spain),
ICEUTE (Conference) 2016 : San Sebastián, Spain),
1 online resource (xxiv, 805 pages) : illustrations
This volume of Advances in Intelligent and Soft Computing contains accepted papers presented at SOCO 2016, CISIS 2016 and ICEUTE 2016, all conferences held in the beautiful and historic city of San Sebastián (Spain), in October 2016. Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena. After a through peer-review process, the 11th SOCO 2016 International Program Committee selected 45 papers. In this relevant edition a special emphasis was put on the organization of special sessions. Two special session was organized related to relevant topics as: Optimization, Modeling and Control Systems by Soft Computing and Soft Computing Methods in Manufacturing and Management Systems. The aim of the 9th CISIS 2016 conference is to offer a meeting opportunity for academic and industry-related researchers belonging to the various, vast communities of Computational Intelligence, Information Security, and Data Mining. The need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, is intended to be the catalyst and the aggregation stimulus for the overall event. After a through peer-review process, the CISIS 2016 International Program Committee selected 20 papers. In the case of 7th ICEUTE 2016, the International Program Committee selected 14 papers.
Preface; SOCO 2016; Contents; SOCO 2016: Classification; Predicting 30-Day Emergency Readmission Risk; Abstract; 1 Introduction; 2 Related Work; 3 Materials and Methods; 3.1 Support Vector Machine; 3.2 Random Forest; 4 Results; 4.1 Class Balancing; 4.2 Feature Selection; 5 Conclusions and Future Work; References; Use of Support Vector Machines and Neural Networks to Assess Boar Sperm Viability; 1 Introduction; 2 Description of the Features; 3 Experimental Results; 3.1 Linear and Quadratic Support Vector Machine; 3.2 Neural Network; 3.3 Discussion; 4 Conclusions; References
Learning Fuzzy Models with a SAX-based Partitioning for Simulated Seizure Recognition1 Introduction; 2 Related Techniques; 2.1 AntMiner+; 2.2 Learning FRBC with ACS; 2.3 Simbolic Aggregation AproXimation; 3 Introducing SAX in the Model Learning; 4 Experiment and Results; 4.1 Materials and Methods; 4.2 Results and Discussion; 5 Conclusion; References; Real Prediction of Elder People Abnormal Situations at Home; 1 Introduction; 2 Dataset Description, Extraction and Reduce Strategy; 3 Modeling the Elders Behaviour; 4 New Raw Data Vectors to Improve Behaviour Modeling
5 The Hierarchical Supervised Classifier Learning6 Use Cases and Conclusion; References; SOCO 2016: Machine Learning; Assisting the Diagnosis of Neurodegenerative Disorders Using Principal Component Analysis and TensorFlow; 1 Introduction; 2 Materials and Methods; 2.1 Data Description; 2.2 Feature Extraction Based on Principal Component Analysis; 2.3 Classification Based on TensorFlow; 3 Experiments and Results; 4 Discussion and Conclusions; References; Cyclone Performance Prediction Using Linear Regression Techniques; Abstract; 1 Introduction; 1.1 Operating Principle
1.2 Collection Efficiency2 Materials; 3 Methodology; 3.1 Linear Regression; 3.2 Enhanced Linear Regression; 3.3 Generalized Linear Regression; 3.4 Enhanced Generalized Linear Regression; 3.5 Method of Validation; 3.6 Model Efficiency; 4 Results; 5 Conclusions; References; Time Analysis of Air Pollution in a Spanish Region Through k-means; Abstract; 1 Introduction; 2 Clustering Techniques and Methods; 2.1 Cluster Evaluation Measures; 2.2 k-means Clustering Technique; 3 Real-Life Case Study; 4 Results and Discussion; 5 Conclusions and Future Work; References
Using Non-invasive Wearables for Detecting Emotions with Intelligent Agents1 Introduction; 2 State of the Art; 3 Problem Description; 4 System Proposal; 4.1 Data Acquisition Process; 4.2 Emotion Recognition; 4.3 Wristband Prototype; 5 Conclusions and Future Work; References; Impulse Noise Detection in OFDM Communication System Using Machine Learning Ensemble Algorithms; Abstract; 1 Introduction; 2 System Model; 3 Multi-classifiers (Ensembles) Algorithms; 3.1 Bagging; 3.2 Boosting (Bos); 3.3 Random Forest (RF); 3.4 Stacking (Stack); 4 Simulations; 4.1 Simulation Set-up; 4.2 Results Discussion
Manuel Graña, José Manuel López-Guede, Oier Etxaniz, Álvaro Herrero, Héctor Quintián, Emilio Corchado, editors.
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Advances in intelligent systems and computing ; 527