{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":32,"n_cols":5,"rows_kept":32,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[19,2,19,3],[20,2,20,3],[22,2,22,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910254984503321"]
[null,null,"Titolo",null,"Artificial Intelligence Applications and Innovations : 12th IFIP WG 12.5 International Conference and Workshops, AIAI 2016, Thessaloniki, Greece, September 16-18, 2016, Proceedings / / edited by Lazaros Iliadis, Ilias Maglogiannis"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016"]
[null,null,"ISBN",null,"3-319-44944-3"]
[null,null,"Edizione",null,"[1st ed. 2016.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XXV, 711 p. 227 illus.)"]
[null,null,"Collana",null,"IFIP Advances in Information and Communication Technology, , 1868-422X ; ; 475"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Pattern recognition systems"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Automated Pattern Recognition"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Computer Communication Networks"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Invited Talks -- Discriminative Dimensionality Reduction for Data Inspection and Classifier Visualization -- Multimodality in Data Clustering: Application to Video Summarization -- Machine Learning of Motor Skills for Robots: From Simple Skills to Table Tennis and Manipulation -- Machine Learning Based Bioinformatics as a Tool for Big-Bata Analytics on Molecular Biology Datasets -- Contents -- Medical Artificial Intelligence Modeling (MAIM) -- A Cumulative Training Approach to Schistosomiasis Vector Density Prediction -- 1 Introduction -- 2 Experiment Data -- 3 Methods -- 3.1 Feature Assessment -- 3.2 Information Gain -- 4 Cumulative Training Approach (CTA) -- 5 Conclusion -- References -- A Mobile and Evolving Tool to Predict Colorectal Cancer Survivability -- "]
[{"$nulls":4},"1 Introduction -- 2 Related Work -- 3 CRCPredictor: An Application for Survivability Prediction -- 3.1 Requirements for the Survivability Prediction Tool -- 3.2 Colon and Rectal Cancer Survivability Prediction Models -- 3.3 Architecture -- 3.4 Use Case -- 4 Analysis and Discussion -- 5 Conclusions and Future Work -- References -- An Implementation of a Decision-Making Algorithm Based on a Novel Health Status Transition Model of Epilepsy -- 1 Introduction -- 2 Transition Model of Epilepsy -- 3 The Implementation of the Model's Decision-Making Algorithm -- 3.1 The Applied Implementation Method -- 3.2 Ontology Engineering for Epilepsy -- 3.3 Implementation of the Decision-Making Algorithm -- 3.4 Input Data to the Decision-Making Algorithm -- 4 Evaluation of the Proposed Model -- 4.1 Kate's Epileptic Medical History and Profile -- 4.2 Kate's Telemonitoring Through the Proposed Algorithm -- 4.3 Discussion -- 5 Conclusions -- References."]
[{"$nulls":4},"Integrative Bioinformatic Analysis of a Greek Epidemiological Cohort Provides Insight into the Pathogenesis of Primary Cutaneous Melanoma -- Abstract -- 1 Introduction -- 2 Materials and Methods -- 2.1 Analysis of Next Generation Exome Sequencing Data -- 2.2 Analysis of Transcriptomic Data -- 3 Results and Discussion -- 3.1 Mutational Data Derived from Exome Sequencing -- 3.2 Transcriptomic Data -- 3.3 Data Integration -- 4 Conclusions and Future Work -- Acknowledgements -- References -- Machine Learning Preprocessing Method for Suicide Prediction -- Abstract -- 1 Introduction -- 2 Suicide - Suicidal Ideation -- 3 What is Depression? -- 4 Data Collection -- 5 Description of Machine Learning Methods -- 5.1 Data Pre-processing Methods -- 5.1.1 Feature Selection -- 5.2 Short Description of Suggested Data Pre-processing Method -- 6 Experimental Results -- 7 Conclusions -- References -- Classification - Pattern Recognition (CLASPR) -- Using Frequent Fixed or Variable-Length POS Ngrams or Skip-Grams for Blog Authorship Attribution -- 1 Introduction -- 2 Related Work -- 3 The Proposed Approach -- 4 Experimental Evaluation -- 4.1 Influence of Parameters n, x, k, and maxgap on Overall Results -- 4.2 Influence of Parameters n, x and k on Authorship Attribution for Each Author -- 5 Conclusions -- References -- Increasing Diversity in Random Forests Using Naive Bayes -- 1 Introduction -- 2 Background Material -- 3 The Proposed Method -- 3.1 Numerical Experiments -- 4 Conclusions and Future Work -- References -- Identifying Asperity Patterns Via Machine Learning Algorithms -- Abstract -- 1 Introduction -- 2 Materials and Methods -- 2.1 Seismic Data -- 2.2 Data Representation -- 2.3 Feature Vector Extraction -- 3 Machine Learning Algorithms -- 4 Experimental Process -- 5 Evaluation -- 6 Conclusion -- Acknowledgments -- References."]
[{"$nulls":4},"Combining Prototype Selection with Local Boosting -- 1 Introduction -- 2 Background Material -- 2.1 Local Weighted Learning and Prototype Selection -- 2.2 Boosting Classifiers -- 3 The Proposed Algorithm -- 4 Numerical Experiments -- 4.1 Prototype Selection -- 4.2 Using Decision Stump as Base Classifier -- 4.3 Using Two-Level Decision Tree as a Base Classifier -- 4.4 Time Analysis -- 5 Synopsis and Future Work -- References -- Convolutional Neural Networks for Pose Recognition in Binary Omni-directional Images -- Abstract -- 1 Introduction -- 2 Methodology -- 2.1 Overview of the Method -- 2.2 Calibration of the Fish-Eye Camera -- 2.3 Synthetically Generated Silhouettes -- 2.4 Convolutional Neural Networks -- 3 Results -- 4 Conclusions and Further Work -- References -- Ontology-Web and Social Media AI Modeling (OWESOM) -- The eLOD Ontology: Modeling Economic Open Data -- Abstract -- 1 Introduction - Related Works -- 2 eLOD Ontology: Modelling Economic Data Under Semantics -- 2.1 Description of Sources and Vocabularies Used -- 2.2 The ELOD "]
[{"$nulls":4},"Ontological Schema -- 2.3 Approach and Reuse -- 3 Asking the Data: A Case Study -- 4 Discussion - Future Work -- Acknowledgements -- Appendix -- References -- Web Image Indexing Using WICE and a Learning-Free Language Model -- 1 Introduction -- 2 Related Work -- 2.1 WICE Methods -- 2.2 Web Image Indexing from Concise Text Fragments -- 3 The Proposed Method -- 3.1 The WICE Algorithm -- 3.2 An English Language Model for Image Retrieval -- 4 Experimental Evaluation -- 5 Conclusion and Further Work -- References -- An Intelligent Internet Search Assistant Based on the Random Neural Network -- Abstract -- 1 Introduction -- 2 Related Work -- 3 The Intelligent Internet Search Assistant Model -- 3.1 Search Model -- 3.2 Result Cost Function -- 3.3 User Iteration -- 3.4 Dimension Learning -- 3.5 Gradient Descent Learning."]
[{"$nulls":4},"3.6 Reinforcement Learning -- 4 Validation -- 4.1 ISA Learning -- 5 Conclusions -- References -- Deep Neural Networks for Web Page Information Extraction -- 1 Introduction -- 2 Related Work -- 3 Architecture Overview -- 4 Neural Network -- 4.1 Spatial Text Encoding -- 4.2 Network Architecture -- 4.3 Training -- 5 Spatial Probability Distribution -- 6 Experiments -- 6.1 Data Set -- 6.2 Baseline Models -- 6.3 Results -- 7 Conclusions -- References -- Environmental AI Modeling (ENAIM) -- Modeling Beach Rotation Using a Novel Legendre Polynomial Feedforward Neural Network Trained by Nonlinear Constrained Optimization -- Abstract -- 1 Introduction -- 2 Experimental Setup and Raw Data Extraction -- 3 The Proposed Legendre Polynomial Feedforward Network -- 4 Simulation Study -- 5 Summary and Conclusions -- Acknowledgments -- References -- Environmental Impact on Predicting Olive Fruit Fly Population Using Trap Measurements -- Abstract -- 1 Introduction -- 2 Data Collection -- 2.1 Environmental Data -- 2.2 Feature Selection -- 2.3 Feature Vector Extraction -- 3 Machine Learning Algorithms -- 4 Experimental Process -- 5 Evaluation -- 6 Conclusion -- Acknowledgments -- References -- A Hybrid Soft Computing Approach Producing Robust Forest Fire Risk Indices -- Abstract -- 1 Introduction -- 1.1 Literature Review -- 1.2 Innovations of the Proposed Methodology -- 1.3 Data -- 1.4 Areas of Study -- 2 Theoretical Framework and Methodology -- 2.1 Fuzzy Inference Systems -- 2.2 T-Norms -- 2.3 Chi-Square Test -- 3 Description of the Proposed Methodology -- 3.1 The Algorithm -- 4 Results and Discussion -- 5 Conclusions and Future Work -- References -- Applying Artificial Neural Networks to Short-Term PM2.5 Forecasting Modeling -- Abstract -- 1 Introduction -- 2 The Artificial Neural Network Approach for Short-Term PM2.5 Forecasting."]
[{"$nulls":4},"3 The PM2.5 Forecasting Model Development Protocol -- 4 Experimental Results -- 5 Conclusions -- Acknowledgements -- References -- AIRuleBased Modeling (AIRUMO) -- Modeling Mental Workload Via Rule-Based Expert System: A Comparison with NASA-TLX and Workload Profile -- 1 Introduction -- 2 Related Work -- 2.1 Mental Workload Assessment Techniques -- 2.2 Mental Workload and Rule-Based Expert System -- 3 Design and Methodology -- 3.1 Knowledge Base (KB) -- 3.2 Inference Engine -- 4 Data Collection, Elicitation of Models and Evaluation -- 4.1 Validity -- 4.2 Sensitivity -- 4.3 Summary of Findings -- 5 Conclusion and Future Work -- References -- Convolutive Audio Source Separation Using Robust ICA and Reduced Likelihood Ratio Jump -- 1 Introduction -- 2 Instantaneous Complex Source Separation -- 2.1 The FastICA Algorithm -- 2.2 The RobustICA Algorithm -- 3 Frequency-Domain Source Separation -- 3.1 Likelihood Ratio Jump -- 3.2 Reduced Likelihood Ratio Jump -- 4 Experiments -- 4.1 Evaluation Process -- 4.2 Performance Comparison -- 5 Conclusion -- References -- Association Rules Mining by Improving the "]
[{"$nulls":4},"Imperialism Competitive Algorithm (ARMICA) -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Imperialism Competitive Algorithm (ICA) -- 3.1 Creating the Initial Empires -- 3.2 Total Empire Power -- 4 Proposed Method -- 4.1 Example -- 5 Evaluation -- 6 Discussion -- 7 Conclusion and Future Work -- Acknowledgements -- References -- Use of Flight Simulators in Analyzing Pilot Behavior -- Abstract -- 1 Introduction -- 2 Mathematical Equation of Human Behavior -- 3 Description of Experimental Workplace and Measurement Procedure -- 3.1 Flight Simulator at the University of Defence -- 3.2 Experimental Flight Task -- 4 Measurement and Data Analysis -- 5 Conclusion -- Acknowledgments -- References -- Machine Learning-Learning (MALL)."]
[{"$nulls":4},"Active Learning Algorithms for Multi-label Data."]
[null,null,null,"Sommario/riassunto","This book constitutes the refereed proceedings of the 12th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2016, and three parallel workshops, held in Thessaloniki, Greece, in September 2016. The workshops are the Third Workshop on New Methods and Tools for Big Data, MT4BD 2016, the 5th Mining Humanistic Data Workshop, MHDW 2016, and the First Workshop on 5G - Putting Intelligence to the Network Edge, 5G-PINE 2016. The 30 revised full papers and 8 short papers presented at the main conference were carefully reviewed and selected from 65 submissions. The 17 revised full papers and 7 short papers presented at the 3 parallel workshops were selected from 33 submissions. The papers cover a broad range of topics such as artificial neural networks, classification, clustering, control systems - robotics, data mining, engineering application of AI, environmental applications of AI, feature reduction, filtering, financial-economics modeling, fuzzy logic, genetic algorithms, hybrid systems, image and video processing, medical AI applications, multi-agent systems, ontology, optimization, pattern recognition, support vector machines, text mining, and Web-social media data AI modeling. ."]