Materias:

Materias:

Materias:

Materias:

Tabla de Contenidos: “…Classical Fuzzy Cluster Analysis -- Visualization of the Clustering Results -- Clustering for Fuzzy Model Identification — Regression -- Fuzzy Clustering for System Identification -- Fuzzy Model based Classifiers -- Segmentation of Multivariate Time-series…”

Tabla de Contenidos: “…An Intelligent Weighted Fuzzy Cluster based Secure Routing Algorithm for Mobile Ad-hoc Networks 3. …”

Tabla de Contenidos: “…Biostatistics -- Discriminant Analysis Based on Continuous and Discrete Variables -- Longitudinal Data Analysis with Linear Regression -- A Kernel Method Used for the Analysis of Replicated Micro-array Experiments -- Kernel Estimates of Hazard Functions for Biomedical Data Sets -- Partially Linear Models -- Analysis of Contingency Tables -- Identifying Coexpressed Genes -- Bootstrap Methods for Testing Interactions in GAMs -- Survival Trees -- A Semiparametric Approach to Estimate Reference Curves for Biophysical Properties of the Skin -- Survival Analysis -- Related Sciences -- Ozone Pollution Forecasting Using Conditional Mean and Conditional Quantiles with Functional Covariates -- Nonparametric Functional Methods: New Tools for Chemometric Analysis -- Variable Selection in Principal Component Analysis -- Spatial Statistics -- Functional Data Analysis -- Analysis of Failure Time with Microearthquakes Applications -- Polychotomous Regression: Application to Landcover Prediction -- The Application of Fuzzy Clustering to Satellite Images Data…”

Tabla de Contenidos: “…7.2 Principles of Cluster Detection7.3 Some General Clustering Concepts; 7.4 Crisp Clustering Techniques; 7.5 Fuzzy Clustering Concepts; 7.6 Fuzzy c-Means Clustering; 7.7 Fuzzy Adaptive Clustering; 7.8 Generating Rule Prototypes; 7.9 Review; Chapter 8. …”

Tabla de Contenidos: “…Machine Learning for Enhanced Waste Quantity Reduction: Insights from the MONSOON Industry 4.0 Project -- Deduction of time-dependent machine tool characteristics by fuzzy-clustering -- Unsupervised Anomaly Detection in Production Lines -- A Random Forest Based Classifer for Error Prediction of Highly Individualized Products -- Web-based Machine Learning Platform for Condition-Monitoring -- Selection and Application of Machine Learning-Algorithms in Production Quality -- Which deep artifificial neural network architecture to use for anomaly detection in Mobile Robots kinematic data -- GPU GEMM-Kernel Autotuning for scalable machine learners -- Process Control in a Press Hardening Production Line with Numerous Process Variables and Quality Criteria -- A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance -- Detection of Directed Connectivities in Dynamic Systems for Different Excitation Signals using Spectral Granger Causality -- Enabling Self-Diagnosis of AutomationDevices through Industrial Analytics -- Making Industrial Analytics work for Factory Automation Applications -- Application of Reinforcement Learning in Production Planning and Control of Cyber Physical Production Systems -- LoRaWan for Smarter Management of Water Network: From metering to data analysis…”

Tabla de Contenidos: “…-- Conceiving ShapeShifting TV: A Computational Language for Truly-Interactive TV -- User Interfaces Based on 3D Avatars for Interactive Television -- Social TV Evaluation -- Perceptions of Value: The Uses of Social Television -- Sociable TV: Exploring User-Led Interaction Design for Older Adults -- Psychological Backgrounds for Inducing Cooperation in Peer-to-Peer Television -- Trends in the Living Room and Beyond -- Personalisation -- SenSee Framework for Personalized Access to TV Content -- AIMED- A Personalized TV Recommendation System -- Fuzzy Clustering Based Ad Recommendation for TV Programs -- Towards Content-Aware Coding: User Study -- Mobile TV -- Personal TV: A Qualitative Study of Mobile TV Users -- “I Just Want to See the News” – Interactivity in Mobile Environments -- Mobile TV in Everyday Life Contexts – Individual Entertainment or Shared Experiences? …”

Tabla de Contenidos: “…General Remarks on Subjective Methods; Chapter 5. FUZZY CLUSTERING METHOD; 5.1. Fuzzy Clustering Techniques; 5.2. …”

Tabla de Contenidos: “…-- Daniel Courgeau -- Case Studies of Combining Sequence Analysis and Modelling: Mervi Eerola -- Part II Sequence Analysis and Event History Analysis: Glass Ceilings, Glass Escalators and Revolving Doors: Lydia Malin and Ramsey Wise -- Modelling Mortality Using Life Trajectories of Disabled andNon-Disabled Individuals in 19th-Century Sweden: Erling Ha ̈ggstro ̈m Lundevaller, Lotta Vikstro ̈m, and Helena Haage -- Sequence History Analysis (SHA): Estimating the Effect of Past Trajectories on an Upcoming Event: Florence Rossignon, Matthias Studer, Jacques-Antoine Gauthier and Jean-Marie Le Goff -- Part III The Sequence Network Approach: Network Analysis of Sequence Structures: Benjamin Cornwell -- Relational Sequence Networks as a Tool for Studying Gendered Mobility Patterns: Klaus Hamberger -- Part IV Unfolding the Process: Multiphase Sequence Analysis: Thomas Collas -- Unpacking Configurational Dynamics: Sequence Analysis and Qualitative Comparative Analysis as a Mixed-Method Design: Camilla Borgna and Emanuela Struffolino -- Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data: Satu Helske, Jouni Helske, and Mervi Eerola -- Part V Advances in Sequence Clustering: Markovian-based Clustering of Internet Addiction Trajectories: Zhivko Taushanov and Andre ́ Berchtold -- Divisive Property-Based and Fuzzy Clustering for Sequence Analysis: Matthias Studer -- From 07.00 to 22.00: A Dual-Earner Couple’s Typical Day in Italy: Ivano Bison and Alessandro Scalcon -- Part VI Appraising Sequence Quality: Measuring Sequence Quality: Anna Manzoni and Irma Mooi-Reci -- An Index of Precarity for Measuring Early Employment Insecurity: Gilbert Ritschard, Margherita Bussi, and Jacqueline O’Reilly -- Subject Index…”

Tabla de Contenidos: “…Shukla Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences Reprinted from: Axioms 2022, 11, 615, doi:10.3390/axioms11110615 . 1 -- Songsong Dai, Lei Du, Haifeng Song and Yingying Xu On the Composition of Overlap and Grouping Functions Reprinted from: Axioms 2021, 10, 272, doi:10.3390/axioms10040272 . 5 -- Eunsuk Yang Basic Core Fuzzy Logics and Algebraic Routley-Meyer-Style Semantics Reprinted from: Axioms 2021, 10, 273, doi:10.3390/axioms10040273 . 15 -- Yadan Jiang and Dong Qiu The Relationships among Three Kinds of Divisions of Type-1 Fuzzy Numbers Reprinted from: Axioms 2022, 11, 77, doi:10.3390/axioms11020077 31 -- Nguyen Van Thanh and Nguyen Thi Kim Lan A New Hybrid Triple Bottom Line Metrics and Fuzzy MCDM Model: Sustainable Supplier Selection in the Food-Processing Industry Reprinted from: Axioms 2022, 11, 57, doi:10.3390/axioms11020057 41 -- Chia-Nan Wang, Nhat-Luong Nhieu, Kim-Phong Tran and Yen-Hui Wang Sustainable Integrated Fuzzy Optimization for Multimodal Petroleum Supply Chain Design with Pipeline System: The Case Study of Vietnam Reprinted from: Axioms 2022, 11, 60, doi:10.3390/axioms11020060 55 -- Juan Carlos Martin, Natalia Soledad Bustamante-S ́anchez and Alessandro Indelicato Analyzing the Main Determinants for Being an Immigrant in Cuenca (Ecuador) Based on a Fuzzy Clustering Approach Reprinted from: Axioms 2022, 11, 74, doi:10.3390/axioms11020074 81 -- Duˇsan J. …”

Tabla de Contenidos: “…3.3 Agglomerative Hierarchical Clustering3.3.1 Overview; 3.3.2 Single Linkage; 3.3.3 Complete Linkage; 3.3.4 Average Linkage; 3.3.5 Other Methods; 3.3.6 Selecting Groups; 3.4 Partitioned-Based Clustering; 3.4.1 Overview; 3.4.2 k-Means; 3.4.3 Worked Example; 3.4.4 Miscellaneous Partitioned-Based Clustering; 3.5 Fuzzy Clustering; 3.5.1 Overview; 3.5.2 Fuzzy k-Means; 3.5.3 Worked Examples; 3.6 Summary; 3.7 Further Reading; 4 PREDICTIVE ANALYTICS; 4.1 Overview; 4.1.1 Predictive Modeling; 4.1.2 Testing Model Accuracy; 4.1.3 Evaluating Regression Models' Predictive Accuracy…”

Tabla de Contenidos: “…Trejos, and A. Xavier: Fuzzy Clustering by Hyperbolic Smoothing -- R. Meng, H. …”

Tabla de Contenidos: “…8.3 Fuzzy-level Subgroup 226 -- 8.4 Fuzzy Normal Subgroup 228 -- 8.5 Fuzzy Subgroups Using T-norms 229 -- 8.6 Product of Fuzzy Subgroups 231 -- 8.7 Summary 234 -- References 235 -- 9 Application of Fuzzy/Intuitionistic Fuzzy Set in Image Processing 237 -- 9.1 Introduction 237 -- 9.2 Digital Images 237 -- 9.3 Image Enhancement 238 -- 9.3.1 Fuzzy Enhancement Method 238 -- 9.3.2 Intuitionistic Fuzzy Enhancement Method 239 -- 9.4 Thresholding 240 -- 9.4.1 Intuitionistic Fuzzy Thresholding Method 242 -- 9.4.2 Fuzzy Thresholding Method 244 -- 9.5 Edge Detection 244 -- 9.5.1 Fuzzy Edge-detection Method 245 -- 9.5.2 Intuitionistic Fuzzy Edge Detection 246 -- 9.6 Clustering 248 -- 9.6.1 Fuzzy c Means Clustering (FCM) 248 -- 9.6.2 Intuitionistic Fuzzy Clustering 249 -- 9.6.3 Kernel Clustering 250 -- 9.7 Mathematical Morphology 252 -- 9.7.1 Fuzzy Approach 254 -- 9.7.2 Intuitionistic Fuzzy Approach 254 -- 9.8 Summary 256 -- References 256 -- 10 Type-2 Fuzzy Set 259 -- 10.1 Introduction 259 -- 10.2 Type-2 Fuzzy Set 260 -- 10.3 Operations on Type-2 Fuzzy Set 263 -- 10.4 Inclusion Measure and Similarity Measure 267 -- 10.4.1 Similarity Measure 268 -- 10.5 Interval Type-2 Fuzzy Set 270 -- 10.6 Application of Interval Type-2 Fuzzy Set in Image Segmentation 271 -- 10.7 Summary 273 -- References 273 -- Beyond Your Doubts 275 -- Index 281.…”

Tabla de Contenidos: “…11.2.2.3 Transport Model-Based Clustering or Distribution Model-Based Clustering -- 11.2.2.4 Progressive Clustering or Hierarchical Clustering -- 11.2.2.5 Fluffy Clustering or Fuzzy Clustering -- 11.3 What are the Utilization of Clustering? …”

Tabla de Contenidos: “…Wu -- A study on operational standardization of urban delivery vehicles / Y.C. Lv -- Fuzzy clustering based on intuitionistic fuzzy similarity relations / J. …”

“…To understand the subject in a practical manner, Part B of this book consists of noteworthy case studies and research papers on management and services and represents theory application of Data mining, Fuzzy Cluster, Game theory, GRID Technology, simulation of Operational Amplifier and Current Controlled Conveyor II in network security, architecture, and integrated circuit application…”