Ravi / Panigrahi / Das Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015)

1. Auflage 2015
ISBN: 978-3-319-27212-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 415, 365 Seiten, eBook

Reihe: Advances in Intelligent Systems and Computing

This proceedings bring together contributions from researchers from academia and industry to report the latest cutting edge research made in the areas of Fuzzy Computing, Neuro Computing and hybrid Neuro-Fuzzy Computing in the paradigm of Soft Computing. The FANCCO 2015 conference explored new application areas, design novel hybrid algorithms for solving different real world application problems. After a rigorous review of the 68 submissions from all over the world, the referees panel  selected 27 papers to be presented at the Conference. The accepted papers have a good, balanced mix of theory and applications. The techniques ranged from fuzzy neural networks, decision trees, spiking neural networks, self organizing feature map, support vector regression, adaptive neuro fuzzy inference system, extreme learning machine, fuzzy multi criteria decision making, machine learning, web usage mining, Takagi-Sugeno Inference system, extended Kalman filter, Goedel type logic, fuzzy formal concept analysis, biclustering etc. The applications ranged from social network analysis, twitter sentiment analysis, cross domain sentiment analysis, information security, education sector, e-learning, information management, climate studies, rainfall prediction, brain studies, bioinformatics, structural engineering, sewage water quality, movement of aerial vehicles,  etc.

Zielgruppe

Research

Autoren/Hrsg.

Ravi, V.

Ravi, Vadlamani

Panigrahi, Bijaya Ketan

Das, Swagatam

Suganthan, Ponnuthurai Nagaratnam

Inhaltsverzeichnis

Tweet Sentiment
Classification Using an Ensemble of Machine Learning Supervised Classifiers
Employing Statistical Feature Selection Methods.- Multiple Fuzzy Correlated
Pattern Tree Mining using Minimum Item All-Confidence Thresholds.- Autonomous Visual Tracking
with Extended Kalman Filter Estimator for Micro Aerial Vehicles.- Identification of Uncertain
Mass and Stiffness Matrices of Multi-Storey Shear Buildings using Fuzzy Neural
Network Modelling.- FuzzyApproach to Rank Global
Climate Models.-  Comparative analysis of
ANFIS and SVR Model performance for Rainfall Prediction.- Cross Domain Sentiment
Analysis using different Machine Learning Techniques.- Combining ELM with Random
Projections for Low and High Dimensional Data Classification and Clustering.- Framework for knowledge driven
optimisation based data encoding for brain data modelling using spiking neural
network architecture.-  ShortCircuit
Evaluations in Gödel Type Logic.-  Coefficient of Variation
based Decision Tree for Fuzzy Classification.-  Predicting protein
coding regions by six-base nucleotide distribution.-  Ontology for Education
System and Ontology based Clustering.-  Neural Networks for Fast
Estimation of Social Network Centrality Measures.-  Scale-free memory to
swiftly generate fuzzy future predictions.-  Assessment of
Vaccination Strategies using Fuzzy Multi-Criteria Decision Making.- Ranking and Dimensionality
Reduction using Biclustering.- Effect of Fractional Order in
Pole Motion.-  Sewage Water Quality
Index of Sewage Treatment Plant using Fuzzy MCDM Approach.-  Fuzzy Formal Concept
Analysis approach for Information Retrieval.- Privacy Preserving
Collaborative Clustering using SOM for Horizontal Data Distribution.-  A Hybrid Approach to
Classification of Categorical Data based on Information-Theoretic Context
Selection.- Robust Stabilization of
Uncertain T-S fuzzy systems: An Integral Error Dependant Sliding Surface Design
Approach.- Extreme learning machine for
eukaryotic and prokaryotic promoter prediction.-  A hybrid approach to the
maximum clique problem in the domain of information management.- An Integrated Network Behavior
and Policy based data exfiltration detection framework.- Web Usages Mining in automatic
detection of Learning Style in Personalized e-Learning System.