The Resource Proceedings of the fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015), Vadlamani Ravi, Bijaya Ketan Panigrahi, Swagatam Das, Ponnuthurai Nagaratnam Suganthan, editors

Proceedings of the fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015), Vadlamani Ravi, Bijaya Ketan Panigrahi, Swagatam Das, Ponnuthurai Nagaratnam Suganthan, editors

Label
Proceedings of the fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015)
Title
Proceedings of the fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015)
Statement of responsibility
Vadlamani Ravi, Bijaya Ketan Panigrahi, Swagatam Das, Ponnuthurai Nagaratnam Suganthan, editors
Title variation
FANCCO 2015
Creator
Contributor
Editor
Subject
Genre
Language
eng
Summary
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
Member of
Cataloging source
N$T
Dewey number
  • 006.3
  • 004
Index
no index present
LC call number
  • QA76.76.E95
  • QA75.5-76.95
Literary form
non fiction
http://bibfra.me/vocab/lite/meetingDate
2015
http://bibfra.me/vocab/lite/meetingName
International Conference on Fuzzy and Neuro Computing
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Ravi, Vadlamani
  • Panigrahi, Bijaya Ketan
  • Das, Swagatam
  • Suganthan, Ponnuthurai Nagaratnam
Series statement
Advances in Intelligent Systems and Computing
Series volume
volume 415
http://library.link/vocab/subjectName
  • Fuzzy expert systems
  • Neural computers
  • COMPUTERS
  • COMPUTERS
  • COMPUTERS
  • COMPUTERS
  • COMPUTERS
  • COMPUTERS
  • COMPUTERS
  • Fuzzy expert systems
  • Neural computers
  • Computer Science
  • Mechanical Engineering - General
  • Mechanical Engineering
  • Engineering & Applied Sciences
  • Artificial intelligence
  • Mathematical modelling
Label
Proceedings of the fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015), Vadlamani Ravi, Bijaya Ketan Panigrahi, Swagatam Das, Ponnuthurai Nagaratnam Suganthan, editors
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-27212-2
Instantiates
Publication
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Preface; Organization; Contents; 1 Tweet Sentiment Classification Using an Ensemble of Machine Learning Supervised Classifiers Employing Statistical Feature Selection Methods; Abstract; 1 Introduction; 2 Related Work; 2.1 Machine Learning (ML) Approach; 2.2 Lexicon Based Approach; 2.3 Hybrid Approach; 2.4 Ensemble Methods; 3 Proposed Work; 3.1 Tweet Pre-processing; 3.2 Feature Selection; 3.3 Point Wise Mutual Information (PMI); 3.4 Chi-square Method; 3.5 Multi-class Sentiment Classification; 3.5.1 Naive Bayes Classifier; 3.5.2 Maximum Entropy Classifier; 3.5.3 Support Vector Machine
  • 3.5.4 Ensemble Learners3.5.5 Bagging; 3.5.6 Boosting; 4 Experiments; 4.1 Baseline and Ensemble Methods; 4.2 Dataset; 5 Results and Analysis; 6 Conclusion; References; 2 Multiple Fuzzy Correlated Pattern Tree Mining with Minimum Item All-Confidence Thresholds; Abstract; 1 Introduction; 1.1 Fuzzy FP-Tree Algorithm; 1.2 Multiple Fuzzy FP-Tree Algorithm; 2 The Multiple Fuzzy Correlated Pattern Tree Construction Algorithm; 2.1 An Example; 3 Proposed Multiple Fuzzy Correlated Pattern Growth Mining Algorithm; 3.1 An Example; 4 Experimental Results; 5 Conclusions and Future Work; Acknowledgments
  • 3 Autonomous Visual Tracking with Extended Kalman Filter Estimator for Micro Aerial VehiclesAbstract; 1 Introduction; 2 Extended Kalman Filter Estimator; 3 Autonomous Visual Tracking System; 4 Simulation and Experimental Results; 5 Conclusions; References; 4 Identification of Uncertain Mass and Stiffness Matrices of Multi-Storey Shear Buildings Using Fuzzy Neural Network Modelling; Abstract; 1 Introduction; 2 Fuzzy Set Theory and Preliminaries; 3 Learning Algorithm for Single Layer Fuzzy Neural Network; 4 System Identification of Structural Parameter in Fuzzified Form
  • 5 Results and Discussion6 Concluding Remarks; References; 5 Fuzzy Approach to Rank Global Climate Models; Abstract; 1 Introduction; 2 Performance Indicators; 3 F-TOPSIS; 4 Results and Discussion; 5 Summary and Conclusions; Acknowledgments; Appendix; Sample Calculation for Computation Of Di+, Di2212, Ci; References; 6 Comparative Analysis of ANFIS and SVR Model Performance for Rainfall Prediction; Abstract; 1 Introduction; 2 Related Works; 3 Methodology; 3.1 Adaptive Neural Fuzzy Inference System (ANFIS); 3.2 Support Vector Regression; 4 Area of Study and Data Set
  • 5 Evaluation Criteria of the Performance of the Models6 Results and Discussion; 7 Conclusions; References; Cross Domain Sentiment Analysis Using Different Machine Learning Techniques; 1 Introduction; 2 Literature Review; 3 Methodology; 3.1 Performance Analysis; 4 Proposed Framework; 4.1 Preprocessing; 4.2 Model Generation; 4.3 Matrix Generation; 5 Results and Analysis; 6 Conclusion; References; Combining ELM with Random Projections for Low and High Dimensional Data Classification and Clustering; 1 Introduction; 2 Extreme Learning Machine; 3 Random Projection
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319272122
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
SpringerLink
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)930600990
  • (OCoLC)ocn930600990
Label
Proceedings of the fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015), Vadlamani Ravi, Bijaya Ketan Panigrahi, Swagatam Das, Ponnuthurai Nagaratnam Suganthan, editors
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-27212-2
Publication
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Preface; Organization; Contents; 1 Tweet Sentiment Classification Using an Ensemble of Machine Learning Supervised Classifiers Employing Statistical Feature Selection Methods; Abstract; 1 Introduction; 2 Related Work; 2.1 Machine Learning (ML) Approach; 2.2 Lexicon Based Approach; 2.3 Hybrid Approach; 2.4 Ensemble Methods; 3 Proposed Work; 3.1 Tweet Pre-processing; 3.2 Feature Selection; 3.3 Point Wise Mutual Information (PMI); 3.4 Chi-square Method; 3.5 Multi-class Sentiment Classification; 3.5.1 Naive Bayes Classifier; 3.5.2 Maximum Entropy Classifier; 3.5.3 Support Vector Machine
  • 3.5.4 Ensemble Learners3.5.5 Bagging; 3.5.6 Boosting; 4 Experiments; 4.1 Baseline and Ensemble Methods; 4.2 Dataset; 5 Results and Analysis; 6 Conclusion; References; 2 Multiple Fuzzy Correlated Pattern Tree Mining with Minimum Item All-Confidence Thresholds; Abstract; 1 Introduction; 1.1 Fuzzy FP-Tree Algorithm; 1.2 Multiple Fuzzy FP-Tree Algorithm; 2 The Multiple Fuzzy Correlated Pattern Tree Construction Algorithm; 2.1 An Example; 3 Proposed Multiple Fuzzy Correlated Pattern Growth Mining Algorithm; 3.1 An Example; 4 Experimental Results; 5 Conclusions and Future Work; Acknowledgments
  • 3 Autonomous Visual Tracking with Extended Kalman Filter Estimator for Micro Aerial VehiclesAbstract; 1 Introduction; 2 Extended Kalman Filter Estimator; 3 Autonomous Visual Tracking System; 4 Simulation and Experimental Results; 5 Conclusions; References; 4 Identification of Uncertain Mass and Stiffness Matrices of Multi-Storey Shear Buildings Using Fuzzy Neural Network Modelling; Abstract; 1 Introduction; 2 Fuzzy Set Theory and Preliminaries; 3 Learning Algorithm for Single Layer Fuzzy Neural Network; 4 System Identification of Structural Parameter in Fuzzified Form
  • 5 Results and Discussion6 Concluding Remarks; References; 5 Fuzzy Approach to Rank Global Climate Models; Abstract; 1 Introduction; 2 Performance Indicators; 3 F-TOPSIS; 4 Results and Discussion; 5 Summary and Conclusions; Acknowledgments; Appendix; Sample Calculation for Computation Of Di+, Di2212, Ci; References; 6 Comparative Analysis of ANFIS and SVR Model Performance for Rainfall Prediction; Abstract; 1 Introduction; 2 Related Works; 3 Methodology; 3.1 Adaptive Neural Fuzzy Inference System (ANFIS); 3.2 Support Vector Regression; 4 Area of Study and Data Set
  • 5 Evaluation Criteria of the Performance of the Models6 Results and Discussion; 7 Conclusions; References; Cross Domain Sentiment Analysis Using Different Machine Learning Techniques; 1 Introduction; 2 Literature Review; 3 Methodology; 3.1 Performance Analysis; 4 Proposed Framework; 4.1 Preprocessing; 4.2 Model Generation; 4.3 Matrix Generation; 5 Results and Analysis; 6 Conclusion; References; Combining ELM with Random Projections for Low and High Dimensional Data Classification and Clustering; 1 Introduction; 2 Extreme Learning Machine; 3 Random Projection
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319272122
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
SpringerLink
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)930600990
  • (OCoLC)ocn930600990

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