Soft computing in information communication technology: [2012 International Conference on Soft Computing in Information communication Technology is to be held in Hong Kong, April 17 - 18 1 Volume 1
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| Format: | Tagungsbericht Buch |
| Sprache: | English |
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2012
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| Schriftenreihe: | Advances in intelligent and soft computing
158 |
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| Beschreibung: | XII, 528 S. Ill., graph. Darst. |
| ISBN: | 9783642291470 |
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| 020 | |a 9783642291470 |c Druckausg. |9 978-3-642-29147-0 | ||
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| 040 | |a DE-604 |b ger |e rakwb | ||
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| 049 | |a DE-473 | ||
| 245 | 1 | 0 | |a Soft computing in information communication technology |b [2012 International Conference on Soft Computing in Information communication Technology is to be held in Hong Kong, April 17 - 18 |n 1 |p Volume 1 |c Jia Luo (ed.) |
| 264 | 1 | |a Berlin ; Heidelberg |b Springer |c 2012 | |
| 300 | |a XII, 528 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Advances in intelligent and soft computing |v 158 | |
| 490 | 0 | |a Advances in intelligent and soft computing |v ... | |
| 655 | 7 | |0 (DE-588)1071861417 |a Konferenzschrift |2 gnd-content | |
| 700 | 1 | |a Luo, Jia |0 (DE-588)138108455 |4 edt | |
| 711 | 2 | |a Soft Computing in Information Communication Technology |d 2012 |c Hong Kong |j Sonstige |0 (DE-588)1029843597 |4 oth | |
| 773 | 0 | 8 | |w (DE-604)BV040662866 |g 1 |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-642-29148-7 |
| 830 | 0 | |a Advances in intelligent and soft computing |v 158 |w (DE-604)BV022825905 |9 158 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025489597&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-025489597 | |
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| adam_text |
Contents
1 Professor
Zdzisław Pawlak
(1926-2006):
Founder of the Polish
School of Artificial Intelligence
.
I
Andrzej
Skowron, Mihir Kr. Chakraborty,
Jerzy
Grz.xmata-Busse,
Vietar
Marek, Sankar K.
Pal, James F.
Perers,
Grzegorz.
Rozenherg, Dominik Slęzak. Roman Słowiński, Shusaku Tsumoto,
Alicja Wakulicz-Deja.
Guoxin
Wang,
Wojciech Ziarko
1.1
Introduction
. 2
1.2
Biography
[51] . 2
1.3
From the Clock to the Pseudo-random Number Generator
. 5
1 4
Engineer and Mathematician
. 6
1.5
Computation Models. Rough Sets and Artificial Intelligence
. 8
1.6
Professor
Pawiaki
Influence on the Development of Computer
Science Community
. 15
1.7
Zdzisław Pawlak
and Artificial Intelligence
. 31
1.8
People and Nature
. 35
1
.9
Conclusions
. 51
References
. 53
2
List of Works by Professor
Zdzisław Pawlak
(1926-2006). 57
Andrzej Skowron
Publications of Professor
Zdzisław Pawlak.
57
Manuscripts and Articles in Newspapers
. 73
3
Rough Sets: From Rudiments to Challenges
. 75
Hung Son Nguyen,
Andrzej
Skowron
3.1
Introduction
. 76
3.2
Vague Concepts
. 78
3.3
Rudiments of Rough Sets
. 79
3.3.1
Indiscernibility and Approximation
. 79
3.3.2
Decision Systems and Decision Rules
. 83
XLIV Contents
3.3.3
Dependency of Attributes
. 85
3.3.4
Reduction of Attributes
. 86
3.3.5
Discernibility and Boolean Reasoning
. 87
3.3.6
Rough Membership
. 88
3.4
Generalizations of Approximation Spaces
. 90
3.5
Rough Sets and Induction
. 93
3.5.1
Rough Sets and Classifiers
. 94
3.5.2
Inducing Relevant Approximation Spaces
. 98
3.5.3
Rough Sets and Higher Order Vagueness
.101
3.6
Information Granulation
.102
3.7
Ontological Framework for Approximation
.103
3.8
Discernibility and Boolean Reasoning: Rough Set Methods
for Machine Learning, Pattern Recognition, and Data Mining
. 104
3.8.1
Reducís
in Information and Decision Systems
.106
3.8.2
Attribute Selection
.110
3.8.3
Value Set Reduction
.112
3.8.3.1
Discretization
.112
3.8.3.2
Symbolic Attribute Value Grouping
.117
3.8.4
Minimal Decision Rules
.121
3.8.5
Example: Learning of Concepts
.124
3.8.6
Association Rules
.126
3.9
Rough Sets, Approximate Boolean Reasoning and Scalability
. 131
3.9.1
Reduct Calculation
.131
3.9.2
Discretization of Large Data Sets Stored in Relational
Databases
.136
3.10
Rough Sets and Logic
.139
3.11
Interactive Rough Granular Computing (IRGC)
.142
3.11.1
Context Inducing and IRGC
.145
3.11.2
Process Mining and IRGC
.149
3.11.3
Perception-Based Computing and IRGC
.151
3.12
Conclusions
.154
References
.155
4
Zdzisław Pawlak,
Databases and Rough Sets
. 175
Victor
W. Marek
4.1
Introduction
.175
4.2
Information Storage and Retrieval Systems, Databases
.179
4.3
Rough Sets
.181
4.4
Conclusions
.183
References
.184
5
jMAF
-
Dominance-Based Rough Set Data Analysis Framework
_185
Jerzy Btaszczyń.ski,
Salvatore
Greco, Benedetto Matarazzo,
Roman
Słowiński, Marcin
Szelçg
5.1
Introduction
.185
5.2
Reminder on the Dominance-Based Rough Set Approach
.186
Contents
XLV
5.2.1
Decision Table
.
|87
5.2.2
Dominance Cones as Granules of Knowledge
.188
5.2.3
Approximation of Ordered Decision Classes
.188
5.2.4
Quality of Approximation
.] 89
5.2.5
Reduction of Attributes
.190
5.2.6
Decision Rules
.190
5.2.7
Variable Consistency Dominance-Based Rough Set
Approaches
.193
5.3
Example of Application of jMAF
.193
5.3.1
RunningjMAF
.194
5.3.2
Decision Table
.194
5.3.3
Data File
.195
5.3.4
Opening Data File
.196
5.3.5
Calculation of Dominance Cones
.197
5.3.6
Calculation of Approximations
.198
5.3.7
Calculation of
Reducís
.199
5.3.8
Induction of Decision Rules
.199
5.3.9
Classification
.202
5.4
Roadmap of Future Development of jMAF
.204
5.5
Exemplary Applications of Dominance-Based Rough Set
Approach
.204
5.6
Glossary
.205
References
.206
6
Dynamic Programming Approach for Exact Decision Rule
Optimization
.211
Talha
Amin,
Igor Chikalov, Mikhail Moshkov,
Beata
Zielosko
6.1
Introduction
.211
6.2
Irredundant Decision Rules
.212
6.3
Directed Acyclic Graph
Δ(Γ)
.215
6.4
Procedure of Optimization Relative to Length
.217
6.5
Procedure of Optimization Relative to Coverage
.220
6.6
Sequential Optimization
.221
6.7
Experimental Results
.224
6.8
Conclusions
.226
References
.226
1
Approaches for Updating Approximations in Set-Valued
Information Systems While Objects and Attributes Vary
with Time
.229
Hongmei Chen, Tianrui Li, Hongmei
Tian
7.1
Introduction
.229
7.2
Rough Set Theory in a Set-Valued Information System
.231
7.3
Principle for Dynamic Maintenance of Approximations While
Objects and Attributes Are Added Simultaneously
.232
XLVI Contents
7.4
Accumulation
Principle for Dynamic Maintenance of
Approximations While Objects and Attributes Are Added
.238
7.5
Example
.240
7.6
Performance Analysis
.244
7.7
Conclusions
.246
References
.247
8
On the Gradual Evolvement of Things
.249
Ivo
Düntsch, Günther Gediga
8.1
Introduction
.249
8.2
The Setup
.250
8.3
Translations
.252
8.3.1
Evolving Things and Properties
.255
8.4
Quadtrees
.256
8.5
Conclusion and Outlook
.258
References
.259
9
An Empirical Comparison of Rule Sets Induced by LERS
and Probabilistic Rough Classification
.261
Jerzy W. Grzymała-Busse,
Shantan
R.
Marepally, Yiyu Yao
9.1
Introduction
.261
9.2
Rough Sets and Three-Way Rules
.262
9.2.1
Indiscernibility Relation
.262
9.2.2
Pawlak
Approximations
.263
9.2.3
Probabilistic Approximations
.264
9.2.4
Three-Way Rules
.265
9.3
Positive and Possible Rule Induction with LERS
.267
9.4
Rules in Probabilistic Rough Sets
.268
9.5
Experiments
.272
9.6
Conclusion
.275
References
.275
10
Exploring Neighborhood Structures with Neighborhood Rough
Sets in Classification Learning
.277
Qinghua
Ни.
Le
ij
u n
Li,
Pengfei Zhu
10.1
Introduction
.277
10.2
Pawlak's Rough Sets and Related Works
.279
10.3
Neighborhood Rough Sets
.280
10.4
Information Entropy for Neighborhood Models
.288
10.5
Boundary Sample Selection with Neighborhood Model
.291
10.6
Feature Selection with Neighborhood Model
.294
10.7
Rule Extraction with Neighborhood Model
.298
10.8
Conclusions and Future Work
.305
References
. .305
Contents XLVII
11
Rough Representations of Ill-Known Sets and Their Manipulations
in Low Dimensional Space
.309
Masahiro Inuiguchi
11.1
Introduction
.309
11.2
Ill-Known Sets
.311
11.3
Possibility and Necessity Measures under Ill-Known Sets
.320
11.4
Concluding Remarks
.329
References
.330
12
Property-Driven Rough Sets Approximations of Relations
.333
Ryszard Janicki
12.1
Introduction
.333
12.2
Relations and Some of Their Basic Classifications
.335
12.3
Classical Rough Relations
.336
12.4
Property-Driven Rough Approximations of Relations
.337
12.5
Properties of a-Approximations
.342
12.6
Composite Properties
.343
12.7
Mixed Approximations
.346
12.8
Approximations by Partial Orders
.347
12.9
Approximations by Equivalence Relations
.35
1
12.10
Final Comment
.355
References
.356
13
Towards a Comprehensive Similarity Analysis of Voting Procedures
Using Rough Sets and Similarity Measures
.359
Janusz Kacprzyk,
Hannu Nurmi,
Sławomir Zadrożny
13.1
Introduction
.
%()
13.2
A Brief Introduction to the Theory of Rough Sets
.362
13.3
A Comparison of Voting Procedures
.363
13.4
Equivalent Voting Procedures and Indispensable Criteria
.367
13.5
Similarity and Distances between Voting Procedures
.372
13.6
Concluding Remarks
.
^77
References
.-^8
14
Algebras for Information Systems
381
Md. Aquil Khan, Mohua Banerjee
14.1
Introduction
.^2
14.1.1
Towards an Algebra for Information Systems
.
3S4
14.2
Algebra for Deterministic Information Systems
.386
14.3
Representation Theorem for Abstract DIS-Algebras
.391
14.4
Logics for Deterministic Information Systems
.393
14.5
Algebra for Incomplete Information Systems
.394
14.6
Algebra for Non-deterministic Information Systems
.395
XLVIII
Contents
14.7
Representation Theorem for Abstract
NIS-
Algebras and
Equational Logic for NISs
.400
14.7.1
Extension to Other Types of Relations Defined
on NISs
.403
14.8
Conclusions
.405
References
.405
15 DNA
Rough-Set Computing in the Development of Decision Rule
Reducts
.409
Ikno Kim, Junzo Watada,
Witold Pedrycz
15.1
Introduction
.409
15.2
Deoxyribonucleic Acid
.411
15.2.1
Nitrogen-Containing Bases
.411
15.2.2
Phosphodiester Bonds
.412
15.2.3
Hydrogen Bonds
.412
15.3 DNA
Molecular Techniques
.413
15.3.1
Restriction Enzyme Technique
.414
15.3.2
Ligation Technique
.416
15.3.3
Polymerase Chain Reaction Technique
.416
15.3.4
Affinity Separation Technique
.417
15.3.5
Gel Electrophoresis Technique
.418
15.4
Rough Sets and a Model Decision Table
.418
15.4.1
Concept of Rough Set Theory
.418
15.4.2
Decision Table as a Model
.420
15.5 DNA
Rough-Set Computing
.421
15.5.1
Digraph in
DNA .421
15.5.2
Encoding Process in
DNA.424
15.6
Experimental Studies
.429
15.6.1
Experiments
.429
15.6.2
Experimental Results
.433
15.7
Conclusions
.435
References
.436
16
Three-Valued Logic for Reasoning about Covering-Based Rough
Sets
.439
Beata
Konikowska
16.1
Introduction
.439
16.2
Covering-Based Rough Sets
.442
16.3
Subordination Relation and Closure Properties of
Approximations and Regions
.443
16.4
Syntax and Semantics of the Language LCRS
.445
16.4.1
Satisfaction and Consequence Relations for Formulas
and Sequents
.447
16.5
Proof System for the Logic LRS
.448
16.6
Sequent Calculus CRS
.448
Contents XLIX
16.7
Soundness of CRS
.450
16.8
Completeness of a Sublanguage
.451
16.9
Conclusions
.459
References
.460
17
Music Information Retrieval in Music Repositories
.463
Bożena Kostek
17.1
Introduction
.463
17.2
Review of Selected Solutions in Terms of Music
Recommendation
.465
17.2.1
Music Retrieval within a Content-Based Analysis
.466
17.2.2
Systems Using a Community Interaction
.467
17.3
Examples of Music Recommendation Systems
.468
17.3.1
Pandora
.468
17.3.2
Last.fm
.469
17.3.3
Examples of Other Systems
.470
17.4
Music Genre Recognition Experiment
.472
17.4.1
Music Database
.472
17.4.2
Music Genre Classification
.476
17.5
Proposal for Objectivization of Annotation Process
.478
17.5.1
Description of Experiment Setup
.478
17.5.2
Platform for Running the Experiment
.479
17.5.3
Annotation-Related Experiment
.481
17.5.3.1
Result Analysis
.485
17.6
Conclusions
.486
References
.487
18
Rough Support Vectors: Classification, Regression, Clustering
491
Pawan Lingras, Parag Bhalchandra, Cory Blitz, S. Asharaf
18.1
Introduction
.491
18.2
Rough Support Vector Machines for Multi-classification
.492
18.2.1
Extending Binary SVM's for Multi-classification
.494
18.2.1.1
Rough Set-Based 1-v-l Approach
.495
18.2.1.2
Rough Set-Based 1-v-r Approach
.495
18.2.2
Experiments with 1-v-l and 1-v-r Approach
.497
18.2.2.1
Experimental Results Using 1-v-l
.498
18.2.2.2
Experimental Results Using 1-v-l
.498
18.2.2.3
Semantics of Rough Set-Based
Multi-classification
.499
18.3
Dual Rough Support Vector Regression
.500
18.3.1
Rough Patterns
.500
18.3.2
Conservative and Aggressive Modeling of Rough
Patterns
.503
18.3.3
Empirical Analysis of Dual RSVR
.505
L
Contents
18.4
Rough Support Vector Clustering
.507
18.4.0.1
Cluster Assignment
.511
18.4.0.2
Role of
υ
and
б
.511
18.4.1
Experimental Results with RSVC
.512
18.4.1.1
Synthetic Data Set
.512
18.4.1.2
Wine Recognition Data Set
.512
18.5
Summary and Conclusions
.513
References
.514
19
Logic-Based Roughiflcation
.517
Linh Anh Nguyen,
Andrzej Szałas
19.1
Introduction
.517
19.2
Preliminaries
.519
19.3
Similarity-Based Roughification
.520
19.3.1
Definitions
.520
19.3.2
Properties
.521
19.3.3
Selected Applications
.522
19.4
Relational Roughification
.523
19.4.1
Definitions
.523
19.4.2
Properties
.524
19.4.3
Granulating Relational Databases
.526
19.5
Terminological Roughification
.526
19.5.1
Description Logics and Information Systems
.527
19.5.2
Bisimulation and Indiscernibility
.531
19.5.3
Concept Learning
.533
19.5.4
Bisimulation-Based Approximation of Concepts
.540
19.6
Conclusions
.541
References
.542
20
How Near Are
Zdzisław Pawlak
's
Paintings?
.545
James
F.
Peters
20.1
Introduction
.546
20.2
Preliminaries
.548
20.3
Perceptual Indiscernibility Relation in Segmenting Paintings
.550
20.4
Neighbourhoods in Paintings by
Z. Pawlak
.552
20.5
ε
-Approach Nearness
.557
20.6
Regions-of-Interest in Z. Pawlak's Paintings
.561
20.7
Concluding Remarks
.565
References
.566
21
An Implementation of the
Zdzisław Pawlak
Idea for Reasoning
about Uncertainty: Approximate Reasoning by Parts
.569
Lech Polkowski, Maria Semeniuk-Polkowska
21.1
Abstract
.569
21.2
Introduction: The Language of Parts
.570
21.3
A Mereological Model for Rough Sets
.570
Contents
ц
21.4
Approximations
.573
21.5
An Extension
to
Parts
to a Degree: Rough Mereology
.574
21.5.1
Rough Inclusions in Information and Decision
Systems
.577
21.6
Approximations to a Degree
.579
21.7
A Characterization of pL
.580
21.8
Acknowledgement
.582
21.9
Conclusions
.582
References
.582
22
Granular Concept Mapping and Applications
.585
Sumalee Sonamthiang, Kanlaya Naruedomkul, Nick
Cercone
22.1
Introduction
.585
22.2
Related Study
.588
22.3
Granular Concept Hierarchy
.590
22.3.1
Formal Definitions of a Granular Concept Hierarchy
. 590
22.3.2
Syntax and Semantics of a Granular Concept
.591
22.4
Granular Concept Hierarchy Construction
.594
22.4.1
An Algorithm for Recursive Granulations
.594
22.4.2
An Algorithm for Level-Wise Attribute Selection
.595
22.4.3
Mapping for Appropriate Granularity in a GCH
.597
22.5
Evaluation
.600
22.6
Conclusion
.601
References
.601
23
Rough Sets and Medical Differential Diagnosis
.605
Shusaku Tsumoto
23.1
Introduction
.605
23.2
Medical Diagnostic Process
.606
23.2.1
Differential Diagnosis of Headache
.606
23.2.2
Classification of Headache
.606
23.2.3
Examples: Migraine and Tension-Type Headache
.607
23.2.4
Focusing Mechanism
.608
23.3
Definition of Rules
.609
23.3.1
Rough Sets
.609
23.3.2
Classification Accuracy and Coverage
.610
23.3.2.1
Definition of Accuracy and Coverage
.610
23.3.3
Probabilistic Rules
.611
23.3.4
Positive Rules
.611
23.3.5
Negative Rules
.612
23.4
Algorithms for Rule Induction
.614
23.5
Extension into Variable Precision Rough Set Model
.615
23.6
Discussion: What Has Not Been Achieved?
.616
23.7
Conclusion
.617
References
.620
24
Science and Semantics:
Λ
Note on
Rough Sets
and
Чациепеѕѕ
623
Mdlí
tu
Whiski
.624
.
ѓ,26
.
62S
.631
.636
.642
.642
.645
24.1
Introduction.
24.2
Obese:
Λ
Case Studs
.
24.3
Roujih Set
Theorş
.
24.4
Supersaluationist Semantics
.
24.5
Vagueness in Science
.
24.6
Conclusions
.
Reterenres
Index
. . . |
| any_adam_object | 1 |
| author2 | Luo, Jia |
| author2_role | edt |
| author2_variant | j l jl |
| author_GND | (DE-588)138108455 |
| author_facet | Luo, Jia |
| building | Verbundindex |
| bvnumber | BV040662869 |
| ctrlnum | (OCoLC)828788956 (DE-599)BVBBV040662869 |
| format | Conference Proceeding Book |
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| genre | (DE-588)1071861417 Konferenzschrift gnd-content |
| genre_facet | Konferenzschrift |
| id | DE-604.BV040662869 |
| illustrated | Illustrated |
| indexdate | 2024-08-21T00:26:23Z |
| institution | BVB |
| institution_GND | (DE-588)1029843597 |
| isbn | 9783642291470 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025489597 |
| oclc_num | 828788956 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG |
| owner_facet | DE-473 DE-BY-UBG |
| physical | XII, 528 S. Ill., graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Springer |
| record_format | marc |
| series | Advances in intelligent and soft computing |
| series2 | Advances in intelligent and soft computing |
| spelling | Soft computing in information communication technology [2012 International Conference on Soft Computing in Information communication Technology is to be held in Hong Kong, April 17 - 18 1 Volume 1 Jia Luo (ed.) Berlin ; Heidelberg Springer 2012 XII, 528 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advances in intelligent and soft computing 158 Advances in intelligent and soft computing ... (DE-588)1071861417 Konferenzschrift gnd-content Luo, Jia (DE-588)138108455 edt Soft Computing in Information Communication Technology 2012 Hong Kong Sonstige (DE-588)1029843597 oth (DE-604)BV040662866 1 Erscheint auch als Online-Ausgabe 978-3-642-29148-7 Advances in intelligent and soft computing 158 (DE-604)BV022825905 158 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025489597&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Soft computing in information communication technology [2012 International Conference on Soft Computing in Information communication Technology is to be held in Hong Kong, April 17 - 18 Advances in intelligent and soft computing |
| subject_GND | (DE-588)1071861417 |
| title | Soft computing in information communication technology [2012 International Conference on Soft Computing in Information communication Technology is to be held in Hong Kong, April 17 - 18 |
| title_auth | Soft computing in information communication technology [2012 International Conference on Soft Computing in Information communication Technology is to be held in Hong Kong, April 17 - 18 |
| title_exact_search | Soft computing in information communication technology [2012 International Conference on Soft Computing in Information communication Technology is to be held in Hong Kong, April 17 - 18 |
| title_full | Soft computing in information communication technology [2012 International Conference on Soft Computing in Information communication Technology is to be held in Hong Kong, April 17 - 18 1 Volume 1 Jia Luo (ed.) |
| title_fullStr | Soft computing in information communication technology [2012 International Conference on Soft Computing in Information communication Technology is to be held in Hong Kong, April 17 - 18 1 Volume 1 Jia Luo (ed.) |
| title_full_unstemmed | Soft computing in information communication technology [2012 International Conference on Soft Computing in Information communication Technology is to be held in Hong Kong, April 17 - 18 1 Volume 1 Jia Luo (ed.) |
| title_short | Soft computing in information communication technology |
| title_sort | soft computing in information communication technology 2012 international conference on soft computing in information communication technology is to be held in hong kong april 17 18 volume 1 |
| title_sub | [2012 International Conference on Soft Computing in Information communication Technology is to be held in Hong Kong, April 17 - 18 |
| topic_facet | Konferenzschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025489597&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV040662866 (DE-604)BV022825905 |
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