Optimization Theory Based on Neutrosophic and Plithogenic Sets

Cover......Page 1
Optimization Theory
Based on Neutrosophic
and Plithogenic Sets
......Page 3
Copyright......Page 4
Contributors......Page 5
Introduction......Page 8
Multi-criteria decision-making (MCDM)......Page 9
The best-worst method (BWM)......Page 10
Plithogenic set......Page 12
Neutrosophic set......Page 13
Proposed model......Page 14
Case 1: Warehouse location problem......Page 15
Case 2: Plant evaluation problem......Page 18
Discussion......Page 23
Conclusion and future research directions......Page 24
References......Page 25
Microservices......Page 27
Statefulness......Page 28
Containers......Page 29
Rules engine......Page 30
Neutrosophic theory......Page 31
Problem definition......Page 32
Case study problem definition: e-Commerce......Page 36
DSL......Page 37
Neutrosophic DSL......Page 39
Neutrosophic rules engine......Page 40
Business rule example......Page 41
ANTLR......Page 42
Decentralized rules engine......Page 44
Conclusion and future work......Page 47
References......Page 48
Introduction......Page 50
Proposed sampling plan......Page 52
Application of the proposed plan......Page 61
References......Page 64
Proposed model......Page 67
Pedagogical eLearning challenges......Page 68
Adaptive eLearning......Page 69
Intelligent eLearning systems......Page 70
Neutrosophic theory......Page 71
Microservices architecture......Page 72
Model components......Page 73
Scenario 1: New student......Page 74
Scenario 4: Suspended student......Page 75
Proposed intelligent microservices......Page 76
Intelligent adaptive online lecture LOs advisor specifications......Page 78
Intelligent cheat depressor......Page 80
Intelligent study plan advisor......Page 81
Intelligent LOs recommender......Page 82
Intelligent meeting manager for suspended students......Page 83
Evaluation......Page 84
Comments on evaluation results......Page 86
Conclusion......Page 87
References......Page 88
Introduction......Page 90
Neutrosophic set (NS)......Page 93
Single valued neutrosophic set (SVNS)......Page 94
Complex fuzzy set (CFS)......Page 95
Complex intuitionistic fuzzy set (CIFS)......Page 96
Complex neutrosophic set (CNS)......Page 97
Complex neutrosophic cosine similarity measure (CNCSM)......Page 99
Weighted complex neutrosophic cosine similarity measure (WCNCSM)......Page 100
Complex neutrosophic Dice similarity measure (CNDSM)......Page 101
Weighted complex neutrosophic Dice similarity measure (WCNDSM)......Page 102
Complex neutrosophic Jaccard similarity measure (CNJSM)......Page 104
Weighted complex neutrosophic Jaccard similarity measure (WCNJSM)......Page 105
Tangent function for CNS......Page 107
Decision-making steps......Page 108
Selection of educational stream for higher secondary education......Page 109
Comparison analysis......Page 110
References......Page 113
Introduction......Page 120
Basic concepts......Page 122
K-means clustering algorithm for SVNS......Page 124
Support vector machine classifier......Page 125
Sentiment analysis using neutrosophic sets......Page 126
Historical significance of the #MeToo movement......Page 127
Description of dataset......Page 128
Analysis of tweets using neutrosophy......Page 129
K-means clustering results......Page 130
Classification of data......Page 131
k-NN classification results......Page 132
SVM classifier results......Page 134
Neutrosophic sentiment analysis......Page 135
Results and further study......Page 136
References......Page 137
Introduction......Page 139
Design of the proposed plan......Page 141
Limitations and advantages......Page 143
Comparison......Page 148
Implementation in real-life datasets......Page 149
Acknowledgments......Page 150
References......Page 151
Introduction......Page 153
Review of literature......Page 155
Markov chain [22]......Page 156
Intuitionistic fuzzy Markov chain [19]......Page 157
Single valued neutrosophic set (SVNS) [44]......Page 158
Interval neutrosophic Markov chain and long-run behavior of the neutrosophic Markov chain using interval neutrosophi .........Page 159
Experimental analysis......Page 160
Comparative analysis......Page 165
Comparative analysis with the existing methods......Page 167
References......Page 168
Recommender systems......Page 171
Neutrosophic sets and theory......Page 172
Stage 1: Synthesization......Page 173
Multicriteria recommender systems (MC-RS)......Page 174
Recommender systems in eLearning......Page 175
Proposed system......Page 176
Popularity-based recommender systems......Page 177
Item-based collaborative filtering......Page 178
Learning objects......Page 179
General learning style......Page 183
ATLAS learning style......Page 184
Phase 1: LOs finding, gathering, and analyzing......Page 185
Phase 2: Personalized supervised generated LOs......Page 187
Neutrosophic theory in the proposed recommender system......Page 189
Students manager service......Page 190
Students usage data manager......Page 194
Crawler module......Page 197
Removing stop words module......Page 199
Intelligent LOs recommender challenges......Page 200
Evaluation results......Page 203
Comments on results and optimized solution......Page 207
Information retrieval evaluation......Page 209
Intelligent LOs classifier evaluation......Page 211
References......Page 214
Preliminaries......Page 216
New types of continuity in FNTSs......Page 219
Interrelations......Page 227
Conflict of interests......Page 233
References......Page 234
Introduction......Page 235
Preliminaries......Page 236
Zimmermann´s method......Page 239
Werners method......Page 241
Guu and Wu´s method......Page 242
Skandari and Ghaznavi´s method......Page 243
Klir and Yuan´s method......Page 244
Verdegay´s method......Page 246
Chanas method......Page 247
Proposed ranking method......Page 248
Comparing with other methods......Page 251
NLPs with fuzzy relation......Page 252
Numerical example......Page 254
Empirical application......Page 255
Conclusion......Page 257
References......Page 258
Further reading......Page 259
Introduction......Page 260
Some concepts related to trapezoidal fuzzy numbers......Page 262
Some concepts related to neutrosophic sets and neutrosophic numbers......Page 263
Dice similarity measure between two vectors......Page 266
Dice similarity measure of trapezoidal neutrosophic fuzzy numbers......Page 267
Jaccard similarity measure of trapezoidal neutrosophic fuzzy numbers......Page 271
Multicriteria decision-making method......Page 275
Illustrative example......Page 277
Ranking method of alternatives based on similarity measure methods......Page 281
Conclusion......Page 283
References......Page 284
Further reading......Page 286
Introduction......Page 287
Neutrosophic set......Page 290
Bipolar neutrosophic set......Page 291
Some refinements on neutrosophic sets......Page 293
Extended neutrosophic optimization and bipolar neutrosophic optimization technique......Page 296
Computational algorithm......Page 298
Application of bipolar neutrosophic in riser design......Page 307
Conclusion......Page 308
References......Page 311
Introduction......Page 313
Neutrosophic set......Page 315
Single valued neutrosophic sets......Page 316
Existing similarity measures......Page 317
A new similarity measure of SVNSs......Page 319
A comparison approach with existing similarity measures......Page 322
Pattern recognition......Page 323
Cluster analysis......Page 326
Discussions and comparison......Page 331
Conclusions......Page 334
References......Page 338
Introduction......Page 340
Literature review......Page 342
Research contribution......Page 345
Description of CLSC network......Page 346
Multiple objective function......Page 351
Constraints related to the capacity of different echelons in the CLSC network......Page 354
Constraints related to production requirement......Page 355
Constraints related to the testing capacity at testing facility centers......Page 356
Proposed CLSC model formulation under uncertainty......Page 357
Treating fuzzy parameters and constraints......Page 360
Neutrosophic fuzzy programming approach......Page 366
Modified neutrosophic fuzzy programming with intuitionistic fuzzy preference relations......Page 371
Computational study......Page 375
Results and discussions......Page 378
Sensitivity analyses......Page 386
Sensitivity analyses of objective functions......Page 388
Sensitivity analyses of intuitionistic fuzzy linguistic preference relations......Page 395
Acknowledgments......Page 397
References......Page 398
Introduction......Page 401
Neutrosophic image......Page 404
Entropy of neutrosophic subsets......Page 405
Concept of optimization......Page 406
Particle swarm optimization......Page 407
OptNS-based CAD medical image processing applications......Page 408
CAD using neutrosophic set without optimizing NS......Page 409
CAD using neutrosophic set with optimization......Page 411
Discussion and future perceptions in OptNS-based CAD systems......Page 412
References......Page 414
Further reading......Page 417
C......Page 418
D......Page 419
F......Page 420
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M......Page 422
N......Page 423
P......Page 424
S......Page 425
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W......Page 427
Back Cover......Page 428