Optimization Theory Based on Neutrosophic and Plithogenic Sets

Description

Optimization Theory Based on Neutrosophic and Plithogenic Sets presents the state-of-the-art research on neutrosophic and plithogenic theories and their applications in various optimization fields. Its table of contents covers new concepts, methods, algorithms, modelling, and applications of green supply chain, inventory control problems, assignment problems, transportation problem, nonlinear problems and new information related to optimization for the topic from the theoretical and applied viewpoints in neutrosophic sets and logic. 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 I......Page 421 M......Page 422 N......Page 423 P......Page 424 S......Page 425 T......Page 426 W......Page 427 Back Cover......Page 428