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Advances in reliability, failure and risk analysis / / edited by Harish Garg
Advances in reliability, failure and risk analysis / / edited by Harish Garg
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore Pte Ltd., , [2023]
Descrizione fisica 1 online resource (XII, 409 p. 160 illus., 119 illus. in color.)
Disciplina 620.00452
Collana Industrial and Applied Mathematics
Soggetto topico Reliability (Engineering) - Statistical methods
Risk assessment - Statistical methods
Avaluació del risc
Fiabilitat (Enginyeria)
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 981-19-9909-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Degradation and Failure Mechanisms of Complex Systems: Principles -- Simplified Approach to Analyse Fuzzy Reliability of a Repairable System -- Fault-Tolerant and Resilient Neural Control for Discrete-Time Nonlinear Systems -- Bayesian Reliability Analysis of the Topp–Leone Model under Different Loss Functions -- Availability Analysis of Non-Markovian Models with Rejuvenation and Check Pointing -- Reliability Metrics of Textile Confection Plant Using Copula Linguistic -- An Application of Soft Computing in Oil Condition Monitoring -- A Multi-Parameter Occupational Safety Risk Assessment Model for Chemicals in the University Laboratories by an MCDM-Sorting Method -- Failure Mode and Effect Analysis (FMEA) for Safety-Critical Systems in the Context of Industry 4.0 -- Optimization of Redundancy Allocation Problem Using QPSO Algorithm under Uncertain Environment -- Resilience: Enterprise Sustainability Based to Risk Management -- Reliability Analysis of Process Systems Using Intuitionistic Fuzzy Set Theory -- Smart Systems Risk Management in IoT-Based Supply Chain -- Risk and Reliability Analysis in the Era of Digital Transformation -- Distributed System Reliability Analysis with Two Coverage Factors: A Copula Approach -- Qualitative Analysis Method for Evaluation of Risk and Failures in Wind Power Plants: A Case Study of Turkey -- Some Discrete Parametric Markov-Chain System Models to Analyze Reliability -- Repair and Maintenance Management System of Food Processing Equipment: A Systematic Literature Review -- Reliability, Availability, Maintainability and Dependability of a Serial Rice Mill Plant with the Incorporation of Coverage Factor.
Record Nr. UNINA-9910686470203321
Singapore : , : Springer Nature Singapore Pte Ltd., , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Proceedings of Sixth International Conference on Soft Computing for Problem Solving : SocProS 2016, Volume 2 / / edited by Kusum Deep, Jagdish Chand Bansal, Kedar Nath Das, Arvind Kumar Lal, Harish Garg, Atulya K. Nagar, Millie Pant
Proceedings of Sixth International Conference on Soft Computing for Problem Solving : SocProS 2016, Volume 2 / / edited by Kusum Deep, Jagdish Chand Bansal, Kedar Nath Das, Arvind Kumar Lal, Harish Garg, Atulya K. Nagar, Millie Pant
Edizione [1st ed. 2017.]
Pubbl/distr/stampa Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017
Descrizione fisica 1 online resource (X, 406 p. 172 illus.)
Disciplina 006.3
Collana Advances in Intelligent Systems and Computing
Soggetto topico Computational intelligence
Signal processing
Image processing
Speech processing systems
Artificial intelligence
Computational Intelligence
Signal, Image and Speech Processing
Artificial Intelligence
ISBN 981-10-3325-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Spammer Classification using Ensemble Methods over Content-Based Features -- A Modified BPDHE Enhancement Algorithm for Low Resolution Images -- Opposition aided Artificial Bee Colony Algorithm for Digital IIR Filter Design -- Cost Optimization of 2-Way Ribbed Slab Using Hybrid Self Organizing Migrating Algorithm -- A Complete Ontological survey of Cloud Forensic in the area of Cloud Computing -- Optimal Path Determination in a Survivable Virtual Topology of an Optical Network using Ant Colony Optimization -- Parameter Optimization for H.265/HEVC encoder using NSGA II -- PSO Based Context Sensitive Thresholding Technique for Automatic Image Segmentation -- Script Identification from Offline Handwritten Characters using Combination of Features -- Multi-Parameter Retrieval in a Porous Fin using Bi-nary-Coded Genetic Algorithm -- Effectiveness of Constrained Laplacian Biogeography Based Optimization for solving Structural Engineering Design Problems -- Soft Computing Based Software Testing – A Concise Travelogue -- Detection and Mitigation of spoofing attacks by using SDN in LAN -- Landslide Early Warning System Development using Statistical Analysis ofSensors’ Data at Tangni Landslide, Uttarakhand, India -- Wearable Haptic Based Pattern Feedback Sleeve System -- Job Scheduling algorithm in cloud environment considering the priority and cost of job -- Automatic Location of Blood Vessel Bifurcations in Digital Eye Fundus Images -- Recommendation System with Sentiment Analysis as Feedback Component -- A second order non-uniform mesh discretization for the numerical treatment of singular two-point boundary value problems with integral forcing function.
Record Nr. UNINA-9910254326503321
Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Proceedings of Sixth International Conference on Soft Computing for Problem Solving : SocProS 2016, Volume 1 / / edited by Kusum Deep, Jagdish Chand Bansal, Kedar Nath Das, Arvind Kumar Lal, Harish Garg, Atulya K. Nagar, Millie Pant
Proceedings of Sixth International Conference on Soft Computing for Problem Solving : SocProS 2016, Volume 1 / / edited by Kusum Deep, Jagdish Chand Bansal, Kedar Nath Das, Arvind Kumar Lal, Harish Garg, Atulya K. Nagar, Millie Pant
Edizione [1st ed. 2017.]
Pubbl/distr/stampa Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017
Descrizione fisica 1 online resource (X, 362 p. 138 illus.)
Disciplina 620
Collana Advances in Intelligent Systems and Computing
Soggetto topico Computational intelligence
Signal processing
Image processing
Speech processing systems
Artificial intelligence
Computational Intelligence
Signal, Image and Speech Processing
Artificial Intelligence
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Adaptive Scale Factor based Differential Evolution Algorithm -- Adaptive Balance Factor in Particle Swarm Optimization -- Community detection in complex networks: a novel approach based on ant lion optimizer -- Hybrid SOMA: A Tool for Optimizing TMD Parameters -- Fast Convergent Spider Monkey Optimization Algorithm -- Bi-level problem and SMD Assessment Delinquent for Single Impartial Bi-level Optimization -- An Adaptive Firefly Algorithm for Load Balancing in Cloud Computing -- Review on Inertia Weight Strategies for Particle Swarm Optimization -- Hybridized Gravitational Search Algorithms with Real Coded Genetic Algorithms for Integer and Mixed Integer Optimization Problems -- Spider Monkey Optimization Algorithm based on Metropolis principle -- An Analysis of modeling and optimization production cost through fuzzy linear programming problem with symmetric and right angle Triangular fuzzy number -- A New Intuitionistic Fuzzy Entropy of Order-  With Applications in Multiple Attribute Decision Making -- The relationship between intuitionistic fuzzy programming and goal programming -- Implementation of Fuzzy Logic on FORTRAN Coded Free Convection around Vertical Tube -- Availability analysis of the Butter Oil Processing Plant using intuitionistic fuzzy differential equations.
Record Nr. UNINA-9910254164803321
Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Pythagorean fuzzy sets : theory and applications / / edited by Harish Garg
Pythagorean fuzzy sets : theory and applications / / edited by Harish Garg
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (443 pages)
Disciplina 016.403
Soggetto topico Mathematical Logic and Foundations
Artificial intelligence
Intel·ligència artificial
Soggetto genere / forma Llibres electrònics
ISBN 981-16-1989-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- About the Editor -- Pythagorean Fuzzy Information Measures -- A Survey on Recent Applications of Pythagorean Fuzzy Sets: A State-of-the-Art Between 2013 and 2020 -- 1 Introduction -- 2 Basic Concepts and Operators of Pythagorean Fuzzy Sets -- 2.1 Basic Concept of the Pythagorean Fuzzy Sets -- 2.2 Principal Operations -- 2.3 Score and Accuracy Functions -- 2.4 Distance Measures -- 2.5 Pythagorean Fuzzy Aggregation Operators -- 3 Literature Review -- 3.1 Survey Methodology -- 3.2 Survey Results -- 4 Conclusion and Future Outlook -- References -- Some New Weighted Correlation Coefficients Between Pythagorean Fuzzy Sets and Their Applications -- 1 Introduction -- 2 Basic Notions of Pythagorean Fuzzy Sets -- 3 Correlation Coefficients Between PFSs -- 3.1 Some Existing/New CCPFSs Methods -- 3.2 Numerical Illustrations for Computing CCPFSs -- 4 Some Existing/New WCCPFSs Methods -- 4.1 Some Existing WCCPFSs Methods -- 4.2 New Methods of Computing WCCPFSs -- 4.3 Numerical Verifications of the WCCPFSs Methods -- 5 Determination of Pattern Recognition and Medical Diagnostic Problem via WCCPFSs -- 5.1 Applicative Example in Pattern Recognition -- 5.2 Applicative Example in Medical Diagnosis -- 6 Conclusion -- References -- Parametric Directed Divergence Measure for Pythagorean Fuzzy Set and Their Applications to Multi-criteria Decision-Making -- 1 Introduction -- 2 Basic Concepts -- 2.1 Intuitionistic Fuzzy Set [1] -- 2.2 Hesitant Fuzzy Set [32, 33] -- 2.3 Pythagorean Fuzzy Set [11, 16] -- 3 Proposed Parametric Directed Divergence Measure for Pythagorean Fuzzy Set (PFS) -- 3.1 Parametric Divergence Measure for PFSs -- 3.2 Parametric Symmetric Divergence Measure for PFSs -- 3.3 Some Properties of Parametric Symmetric Divergence Measure for PFSs.
4 Decision-Making Method Based on Proposed Parametric Directed Divergence Measure for Pythagorean Fuzzy Set (PFS) -- 5 Illustrative Example -- 6 Conclusions -- References -- Some Trigonometric Similarity Measures Based on the Choquet Integral for Pythagorean Fuzzy Sets and Applications to Pattern Recognition -- 1 Introduction -- 2 Preliminaries -- 3 Trigonometric Similarity Measures Defined with the Choquet Integral For PFSs -- 4 Applications -- 4.1 Pattern Recognition Problem -- 4.2 Medical Diagnosis Problem -- 5 Conclusion -- References -- Isomorphic Operators and Ranking Methods for Pythagorean and Intuitionistic Fuzzy Sets -- 1 Introduction -- 2 Preliminaries -- 2.1 Related Definitions of Intuitionistic Fuzzy Sets and Pythagorean Fuzzy Sets -- 2.2 T-Norm and Its Dual T-Conorm -- 2.3 Four Types of Dual Archimedean T-Norm and S-Norm -- 3 Operations Isomorphism -- 3.1 Operations Isomorphism Between IFSs and PFSs -- 3.2 Operations Isomorphism Between IVIFSs and IVPFSs -- 3.3 Operations Isomorphism Between DHFSs and DHPFSs -- 4 Aggregation Operators Isomorphism -- 4.1 Aggregation Operators Isomorphism Between PFSs and IFSs -- 4.2 Aggregation Operators Isomorphism Between IVPFSs and IVIFSs -- 4.3 Aggregation Operators Isomorphism Between HPFSs and DHFSs -- 5 Ranking Methods Isomorphism -- 5.1 Ranking Methods Isomorphism Between IFNs and PFNs -- 5.2 Ranking Methods Isomorphism Between IVIFNs and IVPFNs -- 5.3 Ranking Methods Isomorphism Between DHFSs and DHPFSs -- 6 Proofs -- 7 Conclusion -- References -- Pythagorean Fuzzy Multi-criteria Decision-Making -- A Risk Prioritization Method Based on Interval-Valued Pythagorean Fuzzy TOPSIS and Its Application for Prioritization of the Risks Emerged at Hospitals During the Covid-19 Pandemic -- 1 Introduction -- 2 Literature Review -- 3 Situation Analysis of Turkey's Covid-19 Pandemic -- 4 Applied Methodology.
4.1 Preliminaries -- 4.2 Procedural Steps of the Proposed IVPF-TOPSIS-based Approach -- 5 Case Study: Prioritization of the Risks Emerged at Hospitals During the Covid-19 Pandemic -- 5.1 Comparative Study -- 6 Conclusion -- References -- Assessment of Agriculture Crop Selection Using Pythagorean Fuzzy CRITIC-VIKOR Decision-Making Framework -- 1 Introduction -- 1.1 Motivation and Contributions -- 2 Preliminaries -- 3 Proposed Divergence and Entropy Measures -- 4 Pythagorean Fuzzy-CRITIC-VIKOR (PF-CRITIC-VIKOR) Methodology -- 5 Case Study: Agriculture Crop Selection Problem -- 5.1 Sensitivity Analysis (SA) -- 5.2 Comparative Study -- 6 Conclusions -- References -- Choquet Integral Under Pythagorean Fuzzy Environment and Their Application in Decision Making -- 1 Introduction -- 2 Preliminaries -- 3 Pythagorean Fuzzy Choquet Integral -- 4 Application to Sustainable Solid Waste Management -- 4.1 Experts and Criteria -- 4.2 Computation -- 5 Conclusions -- References -- On Developing Pythagorean Fuzzy Dombi Geometric Bonferroni Mean Operators with Their Application to Multicriteria Decision Making -- 1 Introduction -- 2 Preliminaries -- 2.1 Pythagorean Fuzzy Set -- 2.2 Geometric Bonferroni Mean Operator -- 2.3 Dombi t-Conorm and t-Norm -- 2.4 Operations of PFNs Based on Dombi t-Conorm and t-Norm -- 3 Pythagorean Fuzzy Geometric Bonferroni Mean Operators Based on Dombi Operations -- 3.1 Pythagorean Fuzzy Dombi Geometric Bonferroni Mean Operator -- 3.2 Properties of Pythagorean Fuzzy Dombi Geometric Bonferroni Mean Operator -- 3.3 Some Special Cases of Pythagorean Fuzzy Dombi Geometric Bonferroni Mean Operator -- 3.4 Pythagorean Fuzzy Weighted Dombi Geometric Bonferroni Mean Operator -- 4 An Approach to MCDM with Pythagorean Fuzzy Information -- 5 An Illustrative Example -- 5.1 Description of the MCDM Problem -- 5.2 Results and Discussions.
5.3 Comparative Analysis -- 6 Conclusions -- References -- Schweizer-Sklar Muirhead Mean Aggregation Operators Based on Pythagorean Fuzzy Sets and Their Application in Multi-criteria Decision-Making -- 1 Introduction -- 2 Preliminaries -- 2.1 Pythagorean Fuzzy Sets -- 2.2 Muirhead Mean Operator -- 2.3 Schweizer-Sklar Operations -- 3 Pythagorean Fuzzy Schweizer-Sklar Muirhead Mean Aggregation Operations -- 3.1 Pythagorean Fuzzy Schweizer-Sklar Muirhead Mean Operator -- 3.2 Pythagorean Fuzzy Schweizer-Sklar Weighted Muirhead Mean Operator -- 4 Multi-criteria Decision-Making Problems Based on Pythagorean Fuzzy Schweizer-Sklar Muirhead Mean Aggregation Operations -- 4.1 Advantages of the Explored Operators -- 4.2 Comparative Analysis of the Explored Operators -- 4.3 Graphical Representations of the Explored Operators -- 5 Conclusion -- References -- Pythagorean Fuzzy MCDM Method Based on CODAS -- 1 Introduction -- 2 Preliminaries -- 3 Approach to PF MCDM Based on CODAS -- 3.1 The Description Issue -- 3.2 PF MCDM Method Based on CODAS -- 4 An Illustrative Example -- 5 Conclusion -- References -- A Novel Pythagorean Fuzzy MULTIMOORA Applied to the Evaluation of Energy Storage Technologies -- 1 Introduction -- 2 Review of the Literature -- 2.1 The MULTIMOORA Method -- 2.2 Pythagorean Fuzzy Operators -- 2.3 Energy Storage Technologies -- 3 Preliminaries -- 3.1 Pythagorean Fuzzy Sets -- 3.2 The Classical MULTIMOORA Method -- 4 The Proposed PF-MULTIMOORA -- 4.1 The Ratio System Technique -- 4.2 The Reference Point Technique -- 4.3 The Full Multiplicative Form Technique -- 4.4 The Overall Utility Score -- 5 Evaluation of Energy Storage Technologies -- 5.1 An Overview -- 5.2 A Practical Example -- 6 Conclusion -- References -- Extensions of the Pythagorean Fuzzy Sets -- Application of Linear Programming in Diet Problem Under Pythagorean Fuzzy Environment.
1 Introduction -- 2 Preliminaries -- 3 Proposed Method -- 4 Numerical Example -- 4.1 Diet Problem (T3PFN) -- 5 Result Analysis -- 6 Conclusion -- References -- Maclaurin Symmetric Mean-Based Archimedean Aggregation Operators for Aggregating Hesitant Pythagorean Fuzzy Elements and Their Applications to Multicriteria Decision Making -- 1 Introduction -- 2 Preliminaries -- 2.1 PFS -- 2.2 HPFSs -- 2.3 MSM Operator -- 2.4 At-CN& -- t-Ns -- 3 Development of At-CN& -- t-N-Based MSM Operators for HPFEs -- 4 An Approach to MCDM with HPF Information -- 5 Illustrative Examples -- 6 Comparison and Discussions -- 7 Conclusions -- References -- Extensions of Linguistic Pythagorean Fuzzy Sets and Their Applications in Multi-attribute Group Decision-Making -- 1 Introduction -- 2 Basic Concepts -- 3 Dual Hesitant Linguistic Pythagorean Fuzzy Sets and Their Applications in MAGDM -- 3.1 Motivations and Necessity of Proposing DHLPFSs -- 3.2 Definition of DHLPFSs -- 3.3 Operations of DHLPFEs -- 3.4 Comparison Method of DHLPFEs -- 3.5 Some Basic Aggregation Operators of DHLPFEs -- 3.6 A MAGDM Method Based on DHLPFSs -- 4 Probabilistic Dual Hesitant Linguistic Pythagorean Fuzzy Sets and Their Applications -- 4.1 Motivations of Proposing PDHLPFSs -- 4.2 Definition of PDHLPFSs -- 4.3 Operation of PDHLPFEs -- 4.4 Comparison Method of PDHLPFEs -- 4.5 Aggregation Operators of PDHLPFEs -- 4.6 MAGDM Based on PDHLPFEs -- 5 Conclusion Remarks -- References -- Pythagorean Fuzzy Soft Sets-Based MADM -- 1 Introduction -- 2 Structure of Pythagorean Fuzzy Soft Sets -- 3 Multi-criteria Group Decision-Making Using Pythagorean Fuzzy Soft Information -- 3.1 Comparison Analysis -- 4 TOPSIS Approach for Choice Making with Pythagorean Fuzzy Soft Sets -- 5 Multiple Criteria Group Decision-Making Using PFS-VIKOR Method -- 6 A Similarity Measure for PFSSs.
6.1 Weighted Similarity Measure for PFSSs.
Record Nr. UNINA-9910495163803321
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Pythagorean fuzzy sets : theory and applications / / edited by Harish Garg
Pythagorean fuzzy sets : theory and applications / / edited by Harish Garg
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (443 pages)
Disciplina 016.403
Soggetto topico Mathematical Logic and Foundations
Artificial intelligence
Intel·ligència artificial
Soggetto genere / forma Llibres electrònics
ISBN 981-16-1989-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- About the Editor -- Pythagorean Fuzzy Information Measures -- A Survey on Recent Applications of Pythagorean Fuzzy Sets: A State-of-the-Art Between 2013 and 2020 -- 1 Introduction -- 2 Basic Concepts and Operators of Pythagorean Fuzzy Sets -- 2.1 Basic Concept of the Pythagorean Fuzzy Sets -- 2.2 Principal Operations -- 2.3 Score and Accuracy Functions -- 2.4 Distance Measures -- 2.5 Pythagorean Fuzzy Aggregation Operators -- 3 Literature Review -- 3.1 Survey Methodology -- 3.2 Survey Results -- 4 Conclusion and Future Outlook -- References -- Some New Weighted Correlation Coefficients Between Pythagorean Fuzzy Sets and Their Applications -- 1 Introduction -- 2 Basic Notions of Pythagorean Fuzzy Sets -- 3 Correlation Coefficients Between PFSs -- 3.1 Some Existing/New CCPFSs Methods -- 3.2 Numerical Illustrations for Computing CCPFSs -- 4 Some Existing/New WCCPFSs Methods -- 4.1 Some Existing WCCPFSs Methods -- 4.2 New Methods of Computing WCCPFSs -- 4.3 Numerical Verifications of the WCCPFSs Methods -- 5 Determination of Pattern Recognition and Medical Diagnostic Problem via WCCPFSs -- 5.1 Applicative Example in Pattern Recognition -- 5.2 Applicative Example in Medical Diagnosis -- 6 Conclusion -- References -- Parametric Directed Divergence Measure for Pythagorean Fuzzy Set and Their Applications to Multi-criteria Decision-Making -- 1 Introduction -- 2 Basic Concepts -- 2.1 Intuitionistic Fuzzy Set [1] -- 2.2 Hesitant Fuzzy Set [32, 33] -- 2.3 Pythagorean Fuzzy Set [11, 16] -- 3 Proposed Parametric Directed Divergence Measure for Pythagorean Fuzzy Set (PFS) -- 3.1 Parametric Divergence Measure for PFSs -- 3.2 Parametric Symmetric Divergence Measure for PFSs -- 3.3 Some Properties of Parametric Symmetric Divergence Measure for PFSs.
4 Decision-Making Method Based on Proposed Parametric Directed Divergence Measure for Pythagorean Fuzzy Set (PFS) -- 5 Illustrative Example -- 6 Conclusions -- References -- Some Trigonometric Similarity Measures Based on the Choquet Integral for Pythagorean Fuzzy Sets and Applications to Pattern Recognition -- 1 Introduction -- 2 Preliminaries -- 3 Trigonometric Similarity Measures Defined with the Choquet Integral For PFSs -- 4 Applications -- 4.1 Pattern Recognition Problem -- 4.2 Medical Diagnosis Problem -- 5 Conclusion -- References -- Isomorphic Operators and Ranking Methods for Pythagorean and Intuitionistic Fuzzy Sets -- 1 Introduction -- 2 Preliminaries -- 2.1 Related Definitions of Intuitionistic Fuzzy Sets and Pythagorean Fuzzy Sets -- 2.2 T-Norm and Its Dual T-Conorm -- 2.3 Four Types of Dual Archimedean T-Norm and S-Norm -- 3 Operations Isomorphism -- 3.1 Operations Isomorphism Between IFSs and PFSs -- 3.2 Operations Isomorphism Between IVIFSs and IVPFSs -- 3.3 Operations Isomorphism Between DHFSs and DHPFSs -- 4 Aggregation Operators Isomorphism -- 4.1 Aggregation Operators Isomorphism Between PFSs and IFSs -- 4.2 Aggregation Operators Isomorphism Between IVPFSs and IVIFSs -- 4.3 Aggregation Operators Isomorphism Between HPFSs and DHFSs -- 5 Ranking Methods Isomorphism -- 5.1 Ranking Methods Isomorphism Between IFNs and PFNs -- 5.2 Ranking Methods Isomorphism Between IVIFNs and IVPFNs -- 5.3 Ranking Methods Isomorphism Between DHFSs and DHPFSs -- 6 Proofs -- 7 Conclusion -- References -- Pythagorean Fuzzy Multi-criteria Decision-Making -- A Risk Prioritization Method Based on Interval-Valued Pythagorean Fuzzy TOPSIS and Its Application for Prioritization of the Risks Emerged at Hospitals During the Covid-19 Pandemic -- 1 Introduction -- 2 Literature Review -- 3 Situation Analysis of Turkey's Covid-19 Pandemic -- 4 Applied Methodology.
4.1 Preliminaries -- 4.2 Procedural Steps of the Proposed IVPF-TOPSIS-based Approach -- 5 Case Study: Prioritization of the Risks Emerged at Hospitals During the Covid-19 Pandemic -- 5.1 Comparative Study -- 6 Conclusion -- References -- Assessment of Agriculture Crop Selection Using Pythagorean Fuzzy CRITIC-VIKOR Decision-Making Framework -- 1 Introduction -- 1.1 Motivation and Contributions -- 2 Preliminaries -- 3 Proposed Divergence and Entropy Measures -- 4 Pythagorean Fuzzy-CRITIC-VIKOR (PF-CRITIC-VIKOR) Methodology -- 5 Case Study: Agriculture Crop Selection Problem -- 5.1 Sensitivity Analysis (SA) -- 5.2 Comparative Study -- 6 Conclusions -- References -- Choquet Integral Under Pythagorean Fuzzy Environment and Their Application in Decision Making -- 1 Introduction -- 2 Preliminaries -- 3 Pythagorean Fuzzy Choquet Integral -- 4 Application to Sustainable Solid Waste Management -- 4.1 Experts and Criteria -- 4.2 Computation -- 5 Conclusions -- References -- On Developing Pythagorean Fuzzy Dombi Geometric Bonferroni Mean Operators with Their Application to Multicriteria Decision Making -- 1 Introduction -- 2 Preliminaries -- 2.1 Pythagorean Fuzzy Set -- 2.2 Geometric Bonferroni Mean Operator -- 2.3 Dombi t-Conorm and t-Norm -- 2.4 Operations of PFNs Based on Dombi t-Conorm and t-Norm -- 3 Pythagorean Fuzzy Geometric Bonferroni Mean Operators Based on Dombi Operations -- 3.1 Pythagorean Fuzzy Dombi Geometric Bonferroni Mean Operator -- 3.2 Properties of Pythagorean Fuzzy Dombi Geometric Bonferroni Mean Operator -- 3.3 Some Special Cases of Pythagorean Fuzzy Dombi Geometric Bonferroni Mean Operator -- 3.4 Pythagorean Fuzzy Weighted Dombi Geometric Bonferroni Mean Operator -- 4 An Approach to MCDM with Pythagorean Fuzzy Information -- 5 An Illustrative Example -- 5.1 Description of the MCDM Problem -- 5.2 Results and Discussions.
5.3 Comparative Analysis -- 6 Conclusions -- References -- Schweizer-Sklar Muirhead Mean Aggregation Operators Based on Pythagorean Fuzzy Sets and Their Application in Multi-criteria Decision-Making -- 1 Introduction -- 2 Preliminaries -- 2.1 Pythagorean Fuzzy Sets -- 2.2 Muirhead Mean Operator -- 2.3 Schweizer-Sklar Operations -- 3 Pythagorean Fuzzy Schweizer-Sklar Muirhead Mean Aggregation Operations -- 3.1 Pythagorean Fuzzy Schweizer-Sklar Muirhead Mean Operator -- 3.2 Pythagorean Fuzzy Schweizer-Sklar Weighted Muirhead Mean Operator -- 4 Multi-criteria Decision-Making Problems Based on Pythagorean Fuzzy Schweizer-Sklar Muirhead Mean Aggregation Operations -- 4.1 Advantages of the Explored Operators -- 4.2 Comparative Analysis of the Explored Operators -- 4.3 Graphical Representations of the Explored Operators -- 5 Conclusion -- References -- Pythagorean Fuzzy MCDM Method Based on CODAS -- 1 Introduction -- 2 Preliminaries -- 3 Approach to PF MCDM Based on CODAS -- 3.1 The Description Issue -- 3.2 PF MCDM Method Based on CODAS -- 4 An Illustrative Example -- 5 Conclusion -- References -- A Novel Pythagorean Fuzzy MULTIMOORA Applied to the Evaluation of Energy Storage Technologies -- 1 Introduction -- 2 Review of the Literature -- 2.1 The MULTIMOORA Method -- 2.2 Pythagorean Fuzzy Operators -- 2.3 Energy Storage Technologies -- 3 Preliminaries -- 3.1 Pythagorean Fuzzy Sets -- 3.2 The Classical MULTIMOORA Method -- 4 The Proposed PF-MULTIMOORA -- 4.1 The Ratio System Technique -- 4.2 The Reference Point Technique -- 4.3 The Full Multiplicative Form Technique -- 4.4 The Overall Utility Score -- 5 Evaluation of Energy Storage Technologies -- 5.1 An Overview -- 5.2 A Practical Example -- 6 Conclusion -- References -- Extensions of the Pythagorean Fuzzy Sets -- Application of Linear Programming in Diet Problem Under Pythagorean Fuzzy Environment.
1 Introduction -- 2 Preliminaries -- 3 Proposed Method -- 4 Numerical Example -- 4.1 Diet Problem (T3PFN) -- 5 Result Analysis -- 6 Conclusion -- References -- Maclaurin Symmetric Mean-Based Archimedean Aggregation Operators for Aggregating Hesitant Pythagorean Fuzzy Elements and Their Applications to Multicriteria Decision Making -- 1 Introduction -- 2 Preliminaries -- 2.1 PFS -- 2.2 HPFSs -- 2.3 MSM Operator -- 2.4 At-CN& -- t-Ns -- 3 Development of At-CN& -- t-N-Based MSM Operators for HPFEs -- 4 An Approach to MCDM with HPF Information -- 5 Illustrative Examples -- 6 Comparison and Discussions -- 7 Conclusions -- References -- Extensions of Linguistic Pythagorean Fuzzy Sets and Their Applications in Multi-attribute Group Decision-Making -- 1 Introduction -- 2 Basic Concepts -- 3 Dual Hesitant Linguistic Pythagorean Fuzzy Sets and Their Applications in MAGDM -- 3.1 Motivations and Necessity of Proposing DHLPFSs -- 3.2 Definition of DHLPFSs -- 3.3 Operations of DHLPFEs -- 3.4 Comparison Method of DHLPFEs -- 3.5 Some Basic Aggregation Operators of DHLPFEs -- 3.6 A MAGDM Method Based on DHLPFSs -- 4 Probabilistic Dual Hesitant Linguistic Pythagorean Fuzzy Sets and Their Applications -- 4.1 Motivations of Proposing PDHLPFSs -- 4.2 Definition of PDHLPFSs -- 4.3 Operation of PDHLPFEs -- 4.4 Comparison Method of PDHLPFEs -- 4.5 Aggregation Operators of PDHLPFEs -- 4.6 MAGDM Based on PDHLPFEs -- 5 Conclusion Remarks -- References -- Pythagorean Fuzzy Soft Sets-Based MADM -- 1 Introduction -- 2 Structure of Pythagorean Fuzzy Soft Sets -- 3 Multi-criteria Group Decision-Making Using Pythagorean Fuzzy Soft Information -- 3.1 Comparison Analysis -- 4 TOPSIS Approach for Choice Making with Pythagorean Fuzzy Soft Sets -- 5 Multiple Criteria Group Decision-Making Using PFS-VIKOR Method -- 6 A Similarity Measure for PFSSs.
6.1 Weighted Similarity Measure for PFSSs.
Record Nr. UNISA-996466394203316
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Q-Rung orthopair fuzzy sets : theory and applications. / / edited by Harish Garg
Q-Rung orthopair fuzzy sets : theory and applications. / / edited by Harish Garg
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (558 pages)
Disciplina 511.3223
Soggetto topico Fuzzy sets
Presa de decisions
Matemàtica
Conjunts borrosos
Soggetto genere / forma Llibres electrònics
ISBN 9789811914492
9789811914485
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- About the Editor -- 1 q-Rung Orthopair Fuzzy Supra Topological Applications in Data Mining Process -- 1.1 Introduction -- 1.2 Preliminary -- 1.3 q-Rung Orthopair Fuzzy Supra Topological Spaces -- 1.4 Mappings of q-Rung Orthopair Fuzzy Spaces -- 1.5 Algorithm for Data Mining Problem Via q-Rung Orthopair Fuzzy Supra Topology -- 1.6 Numerical Example -- 1.7 Conclusion and Future Work -- References -- 2 q-Rung Orthopair Fuzzy Soft Topology with Multi-attribute Decision-Making -- 2.1 Introduction -- 2.2 Some Elementary Models -- 2.3 q-Rung Orthopair Fuzzy Soft Sets -- 2.4 q-Rung Orthopair Fuzzy Soft Topology -- 2.4.1 q-ROFS Separation Axioms -- 2.5 Multi-attribute Decision-Making -- 2.5.1 Numerical Application -- 2.5.2 Generalized Choice Value Method -- 2.6 Conclusion -- References -- 3 Decision-Making on Patients' Medical Status Based on a q-Rung Orthopair Fuzzy Max-Min-Max Composite Relation -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 q-Rung Orthopair Fuzzy Sets -- 3.3 q-Rung Orthopair Fuzzy Max-Min-Max Composite Relation -- 3.3.1 Numerical Application -- 3.4 Application of qROFMMMCR in Disease Diagnosis -- 3.4.1 qROFMMMCR in Terms of Patients and Diseases with Respect to Symptoms -- 3.4.2 Experiment of Disease Diagnosis -- 3.5 Conclusion -- References -- 4 Soergel Distance Measures for q-Rung Orthopair Fuzzy Sets and Their Applications -- 4.1 Introduction -- 4.2 Background -- 4.2.1 q-Rung Orthopair Fuzzy Sets -- 4.2.2 Some Existing Information Measures for q-ROFSs -- 4.3 Soergel Distance Measures for q-ROFSs and Their Validation/Efficiency -- 4.3.1 Twelve Types of Soergel Distance Measures for q-ROFSs -- 4.3.2 Twelve Types of Weighted Soergel Distance Measures for q-ROFSs -- 4.3.3 The Validation/Efficiency of SoDMs and SoSMs for q-ROFSs -- 4.4 Applications of SoDMs -- 4.4.1 Proposed Decision-Making Method.
4.4.2 Illustrative Examples -- 4.5 Comparison Analysis -- 4.6 Sensitivity Analysis and Advantages of SoDMs -- 4.6.1 Sensitivity Analysis of SoDMs for the Value of q -- 4.6.2 Advantages of Proposed Approaches -- 4.6.3 Limitations of Proposed Approaches -- 4.7 Conclusion -- References -- 5 TOPSIS Techniques on q-Rung Orthopair Fuzzy Sets and Its Extensions -- 5.1 Introduction -- 5.2 Preliminaries -- 5.2.1 TOPSIS -- 5.3 TOPSIS Techniques on q-ROFS -- 5.4 Combined Weighting TOPSIS MADM Using q-ROHFS -- 5.5 TOPSIS Techniques on q-ROFSfS -- 5.6 Applications -- 5.7 Conclusions -- References -- 6 Knowledge Measure-Based q-Rung Orthopair Fuzzy Inventory Model -- 6.1 Introduction -- 6.1.1 Literature Review -- 6.1.2 Research Gap and the Contribution -- 6.2 Preliminaries -- 6.3 Model Formulation -- 6.3.1 Case (I): Replacement of the Faulty Option by Warranty Claiming and Repair Option -- 6.3.2 Case (ii): Replacement of the Faulty Option by Warranty Claiming and the Emergency Purchase Option -- 6.3.3 Inventory Model with q-Rung Orthopair Fuzzy Variables -- 6.3.4 Vendor's Optimal Policy -- 6.4 Numerical Computation -- 6.4.1 Sensitive Analysis -- 6.4.2 Comparison Study -- 6.5 Conclusion -- Appendix -- References -- 7 Higher Type q-Rung Orthopair Fuzzy Sets: Interval Analysis -- 7.1 Introduction -- 7.2 Basic Concepts of q-RIVOFSs -- 7.3 Some Novel Measures for q-RIVOFSs -- 7.3.1 Cross-Entropy Measure for q-RIVOFSs -- 7.3.2 Hausdorff Distance for q-RIVOFSs -- 7.4 Multi-Attribute Decision-Making Method Under q-RIVOF Circumstances -- 7.4.1 TODIM Method with q-RIVOFSs -- 7.5 Illustrative Example -- 7.5.1 Case Description -- 7.5.2 Illustration of the Proposed Q-RIVOFS-TODIM Approach -- 7.5.3 Sensitivity Analysis -- 7.5.4 Comparative Analysis -- 7.6 Conclusion -- References.
8 Evidence-Based Cloud Vendor Assessment with Generalized Orthopair Fuzzy Information and Partial Weight Data -- 8.1 Introduction -- 8.2 Literature Review -- 8.2.1 CV Selection Using Decision Models -- 8.2.2 GOFS-Based Decision Approaches -- 8.3 A New Scientific Framework for CV Selection -- 8.3.1 Preliminaries -- 8.3.2 Mathematical Model with GOFS -- 8.3.3 Evidence-Based Ranking Algorithm with GOFS -- 8.4 Real Case Example-Selection of CVs -- 8.5 Comparative Analysis -- 8.6 Conclusion -- References -- 9 Supplier Selection Process Based on CODAS Method Using q-Rung Orthopair Fuzzy Information -- 9.1 Introduction -- 9.2 q-Rung Orthopair Fuzzy Sets (q-ROFS) -- 9.2.1 Algebraic Operations q-ROFS -- 9.3 Combinative Distance-Based Assessment (CODAS) -- 9.3.1 Steps for the CODAS Method -- 9.4 CODAS and q-Rung Orthopair Fuzzy Sets for the Supplier Selection Process -- 9.5 Case Numeric -- 9.6 Discussions -- 9.7 Conclusions -- References -- 10 Group Decision-Making Framework with Generalized Orthopair Fuzzy 2-Tuple Linguistic Information -- 10.1 Introduction -- 10.2 Preliminaries -- 10.2.1 The 2-Tuple Linguistic Representation Model -- 10.2.2 The MSM Operator and its Weighted Form -- 10.3 The GOFTLMSM Aggregation Operator and its Weighted Form -- 10.3.1 The GOFTLMSM Operator -- 10.3.2 The GOFTLWMSM Operator -- 10.4 The GOFTLDMSM Aggregation Operator and its Weighted Form -- 10.4.1 The GOFTLDMSM Operator -- 10.4.2 The GOFTLWDMSM Operator -- 10.5 An MAGDM Model with GOFTL Information -- 10.6 Illustrative Example and Discussion -- 10.6.1 Evaluation Process of the Proposed Method -- 10.6.2 Sensitivity Analysis -- 10.6.3 Comparative Analysis -- 10.6.4 Advantages and Superiorities of the Proposed Work -- 10.7 Conclusions -- References -- 11 3PL Service Provider Selection with q-Rung Orthopair Fuzzy Based CODAS Method -- 11.1 Introduction -- 11.2 Literature Survey.
11.3 q-ROF CODAS Method -- 11.3.1 q-Rung Orthopair Fuzzy Sets -- 11.3.2 q-ROF CODAS Methodology -- 11.4 Case Study -- 11.5 Conclusion -- References -- 12 An Integrated Proximity Indexed Value and q-Rung Orthopair Fuzzy Decision-Making Model for Prioritization of Green Campus Transportation -- 12.1 Introduction -- 12.2 Literature Review -- 12.3 Case Study -- 12.3.1 Definition of Alternatives and Criteria -- 12.4 Preliminaries -- 12.5 Proposed Methodologies -- 12.5.1 Proximity Indexed Value (PIV) Method -- 12.5.2 Proposed q-ROF PIV Method -- 12.6 Experimental Results -- 12.7 Discussion -- 12.8 Conclusion -- References -- 13 Platform-Based Corporate Social Responsibility Evaluation with Three-Way Group Decisions Under Q-Rung Orthopair Fuzzy Environment -- 13.1 Introduction -- 13.2 Preliminaries -- 13.2.1 q-rung Orthopair Fuzzy Sets (q-ROFSs) -- 13.2.2 Three Way Decisions (TWDs) -- 13.3 CSR Evaluation Method Based on TWDs with q-ROFSs -- 13.3.1 Information Fusion Method -- 13.3.2 CSR Classification of Platform-Based Enterprises with TWDs -- 13.4 An Illustrative Example -- 13.4.1 Decision Analysis with Our Proposed Method -- 13.4.2 Comparative Experiment -- 13.4.3 Sensitivity Analysis -- 13.5 Conclusions -- References -- 14 MARCOS Technique by Using q-Rung Orthopair Fuzzy Sets for Evaluating the Performance of Insurance Companies in Terms of Healthcare Services -- 14.1 Introduction -- 14.2 Preliminaries -- 14.3 Q-ROF-MARCOS Method -- 14.4 Analysis of Healthcare Services Under Q-ROFS-MARCOS Technique -- 14.5 Sensitivity Analysis -- 14.6 Conclusion -- References -- 15 Interval Complex q-Rung Orthopair Fuzzy Aggregation Operators and Their Applications in Cite Selection of Electric Vehicle -- 15.1 Introduction -- 15.2 Preliminaries -- 15.3 Interval Complex q-Rung Orthopair Fuzzy Sets.
15.4 Interval Complex q-Rung Orthopair Fuzzy Aggregate Operators for MADM Problems -- 15.5 The MADM Model Based on IVCq-ROFWA and IVCq-ROFGA Operators -- 15.6 An Illustrative Example for the Validation of the Proposed MADM Model -- 15.7 Conclusion and Future Work -- References -- 16 A Novel Fermatean Fuzzy Analytic Hierarchy Process Proposition and Its Usage for Supplier Selection Problem in Industry 4.0 Transition -- 16.1 Introduction -- 16.2 Supplier Selection in Industry 4.0 Transition -- 16.3 Preliminaries: Fermatean Fuzzy Sets -- 16.4 A Novel Fermatean Fuzzy AHP Extension -- 16.5 An Application in Turkey -- 16.6 Discussion and Concluding Remarks -- References -- 17 Pentagonal q-Rung Orthopair Numbers and Their Applications -- 17.1 Introduction -- 17.2 Preliminary -- 17.3 Pentagonal q-Rung Orthopair Numbers -- 17.4 Multi-criteria Decision-Making Method Based on Pq-RO-Numbers -- 17.5 Conclusion -- References -- 18 q-Rung Orthopair Fuzzy Soft Set-Based Multi-criteria Decision-Making -- 18.1 Introduction -- 18.2 q-ROFSSs -- 18.2.1 Weighted SM for q-ROFSSs -- 18.3 MCDM Using q-Rung Orthopair Fuzzy Soft Information -- 18.4 MCDM with TOPSIS Approach Based on q-ROFSSs -- 18.5 MCDM Using q-ROFS VIKOR Method -- 18.6 Practical implementation of proposed SM related to COVID-19 -- 18.7 Conclusion -- References -- 19 Development of Heronian Mean-Based Aggregation Operators Under Interval-Valued Dual Hesitant q-Rung Orthopair Fuzzy Environments for Multicriteria Decision-Making -- 19.1 Introduction -- 19.2 Preliminaries -- 19.2.1 DHq-ROFS -- 19.2.2 IVDHq-ROFS -- 19.2.3 Operations on IVDHq-ROFNs -- 19.2.4 HM Operator -- 19.2.5 GHM Operator -- 19.3 HM-Based IVDHq-ROF Aggregation Operators and Its Properties -- 19.3.1 IVDHq-ROFHM Operator -- 19.3.2 IVDHq-ROFWHM Operator -- 19.3.3 IVDHq-OFGHM Operator -- 19.3.4 IVDHq-ROFWGHM Operator.
19.4 Approach to MCDM with HM-Based IVDHq-ROF Information.
Record Nr. UNISA-996490347203316
Singapore : , : Springer, , [2022]
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Q-Rung orthopair fuzzy sets : theory and applications. / / edited by Harish Garg
Q-Rung orthopair fuzzy sets : theory and applications. / / edited by Harish Garg
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (558 pages)
Disciplina 511.3223
Soggetto topico Fuzzy sets
Presa de decisions
Matemàtica
Conjunts borrosos
Soggetto genere / forma Llibres electrònics
ISBN 9789811914492
9789811914485
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- About the Editor -- 1 q-Rung Orthopair Fuzzy Supra Topological Applications in Data Mining Process -- 1.1 Introduction -- 1.2 Preliminary -- 1.3 q-Rung Orthopair Fuzzy Supra Topological Spaces -- 1.4 Mappings of q-Rung Orthopair Fuzzy Spaces -- 1.5 Algorithm for Data Mining Problem Via q-Rung Orthopair Fuzzy Supra Topology -- 1.6 Numerical Example -- 1.7 Conclusion and Future Work -- References -- 2 q-Rung Orthopair Fuzzy Soft Topology with Multi-attribute Decision-Making -- 2.1 Introduction -- 2.2 Some Elementary Models -- 2.3 q-Rung Orthopair Fuzzy Soft Sets -- 2.4 q-Rung Orthopair Fuzzy Soft Topology -- 2.4.1 q-ROFS Separation Axioms -- 2.5 Multi-attribute Decision-Making -- 2.5.1 Numerical Application -- 2.5.2 Generalized Choice Value Method -- 2.6 Conclusion -- References -- 3 Decision-Making on Patients' Medical Status Based on a q-Rung Orthopair Fuzzy Max-Min-Max Composite Relation -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 q-Rung Orthopair Fuzzy Sets -- 3.3 q-Rung Orthopair Fuzzy Max-Min-Max Composite Relation -- 3.3.1 Numerical Application -- 3.4 Application of qROFMMMCR in Disease Diagnosis -- 3.4.1 qROFMMMCR in Terms of Patients and Diseases with Respect to Symptoms -- 3.4.2 Experiment of Disease Diagnosis -- 3.5 Conclusion -- References -- 4 Soergel Distance Measures for q-Rung Orthopair Fuzzy Sets and Their Applications -- 4.1 Introduction -- 4.2 Background -- 4.2.1 q-Rung Orthopair Fuzzy Sets -- 4.2.2 Some Existing Information Measures for q-ROFSs -- 4.3 Soergel Distance Measures for q-ROFSs and Their Validation/Efficiency -- 4.3.1 Twelve Types of Soergel Distance Measures for q-ROFSs -- 4.3.2 Twelve Types of Weighted Soergel Distance Measures for q-ROFSs -- 4.3.3 The Validation/Efficiency of SoDMs and SoSMs for q-ROFSs -- 4.4 Applications of SoDMs -- 4.4.1 Proposed Decision-Making Method.
4.4.2 Illustrative Examples -- 4.5 Comparison Analysis -- 4.6 Sensitivity Analysis and Advantages of SoDMs -- 4.6.1 Sensitivity Analysis of SoDMs for the Value of q -- 4.6.2 Advantages of Proposed Approaches -- 4.6.3 Limitations of Proposed Approaches -- 4.7 Conclusion -- References -- 5 TOPSIS Techniques on q-Rung Orthopair Fuzzy Sets and Its Extensions -- 5.1 Introduction -- 5.2 Preliminaries -- 5.2.1 TOPSIS -- 5.3 TOPSIS Techniques on q-ROFS -- 5.4 Combined Weighting TOPSIS MADM Using q-ROHFS -- 5.5 TOPSIS Techniques on q-ROFSfS -- 5.6 Applications -- 5.7 Conclusions -- References -- 6 Knowledge Measure-Based q-Rung Orthopair Fuzzy Inventory Model -- 6.1 Introduction -- 6.1.1 Literature Review -- 6.1.2 Research Gap and the Contribution -- 6.2 Preliminaries -- 6.3 Model Formulation -- 6.3.1 Case (I): Replacement of the Faulty Option by Warranty Claiming and Repair Option -- 6.3.2 Case (ii): Replacement of the Faulty Option by Warranty Claiming and the Emergency Purchase Option -- 6.3.3 Inventory Model with q-Rung Orthopair Fuzzy Variables -- 6.3.4 Vendor's Optimal Policy -- 6.4 Numerical Computation -- 6.4.1 Sensitive Analysis -- 6.4.2 Comparison Study -- 6.5 Conclusion -- Appendix -- References -- 7 Higher Type q-Rung Orthopair Fuzzy Sets: Interval Analysis -- 7.1 Introduction -- 7.2 Basic Concepts of q-RIVOFSs -- 7.3 Some Novel Measures for q-RIVOFSs -- 7.3.1 Cross-Entropy Measure for q-RIVOFSs -- 7.3.2 Hausdorff Distance for q-RIVOFSs -- 7.4 Multi-Attribute Decision-Making Method Under q-RIVOF Circumstances -- 7.4.1 TODIM Method with q-RIVOFSs -- 7.5 Illustrative Example -- 7.5.1 Case Description -- 7.5.2 Illustration of the Proposed Q-RIVOFS-TODIM Approach -- 7.5.3 Sensitivity Analysis -- 7.5.4 Comparative Analysis -- 7.6 Conclusion -- References.
8 Evidence-Based Cloud Vendor Assessment with Generalized Orthopair Fuzzy Information and Partial Weight Data -- 8.1 Introduction -- 8.2 Literature Review -- 8.2.1 CV Selection Using Decision Models -- 8.2.2 GOFS-Based Decision Approaches -- 8.3 A New Scientific Framework for CV Selection -- 8.3.1 Preliminaries -- 8.3.2 Mathematical Model with GOFS -- 8.3.3 Evidence-Based Ranking Algorithm with GOFS -- 8.4 Real Case Example-Selection of CVs -- 8.5 Comparative Analysis -- 8.6 Conclusion -- References -- 9 Supplier Selection Process Based on CODAS Method Using q-Rung Orthopair Fuzzy Information -- 9.1 Introduction -- 9.2 q-Rung Orthopair Fuzzy Sets (q-ROFS) -- 9.2.1 Algebraic Operations q-ROFS -- 9.3 Combinative Distance-Based Assessment (CODAS) -- 9.3.1 Steps for the CODAS Method -- 9.4 CODAS and q-Rung Orthopair Fuzzy Sets for the Supplier Selection Process -- 9.5 Case Numeric -- 9.6 Discussions -- 9.7 Conclusions -- References -- 10 Group Decision-Making Framework with Generalized Orthopair Fuzzy 2-Tuple Linguistic Information -- 10.1 Introduction -- 10.2 Preliminaries -- 10.2.1 The 2-Tuple Linguistic Representation Model -- 10.2.2 The MSM Operator and its Weighted Form -- 10.3 The GOFTLMSM Aggregation Operator and its Weighted Form -- 10.3.1 The GOFTLMSM Operator -- 10.3.2 The GOFTLWMSM Operator -- 10.4 The GOFTLDMSM Aggregation Operator and its Weighted Form -- 10.4.1 The GOFTLDMSM Operator -- 10.4.2 The GOFTLWDMSM Operator -- 10.5 An MAGDM Model with GOFTL Information -- 10.6 Illustrative Example and Discussion -- 10.6.1 Evaluation Process of the Proposed Method -- 10.6.2 Sensitivity Analysis -- 10.6.3 Comparative Analysis -- 10.6.4 Advantages and Superiorities of the Proposed Work -- 10.7 Conclusions -- References -- 11 3PL Service Provider Selection with q-Rung Orthopair Fuzzy Based CODAS Method -- 11.1 Introduction -- 11.2 Literature Survey.
11.3 q-ROF CODAS Method -- 11.3.1 q-Rung Orthopair Fuzzy Sets -- 11.3.2 q-ROF CODAS Methodology -- 11.4 Case Study -- 11.5 Conclusion -- References -- 12 An Integrated Proximity Indexed Value and q-Rung Orthopair Fuzzy Decision-Making Model for Prioritization of Green Campus Transportation -- 12.1 Introduction -- 12.2 Literature Review -- 12.3 Case Study -- 12.3.1 Definition of Alternatives and Criteria -- 12.4 Preliminaries -- 12.5 Proposed Methodologies -- 12.5.1 Proximity Indexed Value (PIV) Method -- 12.5.2 Proposed q-ROF PIV Method -- 12.6 Experimental Results -- 12.7 Discussion -- 12.8 Conclusion -- References -- 13 Platform-Based Corporate Social Responsibility Evaluation with Three-Way Group Decisions Under Q-Rung Orthopair Fuzzy Environment -- 13.1 Introduction -- 13.2 Preliminaries -- 13.2.1 q-rung Orthopair Fuzzy Sets (q-ROFSs) -- 13.2.2 Three Way Decisions (TWDs) -- 13.3 CSR Evaluation Method Based on TWDs with q-ROFSs -- 13.3.1 Information Fusion Method -- 13.3.2 CSR Classification of Platform-Based Enterprises with TWDs -- 13.4 An Illustrative Example -- 13.4.1 Decision Analysis with Our Proposed Method -- 13.4.2 Comparative Experiment -- 13.4.3 Sensitivity Analysis -- 13.5 Conclusions -- References -- 14 MARCOS Technique by Using q-Rung Orthopair Fuzzy Sets for Evaluating the Performance of Insurance Companies in Terms of Healthcare Services -- 14.1 Introduction -- 14.2 Preliminaries -- 14.3 Q-ROF-MARCOS Method -- 14.4 Analysis of Healthcare Services Under Q-ROFS-MARCOS Technique -- 14.5 Sensitivity Analysis -- 14.6 Conclusion -- References -- 15 Interval Complex q-Rung Orthopair Fuzzy Aggregation Operators and Their Applications in Cite Selection of Electric Vehicle -- 15.1 Introduction -- 15.2 Preliminaries -- 15.3 Interval Complex q-Rung Orthopair Fuzzy Sets.
15.4 Interval Complex q-Rung Orthopair Fuzzy Aggregate Operators for MADM Problems -- 15.5 The MADM Model Based on IVCq-ROFWA and IVCq-ROFGA Operators -- 15.6 An Illustrative Example for the Validation of the Proposed MADM Model -- 15.7 Conclusion and Future Work -- References -- 16 A Novel Fermatean Fuzzy Analytic Hierarchy Process Proposition and Its Usage for Supplier Selection Problem in Industry 4.0 Transition -- 16.1 Introduction -- 16.2 Supplier Selection in Industry 4.0 Transition -- 16.3 Preliminaries: Fermatean Fuzzy Sets -- 16.4 A Novel Fermatean Fuzzy AHP Extension -- 16.5 An Application in Turkey -- 16.6 Discussion and Concluding Remarks -- References -- 17 Pentagonal q-Rung Orthopair Numbers and Their Applications -- 17.1 Introduction -- 17.2 Preliminary -- 17.3 Pentagonal q-Rung Orthopair Numbers -- 17.4 Multi-criteria Decision-Making Method Based on Pq-RO-Numbers -- 17.5 Conclusion -- References -- 18 q-Rung Orthopair Fuzzy Soft Set-Based Multi-criteria Decision-Making -- 18.1 Introduction -- 18.2 q-ROFSSs -- 18.2.1 Weighted SM for q-ROFSSs -- 18.3 MCDM Using q-Rung Orthopair Fuzzy Soft Information -- 18.4 MCDM with TOPSIS Approach Based on q-ROFSSs -- 18.5 MCDM Using q-ROFS VIKOR Method -- 18.6 Practical implementation of proposed SM related to COVID-19 -- 18.7 Conclusion -- References -- 19 Development of Heronian Mean-Based Aggregation Operators Under Interval-Valued Dual Hesitant q-Rung Orthopair Fuzzy Environments for Multicriteria Decision-Making -- 19.1 Introduction -- 19.2 Preliminaries -- 19.2.1 DHq-ROFS -- 19.2.2 IVDHq-ROFS -- 19.2.3 Operations on IVDHq-ROFNs -- 19.2.4 HM Operator -- 19.2.5 GHM Operator -- 19.3 HM-Based IVDHq-ROF Aggregation Operators and Its Properties -- 19.3.1 IVDHq-ROFHM Operator -- 19.3.2 IVDHq-ROFWHM Operator -- 19.3.3 IVDHq-OFGHM Operator -- 19.3.4 IVDHq-ROFWGHM Operator.
19.4 Approach to MCDM with HM-Based IVDHq-ROF Information.
Record Nr. UNINA-9910592994203321
Singapore : , : Springer, , [2022]
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A Roadmap for Enabling Industry 4. 0 by Artificial Intelligence
A Roadmap for Enabling Industry 4. 0 by Artificial Intelligence
Autore Chatterjee Jyotir Moy
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2023
Descrizione fisica 1 online resource (339 pages)
Altri autori (Persone) GargHarish
ThakurR. N
ISBN 1-119-90514-1
1-119-90513-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Artificial Intelligence-The Driving Force of Industry 4.0 -- 1.1 Introduction -- 1.2 Methodology -- 1.3 Scope of AI in Global Economy and Industry 4.0 -- 1.3.1 Artificial Intelligence-Evolution and Implications -- 1.3.2 Artificial Intelligence and Industry 4.0-Investments and Returns on Economy -- 1.3.3 The Driving Forces for Industry 4.0 -- 1.4 Artificial Intelligence-Manufacturing Sector -- 1.4.1 AI Diversity-Applications to Manufacturing Sector -- 1.4.2 Future Roadmap of AI-Prospects to Manufacturing Sector in Industry 4.0 -- 1.5 Conclusion -- References -- Chapter 2 Industry 4.0, Intelligent Manufacturing, Internet of Things, Cloud Computing: An Overview -- 2.1 Introduction -- 2.2 Industrial Transformation/Value Chain Transformation -- 2.2.1 First Scenario: Reducing Waste and Increasing Productivity Using IIoT -- 2.2.2 Second Scenario: Selling Outcome (User Demand)-Based Services Using IIoT -- 2.3 IIoT Reference Architecture -- 2.4 IIoT Technical Concepts -- 2.5 IIoT and Cloud Computing -- 2.6 IIoT and Security -- References -- Chapter 3 Artificial Intelligence of Things (AIoT) and Industry 4.0-Based Supply Chain (FMCG Industry) -- 3.1 Introduction -- 3.2 Concepts -- 3.2.1 Internet of Things -- 3.2.2 The Industrial Internet of Things (IIoT) -- 3.2.3 Artificial Intelligence of Things (AIoT) -- 3.3 AIoT-Based Supply Chain -- 3.4 Conclusion -- References -- Chapter 4 Application of Artificial Intelligence in Forecasting the Demand for Supply Chains Considering Industry 4.0 -- 4.1 Introduction -- 4.2 Literature Review -- 4.2.1 Summary of the First Three Industrial Revolutions -- 4.2.2 Emergence of Industry 4.0 -- 4.2.3 Some of the Challenges of Industry 4.0 -- 4.3 Application of Artificial Intelligence in Supply Chain Demand Forecasting -- 4.4 Proposed Approach.
4.4.1 Mathematical Model -- 4.4.2 Advantages of the Proposed Model -- 4.5 Discussion and Conclusion -- References -- Chapter 5 Integrating IoT and Deep Learning-The Driving Force of Industry 4.0 -- 5.1 Motivation and Background -- 5.2 Bringing Intelligence Into IoT Devices -- 5.3 The Foundation of CR-IoT Network -- 5.3.1 Various AI Technique in CR-IoT Network -- 5.3.2 Artificial Neural Network (ANN) -- 5.3.3 Metaheuristic Technique -- 5.3.4 Rule-Based System -- 5.3.5 Ontology-Based System -- 5.3.6 Probabilistic Models -- 5.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network -- 5.5 Realization of CR-IoT Network in Daily Life Examples -- 5.6 AI-Enabled Agriculture and Smart Irrigation System-Case Study -- 5.7 Conclusion -- References -- Chapter 6 A Systematic Review on Blockchain Security Technology and Big Data Employed in Cloud Environment -- 6.1 Introduction -- 6.2 Overview of Blockchain -- 6.3 Components of Blockchain -- 6.3.1 Data Block -- 6.3.2 Smart Contracts -- 6.3.3 Consensus Algorithms -- 6.4 Safety Issues in Blockchain Technology -- 6.5 Usage of Big Data Framework in Dynamic Supply Chain System -- 6.6 Machine Learning and Big Data -- 6.6.1 Overview of Shallow Models -- 6.6.1.1 Support Vector Machine (SVM) -- 6.6.1.2 Artificial Neural Network (ANN) -- 6.6.1.3 K-Nearest Neighbor (KNN) -- 6.6.1.4 Clustering -- 6.6.1.5 Decision Tree -- 6.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems -- 6.7.1 Replenishment Planning -- 6.7.2 Optimizing Orders -- 6.7.3 Arranging and Organizing -- 6.7.4 Enhanced Demand Structuring -- 6.7.5 Real-Time Management of the Supply Chain -- 6.7.6 Enhanced Reaction -- 6.7.7 Planning and Growth of Inventories -- 6.8 IoT-Enabled Blockchains -- 6.8.1 Securing IoT Applications by Utilizing Blockchain -- 6.8.2 Blockchain Based on Permission -- 6.8.3 Blockchain Improvements in IoT.
6.8.3.1 Blockchain Can Store Information Coming from IoT Devices -- 6.8.3.2 Secure Data Storage with Blockchain Distribution -- 6.8.3.3 Data Encryption via Hash Key and Tested by the Miners -- 6.8.3.4 Spoofing Attacks and Data Loss Prevention -- 6.8.3.5 Unauthorized Access Prevention Using Blockchain -- 6.8.3.6 Exclusion of Centralized Cloud Servers -- 6.9 Conclusions -- References -- Chapter 7 Deep Learning Approach to Industrial Energy Sector and Energy Forecasting with Prophet -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Methodology -- 7.3.1 Splitting of Data (Test/Train) -- 7.3.2 Prophet Model -- 7.3.3 Data Cleaning -- 7.3.4 Model Implementation -- 7.4 Results -- 7.4.1 Comparing Forecast to Actuals -- 7.4.2 Adding Holidays -- 7.4.3 Comparing Forecast to Actuals with the Cleaned Data -- 7.5 Conclusion and Future Scope -- References -- Chapter 8 Application of Novel AI Mechanism for Minimizing Private Data Release in Cyber-Physical Systems -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Proposed Mechanism -- 8.4 Experimental Results -- 8.5 Future Directions -- 8.6 Conclusion -- References -- Chapter 9 Environmental and Industrial Applications Using Internet of Things (IoT) -- 9.1 Introduction -- 9.2 IoT-Based Environmental Applications -- 9.3 Smart Environmental Monitoring -- 9.3.1 Air Quality Assessment -- 9.3.2 Water Quality Assessment -- 9.3.3 Soil Quality Assessment -- 9.3.4 Environmental Health-Related to COVID-19 Monitoring -- 9.4 Applications of Sensors Network in Agro-Industrial System -- 9.5 Applications of IoT in Industry -- 9.5.1 Application of IoT in the Autonomous Field -- 9.5.2 Applications of IoT in Software Industries -- 9.5.3 Sensors in Industry -- 9.6 Challenges of IoT Applications in Environmental and Industrial Applications -- 9.7 Conclusions and Recommendations -- Acknowledgments -- References.
Chapter 10 An Introduction to Security in Internet of Things (IoT) and Big Data -- 10.1 Introduction -- 10.2 Allusion Design of IoT -- 10.2.1 Stage 1-Edge Tool -- 10.2.2 Stage 2-Connectivity -- 10.2.3 Stage 3-Fog Computing -- 10.2.4 Stage 4-Data Collection -- 10.2.5 Stage 5-Data Abstraction -- 10.2.6 Stage 6-Applications -- 10.2.7 Stage 7-Cooperation and Processes -- 10.3 Vulnerabilities of IoT -- 10.3.1 The Properties and Relationships of Various IoT Networks -- 10.3.2 Device Attacks -- 10.3.3 Attacks on Network -- 10.3.4 Some Other Issues -- 10.3.4.1 Customer Delivery Value -- 10.3.4.2 Compatibility Problems With Equipment -- 10.3.4.3 Compatibility and Maintenance -- 10.3.4.4 Connectivity Issues in the Field of Data -- 10.3.4.5 Incorrect Data Collection and Difficulties -- 10.3.4.6 Security Concern -- 10.3.4.7 Problems in Computer Confidentiality -- 10.4 Challenges in Technology -- 10.4.1 Skepticism of Consumers -- 10.5 Analysis of IoT Security -- 10.5.1 Sensing Layer Security Threats -- 10.5.1.1 Node Capturing -- 10.5.1.2 Malicious Attack by Code Injection -- 10.5.1.3 Attack by Fake Data Injection -- 10.5.1.4 Sidelines Assaults -- 10.5.1.5 Attacks During Booting Process -- 10.5.2 Network Layer Safety Issues -- 10.5.2.1 Attack on Phishing Page -- 10.5.2.2 Attacks on Access -- 10.5.2.3 Attacks on Data Transmission -- 10.5.2.4 Attacks on Routing -- 10.5.3 Middleware Layer Safety Issues -- 10.5.3.1 Attack by SQL Injection -- 10.5.3.2 Attack by Signature Wrapping -- 10.5.3.3 Cloud Attack Injection with Malware -- 10.5.3.4 Cloud Flooding Attack -- 10.5.4 Gateways Safety Issues -- 10.5.4.1 On-Boarding Safely -- 10.5.4.2 Additional Interfaces -- 10.5.4.3 Encrypting End-to-End -- 10.5.5 Application Layer Safety Issues -- 10.5.5.1 Theft of Data -- 10.5.5.2 Attacks at Interruption in Service -- 10.5.5.3 Malicious Code Injection Attack.
10.6 Improvements and Enhancements Needed for IoT Applications in the Future -- 10.7 Upcoming Future Research Challenges with Intrusion Detection Systems (IDS) -- 10.8 Conclusion -- References -- Chapter 11 Potential, Scope, and Challenges of Industry 4.0 -- 11.1 Introduction -- 11.2 Key Aspects for a Successful Production -- 11.3 Opportunities with Industry 4.0 -- 11.4 Issues in Implementation of Industry 4.0 -- 11.5 Potential Tools Utilized in Industry 4.0 -- 11.6 Conclusion -- References -- Chapter 12 Industry 4.0 and Manufacturing Techniques: Opportunities and Challenges -- 12.1 Introduction -- 12.2 Changing Market Demands -- 12.2.1 Individualization -- 12.2.2 Volatility -- 12.2.3 Efficiency in Terms of Energy Resources -- 12.3 Recent Technological Advancements -- 12.4 Industrial Revolution 4.0 -- 12.5 Challenges to Industry 4.0 -- 12.6 Conclusion -- References -- Chapter 13 The Role of Multiagent System in Industry 4.0 -- 13.1 Introduction -- 13.2 Characteristics and Goals of Industry 4.0 Conception -- 13.3 Artificial Intelligence -- 13.3.1 Knowledge-Based Systems -- 13.4 Multiagent Systems -- 13.4.1 Agent Architectures -- 13.4.2 JADE -- 13.4.3 System Requirements Definition -- 13.4.4 HMI Development -- 13.5 Developing Software of Controllers Multiagent Environment Behavior Patterns -- 13.5.1 Agent Supervision -- 13.5.2 Documents Dispatching Agents -- 13.5.3 Agent Rescheduling -- 13.5.4 Agent of Executive -- 13.5.5 Primary Roles of High-Availability Agent -- 13.6 Conclusion -- References -- Chapter 14 An Overview of Enhancing Encryption Standards for Multimedia in Explainable Artificial Intelligence Using Residue Number Systems for Security -- 14.1 Introduction -- 14.2 Reviews of Related Works -- 14.3 Materials and Methods -- 14.3.1 Multimedia -- 14.3.2 Artificial Intelligence and Explainable Artificial Intelligence -- 14.3.3 Cryptography.
14.3.4 Encryption and Decryption.
Record Nr. UNINA-9910632498003321
Chatterjee Jyotir Moy  
Newark : , : John Wiley & Sons, Incorporated, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
A roadmap for enabling Industry 4.0 by artificial intelligence / / edited by Jyotir Moy Chatterjee, Harish Garg and R. N. Thakur
A roadmap for enabling Industry 4.0 by artificial intelligence / / edited by Jyotir Moy Chatterjee, Harish Garg and R. N. Thakur
Pubbl/distr/stampa Hoboken, NJ : , : Wiley, , [2023]
Descrizione fisica 1 online resource (339 pages)
Soggetto topico Artificial intelligence - Industrial applications
Industry 4.0
ISBN 1-119-90514-1
1-119-90513-3
9781119904854
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Artificial Intelligence-The Driving Force of Industry 4.0 -- 1.1 Introduction -- 1.2 Methodology -- 1.3 Scope of AI in Global Economy and Industry 4.0 -- 1.3.1 Artificial Intelligence-Evolution and Implications -- 1.3.2 Artificial Intelligence and Industry 4.0-Investments and Returns on Economy -- 1.3.3 The Driving Forces for Industry 4.0 -- 1.4 Artificial Intelligence-Manufacturing Sector -- 1.4.1 AI Diversity-Applications to Manufacturing Sector -- 1.4.2 Future Roadmap of AI-Prospects to Manufacturing Sector in Industry 4.0 -- 1.5 Conclusion -- References -- Chapter 2 Industry 4.0, Intelligent Manufacturing, Internet of Things, Cloud Computing: An Overview -- 2.1 Introduction -- 2.2 Industrial Transformation/Value Chain Transformation -- 2.2.1 First Scenario: Reducing Waste and Increasing Productivity Using IIoT -- 2.2.2 Second Scenario: Selling Outcome (User Demand)-Based Services Using IIoT -- 2.3 IIoT Reference Architecture -- 2.4 IIoT Technical Concepts -- 2.5 IIoT and Cloud Computing -- 2.6 IIoT and Security -- References -- Chapter 3 Artificial Intelligence of Things (AIoT) and Industry 4.0-Based Supply Chain (FMCG Industry) -- 3.1 Introduction -- 3.2 Concepts -- 3.2.1 Internet of Things -- 3.2.2 The Industrial Internet of Things (IIoT) -- 3.2.3 Artificial Intelligence of Things (AIoT) -- 3.3 AIoT-Based Supply Chain -- 3.4 Conclusion -- References -- Chapter 4 Application of Artificial Intelligence in Forecasting the Demand for Supply Chains Considering Industry 4.0 -- 4.1 Introduction -- 4.2 Literature Review -- 4.2.1 Summary of the First Three Industrial Revolutions -- 4.2.2 Emergence of Industry 4.0 -- 4.2.3 Some of the Challenges of Industry 4.0 -- 4.3 Application of Artificial Intelligence in Supply Chain Demand Forecasting -- 4.4 Proposed Approach.
4.4.1 Mathematical Model -- 4.4.2 Advantages of the Proposed Model -- 4.5 Discussion and Conclusion -- References -- Chapter 5 Integrating IoT and Deep Learning-The Driving Force of Industry 4.0 -- 5.1 Motivation and Background -- 5.2 Bringing Intelligence Into IoT Devices -- 5.3 The Foundation of CR-IoT Network -- 5.3.1 Various AI Technique in CR-IoT Network -- 5.3.2 Artificial Neural Network (ANN) -- 5.3.3 Metaheuristic Technique -- 5.3.4 Rule-Based System -- 5.3.5 Ontology-Based System -- 5.3.6 Probabilistic Models -- 5.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network -- 5.5 Realization of CR-IoT Network in Daily Life Examples -- 5.6 AI-Enabled Agriculture and Smart Irrigation System-Case Study -- 5.7 Conclusion -- References -- Chapter 6 A Systematic Review on Blockchain Security Technology and Big Data Employed in Cloud Environment -- 6.1 Introduction -- 6.2 Overview of Blockchain -- 6.3 Components of Blockchain -- 6.3.1 Data Block -- 6.3.2 Smart Contracts -- 6.3.3 Consensus Algorithms -- 6.4 Safety Issues in Blockchain Technology -- 6.5 Usage of Big Data Framework in Dynamic Supply Chain System -- 6.6 Machine Learning and Big Data -- 6.6.1 Overview of Shallow Models -- 6.6.1.1 Support Vector Machine (SVM) -- 6.6.1.2 Artificial Neural Network (ANN) -- 6.6.1.3 K-Nearest Neighbor (KNN) -- 6.6.1.4 Clustering -- 6.6.1.5 Decision Tree -- 6.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems -- 6.7.1 Replenishment Planning -- 6.7.2 Optimizing Orders -- 6.7.3 Arranging and Organizing -- 6.7.4 Enhanced Demand Structuring -- 6.7.5 Real-Time Management of the Supply Chain -- 6.7.6 Enhanced Reaction -- 6.7.7 Planning and Growth of Inventories -- 6.8 IoT-Enabled Blockchains -- 6.8.1 Securing IoT Applications by Utilizing Blockchain -- 6.8.2 Blockchain Based on Permission -- 6.8.3 Blockchain Improvements in IoT.
6.8.3.1 Blockchain Can Store Information Coming from IoT Devices -- 6.8.3.2 Secure Data Storage with Blockchain Distribution -- 6.8.3.3 Data Encryption via Hash Key and Tested by the Miners -- 6.8.3.4 Spoofing Attacks and Data Loss Prevention -- 6.8.3.5 Unauthorized Access Prevention Using Blockchain -- 6.8.3.6 Exclusion of Centralized Cloud Servers -- 6.9 Conclusions -- References -- Chapter 7 Deep Learning Approach to Industrial Energy Sector and Energy Forecasting with Prophet -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Methodology -- 7.3.1 Splitting of Data (Test/Train) -- 7.3.2 Prophet Model -- 7.3.3 Data Cleaning -- 7.3.4 Model Implementation -- 7.4 Results -- 7.4.1 Comparing Forecast to Actuals -- 7.4.2 Adding Holidays -- 7.4.3 Comparing Forecast to Actuals with the Cleaned Data -- 7.5 Conclusion and Future Scope -- References -- Chapter 8 Application of Novel AI Mechanism for Minimizing Private Data Release in Cyber-Physical Systems -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Proposed Mechanism -- 8.4 Experimental Results -- 8.5 Future Directions -- 8.6 Conclusion -- References -- Chapter 9 Environmental and Industrial Applications Using Internet of Things (IoT) -- 9.1 Introduction -- 9.2 IoT-Based Environmental Applications -- 9.3 Smart Environmental Monitoring -- 9.3.1 Air Quality Assessment -- 9.3.2 Water Quality Assessment -- 9.3.3 Soil Quality Assessment -- 9.3.4 Environmental Health-Related to COVID-19 Monitoring -- 9.4 Applications of Sensors Network in Agro-Industrial System -- 9.5 Applications of IoT in Industry -- 9.5.1 Application of IoT in the Autonomous Field -- 9.5.2 Applications of IoT in Software Industries -- 9.5.3 Sensors in Industry -- 9.6 Challenges of IoT Applications in Environmental and Industrial Applications -- 9.7 Conclusions and Recommendations -- Acknowledgments -- References.
Chapter 10 An Introduction to Security in Internet of Things (IoT) and Big Data -- 10.1 Introduction -- 10.2 Allusion Design of IoT -- 10.2.1 Stage 1-Edge Tool -- 10.2.2 Stage 2-Connectivity -- 10.2.3 Stage 3-Fog Computing -- 10.2.4 Stage 4-Data Collection -- 10.2.5 Stage 5-Data Abstraction -- 10.2.6 Stage 6-Applications -- 10.2.7 Stage 7-Cooperation and Processes -- 10.3 Vulnerabilities of IoT -- 10.3.1 The Properties and Relationships of Various IoT Networks -- 10.3.2 Device Attacks -- 10.3.3 Attacks on Network -- 10.3.4 Some Other Issues -- 10.3.4.1 Customer Delivery Value -- 10.3.4.2 Compatibility Problems With Equipment -- 10.3.4.3 Compatibility and Maintenance -- 10.3.4.4 Connectivity Issues in the Field of Data -- 10.3.4.5 Incorrect Data Collection and Difficulties -- 10.3.4.6 Security Concern -- 10.3.4.7 Problems in Computer Confidentiality -- 10.4 Challenges in Technology -- 10.4.1 Skepticism of Consumers -- 10.5 Analysis of IoT Security -- 10.5.1 Sensing Layer Security Threats -- 10.5.1.1 Node Capturing -- 10.5.1.2 Malicious Attack by Code Injection -- 10.5.1.3 Attack by Fake Data Injection -- 10.5.1.4 Sidelines Assaults -- 10.5.1.5 Attacks During Booting Process -- 10.5.2 Network Layer Safety Issues -- 10.5.2.1 Attack on Phishing Page -- 10.5.2.2 Attacks on Access -- 10.5.2.3 Attacks on Data Transmission -- 10.5.2.4 Attacks on Routing -- 10.5.3 Middleware Layer Safety Issues -- 10.5.3.1 Attack by SQL Injection -- 10.5.3.2 Attack by Signature Wrapping -- 10.5.3.3 Cloud Attack Injection with Malware -- 10.5.3.4 Cloud Flooding Attack -- 10.5.4 Gateways Safety Issues -- 10.5.4.1 On-Boarding Safely -- 10.5.4.2 Additional Interfaces -- 10.5.4.3 Encrypting End-to-End -- 10.5.5 Application Layer Safety Issues -- 10.5.5.1 Theft of Data -- 10.5.5.2 Attacks at Interruption in Service -- 10.5.5.3 Malicious Code Injection Attack.
10.6 Improvements and Enhancements Needed for IoT Applications in the Future -- 10.7 Upcoming Future Research Challenges with Intrusion Detection Systems (IDS) -- 10.8 Conclusion -- References -- Chapter 11 Potential, Scope, and Challenges of Industry 4.0 -- 11.1 Introduction -- 11.2 Key Aspects for a Successful Production -- 11.3 Opportunities with Industry 4.0 -- 11.4 Issues in Implementation of Industry 4.0 -- 11.5 Potential Tools Utilized in Industry 4.0 -- 11.6 Conclusion -- References -- Chapter 12 Industry 4.0 and Manufacturing Techniques: Opportunities and Challenges -- 12.1 Introduction -- 12.2 Changing Market Demands -- 12.2.1 Individualization -- 12.2.2 Volatility -- 12.2.3 Efficiency in Terms of Energy Resources -- 12.3 Recent Technological Advancements -- 12.4 Industrial Revolution 4.0 -- 12.5 Challenges to Industry 4.0 -- 12.6 Conclusion -- References -- Chapter 13 The Role of Multiagent System in Industry 4.0 -- 13.1 Introduction -- 13.2 Characteristics and Goals of Industry 4.0 Conception -- 13.3 Artificial Intelligence -- 13.3.1 Knowledge-Based Systems -- 13.4 Multiagent Systems -- 13.4.1 Agent Architectures -- 13.4.2 JADE -- 13.4.3 System Requirements Definition -- 13.4.4 HMI Development -- 13.5 Developing Software of Controllers Multiagent Environment Behavior Patterns -- 13.5.1 Agent Supervision -- 13.5.2 Documents Dispatching Agents -- 13.5.3 Agent Rescheduling -- 13.5.4 Agent of Executive -- 13.5.5 Primary Roles of High-Availability Agent -- 13.6 Conclusion -- References -- Chapter 14 An Overview of Enhancing Encryption Standards for Multimedia in Explainable Artificial Intelligence Using Residue Number Systems for Security -- 14.1 Introduction -- 14.2 Reviews of Related Works -- 14.3 Materials and Methods -- 14.3.1 Multimedia -- 14.3.2 Artificial Intelligence and Explainable Artificial Intelligence -- 14.3.3 Cryptography.
14.3.4 Encryption and Decryption.
Record Nr. UNINA-9910829846403321
Hoboken, NJ : , : Wiley, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui