Data mining and big data : 6th international conference, DMBD 2021, Guangzhou, China, October 20-22, 2021 : proceedings. Part I

Data mining and big data : Part I / 6th international conference, DMBD 2021, Guangzhou, China, October 20-22, 2021 : proceedings. Ying Tan, Yuhui Shi, Albert Zomaya, Hongyang Yan, Jun Cai (eds.).

Sparad:
Bibliografiska uppgifter
Institutionell upphovsman: International Conference on Data Mining and Big Data Guangzhou, China
Övriga upphovsmän: Tan, Ying, 1964- (Utgivare, redaktör, sammanställare), Shi, Yuhui (Utgivare, redaktör, sammanställare), Zomaya, Albert Y. (Utgivare, redaktör, sammanställare), Yan, Hongyang (Utgivare, redaktör, sammanställare), Cai, Jun (Utgivare, redaktör, sammanställare)
Materialtyp: E-bok
Språk:English
Publicerad: Singapore : Springer, [2021]
Serie:Communications in computer and information science ; 1453.
Ämnen:
Data mining > Congresses.
Big data > Congresses.
Big data
Data mining
Electronic books.
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Länkar:Click for online access
Innehållsförteckning:
  • Intro
  • Preface
  • Organization
  • Contents
  • Part I
  • Contents
  • Part II
  • BSMRL: Bribery Selfish Mining with Reinforcement Learning
  • 1 Introduction
  • 1.1 Related Work
  • 2 Preliminaries
  • 2.1 Selfish Mining
  • 2.2 Bribery Attack
  • 2.3 Reinforcement Learning
  • 3 Modeling BSMRL
  • 3.1 Constructing the Environment
  • 3.2 The Attacker's Mining Strategy
  • 4 Simulation
  • 5 Conclusion and Future Work
  • References
  • The Theoretical Analysis of Multi-dividing Ontology Learning by Rademacher Vector
  • 1 Introduction
  • 2 Ontology Learning Framework in Multi-dividing Setting and Prerequisite Knowledge
  • 3 Main Result and Proof
  • 4 Conclusion
  • References
  • A Group Blind Signature Scheme for Privacy Protection of Power Big Data in Smart Grid
  • Abstract
  • 1 Introduction
  • 2 Preliminaries
  • 2.1 Group Blind Signature
  • 2.2 Schnorr Identification Protocol
  • 3 System Model and Adversary Model
  • 3.1 System Model
  • 3.2 Adversary Model
  • 4 Our Scheme
  • 4.1 System Initialization
  • 4.2 User Anonymous Authentication and Data Reporting
  • 4.3 Blindly Signature on the Message
  • 4.4 Verification and Traceability
  • 5 Security Analysis
  • 5.1 Authenticatability
  • 5.2 Privacy Protection
  • 5.3 Anonymity
  • 5.4 Unforgeability
  • 5.5 Traceability
  • 6 Conclusion
  • References
  • MB Based Multi-dividing Ontology Learning Trick
  • 1 Introduction
  • 2 MB Based Multi-dividing Ontology Learning Algorithm
  • 3 Experiments
  • 3.1 Experiment on Mathematics-Physics Disciplines
  • 3.2 Ontology Mapping on Sponge City Rainwater Treatment System Ontologies
  • 3.3 Experiment on Chemical Index Ontology
  • 4 Conclusion
  • References
  • Application of LSTM Model Optimized Based on Adaptive Genetic Algorithm in Stock Forecasting
  • Abstract
  • 1 Introduction
  • 2 Algorithm Background
  • 3 Problem Description
  • 4 Algorithm Description
  • 4.1 Genes Code
  • 4.2 Crossover Operator
  • 4.3 Mutation Operator
  • 4.4 Steps of the Algorithm
  • 5 Experimental Result
  • 6 Conclusion
  • Acknowledgement
  • References
  • A Network Based Quantitative Method for the Mining and Visualization of Music Influence
  • Abstract
  • 1 Introduction
  • 2 Notations
  • 3 LMIFNC Model for Influencer-Follower Network
  • 3.1 Features of "Music Influence."
  • 3.2 The Influence of Artist
  • 3.2.1 The Initial Influence of Artist Drawn from Linkage
  • 3.2.2 Logarithm Function for Time-Offset Correction Coefficient C
  • 3.2.3 Assigning Weight to the Edges of Influencer-Follower Network
  • 3.3 Deriving Influencer-Follower Network and Subnetwork
  • 3.3.1 Definition of Modularity and Increment of Modularity
  • 3.3.2 Louvain Method
  • 3.3.3 Process of Proposed LMIFNC for Influencer-Follower Network Construction
  • 4 Experimental Results and Discussion
  • 4.1 Data Set
  • 4.2 Results and Visualization
  • 5 Conclusion and Future Work
  • References

Liknande verk