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Artificial immune system : applications in computer security / / Ying Tan
| Artificial immune system : applications in computer security / / Ying Tan |
| Autore | Tan Ying <1964-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2016 |
| Descrizione fisica | 1 online resource (240 p.) |
| Disciplina | 005.8 |
| Soggetto topico |
Artificial immune systems
Computer security Computer networks - Security measures |
| ISBN |
1-119-07627-7
1-119-07652-8 1-119-07658-7 |
| Classificazione | COM083000 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
-- Preface xiii -- About Author xxi -- Acknowledgements xxiii -- 1 Artificial Immune System 1 -- 1.1 Introduction 1 -- 1.2 Biological Immune System 2 -- 1.2.1 Overview 2 -- 1.2.2 Adaptive Immune Process 3 -- 1.3 Characteristics of BIS 4 -- 1.4 Artificial Immune System 6 -- 1.5 AIS Models and Algorithms 8 -- 1.5.1 Negative Selection Algorithm 8 -- 1.5.2 Clonal Selection Algorithm 9 -- 1.5.3 Immune Network Model 11 -- 1.5.4 Danger Theory 12 -- 1.5.5 Immune Concentration 13 -- 1.5.6 Other Methods 14 -- 1.6 Characteristics of AIS 15 -- 1.7 Applications of Artificial Immune System 16 -- 1.7.1 Virus Detection 16 -- 1.7.2 Spam Filtering 16 -- 1.7.3 Robots 20 -- 1.7.4 Control Engineering 21 -- 1.7.5 Fault Diagnosis 22 -- 1.7.6 Optimized Design 22 -- 1.7.7 Data Analysis 22 -- 1.8 Summary 22 -- 2 Malware Detection 27 -- 2.1 Introduction 27 -- 2.2 Malware 28 -- 2.2.1 Definition and Features 28 -- 2.2.2 The Development Phases of Malware 29 -- 2.3 Classic Malware Detection Approaches 30 -- 2.3.1 Static Techniques 31 -- 2.3.2 Dynamic Techniques 31 -- 2.3.3 Heuristics 32 -- 2.4 Immune Based Malware Detection Approaches 34 -- 2.4.1 An Overview of Artificial Immune System 34 -- 2.4.2 An Overview of Artificial Immune System for Malware Detection 35 -- 2.4.3 An Immune Based Virus Detection System Using Affinity Vectors 36 -- 2.4.4 A Hierarchical Artificial Immune Model for Virus Detection 38 -- 2.4.5 A Malware Detection Model Based on a Negative Selection Algorithm with Penalty Factor 2.5 Summary 43 -- 3 Immune Principle and Neural Networks Based Malware Detection 47 -- 3.1 Introduction 47 -- 3.2 Immune System for Malicious Executable Detection 48 -- 3.2.1 Non-self Detection Principles 48 -- 3.2.2 Anomaly Detection Based on Thickness 48 -- 3.2.3 Relationship Between Diversity of Detector Representation and Anomaly Detection Hole 48 -- 3.3 Experimental Dataset 48 -- 3.4 Malware Detection Algorithm 49 -- 3.4.1 Definition of Data Structures 49 -- 3.4.2 Detection Principle and Algorithm 49.
3.4.3 Generation of Detector Set 50 -- 3.4.4 Extraction of Anomaly Characteristics 50 -- 3.4.5 Classifier 52 -- 3.5 Experiment 52 -- 3.5.1 Experimental Procedure 53 -- 3.5.2 Experimental Results 53 -- 3.5.3 Comparison With Matthew G. Schultz's Method 55 -- 3.6 Summary 57 -- 4 Multiple-Point Bit Mutation Method of Detector Generation 59 -- 4.1 Introduction 59 -- 4.2 Current Detector Generating Algorithms 60 -- 4.3 Growth Algorithms 60 -- 4.4 Multiple Point Bit Mutation Method 62 -- 4.5 Experiments 62 -- 4.5.1 Experiments on Random Dataset 62 -- 4.5.2 Change Detection of Static Files 65 -- 4.6 Summary 65 -- 5 Malware Detection System Using Affinity Vectors 67 -- 5.1 Introduction 67 -- 5.2 Malware Detection Using Affinity Vectors 68 -- 5.2.1 Sliding Window 68 -- 5.2.2 Negative Selection 68 -- 5.2.3 Clonal Selection 69 -- 5.2.4 Distances 70 -- 5.2.5 Affinity Vector 71 -- 5.2.6 Training Classifiers with Affinity Vectors 71 -- 5.3 Evaluation of Affinity Vectors based malware detection System 73 -- 5.3.1 Dataset 73 -- 5.3.2 Length of Data Fragment 73 -- 5.3.3 Experimental Results 73 -- 5.4 Summary 74 -- 6 Hierarchical Artificial Immune Model 79 -- 6.1 Introduction 79 -- 6.2 Architecture of HAIM 80 -- 6.3 Virus Gene Library Generating Module 80 -- 6.3.1 Virus ODN Library 82 -- 6.3.2 Candidate Virus Gene Library 82 -- 6.3.3 Detecting Virus Gene Library 83 -- 6.4 Self-Nonself Classification Module 84 -- 6.4.1 Matching Degree between Two Genes 84 -- 6.4.2 Suspicious Program Detection 85 -- 6.5 Simulation Results of Hierarchical Artificial Immune Model 86 -- 6.5.1 Data Set 86 -- 6.5.2 Description of Experiments 86 -- 6.6 Summary 89 -- 7 Negative Selection Algorithm with Penalty Factor 91 -- 7.1 Introduction 91 -- 7.2 Framework of NSAPF 92 -- 7.3 Malware signature extraction module 93 -- 7.3.1 Malware Instruction Library (MIL) 93 -- 7.3.2 Malware Candidate Signature Library 94 -- 7.3.3 NSAPF and Malware Detection Signature Library 96 -- 7.4 Suspicious Program Detection Module 97. 7.4.1 Signature Matching 97 -- 7.4.2 Matching between Suspicious Programs and the MDSL 97 -- 7.4.3 Analysis of Penalty Factor 98 -- 7.5 Experiments and Analysis 99 -- 7.5.1 Experimental Datasets 99 -- 7.5.2 Experiments on Henchiri dataset 100 -- 7.5.3 Experiments on CILPKU08 Dataset 103 -- 7.5.4 Experiments on VX Heavens Dataset 104 -- 7.5.5 Parameter Analysis 104 -- 7.6 Summary 105 -- 8 Danger Feature Based Negative Selection Algorithm 107 -- 8.1 Introduction 107 -- 8.1.1 Danger Feature 107 -- 8.1.2 Framework of Danger Feature Based Negative Selection Algorithm 107 -- 8.2 DFNSA for Malware Detection 109 -- 8.2.1 Danger Feature Extraction 109 -- 8.2.2 Danger Feature Vector 110 -- 8.3 Experiments 111 -- 8.3.1 Datasets 111 -- 8.3.2 Experimental Setup 111 -- 8.3.3 Selection of Parameters 112 -- 8.3.4 Experimental Results 113 -- 8.4 Discussions 113 -- 8.4.1 Comparison of Detecting Feature Libraries 113 -- 8.4.2 Comparison of Detection Time 114 -- 8.5 Summary 114 -- 9 Immune Concentration Based Malware Detection Approaches 117 -- 9.1 Introduction 117 -- 9.2 Generation of Detector Libraries 117 -- 9.3 Construction of Feature Vector for Local Concentration 122 -- 9.4 Parameters Optimization based on Particle Swarm Optimization 124 -- 9.5 Construction of Feature Vector for Hybrid Concentration 124 -- 9.5.1 Hybrid Concentration 124 -- 9.5.2 Strategies for Definition of Local Areas 126 -- 9.5.3 HC-based Malware Detection Method 127 -- 9.5.4 Discussions 128 -- 9.6 Experiments 130 -- 9.6.1 Experiments of Local Concentration 130 -- 9.6.2 Experiments of Hybrid Concentration 138 -- 9.7 Summary 142 -- 10 Immune Cooperation Mechanism Based Learning Framework 145 -- 10.1 Introduction 145 -- 10.2 Immune Signal Cooperation Mechanism based Learning Framework 148 -- 10.3 Malware Detection Model 151 -- 10.4 Experiments of Malware Detection Model 152 -- 10.4.1 Experimental setup 152 -- 10.4.2 Selection of Parameters 153 -- 10.4.3 Experimental Results 153 -- 10.4.4 Statistical Analysis 155. 10.5 Discussions 157 -- 10.5.1 Advantages 157 -- 10.5.2 Time Complexity 157 -- 10.6 Summary 158 -- 11 Class-wise Information Gain 161 -- 11.1 Introduction 161 -- 11.2 Problem Statement 163 -- 11.2.1 Definition of the Generalized Class 163 -- 11.2.2 Malware Recognition Problem 163 -- 11.3 Class-wise Information Gain 164 -- 11.3.1 Definition 164 -- 11.3.2 Analysis 166 -- 11.4 CIG-based Malware Detection Method 170 -- 11.4.1 Feature Selection Module 170 -- 11.4.2 Classification Module 171 -- 11.5 Dataset 172 -- 11.5.1 Benign Program Dataset 172 -- 11.5.2 Malware Dataset 172 -- 11.6 Selection of Parameter 174 -- 11.6.1 Experimental Setup 174 -- 11.6.2 Experiments of Selection of Parameter 174 -- 11.7 Experimental Results 175 -- 11.7.1 Experiments on the VXHeavens Dataset 177 -- 11.7.2 Experiments on the Henchiri Dataset 179 -- 11.7.3 Experiments on the CILPKU08 Dataset 180 -- 11.8 Discussions 180 -- 11.8.1 The Relationship Among IG-A, DFCIG-B and DFCIG-M 181 -- 11.8.2 Space Complexity 182 -- 11.9 Summary 183 -- Index 185. |
| Record Nr. | UNINA-9910135024403321 |
Tan Ying <1964->
|
||
| Hoboken, New Jersey : , : Wiley, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial immune system : applications in computer security / / Ying Tan
| Artificial immune system : applications in computer security / / Ying Tan |
| Autore | Tan Ying <1964-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2016 |
| Descrizione fisica | 1 online resource (240 p.) |
| Disciplina | 005.8 |
| Soggetto topico |
Artificial immune systems
Computer security Computer networks - Security measures |
| ISBN |
1-119-07627-7
1-119-07652-8 1-119-07658-7 |
| Classificazione | COM083000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
-- Preface xiii -- About Author xxi -- Acknowledgements xxiii -- 1 Artificial Immune System 1 -- 1.1 Introduction 1 -- 1.2 Biological Immune System 2 -- 1.2.1 Overview 2 -- 1.2.2 Adaptive Immune Process 3 -- 1.3 Characteristics of BIS 4 -- 1.4 Artificial Immune System 6 -- 1.5 AIS Models and Algorithms 8 -- 1.5.1 Negative Selection Algorithm 8 -- 1.5.2 Clonal Selection Algorithm 9 -- 1.5.3 Immune Network Model 11 -- 1.5.4 Danger Theory 12 -- 1.5.5 Immune Concentration 13 -- 1.5.6 Other Methods 14 -- 1.6 Characteristics of AIS 15 -- 1.7 Applications of Artificial Immune System 16 -- 1.7.1 Virus Detection 16 -- 1.7.2 Spam Filtering 16 -- 1.7.3 Robots 20 -- 1.7.4 Control Engineering 21 -- 1.7.5 Fault Diagnosis 22 -- 1.7.6 Optimized Design 22 -- 1.7.7 Data Analysis 22 -- 1.8 Summary 22 -- 2 Malware Detection 27 -- 2.1 Introduction 27 -- 2.2 Malware 28 -- 2.2.1 Definition and Features 28 -- 2.2.2 The Development Phases of Malware 29 -- 2.3 Classic Malware Detection Approaches 30 -- 2.3.1 Static Techniques 31 -- 2.3.2 Dynamic Techniques 31 -- 2.3.3 Heuristics 32 -- 2.4 Immune Based Malware Detection Approaches 34 -- 2.4.1 An Overview of Artificial Immune System 34 -- 2.4.2 An Overview of Artificial Immune System for Malware Detection 35 -- 2.4.3 An Immune Based Virus Detection System Using Affinity Vectors 36 -- 2.4.4 A Hierarchical Artificial Immune Model for Virus Detection 38 -- 2.4.5 A Malware Detection Model Based on a Negative Selection Algorithm with Penalty Factor 2.5 Summary 43 -- 3 Immune Principle and Neural Networks Based Malware Detection 47 -- 3.1 Introduction 47 -- 3.2 Immune System for Malicious Executable Detection 48 -- 3.2.1 Non-self Detection Principles 48 -- 3.2.2 Anomaly Detection Based on Thickness 48 -- 3.2.3 Relationship Between Diversity of Detector Representation and Anomaly Detection Hole 48 -- 3.3 Experimental Dataset 48 -- 3.4 Malware Detection Algorithm 49 -- 3.4.1 Definition of Data Structures 49 -- 3.4.2 Detection Principle and Algorithm 49.
3.4.3 Generation of Detector Set 50 -- 3.4.4 Extraction of Anomaly Characteristics 50 -- 3.4.5 Classifier 52 -- 3.5 Experiment 52 -- 3.5.1 Experimental Procedure 53 -- 3.5.2 Experimental Results 53 -- 3.5.3 Comparison With Matthew G. Schultz's Method 55 -- 3.6 Summary 57 -- 4 Multiple-Point Bit Mutation Method of Detector Generation 59 -- 4.1 Introduction 59 -- 4.2 Current Detector Generating Algorithms 60 -- 4.3 Growth Algorithms 60 -- 4.4 Multiple Point Bit Mutation Method 62 -- 4.5 Experiments 62 -- 4.5.1 Experiments on Random Dataset 62 -- 4.5.2 Change Detection of Static Files 65 -- 4.6 Summary 65 -- 5 Malware Detection System Using Affinity Vectors 67 -- 5.1 Introduction 67 -- 5.2 Malware Detection Using Affinity Vectors 68 -- 5.2.1 Sliding Window 68 -- 5.2.2 Negative Selection 68 -- 5.2.3 Clonal Selection 69 -- 5.2.4 Distances 70 -- 5.2.5 Affinity Vector 71 -- 5.2.6 Training Classifiers with Affinity Vectors 71 -- 5.3 Evaluation of Affinity Vectors based malware detection System 73 -- 5.3.1 Dataset 73 -- 5.3.2 Length of Data Fragment 73 -- 5.3.3 Experimental Results 73 -- 5.4 Summary 74 -- 6 Hierarchical Artificial Immune Model 79 -- 6.1 Introduction 79 -- 6.2 Architecture of HAIM 80 -- 6.3 Virus Gene Library Generating Module 80 -- 6.3.1 Virus ODN Library 82 -- 6.3.2 Candidate Virus Gene Library 82 -- 6.3.3 Detecting Virus Gene Library 83 -- 6.4 Self-Nonself Classification Module 84 -- 6.4.1 Matching Degree between Two Genes 84 -- 6.4.2 Suspicious Program Detection 85 -- 6.5 Simulation Results of Hierarchical Artificial Immune Model 86 -- 6.5.1 Data Set 86 -- 6.5.2 Description of Experiments 86 -- 6.6 Summary 89 -- 7 Negative Selection Algorithm with Penalty Factor 91 -- 7.1 Introduction 91 -- 7.2 Framework of NSAPF 92 -- 7.3 Malware signature extraction module 93 -- 7.3.1 Malware Instruction Library (MIL) 93 -- 7.3.2 Malware Candidate Signature Library 94 -- 7.3.3 NSAPF and Malware Detection Signature Library 96 -- 7.4 Suspicious Program Detection Module 97. 7.4.1 Signature Matching 97 -- 7.4.2 Matching between Suspicious Programs and the MDSL 97 -- 7.4.3 Analysis of Penalty Factor 98 -- 7.5 Experiments and Analysis 99 -- 7.5.1 Experimental Datasets 99 -- 7.5.2 Experiments on Henchiri dataset 100 -- 7.5.3 Experiments on CILPKU08 Dataset 103 -- 7.5.4 Experiments on VX Heavens Dataset 104 -- 7.5.5 Parameter Analysis 104 -- 7.6 Summary 105 -- 8 Danger Feature Based Negative Selection Algorithm 107 -- 8.1 Introduction 107 -- 8.1.1 Danger Feature 107 -- 8.1.2 Framework of Danger Feature Based Negative Selection Algorithm 107 -- 8.2 DFNSA for Malware Detection 109 -- 8.2.1 Danger Feature Extraction 109 -- 8.2.2 Danger Feature Vector 110 -- 8.3 Experiments 111 -- 8.3.1 Datasets 111 -- 8.3.2 Experimental Setup 111 -- 8.3.3 Selection of Parameters 112 -- 8.3.4 Experimental Results 113 -- 8.4 Discussions 113 -- 8.4.1 Comparison of Detecting Feature Libraries 113 -- 8.4.2 Comparison of Detection Time 114 -- 8.5 Summary 114 -- 9 Immune Concentration Based Malware Detection Approaches 117 -- 9.1 Introduction 117 -- 9.2 Generation of Detector Libraries 117 -- 9.3 Construction of Feature Vector for Local Concentration 122 -- 9.4 Parameters Optimization based on Particle Swarm Optimization 124 -- 9.5 Construction of Feature Vector for Hybrid Concentration 124 -- 9.5.1 Hybrid Concentration 124 -- 9.5.2 Strategies for Definition of Local Areas 126 -- 9.5.3 HC-based Malware Detection Method 127 -- 9.5.4 Discussions 128 -- 9.6 Experiments 130 -- 9.6.1 Experiments of Local Concentration 130 -- 9.6.2 Experiments of Hybrid Concentration 138 -- 9.7 Summary 142 -- 10 Immune Cooperation Mechanism Based Learning Framework 145 -- 10.1 Introduction 145 -- 10.2 Immune Signal Cooperation Mechanism based Learning Framework 148 -- 10.3 Malware Detection Model 151 -- 10.4 Experiments of Malware Detection Model 152 -- 10.4.1 Experimental setup 152 -- 10.4.2 Selection of Parameters 153 -- 10.4.3 Experimental Results 153 -- 10.4.4 Statistical Analysis 155. 10.5 Discussions 157 -- 10.5.1 Advantages 157 -- 10.5.2 Time Complexity 157 -- 10.6 Summary 158 -- 11 Class-wise Information Gain 161 -- 11.1 Introduction 161 -- 11.2 Problem Statement 163 -- 11.2.1 Definition of the Generalized Class 163 -- 11.2.2 Malware Recognition Problem 163 -- 11.3 Class-wise Information Gain 164 -- 11.3.1 Definition 164 -- 11.3.2 Analysis 166 -- 11.4 CIG-based Malware Detection Method 170 -- 11.4.1 Feature Selection Module 170 -- 11.4.2 Classification Module 171 -- 11.5 Dataset 172 -- 11.5.1 Benign Program Dataset 172 -- 11.5.2 Malware Dataset 172 -- 11.6 Selection of Parameter 174 -- 11.6.1 Experimental Setup 174 -- 11.6.2 Experiments of Selection of Parameter 174 -- 11.7 Experimental Results 175 -- 11.7.1 Experiments on the VXHeavens Dataset 177 -- 11.7.2 Experiments on the Henchiri Dataset 179 -- 11.7.3 Experiments on the CILPKU08 Dataset 180 -- 11.8 Discussions 180 -- 11.8.1 The Relationship Among IG-A, DFCIG-B and DFCIG-M 181 -- 11.8.2 Space Complexity 182 -- 11.9 Summary 183 -- Index 185. |
| Record Nr. | UNINA-9910830511803321 |
|
Tan Ying <1964->
|
||
| Hoboken, New Jersey : , : Wiley, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data Mining and Big Data [[electronic resource] ] : 7th International Conference, DMBD 2022, Beijing, China, November 21–24, 2022, Proceedings, Part II / / edited by Ying Tan, Yuhui Shi
| Data Mining and Big Data [[electronic resource] ] : 7th International Conference, DMBD 2022, Beijing, China, November 21–24, 2022, Proceedings, Part II / / edited by Ying Tan, Yuhui Shi |
| Autore | Tan Ying <1964-> |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (474 pages) |
| Disciplina | 005.7 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Application software
Artificial intelligence Image processing—Digital techniques Computer vision Computer and Information Systems Applications Artificial Intelligence Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 981-19-8991-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Identification and Recognition Methods -- Complementary Convolutional Restricted Boltzmann Machine and Its Applications in Image Recognition -- Text-independent Speaker Identification Using a Single-scale SincNet-DCGAN Model -- Genome-wide Feature Selection of Robust mRNA Biomarkers for Body Fluid Identification -- HOS-YOLOv5: An Improved High-precision Remote Sensing Image Target Detection Algorithm Based on YOLOv5 -- A Multi-Module 3D U-Net Learning Architecture for Brain Tumor Segmentation -- Problems with Regression-line in Data-mining Applications and A Better Alternate Linear-model -- Research on Hot Spot Mining Technology for Network Public Opinion -- Research Hotspots, Emerging Trend and Front of Fraud Detection Rearch: A Scientometric Analysis (1984 - 2021) -- Optimization Methods -- An Algorithm of Set-Based Differential Evolution for Discrete Optimization Problem -- Multi-objective Optimization Technique for RSU Deployment -- Knowledge Learning-based Brain Storm Optimization Algorithm for Multimodal Optimization -- Market Investment Methods -- Non-Local Graph Aggregation for Diversified Stock Recommendation -- Novel Sentiment Analysis from Twitter for Stock Change Prediction -- A Novel Investment Strategy for Mixed Asset Allocation Based on Entropy-based Time Series Prediction -- A Novel Investment Strategy for Mixed Asset Allocation Based on Entropy-based Time Series Prediction -- Community Detection and Diagnosis Systems -- A Self-Adaptive Two-Stage Local Expansion Algorithm for Community Detection on Complex Networks -- Supervised Prototypical Variational Autoencdoer for Shilling Attack Detection in Recommender Systems -- Knowledge Graph based Chicken Disease Diagnosis Question Answering System -- Therapeutic effects of corticosteroids for critical and severe COVID-19 patients -- Big Data Analysis -- Secure Cross-User Fuzzy Deduplication for Images in Cloud Storage -- Blockchain-based Integrity Auditing with Secure Deduplication in Cloud Storage -- Name Disambiguation Based on Entity Relationship Graph in Big Data -- Ontology-based metadata model design of data governance system -- Ontology-based Combat Force Modeling and Its Intelligent Planning Using Genetic Algorithm -- Research on Multi-channel Retrieve Mechanism Based on Heuristic -- Big-Model Methods -- PoetryBERT: Pre-Training with Sememe Knowledge for Classical Chinese Poetry -- Image hide with Invertible Network and Swin Transformer -- Modeling and Analysis of Combat System Confrontation Based on Large-scale Knowledge Graph Network -- Generating Aversarial Exmaples and Other Applications -- Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN -- Defending Adversarial Examples by Negative Correlation Ensemble -- Accurate Decision-Making Method for Air Combat Pilots based on Data-Driven -- Establishment of Empirical Expression of Atmospheric Scattering Coefficient for Line-of-sight Ultraviolet Propagation in Coastal Area -- Deep Reinforcement Learning Approach -- Heterogeneous Multi-unit Control with Curriculum Learning for Multi-agent Reinforcement Learning -- A Deep Reinforcement Learning Approach for Cooperative Target Defense -- Particle Swarm Based Reinforcement Learning -- User’s Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning in Data Center -- SMPG: Adaptive Soft Update for Masked MADDPG -- Attentive Relational State Representation for Intelligent Joint Operation Simulation -- Graph Neural Networks -- Flow Prediction via Multi-view Spatial-temporal Graph Neural Network -- RotatSAGE: A Scalable Knowledge Graph Embedding Model based on Translation Assumptions and Graph Neural Networks -- Denoise Network Structure for User Alignment across Networks via Graph Structure Learning -- OLPGP: An optimized label propagation-based distributed graph partitioning algorithm -- Deep Neural Networks -- DRGS: Low-Precision Full Quantization of Deep Neural Network with Dynamic Rounding and Gradient Scaling for Object Detection -- Emotion Recognition Based on Multi-scale Convolutional Neural Network.-Pose Sequence Model Using the Encoder-decoder Structure for 3d Pose Estimation.-Research and Analysis of Video-Based Human Pose Estimation.-Action Recognition for Solo-militant Based on ResNet and Rule Matching -- Multiple Residual Quantization of Pruning -- Clustering Methods -- Deep Structured Graph Clustering Network -- Improved Clustering Strategies for Learning Style Identification in Massive Open Online Courses -- CSHEM - A Compressed Sensing Based Secure Data Processing Method for Electrical Data -- Prediction Methods -- An Improved Multi-Source Spatiotemporal Data Fusion Model based on the Nearest Neighbor Grids for PM2.5 Concentration Interpolation and Prediction -- Study on the Prediction of Rice Noodle Raw Material Index Content by Deep Feature Fusion -- GAP: Goal-Aware Prediction with Hierarchical Interactive Representation for Vehicle Trajectory -- Multi-Cause Learning for Diagnosis Prediction -- Prediction of Postoperative Survival Level of Esophageal Cancer Patients Based on Kaplan-Meier(K-M) Survival Analysis and Gray Wolf Optimization (GWO)-BP Model -- Classification Methods -- Possibilistic Reject-Classification based on Contrastive Learning in Vector Quantization Networks -- A Classification Method for Imbalanced Data Based on Ant Lion Optimizer -- Learnable Relation With Triplet Formulation For Semi-supervised Medical Image Classification -- Multi-view Classification via Twin Projection Vector Machine with Application to EEG-based Driving Fatigue Detection -- An Interpretable Conditional Augmentation Classification Approach for Imbalanced EHRs Mortality Prediction -- Combining Statistical and Semantic Features For Trajectory Point Classification. |
| Record Nr. | UNISA-996508666703316 |
|
Tan Ying <1964->
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Data Mining and Big Data : 7th International Conference, DMBD 2022, Beijing, China, November 21–24, 2022, Proceedings, Part II / / edited by Ying Tan, Yuhui Shi
| Data Mining and Big Data : 7th International Conference, DMBD 2022, Beijing, China, November 21–24, 2022, Proceedings, Part II / / edited by Ying Tan, Yuhui Shi |
| Autore | Tan Ying <1964-> |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (474 pages) |
| Disciplina | 005.7 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Application software
Artificial intelligence Image processing—Digital techniques Computer vision Computer and Information Systems Applications Artificial Intelligence Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 981-19-8991-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Identification and Recognition Methods -- Complementary Convolutional Restricted Boltzmann Machine and Its Applications in Image Recognition -- Text-independent Speaker Identification Using a Single-scale SincNet-DCGAN Model -- Genome-wide Feature Selection of Robust mRNA Biomarkers for Body Fluid Identification -- HOS-YOLOv5: An Improved High-precision Remote Sensing Image Target Detection Algorithm Based on YOLOv5 -- A Multi-Module 3D U-Net Learning Architecture for Brain Tumor Segmentation -- Problems with Regression-line in Data-mining Applications and A Better Alternate Linear-model -- Research on Hot Spot Mining Technology for Network Public Opinion -- Research Hotspots, Emerging Trend and Front of Fraud Detection Rearch: A Scientometric Analysis (1984 - 2021) -- Optimization Methods -- An Algorithm of Set-Based Differential Evolution for Discrete Optimization Problem -- Multi-objective Optimization Technique for RSU Deployment -- Knowledge Learning-based Brain Storm Optimization Algorithm for Multimodal Optimization -- Market Investment Methods -- Non-Local Graph Aggregation for Diversified Stock Recommendation -- Novel Sentiment Analysis from Twitter for Stock Change Prediction -- A Novel Investment Strategy for Mixed Asset Allocation Based on Entropy-based Time Series Prediction -- A Novel Investment Strategy for Mixed Asset Allocation Based on Entropy-based Time Series Prediction -- Community Detection and Diagnosis Systems -- A Self-Adaptive Two-Stage Local Expansion Algorithm for Community Detection on Complex Networks -- Supervised Prototypical Variational Autoencdoer for Shilling Attack Detection in Recommender Systems -- Knowledge Graph based Chicken Disease Diagnosis Question Answering System -- Therapeutic effects of corticosteroids for critical and severe COVID-19 patients -- Big Data Analysis -- Secure Cross-User Fuzzy Deduplication for Images in Cloud Storage -- Blockchain-based Integrity Auditing with Secure Deduplication in Cloud Storage -- Name Disambiguation Based on Entity Relationship Graph in Big Data -- Ontology-based metadata model design of data governance system -- Ontology-based Combat Force Modeling and Its Intelligent Planning Using Genetic Algorithm -- Research on Multi-channel Retrieve Mechanism Based on Heuristic -- Big-Model Methods -- PoetryBERT: Pre-Training with Sememe Knowledge for Classical Chinese Poetry -- Image hide with Invertible Network and Swin Transformer -- Modeling and Analysis of Combat System Confrontation Based on Large-scale Knowledge Graph Network -- Generating Aversarial Exmaples and Other Applications -- Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN -- Defending Adversarial Examples by Negative Correlation Ensemble -- Accurate Decision-Making Method for Air Combat Pilots based on Data-Driven -- Establishment of Empirical Expression of Atmospheric Scattering Coefficient for Line-of-sight Ultraviolet Propagation in Coastal Area -- Deep Reinforcement Learning Approach -- Heterogeneous Multi-unit Control with Curriculum Learning for Multi-agent Reinforcement Learning -- A Deep Reinforcement Learning Approach for Cooperative Target Defense -- Particle Swarm Based Reinforcement Learning -- User’s Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning in Data Center -- SMPG: Adaptive Soft Update for Masked MADDPG -- Attentive Relational State Representation for Intelligent Joint Operation Simulation -- Graph Neural Networks -- Flow Prediction via Multi-view Spatial-temporal Graph Neural Network -- RotatSAGE: A Scalable Knowledge Graph Embedding Model based on Translation Assumptions and Graph Neural Networks -- Denoise Network Structure for User Alignment across Networks via Graph Structure Learning -- OLPGP: An optimized label propagation-based distributed graph partitioning algorithm -- Deep Neural Networks -- DRGS: Low-Precision Full Quantization of Deep Neural Network with Dynamic Rounding and Gradient Scaling for Object Detection -- Emotion Recognition Based on Multi-scale Convolutional Neural Network.-Pose Sequence Model Using the Encoder-decoder Structure for 3d Pose Estimation.-Research and Analysis of Video-Based Human Pose Estimation.-Action Recognition for Solo-militant Based on ResNet and Rule Matching -- Multiple Residual Quantization of Pruning -- Clustering Methods -- Deep Structured Graph Clustering Network -- Improved Clustering Strategies for Learning Style Identification in Massive Open Online Courses -- CSHEM - A Compressed Sensing Based Secure Data Processing Method for Electrical Data -- Prediction Methods -- An Improved Multi-Source Spatiotemporal Data Fusion Model based on the Nearest Neighbor Grids for PM2.5 Concentration Interpolation and Prediction -- Study on the Prediction of Rice Noodle Raw Material Index Content by Deep Feature Fusion -- GAP: Goal-Aware Prediction with Hierarchical Interactive Representation for Vehicle Trajectory -- Multi-Cause Learning for Diagnosis Prediction -- Prediction of Postoperative Survival Level of Esophageal Cancer Patients Based on Kaplan-Meier(K-M) Survival Analysis and Gray Wolf Optimization (GWO)-BP Model -- Classification Methods -- Possibilistic Reject-Classification based on Contrastive Learning in Vector Quantization Networks -- A Classification Method for Imbalanced Data Based on Ant Lion Optimizer -- Learnable Relation With Triplet Formulation For Semi-supervised Medical Image Classification -- Multi-view Classification via Twin Projection Vector Machine with Application to EEG-based Driving Fatigue Detection -- An Interpretable Conditional Augmentation Classification Approach for Imbalanced EHRs Mortality Prediction -- Combining Statistical and Semantic Features For Trajectory Point Classification. |
| Record Nr. | UNINA-9910645895603321 |
|
Tan Ying <1964->
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||