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Advanced data mining and applications . Part II : 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2-4, 2022, Proceedings Edited by Bohan Li [and seven others]
Advanced data mining and applications . Part II : 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2-4, 2022, Proceedings Edited by Bohan Li [and seven others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (430 pages)
Disciplina 006.3
Collana Lecture notes in computer science
Soggetto topico Data mining
Mineria de dades
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-95408-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996464552303316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advanced data mining and applications : 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, Proceedings, Part II / / Weitong Chen [and five others] editors
Advanced data mining and applications : 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, Proceedings, Part II / / Weitong Chen [and five others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (500 pages)
Disciplina 006.31
Collana Lecture notes in computer science. Lecture notes in artificial intelligence
Soggetto topico Data mining
ISBN 3-031-22137-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Text Mining -- Towards Idea Mining: Problem-Solution Phrase Extraction from Text -- 1 Introduction -- 2 Related Work -- 2.1 Problem Formation -- 3 Methodology -- 3.1 Models for Extracting Problem-Solution Phrases -- 4 Experiment -- 4.1 Dataset UCCL1000 -- 4.2 Dataset NIPS488 -- 4.3 Dataset Summary -- 4.4 Text Preprocessing -- 4.5 Input Representations -- 4.6 Training and Evaluation -- 4.7 Result Analysis -- 5 Discussion -- 6 Future Work -- 7 Conclusion -- References -- Spam Email Categorization with NLP and Using Federated Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Federated Phishing Filter (FPF) -- 3.1 Natural Language Processing -- 3.2 Deep Learning Model for Spam Categorization -- 3.3 Spam Detection and Categorization Model -- 3.4 Federated Learning -- 3.5 Federated Training Models -- 3.6 Federated Averaging (FA) -- 3.7 Federated Averaging Strategies -- 3.8 Equal Weighting (EWS) -- 3.9 Weighted Average (WAS) -- 3.10 Datasets -- 4 Empirical Evaluation -- 4.1 Comparison of EWS and AWS Averaging Strategies -- 4.2 Features Performance Comparison -- 5 Conclusion and Future Work -- References -- SePass: Semantic Password Guessing Using k-nn Similarity Search in Word Embeddings*-12pt -- 1 Introduction -- 2 Related Work -- 3 Semantic Password Guessing -- 3.1 Generation of New Password Candidates -- 3.2 Sorting of the Password Candidates -- 4 Test Bed -- 4.1 Data Sets -- 4.2 Compared Methods -- 4.3 Experimental Set-Up and Evaluation Metric -- 5 Results and Discussion -- 5.1 Accuracy Results -- 5.2 Unseen Base Words -- 6 Conclusion -- References -- DeMRC: Dynamically Enhanced Multi-hop Reading Comprehension Model for Low Data -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Sentence Filtering Model -- 3.2 Answer Prediction Model.
3.3 Self-training Augmentation Based on External Data -- 4 Experiments -- 4.1 Data Set -- 4.2 Implementation Details -- 5 Results -- 6 Conclusion -- References -- ESTD: Empathy Style Transformer with Discriminative Mechanism -- 1 Introduction -- 2 Related Work -- 2.1 NLP for Online Mental Health Assistance -- 2.2 Text Style Transfer -- 2.3 Discriminatory Mechanism -- 3 Methodology -- 3.1 Empathic Expression Calculation -- 3.2 ESTD Framework -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Evaluation Metrics -- 4.4 Ablation Study -- 4.5 Results -- 5 Conclusion -- References -- Detection Method of User Behavior Transition on Computer -- 1 Introduction -- 2 Related Work -- 2.1 Image Classification and Clustering -- 2.2 Search and Operation Automation -- 2.3 User Behavior Analytics -- 3 Detection Method of User Behavior Transition -- 3.1 Overview -- 3.2 Feature Extraction -- 3.3 Time-Series Grouping Function -- 3.4 Time-Series Features Grouping Function -- 3.5 User Behavior Transition Detection Function -- 4 Experiment -- 4.1 Our Dataset -- 4.2 Experiment Results -- 4.3 Discussion -- 5 Conclusion -- References -- Image, Multimedia and Time Series Data Mining -- Ensemble Image Super-Resolution CNNs for Small Data and Diverse Compressive Models -- 1 Introduction -- 1.1 Contribution -- 2 Foundational Work and Background -- 2.1 Sparse Representations -- 2.2 Miralon Areal Density Maps -- 3 Proposed Method -- 4 Experimental Results -- 4.1 Training Details -- 4.2 Reconstruction Quality on Testing Images -- 4.3 Application of Miralon Areal Density Maps -- 5 Conclusion -- References -- Optimizing MobileNetV2 Architecture Using Split Input and Layer Replications for 3D Face Recognition Task -- 1 Introduction -- 2 Backgrounds -- 2.1 Related Works -- 2.2 Convolutional Neural Network (CNN) -- 3 Methodology -- 3.1 Data Gathering.
3.2 Preprocessing -- 3.3 Model Overview -- 3.4 Metrics -- 3.5 Training Configuration -- 3.6 Automatic Model Finding -- 4 Experimental Results -- 4.1 Comparison Between 2D and 3D Face Recognition Models -- 4.2 Comparison Between RGBD and RGB+D Face Recognition Models -- 4.3 Comparison Between Baseline MobileNetV2 and RGB+D MobileNetV2 with Layer Replication -- 4.4 Comparison Between Our Baseline Model and EffiencientNet on CelebA Dataset -- 5 Conclusion and Future Work -- References -- GANs for Automatic Generation of Data Plots -- 1 Introduction -- 2 Generative Adversarial Networks -- 3 Related Work -- 4 Methodology -- 5 Results -- 6 Conclusion -- References -- An Explainable Approach to Semantic Link Mining in Multi-sourced Dynamic Data -- 1 Introduction -- 2 Related Work -- 2.1 Knowledge Graph Link Prediction -- 2.2 Semantic Data Integration -- 3 Preliminaries -- 4 Our Approach -- 4.1 Our Framework -- 4.2 KG-Based Integration -- 4.3 Rule-Based Link Prediction -- 5 An Application Case -- 6 Evaluation -- 6.1 Static Link Prediction -- 6.2 Dynamic Link Prediction -- 7 Conclusion -- References -- Information Mining from Images of Pipeline Based on Knowledge Representation and Reasoning -- 1 Introduction -- 2 Related Work -- 2.1 Pipeline Defects Identification -- 2.2 Ontology for Knowledge Formalization -- 3 PDI Ontology Construction -- 3.1 Knowledge Resource -- 3.2 Ontology Development for PDI -- 3.3 Reasoning Rules for PDI -- 4 Case Study -- 4.1 Selected Pipeline Images with Common Defect Types -- 4.2 The Attribute Information of Pipeline Images -- 4.3 Mapping Rules for Images Instantiation in PDI Ontology -- 4.4 Knowledge Reasoning -- 4.5 Discussion -- 5 Conclusion -- References -- Binary Gravitational Subspace Search for Outlier Detection in High Dimensional Data Streams -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation.
4 Binary Gravitational Subspace Search for Outlier Detection in High Dimensional Data Streams -- 4.1 Subspace Search with Adapted Binary GSA -- 4.2 Solution Overview -- 5 Experimental Study and Results Analysis -- 5.1 Experimentation Setting -- 5.2 Results and Analysis -- 6 Conclusion and Future Works -- References -- Classification, Clustering and Recommendation -- Signal Classification Using Smooth Coefficients of Multiple Wavelets to Achieve High Accuracy from Compressed Representation of Signal -- 1 Introduction -- 2 Wavelets -- 2.1 DWT -- 2.2 MDWT -- 2.3 Energy Distribution -- 3 Proposed Technique -- 3.1 Advantages -- 3.2 Steps in the Proposed Technique: MWCSC -- 4 Experimental Results -- 4.1 Classification Methods Used -- 4.2 Arrowhead Data -- 4.3 Mallat Data -- 4.4 Ford Data -- 5 Conclusion -- References -- On Reducing the Bias of Random Forest -- 1 Introduction -- 2 The Proposed Technique -- 3 Experimental Results -- 4 Conclusion -- References -- A Collaborative Filtering Recommendation Method with Integrated User Profiles*-12pt -- 1 Introduction -- 2 Proposed Method -- 2.1 User Profile Labeling System -- 2.2 User Profile Construction and Similarity Calculation -- 2.3 User Clustering -- 2.4 Collaborative Filtering -- 3 Performance Analysis -- 3.1 Experimental Method -- 3.2 Experimental Result -- 4 Conclusion -- References -- A Quality Metric for K-Means Clustering Based on Centroid Locations -- 1 Introduction -- 2 Related Work -- 3 New Quality Metrics -- 3.1 Reduced 2R Metric -- 3.2 Implicit Assumptions in K-Means Algorithm -- 3.3 Covariant Metric (MC) -- 3.4 Quantifying Index Performance -- 4 Experiments on Synthetic Data -- 4.1 Data Generation -- 4.2 Analysis of Synthetic Data -- 4.3 Results and Discussion -- 5 Experiments on Real Data -- 5.1 Variable Selection -- 5.2 Data Sets -- 6 Comparison with Other Indexes -- 7 Limitations.
8 Conclusion -- References -- Clustering Method for Touristic Photographic Spots Recommendation -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Global Clustering -- 3.2 Local Clustering -- 3.3 Indexes and Validation -- 3.4 TPS Qualification -- 4 Experiments -- 4.1 Data Processing -- 4.2 Global Clustering Comparison -- 4.3 Local Clustering Comparison -- 4.4 Spot Qualification -- 5 Conclusion and Future Work -- References -- Personalized Federated Learning with Robust Clustering Against Model Poisoning -- 1 Introduction -- 2 Related Work -- 2.1 PFL -- 2.2 Robust Clustering -- 2.3 Model Poisoning and Anomaly Detection -- 3 Methodology -- 3.1 PFL -- 3.2 LOF -- 3.3 Proposed Method -- 4 Algorithm -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Experimental Study -- 6 Conclusion -- References -- A Data-Driven Framework for Driving Style Classification -- 1 Introduction -- 2 State of the Art -- 3 Problem Statement -- 4 Proposed Solution -- 4.1 Dataset Description -- 4.2 Pre-processing -- 4.3 Feature Engineering -- 4.4 Neural Architecture Search -- 5 Results -- 5.1 Selection of Time-Window for Aggregation -- 5.2 Comparison of Different Models -- 6 Conclusion and Future Work -- References -- Density Estimation in High-Dimensional Spaces: A Multivariate Histogram Approach -- 1 Introduction -- 2 Background and Related Work -- 2.1 Basic Concepts -- 2.2 Approaches to Density Estimation -- 2.3 Applications in Research -- 2.4 Example: Density of the Old Faithful Dataset -- 3 A Multivariate Histogram-Based Approach -- 3.1 Define Hypergrid -- 3.2 Calculate Relative Frequencies -- 3.3 Calculate Hypervolumes and Density Estimates -- 3.4 Estimate Density for Datasets with Missing Values -- 4 Evaluation and Results -- 4.1 Computational Performance -- 4.2 Measuring Density with Categorical Variables -- 4.3 Measuring Density with Missing Values.
5 Conclusions.
Record Nr. UNISA-996500062003316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advanced data mining and applications : 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, Proceedings, Part II / / Weitong Chen [and five others] editors
Advanced data mining and applications : 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, Proceedings, Part II / / Weitong Chen [and five others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (500 pages)
Disciplina 006.31
Collana Lecture notes in computer science. Lecture notes in artificial intelligence
Soggetto topico Data mining
ISBN 3-031-22137-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Text Mining -- Towards Idea Mining: Problem-Solution Phrase Extraction from Text -- 1 Introduction -- 2 Related Work -- 2.1 Problem Formation -- 3 Methodology -- 3.1 Models for Extracting Problem-Solution Phrases -- 4 Experiment -- 4.1 Dataset UCCL1000 -- 4.2 Dataset NIPS488 -- 4.3 Dataset Summary -- 4.4 Text Preprocessing -- 4.5 Input Representations -- 4.6 Training and Evaluation -- 4.7 Result Analysis -- 5 Discussion -- 6 Future Work -- 7 Conclusion -- References -- Spam Email Categorization with NLP and Using Federated Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Federated Phishing Filter (FPF) -- 3.1 Natural Language Processing -- 3.2 Deep Learning Model for Spam Categorization -- 3.3 Spam Detection and Categorization Model -- 3.4 Federated Learning -- 3.5 Federated Training Models -- 3.6 Federated Averaging (FA) -- 3.7 Federated Averaging Strategies -- 3.8 Equal Weighting (EWS) -- 3.9 Weighted Average (WAS) -- 3.10 Datasets -- 4 Empirical Evaluation -- 4.1 Comparison of EWS and AWS Averaging Strategies -- 4.2 Features Performance Comparison -- 5 Conclusion and Future Work -- References -- SePass: Semantic Password Guessing Using k-nn Similarity Search in Word Embeddings*-12pt -- 1 Introduction -- 2 Related Work -- 3 Semantic Password Guessing -- 3.1 Generation of New Password Candidates -- 3.2 Sorting of the Password Candidates -- 4 Test Bed -- 4.1 Data Sets -- 4.2 Compared Methods -- 4.3 Experimental Set-Up and Evaluation Metric -- 5 Results and Discussion -- 5.1 Accuracy Results -- 5.2 Unseen Base Words -- 6 Conclusion -- References -- DeMRC: Dynamically Enhanced Multi-hop Reading Comprehension Model for Low Data -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Sentence Filtering Model -- 3.2 Answer Prediction Model.
3.3 Self-training Augmentation Based on External Data -- 4 Experiments -- 4.1 Data Set -- 4.2 Implementation Details -- 5 Results -- 6 Conclusion -- References -- ESTD: Empathy Style Transformer with Discriminative Mechanism -- 1 Introduction -- 2 Related Work -- 2.1 NLP for Online Mental Health Assistance -- 2.2 Text Style Transfer -- 2.3 Discriminatory Mechanism -- 3 Methodology -- 3.1 Empathic Expression Calculation -- 3.2 ESTD Framework -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Evaluation Metrics -- 4.4 Ablation Study -- 4.5 Results -- 5 Conclusion -- References -- Detection Method of User Behavior Transition on Computer -- 1 Introduction -- 2 Related Work -- 2.1 Image Classification and Clustering -- 2.2 Search and Operation Automation -- 2.3 User Behavior Analytics -- 3 Detection Method of User Behavior Transition -- 3.1 Overview -- 3.2 Feature Extraction -- 3.3 Time-Series Grouping Function -- 3.4 Time-Series Features Grouping Function -- 3.5 User Behavior Transition Detection Function -- 4 Experiment -- 4.1 Our Dataset -- 4.2 Experiment Results -- 4.3 Discussion -- 5 Conclusion -- References -- Image, Multimedia and Time Series Data Mining -- Ensemble Image Super-Resolution CNNs for Small Data and Diverse Compressive Models -- 1 Introduction -- 1.1 Contribution -- 2 Foundational Work and Background -- 2.1 Sparse Representations -- 2.2 Miralon Areal Density Maps -- 3 Proposed Method -- 4 Experimental Results -- 4.1 Training Details -- 4.2 Reconstruction Quality on Testing Images -- 4.3 Application of Miralon Areal Density Maps -- 5 Conclusion -- References -- Optimizing MobileNetV2 Architecture Using Split Input and Layer Replications for 3D Face Recognition Task -- 1 Introduction -- 2 Backgrounds -- 2.1 Related Works -- 2.2 Convolutional Neural Network (CNN) -- 3 Methodology -- 3.1 Data Gathering.
3.2 Preprocessing -- 3.3 Model Overview -- 3.4 Metrics -- 3.5 Training Configuration -- 3.6 Automatic Model Finding -- 4 Experimental Results -- 4.1 Comparison Between 2D and 3D Face Recognition Models -- 4.2 Comparison Between RGBD and RGB+D Face Recognition Models -- 4.3 Comparison Between Baseline MobileNetV2 and RGB+D MobileNetV2 with Layer Replication -- 4.4 Comparison Between Our Baseline Model and EffiencientNet on CelebA Dataset -- 5 Conclusion and Future Work -- References -- GANs for Automatic Generation of Data Plots -- 1 Introduction -- 2 Generative Adversarial Networks -- 3 Related Work -- 4 Methodology -- 5 Results -- 6 Conclusion -- References -- An Explainable Approach to Semantic Link Mining in Multi-sourced Dynamic Data -- 1 Introduction -- 2 Related Work -- 2.1 Knowledge Graph Link Prediction -- 2.2 Semantic Data Integration -- 3 Preliminaries -- 4 Our Approach -- 4.1 Our Framework -- 4.2 KG-Based Integration -- 4.3 Rule-Based Link Prediction -- 5 An Application Case -- 6 Evaluation -- 6.1 Static Link Prediction -- 6.2 Dynamic Link Prediction -- 7 Conclusion -- References -- Information Mining from Images of Pipeline Based on Knowledge Representation and Reasoning -- 1 Introduction -- 2 Related Work -- 2.1 Pipeline Defects Identification -- 2.2 Ontology for Knowledge Formalization -- 3 PDI Ontology Construction -- 3.1 Knowledge Resource -- 3.2 Ontology Development for PDI -- 3.3 Reasoning Rules for PDI -- 4 Case Study -- 4.1 Selected Pipeline Images with Common Defect Types -- 4.2 The Attribute Information of Pipeline Images -- 4.3 Mapping Rules for Images Instantiation in PDI Ontology -- 4.4 Knowledge Reasoning -- 4.5 Discussion -- 5 Conclusion -- References -- Binary Gravitational Subspace Search for Outlier Detection in High Dimensional Data Streams -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation.
4 Binary Gravitational Subspace Search for Outlier Detection in High Dimensional Data Streams -- 4.1 Subspace Search with Adapted Binary GSA -- 4.2 Solution Overview -- 5 Experimental Study and Results Analysis -- 5.1 Experimentation Setting -- 5.2 Results and Analysis -- 6 Conclusion and Future Works -- References -- Classification, Clustering and Recommendation -- Signal Classification Using Smooth Coefficients of Multiple Wavelets to Achieve High Accuracy from Compressed Representation of Signal -- 1 Introduction -- 2 Wavelets -- 2.1 DWT -- 2.2 MDWT -- 2.3 Energy Distribution -- 3 Proposed Technique -- 3.1 Advantages -- 3.2 Steps in the Proposed Technique: MWCSC -- 4 Experimental Results -- 4.1 Classification Methods Used -- 4.2 Arrowhead Data -- 4.3 Mallat Data -- 4.4 Ford Data -- 5 Conclusion -- References -- On Reducing the Bias of Random Forest -- 1 Introduction -- 2 The Proposed Technique -- 3 Experimental Results -- 4 Conclusion -- References -- A Collaborative Filtering Recommendation Method with Integrated User Profiles*-12pt -- 1 Introduction -- 2 Proposed Method -- 2.1 User Profile Labeling System -- 2.2 User Profile Construction and Similarity Calculation -- 2.3 User Clustering -- 2.4 Collaborative Filtering -- 3 Performance Analysis -- 3.1 Experimental Method -- 3.2 Experimental Result -- 4 Conclusion -- References -- A Quality Metric for K-Means Clustering Based on Centroid Locations -- 1 Introduction -- 2 Related Work -- 3 New Quality Metrics -- 3.1 Reduced 2R Metric -- 3.2 Implicit Assumptions in K-Means Algorithm -- 3.3 Covariant Metric (MC) -- 3.4 Quantifying Index Performance -- 4 Experiments on Synthetic Data -- 4.1 Data Generation -- 4.2 Analysis of Synthetic Data -- 4.3 Results and Discussion -- 5 Experiments on Real Data -- 5.1 Variable Selection -- 5.2 Data Sets -- 6 Comparison with Other Indexes -- 7 Limitations.
8 Conclusion -- References -- Clustering Method for Touristic Photographic Spots Recommendation -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Global Clustering -- 3.2 Local Clustering -- 3.3 Indexes and Validation -- 3.4 TPS Qualification -- 4 Experiments -- 4.1 Data Processing -- 4.2 Global Clustering Comparison -- 4.3 Local Clustering Comparison -- 4.4 Spot Qualification -- 5 Conclusion and Future Work -- References -- Personalized Federated Learning with Robust Clustering Against Model Poisoning -- 1 Introduction -- 2 Related Work -- 2.1 PFL -- 2.2 Robust Clustering -- 2.3 Model Poisoning and Anomaly Detection -- 3 Methodology -- 3.1 PFL -- 3.2 LOF -- 3.3 Proposed Method -- 4 Algorithm -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Experimental Study -- 6 Conclusion -- References -- A Data-Driven Framework for Driving Style Classification -- 1 Introduction -- 2 State of the Art -- 3 Problem Statement -- 4 Proposed Solution -- 4.1 Dataset Description -- 4.2 Pre-processing -- 4.3 Feature Engineering -- 4.4 Neural Architecture Search -- 5 Results -- 5.1 Selection of Time-Window for Aggregation -- 5.2 Comparison of Different Models -- 6 Conclusion and Future Work -- References -- Density Estimation in High-Dimensional Spaces: A Multivariate Histogram Approach -- 1 Introduction -- 2 Background and Related Work -- 2.1 Basic Concepts -- 2.2 Approaches to Density Estimation -- 2.3 Applications in Research -- 2.4 Example: Density of the Old Faithful Dataset -- 3 A Multivariate Histogram-Based Approach -- 3.1 Define Hypergrid -- 3.2 Calculate Relative Frequencies -- 3.3 Calculate Hypervolumes and Density Estimates -- 3.4 Estimate Density for Datasets with Missing Values -- 4 Evaluation and Results -- 4.1 Computational Performance -- 4.2 Measuring Density with Categorical Variables -- 4.3 Measuring Density with Missing Values.
5 Conclusions.
Record Nr. UNINA-9910632469803321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced data mining and applications . Part II : 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2-4, 2022, Proceedings Edited by Bohan Li [and seven others]
Advanced data mining and applications . Part II : 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2-4, 2022, Proceedings Edited by Bohan Li [and seven others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (430 pages)
Disciplina 006.3
Collana Lecture notes in computer science
Soggetto topico Data mining
Mineria de dades
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-95408-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910523766303321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced data mining and applications . Part I : 17th international conference, ADMA 2021, Sydney, NSW, Australia, February 2-4, 2022, proceedings / / Bohan Li [and seven others], editors
Advanced data mining and applications . Part I : 17th international conference, ADMA 2021, Sydney, NSW, Australia, February 2-4, 2022, proceedings / / Bohan Li [and seven others], editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer International Publishing, , [2022]
Descrizione fisica 1 online resource (449 pages)
Disciplina 006.312
Collana Lecture Notes in Computer Science
Soggetto topico Data mining
Data mining - Data processing
Mineria de dades
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-95405-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996464538703316
Cham, Switzerland : , : Springer International Publishing, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advanced data mining and applications : 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, proceedings / / edited by Weitong Chen, [and three others]
Advanced data mining and applications : 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, proceedings / / edited by Weitong Chen, [and three others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (552 pages)
Disciplina 943.005
Collana Lecture Notes in Computer Science
Soggetto topico Data mining
ISBN 3-031-22064-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Finance and Healthcare -- Application of Supplemental Sampling and Interpretable AI in Credit Scoring for Canadian Fintechs: Methods and Case Studies -- 1 Introduction -- 2 Supplementary Sampling -- 2.1 Notations -- 2.2 Theories -- 2.3 Sampling Strategies -- 3 Techniques of Credit Scoring -- 4 Empirical Studies -- 4.1 Data Source and Sample Facts -- 4.2 Model Development and Comparisons -- 4.3 Model Evaluation -- 5 Conclusion -- References -- A Deep Convolutional Autoencoder-Based Approach for Parkinson's Disease Diagnosis Through Speech Signals -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 Dataset -- 3.2 Deep Convolutional AutoEncoder (DCAE) -- 3.3 MultiLayer Perceptron (MLP) -- 4 Experimental Results -- 5 Conclusion -- References -- Mining the Potential Relationships Between Cancer Cases and Industrial Pollution Based on High-Influence Ordered-Pair Patterns -- 1 Introduction -- 2 High-Influence Ordered-Pair Pattern -- 3 Basic Algorithm for Mining HIOPPs -- 3.1 Property Analysis of HIOPP -- 3.2 Description of Basic Algorithm -- 4 Optimizing Algorithm for Mining HIOPPs -- 4.1 Feasibility of Participation Instances -- 4.2 Obtaining Participating Instances -- 5 Experiments -- 5.1 Effectiveness of Mining Results -- 5.2 Performance Evaluation -- 6 Conclusion -- References -- Finding Hidden Relationships Between Medical Concepts by Leveraging Metamap and Text Mining Techniques -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Data Collection and Analysis -- 4.1 Data Extraction from the Source -- 4.2 MetaMap Module - Processing Phase -- 4.3 MetaMap Module - Preparation Phase -- 4.4 Title and Abstract Fetching Module -- 4.5 Closed Discovery Module -- 5 System Evaluation -- 6 Result Evaluation -- 7 Conclusion and Future Work -- References.
Causality Discovery Based on Combined Causes and Multiple Causes in Drug-Drug Interaction -- 1 Introduction -- 2 Background -- 2.1 Combined Causes and Multiple Causes in DDI -- 2.2 Limitations of CBN -- 3 Proposed Method -- 4 Empirical Evaluation -- 5 Results and Discussions -- 6 Conclusion -- References -- An Integrated Medical Recommendation Mechanism Combining Promote Product Singular Value Decomposition and Knowledge Graph -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Promote Product Singular Value Decomposition Algorithm -- 2.2 Knowledge Graph to Recommendation -- 3 Experiment -- 3.1 Environment and Dataset -- 3.2 Evaluation Metrics -- 3.3 Results and Analysis -- 4 Conclusion -- References -- Web and IoT Applications -- Joint Extraction of Entities and Relations in the News Domain -- 1 Introduction -- 2 Research Status -- 3 Methodology -- 3.1 Labeling Strategy for Central Entities -- 3.2 RoBERTa Presentation Layer -- 3.3 Improved BiLSTM* Layer -- 4 Experiment -- 4.1 Experimental Data and Experimental Environment -- 4.2 Evaluation Standard -- 4.3 Experimental Parameters -- 4.4 Experimental Design -- 4.5 Result Analysis -- 5 Conclusion -- References -- Event Detection from Web Data in Chinese Based on Bi-LSTM with Attention*-8pt -- 1 Introduction -- 2 Related Work -- 2.1 Pattern Matching Based Methods -- 2.2 Machine Learning Based Methods -- 3 ABiLSTM Model -- 3.1 Problem Formulation -- 3.2 Static Classification Model -- 3.3 Dynamic Model Maintenance -- 4 Experimental Evaluation -- 4.1 Dataset and Experimental Setup -- 4.2 Chinese Text Preprocessing -- 4.3 Sensitivity Analysis -- 4.4 Effectiveness Analysis -- 4.5 Dynamic Maintenance Comparison -- 5 Conclusion and Future Work -- References -- Sentiment Analysis of Tweets Using Deep Learning -- 1 Introduction -- 2 Related Work -- 2.1 Sentiment Analysis on Tweets.
2.2 Sentiment Analysis on Coronavirus Related Tweets -- 3 Data Collection and Pre-Processing -- 4 Methodology -- 4.1 Text Tokenization and Padding -- 4.2 Convolutional Neural Network Model (CNN) -- 4.3 Long Short-Term Memory (LSTM) -- 4.4 CNN-LSTM -- 4.5 Distiled Bidirectional Encoder Representation from Transformer (DistilBERT) -- 4.6 Stratified K-Fold Cross Validation -- 5 Experiments and Results -- 6 Conclusions -- References -- Cyber Attack Detection in IoT Networks with Small Samples: Implementation And Analysis -- 1 Introduction -- 2 Related Work -- 3 System Architecture -- 3.1 Network Topology -- 3.2 Attack Model -- 4 Threat Detection System -- 5 Modelling the Traffic Data and Evaluation -- 5.1 Results and Discussion -- 5.2 Supervised Methods -- 5.3 Unsupervised Methods -- 5.4 Comparison with Relatively Larger Dataset -- 6 Conclusion -- References -- SATB: A Testbed of IoT-Based Smart Agriculture Network for Dataset Generation -- 1 Introduction -- 2 Background and Related Work -- 2.1 Smart Agriculture -- 2.2 LoRaWAN -- 2.3 Related Work -- 3 SATB: A LoRaWAN SA Testbed -- 3.1 Components of SATB -- 3.2 Functionalities of SATB -- 4 A Case Study: Constructing an SA Dataset with SATB -- 4.1 Test Cases and Data Collection -- 4.2 Data Preprocessing -- 4.3 A Preliminary Study of the Dataset -- 5 Usage of the SATB Testbed -- 5.1 Development of Intrusion Detection Systems for SA -- 5.2 Preservation of Data Privacy and Integrity for SA -- 5.3 Development of Data-Driven Applications for SA -- 6 Conclusion -- References -- An Overview on Reducing Social Networks' Size -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem Definition -- 2.2 Network Properties -- 3 Graph Sampling -- 3.1 Node Sampling -- 3.2 Edge Sampling -- 3.3 Traversal Based Sampling -- 4 Graph Coarsening -- 5 Recent Directions -- 6 Conclusion -- References.
AuCM: Course Map Data Analytics for Australian IT Programs in Higher Education -- 1 Introduction -- 2 Related Work -- 3 The AuCM Dataset -- 3.1 Data Scraping -- 3.2 Data Processing -- 4 Statistical Analysis of AuCM -- 4.1 Analysis of the Number of Courses -- 4.2 Analysis of Curriculum Design -- 4.3 Analysis of Core Curriculum -- 4.4 Analysis of Prerequisites -- 5 Concept Semantics in AuCM -- 5.1 Semantic Feature Extraction and Analysis -- 5.2 Concept Map Learning -- 6 Conclusion -- References -- Profit Maximization Using Social Networks in Two-Phase Setting -- 1 Introduction -- 2 Background and Problem Definition -- 3 Mathematical Model and Solution Methodologies -- 4 Experimental Evaluation -- 5 Conclusion and Future Direction -- References -- On-Device Application -- SESA: Fast Trajectory Compression Method Using Sub-trajectories Segmented by Stay Areas -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Extraction of Stay Area -- 3.2 Segmentation -- 3.3 SQUISH-E() -- 3.4 Integration -- 4 Experiment -- 4.1 Experimental Conditions -- 4.2 Experiment Results -- 4.3 Discussion -- 5 Conclusion and Future Work -- References -- Android Malware Detection Based on Stacking and Multi-feature Fusion -- 1 Introduction -- 2 Related Work -- 2.1 Features of Android Malware Detection -- 2.2 Feature Selection -- 2.3 Stacking Technique -- 3 Framework and Implementation of Android Malware -- 3.1 Feature Extraction and Preprocessing -- 3.2 Two-Level Feature Selection -- 3.3 Malware Detection Based on Stacking Structure -- 4 Experiments and Evaluations -- 4.1 Data Set and Experimental Environment -- 4.2 Experimental Process and Results -- 5 Conclusion -- References -- Influential Billboard Slot Selection Using Pruned Submodularity Graph -- 1 Introduction -- 2 Preliminaries and Problem Definition -- 3 Proposed Solution Approach -- 4 Experimental Evaluation.
4.1 Datasets Used -- 4.2 Experimental Setup -- 4.3 Algorithms Compared -- 4.4 Goals of the Experiments -- 4.5 Observations with Explanation -- 5 Conclusion and Future Research Directions -- References -- Quantifying Association Between Street-Level Urban Features and Crime Distribution Around Manhattan Subway Entrances -- 1 Introduction -- 2 Literature Review -- 2.1 Key Dimensions in Assessing Crimes Around Subway Stations -- 2.2 SVI, CV, and ML for Street Measures -- 3 Data and Methods -- 3.1 Hotspots of Crime Around Subway Stations in Manhattan -- 3.2 Analytical Framework -- 3.3 Data for Constructing Variables -- 4 Findings and Discussion -- 4.1 Regression Results -- 4.2 Urban Design Quality Matters -- 5 Conclusion -- References -- The Coherence and Divergence Between the Objective and Subjective Measurement of Street Perceptions for Shanghai -- 1 Introduction -- 2 Literature Review -- 3 Methods and Process -- 3.1 Analytical Framework -- 3.2 Site Investigation and Data Preparation -- 3.3 Quantifying Objective and Subjective Perception Scores -- 3.4 Coherence and Divergence of the Subjective and Objective Perceptions -- 3.5 Features that Cause Differences Between Objective and Subjective Measures -- 4 Results and Discussion -- 4.1 Spatial Mismatch Between Subjective and Objective Perceptions -- 4.2 Key Urban Features for Variances Between Two Models -- 5 Conclusion -- Appendix -- References -- Other Application -- A Comparative Study of Question Answering over Knowledge Bases -- 1 Introduction -- 2 Methodology -- 2.1 Problem Setting -- 2.2 KBQA Approaches -- 2.3 Summary -- 3 Experimental Setup -- 4 Results -- 4.1 End-to-end Comparison -- 4.2 Running Time -- 4.3 Influence of Question Taxonomy -- 4.4 Effects of Quantity of Questions -- 5 Conclusion -- References.
A Deep Learning Framework for Removing Bias from Single-Photon Emission Computerized Tomography.
Record Nr. UNISA-996500062303316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advanced data mining and applications : 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, proceedings / / edited by Weitong Chen, [and three others]
Advanced data mining and applications : 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, proceedings / / edited by Weitong Chen, [and three others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (552 pages)
Disciplina 943.005
Collana Lecture Notes in Computer Science
Soggetto topico Data mining
ISBN 3-031-22064-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Finance and Healthcare -- Application of Supplemental Sampling and Interpretable AI in Credit Scoring for Canadian Fintechs: Methods and Case Studies -- 1 Introduction -- 2 Supplementary Sampling -- 2.1 Notations -- 2.2 Theories -- 2.3 Sampling Strategies -- 3 Techniques of Credit Scoring -- 4 Empirical Studies -- 4.1 Data Source and Sample Facts -- 4.2 Model Development and Comparisons -- 4.3 Model Evaluation -- 5 Conclusion -- References -- A Deep Convolutional Autoencoder-Based Approach for Parkinson's Disease Diagnosis Through Speech Signals -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 Dataset -- 3.2 Deep Convolutional AutoEncoder (DCAE) -- 3.3 MultiLayer Perceptron (MLP) -- 4 Experimental Results -- 5 Conclusion -- References -- Mining the Potential Relationships Between Cancer Cases and Industrial Pollution Based on High-Influence Ordered-Pair Patterns -- 1 Introduction -- 2 High-Influence Ordered-Pair Pattern -- 3 Basic Algorithm for Mining HIOPPs -- 3.1 Property Analysis of HIOPP -- 3.2 Description of Basic Algorithm -- 4 Optimizing Algorithm for Mining HIOPPs -- 4.1 Feasibility of Participation Instances -- 4.2 Obtaining Participating Instances -- 5 Experiments -- 5.1 Effectiveness of Mining Results -- 5.2 Performance Evaluation -- 6 Conclusion -- References -- Finding Hidden Relationships Between Medical Concepts by Leveraging Metamap and Text Mining Techniques -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Data Collection and Analysis -- 4.1 Data Extraction from the Source -- 4.2 MetaMap Module - Processing Phase -- 4.3 MetaMap Module - Preparation Phase -- 4.4 Title and Abstract Fetching Module -- 4.5 Closed Discovery Module -- 5 System Evaluation -- 6 Result Evaluation -- 7 Conclusion and Future Work -- References.
Causality Discovery Based on Combined Causes and Multiple Causes in Drug-Drug Interaction -- 1 Introduction -- 2 Background -- 2.1 Combined Causes and Multiple Causes in DDI -- 2.2 Limitations of CBN -- 3 Proposed Method -- 4 Empirical Evaluation -- 5 Results and Discussions -- 6 Conclusion -- References -- An Integrated Medical Recommendation Mechanism Combining Promote Product Singular Value Decomposition and Knowledge Graph -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Promote Product Singular Value Decomposition Algorithm -- 2.2 Knowledge Graph to Recommendation -- 3 Experiment -- 3.1 Environment and Dataset -- 3.2 Evaluation Metrics -- 3.3 Results and Analysis -- 4 Conclusion -- References -- Web and IoT Applications -- Joint Extraction of Entities and Relations in the News Domain -- 1 Introduction -- 2 Research Status -- 3 Methodology -- 3.1 Labeling Strategy for Central Entities -- 3.2 RoBERTa Presentation Layer -- 3.3 Improved BiLSTM* Layer -- 4 Experiment -- 4.1 Experimental Data and Experimental Environment -- 4.2 Evaluation Standard -- 4.3 Experimental Parameters -- 4.4 Experimental Design -- 4.5 Result Analysis -- 5 Conclusion -- References -- Event Detection from Web Data in Chinese Based on Bi-LSTM with Attention*-8pt -- 1 Introduction -- 2 Related Work -- 2.1 Pattern Matching Based Methods -- 2.2 Machine Learning Based Methods -- 3 ABiLSTM Model -- 3.1 Problem Formulation -- 3.2 Static Classification Model -- 3.3 Dynamic Model Maintenance -- 4 Experimental Evaluation -- 4.1 Dataset and Experimental Setup -- 4.2 Chinese Text Preprocessing -- 4.3 Sensitivity Analysis -- 4.4 Effectiveness Analysis -- 4.5 Dynamic Maintenance Comparison -- 5 Conclusion and Future Work -- References -- Sentiment Analysis of Tweets Using Deep Learning -- 1 Introduction -- 2 Related Work -- 2.1 Sentiment Analysis on Tweets.
2.2 Sentiment Analysis on Coronavirus Related Tweets -- 3 Data Collection and Pre-Processing -- 4 Methodology -- 4.1 Text Tokenization and Padding -- 4.2 Convolutional Neural Network Model (CNN) -- 4.3 Long Short-Term Memory (LSTM) -- 4.4 CNN-LSTM -- 4.5 Distiled Bidirectional Encoder Representation from Transformer (DistilBERT) -- 4.6 Stratified K-Fold Cross Validation -- 5 Experiments and Results -- 6 Conclusions -- References -- Cyber Attack Detection in IoT Networks with Small Samples: Implementation And Analysis -- 1 Introduction -- 2 Related Work -- 3 System Architecture -- 3.1 Network Topology -- 3.2 Attack Model -- 4 Threat Detection System -- 5 Modelling the Traffic Data and Evaluation -- 5.1 Results and Discussion -- 5.2 Supervised Methods -- 5.3 Unsupervised Methods -- 5.4 Comparison with Relatively Larger Dataset -- 6 Conclusion -- References -- SATB: A Testbed of IoT-Based Smart Agriculture Network for Dataset Generation -- 1 Introduction -- 2 Background and Related Work -- 2.1 Smart Agriculture -- 2.2 LoRaWAN -- 2.3 Related Work -- 3 SATB: A LoRaWAN SA Testbed -- 3.1 Components of SATB -- 3.2 Functionalities of SATB -- 4 A Case Study: Constructing an SA Dataset with SATB -- 4.1 Test Cases and Data Collection -- 4.2 Data Preprocessing -- 4.3 A Preliminary Study of the Dataset -- 5 Usage of the SATB Testbed -- 5.1 Development of Intrusion Detection Systems for SA -- 5.2 Preservation of Data Privacy and Integrity for SA -- 5.3 Development of Data-Driven Applications for SA -- 6 Conclusion -- References -- An Overview on Reducing Social Networks' Size -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem Definition -- 2.2 Network Properties -- 3 Graph Sampling -- 3.1 Node Sampling -- 3.2 Edge Sampling -- 3.3 Traversal Based Sampling -- 4 Graph Coarsening -- 5 Recent Directions -- 6 Conclusion -- References.
AuCM: Course Map Data Analytics for Australian IT Programs in Higher Education -- 1 Introduction -- 2 Related Work -- 3 The AuCM Dataset -- 3.1 Data Scraping -- 3.2 Data Processing -- 4 Statistical Analysis of AuCM -- 4.1 Analysis of the Number of Courses -- 4.2 Analysis of Curriculum Design -- 4.3 Analysis of Core Curriculum -- 4.4 Analysis of Prerequisites -- 5 Concept Semantics in AuCM -- 5.1 Semantic Feature Extraction and Analysis -- 5.2 Concept Map Learning -- 6 Conclusion -- References -- Profit Maximization Using Social Networks in Two-Phase Setting -- 1 Introduction -- 2 Background and Problem Definition -- 3 Mathematical Model and Solution Methodologies -- 4 Experimental Evaluation -- 5 Conclusion and Future Direction -- References -- On-Device Application -- SESA: Fast Trajectory Compression Method Using Sub-trajectories Segmented by Stay Areas -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Extraction of Stay Area -- 3.2 Segmentation -- 3.3 SQUISH-E() -- 3.4 Integration -- 4 Experiment -- 4.1 Experimental Conditions -- 4.2 Experiment Results -- 4.3 Discussion -- 5 Conclusion and Future Work -- References -- Android Malware Detection Based on Stacking and Multi-feature Fusion -- 1 Introduction -- 2 Related Work -- 2.1 Features of Android Malware Detection -- 2.2 Feature Selection -- 2.3 Stacking Technique -- 3 Framework and Implementation of Android Malware -- 3.1 Feature Extraction and Preprocessing -- 3.2 Two-Level Feature Selection -- 3.3 Malware Detection Based on Stacking Structure -- 4 Experiments and Evaluations -- 4.1 Data Set and Experimental Environment -- 4.2 Experimental Process and Results -- 5 Conclusion -- References -- Influential Billboard Slot Selection Using Pruned Submodularity Graph -- 1 Introduction -- 2 Preliminaries and Problem Definition -- 3 Proposed Solution Approach -- 4 Experimental Evaluation.
4.1 Datasets Used -- 4.2 Experimental Setup -- 4.3 Algorithms Compared -- 4.4 Goals of the Experiments -- 4.5 Observations with Explanation -- 5 Conclusion and Future Research Directions -- References -- Quantifying Association Between Street-Level Urban Features and Crime Distribution Around Manhattan Subway Entrances -- 1 Introduction -- 2 Literature Review -- 2.1 Key Dimensions in Assessing Crimes Around Subway Stations -- 2.2 SVI, CV, and ML for Street Measures -- 3 Data and Methods -- 3.1 Hotspots of Crime Around Subway Stations in Manhattan -- 3.2 Analytical Framework -- 3.3 Data for Constructing Variables -- 4 Findings and Discussion -- 4.1 Regression Results -- 4.2 Urban Design Quality Matters -- 5 Conclusion -- References -- The Coherence and Divergence Between the Objective and Subjective Measurement of Street Perceptions for Shanghai -- 1 Introduction -- 2 Literature Review -- 3 Methods and Process -- 3.1 Analytical Framework -- 3.2 Site Investigation and Data Preparation -- 3.3 Quantifying Objective and Subjective Perception Scores -- 3.4 Coherence and Divergence of the Subjective and Objective Perceptions -- 3.5 Features that Cause Differences Between Objective and Subjective Measures -- 4 Results and Discussion -- 4.1 Spatial Mismatch Between Subjective and Objective Perceptions -- 4.2 Key Urban Features for Variances Between Two Models -- 5 Conclusion -- Appendix -- References -- Other Application -- A Comparative Study of Question Answering over Knowledge Bases -- 1 Introduction -- 2 Methodology -- 2.1 Problem Setting -- 2.2 KBQA Approaches -- 2.3 Summary -- 3 Experimental Setup -- 4 Results -- 4.1 End-to-end Comparison -- 4.2 Running Time -- 4.3 Influence of Question Taxonomy -- 4.4 Effects of Quantity of Questions -- 5 Conclusion -- References.
A Deep Learning Framework for Removing Bias from Single-Photon Emission Computerized Tomography.
Record Nr. UNINA-9910632486403321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced data mining and applications . Part I : 17th international conference, ADMA 2021, Sydney, NSW, Australia, February 2-4, 2022, proceedings / / Bohan Li [and seven others], editors
Advanced data mining and applications . Part I : 17th international conference, ADMA 2021, Sydney, NSW, Australia, February 2-4, 2022, proceedings / / Bohan Li [and seven others], editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer International Publishing, , [2022]
Descrizione fisica 1 online resource (449 pages)
Disciplina 006.312
Collana Lecture Notes in Computer Science
Soggetto topico Data mining
Data mining - Data processing
Mineria de dades
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-95405-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910522573703321
Cham, Switzerland : , : Springer International Publishing, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced data mining and applications : 16th international conference, ADMA 2020 Foshan, China, November 12-14, 2020, proceedings / / Xiaochun Yang [and three others] (editors)
Advanced data mining and applications : 16th international conference, ADMA 2020 Foshan, China, November 12-14, 2020, proceedings / / Xiaochun Yang [and three others] (editors)
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (XV, 670 p. 241 illus., 165 illus. in color.)
Disciplina 006.3
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Data mining
ISBN 3-030-65390-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine Learning -- Subspace-Weighted Consensus Clustering for High-Dimensional Data -- NOV-RSI: A Novel Optimization Algorithm for Mining Rare Significance Itemsets -- MSPP: A Highly Efficient and Scalable Algorithm for Mining Similar Pairs of Points -- Discovering High Utility Itemsets Using Set-Based Particle Swarm Optimization -- SS-AOE: Subspace based classification framework for avoiding over-confident errors -- Inuence Maximization based Active Learning in Noisy Setting -- Text Mining -- DGRL: Text Classification with Deep Graph Residual Learning -- Densely Connected Bidirectional LSTM With Max-pooling of CNN Network for Text Classification -- A Context-aware Computing Method of Sentence Similarity Based on Frame Semantics -- Learning the Concept Embeddings of Ontology -- ATextCNN Model: A New Multi-Classification Method for Police Situation -- Hierarchical and Pairwise Document Embedding for Plagiarism Detection -- Graph Mining -- Evolutionary strategy for graph embedding -- D2NE: Deep Dynamic Network Embedding -- Elaborating the Bayesian Priors in Unsupervised Graph Embedding via Graph Concepts -- Tuser3: A profile matching based algorithm across three heterogeneous social networks -- Encrypted Traffic Classification using Graph Convolutional Networks -- Representing EHRs with Temporal Tree and Sequential Pattern Mining for Similarity Computing -- Research of Medical Aided Diagnosis System Based on Temporal Knowledge Graph -- TOP-R Keyword-Aware Community Search -- Online Community Identification Over Heterogeneous Attributed Directed Graphs -- Predictive Analytics MPB: Multi-Peak Binarization for Pupil Detection -- Rice Leaf Diseases Recognition using Convolutional Neural Networks -- STCNet: Spatial-Temporal Convolution Network for Traffic Speed Prediction -- Discriminative Features Generation for Mortality Prediction in ICU -- Pre-trained StyleGAN based data augmentation for small sample brain CT motion artifacts detection -- Motion Artifacts Detection from Computed Tomography Images -- Loners stand out. Identification of anomalous subsequences based on group performance -- Brain CT Image Augmentation based on PGGAN and FBP for Artifact Detection -- Recursive RNN based Shift Representation Learning for Dynamic User-Item Interaction Prediction -- Computational methods for predicting Autism Spectrum Disorder from gene expression data -- Recommender Systems -- Declarative User-Item Profiling Based Context-Aware Recommendation -- HisRec: Bridging Heterogeneous Information Spaces for Recommendation via Attentive Embedding -- A Neighbor-aware Group Recommendation Algorithm -- Cross Product And Attention Based Deep Neural Collaborative Filtering -- Privacy and Security -- Blockchain-based Privacy Preserving Trust Management Model in VANET -- SecureRec: Privacy-Preserving Recommendation with Distributed Matrix Factorization -- Query Processing -- Optimizing Scoring and Sorting Operations for Faster WAND Processing -- Query-Based Recommendation by HIN Embedding with PRE-LSTM -- Data Mining Applications -- Applications of Big Data in Tourism: A Survey -- High-quality Plane Wave Compounding using Deep Learning for Hand-held Ultrasound Devices -- IPMM: Cancer Subtype Clustering Model Based on Multiomics Data and Pathway and Motif Information -- Personal Health Index based on Residential Health Examination -- Decision support system for acupuncture treatment of ischemic stroke -- Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media -- FabricGene: A higher-level feature representation of fabric patterns for nationality classification -- Low-Light Image Enhancement With Color Transfer Based On Local Statistical Feature -- Role-aware Enhanced Matching Network for Multi-Turn Response Selection in Customer Service Chatbots.
Record Nr. UNISA-996418304203316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advanced data mining and applications : 16th international conference, ADMA 2020 Foshan, China, November 12-14, 2020, proceedings / / Xiaochun Yang [and three others] (editors)
Advanced data mining and applications : 16th international conference, ADMA 2020 Foshan, China, November 12-14, 2020, proceedings / / Xiaochun Yang [and three others] (editors)
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (XV, 670 p. 241 illus., 165 illus. in color.)
Disciplina 006.3
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Data mining
ISBN 3-030-65390-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine Learning -- Subspace-Weighted Consensus Clustering for High-Dimensional Data -- NOV-RSI: A Novel Optimization Algorithm for Mining Rare Significance Itemsets -- MSPP: A Highly Efficient and Scalable Algorithm for Mining Similar Pairs of Points -- Discovering High Utility Itemsets Using Set-Based Particle Swarm Optimization -- SS-AOE: Subspace based classification framework for avoiding over-confident errors -- Inuence Maximization based Active Learning in Noisy Setting -- Text Mining -- DGRL: Text Classification with Deep Graph Residual Learning -- Densely Connected Bidirectional LSTM With Max-pooling of CNN Network for Text Classification -- A Context-aware Computing Method of Sentence Similarity Based on Frame Semantics -- Learning the Concept Embeddings of Ontology -- ATextCNN Model: A New Multi-Classification Method for Police Situation -- Hierarchical and Pairwise Document Embedding for Plagiarism Detection -- Graph Mining -- Evolutionary strategy for graph embedding -- D2NE: Deep Dynamic Network Embedding -- Elaborating the Bayesian Priors in Unsupervised Graph Embedding via Graph Concepts -- Tuser3: A profile matching based algorithm across three heterogeneous social networks -- Encrypted Traffic Classification using Graph Convolutional Networks -- Representing EHRs with Temporal Tree and Sequential Pattern Mining for Similarity Computing -- Research of Medical Aided Diagnosis System Based on Temporal Knowledge Graph -- TOP-R Keyword-Aware Community Search -- Online Community Identification Over Heterogeneous Attributed Directed Graphs -- Predictive Analytics MPB: Multi-Peak Binarization for Pupil Detection -- Rice Leaf Diseases Recognition using Convolutional Neural Networks -- STCNet: Spatial-Temporal Convolution Network for Traffic Speed Prediction -- Discriminative Features Generation for Mortality Prediction in ICU -- Pre-trained StyleGAN based data augmentation for small sample brain CT motion artifacts detection -- Motion Artifacts Detection from Computed Tomography Images -- Loners stand out. Identification of anomalous subsequences based on group performance -- Brain CT Image Augmentation based on PGGAN and FBP for Artifact Detection -- Recursive RNN based Shift Representation Learning for Dynamic User-Item Interaction Prediction -- Computational methods for predicting Autism Spectrum Disorder from gene expression data -- Recommender Systems -- Declarative User-Item Profiling Based Context-Aware Recommendation -- HisRec: Bridging Heterogeneous Information Spaces for Recommendation via Attentive Embedding -- A Neighbor-aware Group Recommendation Algorithm -- Cross Product And Attention Based Deep Neural Collaborative Filtering -- Privacy and Security -- Blockchain-based Privacy Preserving Trust Management Model in VANET -- SecureRec: Privacy-Preserving Recommendation with Distributed Matrix Factorization -- Query Processing -- Optimizing Scoring and Sorting Operations for Faster WAND Processing -- Query-Based Recommendation by HIN Embedding with PRE-LSTM -- Data Mining Applications -- Applications of Big Data in Tourism: A Survey -- High-quality Plane Wave Compounding using Deep Learning for Hand-held Ultrasound Devices -- IPMM: Cancer Subtype Clustering Model Based on Multiomics Data and Pathway and Motif Information -- Personal Health Index based on Residential Health Examination -- Decision support system for acupuncture treatment of ischemic stroke -- Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media -- FabricGene: A higher-level feature representation of fabric patterns for nationality classification -- Low-Light Image Enhancement With Color Transfer Based On Local Statistical Feature -- Role-aware Enhanced Matching Network for Multi-Turn Response Selection in Customer Service Chatbots.
Record Nr. UNINA-9910447247103321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui

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