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]

• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (552 pages)
• Disciplina: 943.005
• Soggetto topico: Data mining
• Persona (resp. second.): ChenWeitong
• Nota di bibliografia: Includes bibliographical references and index.
• 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.
• Titolo autorizzato: Advanced Data Mining and Applications
• ISBN: 3-031-22064-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910632486403321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science