Advanced Data Mining and Applications : 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, Proceedings, Part I / / edited by Bohan Li, Lin Yue, Jing Jiang, Weitong Chen, Xue Li, Guodong Long, Fei Fang, Han Yu

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• Edizione: 1st ed. 2022.
• Descrizione fisica: 1 online resource (449 pages)
• Disciplina: 006.312
• Soggetto topico: Artificial intelligence; Image processing - Digital techniques; Computer vision; Computer engineering; Computer networks; Social sciences - Data processing; Artificial Intelligence; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Engineering and Networks; Computer Application in Social and Behavioral Sciences
• Persona (resp. second.): LiBohan
• Nota di contenuto: Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Healthcare -- Deep Learning Based Cardiac Phase Detection Using Echocardiography Imaging -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Phase I: Model Training -- 3.2 Phase II: Model Testing -- 4 Experiments -- 4.1 Datasets -- 4.2 Competing Approaches -- 4.3 Parameter Configuration -- 4.4 Hardware and Software Configuration -- 5 Evaluation -- 5.1 Evaluation Metrics -- 5.2 Comparison Among Competing Approaches -- 5.3 Effect of Image Preprocessing -- 5.4 Evaluation of Parameter Sensitivity -- 5.5 Performance Analysis of the Custom Loss Function -- 5.6 Evidence of Generalization of DeepPhase -- 6 Conclusion -- References -- An Empirical Study on Human Flying Imagery Using EEG -- 1 Introduction -- 2 Method -- 2.1 Experiment and EEG Recording -- 2.2 Classification and Feature Analysis -- 3 Results -- 3.1 Overall Classification Results -- 3.2 Frequency Band Specific Classification Results -- 3.3 Time Window Specific Classification Results -- 3.4 Time-frequency Specific Classification Results -- 3.5 EEG Activity Patterns in Most Significant Time-frequency Bin -- 4 Discussion -- 5 Conclusion and Future Work -- References -- Network Graph Analysis of Hospital and Health Services Functional Structures -- 1 Introduction -- 2 Subjects and Methods -- 3 Network Graphs -- 4 Results -- 5 Discussion -- 6 Conclusion and Future Work -- References -- Feature Selection in Gene Expression Profile Employing Relevancy and Redundancy Measures and Binary Whale Optimization Algorithm (BWOA) -- 1 Introduction -- 1.1 Objective and Contributions -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Feature Scaling -- 3.2 Phase 1 Feature Selection: Relevance Analysis -- 3.3 Phase 2 Feature Selection: Redundancy Analysis -- 3.4 Phase 3 Feature Selection: Meta-heuristic Optimization. ; 3.5 Binary Whale Optimization Algorithm (BWOA) -- 3.6 BWOA for Gene Selection -- 3.7 Classification -- 4 Datasets and Baselines -- 4.1 Gene Expression Datasets -- 4.2 Performance Metrics -- 4.3 Baseline Methods -- 5 Results and Discussion -- 6 Summary and Conclusions -- References -- Hand Bone Age Estimation Using Deep Convolutional Neural Networks -- 1 Introduction -- 2 Background and Related Works -- 3 Materials and Methods -- 3.1 Normalization -- 3.2 Hand Detection -- 3.3 Vision Pipeline -- 3.4 Proposed Bone Age Prediction Model -- 3.5 Dataset -- 4 Experimental Results and Discussion -- 5 Conclusion -- References -- An Interpretable Machine Learning Approach for Predicting Hospital Length of Stay and Readmission -- 1 Introduction -- 2 Methods -- 3 Results and Discussion -- 4 Conclusion -- References -- STCT: Spatial-Temporal Conv-Transformer Network for Cardiac Arrhythmias Recognition -- 1 Introduction -- 2 Related Works -- 2.1 Diagnosis of Cardiac Arrhythmias -- 2.2 Deep Learning-Based Cardiac Arrhythmias Diagnose -- 3 Methodology -- 3.1 Data Segmentation -- 3.2 the Proposed Model -- 4 Experiment -- 4.1 Datasets and Model Implementation -- 4.2 Comparison Model -- 4.3 Experimental Results -- 5 Conclusion -- References -- Education -- Augmenting Personalized Question Recommendation with Hierarchical Information for Online Test Platform -- 1 Introduction -- 2 Preliminary -- 2.1 Definitions and Problem Statement -- 2.2 Framework Overview -- 3 Method -- 3.1 Incorporating the Student and Question Hierarchical Information -- 3.2 Student Performance Predicting -- 3.3 The Framework APQR -- 4 Experiments -- 4.1 Online Test Dataset -- 4.2 Evaluation Metric -- 4.3 Baseline Algorithms -- 4.4 Overall Performance -- 4.5 Parameter Analysis -- 5 Related Work -- 5.1 Recommender System -- 5.2 Student Performance Modeling -- 6 Conclusion -- References. ; Smart Online Exam Proctoring Assist for Cheating Detection -- 1 Intruduction -- 2 Related Work -- 3 Proposed Technique Highlight -- 3.1 Problem Statement -- 3.2 High Level Architecture -- 4 Proposed Work Details -- 4.1 Exam Recording -- 4.2 Video Characteristic Analysis -- 4.3 Videos Transformed to Feature Vector -- 4.4 Data Uniforming by Video Length Equalizing -- 4.5 Training -- 5 Experiments -- 5.1 Dataset -- 5.2 Competing Approaches -- 5.3 Parameters, Hardware and Software -- 5.4 Evaluation -- 6 Conclusion -- References -- Design and Development of Real-Time Barrage System for College Class -- 1 Introduction -- 2 System Design -- 2.1 System Framework and Function Design -- 2.2 System Flow Design -- 3 Analysis of Sensitive Word Filtering Algorithm -- 4 System Realization -- 4.1 PC Function Realization -- 4.2 Mobile Function Realization -- 4.3 Server-Side Function Realization -- 5 Conclusion -- References -- Recommendation for Higher Education Candidates: A Case Study on Engineering Programs -- 1 Introduction -- 2 Related Work -- 3 ESTHER -- 3.1 Students Profiler -- 3.2 Programs Recommender -- 3.3 System Dependencies and Limitations -- 4 Case Study -- 4.1 Students Profiler -- 4.2 Programs Recommender -- 4.3 ESTHER Overview -- 5 Conclusions -- References -- Web Application -- UQ-AAS21: A Comprehensive Dataset of Amazon Alexa Skills -- 1 Introduction -- 2 Background and Related Work -- 2.1 Background -- 2.2 Related Work -- 3 The UQ-AAS21 Dataset -- 3.1 Data Scraping -- 3.2 Data Processing -- 3.3 Dataset Features -- 4 Preliminary Studies Based on UQ-AAS21 Datasets -- 4.1 Demographic Study -- 4.2 Analysis of Privacy Policy and Term of Use Document -- 5 Potential Usage of UQ-AAS21 -- 6 Conclusion -- References -- Are Rumors Always False?: Understanding Rumors Across Domains, Queries, and Ratings -- 1 Introduction -- 2 Background. ; 2.1 Detecting Rumors on the Web -- 2.2 Actions Against Detected Rumors -- 2.3 Research Gap -- 3 Research Questions -- 4 Methodology -- 4.1 Data Collection -- 5 Empirical Analyses and Findings -- 5.1 What Are the Rumors About? -- 5.2 Where Do the Rumors Come From? -- 5.3 Who Contribute to Rumors? -- 5.4 When Are the Rumors Reported? -- 5.5 How Do Rumors Propagate? -- 6 Discussion -- 6.1 Key Findings -- 6.2 Implications for Public Trust and Explainable Rumor Detection -- 6.3 Limitations and Future Work -- 7 Conclusion -- References -- A Green Pipeline for Out-of-Domain Public Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Pre-trained Transformer Encoder -- 3.3 Pipeline Sentiment Analysis Model -- 4 Experiments -- 4.1 Experimental Setups -- 4.2 Sentiment Analysis Evaluation -- 4.3 Performance of Sub-models -- 4.4 Analysis and Case Study -- 5 Conclusion -- References -- Profiling Fake News: Learning the Semantics and Characterisation of Misinformation -- 1 Introduction -- 2 Experimental Dataset -- 2.1 Data Pre-processing -- 3 Proposed Solution Approach -- 3.1 Features Extraction and Selection -- 3.2 Classification Models -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- Mining Social Networks for Dissemination of Fake News Using Continuous Opinion-Based Hybrid Model -- 1 Introduction -- 2 Proposed Model -- 2.1 Initialization -- 2.2 Propagation -- 3 Results and Discussions -- 4 Conclusion -- References -- Predicting Network Threat Events Using HMM Ensembles -- 1 Introduction -- 2 Background and Related Work -- 3 Ensemble of Hidden Markov Models -- 3.1 Hidden Markov Model Structure -- 3.2 Event Sequence Clustering -- 3.3 Ensemble Creation and Prediction Methods -- 4 Data Set -- 5 Evaluation -- 6 Conclusion -- References -- On-device Application. ; Group Trip Planning Queries on Road Networks Using Geo-Tagged Textual Information -- 1 Introduction -- 2 Background and Problem Definition -- 3 Proposed Solution Methodologies -- 3.1 Brute Force Approach with Precomputed Distance -- 3.2 Group Nearest Neighbor (GNN) to Compute GTP Queries -- 3.3 Using R-trees to Compute GTP Queries -- 4 Experimental Evaluation -- 5 Conclusion and Future Direction -- References -- Deep Reinforcement Learning Based Iterative Participant Selection Method for Industrial IoT Big Data Mobile Crowdsourcing -- 1 Introduction -- 2 System Model and Deep Neural Network -- 2.1 System Model -- 2.2 Participant Selection Problem -- 3 System Framework and Deep Q-Network -- 3.1 System Framework -- 3.2 Deep Q-Network -- 4 Evaluation -- 4.1 Dataset and Experiment Setups -- 4.2 BaseLine Method -- 4.3 The Performance Evaluation and Comparison -- 5 Related Work -- 6 Conclusion -- References -- Know Your Limits: Machine Learning with Rejection for Vehicle Engineering -- 1 Introduction -- 2 Background -- 2.1 Vehicle Engineering: The Need for Usage Profiling -- 2.2 Related Work on Machine Learning with a Reject Option -- 3 Usage Profiling: Data Science Challenge -- 4 Our Approach for Vehicle Usage Profiling -- 4.1 Predictor h -- 4.2 Rejector r -- 4.3 Combined Model h' -- 5 Use-Case: Road-Roughness Analysis -- 5.1 Data Collection and Preprocessing -- 5.2 Experimental Methodology -- 5.3 Results -- 5.4 Discussion and Lessons Learned -- 6 Conclusion -- References -- Towards Generalizable Machinery Prognostics -- 1 Introduction -- 2 Background and Related Work -- 3 A Generic Approach to Incipient Failure Prediction -- 4 Learning the Prediction Horizon -- 5 Ablation Study -- 6 Results and Discussion -- 7 Conclusion -- 8 Future Work -- References -- A Trust Management-Based Route Planning Scheme in LBS Network -- 1 Introduction -- 2 Related Work. ; 3 Framework of System.
• Sommario/riassunto: This book constitutes the proceedings of the 17th International Conference on Advanced Data Mining and Applications, ADMA 2021, held in Sydney, Australia in February 2022.* The 26 full papers presented together with 35 short papers were carefully reviewed and selected from 116 submissions. The papers were organized in topical sections in Part I, including: Healthcare, Education, Web Application and On-device application. * The conference was originally planned for December 2021, but was postponed to 2022. .
• Titolo autorizzato: Advanced Data Mining and Applications
• ISBN: 3-030-95405-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910522573703321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Artificial Intelligence, . 2945-9141 ; ; 13087