11375nam 2200517 450 991059005560332120230114073914.03-031-08530-2(MiAaPQ)EBC7078336(Au-PeEL)EBL7078336(CKB)24765686800041(PPN)264191153(EXLCZ)992476568680004120230114d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAdvances and trends in artificial intelligence. Theory to practice 35th international conference on industrial, engineering and other applications of applied intelligent systems, IEA/AIE 2022, Kitakyushu, Japan, July 19-22, 2022, proceedings /Hamido Fujita, [and three others], editorsCham, Switzerland :Springer,[2022]©20221 online resource (932 pages)Lecture notes in computer science. Lecture notes in artificial intelligenceIncludes index.Print version: Fujita, Hamido Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence Cham : Springer International Publishing AG,c2022 9783031085291 Intro -- Preface -- Organization -- Contents -- Industrial Applications -- Comparative Study of Methods for the Real-Time Detection of Dynamic Bottlenecks in Serial Production Lines -- 1 Introduction -- 1.1 On the Dynamic Nature of Bottlenecks -- 1.2 The Need for Real-Time Bottleneck Detection -- 2 Related Work on Bottleneck Detection -- 2.1 Detection Using Bottleneck Walk with Buffer Levels -- 2.2 Detection Using Active Period Method with Machine States -- 2.3 Detection Using Interdeparture Time Variance with Process Times -- 3 Design of the Comparative Study for Bottleneck Detection -- 4 Detection Results using BNW, APM and ITV -- 4.1 Bottleneck Detection with Bottleneck Walk -- 4.2 Bottleneck Detection Using the Active Period Method -- 4.3 Bottleneck Detection Using Interdeparture Time Variances -- 5 Comparison -- 5.1 Comparison of 20%-Bottleneck Results -- 5.2 Results for Varying Bottleneck Process Times (10% to 100%) -- 6 Conclusion -- References -- Ultra-short-Term Load Forecasting Model Based on VMD and TGCN-GRU -- 1 Introduction -- 2 Methodology -- 2.1 Variational Mode Decomposition -- 2.2 Temporal Graph Convolution Network -- 2.3 VTGG Model -- 3 Experiments and Discussions -- 3.1 Data -- 3.2 Evaluation Method -- 3.3 Contrast Experimental Model -- 3.4 Experimental Environment and Parameter Settings -- 3.5 Experimental Results -- 4 Conclusion -- References -- Learning to Match Product Codes -- 1 Introduction -- 2 Related Work -- 3 Data Wrangling -- 4 Approximate String Matching -- 5 Deep Learning -- 6 System Structure Design -- 7 Experiments and Results -- 7.1 Exploratory Data Analysis -- 7.2 Comparison of Approximate String Matching Methods -- 7.3 Comparison of Deep Learning Methods -- 8 Conclusion and Future Work -- References -- ResUnet: A Fully Convolutional Network for Speech Enhancement in Industrial Robots -- 1 Instruction.2 Related Work -- 2.1 U-Net -- 2.2 ResNet -- 2.3 Huber Loss Function -- 3 The Proposed Method -- 3.1 Overview of the Proposed Method -- 3.2 Structure of Res-Unet -- 3.3 Optimization Function -- 4 Experimental Methods -- 4.1 Dataset -- 4.2 Feature Transformation -- 4.3 Training Schemes -- 4.4 Evaluation Score -- 5 Experimental Results -- 6 Conclusion -- References -- Surface Defect Detection and Classification Based on Fusing Multiple Computer Vision Techniques -- 1 Introduction -- 2 Technical Framework -- 3 Online Defect Detection -- 3.1 Defect Detection Based on Conventional CV Technology -- 3.2 Defect Detection Based on CNN -- 3.3 Detection Result Fusion -- 4 Offline Defect Classification -- 5 Case Study and Experiment -- 5.1 Overall System Architecture -- 5.2 Data Acquisition -- 5.3 Online Defect Detection -- 6 Conclusion -- References -- Development of a Multiagent Based Order Picking Simulator for Optimizing Operations in a Logistics Warehouse -- 1 Introduction -- 2 Order Picking Simulator -- 2.1 Setting of Simulator -- 2.2 Cart Behavior Decision Algorithm -- 3 Experiments for Simulator Performance Evaluation -- 3.1 Experimental Setting -- 3.2 Results -- 4 Discussion -- 5 Conclusion -- References -- Health Informatics -- Predicting Infection Area of Dengue Fever for Next Week Through Multiple Factors -- 1 Introduction -- 2 Related Work -- 2.1 Study on the Factor of Dengue Fever Model -- 3 Research Methodology -- 3.1 Research Characteristics -- 3.2 Model Scoring -- 4 Research Experiment -- 4.1 Data Collection -- 4.2 Data Preprocessing -- 4.3 Model Parameter Adjustment -- 4.4 Experimental Results and Analysis -- 4.5 Important Characteristics of the Model -- 4.6 Adjusted Model Results and Analysis -- 5 Conclusion and Future Research -- References.Hospital Readmission Prediction via Personalized Feature Learning and Embedding: A Novel Deep Learning Framework -- 1 Introduction -- 2 Basic Notation and Problem Definition -- 3 The Proposed Framework -- 3.1 Personalized Feature Learning and Embedding -- 3.2 Personalized Prediction -- 4 Experimental Setup -- 4.1 Dataset Description -- 4.2 Data Preprocessing -- 4.3 Baseline Approaches -- 4.4 Implementation Details and Evaluation Strategies -- 5 Results and Discussion -- 5.1 Performance Evaluation -- 5.2 Clinical Feature Interdependencies -- 6 Conclusion -- References -- Intelligent Medical Interactive Educational System for Cardiovascular Disease -- 1 Introduction -- 2 Materials and Methods -- 2.1 Medical Teaching Materials -- 2.2 Patient-Orient Healthcare Documents -- 2.3 System Design -- 2.4 DAG Structure -- 2.5 Keyword Statistics Architecture -- 3 Result and Discussion -- 3.1 Develop a Patient-Centered Educational Interaction System -- 3.2 Evaluation of Cardiovascular Health Education Data -- 4 Future Work -- References -- Evolutionary Optimization for CNN Compression Using Thoracic X-Ray Image Classification -- 1 Introduction -- 2 Related Work -- 2.1 CNN for Xray Images Classification -- 2.2 Channel Pruning -- 3 Proposed Method -- 3.1 Compression-CNN-XRAY -- 4 Experiments -- 4.1 Experiment Configuration and Setup -- 4.2 Results and Discussion -- 5 Conclusion -- References -- An Oriented Attention Model for Infectious Disease Cases Prediction -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 The Proposed OAM -- 4.1 Oriented Attention Unit (OAU) -- 4.2 Temporal Fusion Layer -- 5 Experiments -- 5.1 Settings -- 5.2 Study on Attention Combinations -- 5.3 Performance Comparisons -- 6 Conclusions -- References -- The Differential Gene Detecting Method for Identifying Leukemia Patients -- 1 Introduction -- 2 Proposed Method.3 Experiments and Results -- 4 Conclusions -- References -- Epidemic Modeling of the Spatiotemporal Spread of COVID-19 over an Intercity Population Mobility Network -- 1 Introduction -- 2 The Proposed Approach -- 2.1 SEIR Model (Single-Network) -- 2.2 M-Urb-SEIR (Urban Network Epidemic Framework) -- 2.3 Addressing the Challenges of a Deterministic Epidemic Model -- 3 Experimental Settings -- 3.1 Datasets -- 3.2 Competitors -- 3.3 Evaluation Metrics -- 4 Experimental Results -- 5 Conclusion -- References -- Skin Cancer Classification Using Different Backbones of Convolutional Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Model Configuration -- 5 Experimental Results -- 6 Conclusion and Future Work -- References -- Cardiovascular Disease Detection on X-Ray Images with Transfer Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Data Pre-processing -- 3.2 Proposed Model for Cardiovascular Disease Detection -- 4 Experiments -- 4.1 Data Set -- 4.2 Evaluation Methods and Baselines -- 4.3 Experimental Results -- 4.4 Discussion on Experimental Results -- 5 Conclusion -- References -- Causal Reasoning Methods in Medical Domain: A Review -- 1 Introduction -- 2 Probability-Based Reasoning Methods -- 2.1 Causal Bayesian Networks -- 2.2 Causal Graph -- 2.3 Probability Tree -- 3 Model-Based Reasoning Methods -- 3.1 SCM -- 3.2 RCM -- 3.3 MSM -- 4 Regression-Based Reasoning Methods -- 4.1 Granger Causality Test -- 5 Balancing-Based Reasoning Methods -- 5.1 Propensity Score Matching -- 5.2 Re-weighting -- 5.3 Confounder Balancing -- 6 Conclusion and Discussion -- References -- Optimization -- Enhancing a Multi-population Optimisation Approach with a Dynamic Transformation Scheme -- 1 Introduction -- 2 Related Work -- 2.1 The Original AMPO Algorithm -- 2.2 Other Metaheuristic Algorithms -- 3 The Enhanced Search Framework.4 The Empirical Evaluation -- 5 Concluding Remarks -- References -- A Model Driven Approach to Transform Business Vision-Oriented Decision-Making Requirement into Solution-Oriented Optimization Model -- 1 Introduction -- 2 Past Related Studies -- 2.1 Theorical Foundation of MDE -- 2.2 Previous Experiences in M2M -- 3 MDE for Decision-Making Process Design -- 3.1 Cognitive Process for Decision-Making System -- 3.2 Cognitive Process-Based Model Driven Architecture -- 4 PIM to PSM Transformation Applied to TSP -- 4.1 Specification of Solution-Oriented Mathematical Meta-model (SMM) -- 4.2 Transformation Process -- 5 Case Study -- 6 Conclusion and Research Perspectives -- References -- A Hybrid Approach Based on Genetic Algorithm with Ranking Aggregation for Feature Selection -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 The Filter Based Ranking Aggregation -- 3.2 The RA-GA Algorithm -- 4 Empirical Settings -- 5 Experimental Results -- 5.1 RQ1: How Does the Proposed Approach Perform Comparing with Some State-of-the-Art Methods? -- 5.2 RQ2: What is the Impact of the Subset's Size Produced by RA-GA? -- 6 Conclusion -- References -- A Novel Type-Based Genetic Algorithm for Extractive Summarization -- 1 Introduction -- 2 Our Proposed Type-Based GA for Extractive Summarization -- 2.1 Chromosome Encoder -- 2.2 Fitness Function -- 2.3 The Proposed Type-Based GA -- 3 Related Works -- 4 Empirical Settings -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Tuning Parameters -- 5 Results -- 6 Conclusion -- References -- Dragonfly Algorithm for Multi-target Search Problem in Swarm Robotic with Dynamic Environment Size -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Simulation Parameters Setup -- 3.2 Environment Setup -- 4 Results and Discussion -- 5 Conclusion -- References -- Video and Image Processing.Improved Processing of Ultrasound Tongue Videos by Combining ConvLSTM and 3D Convolutional Networks.Lecture notes in computer science. Lecture notes in artificial intelligence. .Artificial intelligenceCongressesArtificial intelligenceIndustrial applicationsCongressesArtificial intelligenceArtificial intelligenceIndustrial applications006.3Fujita HamidoMiAaPQMiAaPQMiAaPQBOOK9910590055603321Advances and trends in artificial intelligence. Theory to practice3362816UNINA