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[null,"1.","Record Nr.",null,"UNISA996464498403316"]
[null,null,"Titolo",null,"Applied informatics : fourth international conference, ICAI 2021, Buenos Aires, Argentina, October 28-30, 2021 : proceedings / / edited by Hector Florez and Ma Florencia Pollo-Cattaneo"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-89654-4"]
[null,null,"Descrizione fisica",null,"1 online resource (512 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science Ser. ; ; v.1455"]
[null,null,"Disciplina",null,"004"]
[null,null,"Soggetti",null,"Computer networks"]
[{"$nulls":4},"Education - Data processing"]
[{"$nulls":4},"Application software"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Artificial Intelligence -- A Chatterbot Based on Genetic Algorithm: Preliminary Results -- 1 Introduction -- 2 Related Works -- 3 Database -- 4 The Use of Genetic Algorithm -- 4.1 Initial Population Generation -- 4.2 Mutation -- 4.3 Evaluation -- 4.4 Pseudo-code -- 5 Experimental Setup -- 6 Experimental Results -- 7 Conclusions -- References -- A Supervised Approach to Credit Card Fraud Detection Using an Artificial Neural Network -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Data Collection and Exploration -- 3.2 Feature Scaling -- 3.3 Model Development -- 3.4 Training and Testing of the Model -- 4 Results and Discussion -- 4.1 Data Exploration -- 4.2 ANN Model Built with Phython's Keras -- 4.3 Performance Evaluation of the Proposed Model -- 5 Conclusion -- References -- Machine Learning Classification Based Techniques for Fraud Discovery in Credit Card Datasets -- 1 Introduction -- 2 Literature Review -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Proposed Method -- 3.3 Performance Evaluation -- 4 Result and Discussions -- 4.1 Discussion -- 5 Conclusion and Future Research Direction -- References -- Object Detection Based Software System for Automatic Evaluation of CursogramasImages -- 1 Introduction -- 2 Problem Description -- 3 Proposed Solution -- 3.1 Construction of the Symbol Detection Model -- 3.2 Implementation of the Evaluation Program -- 4 Results -- 4.1 "]
[{"$nulls":4},"Generation of Test Cases -- 4.2 Analysis of a Case Study -- 4.3 Verification of the Robustness with \"Noisy\" Images -- 4.4 Discussions of the Evaluation Results -- 5 Conclusions -- References -- Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques -- 1 Introduction -- 2 Materials and Methods -- 2.1 User Disposition for Data Acquisition."]
[{"$nulls":4},"2.2 Dataset -- 2.3 Data Cleansing and Segmentation -- 2.4 Data Transformation and Sampling -- 2.5 Feature Extraction -- 2.6 Classification -- 3 Results and Discussion -- 4 Conclusions -- References -- Solar Radiation Prediction Using Machine Learning Techniques -- 1 Introduction -- 2 Related Work -- 3 Estimation Model Using Machine Learning -- 4 Estimation Model Results Using Machine Learning -- 5 Conclusions -- References -- Towards More Robust GNN Training with Graph Normalization for GraphSAINT -- 1 Introduction -- 2 Related Work -- 2.1 The Sampling Strategy of GraphSAINT -- 2.2 The Typical Normalization Technique -- 3 Methodology -- 4 Experiments -- 5 Conclusions -- References -- Data Analysis -- A Complementary Approach in the Analysis of the Human Gut Microbiome Applying Self-organizing Maps and Random Forest -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Self-organizing Maps (SOMs) -- 2.3 Random Forest -- 3 Results -- 3.1 Characteristics of the Study Population -- 3.2 SOM Analysis -- 3.3 Random Forest -- 4 Discussion -- 5 Conclusions -- References -- An Evaluation of Intrinsic Mode Function Characteristic of Non-Gaussian Autorregresive Processes -- 1 Introduction -- 2 Empirical Mode Decomposition Features and Stochastic Processes -- 2.1 Stochastic Processes -- 2.2 Empirical Mode Decomposition Features -- 2.3 Time-Frequency Related Features -- 2.4 Energy-Entropy Related Features -- 3 Numerical Results and Discussion -- 4 Conclusions -- References -- Analysing Documents About Colombian Indigenous Peoples Through Text Mining -- 1 Introduction -- 2 Related Work -- 3 Definition of the Corpus -- 3.1 Characterisation Documents -- 3.2 News Articles -- 3.3 Constitutional Court Rulings -- 4 Overall Analysis of the Corpus -- 5 News Article Sentiment Analysis -- 6 Timeline Generation -- 7 Conclusions -- References."]
[{"$nulls":4},"Application of a Continuous Improvement Method for Graphic User Interfaces Through Intelligent Systems for a Language Services Company -- 1 Introduction -- 2 Proposed Method -- 2.1 Interactive Genetic Algorithms -- 2.2 General Structure of the Proposed Method -- 3 Results -- 3.1 Induction -- 3.2 Intervention Elements Selection -- 3.3 Configuration -- 3.4 Initial Population Generation -- 3.5 GUIs Modification -- 3.6 Deployment -- 3.7 Measurement -- 3.8 Evaluation -- 3.9 Crossover and Mutation -- 3.10 Subsequent Generations -- 3.11 Closing -- 4 Analysis of the Results -- 5 Conclusions -- References -- Descriptive Analysis Model to Improve the Pension Contributions Collection Process for Colpensiones -- 1 Introduction -- 2 Methodology and Materials -- 2.1 Background -- 3 Related Work -- 4 Methodology -- 5 Results -- 5.1 Geographical Zones -- 6 Conclusions and Future Work -- References -- User Response-Based Fake News Detection on Social Media -- 1 Introduction -- 2 Related Work -- 2.1 News Content-Based Approach -- 2.2 Social Context Approach -- 3 Methodologies -- 3.1 Extract User Response Attributes -- 3.2 Apply Text Preprocessing -- 3.3 Encode Emotions -- 3.4 Extract Features -- 3.5 Merge Attributes -- 3.6 Build Model -- 4 Experimental Results and Analysis -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Performance Comparison (RQ1) -- 4.4 Which Algorithm Fits Best? (RQ2) -- 5 Discussion -- 6 Conclusion and Future Work -- References -- Decision Systems -- A Multi-objective Mathematical Optimization "]
[{"$nulls":4},"Model and a Mobile Application for Finding Best Pedestrian Routes Considering SARS-CoV-2 Contagions -- 1 Introduction -- 2 Related Work -- 3 Multi-objective Mathematical Optimization Model -- 3.1 Interaction Cost Function -- 3.2 Pedestrian Traffic Simulation on the University Campus -- 3.3 Pareto Front Development -- 4 Heuristic."]
[{"$nulls":4},"4.1 Bi-Objective Problem Understanding -- 4.2 Single Cost Approach to Solution Heuristic -- 4.3 Results Comparisson and Validation with the Pareto Front -- 4.4 Results Comparisons and Validation Based on the Mathematical Model -- 5 Mobile Application Design -- 5.1 Validations -- 6 Conclusions -- References -- An Improved Course Recommendation System Based on Historical Grade Data Using Logistic Regression -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Pre-processing -- 3.3 Feature Selection -- 3.4 Division of Data -- 3.5 Wide and Deep Recommender -- 3.6 Performance Evaluation Metrics -- 4 Results and Discussion -- 5 Conclusion -- References -- Predictive Academic Performance Model to Support, Prevent and Decrease the University Dropout Rate -- 1 Introduction -- 2 Preliminaries -- 2.1 Student Desertion -- 2.2 Functioning of Notes at University and in General at Colombian Universities -- 3 Methods -- 3.1 Data Preparation -- 3.2 Data Analysis -- 4 Modeling Academic Performance with Machine Learning -- 4.1 Models Used -- 4.2 Training Process -- 4.3 TESTING the MODEL -- 5 Discussion -- 6 Conclusion -- References -- Proposed Algorithm to Digitize the Root-Cause Analysis and 8Ds of the Automotive Industry -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Problem Statement -- 4.1 Stage 1. -- 4.2 Stage 2: -- 5 Smart QRCI Design -- 5.1 Stage 3: -- 6 Conclusions -- References -- Health Care Information Systems -- Blood Pressure Morphology as a Fingerprint of Cardiovascular Health: A Machine Learning Based Approach -- 1 Introduction -- 2 Methods -- 2.1 Signal Analysis -- 2.2 Models -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Characterizing Musculoskeletal Disorders. A Case Study Involving Kindergarten Employees -- 1 Introduction -- 2 Materials and Methods -- 2.1 Evalu@ Parametrization."]
[{"$nulls":4},"2.2 The Kuourika Questionnaire (KQ) Administration -- 3 Results -- 3.1 Discomfort in the Upper Body -- 3.2 Analysing Discomfort by Upper Body Joint -- 3.3 General Discomfort Timing in the Last 12 Months -- 3.4 Upper Body Discomfort Timing in the Last 12 Months -- 3.5 Discomfort in the Last Seven Days -- 3.6 Duration of the Discomfort Episodes -- 3.7 Annoyance Vs. Disability Time -- 4 Discussion -- 5 Conclusion -- References -- Comparison of Heart Rate Variability Analysis with Empirical Mode Decomposition and Fourier Transform -- 1 Introduction -- 2 Materials and Methods -- 2.1 Simulated Signal -- 2.2 Dataset -- 2.3 Resampling -- 2.4 HRV Signal Analysis -- 2.5 Software Tools -- 3 Results -- 4 Discussion -- 4.1 Conclusions -- References -- Evalu@ + Sports. Creatine Phosphokinase and Urea in High-Performance Athletes During Competition. a Framework for Predicting Injuries Caused by Fatigue -- 1 Introduction -- 2 Materials and Methods -- 2.1 EV-box Configuration -- 2.2 EV-Surv Configuration -- 2.3 Variable Independence -- 2.4 Studied Indexes -- 3 Results -- 3.1 Tracking CPK and Urea -- 3.2 Joint Analysis of the Variables -- 4 Discussion -- 5 Conclusion -- References -- Heart Rate Variability: Validity of Autonomic Balance Indicators in Ultra-Short Recordings -- 1 Introduction -- 2 Materials and Methods -- 2.1 Subjects -- 2.2 HRV Analysis -- 2.3 Poincaré Geometric Domain -- 2.4 Statistical Analysis -- 2.5 Results -- 2.6 Discussion -- 2.7 Conclusions -- References -- Image Processing -- An Improved Machine Learnings Diagnosis Technique for COVID-19 Pandemic Using Chest X-ray "]
[{"$nulls":4},"Images -- 1 Introduction -- 2 Review of Related Work -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 The Flowchart for the Propose Model -- 4 Experimentation of the Proposed Model -- 4.1 Pre-processing -- 4.2 Diagnosis Models, Parameters and Metrics -- 5 Results and Discussion."]
[{"$nulls":4},"5.1 ROC Curves."]