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[null,"1.","Record Nr.",null,"UNISA996490363703316"]
[null,null,"Titolo",null,"Progress in artificial intelligence : 21st EPIA conference on artificial intelligence, EPIA 2022, Lisbon, Portugal, August 31-September 2, 2022, proceedings / / edited by Goreti Marreiros [and four others]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-16474-1"]
[null,null,"Descrizione fisica",null,"1 online resource (818 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.13566"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[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 -- AI4IS - Artificial Intelligence for Industry and Societies -- Estimating the Temperature on the Reinforcing Bars of Composite Slabs Under Fire Conditions -- 1 Introduction -- 2 Transient Thermal Problem -- 2.1 Physical Multidomains -- 2.2 Boundary Conditions Corresponding to a Standard Fire -- 2.3 Analytical Method Provided by the Standard Eurocode -- 3 Improving the Analytical Method with Numerical Results -- 3.1 Computational Solution by Finite Elements Method -- 3.2 Improving the New Proposal with an Optimization Method -- 3.3 Improving the New Proposal by the Linear Least Squares Method -- 3.4 Comparison of the Results -- 4 Conclusion -- References -- Hierarchically Structured Scheduling and Execution of Tasks in a Multi-agent Environment -- 1 Introduction -- 2 Related Work -- 2.1 Resource Management -- 2.2 Hierarchical Reinforcement Learning -- 3 Background -- 3.1 Markov Decision Problems -- 3.2 Reinforcement Learning -- 3.3 Multi-agent Markov Decision Problems -- 3.4 Multi-agent Reinforcement Learning -- 3.5 Hierarchical Reinforcement Learning -- 4 Problem Setting -- 4.1 High-Level -- 4.2 Low-Level -- 4.3 Implementation Details -- 5 Evaluation -- 5.1 Environment Settings -- 5.2 Experimental Results -- 5.3 Additional Experiments -- 6 Conclusion -- References -- AIL - Artificial Intelligence and Law -- Content-Based Lawsuits Document Image Retrieval -- 1 Introduction -- 2 Proposal -- 3 Application -- 3.1 Contextualization and Database -- 3.2 Preprocessing and Feature Extraction -- 3.3 Similarity Calculation and Result Presentation -- 4 Experiments -- 4.1 CNN's Choice -- 4.2 Hybrid Algorithm Evaluation "]
[{"$nulls":4},"-- 5 Conclusion -- References -- Lawsuits Document Images Processing Classification -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Proposal -- 4.1 Preprocessing -- 4.2 Classification."]
[{"$nulls":4},"5 Experiment -- 5.1 Preprocessing -- 5.2 Classification -- 6 Conclusion -- References -- A Rapid Semi-automated Literature Review on Legal Precedents Retrieval -- 1 Introduction -- 2 Materials and Methods -- 2.1 Rapid Reviews -- 2.2 Literature Review Automation -- 2.3 Keyword Identification -- 2.4 Electronic Databases and Eligibility Criteria -- 2.5 Data Extraction and Pre-processing -- 2.6 Screening Based on Keyword Frequency -- 2.7 Topic Modelling for Eligibility -- 2.8 Full-Text Screening for Inclusion -- 3 Results -- 3.1 Descriptive Analytics -- 3.2 Content Analysis -- 4 Discussion -- 4.1 RQ1 (How has the Task of Automating the Identification of Previous Relevant Cases been Addressed by the Researchers?) and RQ2 (What Types of Techniques are Covered in the Reviewed Studies?) -- 4.2 RQ3: What are the Most Promising Strategies and Research Gaps in Automating the Retrieval of Legal Precedents? -- 5 Conclusions, Limitations, and Future Research -- References -- The European Draft Regulation on Artificial Intelligence: Houston, We Have a Problem -- 1 Presentation of the European Draft Regulation -- 2 Risk-Based Approach -- 3 The Conformity Assessment -- 4 Difficulty in Complying with Some Requirements -- 5 Contextualization Within the EU Legal Framework -- 6 Lack of Protection of CITizen's Rights -- 7 Discouragement to Technological Development -- 8 Houston, We Have a Problem -- References -- Traffic Stops in the Age of Autonomous Vehicles -- 1 Background -- 1.1 Autonomous Vehicle Development -- 1.2 Likely Impacts of Autonomous Vehicles on Traffic Stops -- 2 The Legal Complexities of AV Traffic Stops -- 2.1 Level 2 (Semi-autonomous) Vehicles -- 2.2 Level 3 (Semi-autonomous) Vehicles -- 2.3 Level 4 &amp -- 5 (Fully Autonomous) Vehicles -- 3 Potential Solutions -- References -- UlyssesSD-Br: Stance Detection in Brazilian Political Polls."]
[{"$nulls":4},"1 Introduction -- 2 Related Works -- 3 UlyssesSD-Br Corpus -- 3.1 Data Collection -- 3.2 Annotation -- 3.3 Data Analysis -- 4 Experimental Setup -- 5 Results and Discussion -- 5.1 Experiments in UlyssesSD-Br Corpus -- 5.2 Experiments in Elicited and Twitter Corpora -- 5.3 Experiments in Elicited and Twitter Corpora Using the UlyssesSD-Br Model Knowledge -- 6 Conclusion -- References -- Unraveling the Algorithms for Humanized Digital Work Oriented Artificial Intelligence -- 1 Introduction -- 2 Living the Age of Artificial Intelligence -- 3 Algorithms in the Constitution of AI -- 4 Platforms and Algorithms: Working Against Workers -- 5 Conclusion -- References -- The Compatibility of AI in Criminal System with the ECHR and ECtHR Jurisprudence -- 1 The Use of AI Systems as an Aid in Determining Sentence Length -- 1.1 Supporting Decision in the Criminal Sentencing -- 2 The Use of AI in Light of ECHR -- 2.1 Right to a Fair Trial -- 2.2 No Punishment Without Law -- 2.3 Prohibition of Discrimination -- 3 Conclusions -- References -- Enriching Legal Knowledge Through Intelligent Information Retrieval Techniques: A Review -- 1 Introduction -- 2 Legal Knowledge Representation -- 3 Approaches for Information Retrieval in Legal Documents -- 3.1 Information Science -- 3.2 Artificial Intelligence -- 3.3 An Overview of the Related Background and Prior Research -- 4 Conclusion -- References -- AIM - Artificial Intelligence in Medicine -- Region of Interest Identification in the Cervical Digital Histology Images -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Cervical Slide Image Processing -- 3.2 Bounding Box (bb) Construction Procedure -- 3.3 Extracted bb Classification -- 3.4 Epithelium RoI Identification -- 4 Experiments -- 4.1 Dataset -- 4.2 Procedure -- 4.3 Assessment -- 4.4 "]
[{"$nulls":4},"Comparison with State-Of-The-Arts -- 5 Conclusion -- References."]
[{"$nulls":4},"Audio Feature Ranking for Sound-Based COVID-19 Patient Detection -- 1 Introduction -- 1.1 The Paper's Contributions -- 2 Audio Features Overview -- 2.1 Time Domain -- 2.2 Frequency Domain -- 2.3 Time-Frequency Domain -- 3 Experimental Method and Results -- 3.1 Research Questions -- 3.2 The Datasets -- 3.3 Feature Engineering -- 3.4 Results Description and Analysis -- 4 Related Work -- 5 Conclusion and Future Work -- References -- Using a Siamese Network to Accurately Detect Ischemic Stroke in Computed Tomography Scans -- 1 Introduction -- 2 Related Work -- 3 Dataset and Methods -- 3.1 Image Preprocessing -- 3.2 Symmetry Detection -- 3.3 Proposed Architecture -- 3.4 Loss Function -- 4 Results -- 5 Conclusions and Future Work -- References -- Determining Internal Medicine Length of Stay by Means of Predictive Analytics -- 1 Introduction -- 2 Background -- 2.1 Resources Planning in Hospital Settings -- 2.2 Related Works -- 3 Materials and Methods -- 3.1 Methodologies -- 3.2 Tools and Algorithms -- 3.3 Data Sets -- 4 Case Study -- 4.1 Business Understanding -- 4.2 Data Understanding -- 4.3 Data Preparation -- 4.4 Modeling -- 4.5 Evaluation -- 5 Results and Discussion -- 6 Conclusions -- References -- Improving the Prediction of Age of Onset of TTR-FAP Patients Using Graph-Embedding Features -- 1 Introduction -- 2 Feature Construction and Embedding-Based Network Representation Learning -- 3 Model Overview -- 3.1 Problem Definition -- 3.2 Defining and Using Embeddings -- 3.3 Experimental Setup -- 4 Results and Discussion -- 4.1 Overall Evaluation of the Different Embedding Variants -- 4.2 Studying the Effect of Predicting the Age of Onset of Asymptomatic Patients at 22 Years Old -- 5 Conclusions and Future Work -- References -- Cloud-Based Privacy-Preserving Medical Imaging System Using Machine Learning Tools -- 1 Introduction -- 2 Related Work."]
[{"$nulls":4},"3 MedCloudCare -- 3.1 React Frontend -- 3.2 Django Backend API -- 3.3 Machine Learning Modelling -- 3.4 Docker and Kubernetes for ML Inference -- 4 Results -- 5 Conclusions and Future Work -- References -- An Active Learning-Based Medical Diagnosis System -- 1 Introduction -- 2 Related Work -- 3 Materials -- 4 Methods -- 4.1 Preprocessing -- 4.2 Active Learning Component -- 4.3 Metrics -- 4.4 Experimental Design -- 5 Results -- 6 Discussion -- 7 Conclusion -- References -- Comparative Evaluation of Classification Indexes and Outlier Detection of Microcytic Anaemias in a Portuguese Sample -- 1 Background -- 2 Methods -- 2.1 Data Description -- 2.2 Classification Indexes Evaluation -- 3 Results and Discussion -- 3.1 Descriptive Data Analysis -- 3.2 Indexes Evaluation -- 3.3 Binary and Multi-class Microcytic Anaemia Classifiers -- 3.4 Outliers Detection -- 4 Conclusions -- References -- A General Preprocessing Pipeline for Deep Learning on Radiology Images: A COVID-19 Case Study -- 1 Introduction -- 2 Proposed Approach -- 2.1 CT Scans Preprocessing for 3D Deep Learning Architecture -- 3 CT Scan Normalization -- 3.1 Deep Learning Architecture for CT Scan Classification -- 4 Experiment and Results -- 4.1 Dataset and Preprocessing -- 4.2 Implementation Details and Model Training -- 4.3 Results -- 5 Conclusion -- References -- AIPES - Artificial Intelligence in Power and Energy Systems -- Automatic Configuration of Genetic Algorithm for the Optimization of Electricity Market Participation Using Sequential Model Algorithm Configuration -- 1 Introduction -- 2 Related Work -- 3 Automatic Configuration of Genetic Algorithm for Market Participation Portfolio Optimization -- 3.1 Portfolio Optimization -- 3.2 Genetic Algorithm -- 3.3 Sequential Model Algorithm Configuration (SMAC) -- 4 Experimental Findings -- 5 Conclusions -- References."]
[{"$nulls":4},"Modeling Stand-Alone Photovoltaic Systems with Matlab/Simulink."]