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[null,"1.","Record Nr.",null,"UNISA996601560803316"]
[null,null,"Autore",null,"Ferrández Vicente José Manuel"]
[null,null,"Titolo",null,"Bioinspired Systems for Translational Applications : 10th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2024, Olhâo, Portugal, June 4-7, 2024, Proceedings, Part II"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"3-031-61137-3"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (553 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Series ; ; v.14675"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Machine Learning in Computer Vision and Robotics -- Unsupervised Detection of Incoming and Outgoing Traffic Flows in Video Sequences -- 1 Introduction -- 2 Methodology -- 3 Experimental Results -- 3.1 Methods -- 3.2 Datasets -- 3.3 Results -- 4 Conclusions -- References -- A Decentralized Collision Avoidance Algorithm for Individual and Collaborative UAVs -- 1 Introduction -- 2 State of Art -- 3 Methodology -- 3.1 Collision Avoidance -- 3.2 System Formation -- 4 Experiments and Results -- 5 Conclusions -- References -- Improved Surface Defect Classification from a Simple Convolutional Neural Network by Image Preprocessing and Data Augmentation -- 1 Introduction -- 2 Materials -- 2.1 The NEU Dataset -- 2.2 Image Preprocessing -- 2.3 Data Augmentation -- 3 Methodology -- 3.1 Simple Convolutional Neural Network -- 3.2 Training Strategy -- 4 Results and Discussion -- 5 Conclusions -- References -- Prediction of Optimal Locations for 5G Base Stations in Urban Environments Using Neural Networks and Satellite Image Analysis -- 1 Introduction -- 2 Methodology -- 2.1 Segmentation of Satellite Images -- 2.2 Base Station Deployment -- 3 Experiments -- 3.1 Convolutional Neural Networks -- 3.2 Dataset -- 3.3 Evaluation -- 3.4 Results -- 4 Conclusions -- References -- Enhanced Cellular Detection Using Convolutional Neural Networks and Sliding Window Super-Resolution Inference*-6pt -- 1 Introduction -- 2 Methodology -- 3 Experiments -- 3.1 Dataset -- 3.2 Super-Resolution Model -- 3.3 "]
[{"$nulls":4},"Object Detection Models -- 3.4 Results -- 4 Conclusions and Future Lines -- References -- Exploring Text-Driven Approaches for Online Action Detection -- 1 Introduction -- 2 Related Works -- 2.1 Online Action Detection -- 2.2 Vision-Language Models -- 3 Methodology -- 4 Experiments."]
[{"$nulls":4},"4.1 Experimental Setup -- 4.2 Zero-Shot/Few-Shot Action Detection -- 4.3 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Deep Learning for Assistive Decision-Making in Robot-Aided Rehabilitation Therapy -- 1 Introduction -- 2 Materials and Methods -- 2.1 Subjects -- 2.2 Experimental Setup and Data Collection -- 2.3 Data Processing -- 2.4 Model Architecture -- 3 Results and Discussion -- 4 Conclusion -- References -- Text-Driven Data Augmentation Tool for Synthetic Bird Behavioural Generation -- 1 Introduction -- 2 Related Works -- 2.1 Birds Datasets -- 2.2 Generative Models -- 3 Synthetic Video Generation -- 3.1 Enhancing Captions -- 3.2 Generative Video Models -- 4 Results -- 5 Conclusions -- References -- Deep Learning for Enhanced Risk Assessment in Home Environments -- 1 Introduction -- 2 Related Work -- 2.1 Risks Assessment -- 2.2 Object Detection -- 2.3 Video Captioning -- 3 Methodology -- 3.1 Objects Extraction -- 3.2 Risks Identification -- 4 Experiments -- 4.1 Setup and Data -- 4.2 Results -- 5 Conclusion -- References -- Lightweight CNNs for Advanced Bird Species Recognition on the Edge -- 1 Introduction -- 2 Related Works -- 2.1 Bird Species Recognition -- 2.2 Edge Computing -- 3 Methodology -- 3.1 Datasets -- 3.2 Training -- 4 Experiments -- 4.1 Setup -- 4.2 Results -- 5 Conclusion -- References -- Learning Adaptable Utility Models for Morphological Diversity -- 1 Introduction -- 2 Motivational System for Open-Ended Learning -- 2.1 Novelty-Based Intrinsic Motivation. Enhancing Exploration -- 2.2 Frustration-Based Intrinsic Motivation. Preventing Learning Stagnation -- 3 Deliberative Decision-Making with World and Utility Models -- 3.1 World Model Learning -- 3.2 Utility Model Learning -- 4 Experimental Setup: EMERGE Robot -- 5 Experimental Results -- 6 Conclusion -- References."]
[{"$nulls":4},"Deep Learning-Based Classification of Invasive Coronary Angiographies with Different Patch-Generation Techniques -- 1 Introduction -- 2 Methodology -- 2.1 Dataset -- 2.2 Data Preprocessing -- 3 Experimental Results -- 3.1 Training and Experiments Description -- 3.2 Results -- 4 Conclusions -- References -- Bio-inspired Computing Approaches -- Refinement of Protein Structures with a Memetic Algorithm. Examples with SARS-CoV-2 Proteins -- 1 Introduction -- 2 Methods -- 2.1 Rosetta Relax Process -- 2.2 Relax-DE -- 3 Results -- 3.1 Setup of the Refinement Approaches -- 3.2 Refinement of Predicted Structures -- 4 Conclusions -- References -- Evolutionary Algorithms for Bin Packing Problem with Maximum Lateness and Waste Minimization -- 1 Introduction -- 2 Problem Definition -- 3 The Solution Method -- 4 Evolutionary Algorithms -- 4.1 Genetic Programming -- 4.2 Genetic Algorithm -- 5 Experimental Analysis -- 5.1 Set up -- 5.2 Results -- 6 Conclusions and Future Work -- References -- Stationary Wavelet Entropy and Cat Swarm Optimization to Detect COVID-19 -- 1 Introduction -- 2 Background -- 3 Dataset -- 4 Methodology -- 4.1 Feed-Forward Neural Network -- 4.2 Stationary Wavelet Entropy -- 4.3 Cat Swarm Optimization -- 4.4 K-Fold Cross-Validation -- 4.5 Evaluation -- 5 Experiment and Discussion -- 5.1 Statistical Evaluation -- 5.2 Comparison to State-of-the-Art Methods -- 5.3 ROC Curve -- 6 Conclusion and Future Research -- References -- Private Inference on Layered Spiking Neural P Systems -- 1 Introduction -- 2 Related Work -- 3 Layered Spiking Neural P Systems -- 4 Private Inference -- 4.1 The Protocol -- 4.2 Security Discussion -- "]
[{"$nulls":4},"5 Conclusions and Further Directions of Research -- References -- Cooperative Multi-fitness Evolutionary Algorithm for Scientific Workflows Scheduling -- 1 Introduction -- 2 The Scientific Workflow Scheduling Model."]
[{"$nulls":4},"2.1 Workflow Scheduling Problem Overview -- 3 Overview of the Genetic Algorithm Approach -- 4 Cooperative Multi-fitness Functions Evaluation -- 5 Experimental Study -- 5.1 Benchmark Instances -- 5.2 Benchmark Platform -- 5.3 Efficiency of the Cooperative Multi-fitness Approach -- 6 Conclusion -- References -- A Genetic Approach to Green Flexible Job Shop Problem Under Uncertainty -- 1 Introduction -- 2 Problem Definition -- 3 Solving Methodology -- 4 Experimental Results -- 5 Conclusion -- References -- Social and Civil Engineering Through Human AI Translations -- AI Emmbedded in Drone Control -- 1 Introduction -- 2 Drone Operations Supported by AI Algorithms -- 2.1 Delivery Systems -- 2.2 Optimization and Complexity Associated with Cargo and Resources -- 2.3 Emergency Situations -- 2.4 Drone Identification and Detection -- 2.5 Flight Control and Safety -- 2.6 Agricultural Operations -- 3 Conclusions and Future Work -- References -- Dual-System Recommendation Architecture for Adaptive Reading Intervention Platform for Dyslexic Learners -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data and Exploratory Analysis -- 2.2 Description of the Intervention Trial -- 2.3 Word Generator -- 2.4 Embedded Intra/Inter-user Recommender Engines -- 2.5 Surmounting Cold Start and Limited Data Hurdles -- 3 Results -- 4 Conclusions -- References -- Accurate LiDAR-Based Semantic Classification for Powerline Inspection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Online Segmentation -- 3.2 Full Map Refinement -- 4 Validation -- 5 Conclusions -- References -- RESISTO Project: Automatic Detection of Operation Temperature Anomalies for Power Electric Transformers Using Thermal Imaging -- 1 Introduction -- 1.1 Introduction to the RESISTO Project -- 1.2 Mitigating Transformer Risks in Electricity Networks -- 2 Materials and Methods."]
[{"$nulls":4},"2.1 Thermographic Data Acquisition -- 2.2 Thermal Anomalies Detection System -- 2.3 Synthetic Data Generation -- 3 Results and Discussion -- 3.1 Simulation Results -- 3.2 Registered Temperature Time Series -- 4 Conclusions -- References -- RESISTO Project: Safeguarding the Power Grid from Meteorological Phenomena -- 1 Introduction -- 1.1 Objectives -- 1.2 Project Innovations -- 2 Proposed Solution -- 2.1 Electrical Resilience Platform: GridWatch -- 2.2 Automatic Detection of Operation Temperature Anomalies Using Thermal Imaging -- 2.3 Fleet of Drones -- 3 Discussion -- 4 Conclusions -- References -- Multi-UAV System for Power-Line Failure Detection Within the RESISTO Project -- 1 Introduction -- 2 System Description -- 2.1 Planner Description -- 2.2 Software Implementation -- 2.3 Hardware Implementation -- 3 Validation -- 3.1 Planning Approach Simulation -- 3.2 Test Flights -- 4 Conclusions and Future Works -- References -- Smart Renewable Energies: Advancing AI Algorithms in the Renewable Energy Industry -- Machine Learning Health Estimation for Lithium-Ion Batteries Under Varied Conditions -- 1 Introduction -- 2 Methods -- 2.1 Experimental Design and Data Processing -- 3 Results and Discussion -- 4 Conclusions -- References -- Energy Flux Prediction Using an Ordinal Soft Labelling Strategy -- 1 Introduction -- 2 Data Description and Processing -- 2.1 Buoys Measurements and Reanalysis Data -- 2.2 Obtaining Ordinal Labels -- 3 Experimental Settings -- 3.1 Compared Methodologies -- 3.2 Model Training -- 4 Results and Discussion -- 5 Conclusions -- References -- Medium- and Long-Term Wind Speed Prediction Using the Multi-task Learning Paradigm -- 1 Introduction -- 2 Data Description -- 2.1 "]
[{"$nulls":4},"Wind Speed Data -- 2.2 Predictive Variables -- 3 Multi-task Artificial Neural Networks -- 4 Experimental Settings -- 5 Results and Discussion -- 6 Conclusions."]
[{"$nulls":4},"References."]