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Biblioteca

MARC Lista (tabellare)
Automated Machine Learning and Industrial Applications
Automated Machine Learning and Industrial Applications
Autore Gangadevi E
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica 1 online resource (351 pages)
Disciplina 006.3/1
Altri autori (Persone) ShriM. Lawanya
BalusamyBalamurugan
DhanarajRajesh Kumar
Soggetto topico Machine learning
ISBN 1-394-27242-1
1-394-27241-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Design and Architecture of AutoML for Data Science in Next-Generation Industries -- 1.1 Introduction -- 1.2 Modular Design -- 1.3 Data Handling -- 1.4 Model Training and Selection -- Conclusion -- References -- Chapter 2 Automated Machine Learning Model in Secure Data Transmission in Sustainable Healthcare Sensor Network Using Quantum Blockchain Architecture -- 2.1 Introduction -- 2.2 Related Works -- 2.3 Proposed Model -- 2.4 Results and Discussion -- 2.5 Conclusion -- References -- Chapter 3 Automated Machine Learning in the Biological and Medical Healthcare Industries: Analysis Interpretation and Evaluation -- 3.1 Introduction -- 3.1.1 Rise of AutoML -- 3.1.2 Significance of AutoML in Biological and Medical Healthcare -- 3.2 Methodology for Effective Data Management -- 3.3 Foundations of Automated Machine Learning -- 3.3.1 Understanding Automated Machine Learning -- 3.3.2 Components and Workflow -- 3.3.3 Pros of AutoML Implementation -- 3.3.4 Cons of AutoML Implementation -- 3.4 Applications in Healthcare -- 3.4.1 Disease Diagnosis -- 3.4.2 Drug Discovery and Development -- 3.4.3 Personalized Medicine -- 3.4.4 Predictive Analytics in Healthcare -- 3.5 Case Studies and Success Stories -- 3.5.1 Noteworthy Implementations -- 3.5.2 Impact on Patient Outcomes -- 3.5.3 Challenges Encountered and Overcome -- 3.6 Ethical Implications -- 3.6.1 Data Privacy and Security -- 3.6.2 Fairness and Bias Considerations -- 3.7 Practical Implementation: From Concept to Application -- 3.7.1 Problem Formulation and Data Preparation -- 3.7.2 Tool Selection -- 3.7.3 Training and Evaluation -- 3.7.4 Explainability and Interpretability -- 3.7.5 Deployment and Monitoring -- 3.8 Future Directions and Trends -- 3.8.1 Integration with Emerging Technologies.
3.8.2 AutoML in Clinical Trials and Research -- 3.9 Conclusion -- References -- Chapter 4 Advancements in AI and AutoML for Plant Leaf Disease Identification in Sustainable Agriculture -- 4.1 Introduction -- 4.2 Literature Survey -- 4.3 Preliminary Analysis for Agricultural Diseases -- 4.3.1 Datasets and Descriptions -- 4.3.2 Normalization and Scaling -- 4.3.3 Feature Extraction and Classification -- 4.3.4 Spectral Image Analysis -- 4.4 Proposed Methods -- 4.4.1 Leaf Disease Identification Using ResNet -- 4.4.2 Pixel-Based Information Extraction and Ant Colony Optimization -- 4.4.3 Image Enhancement and Segmentation -- 4.5 Conclusion -- References -- Chapter 5 Predictive Maintenance in Industrial Settings: Video Analytics at the Edge with AutoML -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Proposed Design of an Efficient Model for Enhancing Predictive Maintenance in Industrial Settings -- 5.4 Result Evaluation and Comparative Analysis -- 5.5 Conclusion and Future Scope -- Future Scope -- References -- Chapter 6 AutoCRM-An Automated Customer Relationship Management Learning System with Random Search Hyper-Parameter Optimization -- 6.1 Introduction -- 6.1.1 Opinion Mining or Sentiment Analysis -- 6.1.2 Machine Learning Approaches -- 6.1.3 Machine Learning Pipeline (ML) -- 6.1.4 Automated Machine Learning (AutoML) -- 6.1.4.1 AutoML Core Goals -- 6.1.4.2 AutoML Tools -- 6.1.5 Objectives of this Research -- 6.1.6 Outline -- 6.2 Literature Review -- 6.3 Methodology -- 6.3.1 Data Preparation -- 6.3.1.1 Data Collection -- 6.3.1.2 Data Cleaning and Labeling -- 6.3.1.3 Data Visualization -- 6.3.1.4 Feature Engineering -- 6.3.2 AutoKeras -- 6.3.2.1 Neural Architecture Search and Hyper-Parameter Tuning -- 6.3.3 Model Selection -- 6.4 Results and Discussions -- 6.4.1 Comparative Analysis: AutoML vs ML -- 6.5 Conclusion -- References.
Chapter 7 The Competence of Customer Support Team for Sentiment Analysis in Chatbots Using AutoML -- 7.1 Introduction -- 7.1.1 Background -- 7.1.2 Problem Definition -- 7.1.3 Scope -- 7.1.4 Technical Highlights -- 7.1.5 Objectives -- 7.1.6 Common Chatbot Use Cases -- 7.1.7 The Basics of Sentiment Analysis -- 7.1.8 Levels of Sentiment Analysis -- 7.2 Literature Survey -- 7.3 Methodology for Chatbot Sentiment Analysis -- 7.3.1 AutoML-Based Exploratory Data Analysis and Subjectivity Detection -- 7.3.2 Trilateral Modifier Utilization -- 7.3.3 Sentiment Polarity Detection -- 7.3.4 Workflow of Customer Service Inquiry-Chatbot Response -- 7.3.5 Scoring -- 7.4 Experimentation and Results -- 7.4.1 Performance Metrics -- 7.4.2 Data Collection -- 7.4.3 Evaluation -- 7.5 Conclusion -- References -- Chapter 8 Financial Risk Prediction with Banking Monitoring for Cyber Security Analysis Using Automated Machine Learning -- 8.1 Introduction -- 8.2 Related Works -- 8.3 System Model -- 8.3.1 Cyber Security Detection Using Gaussian Encoder Belief Network -- 8.4 Results and Discussion -- 8.5 Conclusion -- References -- Chapter 9 AutoML Ecosystem and Open-Source Platforms: Challenges and Limitations -- 9.1 Introduction -- 9.2 Related Study -- 9.3 Ecosystem of AutoML -- 9.3.1 Data Preprocessing -- 9.3.2 Model Selection -- 9.3.3 Hyperparameter Tuning -- 9.3.4 Model Evaluation and Deployment -- 9.4 AutoML Frameworks -- 9.4.1 Google AutoML -- 9.4.2 IBM Watson AutoAI -- 9.4.3 Microsoft Azure AutoML -- 9.4.4 H2O.ai -- 9.4.5 Data Robot -- 9.4.6 Databricks AutoML -- 9.4.7 Tune -- 9.4.8 AutoKeras -- 9.4.9 H2O Driverless AI -- 9.4.10 RapidMiner -- 9.4.11 Google Cloud AutoML Tables -- 9.4.12 H2O Sparkling Water -- 9.4.13 Turi Create -- 9.4.14 Big ML -- 9.4.15 Hail -- 9.5 Open-Source AutoML Libraries -- 9.5.1 Auto-Sklearn -- 9.5.2 TPOT (Tree-Based Pipeline Optimization Tool).
9.5.3 AutoKeras -- 9.5.4 MLBox -- 9.5.5 AutoGluon -- 9.5.6 H2O AutoML -- 9.5.7 Auto-WEKA -- 9.5.8 AutoGluon Tabular -- 9.5.9 FLAML -- 9.5.10 Ludwig -- 9.6 Types of AutoML Approaches -- 9.6.1 Fully Automated -- 9.6.2 Human-in-the-Loop -- 9.6.3 Model Assisted -- 9.7 Benefits of AutoML -- 9.8 Challenges and Limitations -- 9.9 Conclusion -- References -- Chapter 10 Plant Disease Identification Using Extended-EfficientNet Deep Learning Model in Smart Farming -- 10.1 Introduction -- 10.1.1 Obstacles in the Agricultural Sector -- 10.1.1.1 Soil Erosion -- 10.1.1.2 Absence of High-Quality Seeds -- 10.1.1.3 Lack of Contemporary Farming Machinery -- 10.1.2 Challenges of AI in Agriculture -- 10.1.3 Existing Plant Disease Identification Methods -- 10.2 Literature Review -- 10.3 Materials and Methods -- 10.3.1 Dataset -- 10.3.2 Existing CNN Models -- 10.3.2.1 AlexNet -- 10.3.2.2 VGG16 -- 10.3.2.3 ResNet50 -- 10.3.2.4 Inception V3 -- 10.4 Methodology-E-ENet -- 10.4.1 Localization of the Leaf -- 10.4.2 Segmentation of Leaf Image -- 10.4.3 The Diseased Leaf Identification -- 10.5 Experimental Analysis -- 10.5.1 The Acquisition of Data -- 10.5.2 The Parameter Setup -- 10.5.2.1 The Configuration of Parameters for Leaf Localization -- 10.5.2.2 The Configuration of Parameters for Leaf Segmentation -- 10.5.2.3 The Configuration of Parameters for Leaf Retrieval -- 10.6 Results -- 10.6.1 The Leaf Localization Outcome -- 10.6.2 The Outcomes of Leaf Segmentation -- 10.6.3 The Result of Disease Identification -- 10.7 Comparative Test -- 10.8 Summary -- References -- Chapter 11 AutoML-Driven Deep Learning for Fake Currency Recognition -- 11.1 Introduction -- 11.2 Literature Review -- 11.2.1 Scope -- 11.2.2 Objectives -- 11.3 Proposed System -- 11.4 Methodology -- 11.5 Convolutional Neural Network -- 11.6 Analysis Modeling -- 11.6.1 Behavioral Modeling -- 11.7 Software Testing.
11.7.1 Types of Testing -- 11.7.2 Test Cases -- 11.8 Results and Discussions -- 11.9 Conclusion -- References -- Chapter 12 Blockchain and Automated Machine Learning-Based Advancements for Banking and Financial Sectors -- 12.1 Introduction -- 12.2 Understanding Blockchain and AutoML -- 12.3 Need of Blockchain -- 12.4 Synergies Between Blockchain and AutoML -- 12.5 Applications in Banking and Finance -- 12.6 Applications of AutoML in Industries -- 12.7 Case Studies and Real-World Applications -- 12.8 Blockchain in Finance -- 12.9 Real-World Examples and Case Studies -- 12.10 Benefits and Challenges -- 12.11 Discussion -- 12.12 Limitations -- 12.13 Recommendations for Implementation -- 12.14 Ethical Considerations and Responsible AI -- 12.15 Future Directions and Emerging Trends -- 12.16 Future Scope -- 12.17 Conclusion -- References -- Chapter 13 Advances in Automated Machine Learning for Precision Healthcare and Biomedical Discoveries -- 13.1 Introduction -- 13.1.1 Some of the Recent Publications and their Findings -- 13.2 Current Day Usage of AI -- 13.2.1 Deep Learning and Neural Networks -- 13.2.2 Natural Language Processing (NLP) -- 13.2.2.1 Clinical Documentation -- 13.2.2.2 Disease Prediction -- 13.2.2.3 Chatbots and Virtual Assistants -- 13.2.2.4 Report Analysis -- 13.2.3 Automation -- 13.2.3.1 Appointment Scheduling -- 13.2.3.2 Medication Dispensary -- 13.2.3.3 Robotic Surgeries -- 13.2.3.4 Inventory Management -- 13.3 Data Management and Security in Healthcare AI -- 13.3.1 Data Acquisition and Storage -- 13.3.2 Data Processing and Analysis -- 13.3.3 Data Protection and Privacy -- 13.3.4 Balancing Technological Advancements with Data Governance -- 13.3.5 The Evolving Role of AI in Data Security -- 13.3.6 Continuous Education in Data Management and AI -- 13.3.7 Preparing for the Future of Healthcare AI and Data Management.
13.4 Challenges in Integrating AI into Healthcare Systems.
Record Nr. UNINA-9911019472703321
Gangadevi E  
Newark : , : John Wiley & Sons, Incorporated, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational Intelligence in Bioprinting : Challenges and Future Directions
Computational Intelligence in Bioprinting : Challenges and Future Directions
Autore Gangadevi E
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (346 pages)
Disciplina 610.285
Altri autori (Persone) ShriM. Lawanya
BalusamyBalamurugan
Soggetto topico Computational intelligence
Tissue engineering
ISBN 9781394204878
1394204876
9781394204861
1394204868
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 The Emergence of Bioprinting and Computational Intelligence -- 1.1 Introduction -- 1.2 Related Study -- 1.3 Understanding the Basics of Bioprinting and Computational Intelligence -- 1.3.1 Bioprinting: The Basics -- 1.3.2 Computational Intelligence: The Basics -- 1.3.3 Applications of Bioprinting and Computational Intelligence -- 1.4 The Role of Computational Intelligence in Bioprinting -- 1.5 Applications of Bioprinting and Computational Intelligence in Medicine -- 1.6 Bioprinting and Computational Intelligence in Tissue Engineering and Regenerative Medicine -- 1.7 Advancements in Bioprinting and Computational Intelligence Technologies -- 1.8 The Ethical and Regulatory Implications of Bioprinting and Computational Intelligence -- 1.9 The Future of Bioprinting and Computational Intelligence: Opportunities and Challenges -- 1.10 Case Studies: Bioprinting and Computational Intelligence in Action -- 1.10.1 Trends in Computational Intelligence and Bioprinting -- 1.10.2 Challenges in Computational Intelligence and Bioprinting -- 1.11 Conclusion -- References -- Chapter 2 Design, Architecture, Implementation, and Evaluation of Bioprinting Technology for Tissue Engineering -- 2.1 Introduction -- 2.2 3D Bioprinting -- 2.3 Material Characteristics -- 2.3.1 Printability -- 2.4 Mechanical Properties -- 2.5 Biomaterials -- 2.6 Design, Architecture of 3D Bioprinting -- 2.6.1 Inkjet Bioprinting -- 2.6.2 Laser-Assisted Bioprinting (LAB) -- 2.6.3 Extrusion Bioprinting -- 2.7 3D Bioprinting Tissue Models -- 2.8 3D Multimaterial Bioprinting-Development of Complex Architectures -- 2.9 Implementation and Evaluation -- 2.10 Bone -- 2.11 Cartilage -- 2.12 Soft Tissue Engineering -- 2.13 Vascular Tissue -- 2.14 Skin -- 2.15 Biocompatibility and Control of Degradation and Byproducts.
2.16 Conclusion -- References -- Chapter 3 Design and Development of IoT Devices: Methods, Tools and Technologies -- 3.1 Introduction to IoT Devices and 3D Bioprinting -- 3.2 Methodology for Designing IoT Devices for 3D Bioprinting -- 3.3 Additional Considerations in IoT Device Design for 3D Bioprinting -- 3.4 Tools for Developing IoT Devices for 3D Bioprinting -- 3.4.1 Microcontrollers and Development Boards -- 3.4.2 Sensors and Actuators -- 3.4.3 Communication Protocols -- 3.4.4 Software Development Kits -- 3.4.5 Cloud Platforms -- 3.4.6 3D Printing Software -- 3.4.7 CAD Software -- 3.4.8 Simulation Software -- 3.4.9 Data Analytics Tools -- 3.4.10 Cybersecurity Tools -- 3.5 Techniques for Developing IoT Devices for 3D Bioprinting -- 3.5.1 Agile Development -- 3.5.2 Rapid Prototyping -- 3.5.3 Test-Driven Development -- 3.5.4 Continuous Integration -- 3.5.5 Modular Design -- 3.5.6 Power Optimization -- 3.5.7 Data Processing Techniques -- 3.5.8 Quality Assurance -- 3.5.9 Cybersecurity Techniques -- 3.5.10 Standardization -- 3.6 Case Studies of IoT Devices for 3D Bioprinting -- 3.7 Future Directions in IoT Devices for 3D Bioprinting -- 3.8 Conclusion -- References -- Chapter 4 AI-Based AR/VR Models in Biomedical Sustainable Industry 4.0 -- 4.1 Introduction -- 4.2 Mixed Augmented Reality -- 4.2.1 SDK in Augmented Reality -- 4.2.2 Application Scope of Augmented Reality -- 4.2.2.1 Video Capabilities -- 4.2.2.2 AR Toolkit Technology -- 4.2.2.3 Quality of Tracking System -- 4.3 AR Technology -- 4.3.1 High Level Augmented Reality -- 4.3.2 Limitations of Enhanced Image -- 4.3.3 Limitations of CAD Model -- 4.3.4 Augmented Reality in Manufacturing Sector -- 4.4 Requirement of Augmented Reality -- 4.4.1 Capability of AR -- 4.4.2 Computational Hardware Capabilities -- 4.4.3 Symbol-Based Tracking Software -- 4.5 Conclusions -- References.
Chapter 5 Computational Intelligence-Based Image Classification for 3D Printing: Issues and Challenges -- 5.1 Introduction -- 5.2 Brief Concepts -- 5.2.1 3D Printing Tools -- 5.2.2 Artificial Intelligence-Based Digital Marketing -- 5.2.3 Automated Machine Learning Prediction System -- 5.3 Role of Artificial Intelligence in Industry 4.0 -- 5.3.1 3D Printing Process -- 5.3.2 Enhancement in Machine Learning -- 5.3.3 Genetics-Based Machine Learning -- 5.3.4 Slicing Technique in 3D Model -- 5.3.5 Printing Path Trajectory -- 5.3.6 Improvement in Computational Simulation -- 5.3.7 Improving Service-Oriented Architecture -- 5.3.8 Capabilities of Cloud Computing -- 5.3.9 Hamming Distance Technique -- 5.3.10 Improving Knowledge Skills -- 5.3.11 Object Detection Algorithm -- 5.3.12 Improvement in Manufacturing Defects -- 5.4 Conclusion -- References -- Chapter 6 Role of Cybersecurity to Safeguard 3D Bioprinting in Healthcare: Challenges and Opportunities -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Creation of 3D Objects and Printing -- 6.3.1 Benefits of 3D Printing -- 6.3.2 Bioprinting -- 6.3.3 A Flow Diagram Depicting the Bioprinting Process -- 6.3.4 Datasets Used in Bioprinting -- 6.4 Schematic Diagram of 3D Bioprinting -- 6.4.1 3D Bioprinting Strategies -- 6.4.2 Comparison Among the 3D Bioprinting Approaches -- 6.4.3 Materials Used in Bioprinting -- 6.4.4 Bioprinting in Diverse Domains -- 6.5 Cyberthreats Posed to Bioprinting -- 6.5.1 Challenges and Opportunities of Cybersecurity in Bioprinting -- 6.5.2 Proposed Solutions -- 6.5.3 Combating the Cybersecurity Risks of 3D Bioprinting -- 6.5.4 Blockchain Technology and Bioprinting -- 6.5.5 A Comparative Survey of Cyberthreats in Additive Manufacturing Technology -- 6.6 Conclusion -- References -- Chapter 7 Legal and Bioethical View of Educational Sectors and Industrial Areas of 3D Bioprinting.
7.1 Introduction -- 7.2 Current 3D Bioprinting Market Trends -- 7.3 Legal and Ethical Perspectives -- 7.4 Regarding the Introduction and Advancement of 3D Bioprinting -- 7.4.1 Current and Potential Paths for Bioethical Discourse -- 7.4.2 Legal Concerns with the Introduction of 3D Bioprinting Into Clinical Practice -- 7.4.3 Ethical Concerns with the 3D Bioprinting of Artificial Ovaries and Their Use in Clinical Settings -- 7.5 Conclusion -- 7.6 Future Scope -- References -- Chapter 8 Optimizing 3D Bioprinting Using Advanced Deep Learning Techniques A Comparative Study of CNN, RNN, and GAN -- 8.1 Introduction -- 8.2 Convolutional Neural Networks in Optimization of 3D Bioprinting -- 8.3 RNN in Optimization of 3D Bioprinting -- 8.4 Generative Adversarial Networks (GAN) in Optimization of 3D Bioprinting -- 8.5 Datasets Used for Optimization of 3D Bioprinting -- 8.6 3D Slicer Medical Image Segmentation Dataset -- 8.7 Sensor Data -- 8.8 Open Organ Database Dataset -- 8.9 Proposed Model -- 8.10 CNN U-Net -- 8.11 RNN Long Short-Term Memory -- 8.12 Wasserstein Generative Adversarial Network -- 8.13 Process of Combined Model -- 8.14 Conclusion -- References -- Chapter 9 Research Trends in Intelligence-Based Bioprinting for Construction Engineering Applications -- 9.1 Introduction -- 9.2 Analysis of Bioprinting -- 9.3 Model Development in Bioprinting Technology -- 9.4 3D Bioprinting Academic Institutions in the World -- 9.5 Emerging Bioprinting Technology -- 9.5.1 Opportunities -- 9.5.2 Challenges -- 9.6 Development in Bioengineering -- 9.7 Evolution of Patent Trends in Bioprinting -- 9.8 Conclusions -- References -- Chapter 10 Design and Development to Collect and Analyze Data Using Bioprinting Software for Biotechnology Industry -- 10.1 Introduction -- 10.2 Digital Technology in Bioprinting -- 10.2.1 Shape of Bioprinting.
10.2.2 Heterogeneity Units of Material -- 10.2.2.1 Tissue Improvement -- 10.2.2.2 Formation of Biomaterials -- 10.2.2.3 Biomaterial and Biological Factors -- 10.2.3 Dynamic Changes in Fabrication Process -- 10.3 Designing Techniques in Bioprinting -- 10.3.1 Data Processing in Biomedical Imaging -- 10.3.2 Process Bioprinting Techniques -- 10.3.3 Interaction of Bioink Formulation -- 10.4 3D Bioprinting -- 10.4.1 Optimized Bioprinting -- 10.4.2 Modifying Crosslinking -- 10.4.3 Multiple Crosslinking -- 10.4.4 Enhance Bioprinting -- 10.4.5 Hybrid Bioprinting -- 10.5 Enhanced Biotissue Printing -- 10.5.1 Integrating Thickness of Engineered Tissue -- 10.5.2 Integration and Enhancement of Cellular Interaction -- 10.5.3 DNA with a Smart Biomaterial -- 10.5.3.1 Biomaterials -- 10.5.3.2 Reactive Hydrogel to External Stimuli -- 10.5.4 Simulation -- 10.6 Conclusion -- 10.7 Future Work -- References -- Chapter 11 Cyborg Intelligence for Bioprinting in Computational Design and Analysis of Medical Application -- 11.1 Introduction -- 11.2 Next Generation of Bioprinting -- 11.2.1 Medicine Management -- 11.2.2 Varieties of Bioprinting Material -- 11.2.2.1 Thermoresponsive Materials -- 11.2.2.2 Biocompatible Polymers Materials -- 11.2.2.3 Endophyte Biocompatible Polymers Materials -- 11.2.2.4 Photo-Conductive Polymer Materials -- 11.2.2.5 UV-Assisted in 3D Printing -- 11.2.2.6 Sensitivity Polymeric Materials -- 11.2.2.7 Macromolecules Materials -- 11.2.2.8 Dual-Sensitive Materials -- 11.2.3 Biosensing Scaffolds -- 11.3 Biosensors and Actuators -- 11.3.1 Fabricated Scaffold Tissues -- 11.3.2 Vascularizing Tissues -- 11.3.3 4D Bioprinting Neural Tissue -- 11.3.4 Longitudinal Deformation -- 11.3.5 Uses of Biomedical Appliances -- 11.4 Enhancing Technology in Bioprinting -- 11.5 Conclusion and Future Work -- References.
Chapter 12 Computer Vision-Aides 3D Bioprinting in Ophthalmology Recent Trends and Advancements.
Record Nr. UNINA-9911019590603321
Gangadevi E  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui