Pubbl/distr/stampa	Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica	1 online resource (273 pages)
Disciplina	006.301
Collana	Intelligent systems reference library
Soggetto topico	Artificial intelligence - Philosophy
ISBN	3-031-12807-9
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Intro -- Preface -- Contents -- Contributors -- Abbreviations -- 1 Black Box Models for eXplainable Artificial Intelligence -- 1.1 Introduction to Machine Learning -- 1.1.1 Motivation -- 1.1.2 Scope of the Paper -- 1.2 Importance of Cyber Security in eXplainable Artificial Intelligence -- 1.2.1 Importance of Trustworthiness -- 1.3 Deep Learning (DL) Methods Contribute to XAI -- 1.4 Intrusion Detection System -- 1.4.1 Classification of Intrusion Detection System -- 1.5 Applications of Cyber Security and XAI -- 1.6 Comparison of XAI Using Black Box Methods -- 1.7 Conclusion -- References -- 2 Fundamental Fallacies in Definitions of Explainable AI: Explainable to Whom and Why? -- 2.1 Introduction -- 2.1.1 A Short History of Explainable AI -- 2.1.2 Diversity of Motives for Creating Explainable AI -- 2.1.3 Internal Inconsistency of Motives for Creating XAI -- 2.1.4 The Contradiction Between the Motives for Creating Explainable AI -- 2.1.5 Paradigm Shift of Explainable Artificial Intelligence -- 2.2 Proposed AI Model -- 2.2.1 The Best Way to Optimize the Interaction Between Human and AI -- 2.2.2 Forecasts Are not Necessarily Useful Information -- 2.2.3 Criteria for Evaluating Explanations -- 2.2.4 Explainable to Whom and Why? -- 2.3 Proposed Architecture -- 2.3.1 Fitness Function for Explainable AI -- 2.3.2 Deep Neural Network is Great for Explainable AI -- 2.3.3 The More Multitasking the Better -- 2.3.4 How to Collect Multitasking Datasets -- 2.3.5 Proposed Neural Network Architecture -- 2.4 Conclusions -- References -- 3 An Overview of Explainable AI Methods, Forms and Frameworks -- 3.1 Introduction -- 3.2 XAI Methods and Their Classifications -- 3.2.1 Based on the Scope of Explainability -- 3.2.2 Based on Implementation -- 3.2.3 Based on Applicability -- 3.2.4 Based on Explanation Level -- 3.3 Forms of Explanation -- 3.3.1 Analytical Explanation. 3.3.2 Visual Explanation -- 3.3.3 Rule-Based Explanation -- 3.3.4 Textual Explanation -- 3.4 Frameworks for Model Interpretability and Explanation -- 3.4.1 Explain like I'm 5 -- 3.4.2 Skater -- 3.4.3 Local Interpretable Model-Agnostic Explanations -- 3.4.4 Shapley Additive Explanations -- 3.4.5 Anchors -- 3.4.6 Deep Learning Important Features -- 3.5 Conclusion and Future Directions -- References -- 4 Methods and Metrics for Explaining Artificial Intelligence Models: A Review -- 4.1 Introduction -- 4.1.1 Bringing Explainability to AI Decision-Need for Explainable AI -- 4.2 Taxonomy of Explaining AI Decisions -- 4.3 Methods of Explainable Artificial Intelligence -- 4.3.1 Techniques of Explainable AI -- 4.3.2 Stages of AI Explainability -- 4.3.3 Types of Post-model Explaination Methods -- 4.4 Metrics for Explainable Artificial Intelligence -- 4.4.1 Evaluation Metrics for Explaining AI Decisions -- 4.5 Use-Case: Explaining Deep Learning Models Using Grad-CAM -- 4.6 Challenges and Future Directions -- 4.7 Conclusion -- References -- 5 Evaluation Measures and Applications for Explainable AI -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Basics Related to XAI -- 5.3.1 Understanding -- 5.3.2 Explicability -- 5.3.3 Explainability -- 5.3.4 Transparency -- 5.3.5 Explaining -- 5.3.6 Interpretability -- 5.3.7 Correctability -- 5.3.8 Interactivity -- 5.3.9 Comprehensibility -- 5.4 What is Explainable AI? -- 5.4.1 Fairness -- 5.4.2 Causality -- 5.4.3 Safety -- 5.4.4 Bias -- 5.4.5 Transparency -- 5.5 Need for Transparency and Trust in AI -- 5.6 The Black Box Deep Learning Models -- 5.7 Classification of XAI Methods -- 5.7.1 Global Methods Versus Local Methods -- 5.7.2 Surrogate Methods Versus Visualization Methods -- 5.7.3 Model Specific Versus Model Agnostic -- 5.7.4 Pre-Model Versus In-Model Versus Post-Model -- 5.8 XAI's Evaluation Methods. 5.8.1 Mental Model -- 5.8.2 Explanation Usefulness and Satisfaction -- 5.8.3 User Trust and Reliance -- 5.8.4 Human-AI Task Performance -- 5.8.5 Computational Measures -- 5.9 XAI's Explanation Methods -- 5.9.1 Lime -- 5.9.2 Sp-Lime -- 5.9.3 DeepLIFT -- 5.9.4 Layer-Wise Relevance Propagation -- 5.9.5 Characteristic Value Evaluation -- 5.9.6 Reasoning from Examples -- 5.9.7 Latent Space Traversal -- 5.10 Explainable AI Stakeholders -- 5.10.1 Developers -- 5.10.2 Theorists -- 5.10.3 Ethicists -- 5.10.4 Users -- 5.11 Applications -- 5.11.1 XAI for Training and Tutoring -- 5.11.2 XAI for 6G -- 5.11.3 XAI for Network Intrusion Detection -- 5.11.4 XAI Planning as a Service -- 5.11.5 XAI for Prediction of Non-Communicable Diseases -- 5.11.6 XAI for Scanning Patients for COVID-19 Signs -- 5.12 Possible Research Ideology and Discussions -- 5.13 Conclusion -- References -- 6 Explainable AI and Its Applications in Healthcare -- 6.1 Introduction -- 6.2 The Multidisciplinary Nature of Explainable AI in Healthcare -- 6.2.1 Technological Outlook -- 6.2.2 Legal Outlook -- 6.2.3 Medical Outlook -- 6.2.4 Ethical Outlook -- 6.2.5 Patient Outlook -- 6.3 Different XAI Techniques Used in Healthcare -- 6.3.1 Methods to Explain Deep Learning Models -- 6.3.2 Explainability by Using White-Box Models -- 6.3.3 Explainability Methods to Increase Fairness in Machine Learning Models -- 6.3.4 Explainability Methods to Analyze Sensitivity of a Model -- 6.4 Application of XAI in Healthcare -- 6.4.1 Medical Diagnostics -- 6.4.2 Medical Imaging -- 6.4.3 Surgery -- 6.4.4 Detection of COVID-19 -- 6.5 Conclusion -- References -- 7 Explainable AI Driven Applications for Patient Care and Treatment -- 7.1 General -- 7.2 Benefits of Technology and AI in Healthcare Sector -- 7.3 Most Common AI-Based Healthcare Applications -- 7.4 Issues/Concerns of Using AI in Health Care. 7.5 Why Explainable AI? -- 7.6 History of XAI -- 7.7 Explainable AI's Benefits in Healthcare -- 7.8 XAI Has Proposed Applications for Patient Treatment and Care -- 7.9 Future Prospects of XAI in Medical Care -- 7.10 Case Study on Explainable AI -- 7.11 Framework for Explainable AI -- 7.12 Conclusion -- References -- 8 Explainable Machine Learning for Autonomous Vehicle Positioning Using SHAP -- 8.1 Introduction -- 8.1.1 Global Navigation Satellite System (GNSS) and Autonomous Vehicles -- 8.1.2 Navigation Using Inertial Measurement Sensors -- 8.1.3 Inertial Positioning Using Wheel Encoder Sensors -- 8.1.4 Motivation for Explainability in AV Positioning -- 8.2 eXplainable Artificial Intelligence (XAI): Background and Current Challenges -- 8.2.1 Why XAI in Autonomous Driving? -- 8.2.2 What is XAI? -- 8.2.3 Types of XAI -- 8.3 XAI in Autonomous Vehicle and Localisation -- 8.4 Methodology -- 8.4.1 Dataset: IO-VNBD (Inertial and Odometry Vehicle Navigation Benchmark Dataset) -- 8.4.2 Mathematical Formulation of the Learning Problem -- 8.4.3 WhONet's Learning Scheme -- 8.4.4 Performance Evaluation Metrics -- 8.4.5 Training of the WhONet Models -- 8.4.6 WhONet's Evaluation -- 8.4.7 SHapley Additive exPlanations (SHAP) Method -- 8.5 Results and Discussions -- 8.6 Conclusions -- References -- 9 A Smart System for the Assessment of Genuineness or Trustworthiness of the Tip-Off Using Audio Signals: An Explainable AI Approach -- 9.1 Introduction -- 9.2 Background -- 9.3 Proposed Methodology -- 9.3.1 Dataset Used -- 9.3.2 Pre-processing -- 9.3.3 Feature Extracted -- 9.3.4 Feature Selected -- 9.3.5 Machine Learning in SER -- 9.3.6 Performance Index -- 9.4 Results and Discussion -- 9.5 Conclusion -- References -- 10 Face Mask Detection Based Entry Control Using XAI and IoT -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Methodology. 10.3.1 Web Application Execution -- 10.3.2 Implementation -- 10.3.3 Activation Functions -- 10.3.4 Raspberry Pi Webserver -- 10.4 Results -- 10.4.1 Dataset -- 10.4.2 Model Summary -- 10.4.3 Model Evaluation -- 10.5 Conclusion -- References -- 11 Human-AI Interfaces are a Central Component of Trustworthy AI -- 11.1 Introduction -- 11.2 Regulatory Requirements for Trustworthy AI -- 11.3 Explicability-An Ethical Principle for Trustworthy AI -- 11.4 User-Centered Approach to Trustworthy AI -- 11.4.1 Stakeholder Analysis and Personas for AI -- 11.4.2 User-Testing for AI -- 11.5 An Example Use Case: Computational Pathology -- 11.5.1 AI in Computational Pathology -- 11.5.2 Stakeholder Analysis for Computational Pathology -- 11.5.3 Human-AI Interface in Computational Pathology -- 11.6 Conclusion -- 11.7 List of Abbreviations -- References.
Record Nr.	UNINA-9910627258703321

Autore	Aluvalu Rajanikanth
Edizione	[1st ed. 2024.]
Pubbl/distr/stampa	Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica	1 online resource (287 pages)
Disciplina	006.3
Altri autori (Persone)	MehtaMayuri SiarryPatrick
Collana	Computational Intelligence Methods and Applications
Soggetto topico	Artificial intelligence Medical informatics Biomedical engineering Artificial Intelligence Health Informatics Medical and Health Technologies
ISBN	9789819737055 9789819737048
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Chapter 1. Introduction to Explainable AI -- Chapter 2. Explainable AI Methods and Applications -- Chapter 3. Unveil the Black Box Model for Healthcare Explainable AI -- Chapter 4. Explainable AI: Methods, Frameworks, and Tools for Healthcare 5.0 -- Chapter 5. Explainable AI in Disease Diagnosis -- Chapter 6. Explainable Artificial Intelligence in Drug Discovery -- Chapter 7. Explainable AI for Big Data Control -- Chapter 8. Patient Data Analytics using XAI- Existing Tools & Case Studies -- Chapter 9. Enhancing Diagnosis of Kidney Ailments from CT Scan with Explainable AI -- Chapter 10. Explainable AI for Colorectal Cancer Classification -- Chapter 11. Explainable AI (XAI)-based Robot-Assisted Surgical classification Procedure -- Chapter 12. Explainable AI Case Studies in Healthcare.
Record Nr.	UNINA-9910872197303321

Edizione	[1st ed. 2023.]
Pubbl/distr/stampa	Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Descrizione fisica	1 online resource (244 pages)
Disciplina	352.3802854678 350.000285
Collana	Artificial Intelligence-Enhanced Software and Systems Engineering
Soggetto topico	Engineering - Data processing Computational intelligence Artificial intelligence Data Engineering Computational Intelligence Artificial Intelligence
ISBN	3-031-22408-6
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Conceptual Model and Data algorithm for Modernization of E-governance towards Sustainable E-Government Services -- E-School Initiatives that Instigated the Digital Transformation of Education: A Case Study according to SABER-ICT Framework -- New Architecture to facilitate the expansion of E-Government -- Struggling against tax fraud, a holistic approach using artificial intelligence -- e-Government and Green IT: the intersection point -- Machine Learning Technique for Predicting the Rural Citizens’ Trust on using E-Governance Health Care Applications during COVID-19 -- Artificial Intelligence (AI) Use for e-Governance in Agriculture: Exploring the Bioeconomy Landscape -- Enhancing government actions against Covid-19 using computer science -- From paper to digital: e-government's evolution and pitfalls in Brazil -- The role of public libraries in improving public literacy through Twitter social media in Indonesia.
Record Nr.	UNINA-9910678245503321

Edizione	[1st ed. 2022.]
Pubbl/distr/stampa	Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica	1 online resource (266 pages)
Disciplina	610.285
Collana	Intelligent Systems Reference Library
Soggetto topico	Computational intelligence Biomedical engineering Artificial intelligence Computational Intelligence Biomedical Engineering and Bioengineering Artificial Intelligence
ISBN	3-030-76732-9
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Stress Identification from Speech using Clustering techniques -- Comparative Study and Detection of COVID-19 and Related Viral Pneumonia using a Fine-tuned Deep Transfer Learning -- Predicting Glaucoma Diagnosis using AI -- Diagnosis and Analysis of Tuberculosis Disease using Simple Neural Network and Deep Learning Approach for Chest X-ray Images -- Adaptive Machine Learning Algorithm and Analytics of Big Genomic Data for Gene Prediction.
Record Nr.	UNINA-9910522913203321

Autore	Gaie Christophe
Edizione	[1st ed. 2024.]
Pubbl/distr/stampa	Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
Descrizione fisica	1 online resource (247 pages)
Disciplina	620.00285
Altri autori (Persone)	MehtaMayuri
Collana	Intelligent Systems Reference Library
Soggetto topico	Engineering - Data processing Computational intelligence Big data Data Engineering Computational Intelligence Big Data
ISBN	3-031-55575-9
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Digital Transformation of public services: Introduction, Current Trends and Future -- Enhancing Citizen Satisfaction Using Citizen-Facing Process Mining -- Thinking inside the Sandbox: Beyond public services digitalization with co-production -- Thinking inside the Sandbox: Beyond public services digitalization with co-production -- Usage of Modern API for automization of Government procedures -- Epidemiology inspired Cybersecurity Threats Forecasting Models applied to e-Government -- The provision of e-services by public administration bodies and their cybersecurity -- The provision of e-services by public administration bodies and their cybersecurity -- Fuzzy Logic Architecture for Availing the E-Governance Health Care Services by Rural Citizens -- Enhancing the efficiency and the security of e-Government: the French case study of human resources applications.
Record Nr.	UNINA-9910855395503321