Le mari sentimental, ou Le marriage comme il y en a quelques-uns : lettres d'un homme du pays de Vaud, écrites en 178... / Samuel de Constant ; introduzione e note al testo di Giovanni Riccioli

• Record Nr.: UNISALENTO991003529719707536
• Autore: Constant, Samuel : de
• Pubbl/distr/stampa: Milano : Cisalpino-Goliardica, 1975
• Descrizione fisica: 185 p. ; 19 cm
• Collana: Testi di letteratura francese ; 4
• Altri autori (Persone): Riccioli, Giovanni
• Disciplina: 843.5
• Lingua di pubblicazione: Francese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Weakly differentiable functions : Sobolev spaces and functions of bounded variation / William P. Ziemer

• Record Nr.: UNICAMPANIAVAN0269255
• Autore: Ziemer, William P.
• Pubbl/distr/stampa: New York, : Springer, 1989
• Titolo uniforme: Weakly differentiable functions : Sobolev spaces and functions of bounded variation
• Descrizione fisica: xvi, 308 p. ; 25 cm
• Soggetti: 46E35 - Sobolev spaces and other spaces of “smooth” functions, embedding theorems, trace theorems [MSC 2020]; 31B15 - Potentials and capacities, extremal length and related notions in higher dimensions [MSC 2020]; 26B30 - Absolutely continuous real functions of several variables, functions of bounded variation [MSC 2020]
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Artificial Intelligence and Machine Learning for Industry 4. 0

• Record Nr.: UNINA9911020074303321
• Autore: Thirunavukkarasan M
• Pubbl/distr/stampa: Newark : , : John Wiley & Sons, Incorporated, , 2025; ©2025
• ISBN: 1-394-27505-6; 1-394-27506-4; 1-394-27507-2
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (276 pages)
• Altri autori (Persone): MaryS. A. Sahaaya Arul; RSathiyaraj; GhantasalaG S Pradeep; KhanMudassir
• Disciplina: 006.3
• Soggetti: Artificial intelligence; Machine learning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di contenuto: Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Industry 4.0 and the AI/ML Era: Revolutionizing Manufacturing -- 1.1 Introduction -- 1.1.1 Key Traits of Industry 4.0 -- 1.2 Literature Survey -- 1.2.1 Foundations of Industry 4.0 -- 1.2.2 Integration of AI and ML -- 1.2.3 Smart Automation and Human-Robotic Collaboration -- 1.2.4 Cognitive Manufacturing -- 1.2.5 Disturbing Situations and Opportunities -- 1.3 The AI/ML Era Within the Industrial Revolution -- 1.3.1 The Role of AI and ML -- 1.3.2 Opportunities -- 1.4 The Nexus of Industry 4.0 and the AI/ML Era: A Symbiotic Evolution -- 1.5 Challenges and Opportunities in the Integration of Industry 4.0 and the AI/ML Era -- 1.6 Implementation Techniques -- 1.6.1 Future Suggestions -- 1.7 Conclusion -- References -- Chapter 2 Business Intelligence and Big Data Analytics for Industry 4.0 -- 2.1 Introduction -- 2.1.1 The Biggest Challenge of Industry 4.0 -- 2.2 Literature Review -- 2.3 Business Intelligence -- 2.3.1 Challenges of Business Intelligence in Industry 4.0 -- 2.4 Big Data Analytics -- 2.4.1 Five Pillars of Big Data -- 2.4.2 Big Data to the ; Rescue -- 2.4.3 Challenges in Big Data Analytics for Industry 4.0 -- 2.4.4 Advantage in Big Data Analysis for Industry 4.0 -- 2.5 Result and Discussion -- 2.6 Conclusion -- References -- Chapter 3 "AI-Powered Mental Health Innovations": Handling the Effects of Industry 4.0 on Health -- 3.1 Introduction -- 3.1.1 An Overview of Industry 4.0's Development in Healthcare Over Time -- 3.1.2 The Advancement of AI in Mental Health -- 3.2 Related Work -- 3.2.1 Recognizing AI's Place in Healthcare -- 3.2.2 Comprehending AI's Impact on Mental Health -- 3.3 Machine Learning in Healthcare -- 3.3.1 SML-Supervised Machine Learning -- 3.3.2 Unsupervised Machine Learning (UML) -- 3.3.3 Deep Learning (DL).; 3.3.4 NLP - Natural Language Processing -- 3.4 Genetics and Machine Learning for Understanding and Prediction of Complicated Illnesses -- 3.5 AI-Driven Virtual Healthcare Support for Patient Care -- 3.6 AI's Advantages for Mental Health Treatment -- 3.7 AI's Predictive Capabilities: Revolutionizing Mental Health Treatment -- 3.8 AI's Limitations and Research on Mental Health -- 3.9 Ethical Issues and Difficulties with AI-Powered Mental Health -- 3.10 Healthcare AI Governance -- 3.11 Artificial Intelligence in Augmented and Virtual Reality (AR &amp -- VR) -- 3.12 Methodology -- 3.13 Results and Discussions -- 3.13.1 Synopsis of AI Research in Mental Health -- 3.13.2 AI-Driven Intervention as the Future of Mental Healthcare -- 3.14 Conclusion -- References -- Chapter 4 AI ML Empowered Smart Buildings and Factories -- 4.1 Introduction -- 4.1.1 An Account of How Machine Learning Contributes to Task Automation -- 4.1.2 A Description of How Mobile Phones and Computers Facilitate the Completion of Tasks in Intelligent Buildings -- 4.1.3 Intelligent Buildings as well as IoT -- 4.1.4 Utilizing the Ubiquitous Internet of Things Plus the Global Web to Link Buildings -- 4.2 The Advancement of Computational Intelligence within Smart Building Technology and Its Worldwide Consequences -- 4.2.1 Industrial 4.0 Along with IoT -- 4.2.2 An Exploration of the Web of Things and Its Role in Making 4.0 -- 4.3 An Examination on ML, DL and AI Algorithms Used for Engineering and Construction -- 4.3.1 Utilization in Intelligent Structures -- 4.3.2 A Few Examples of the Numerous Uses in Smart Buildings are Automation, Material Efficiency, Off-Site Production, Designing Buildings, and the Combination of Big Data -- 4.3.3 Detectors, and Computational AI Enabling Intelligent Management and Energy Efficiency -- 4.4 Conclusion.; 4.5 Future Advances in Urban Energy Efficiency and Smart Building Technologies -- References -- Chapter 5 Applications of Artificial Intelligence and Machine Learning in Industry 4.0 -- 5.1 Introduction -- 5.1.1 Overview of Industry 4.0 -- 5.1.2 Key Components and Technologies -- 5.2 Smart Manufacturing and Predictive Maintenance -- 5.2.1 Integration of AI/ML in Manufacturing Process -- 5.2.2 Predictive Maintenance Strategies -- 5.3 Supply Chain Optimization -- 5.3.1 AI/ML for Supply Chain Management -- 5.3.2 Optimizing Logistics and Inventory -- 5.4 Quality Control and Defect Detection -- 5.4.1 AI/ML for Quality Assurance -- 5.4.2 Automated Defect Detection System -- 5.5 Robotics and Automation -- 5.5.1 Robotics in Smart Factories -- 5.5.2 AI-Driven Automation Process -- 5.6 Data Analytics and Decision Support -- 5.6.1 Big Data Analytics in Industry 4.0 -- 5.6.2 Decision Support System with AI/ML -- 5.7 Cybersecurity in Industry 4.0 -- 5.7.1 Challenges and Threats -- 5.7.2 AI-Enhanced Cybersecurity Solutions -- 5.8 Human-Machine Collaboration -- 5.8.1 Human-Centric AI Applications -- 5.8.2 Collaboration Interfaces in Smart Manufacturing -- 5.9 Energy Efficiency and Sustainability -- 5.9.1 Role of AI ML in Energy Management -- 5.9.2 Sustainable ; Practices in Industry 4.0 -- 5.10 Emerging Trends and Future Prospects -- Conclusion -- References -- Chapter 6 Application of Machine Learning in Moisture Content Prediction of Coffee Drying Process -- 6.1 Introduction -- 6.2 Literature Reviews -- 6.2.1 Related Works -- 6.2.2 Background of Machine Learning and Credit Risk Prediction Techniques -- 6.2.2.1 Non-Linear Regression -- 6.2.2.2 Artificial Neural Networks (ANN) -- 6.2.2.3 Adaptive Network-Based Fuzzy Inference System (ANFIS) -- 6.3 Methodology -- 6.3.1 Data Collection -- 6.3.2 Data Preprocessing.; 6.3.2.1 Missing Value Detection and Attribute Visualization -- 6.3.2.2 Normalization -- 6.3.2.3 Standardization -- 6.3.2.4 Cross-Validation -- 6.3.3 Research Methodology -- 6.3.3.1 Multi-Layer Perceptron (MLP) Regression -- 6.3.3.2 Adaptive Neuro-Fuzzy Inference System - ANFIS -- 6.3.3.3 Feature Selection Techniques -- 6.4 Results and Analysis -- 6.4.1 Model Evaluation -- 6.4.2 Analysis Results -- 6.4.3 Analysis Results with Feature Selection -- 6.4.3.1 Feature Selection with ANN -- 6.4.3.2 Feature Selection with ANFIS -- 6.5 Conclusion -- References -- Chapter 7 Survivable AI for Defense Strategies in Industry 4.0 -- 7.1 Introduction -- 7.2 Purpose -- 7.3 Scope -- 7.4 History of AI for Defense Strategies in Industry 4.0 -- 7.4.1 AI in Defense -- 7.4.2 AI in Defense Strategies in Industry 4.0 -- 7.5 AI Applications in Defense Strategies in Industry 4.0 -- 7.6 Era of AI in Industry -- 7.6.1 Era of AI Applications in Industry 4.0 -- 7.7 Importance of AI in the Defense Industry -- 7.8 Future of AI in the Defense Industry -- 7.8.1 Cyberattacks in Defense Industry -- 7.8.2 Trade-Offs of AI in Industry 4.0 -- 7.8.3 Cyberattacks in Defense Industry 4.0 -- 7.9 Conclusion -- References -- Chapter 8 Industry 4.0 Based Turbofan Performance Prediction -- 8.1 Introduction -- 8.2 Search Methodology -- 8.2.1 Sensor-Based Technique -- 8.2.2 Data-Driven Approaches -- 8.2.3 Benefits and Challenges of Machine Learning for PdM -- 8.2.4 Challenges -- 8.3 Literature Review -- 8.3.1 Identification of Problem -- 8.3.2 Objectives -- 8.4 Methodology -- 8.5 Experimental Results -- 8.5.1 Data Preprocessing -- 8.5.2 Developing Models -- 8.5.3 Training and Validation -- 8.5.4 Evaluation -- 8.5.5 Comparison with Baseline -- 8.5.6 Sensitivity Analysis -- 8.6 Conclusion and Future Work -- 8.7 Additional Considerations -- References.; Chapter 9 Industrial Predictive Maintenance for Sustainable Manufacturing -- 9.1 Introduction -- 9.1.1 IoT Internet of Things -- 9.1.2 Industry 4.0 -- 9.2 Search Methodology -- 9.3 Methodology -- 9.3.1 Types of Maintenance -- 9.3.2 IoT Technologies for Predictive Maintenance -- 9.3.3 Predictive Maintenance Workflow -- 9.3.4 Predictive Maintenance Model -- 9.3.5 Data Collection Techniques -- 9.3.6 Data Analysis Techniques -- 9.3.7 Predictive Analytics Algorithms -- 9.3.8 Machine Learning Techniques in PdM -- 9.3.9 Comparative Analysis -- 9.3.10 Limitations and Considerations -- 9.4 Conclusion -- References -- Chapter 10 Enhanced Security Framework with Blockchain for Industry 4.0 Cyber-Physical Systems, Exploring IoT Integration Challenges and Applications -- 10.1 Introduction -- 10.2 Related Works -- 10.3 Industry 4.0 Elements -- 10.3.1 CPS in Critical Industry 4.0 -- 10.3.2 Challenges in IoT Integration -- 10.3.3 Security Provided Through Blockchain -- 10.3.4 Blockchain Replaces the Certificate Authority -- 10.4 Results and Discussions -- 10.5 Conclusions -- References -- Chapter 11 Integrating Artificial Intelligence and Machine Learning for Enhanced Cyber Security in Industry 4.0: Designing a Smart Factory with IoT and CPS -- 11.1 Introduction -- 11.2 Related Works -- 11.3 Proposed Model -- 11.3.1 Smart Factory -- 11.3.2 The Mechanical Design -- 11.3.3 Proposed IDS Architecture -- 11.3.4 IDS in CCPS -- 11.4 Results and Discussions -- ; 11.5 Conclusions -- References -- Chapter 12 Application of AI and ML in Industry 4.0 -- 12.1 Introduction -- 12.2 Application of AI and ML in Industry 4.0 -- 12.3 Benefits of AI and ML in Industry 4.0 -- 12.4 Challenges and Considerations in Adopting AI and ML in Industry 4.0 -- 12.5 Case Studies and Examples of AI and ML in Industry 4.0 -- 12.6 Emerging AI and ML Technologies in Industry 4.0 -- 12.7 Conclusion.; R eferences.
• Sommario/riassunto: This book is essential for any leader seeking to understand how to leverage intelligent automation and predictive maintenance to drive innovation, enhance productivity, and minimize downtime in their manufacturing processes. Intelligent automation is widely considered to have the greatest potential for Industry 4.0 innovations for corporations. Industrial machinery is increasingly being upgraded to intelligent machines that can perceive, act, evolve, and interact in an industrial environment. The innovative technologies featured in this machinery include the Internet of Things, cyber-physical systems, and artificial intelligence. Artificial intelligence enables computer systems to learn from experience, adapt to new input data, and perform intelligent tasks. The significance of AI is not found in its computational models, but in how humans can use them. Consistently observing equipment to keep it from malfunctioning is the procedure of predictive maintenance. Predictive maintenance includes a periodic maintenance schedule and anticipates equipment failure rather than responding to equipment problems. Currently, the industry is struggling to adopt a viable and trustworthy predictive maintenance plan for machinery. The goal of predictive maintenance is to reduce the amount of unanticipated downtime that a machine experiences due to a failure in a highly automated manufacturing line. In recent years, manufacturing across the globe has increasingly embraced the Industry 4.0 concept. Greater solutions than those offered by conventional maintenance are promised by machine learning, revealing precisely how AI and machine learning-based models are growing more prevalent in numerous industries for intelligent performance and greater productivity. This book emphasizes technological developments that could have great influence on an industrial revolution and introduces the fundamental technologies responsible for directing the development of innovative firms. Decision-making requires a vast intake of data and customization in the manufacturing process, which managers and machines both deal with on a regular basis. One of the biggest issues in this field is the capacity to foresee when maintenance of assets is necessary. Leaders in the sector will have to make careful decisions about how, when, and where to employ these technologies. Artificial Intelligence and Machine Learning for Industry 4.0offers contemporary technological advancements in AI and machine learning from an Industry 4.0 perspective, looking at their prospects, obstacles, and potential applications.