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[null,"1.","Record Nr.",null,"UNIORUON00058929"]
[null,null,"Autore",null,"WAKEFIELD, Francis"]
[null,null,"Titolo",null,"Sawahid islamiyya wa masihiyya wa yaduhiyya / Francis Wakefield"]
[null,null,"Pubbl/distr/stampa",null,"al-Qahira, : Matbu'at al-Nil al-masihiyya, 1344h [1926]"]
[null,null,"Descrizione fisica",null,"228 p. ; 28 cm"]
[null,null,"Classificazione",null,"ARA VII G"]
[null,null,"Soggetti",null,"RELIGIONI COMPARATE - CRISTIANESIMO-ISLAM"]
[null,null,"Lingua di pubblicazione",null,"Arabo"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9911020074303321"]
[null,null,"Autore",null,"Thirunavukkarasan M"]
[null,null,"Titolo",null,"Artificial Intelligence and Machine Learning for Industry 4. 0"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2025"]
[{"$nulls":4},"©2025"]
[null,null,"ISBN",null,"1-394-27505-6"]
[{"$nulls":4},"1-394-27506-4"]
[{"$nulls":4},"1-394-27507-2"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (276 pages)"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Machine learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"in Mental Health -- 3.2 Related Work -- 3.2.1 Recognizing AI's Place in Healthcare -- 3.2.2 Comprehending AI's Impact on Mental Health -- 3.3 Machine Learning in Healthcare -- 3.3.1 SML-Supervised Machine Learning -- 3.3.2 Unsupervised Machine Learning (UML) -- 3.3.3 Deep Learning (DL)."]
[{"$nulls":4},"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."]
[{"$nulls":4},"4.5 Future Advances in Urban Energy Efficiency and Smart Building Technologies -- References -- Chapter 5 Applications of Artificial Intelligence and Machine Learning in Industry 4.0 -- 5.1 Introduction -- 5.1.1 Overview of Industry 4.0 -- 5.1.2 Key Components and Technologies -- 5.2 Smart Manufacturing and Predictive Maintenance -- 5.2.1 Integration of AI/ML in Manufacturing Process -- 5.2.2 Predictive Maintenance Strategies -- 5.3 Supply Chain Optimization -- 5.3.1 AI/ML for Supply Chain Management -- 5.3.2 Optimizing Logistics and Inventory -- 5.4 Quality Control and Defect Detection -- 5.4.1 AI/ML for Quality Assurance -- 5.4.2 Automated Defect Detection System -- 5.5 Robotics and Automation -- 5.5.1 Robotics in Smart Factories -- 5.5.2 AI-Driven Automation Process -- 5.6 Data Analytics and Decision Support -- 5.6.1 Big Data Analytics in Industry 4.0 -- 5.6.2 Decision Support System with AI/ML -- 5.7 Cybersecurity in Industry 4.0 -- 5.7.1 Challenges and Threats -- 5.7.2 AI-Enhanced Cybersecurity Solutions -- 5.8 Human-Machine Collaboration -- 5.8.1 Human-Centric AI Applications -- 5.8.2 Collaboration Interfaces in Smart Manufacturing -- 5.9 Energy Efficiency and Sustainability -- 5.9.1 Role of AI ML in Energy Management -- 5.9.2 Sustainable Practices in Industry 4.0 -- 5.10 Emerging Trends and Future Prospects -- Conclusion -- References -- Chapter 6 Application of Machine Learning in Moisture Content Prediction of Coffee Drying Process -- 6.1 Introduction -- 6.2 Literature Reviews -- 6.2.1 Related Works -- 6.2.2 Background of Machine Learning and Credit Risk Prediction Techniques -- 6.2.2.1 Non-Linear Regression -- 6.2.2.2 Artificial Neural Networks "]
[{"$nulls":4},"(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."]
[{"$nulls":4},"6.3.2.1 Missing Value Detection and Attribute Visualization -- 6.3.2.2 Normalization -- 6.3.2.3 Standardization -- 6.3.2.4 Cross-Validation -- 6.3.3 Research Methodology -- 6.3.3.1 Multi-Layer Perceptron (MLP) Regression -- 6.3.3.2 Adaptive Neuro-Fuzzy Inference System - ANFIS -- 6.3.3.3 Feature Selection Techniques -- 6.4 Results and Analysis -- 6.4.1 Model Evaluation -- 6.4.2 Analysis Results -- 6.4.3 Analysis Results with Feature Selection -- 6.4.3.1 Feature Selection with ANN -- 6.4.3.2 Feature Selection with ANFIS -- 6.5 Conclusion -- References -- Chapter 7 Survivable AI for Defense Strategies in Industry 4.0 -- 7.1 Introduction -- 7.2 Purpose -- 7.3 Scope -- 7.4 History of AI for Defense Strategies in Industry 4.0 -- 7.4.1 AI in Defense -- 7.4.2 AI in Defense Strategies in Industry 4.0 -- 7.5 AI Applications in Defense Strategies in Industry 4.0 -- 7.6 Era of AI in Industry -- 7.6.1 Era of AI Applications in Industry 4.0 -- 7.7 Importance of AI in the Defense Industry -- 7.8 Future of AI in the Defense Industry -- 7.8.1 Cyberattacks in Defense Industry -- 7.8.2 Trade-Offs of AI in Industry 4.0 -- 7.8.3 Cyberattacks in Defense Industry 4.0 -- 7.9 Conclusion -- References -- Chapter 8 Industry 4.0 Based Turbofan Performance Prediction -- 8.1 Introduction -- 8.2 Search Methodology -- 8.2.1 Sensor-Based Technique -- 8.2.2 Data-Driven Approaches -- 8.2.3 Benefits and Challenges of Machine Learning for PdM -- 8.2.4 Challenges -- 8.3 Literature Review -- 8.3.1 Identification of Problem -- 8.3.2 Objectives -- 8.4 Methodology -- 8.5 Experimental Results -- 8.5.1 Data Preprocessing -- 8.5.2 Developing Models -- 8.5.3 Training and Validation -- 8.5.4 Evaluation -- 8.5.5 Comparison with Baseline -- 8.5.6 Sensitivity Analysis -- 8.6 Conclusion and Future Work -- 8.7 Additional Considerations -- References."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"Conclusion."]
[{"$nulls":4},"R eferences."]
[null,null,null,"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."]
[null,"3.","Record Nr.",null,"UNINA9910137138503321"]
[null,null,"Autore",null,"Inter-American Development Bank"]
[null,null,"Titolo",null,"Saving for Development : How Latin America and the Caribbean Can Save More and Better / / by Inter-American Development Bank ; edited by Eduardo Cavallo, Tomás Serebrisky"]
[null,null,"Pubbl/distr/stampa",null,"2016"]
[{"$nulls":4},"New York : , : Palgrave Macmillan US : , : Imprint : Palgrave Macmillan, , 2016"]
[null,null,"ISBN",null,"9781349949298"]
[{"$nulls":4},"1349949299"]
[null,null,"Edizione",null,"[1st ed. 2016.]"]
[null,null,"Descrizione fisica",null,"1 online resource (xxvi, 329 pages) : 94 illustrations, 91 illustrations in colour; digital, PDF file(s)"]
[null,null,"Classificazione",null,"BUS067000BUS092000POL024000"]
[null,null,"Disciplina",null,"338.9"]
[null,null,"Soggetti",null,"Development economics"]
[{"$nulls":4},"Economic policy"]
[{"$nulls":4},"Regional economics"]
[{"$nulls":4},"Space in economics"]
[{"$nulls":4},"Development Economics"]
[{"$nulls":4},"Economic Policy"]
[{"$nulls":4},"Regional and Spatial Economics"]
[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 -- Contents -- List of Tables -- List of Figures -- List of Boxes -- Preface -- Acknowledgments -- Contributors -- 1 Saving for a Sunny Day -- Savings: The Sum of its Parts -- Why Care about National Saving? -- Sharing the Blame for Low Saving -- How to Promote Saving for Development -- Tackle the pension problem -- Focus on infrastructure and capital spending -- Target tax policy better -- Promote household saving and create a savings culture -- Improve productivity growth -- Fix the financial system -- The Many Faces of Saving -- A Policy Agenda for the Future -- Notes -- 2 The State of Saving in Latin America and the Caribbean -- National Saving Rates: Comparatively Low -- The Private Sector: Taking the Lead -- Foreign Savings: A Secondary Actor -- Businesses: The Biggest Savers-Worldwide -- Farewell to the Demographic Dividend -- Too Old to Save? -- Higher Income, Greater Saving -- Lower-Income Savers: Little to Show for their Efforts -- The Bottom Line -- Notes -- 3 Financial "]
[{"$nulls":4},"Systems to Make Savings Count -- In Financial Systems, Small Is Not Beautiful -- Formal vs. Informal Saving: Quality Counts -- Accounting for the Unbanked -- The Link between Financial Access and Savings: The Case of Mexico -- The Missing Link -- Notes -- 4 More and Better Saving for Productive Investment -- Investment and National Saving: Low, Lower, Lowest -- Financing Investment: No Place Like Home -- For Policy, Which Comes First: Saving or Investment? -- Investment in Infrastructure: First among Equals? -- A Catalyst for Productivity and Growth -- Public or Private Investment: Both Is Best -- The Other Half -- Understanding the Infrastructure Financing Market -- Debt Stands Out -- Infrastructure as an Asset Class -- Institutional Investors: An Untapped Source of Financing -- Building a Better Investment Strategy -- Notes -- 5 Saving for Stability."]
[{"$nulls":4},"Foreign Financing: A Different Animal -- Risky Business: Absorbing Foreign Saving -- Not All Foreign Saving Is Created Equal -- Different Risks for Different Financial Flows -- Financial Integration Is No Cure -- Safety First -- Notes -- 6 Running Out of Time: The Demographics of Saving -- More Elderly with More Needs -- Facing the Challenge: More and Better Savings -- Fulfilling Promises -- Saving for the Future -- More-and Better-Savings to Enhance Growth -- How Is the Region Preparing for the Future? -- Pension Systems: Not an Option Today -- Plan B: Household Savings in Other Assets -- The Last Resort: Taking Care of Grandma -- Act Today, for a Better Tomorrow -- Notes -- 7 Saving for the Future: Pension Systems -- Newer Systems for Older Populations -- PAYG/Defined-Benefit Systems: Promises, Promises -- Sustainability -- Adequacy and Redistribution -- Institutional Arrangements -- Recommendations -- Defined Contribution Systems: A Work in Progress -- Transition Costs -- Investments, Returns, and Costs -- Retirement Products and Insurance Arrangements -- Financial Literacy, Legitimacy, and Confidence -- Appropriate Regulation and Supervision -- Recommendations -- When All Else Fails: Noncontributory Pensions -- Recommendations -- Pensions Count -- Notes -- 8 A Better Way for Government to Save -- Current vs. Capital Expenditures: Fix the Mix -- Efficiency: A Path to Saving -- Energy -- Social Programs -- Tax Expenditures -- Education and Health -- Education -- Health -- It All Adds Up -- Saving, from the Top Down -- Notes -- 9 Saving Begins at Home -- Constraints to Saving -- Too Hard to Save Formally? -- In the Hearts and Minds of Savers -- Family and Friends First -- A Behavioral Economics Tale -- The Penchant for Instant Gratification -- Inertia and Limited Attention: Not All Bad -- Policy Recommendations: What Really Counts."]
[{"$nulls":4},"Product Innovation -- Keep It Simple -- Tackle Behavioral Biases -- Incorporate Technology -- Keep Testing -- Bridging the Gap between Informal and Formal Mechanisms -- Getting an Early Start -- Redefining Financial Inclusion -- Notes -- 10 Firm Productivity as an Engine of Saving -- From Japan to the World: The Empirical Link between TFP and Savings -- Incentives to Save -- Quantifying the Link from Productivity to Saving -- The Fine Print -- Zooming in on Firms' Saving Decisions -- Firm Saving: A Way Out for Productive Firms -- A Productive Approach to Policy -- Notes -- 11 Breaking the Vicious Circle: Financial Policies for High-Quality Saving -- Toward a Well-Oiled Financial Machine -- Broadening the Base -- Reducing Service Costs -- Keeping it Simple -- Fostering a Culture of Saving -- Banking-The Old Fashioned Way -- More than Brick-and-Mortar Banking -- Dialing up Technology -- Financial Saving beyond Banking -- Creating a Virtuous Circle -- Notes -- References -- Index."]
[null,null,null,"Sommario/riassunto","Why should people - and economies - save? This book on the savings problem in Latin America and the Caribbean suggests that, while saving "]
[{"$nulls":4},"to survive the bad times is important, saving to thrive in the good times is what really counts. People must save to invest in health and education, live productive and fulfilling lives, and make the most of their retirement years. Firms must save to grow their enterprises, employ more workers in better jobs, and produce quality goods. Governments must save to build the infrastructure required by a productive economy, provide quality services to their citizens, and assure their senior citizens a dignified, worry-free retirement. In short, countries must save not for the proverbial rainy day, but for a sunny day - a time when everyone can bask in the benefits of growth, prosperity, and well-being. This book is open access under a CC BY-NC-ND 3.0 IGO license."]