01625nam 2200397 n 450 99639503080331620200824121034.0(CKB)3810000000010454(EEBO)2248544051(UnM)99835313e(UnM)99835313(EXLCZ)99381000000001045419920317d1640 uy |engurbn||||a|bb|The Greeks and Trojans vvarres[electronic resource] Caus'd by that wanton Trojan knight Sir Paris who ravishes Hellen and her to Troy carries the Greeks in revenge (and to fetch her again) a mighty great army do quickly ordain. Imagine you see them besiedging old Troy, which after ten years they at th'last destroy, with a fit allusion, before the conclusion. Tune is, A conscionable caveatLondon Printed for F. Grove[1650?]1 sheet ([1] p.) ill. (woodcuts)Verse - "Of Grece and Troy I shall you tell.".Signed: H.C., i.e. Humphrey Crouch.Publication date from Wing.In two parts; woodcuts at head of each part.Reproduction of the original in the British Library.eebo-0018Ballads, English17th centuryBroadsidesEnglandLondon17th century.rbgenrBallads, EnglishCrouch Humphreyfl. 1635-1671.1001249Cu-RivESCu-RivESCStRLINWaOLNBOOK996395030803316The Greeks and Trojans vvarres2320542UNISA03477oam 2200577 450 991048417180332120221003160811.03-030-66501-110.1007/978-3-030-66501-2(CKB)5590000000433615(DE-He213)978-3-030-66501-2(MiAaPQ)EBC6466024(PPN)253254485(EXLCZ)99559000000043361520210629d2021 uy 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierProgress in intelligent decision science proceeding of IDS 2020 /editors, Tofigh Allahviranloo, Soheil Salahshour, Nafiz Arica1st edition 2021.Cham, Switzerland :Springer,[2021]©20211 online resource (XI, 989 p. 293 illus., 194 illus. in color.)Advances in Intelligent Systems and Computing,2194-5357 ;13013-030-66500-3 Identification of Sport News in Turkish Tweets using Deep Learning Architectures -- Real-time News Grouping: Detecting the Same-content News on Turkish News Stream -- Statistical Analysis of Behavioural Intention Towards Private Umbilical Cord Blood Banking -- Improved Weighted Random Forest for Classification Problems -- A new Combination Method for Fuzzy Optimal Control -- Smart Room Temperature Controller IoT System -- Audio to Video: Generating a Talking Fake Agent -- Cluster-Based Monitoring and Location Estimation for Crowd Counting -- Genetic Algorithms and Neural Networks by Data Mining -- A new solution for the generalized shortest path problem -- An extension of DEMATEL under Pythagorean fuzzy environment -- Multi-Objective Genetic Algorithm and Interpolation Based Nonlinear Control Model -- The Design of a Novel Torque Wrench Based on TRIZ Decision Procedures -- Solving Linear Systems based on Z-numbers -- Z-numbers for uncertainty formulation.This book contains the topics of artificial intelligence and deep learning that do have much application in real-life problems. The concept of uncertainty has long been used in applied science, especially decision making and a logical decision must be made in the field of uncertainty or in the real-life environment that is formed and combined with vague concepts and data. The chapters of this book are connected to the new concepts and aspects of decision making with uncertainty. Besides, other chapters are involved with the concept of data mining and decision making under uncertain computations.Advances in Intelligent Systems and Computing,2194-5357 ;1301Big dataCongressesComputational intelligenceCongressesDecision makingData processingCongressesMachine learningCongressesUncertainty (Information theory)CongressesBig dataComputational intelligenceDecision makingData processingMachine learningUncertainty (Information theory)658.403Allahviranloo TofighSalahshour SoheilArica NafizMiAaPQMiAaPQUtOrBLWBOOK9910484171803321Progress in intelligent decision science2851792UNINA05084nam 22004213 450 991080556950332120240131080237.03-031-49979-4(MiAaPQ)EBC31084680(Au-PeEL)EBL31084680(EXLCZ)993011141950004120240131d2024 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierArtificial Intelligence for Sustainability Innovations in Business and Financial Services1st ed.Cham :Palgrave Macmillan,2024.©2024.1 online resource (250 pages)Print version: Walker, Thomas Artificial Intelligence for Sustainability Cham : Palgrave Macmillan,c2024 9783031499784 Intro -- Preface -- Acknowledgements -- Contents -- About the Editors and Contributors -- About the Editors -- Notes on Contributors -- Copy Editing Team -- List of Figures -- List of Tables -- 1: Artificial Intelligence for Sustainability: An Overview -- 1.1 Introduction -- 1.2 Overview of Content -- References -- Part I: AI and Sustainable Industry Applications -- 2: Fast Fashion's Fate: Artificial Intelligence, Sustainability, and the Apparel Industry -- 2.1 Silhouette: Chapter's Contour -- 2.2 Pattern: AI and Algorithms -- 2.3 Ensemble: Apparel, Sustainability, and AI -- 2.4 Samples: Cases and Companies -- 2.5 Trends: Fashion's Future -- References -- 3: Artificial Intelligence and the Global Automotive Industry -- 3.1 Introduction -- 3.2 The Six Stages of Self-Driving and Their Implications -- 3.3 Artificial Intelligence and Technology Features -- 3.4 Autonomous Vehicles (AV) Industry Forces -- 3.5 Conclusion and Path Forward -- References -- 4: The Emergence of the Nighttime Artificial Intelligence-Robot-Driven Economy -- 4.1 Introduction -- 4.2 Transitions Toward an AI-Robot-Driven Economy -- 4.3 Decentralization, Localization, and Customization -- 4.4 AI-Robot-Driven Circular Economy (CE) and Value Chain Optimization -- 4.5 The Emergence of the AI-Robot-Driven Nighttime Economy -- 4.6 Conclusion -- References -- Part II: AI and Sustainable Business Operations -- 5: Strengthening the Sustainability of Artificial Intelligence: Fostering Green Intelligence for a More Ethical Future -- 5.1 Introduction -- 5.2 Bounded AI -- 5.3 The Greening of Bounded AI -- 5.4 Further Considerations for the Business Sector -- 5.5 Recommendations for Business Leaders, Policymakers, and Other Stakeholders -- 5.6 Conclusion -- References.6: Predictive Machine Learning in Assessing Materiality: The Global Reporting Initiative Standard and Beyond -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Methodology and Results -- 6.3.1 Methodology -- 6.3.2 Results -- 6.4 Conclusions and Limitations -- References -- 7: Artificial Intelligence and the Food Value Chain -- 7.1 Introduction -- 7.2 Background -- 7.2.1 The Sustainability of Value Chains -- 7.2.2 Sustainable Food Value Chains -- 7.2.3 Artificial Intelligence (AI) -- 7.3 Sustainability Benefits from Artificial Intelligence in the Food Value Chain -- 7.3.1 Food Production -- 7.3.2 Food Aggregation -- 7.3.3 Food Processing -- 7.3.4 Food Distribution -- 7.4 Discussion and Conclusions -- References -- Part III: The Role of AI in Sustainable Development and the 2030 Agenda -- 8: Analysis of Smart Meter Data for Energy Waste Management -- 8.1 Introduction -- 8.2 Sustainability Benefits of Smart Grids and Smart Meters -- 8.3 Challenges of Using AI for Smart Meter Data Analysis -- 8.4 The Future of AI for Smart Meter Data Analytics -- References -- 9: Leveraging AI to Map SDG Coverage and Uncover Partnerships in Swiss Philanthropy -- 9.1 Overview -- 9.2 Contextualizing Sustainability - The SDG Framework -- 9.3 The Landscape of AI for SDGs -- 9.4 Philanthropy for the SDGs -- 9.5 Mapping SDG-Alignment of the Swiss Philanthropic Ecosystem -- 9.6 Risks of AI in Philanthropy and SDGs -- 9.7 Conclusion -- References -- 10: The Potential Role of Artificial Intelligence in the Commercialization of Traditional Medicines in Tropical Regions -- 10.1 Introduction -- 10.2 Sustainability Challenges with Developing Traditional and Complementary Medicines -- 10.3 Examples of Sources of Information on Traditional and Complementary Medicines.10.4 Potential Role for AI in Developing a Comprehensive Process to Identify Efficacious Traditional Medicines -- 10.5 Discussion and Recommendations -- References -- Index.658.0563Walker Thomas158671Wendt Stefan1588920Goubran Sherif1588921Schwartz Tyler1588922MiAaPQMiAaPQMiAaPQBOOK9910805569503321Artificial Intelligence for Sustainability3883194UNINA