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1. |
Record Nr. |
UNINA9910461964203321 |
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Autore |
Teshima Shoichi |
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Titolo |
Quality recognition and prediction : smarter pattern technology with the Mahalanobis-Taguchi system / / Shoichi Teshima, Yoshiko Hasegawa, Kazuo Tatebayashi |
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Pubbl/distr/stampa |
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New York : , : Momentum Press, LLC, , [2012] |
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©2012 |
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ISBN |
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1-283-89601-X |
1-60650-344-8 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (242 p.) |
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Disciplina |
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Soggetti |
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Pattern recognition systems |
Taguchi methods (Quality control) |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Foreword -- Preface -- Acknowledgments -- |
1. Pattern recognition and the MT system -- 1.1 Overview of pattern recognition and the fields of application -- 1.2 Standard execution procedure for pattern recognition -- 1.3 Fields with substantial experience in the use of MT system applications -- |
2. Merits of the MT system and its computation methods -- 2.1 Characteristics shared by all MT system components -- 2.2 Features of the MT method -- 2.3 Features of the T method -- 2.4 The MT system computation formulas -- |
3. Data handled by the MT system and feature extraction -- 3.1 Use of measured values in an unmodified form -- 3.2 Performing feature extraction -- 3.3 Feature extraction technique from character pattern -- 3.4 Feature extraction technique from waveform pattern -- 3.5 Differences between other waveform features and variation values/abundance values -- |
4. MT method application procedure and important points to heed -- 4.1 Example of character recognition -- 4.2 Example of weather prediction -- |
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5. T method application procedures and key points -- 5.1 Yield prediction for manufacturing-production using T method-1 -- 5.2 Character pattern recognition using the RT method -- |
6. Examples of actual applications -- 6.1 Blade wear monitoring via cutting vibration waveform (MT method) -- 6.2 Appearance inspection of a clutch disk -- 6.3 Monitoring of machine conditions (MT method) -- 6.4 Application to medical diagnosis (MT method) -- 6.5 Strength estimation based on raw material mixing (T method-1) -- 6.6. Real estate price prediction by T method-1 -- |
Appendices -- A. Differences between the MT system and artificial intelligence -- B. Difference between the MT system and traditional statistical theory -- C. Supplementary considerations concerning mathematical formulas -- D. Strategy to use when data incorporates unmeasured values -- E. Fusion with artificial intelligence and other resources -- F. Mahalanobis distance computation using Microsoft Excel -- G. Paley's construct for generation of Hadamard matrice -- |
Bibliography and reference sources -- Bibliography (in English) -- Bibliography (in Japanese) -- References -- Glossary: definition of terms -- Index -- About the authors. |
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Sommario/riassunto |
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The MT system is a diagnostic and predictive method for analyzing patterns in multivariate data that has provided benefits in many diverse applications over the past decade or so. It has proven itself superior in many cases to more traditional artificial intelligence applications such as neural nets. |
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2. |
Record Nr. |
UNINA9910983070403321 |
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Autore |
Lau Theodora (Fintech expert) |
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Titolo |
Banking on (Artificial) Intelligence : Navigating the Realities of AI in Financial Services / / by Theodora Lau |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Palgrave Macmillan, , 2025 |
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ISBN |
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9783031816468 |
9783031816475 |
3031816471 |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (xvii, 216 pages) |
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Disciplina |
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Soggetti |
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Financial engineering |
Financial services industry |
Artificial intelligence |
Financial Technology and Innovation |
Financial Services |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographies and index. |
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Nota di contenuto |
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Chapter 1: Introduction -- Chapter 2: The rise of AI and generative AI -- Chapter 3: The key pillars of responsible AI -- Chapter 4: Size matters when adopting and scaling AI -- Chapter 5: Trust and evolution of risk -- Chapter 6: Representation matters -- Chapter 7: Future of work -- Chapter 8: Win at all costs? -- Chapter 9: Ownership, rights, and governance -- Chapter 10: Trustworthy and Human-Centered AI. |
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Sommario/riassunto |
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There is no lack of hype around artificial intelligence. We have only begun to scratch the surface of what this powerful technology can do. While tech and financial services become more intertwined, cutting through the noise has become more difficult but also more crucial. As a technology, AI is essential to advancing innovation, to creating efficiencies, and enhancing productivity while capturing opportunities by both incumbent financial institutions as well as fintechs. But it also |
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comes with risks and potential for biases and disinformation, that can deepen inequalities and erode trust in our society. Responsible innovation must become part of our DNA and not as an afterthought. This book provides a tailored overview of what AI specifically means for financial services, a highly regulated yet also disrupted industry. It investigates the current state of AI applications in financial services today along with the state of funding and partnerships between tech and banking industries. It also examines the key pillars of responsible AI and the importance of keeping humans in the loop. The book takes a deep dive into the use cases in the financial services industry, the challenges and opportunities, and the fragmented regulatory landscape. How can we effectively assess risks, and balance innovation and customer centricity with trust in AI in financial services? Can smaller organizations reap the benefits of the technology? How can institutions deploy AI responsibly and securely, and promote a fairer and more equitable future for more people? While data is about bits and bytes, the realities of AI is very much human. This book will help spark dialogue and collaboration as we journey into the future. Theodora Lau is the founder of Unconventional Ventures, a public speaker, and an advisor. She is the co-author of The Metaverse Economy (2023) and Beyond Good (2021), and host of One Vision, a podcast on fintech and innovation. Her monthly column on FinTech Futures explores the intersection of financial services, tech, and humanity. She was named one of American Banker’s Most Influential Women in FinTech in 2023. She is a regular contributor and commentator for top industry events and publications, including Finovate, American Banker, BBC News, Fintech Times, and The Power 50. Her work has also been published by the Journal of Digital Banking, The Banker, MIT Tech Review, and Harvard Business Review. |
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