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Using traditional design methods to enhance AI-driven decision making

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Using traditional design methods to enhance AI-driven decision making/ Tien V. T. Nguyen, Nhut T. M. Vo, editors.
其他題名:	traditional design methods to enhance artificial intelligence-driven decision making
其他作者:	Vo, Nhut Thi Minh,
出版者:	Hershey, Pennsylvania :IGI Global, : 2024.,
面頁冊數:	1 online resource (xx, 503 p.) :ill.
內容註:	Section 1. AI-driven decision making in healthcare and environmental sciences: nurturing wellness, sustaining nature - navigating AI frontiers in healthcare and environmental science. Chapter 1. AI-driven decision-making applications in pharmaceutical sciences ; Chapter 2. AI clinical decision support system (AI-CDSS) for cardiovascular diseases ; Chapter 3. AI-driven IoT (AIIoT) in healthcare monitoring ; Chapter 4. Artificial intelligence and machine learning modelsfor Alzheimer's disease ; Chapter 5. A smart healthcare diabetes prediction system using ensemble of classifiers ; Chapter 6. AI-driven powered solution selection: navigating forests and fires for a sustainable future ; Chapter 7. AI-drivensolution selection: prediction of water quality using machine learning ; Chapter 8. AI-decision support system: engineering, geology, climate, and socioeconomic aspects' implications on machine learning -- Section 2. Intelligent systems from optimal-MCDM shaping tomorrow: an in-depth analysis of decision-making applications in agriculture, judiciary, education, and others. Chapter 9. AI-driven learning analytics for personalized feedback and assessment in higher education ;Chapter 10. Integrating artificial intelligence in education for sustainable development ; Chapter 11. AI-driven decision-making applications in higher education ; Chapter 12. AI-driven decision-making and optimization in modern agriculture sectors ; Chapter 13. IoT-integrated machine learning-based automated precision agriculture-indoor farming techniques ; Chapter 14. Automated plant disease detection using efficient deep ensemble learning model for smart agriculture ; Chapter 15. Exploring the power of AI-driven decision making in the judicial domain: case studies, benefits, challenges, and solutions ; Chapter 16. AI-driven decision support system for intuitionistic fuzzy assignment problems ; Chapter 17. Enhanced YOLO algorithm for robust object detection in challenging nighttime and blurry, low vision ; Chapter 18. Smart speakers: a new normal lifestyle.
標題:	Artificial intelligence - Educational applications. -
電子資源:	https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-0639-0
ISBN:	9798369306406

Using traditional design methods to enhance AI-driven decision making
Using traditional design methods to enhance AI-driven decision making[electronic resource] /traditional design methods to enhance artificial intelligence-driven decision makingTien V. T. Nguyen, Nhut T. M. Vo, editors. - Hershey, Pennsylvania :IGI Global,2024. - 1 online resource (xx, 503 p.) :ill.

Includes bibliographical references and index.

Section 1. AI-driven decision making in healthcare and environmental sciences: nurturing wellness, sustaining nature - navigating AI frontiers in healthcare and environmental science. Chapter 1. AI-driven decision-making applications in pharmaceutical sciences ; Chapter 2. AI clinical decision support system (AI-CDSS) for cardiovascular diseases ; Chapter 3. AI-driven IoT (AIIoT) in healthcare monitoring ; Chapter 4. Artificial intelligence and machine learning modelsfor Alzheimer's disease ; Chapter 5. A smart healthcare diabetes prediction system using ensemble of classifiers ; Chapter 6. AI-driven powered solution selection: navigating forests and fires for a sustainable future ; Chapter 7. AI-drivensolution selection: prediction of water quality using machine learning ; Chapter 8. AI-decision support system: engineering, geology, climate, and socioeconomic aspects' implications on machine learning -- Section 2. Intelligent systems from optimal-MCDM shaping tomorrow: an in-depth analysis of decision-making applications in agriculture, judiciary, education, and others. Chapter 9. AI-driven learning analytics for personalized feedback and assessment in higher education ;Chapter 10. Integrating artificial intelligence in education for sustainable development ; Chapter 11. AI-driven decision-making applications in higher education ; Chapter 12. AI-driven decision-making and optimization in modern agriculture sectors ; Chapter 13. IoT-integrated machine learning-based automated precision agriculture-indoor farming techniques ; Chapter 14. Automated plant disease detection using efficient deep ensemble learning model for smart agriculture ; Chapter 15. Exploring the power of AI-driven decision making in the judicial domain: case studies, benefits, challenges, and solutions ; Chapter 16. AI-driven decision support system for intuitionistic fuzzy assignment problems ; Chapter 17. Enhanced YOLO algorithm for robust object detection in challenging nighttime and blurry, low vision ; Chapter 18. Smart speakers: a new normal lifestyle.

"In the rapidly evolving landscape of industrial activities, artificial intelligence (AI) has emerged as a powerful force driving transformative change. Among its many applications, AI has proven to be instrumental in reducing processing costs associated with optimization challenges. The intersection of AI with optimization and multi-criteria decision making (MCDM) techniques has led to practical solutions in diverse fields such as manufacturing, transportation, finance, economics, and artificial intelligence. Using Traditional Design Methods to Enhance AI-Driven Decision Making delves into a wide array of topics related to optimization, decision-making, and their applications. Drawing on foundational contributions, system developments, and innovative techniques, the book explores the synergy between traditional design methods and AI-driven decision-making approaches. The book is ideal for higher education faculty and administrators, students of higher education, librarians, researchers, graduate students, and academicians. Contributors are invited to explore a wide range of topics, including the role of AI-driven decision-making in leadership, trends in AI-driven decision-making in Industry 5.0, applications in various industries such as manufacturing, transportation, healthcare, and banking services, as well as AI-driven optimization in mechanical engineering and materials."--

Mode of access: World Wide Web.

ISBN: 9798369306406Subjects--Topical Terms:

567577
Artificial intelligence
--Educational applications.Subjects--Index Terms:

AI-Driven Applications.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: LB1028.43 / .U83 2024eb

Dewey Class. No.: 378.1/01

Using traditional design methods to enhance AI-driven decision making
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300 $a 1 online resource (xx, 503 p.) : $b ill.
504 $a Includes bibliographical references and index.
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520 3 $a "In the rapidly evolving landscape of industrial activities, artificial intelligence (AI) has emerged as a powerful force driving transformative change. Among its many applications, AI has proven to be instrumental in reducing processing costs associated with optimization challenges. The intersection of AI with optimization and multi-criteria decision making (MCDM) techniques has led to practical solutions in diverse fields such as manufacturing, transportation, finance, economics, and artificial intelligence. Using Traditional Design Methods to Enhance AI-Driven Decision Making delves into a wide array of topics related to optimization, decision-making, and their applications. Drawing on foundational contributions, system developments, and innovative techniques, the book explores the synergy between traditional design methods and AI-driven decision-making approaches. The book is ideal for higher education faculty and administrators, students of higher education, librarians, researchers, graduate students, and academicians. Contributors are invited to explore a wide range of topics, including the role of AI-driven decision-making in leadership, trends in AI-driven decision-making in Industry 5.0, applications in various industries such as manufacturing, transportation, healthcare, and banking services, as well as AI-driven optimization in mechanical engineering and materials."-- $c Provided by publisher.
538 $a Mode of access: World Wide Web.
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650 0 $a Education, Higher $x Decision making. $3 3793176
650 0 $a Educational leadership. $3 529436
653 $a AI-Driven Applications.
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653 $a Decision-Making Approaches.
653 $a Healthcare.
653 $a Higher Education.
653 $a Industrial Transformation.
653 $a Leadership Pathways.
653 $a Manufacturing.
653 $a Materials Optimization.
653 $a Mechanical Engineering.
653 $a Multi-Criteria Decision Making (MCDM).
653 $a Optimization.
653 $a Smart Building.
653 $a Sustainable Development.
653 $a Transportation.
655 4 $a Electronic books. $2 lcsh $3 542853
700 1 $a Vo, Nhut Thi Minh, $d 1986- $3 3793174
700 1 $a Nguyen, Tien V. T., $d 1987- $3 3793175
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