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This book presents computational intelligence methodologies and its applications to sustainable development goals. Along 18 chapters prepared by reputed scientists around the world, this book explores and focuses on the impacts produced by the application of artificial intelligence and mainly of computational intelligence, in sustainable development goals and on analysing how particularly computational intelligence can influence the ability to comply in a timely manner with all the sustainable development goals. Specialists from STEM areas will find in this book an attractive showcase of instances and research lines to be explored.
Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development present
This book highlights the latest advances in the field of artificial intelligence and related technologies, with a special focus on sustainable development and environmentally friendly artificial intelligence applications. Discussing theory, applications and research, it covers all aspects of artificial intelligence in the context of sustainable development.
New technologies and computing methodologies are now used to address the existing issues of urban traffic systems. The development of computational intelligence methods such as machine learning and deep learning, enables engineers to find innovative solutions to guide traffic in order to reduce transportation and mobility problems in urban areas. This volume, Computational Intelligence for Sustainable Transportation and Mobility, presents several computing models for intelligent transportation systems, which may hold the key to achieving sustainable development goals by optimizing traffic flow and minimizing associated risks. The book begins with the basic computational Intelligence techniques for traffic systems and explains its applications in vehicular traffic prediction, model optimization, behavior analysis, traffic density estimation, and more. The main objectives of this book are to present novel techniques developed, new technologies and computational intelligence for sustainable mobility and transportation solutions, as well as giving an understanding of some Industry 4.0 trends. Readers will learn how to apply computational intelligence techniques such as multiagent systems (MAS), whale optimization, artificial Intelligence (AI), deep neural networks (DNNs) so that they can to develop algorithms, models, and approaches for sustainable transportation operations. Key Features: - Provides an overview of machine learning models and their optimization for intelligent transportation systems in urban areas - Covers classification of traffic behavior - Demonstrates the application of artificial immune system algorithms for traffic prediction - Covers traffic density estimation using deep learning models - Covers Fog and edge computing for intelligent transportation systems - Gives an IoT and Industry 4.0 perspective about intelligent transportation systems to readers - Presents a current perspective on an urban hyperloop system for India
Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. - Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery - Includes considerations sustainability analysts must evaluate when applying big data - Features case studies illustrating the application of data science in sustainability analyses
The book draws upon the expertise and international research collaborations forged by the Worldwide Universities Network Global Africa Group to critically engage with the intersection, in theory and practice, of the Sustainable Development Goals (SDGs) and Africa’s development agendas and needs. Further, it argues that – and demonstrates how – the SDGs should be understood as an aspirational blueprint for development with multiple meanings that are situated in dynamic and contested terrains. As the SDGs have substantial implications for development policy and resourcing at both the macro and micro levels, their relevance is not only context-specific but should also be assessed in terms of the aspirations and needs of ordinary citizens across the continent. Drawing on analyses and evidence from both the natural and social sciences, the book demonstrates that progress towards the SDGs must meet demands for improving human well-being under diverse and challenging socio-economic, political and environmental conditions. Examples include those from the mining industry, public health, employment and the media. In closing, it highlights how international collaboration in the form of research networks can enhance the production of critical knowledge on and engagement with the SDGs in Africa.
A new challenge has become present in the field of generative artificial intelligence (AI). The fundamental nature of education, a vital element for advancing the United Nations' Sustainable Development Goals (SDGs), now grapples with the transformative impact of AI technologies. As we stand at this intersection of progress and pedagogy, critical questions surface about the future roles of educators and the integrity of assessment processes. AI’s rapid progression prompts an exploration of the competencies our education systems must cultivate in a world where human and machine intelligence are becoming increasingly interconnected. Against this backdrop of transformative uncertainty, Integrating Generative AI in Education to Achieve Sustainable Development Goals addresses profound challenges and offers promising solutions at the crossroads of AI and education. This book assembles distinguished academics, researchers, and practitioners, forming a collective voice on the intersection of Generative AI and education. The three-part structure dissects the technical aspects of AI-powered innovations in educational design, explores multidisciplinary applications enhancing educational content, and highlights AI-driven solutions to address equality and inclusion concerns within educational systems. The book also underscores the importance of ethical considerations of generative AI to ensure a future where technology serves the broader goals of sustainability and equitable education.
Sustainable supply chain management involves integrating environmentally and financially viable practices into the complete supply chain lifecycle, from product design and development to material selection and sourcing, manufacturing, packaging, transportation, and distribution. A sustainable supply chain ensures balance between economic, social, and environmental performances – such as better assurance of human rights, ethical work practices, carbon footprint reduction, waste management, and resource efficiency. Computational Intelligence Techniques for Sustainable Supply Chain Management presents state-of-the-art computational intelligence techniques and applications for supply chain sustainability issues and logistic problems, filling the gap between general textbooks on sustainable supply chain management and more specialized literature dealing with methods for computational intelligence. This book focuses on addressing problems in advanced topics in the sustainable supply chain, and will appeal to practitioners, managers, researchers, academicians, students, and professionals interested in sustainable logistics, sustainable procurement, sustainable manufacturing, sustainable inventory and production management, sustainable scheduling, sustainable transportation, and sustainable network design. - Serves as a reference on computational intelligence–enabled sustainable supply chains for graduate students in computer/data science, industrial engineering, industrial ecology, and business - Explores key topics in sustainable supply chain informatics, that is, heuristics, metaheuristics, robotics, simulation, machine learning, big data analytics and artificial intelligence - Provides a foundation for industry leaders and professionals to understand recent and cutting-edge methodologies and technologies in the domain of sustainable supply chain powered by computational intelligence techniques
This book presents insights into how Intelligent Computing and Optimization techniques can be used to attain the goals of Sustainable Development. It provides a comprehensive overview of the latest breakthroughs and recent developments in sustainable, intelligent computing technologies, applications, and optimization techniques across various industries, including business process management, manufacturing, financial sector, agriculture, financial sector, supply chain management, and healthcare. It focuses on computational intelligent techniques and optimization techniques to provide sustainable solutions to many problems. Features: • Provides insights into the theory, implementation, and application of computational intelligence techniques in many industries. • Includes industry practitioner perspectives and case studies for a better understanding of sustainable solutions. • Highlights the role of intelligent computing and optimization as key technologies in decision-making processes and in providing cutting-edge solutions to real-world problems. • Addresses the challenges and limitations of computational approaches in sustainability, such as data availability, model uncertainty, and computational complexity, while also discusses emerging opportunities and future directions in the field. This book will be useful for professionals and scholars looking for up-to-date research on cutting-edge perspectives in the field of computational intelligent and optimization techniques in the areas of agriculture, industry, financial sector, business automation, renewable energy, optimization, and smart cities.