Search results for: machine-learning-for-sustainable-development

Machine Learning for Sustainable Development

Author : Kamal Kant Hiran
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The book will focus on the applications of machine learning for sustainable development. Machine learning (ML) is an emerging technique whose diffusion and adoption in various sectors (such as energy, agriculture, internet of things, infrastructure) will be of enormous benefit. The state of the art of machine learning models is most useful for forecasting and prediction of various sectors for sustainable development.

Machine Learning for Ecology and Sustainable Natural Resource Management

Author : Grant Humphries
File Size : 45.48 MB
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Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.

Machine Learning for Sustainable Development

Author : Kamal Kant Hiran
File Size : 33.33 MB
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The book will focus on the applications of machine learning for sustainable development. Machine learning (ML) is an emerging technique whose diffusion and adoption in various sectors (such as energy, agriculture, internet of things, infrastructure) will be of enormous benefit. The state of the art of machine learning models is most useful for forecasting and prediction of various sectors for sustainable development.

Computational Intelligent Data Analysis for Sustainable Development

Author : Ting Yu
File Size : 29.73 MB
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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

Linking Aid to the Sustainable Development Goals a Machine Learning Approach

Author : Arnaud Pincet
File Size : 80.88 MB
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Emerging Trends in Disruptive Technology Management for Sustainable Development

Author : Rik Das
File Size : 82.43 MB
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Interdisciplinary approaches using Machine Learning and Deep Learning techniques are smartly addressing real life challenges and have emerged as an inseparable element of disruption in current times. Applications of Disruptive Technology in Management practices are an ever interesting domain for researchers and professionals. This volume entitled Emerging Trends in Disruptive Technology Management for Sustainable Development has attempted to collate five different interesting research approaches that have innovatively reflected diverse potential of disruptive trends in the era of 4th. Industrial Revolution. The uniqueness of the volume is going to cater the entrepreneurs and professionals in the domain of artificial intelligence, machine learning, deep learning etc. with its unique propositions in each of the chapters. The volume is surely going to be a significant source of knowledge and inspiration to those aspiring minds endeavouring to shape their futures in the area of applied research in machine learning and computer vision. The expertise and experiences of the contributing authors to this volume is encompassing different fields of proficiencies. This has set an excellent prelude to discover the correlation among multidisciplinary approaches of innovation. Covering a broad range of topics initiating from IoT based sustainable development to crowd sourcing concepts with a blend of applied machine learning approaches has made this volume a must read to inquisitive wits. Features Assorted approaches to interdisciplinary research using disruptive trends Focus on application of disruptive technology in technology management Focus on role of disruptive technology on sustainable development Promoting green IT with disruptive technology The book is meant to benefit several categories of students and researchers. At the students' level, this book can serve as a treatise/reference book for the special papers at the masters level aimed at inspiring possibly future researchers. Newly inducted PhD aspirants would also find the contents of this book useful as far as their compulsory course-works are concerned. At the researchers' level, those interested in interdisciplinary research would also be benefited from the book. After all, the enriched interdisciplinary contents of the book would always be a subject of interest to the faculties, existing research communities and new research aspirants from diverse disciplines of the concerned departments of premier institutes across the globe. This is expected to bring different research backgrounds (due to its cross platform characteristics) close to one another to form effective research groups all over the world. Above all, availability of the book should be ensured to as much universities and research institutes as possible through whatever graceful means it may be. Hope this volume will cater as a ready reference to your quest for diving deep into the ocean of technology management for 4th. Industrial Revolution.

Computer Vision and Machine Learning in Sustainable Mobility The Case of Road Surface Defects

Author : Sromona Chatterjee
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Road maintenance has traditionally been a time consuming, expensive, and manual process. Timely maintenance of roads helps in lowering rehabilitation costs, accidents, environmental pollution, while facilitating increased connectivity, trade, and growth. Easily acquirable front-view scene images are seen to be used lately for infrastructure management and road maintenance as they provide quicker, low-cost, and flexible solutions. Such scene images can easily be acquired using standard commodity cameras. In this dissertation, machine learning based approaches have been developed to analyze front-view scene images for detecting cracks automatically on road surfaces across different locations and under various conditions. This work thus contributes toward automated approaches to detect different kinds of cracks on road surfaces, thereby proposing a low-cost solution to road maintenance practices. As a result, different components are developed in this work which are sketched together to form a Decision Support System for the task of crack detection. In this study primarily three algorithmic approaches have been developed. Firstly, an unsupervised graph-based hierarchical clustering technique for road area segmentation has been developed, thus helping in detecting the road area in scene images. Secondly, a classifier and superpixel based supervised learning approach consisting of systematically identifying relevant features for detecting superpixels containing cracks has been developed. Thirdly, an unsupervised learning approach consisting of Gamma Mixture Fuzzy Model based clustering technique and keypoint matching mechanisms have been designed in this work for detecting which road pixels are crack pixels in images. Finally, this study integrates the findings and approaches to propose a Decision Support System for crack detection on road surfaces of easily acquirable front-view scene images. Evaluations performed on an experimentally collected diverse front-view scene image dataset show promising results for crack detection using the developed approaches in this work.

Sustainable Intelligent Systems

Author : Amit Joshi
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This book discusses issues related to ICT, intelligent systems, data science, AI, machine learning, sustainable development and overall their impacts on sustainability. It provides an overview of the technologies of future. The book also discusses novel intelligent algorithms and their applications to move from a data-centric world to sustainable world. It includes research paradigms on sustainable development goals and societal impacts. The book provides an overview of cutting-edge techniques toward sustainability and ideas to help researchers who want to understand the challenges and opportunities of using smart management perspective for sustainable society. It serves as a reference to wide ranges of readers from computer science, data analysts, AI technocrats and management researchers.

The Smart Cyber Ecosystem for Sustainable Development

Author : Pardeep Kumar
File Size : 68.75 MB
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The Cyber Ecosystem can be a replica of our natural ecosystem where different living and non-living things interact with each other to perform specific tasks. Similarly, the different entities of the cyber ecosystem collaborate digitally with each other to revolutionize our lifestyle by creating smart, intelligent, and automated systems/processes. The main actors of the cyber ecosystem, among others, are the Internet of Things (IoT), Artificial Intelligence (AI), and the mechanisms providing cybersecurity. This book documents how this blend of technologies is powering a digital sustainable socio-economic infrastructure which improves our life quality. It offers advanced automation methods fitted with amended business and audits models, universal authentication schemes, transparent governance, and inventive prediction analysis.

Advanced Intelligent Systems for Sustainable Development AI2SD 2019

Author : Mostafa Ezziyyani
File Size : 50.7 MB
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This book contains the latest researches on advanced intelligent systems applied in the field of education presented during the second edition of the International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2019) held on July 08–11, 2019, in Marrakech, Morocco. The book proposes new approaches and innovative strategies for the manipulation of data and big data collected from the educational environment, exploiting the analysis tools, algorithms of artificial intelligence, and machine learning techniques, in order to extract results, which allow improving the performance and effectiveness of the education field, which is a strategic lever for sustainable development. The book deals with concepts, strategies, and approaches developed on various current axes of scientific research in the field of education, such as smart e-learning, smart education (smart classroom, smart assessment and smart teaching and learning technologies), massive open online courses (MOOC), courseware design, and development for smart learning, cloud learning, and mobile learning. The book is intended for all actors in the educational sector, namely students, professors, academic researchers, and stakeholders. It constitutes a large-scale forum for the exchange of ideas, approaches, and innovative techniques between these actors on the development and innovation of the field of education with the revolution 4.0. The authors of each chapter report the state of the art of the various topics addressed and present results of their own research, laboratory experiments, and successful applications. The purpose of this session is to share the idea of advanced intelligent systems with appropriate tools and techniques for modeling, management, and decision support in the field of education.

Emerging Trends in ICT for Sustainable Development

Author : Mohamed Ben Ahmed
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This book features original research and recent advances in ICT fields related to sustainable development. Based the International Conference on Networks, Intelligent systems, Computing & Environmental Informatics for Sustainable Development, held in Marrakech in April 2020, it features peer-reviewed chapters authored by prominent researchers from around the globe. As such it is an invaluable resource for courses in computer science, electrical engineering and urban sciences for sustainable development. This book covered topics including • Green Networks • Artificial Intelligence for Sustainability• Environment Informatics• Computing Technologies

Deep Learning for Sustainable Agriculture

Author : Ramesh Chandra Poonia
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Deep Learning for Sustainable Agriculture reviews the fundamental concepts of gathering, processing, and analyzing different deep learning models, followed by a review of methods that can be used in this direction. The book also covers the novel Deep Learning techniques for effective agriculture data management, with the standards laid by international organizations in related fields. The book is centered around the evolving novel intelligent/deep learning models to solve the mitigation of agriculture. There are several deep learning models know among which few are used for weather forecasting, plant disease detection, underground water detection, quality of soil, and many more issues in agriculture. This book provides such models developed in deep learning and their applications at a single platform. Traditional methods of agriculture are major reasons behind inefficient utilization & wastage of the resources. Utilizing the deep learning methods in the field of agriculture will increase the efficiency of the farmers and use the resources in an optimized way. Introduces novel deep learning models needed to address sustainable solutions for the issues related to agriculture by creating a sustainable solution Provides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of sustainable agriculture Offers perspectives for design, development, and commissioning of intelligent applications

Geo intelligence for Sustainable Development

Author : T. P. Singh
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Globally, concerns for the environment and human well-being have increased as results of threats imposed by climate change and disasters, environmental degradation, pollution of natural resources, water scarcity and proliferation of slums. Finding appropriate solutions to these threats and challenges is not simple, as these are generally complex and require state-of-the-art technology to collect, measure, handle and analyse large volumes of varying data sets. However, the recent advances in sensor technology, coupled with the rapid development of computational power, have greatly enhanced our abilities to capture, store and analyse the surrounding physical environment. This book explores diverse dimensions of geo-intelligence (GI) technology in developing a computing framework for location-based, data-integrating earth observation and predictive modelling to address these issues at all levels and scales. The book provides insight into the applications of GI technology in several fields of spatial and social sciences and attempts to bridge the gap between them.

Creative Solutions for a Sustainable Development

Author : Yuri Borgianni
File Size : 60.42 MB
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Advanced Intelligent Systems for Sustainable Development AI2SD 2019

Author : Mostafa Ezziyyani
File Size : 20.62 MB
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This book highlights the latest research in the fields of health care and agriculture, presented at the second installment of the International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD-2019), held on July 08–11, 2019 in Marrakech, Morocco. Gathering contributions by respected researchers in the field of agriculture, the book is intended to stimulate debate in this field, and proposes new solutions, tools and effective techniques concerning various current topics in the field of agriculture, such as ICT, IoT and big data analytics for agriculture, smart systems for plant productivity, and data analytics of socio-economic dimensions for sustainable agriculture and aquaculture. With regard to the field of health, the book addresses several areas of research, including E-health services in smart environments (smart homes, smart medical institutions, smart cities), E-health and big data analysis, IoT for health, network interoperability in E-health ecosystems, current and emerging web norms and communication technologies for E-health, heterogeneity of E-health environments and platforms (sensors and actuators, heterogeneous access technologies, security), human–computer interaction, RFID and localization techniques, E-health virtual communities, and business intelligence in health care. This book is intended for academic and professional researchers, decision-makers and all stakeholders in the fields of health and agriculture whose work involves the development and improvement of this field with modern I4.0 technologies and approaches. The authors of each chapter report on the state of the art and present the outcomes of their own research, laboratory experiments, and successful applications. The purpose of the book is to combine the idea of advanced intelligent systems with appropriate tools and techniques for modeling, management, and decision support in the fields of health and agriculture.

Artificial Intelligence for Sustainable Development Theory Practice and Future Applications

Author : Aboul Ella Hassanien
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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.

GIS and Machine Learning for Small Area Classifications in Developing Countries

Author : Adegbola Ojo
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Since the emergence of contemporary area classifications, population geography has witnessed a renaissance in the area of policy related spatial analysis. Area classifications subsume geodemographic systems which often use data mining techniques and machine learning algorithms to simplify large and complex bodies of information about people and the places in which they live, work and undertake other social activities. Outputs developed from the grouping of small geographical areas on the basis of multi- dimensional data have proved beneficial particularly for decision-making in the commercial sectors of a vast number of countries in the northern hemisphere. This book argues that small area classifications offer countries in the Global South a distinct opportunity to address human population policy related challenges in novel ways using area-based initiatives and evidence-based methods. This book exposes researchers, practitioners, and students to small area segmentation techniques for understanding, interpreting, and visualizing the configuration, dynamics, and correlates of development policy challenges at small spatial scales. It presents strategic and operational responses to these challenges in cost effective ways. Using two developing countries as case studies, the book connects new transdisciplinary ways of thinking about social and spatial inequalities from a scientific perspective with GIS and Data Science. This offers all stakeholders a framework for engaging in practical dialogue on development policy within urban and rural settings, based on real-world examples. Features: The first book to address the huge potential of small area segmentation for sustainable development, combining explanations of concepts, a range of techniques, and current applications. Includes case studies focused on core challenges that confront developing countries and provides thorough analytical appraisal of issues that resonate with audiences from the Global South. Combines GIS and machine learning methods for studying interrelated disciplines such as Demography, Urban Science, Sociology, Statistics, Sustainable Development and Public Policy. Uses a multi-method approach and analytical techniques of primary and secondary data. Embraces a balanced, chronological, and well sequenced presentation of information, which is very practical for readers.

Sustainable Development and Social Responsibility Volume 2

Author : Ahmed N. Al-Masri
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This book gathers high-quality research papers presented at the 2nd AUE international research conference, AUEIRC 2018, which was organized by the American University in the Emirates, Dubai, and held on November 13th-15th, 2018. The book is broadly divided into two main sections: Sustainability and Smart Business, and Sustainability and Creative Industries. The broad range of topics covered under these sections includes: risk assessment in agriculture, corporate social responsibility and the role of intermediaries, the impact of privatizing health insurance, political events and their effect on foreign currency exchange, the effect of sustainable HR practices on financial performance, sustainability integration in the supply chain and logistics, gender inequality in the MENA economies, the panel data model, the model of sustainable marketing in the era of Industry 4.0, micro-enterprises as a tool for combating unemployment, the impact of financial education and control on financial behavior, measuring financial and asset performance in agricultural firms, a comprehensive strategic approach to sustainability in the UAE, sustainability and project finance, HR analytics, FaD or fashion for organizational sustainability, a conceptual framework of sustainable competitive advantages, psychology of organizational sustainability, Blockchain technology and sustainability, veganism and sustainability, institution building from an emotional intelligence perspective, sustainable concrete production using CWP, occupants’ behavior and energy usage in Emirati houses, the effect of shop lighting on consumer behavior, multimedia applications in digital transformation art, integrating biomimicry principles in sustainable architecture, experimental sustainable practices in fashion education, technology-assisted student-centered learning for civil engineering, and a 10-step design process for architectural design studios. All contributions present high-quality original research work, findings and lessons learned in practical development.

Agile Machine Learning

Author : Eric Carter
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Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment. The authors’ approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll Learn Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused Make sound implementation and model exploration decisions based on the data and the metrics Know the importance of data wallowing: analyzing data in real time in a group setting Recognize the value of always being able to measure your current state objectively Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations Who This Book Is For Anyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data.

Machine Learning and Knowledge Extraction

Author : Andreas Holzinger
File Size : 46.68 MB
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