Guest Editors
Prof. Sunil K. Singh, Panjab University, India.
Prof. Dragan Peraković, University of Zagreb, Croatia.
Prof. Brij B Gupta, Asia University, Taiwan.
Summary
The Sustainable Development Goals (SDGs) were developed at the United Nations Conference on Sustainable Development, held in Rio de Janeiro, Brazil, in 2012. The purpose was to create a set of global goals, related to the environmental, political, and economic challenges that we face as humanity. In this way, in 2015 the UN chose through a vote 17 objectives applicable universally to transform the world and has promoted them as global goals of sustainable development for the period 2015-2030, thus replacing the 8 Millennium Development Goals. The SDGs are a commitment that seeks to address the most urgent problems of the world and they are all interrelated. They are a universal call to action to respond sustainably against the threat of climate change, having a positive impact on the way we manage our fragile natural resources, promoting peace and inclusive societies, reducing inequalities, and contributing to the prosperity of economies.
Since the beginning of the SDGs, they have aimed at the so-called 2030 Agenda, which has provided a model for shared prosperity in a sustainable world, in which all people can lead productive lives, living peacefully and on a healthy planet. There is a growing number of AI applications in the environmental sector, including those within the energy (eg. smart grids), agricultural, and monitoring sectors. This is made possible by the recent advances in IoT hardware and the accompanying AI algorithms in vision and sensor fusion. The next wave of AI can play an instrumental role in realizing the UN’s 17 SDGs. A number of initiatives have been initiated by national administrations and nongovernmental organizations toward directing the use of AI to achieve the SDGs. Examples include the Robotics for Good and the Drones for Good competitions launched by the United Arab Emirates, IBM Watson AI XPRIZE, Microsoft AI for Good, and the Institute for Human-Centered Artificial Intelligence. AI can help to map poverty through big data analytics. It opens doors to create new business models, such as the sharing economy, that replace ownership with mutual arrangements to boost incomes and, sometimes, create jobs.
This special issue aims to present a machine learning approach to achieve sustainable development goals. There are many goals that are defined by the UN if any country has achieved these goals then they have achieved sustainable development. We have presented an approach on the historical dataset of various goals that align with the goals of SDG’s. We have review what exactly is Machine Learning and how it can be used in various industries providing a Competitive Advantage to Organizations to outperform its competitors and also sustain in the market. Machine Learning helps an organization or an industry to grow leading to growth in economy also while being committed to the various Sustainable Development Goals. This study uses advanced data analytics techniques to analyses and predict the sustainability of countries and organizations related to various goals of SDG’s and how ML can be useful in decision making.
The special issue is to provide a global platform for collection of innovative algorithms, theories and applications related to all the aspects of perception, reasoning, and navigation of SDG. AI and machine learning can help meet the SDGs because it augments, rather than replaces, our own intelligence and capabilities. Any actions humans take to achieve the SDGs can be augmented with artificial intelligence. This issue will also help to provide more concise solution to the complex problems of SDG that helps balance the economy and sustainability. We also encourage for the submission of datasets and benchmarks collected in real challenging conditions or methods optimized for real-time perception and reasoning of SDGs are encouraged.
Keywords
The topics relevant to this special issue include but are not limited to:
• Machine Learning Application in SDG-Oriented Artefact detection
• Statistical Machine Learning (SML) Methonds of remote sensing data to produce measurements of environment, agriculture, and sustainable development
• Monitoring sustainable development by means of earth observation data and machine learning
• Spatial and machine learning methods of satellite imagery analysis for Sustainable Development Goals
• Accelerating evidence-informed decision-making for the Sustainable Development Goals using machine learning
• Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
• Using machine learning designed to link project-based flows to the Sustainable Development Goals
• Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space
• Role of Artificial Intelligence (AI) in the construction of sustainable business models (SBMs)
• Artificial Intelligence Applications to Achieve Water-related Sustainable Development Goals
• How exactable computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals
• A Deep Neural Network-Based Framework for Attainment of Sustainable Development Goals 1-6
• Disposition of youth in predicting sustainable development goals using machine learning algorithms.
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