New Machine Learning Methods for the Sustainable Development Goals

This book is a collection of new artificial intelligence methods, including new regression, machine learning, deep learning, hybrid, and ensemble models, published in recent years.

This book is a collection of new artificial intelligence methods, including new regression, machine learning, deep learning, hybrid, and ensemble models, published in recent years. The algorithms and structures of these methods are clearly explained such that the readers can use them in their studies and projects. In addition, by presenting several examples for models, the strengths and weaknesses of each model are explained to help the readers to select the best model for each kind of dataset. Therefore, the readers can purposefully and usefully select the appropriate models among all models. As a result, the first advantage of this book in comparison to other books refers to presentation of the new AI models in details, comparison them with respect to their capability and feature, and some criteria to select the appropriate model for the various dataset. Moreover, this book provides the required information on sustainable development, which the students need to know, as well as the advanced information and details for using new AI approaches for sustainable development goals. To improve the criterion in the sustainable development, management of the current and future situation is one the important factors to have better situation with respect to sustainability, which function needs the prediction approaches for future; for example, for environmental indicators, like the management of air pollution, climate change, waste, and energy consumption. Als, the sustainability has other variables, such as social, technical, and economic indicators that to management of these indicators, the new AI can efficiently be used. Furthermore, the new AI can increase the number of models and estimate more indicators in multi-criteria decision analysis; therefore, the accuracy of this type of analyses to have a sustainable production and development can improve regarding an increase in the number of options and indicators, which the cost and time of analyses will be decreased using the AI methods. Briefly, this book provides many new stronger AI approaches for the various studies in sustainable development that are very interesting for the researchers and students.