Explainable Artificial Intelligence for Smart Cities

Thanks to rapid technological developments in terms of Computational Intelligence, smart tools have been playing active roles in daily life. It is clear that the 21st century has brought about many advantages in using high-level computation and communication solutions to deal with real-world problems; however, more technologies bring more changes to society. In this sense, the concept of smart cities has been a widely discussed topic in terms of society and Artificial Intelligence-oriented research efforts. The rise of smart cities is a transformation of both community and technology use habits, and there are many different research orientations to shape a better future.

The objective of this book is to focus on Explainable Artificial Intelligence (XAI) in smart city development. As recently designed, advanced smart systems require intense use of complex computational solutions (i.e., Deep Learning, Big Data, IoT architectures), the mechanisms of these systems become ?black-box? to users. As this means that there is no clear clue about what is going on within these systems, anxieties regarding ensuring trustworthy tools also rise. In recent years, attempts have been made to solve this issue with the additional use of XAI methods to improve transparency levels. This book provides a timely, global reference source about cutting-edge research efforts to ensure the XAI factor in smart city-oriented developments. The book includes both positive and negative outcomes, as well as future insights and the societal and technical aspects of XAI-based smart city research efforts.

This book contains nineteen contributions beginning with a presentation of the background of XAI techniques and sustainable smart-city applications. It then continues with chapters discussing XAI for Smart Healthcare, Smart Education, Smart Transportation, Smart Environment, Smart Urbanization and Governance, and Cyber Security for Smart Cities.

Contributors. 1 An Overview of Explainable Artificial Intelligence (XAI) from a Modern Perspective. 2 Explainable Artificial Intelligence for Services Exchange in Smart Cities. 3 IoT- and XAI-Based Smart Medical Waste Management. 4 The Impact and Usage of Smartphone among Generation Z: A Study Based on Data Mining Techniques. 5 Explainable Artificial Intelligence: Guardian for Cancer Care. 6 ANN-Based Brain Tumor Classification: Performance Analysis Using K-Means and FCM Clustering with Various Training Functions. 7 Recognition of the Most Common Trisomies through Automated Identification of Abnormal Metaphase Chromosome Cells. 8 Smart Learning Solutions for Combating COVID-19. 9 An Analysis of Machine Learning for Smart Transportation System (STS). 10 Classification of Kinematic Data Using Explainable Artificial Intelligence (XAI) for Smart Motion. 11 Smart Urban Traffic Management for an Efficient Smart City. 12 Systematic Comparison of Feature Selection Methods for Solar Energy Forecasting. 13 Indoor Environment Assistance Navigation System Using Deep Convolutional Neural Networks. 14 Pixel-Based Classification of Land Use/Land Cover Built-Up and Non-Built-Up Areas Using Google Earth Engine in an Urban Region (Delhi, India). 15 Emergence of Smart Home Systems Using IoT: Challenges and Limitations. 16 Acceptance of Blockchain in Smart City Governance from the User Perspective. 17 Explainable AI in Machine/Deep Learning for Intrusion Detection in Intelligent Transportation Systems for Smart Cities. 18 Real-Time Identity Censorship of Videos to Enable Live Telecast Using NVIDIA Jetson Nano. 19 Smart Cities’ Information Security: Deep Learning-Based Risk Management. Index.

Mohamed LAHBY is Associate Professor at the Higher Normal School (ENS) University Hassan II of Casablanca, Morocco. His PhD in Computer Science from Faculty of Sciences and Technology of Mohammedia, University Hassan II of Casablanca, in 2013. His research interests are wireless communication and network, mobility management, QoS/QoE, Internet of things, Smart cities, Optimization and Machine learning. He has published more than 35 papers (book chapters, international journals, and conferences), 1 edited book, and 1 authored book. He has served and continues to serve on executive and technical program committees of numerous international conferences such as IEEE PIMRC, ICC, NTMS, IWCMC, WINCOM, ISNCC. He also serves as a referee of many prestigious Elsevier journals : Ad Hoc Networks, Applied Computing and Informatics and International journal of disaster risk reduction. He organized and participated more than 40 conferences and workshops. He is the chair of many international workshops and special sessions such as MLNGSN’19, CSPSC’19, MLNGSN’20, AI2SC ’20 and WCTCP’20. Utku Kose received the B.S. degree in 2008 from computer education of Gazi University, Turkey as a faculty valedictorian. He received M.S. degree in 2010 from Afyon Kocatepe University, Turkey in the field of computer and D.S. / Ph. D. degree in 2017 from Selcuk University, Turkey in the field of computer engineering. Between 2009 and 2011, he has worked as a Research Assistant in Afyon Kocatepe University. Following, he has also worked as a Lecturer and Vocational School – Vice Director in Afyon Kocatepe University between 2011 and 2012, as a Lecturer and Research Center Director in Usak University between 2012 and 2017, and as an Assistant Professor in Suleyman Demirel University between 2017 and 2019. Currently, he is an Associate Professor in Suleyman Demirel University, Turkey. He has more than 100 publications including articles, authored and edited books, proceedings, and reports. He is al