INTRODUCTION: The continued growth of urbanization presents new challenges. This, in turn, will lead to pressure for sustainable environment initiatives, with demands for more and better infrastructure in the diminishing space available and improved quality of life for city dwellers at a more affordable cost. Smart Cities are part of the solution to the growing challenges of urbanization. The adoption of new technologies like artificial intelligence (AI) is transforming cities, making them smarter, faster, and predicting opportunities for improvement. OBJECTIVES: This study is conducting a detailed bibliometric survey to investigate the applications and trends of Artificial Intelligence research for different areas of smart cities and emphasizing the potential effects and challenges of AI adaptation in smart cities over the past 30.5 years. METHODS: For this study, the Scopus database was used to collect a total of 1925 documents published between 1991-2021 (July). The bibliometric analysis includes document types, subject categorization, document growth, as well as top contributing sources, countries, authors, and funding sponsors. It also analyses keywords, abstracts, titles, and characteristics of most cited documents. RESULTS: The analyzed findings of this research study reflect not only the significance of AI technology for various applications within numerous sectors in the smart city but also major obstacles in AI research for various sectors of smart cities. CONCLUSION: The research demonstrates that AI has the ability to construct today’s and tomorrow’s smart cities, but that each region’s potentials, conditions, and circumstances must be addressed in order to achieve a smooth internet city development.

This study explores the complexities of urban planning and addresses major issues by carefully weighing four options for smart city technology, community-based development, green infrastructure investment, and transit-oriented development. Unlike traditional evaluations, our study applies the novel SWARA-WASPAS model to spherical fuzzy sets (SFSs), thus identifying and navigating the uncertainty present in decision making. This methodological approach improves the accuracy of our assessment by providing detailed information about the advantages and disadvantages of each option. Our study offers useful insights for urban policymakers and planners using carefully weighted criteria and employing a methodical ranking procedure. The aim is to provide insights for decisions that promote equity, environmental consciousness, resilience, and symmetry in urban environments. The application of the SWARA-WASPAS approach not only advances the field but also provides a strong basis for informed decision making. This improves the accuracy of our evaluations and provides detailed insights into each option’s pros and cons. Our study uses weighted criteria and systematic ranking to advise urban policymakers and planners. Our main goal is to help urban populations make resilient, environmentally responsible, equitable, and symmetrical decisions. Our research aims to further the conversation on sustainable urban development by offering a framework based on data that addresses the difficulties associated with dynamic urban environments. In the end, we want our humanized viewpoint to speak to a wider audience and inspire a shared dedication to creating cities that flourish in the face of changing urban environments.

Companies and organizations aim to improve the performance of their business processes to stay competitive. Recently, researchers have shown significant interest in process mining, particularly its ability to extract accurate information from process-related data. Process enhancement is a crucial aspect of process mining, involving the extraction of information from the actual process event log to extend or improve existing processes. Enhancement can be classified into two types: extension and repair. This paper specifically focuses on the repair type of enhancement. Information systems commonly encounter logging errors or exhibit special behaviors that introduce noise into the event log. In this research, we investigate the process mining model in the presence of noise in the event log. We propose a method for repairing event logs by decomposing them into sub-logs and eliminating the noisy behavior within these sub-logs using covering probability. The repaired sub-logs are then integrated into the original event log at the appropriate location. Additionally, we propose a probabilistic method that considers the frequency of occurrence for activities in a given situation. This method allows for the removal of noisy and abnormal behavior from the event log, providing an overall perspective on the process. To validate our approach, we generate artificial event logs with the presence of noisy behavior using the ProM framework. By using the RapidMiner-based ProM Extension, we generate a test set to illustrate how various types of noisy behavior in an event log can be identified and repaired. Our findings clearly demonstrate that repairing the event log improves the performance of a process mining model.