CITYADAPTAI: AI-DRIVEN COGNITIVE MODEL ENHANCING, ENGAGEMENT, AND REAL-TIME PERSONALIZATION TO FILL GAPS IN SMART CITY SERVICES FOR SOCIETY

Abstract

In 21st century deployments of smart cities, which generate huge amounts of data every day, the data can be from the transport systems and utility supply network, hospitals, schools and education, the police, professional services, or communities. Although this information can improve the quality of people's lives, services often fragment and fit one size for all, and people find services difficult as the number of choices increases. Smart systems that can help citizens find needed services and give personalized recommendations in a timely way are necessary. To address this issue, we need to use a smart city services recommendation system called "CityAdaptAI" that can personalize and optimize citizen experience by learning with machines, learning deeply, and processing natural language. The system can employ various recommendation algorithms like Collaborative Filtering, Content-Based Filtering, and Hybrid recommendation algorithms that can adapt to user choices and habits. Contextual data like time, place, and weather must also be taken into account. Cloud services will be used to handle big data and near-real time decisions. Using APIs, these platforms will allow interoperability with third-party services, for real-time information on things like transport schedules, availability of parking spaces, and health care and city events. Big data analytics will be used to analyse and manage the information generated by the smart city. The goal of this project is to establish a citizen-centric technology platform that improves service delivery, improves data-driven decision-making, supports sustainable urban development, and closes the divide between city services and citizens, resulting in smarter, more equitable and sustainable urban centres.