This article delves into the impact of intelligence (AI), on easing traffic congestion as cities strive to become more intelligent. It highlights advancements in AI technologies like networks (ANNs) and genetic algorithms (GAs), within the context of urban transportation and movement. The incorporation of intelligence, into transportation systems is motivated by the necessity to adapt to evolving circumstances while emphasizing improvements, in the effectiveness and environmental friendliness of transportation networks. Mobility as a Service (MaaS) combines options like transit, ride-sharing and bike rentals. Through the use of networks AI enhances urban traffic management by predicting congestion and optimizing traffic signal control at intersections. Genetic algorithms play a role, in optimizing vehicle routes by taking into account variables such as travel time and associated expenses. The research indicates a rise in the use of AI in the transportation industry signaling a change, in commuting patterns. This study highlights the significance of progressing AI technologies to meet changing infrastructure requirements and fluctuating traffic trends emphasizing the impact of AI on shaping the urban transportation landscape.

The rising complexity and operational demands of modern healthcare systems have significantly increased resource usage and associated costs. This trend highlights the need for innovative approaches to optimize workflows and enhance decision-making. From this perspective, the present study explores how artificial intelligence (AI) can contribute to improving efficiency and information access in the medical field. The article begins with an introduction and a concise literature review focused on the integration of AI in healthcare platforms. Also, three main research questions are presented here. Our research employs an evaluation and a comparison for five existing medical-based applications. Each of these platforms was assessed to determine whether and how AI technologies have been integrated into their functionalities. The findings from this analysis inspired us to the design of a novel AI-based architecture, which we propose in section three of the article. This proposed architecture aims to assist medical professionals by providing streamlined access to relevant patient information, using machine learning (ML) techniques. Also, at the end of this section we address the initial research questions. In the final section of the article, we conclude that the insights gained from analyzing existing medical chatbot platforms has informed the design of our AI-based solution, aimed at supporting both patients and healthcare professionals through an integrated and intelligent system. The findings highlight the necessity for systems that not only align with user expectations but also demonstrate seamless integration within clinical workflows. Future research should prioritize advancing the reliability, personalization, and regulatory compliance of these platforms, thereby fostering enhanced patient engagement and enabling healthcare professionals to deliver care that is both more efficient and more accessible.

Sustainable development, smart waste management, and circular economy principles are paramount to the significant worldwide trend of smart city-related research and projects. The basic hypothesis of our research is that artificial intelligence (AI)-based IT applications have an increasingly important role in the field of smart cities in terms of issues related to waste management. In our present article, we set out to analyze the characteristics of chatbot applications dedicated to waste recycling in the case of smart cities and propose some innovative ideas to improve the efficiency of such applications. Based on the consultation and analysis of a whole series of chatbot-type applications used to facilitate the recycling activity, we systematically analyze and evaluate five illustrative examples of chatbots employed in the context of material recycling. We provide performance comparisons in a table based on specific relevant criteria. Furthermore, the detailed analysis of these chatbots has led to the idea of improving the performance of this type of application. In this sense, we propose a series of innovative concepts that can be successfully implemented in future chatbots dedicated to the field of the circular economy. Here, we detail the innovative ideas that can promote the circular economy and capitalize on the potential of chatbots in the waste recycling activity. We also identify some possible limitations of these new ideas that we propose to be implemented. As for future research directions, our goal is to develop a chatbot dedicated to improving waste recycling practices within the framework of a smart city. Such innovation holds promise in improving sustainability efforts and fostering environmental stewardship within urban environments.

Smart cities represent a transformative approach that combines advanced technology and smart applications to address contemporary urban challenges. The study explores the implementation of smart city solutions in Romania, with a focus on five key areas: transportation and mobility, smart governance, smart health, public safety, and environmental monitoring. By analyzing representative applications, the research highlights the positive impact of digitization on infrastructure efficiency and citizens’ quality of life. However, initiatives are accompanied by challenges such as financial limitations, lack of public trust, and regional disparities. The article highlights the importance of public-private collaboration and a coherent national strategy to maximize the benefits of smart cities, promoting social inclusion and sustainability. The results provide insight into Romania’s potential to become a best-practice model in Eastern Europe.