Cities have grown in development and sophistication throughout human history. Smart cities are the current incarnation of this process, with increased complexity and social importance. This complexity has come to involve significant digital components and has thus come to raise the associated cybersecurity concerns. Major security relevant events can cascade into the connected systems making up a smart city, causing significant disruption of function and economic damage. The present paper aims to survey the landscape of scientific publication related to cybersecurity-related issues in relation to smart cities. Relevant papers were selected based on the number of citations and the quality of the publishing journal as a proxy indicator for scientific relevance. Cybersecurity will be shown to be reflected in the selected literature as an extremely relevant concern in the operation of smart cities. Generally, cybersecurity is implemented in actual cities through the concerted application of both mature existing technologies and emerging new approaches.

The increasing complexity of IT operations necessitates advanced automation to ensure system availability, resilience, continuity, performance, security, and maintainability. Traditional IT management frameworks, such as the Information Technology Infrastructure Library (ITIL), have standardized service management processes, while models like IBM’s Process Reference Model for IT (PRM-IT) have facilitated automation through structured workflows. However, critical tasks, such as incident resolution and problem management, still require significant human intervention. The adoption of development, security, and operations (DevSecOps) introduced standardized remediation playbooks, further enhancing automation. More recently, Generative AI (GenAI) has expanded automation possibilities, opening new avenues for hyper-automated IT operations. This study explored Agentic Artificial Intelligence for IT Operations (Agentic AIOps), an approach that integrates AI-driven agents to autonomously manage IT operations. Our methodology features a framework for designing and developing an Agentic AIOps system that proactively detects anomalies, classifies incidents, and autonomously executes resolution workflows across infrastructure, middleware, data, and applications in complex, heterogeneous Enterprise IT environments. Our findings suggest that such a system can significantly reduce incident response time, enhance predictive maintenance, and enable self-healing IT environments. By leveraging AI agents for real-time decision making, this approach enhances operational efficiency and reduces human workload. We conclude that this Agentic AIOps system represents a transformative step toward fully autonomous IT operations. We outline a framework for defining its architecture and planning its subsequent development, ensuring continuous evolution and adaptability in complex IT landscapes.

This paper provides an in-depth analysis of the Big Data application in the field of medicine. This innovative technology is a significant topic because Big Data introduces new opportunities for medical practitioners and patients, including the improvement of patients’ quality of life and healthcare systems, the mitigation of specific illnesses, and the reduction of state-level healthcare costs. This paper discusses implications relative to wearable sensing devices as well as personalised medicine. The advantages of the analytical capabilities of Big Data are also discussed. For instance, Big Data analytics can aid in the improvement of healthcare cost-efficiency and profit generating capabilities. The limitations and vulnerabilities of Big Data application in the healthcare field, such as the development of the domain-specific system architecture, are exposed. This paper examines the elements of architecture (data acquisition & transmission, cloud processing, and analytics) adapted to healthcare needs. A brief outline of the restrictions for official approval of the new devices and methodologies is presented. Specifically, the sensor data are heterogeneous and untagged. Previously, the implementation of clinical records into models was required. Currently, adaptation of machine-learning techniques is required. Ethical issues relative to the personal privacy involved in healthcare data acquisition, storage, and processing are considered.

When we speak of solutions that can be designed, developed, and implemented to solve a determined problem in a specific domain, human intelligence shows that it cannot be kept within regular bounds. In addition, it consistently manages to produce innovative elements that increase efficiency, applicability, and abstraction, with the drawback of increasing the overall underlying complexity. As technology advanced and time passed, in every system there was a visible need to automate manual, repetitive, and mundane tasks to help save time and let humans focus on more important things. A practical example of such a system is the banking system. In this paper, our aim is to present how Artificial Intelligence (AI) helps this field through the use of chatbots. In this sense, we present and analyse five examples of such applications that are already used in the international banking system, highlighting the main benefits they bring. Finally, we present some conclusions regarding the chatbot systems analysed, acknowledging that the future will increasingly belong to IT applications of this kind.

The fact that advanced technologies and their economic applications have generated increasing resource costs justifies the transition from a linear approach to a circular one in order to control these costs. From this perspective, this study presents how artificial intelligence can help achieve this goal. Therefore, at the beginning of this article, we begin with an introduction and brief review of the literature on the subject. Our research procedure involved the combination of qualitative and quantitative forms of research using mixed methods. In this study, we presented and analyzed five chatbot solutions used in the field of the circular economy. The analysis of these five chatbots helped us design, in the second part of this paper, the procedures for data collection, training, development, and testing of a chatbot using various natural language processing (NLP) and deep processing (DP) techniques. Additionally, we include discussions and some conclusions regarding all aspects of the subject to see how they can help us in future studies. Furthermore, our future research with this topic will have as the goal the effective construction of a chatbot dedicated to the circular economy.

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.