There are severe logistic and environmental problems associated with increased global garbage production and rapid urbanization. The traditional management system of trash usually suffers from overflowing containers, unpredictable collection times, and costly operating costs. The main focus of this lecture is on an IoT-enabled smart trash management system comprising a combination of robotics and AI along with its mobile application. It happens to be a modern and environmentally friendly solution. The proposed solution also uses IoT sensors to monitor the real-time waste levels, and optimization of collection routes is mainly through AI algorithms. The process is further automated with a robot that collects waste, thus increasing productivity and the requirement for less human intervention. The integration of a mobile app will allow users and authorities to update schedules for garbage collection, receive warnings, and track the status of bins, thus keeping them on time and ensuring that decisions were based on actual facts. It encourages resource optimization, reduces environmental impact, and decreases operational expenses.

In the era of cloud computing, the proliferation of distributed denial-of-service(DDoS) attacks pose significant challenges to the integrity, availability, and reliability of services. This paper explores advanced detection and prevention.mechanisms tailored for cloud environments to mitigate the impact of DDoSattacks. Due to the dynamic behavior, distributed paradigm, and heterogeneity present among the processing elements, devices, and service-oriented pay per use policies; the cloud computing environment is having its availability, security, and privacy issues. Among these various issues, one of the important issues in cloud computing paradigm is DDoS attack. This paper displays various detection methods, including traffic analysis, machine learning algorithms, and anomaly detection techniques, which enhance realtime identification of malicious activities. For prevention, we discuss strategies such as rate-limiting, traffic redirection, and the deployment of cloud-based security services that leverage elastic resources for dynamic response. By providing a comprehensive overview of current strategies and future trends, this study aims to equip cloud service providers and users with the knowledge to enhance their defenses against evolving DDoS attack vectors.