Distributed Deep Reinforcement Learning for Autonomous Iot Healthcare Devices in the Cloud
Aasheesh Shukla, Hemant Singh Pokhariya, Jacob Michaelson, K Laxminarayanamma, Mukesh Kumar, Om Krishna · 2023
The ethical and philosophical problems concerning the cooperation of AI systems and human artists are also examined in this study. In addressing authorship, agency, and the very essence of creation, the changing position of artists as co-creators with intelligent algorithms is explored. It also looks at how AI can question and change conventional ideas of creative competence. Additionally, this study looks into how AI affects the promotion and distribution of art. AI-driven marketing tactics provide improved targeting of customers, personalized experiences, and optimal promotional efforts by utilizing insights based on data and predictive analytics. The study focuses on how these developments transform the relationships between galleries and artists and their patrons, ultimately fostering a more varied and inclusive art scene. The system's potential in many healthcare scenarios has been validated through simulations and practical experiments, which have received excellent feedback from healthcare providers. A critical review emphasizes the need to address practical deployment issues and data security concerns, while also highlighting the exciting convergence of IoT healthcare devices and DDRL. The paper ends with suggestions for more research, highlighting the significance of ethical issues, user interface improvements, and real-world validation. Through the smooth integration of DDRL-enhanced Internet of Things (IoT) medical equipment into clinical practice, our research eventually improves patient care and transforms the delivery of healthcare.