Recognition and Integration of AI with IoT for Innovative Decision Making Techniques in Cyber Physical Systems

With the integration of Internet of Things (IoT) devices and the demand for effective decision-making methods, cyber-physical systems (CPS) are getting more complicated. In CPS, machine learning (ML) has become a potent technique for processing and analyzing massive volumes of data. The problem remains in optimizing ML algorithms to accomplish quick and accurate decision-making. To overcome this difficulty, we provide a novel strategy in this paper that integrates ML with Particle Swarm Optimisation (PSO) and IoT. The suggested framework optimizes ML algorithms for CPS using PSO, an optimization technique inspired by nature. To find the optimum solution in a multidimensional space, PSO imitates the behavior of a swarm of particles. Our goal is to improve the precision and effectiveness of decision-making processes in CPS by combining PSO with ML techniques. Additionally, by integrating IoT devices into CPS, a wealth of real-time data is made available, enabling the collection of diverse and dynamic data from numerous sources. Our method uses this data to analyze patterns, trends, and anomalies using ML algorithms, enabling wise decision-making in real time. The advantages of our suggested strategy are dual. First, PSO optimization increases the efficiency of ML algorithms and lessens the computational complexity related to large-scale datasets. Second, the framework's inclusion of IoT devices makes it possible to employ real-time data, leading to more precise and rapid decision-making.