LEVERAGING DEEP LEARNING AND MACHINE LEARNING IN IOT NETWORKS: ARTIFICIAL INTELLIGENCE AND CLOUD-BASED FRAMEWORKS FOR SMART AUTOMATION

Abstract

The article sheds light on the ways of integrating machine learning and deep learning algorithms in Internet of Things (IoT) networks with the help of cloud-computing structures and enable the powering of intelligent and efficient smart automation systems. The paper has compared the performance of various artificial intelligence systems including the random forest, XGBoost, and Neural Network in processing the IoT big data. The findings show that deep learning models and in particular the Neural Networks are the best predictors with high accuracy of 93 percent that exceeds the traditional machine learning. The paper also indicates that AI-based IoT systems would significantly enhance efficiency and performance of operations of a system would be enhanced 65 percent in conventional systems to 89 percent with the introduction of the system.

Cloud-based processing is among the critical enablers, and it increases the capacity of data processing to 90 percent and processing speed, which was 92 times more excellent compared to the performance of local processing environments. The results also focus on high accuracy of automation in all the areas of application of the industrial system, medical, agriculture and energy management with the level of performance exceeding 90 percent. Security models based on machine learning can also improve the threat detection rate to 91 percent and minimize the number of false alarms and thereby enable IoT networks to be more reliable.

The paper succeeds in concluding that the true intersection of artificial intelligence, IoT, and cloud computing presents an effective and scalable framework of intelligent automation. However, despite the problems related to the complexity of the computations and data dependency, the proposed solution demonstrates that the possibility of transforming the conventional IoT systems into intelligent, flexible, and secure ecosystems is immense. The research will result in the development of the AI-based IoT architectures of the future, and will also offer the practical recommendations on the way to make the smart environment more efficient, scalable, and decision-making.