Using Machine Learning for Industry 5.0 Efficiency Prediction Based on Security and Proposing Models to Enhance Efficiency

Machine learning, with its huge untapped potential, is being researched all over the world to develop truly intelligent systems. Its applications are not enclosed in just one domain but in almost everything, from prediction models, recommender systems, and anomaly detection to automation and teaching a computer how to fly a helicopter. In this research, Multivariate Linear regression of supervised machine learning is studied to predict the efficiency of Industry 5.0, however, the efficiency of the model would be dependent on many factors and components such as security protocols and models, Industrial IoT - performance, connectivity, reachability, availability and many more. These factors and components would be categorized as the features of the algorithm which would be assigned weight ‘w’ and bias ‘b’. To improve the efficiency of the model, these components could be changed and updated in order to enhance the overall model. Previous research papers discussed the integration of “hot” technologies like 5G, Blockchain, AI, and IIoT in the industry 5.0 model, but this research is presented as their future work as it proposes to determine the efficiency of the model based on the features provided so that ultimate and optimal model could be determined. Later it proposes security and IIoT models that could improve the overall Industry 5.0. Quorum blockchain is proposed by the research in order to implement the ultimate security in the Industry 5.0.