This paper is modelled in details, and it describes an integrated MRP II agent system for use in a make-to-order manufacturing environment by demonstrating potential benefits on purchasing and manufacturing orders generated. MRP II activities were modelled in a multi-agent based system; the information exchanges and activities to occur within the system were identified and the system simulation was prepared by applying the Petri net method using the estimated operation times for these activities. Multi-agent systems were preferred for modelling due to the fact that these systems were intelligent software systems and they included discrete manufacturing systems as well as communication and software systems. Also, the Petri net system was preferred in simulation because it was one of the distributed artificial intelligence methods and used in the analysis of the status and information exchange in the software systems. The obtained results will provide information about the possible bottlenecks and interruptions to occur before implemented within a huge and complex system structure.
In this chapter, an agent-based fuzzy data mining structure was developed to process and evaluate data with an enlargement in the knowledge dimension, and to build a rule structure for the system. Within the developed system, the focus was on the operation feature of the fuzzy data mining structure, which is the same for each agent composing the system. The suggested association rules are derived from a relational database. Future tasks of the system will be estimated when the system performs fuzzy data mining more quickly thanks to the distributed, autonomous, intelligent, and communicative agent structure of the suggested agent-based fuzzy rule mining system. In fuzzy rule mining, the system will primarily examine and group the relational database in databases of the agents with fuzzy logic and then will shape the rule base of the system by applying the fuzzy data mining method to these data.
Recently, health management systems have some troubles such as insufficient sharing of medical data, security problems of shared information, tampering and leaking of private data with data modeling probes and developing technology. Local learning is performed together with federated learning and differential entropy method to prevent the leakage of medical confidential information, so blockchain-based learning is preferred to completely eliminate the possibility of leakage while in global learning. Qualitative and quantitative analysis of information can be made with information entropy technology for the effective and maximum use of medical data in the local learning process. The blockchain is used the distributed network structure and inherent security features, at the same time information is treated as a whole, not as islands of data. All the way through this work, data sharing between medical systems can be encouraged, access records tampered with, and better support medical research and definitive medical treatment. The M/M/1 queue for the memory pool and M/M/C queue to combine integrated blockchains with a unified learning structure. With the proposed model, the number of transactions per block, mining of each block, learning time, index operations per second, number of memory pools, waiting time in the memory pool, number of unconfirmed transactions in the whole system, total number of transactions were examined. Thanks to this study, the protection of the medical privacy information of the user during the service process and the autonomous management of the patient’s own medical data will benefit the protection of privacy within the scope of medical data sharing. Motivated by this, proposed a blockchain and federated learning-based data management system able to develop in next studies.
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