Improving data delivery, quality, and precision are especially important for ML. The combination of ML and Blockchain technologies make exact outcomes. The IIoT, has quickly been recognized and is attainment huge atte...
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Improving data delivery, quality, and precision are especially important for ML. The combination of ML and Blockchain technologies make exact outcomes. The IIoT, has quickly been recognized and is attainment huge attention in educational areas with manufacturing, but IoT solitude danger and privacy exposures are emerging by lack of significant security technology. The Blockchain Driven Cyber-Physical system (BDCPS) is supported by IoT-cloud services. In this paper, Integrated Fuzzy Decision Tree based Blockchain Federated Safety-as-a-Service for IIoT (IFDT-BCF-SAS-IIOT) is proposed. In this study, lightweighthomomorphiccryptographicalgorithm is run on separate board system. The suggested system uses Delegated Proof of Work Consensus Protocol to resolve problems likes's lightweight, warehousing transactions, evaporation, and shipment time. The data flow of blockchain is proposed to establish application of integrated fuzzy decision tree to food traceability. The proposed method is executed in Python, performance of proposed technique is analysed with performance metrics like, accuracy, precision, F1-score, recall, specificity, Mean Absolute Error, MSE, RMSE are *** proposed IFDT-BCF-SAS-IIOT method attains 32.58%,26.73%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$32.58\%, 26.73\%$$\end{document} and 24.22%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$24.22\%$$\end{document}, higher Specificity, 23.58%,20.73%,19.32%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \set
More vertical service areas than only data processing, storing, and communication are promised by fog-cloud computing. Due to its great efficiency and scalability, distributed deep learning (DDL) across fog-cloud comp...
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More vertical service areas than only data processing, storing, and communication are promised by fog-cloud computing. Due to its great efficiency and scalability, distributed deep learning (DDL) across fog-cloud computing environments is a widely used application among them. With training limited to sharing parameters, DDL can offer more privacy protection than centralized deep learning. Nevertheless, DDL still faces two significant security obstacles when it comes to fog-cloud computing are How to ensure that users' identities are not stolen by outside enemies, and How to prevent users' privacy from being disclosed to other internal participants in the process of training. In this manuscript, Interference Tolerant Fast Convergence Zeroing Neural Network for Security and Privacy Preservation with Reptile Search Optimization algorithm in Fog-Cloud Computing environment (SPP-ITFCZNN-RSOA-FCC) is proposed. ITFCZNN is proposed for security and privacy preservation, Then Reptile Search Optimization algorithm (RSOA) is proposed to optimize the ITFCZNN, and effective lightweight homomorphic cryptographic algorithm (ELHCA) is used to encrypt and decrypt the local gradients. The proposed SPP-ITFCZNN-RSOA-FCC system attains a better security balance, efficiency, and functionality than existing efforts. The proposed SPP-ITFCZNN-RSOA-FCC is implemented using Python. The performance metrics like accuracy, resource overhead, computation overhead, and communication overhead are considered. The performance of the SPP-ITFCZNN-RSOA-FCC approach attains 29.16%, 20.14%, and 18.93% high accuracy, and 11.03%, 26.04%, and 23.51% lower Resource overhead compared with existing methods including FedSDM: Federated learning dependent smart decision making component for ECG data at internet of things incorporated Edge-Fog-Cloud computing (SPP-FSDM-FCC), A collaborative computation with offloading in dew-enabled vehicular fog computing to compute-intensive with latency-sensitive dependence-awar
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