Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT.

The Industrial Internet of Things (IIoT) has led to the growth and expansion of various new opportunities in the new Industrial Transformation. There have been notable challenges regarding the security of data and challenges related to privacy when collecting real-time and automatic data while observing applications in the industry. This paper proposes an Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT. In FT-Block (Federated transfer learning blockchain), several blockchains are applied to preserve privacy and security for all types of industrial applications. Additionally, by introducing the authentication mechanism based on transfer learning, blockchains can enhance the preservation and security standards for industrial applications. Specifically, Novel Supportive Twin Delayed DDPG trains the user model to authenticate specific regions. As it is considered one of the most open and scalable interacting platforms of information, it successfully helps in the positive transfer of different kinds of data between devices in more significant and local operations of the industry. It is mainly due to a single authentication factor, and the poor adaptation to regular increases in the number of users and different requirements that make the current authentication mechanism suffer a lot in IIoT. As a result, it has been very clearly observed that the given solutions are very useful.

Statistical optimization of exopolysaccharide production by Lactobacillus plantarum NTMI05 and NTMI20.

In this study, 27 strains of Lactic acid bacteria (LAB) were isolated and identified from different milk sources. All the isolates were biochemically characterized and screened for their ability to produce exopolysaccharides (EPS), among which two isolates namely Lactobacillus plantarum NTMI05 (197mg/L) and Lactobacillus plantarum NTMI20 (187mg/L) showed higher EPS production. Both the isolates were molecular characterized and tested for their probiotic properties. The chemical composition of EPS from L. plantarum NTMI05 and NTMI20 revealed the presence of 95.45% and 92.35% carbohydrates, 14±0.1and 11±0.15mg/L lactic acid, 10.5±0.2 and 9±0.1mg/mL of reducing sugar, respectively. HPLC analysis showed galactose at the retention time of 2.29.The maximum EPS yield was optimized for the media components like glucose (20g/L), yeast extract (25g/L) and ammonium sulphate (2g/L) using Central Composite Design and Response Surface Methodology (RSM). Under optimum conditions the predicted maximum EPS production was 0.891g/L, 0.797g/L, while the actual experimental value was 0.956g/L and 0.827g/L for L. plantarum NTMI05 and NTMI20, respectively. The antioxidant capacity was also evaluated by DPPH and reducing power assay proving the potentiality of these organisms in food and dairy industries.