The Industrial Internet of Things (IIoT) has received significant attention from several leading industries like agriculture, mining, transport, energy, and healthcare. IIoT acts as a vital part of Industry 4.0 that mainly employs machine learning (ML) to investigate the interconnection and massive quantity of the IIoT data. As the data are generally saved at the cloud server, security and privacy of the collected data from numerous distributed and heterogeneous devices remain a challenging issue. This article develops a novel multi-agent system (MAS) with deep learning-based privacy preserving data transmission (BDL-PPDT) scheme for clustered IIoT environment. The goal of the BDL-PPDT technique is to accomplish secure data transmission in clustered IIoT environment. The BDL-PPDT technique involves a two-stage process. Initially, an enhanced moth swarm algorithm-based clustering (EMSA-C) technique is derived to choose a proper set of clusters in the IIoT system and construct clusters. Besides, multi-agent system is used to enable secure inter-cluster communication. Moreover, multi-head attention with bidirectional long short-term memory (MHA-BLSTM) model is applied for intrusion detection process. Furthermore, the hyperparameter tuning process of the MHA-BLSTM model can be carried out by the stochastic gradient descent with momentum (SGDM) model to improve the detection rate. For examining the promising performance of the BDL-PPDT technique, an extensive comparison study takes place and the results are assessed under varying measures. A significant amount of capital is required. It goes without saying that one of the most obvious industrial IoT concerns is the high cost of adoption. Secure data storage and management connectivity failures are common among IoT devices due to the massive amount of data they create. The simulation results demonstrate the enhanced outcomes of the BDL-PPDT technique over the recent methods. Despite the fact that the offered BDL-PPDT technique has an accuracy of just 98.15 percent, it produces the best feasible outcome. Because of the data analysis conducted as detailed above, it was determined that the BDL-PPDT technique outperformed the other current techniques on a range of different criteria and was thus recommended.
Dentistry frequently makes use of intraoral scanning technologies to digitally acquire the three‐dimensional (3D) geometry of teeth. In recent times, dental clinics over the globe utilize used computer aided diagnosis (CAD) models to make treatment plans, for example, orthodontics. Orthodontic CAD system acts as a vital part of the advanced dentistry field. A 3D dental model, computed by patient impression, as input and aids dentist in the extraction, moving, deletion, and rearranging of teeth to simulate treatment output. Tooth segmentation and labelling is the basic and foremost element of the CAD model which needs to be addressed. Automated segmentation and classification of 3D dental images using advanced machine learning and deep learning (DL) models become essential. This article introduces a new 3D dental image segmentation and classification using DL with tunicate swarm algorithm (3DDISC‐DLTSA) model. The major intention of the 3DDISC‐DLTSA system is to segment the tooth model and identify seven distinct tooth types. To accomplish this, the presented 3DDISC‐DLTSA model performs image pre‐processing in two stages namely image filtering and U‐Net segmentation. In addition, the 3DDISC‐DLTSA model derives DenseNet‐169 model for feature extraction purposes. For the recognition and classification of tooth type, the TSA based hyperparameter tuning process is carried out which helps to accomplish maximum classification performance. A wide range of experimental analyses is performed and the outcomes are inspected under many aspects. On dataset‐1, 3DDISC‐DLTSA model accuracy rose by 96.67%. On dataset‐3, 3DDISC‐DLTSA model accuracy rose by 97.48% and algorithm accuracy by 97.35%. The 3DDISC‐DLTSA model outperformed more modern models, according to the comparative investigation.
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