We present a simple dual band absorber design and investigate it in the terahertz (THz) region. The proposed absorber works in dual operating bands at 5.1 THz and 11.7 THz. By adjusting the graphene chemical potential, the proposed absorber has the controllability of the resonance frequency to have perfect absorption at various frequencies. The graphene surface plasmon resonance results in sharp and narrow resonance absorption peaks. For incident angles up to 8°, the structure possesses near-unity absorption. The proposed sensor absorber’s functionality is evaluated using sensing medium with various refractive indices. The proposed sensor is simulated for glucose detection and a maximum sensitivity of 4.72 THz/RIU is observed. It has a maximum figure of merit (FOM) and Quality factor (Q) value of 14 and 32.49, respectively. The proposed optimal absorber can be used to identify malaria virus and cancer cells in blood. Hence, the proposed plasmonic sensor is a serious contender for biomedical uses in the diagnosis of bacterial infections, cancer, malaria, and other diseases.

With the growing demand for smart, secure, and intelligent solutions, Industry 4.0 has emerged as the future of various applications. One of the primary sectors that are becoming more vulnerable to security assaults like ransomware is the healthcare sector. Researchers have proposed various mechanisms in smart and secure health care systems with this vision in mind. Existing systems are vulnerable to security attacks on medical data. It is required to build a real-time diagnosis device using a cyber-physical system with blockchain technology in a considerable manner. The proposed work’s main purpose is to build secure, real-time preservation and tamper-proof control of medical data. In this work, the Bayesian grey filter-based convolution neural network (BGF-CNN) approach is used to enhance accuracy and reduce time complexity and overhead. Additionally, PSO and GWO optimization techniques are used to improve network performance. As an outcome of the proposed work, the privacy preservation of medical data is improved with a high accuracy rate by a blockchain-based cyber-physical system using a deep neural network (BGF Blockchain). To summarize, the proposed system helps in the privacy preservation of medical data along with a reduction in communication overhead using the Bayesian Grey Filter–CNN.