When it is important to authenticate a person based on his or her biometric qualities, most systems use a single modality (e.g. fingerprint or palm print) for further analysis at higher levels. Rather than using higher levels, this research recommends using two biometric features at the sensor level. The Log-Gabor filter is used to extract features and, as a result, recognize the pattern, because the data acquired from images is sampled at various spacing. Using the two fused modalities, the suggested system attained greater accuracy. Principal component analysis was performed to reduce the dimensionality of the data. To get the optimum performance between the two classifiers, fusion was performed at the sensor level utilizing different classifiers, including k-nearest neighbors and support vector machines. The technology collects palm prints and veins from sensors and combines them into consolidated images that take up less disk space. The amount of memory needed to store such photos has been lowered. The amount of memory is determined by the number of modalities fused.

The Internet of Medical Things (IoMT) plays a crucial role in smart healthcare by leveraging medical devices that generate vast amounts of data for monitoring and diagnosis. IoMT networks are prone to challenges like dynamic topology, computational limitations, and power constraints that make it difficult to process data. Offloading computationally intensive operations to high-capacity computational nodes is necessary. However, medical devices provide nonindependent and identically distributed (non-i.i.d.) data in different formats, making it more complex. Conventional rule-based offloading schemes are inefficient in enhancing network performance and hence have high latency and energy consumption. This article introduces a secure computational offloading framework using deep reinforcement learning. With deep deterministic policy gradient (DDPG) and federated learning as the basis, the framework: 1) observes the network; 2) learns offloading decisions; and 3) safeguards data privacy. It reduces energy and latency with a security guarantee. A~cloud-fog architecture is implemented with a DDPG-based local model, which is executed at the fog broker, and a global model that is executed at the cloud server. We use advance encryption standard (AES) + encryption for secure transmission of data (ECC). MATLAB simulations are conducted for encrypted and unencrypted offloading and exhibit up to 12% improvement in encrypted cases and 13% in unencrypted cases compared to existing solutions.