Cloud computing is a potential platform transforming the health sector by allowing clinicians to monitor patients in real-time using sensor technologies. However, the users tend to transmit sensitive and classified medical data back and forth to cloud service providers for centralized processing and storage. This presents opportunities for hackers to steal data, intercept data in transit, and deprive patients and healthcare providers of private information. Consequently, Security and privacy are the primary concerns that must be addressed for the healthcare organization to trust and adopt the cloud computing platform. We present data sanitization and restoration processes to generate the keys from the acquired data and develop a multi-objective function for the hiding ratio, degree of modification, and information preservation ratio. We then employed the Bee-Foraging Learning-based Particle Swarm Optimization (BFL-PSO) algorithm to acquire the optimal key while transferring healthcare data into the cloud to ensure high Security. The experiment is carried out on the UHDDS dataset. The performance is assessed in terms of Security, delay time, encryption time, error rate, and convergence speed, with the results contrasted to state-of-the-art works. The performance study demonstrates that the suggested algorithm has higher Security than cutting-edge security algorithms.

Alzheimer’s disease (AD) is the most frequent method of dementia and ranks the fifth-leading disease. Brain imaging or neuroimaging like magnetic resonance imaging (MRI) was utilized in the medicinal analysis of brain condition to allow visualization of infrastructure and brain functionality. Machine learning (ML) approaches on MRI are utilized in the analysis of AD to accelerate the analysis procedure and assist doctors. But, in typical ML approaches utilizing handcrafted extracting feature systems on MRI are difficult, and requires the contribution of expert users. So, executing deep learning (DL) as an automatic extracting feature system is minimizing require for extracting features and automate the procedure. This article introduces a novel Biomedical Alzheimer’s Disease Diagnosis using Enhanced Manta ray Foraging Optimization based Deep Learning (ADD-EMRFODL) technique on brain MRI. The presented ADD-EMRFODL technique exploits Gabor filtering (GF) technique as a pre-processing step. Also, the presented ADD-EMRFODL technique utilizes densely connected network (DenseNet-121) model to derive feature vectors. Finally, the ADD process is performed via EMRFO with back propagation neural network (BPNN) classifier. The parameter tuning of the BPNN occurs utilizing the EMRFO algorithm with the intention of enhanced classification accuracy. To depict the improvised ADD performance of the ADD-EMRFODL methodology, a comprehensive set of simulations was effectuated. The results stated the improved outcomes of the ADD-EMRFODL algorithm over other current methodologies.

The integration of lattice‐based cryptography principles with Quantum Key Distribution (QKD) protocols is explored to enhance security in the context of Internet of Things (IoT) ecosystems. With the advent of quantum computing, traditional cryptographic methods are increasingly susceptible to attacks, necessitating the development of quantum‐resistant approaches. By leveraging the inherent resilience of lattice‐based cryptography, a synergistic fusion with QKD is proposed to establish secure and robust communication channels among IoT devices. Through comprehensive Qiskit simulations and theoretical analysis, the feasibility, security guarantees, and performance implications of this novel hybrid approach are thoroughly investigated. The findings not only demonstrate the efficacy of lattice‐based QKD in mitigating quantum threats, but also highlight its potential to fortify IoT communications against emerging security challenges. Moreover, the authors provide valuable insights into the practical implementation considerations and scalability aspects of this fusion approach. This research contributes to advancing the understanding of quantum‐resistant cryptography for IoT applications and paves the way for further exploration and development in this critical domain.

In this paper, we propose a design and implementation of a Direct Memory Access Controller (DMAC) as a part of an SoC. The main purpose of the DMAC design is to integrate it into a System on a Chip (SoC) for the exchange of a large volume of data between the memory and peripherals at high speed. The proposed DMAC works on Advanced Microcontroller Bus Architecture (AMBA) specifications. Internally, these specifications define two buses, Advanced High-performance Bus (AHB) and Advanced Peripheral Bus (APB). The Direct Memory Access (DMA) controller functions as the bridge between AHB and APB and allows them to work in parallel. It works either in buffer or non-buffer data transfer mode, according to the peripheral speed. This is synchronized with an asynchronous FIFO. Fast data reads can be achieved by using an AMBA based DMA controller with a processor in the SoC. This means that the DMAC provides a high volume of data transfer. Hence, the proposed DMAC is a better option for high volumes of data, as well as for timing. It can be concluded that if using this AMBA-based DMA controller the issues of high speed and high volume data have been resolved. Comparison is made with ARM processors, such as Cortex A8 and ZC702, and design comparison with Xilinx DMA is also made. The DMAC is viewed as a more appropriate choice.