Securing medical images is a very essential process in medical image authentication. Medical image watermarking is a very popular tool to achieve this goal. In this paper, an extremely fast, highly accurate and robust algorithm is proposed for watermarking both gray-level and color medical images. In the proposed method, a scrambled binary watermark is embedded in the host medical image. Simplified exact kernels are used to compute the moments of the polar complex exponential transform (PCET) for the host gray-level images and the moments of the quaternion polar complex exponential transform (QPCET) for the host color images without approximation errors. The stability of the computed moments enables us to use higher order moments in a perfect reconstruction of the watermarked medical images. The accurate moment invariant to rotation, scaling, and translation ensures the robustness of the proposed watermarking algorithm against geometric attacks. Performed experiments clearly show very high visual imperceptibility and robustness to different levels of geometric distortions and common signal processing attacks. The implementation of parallel multi-core CPU and GPU results in a tremendous reduction of the overall watermarking times. For a color image of size 256×256, the watermarking time is accelerated by 20x and 11x using a GPU and a CPU with 16 cores, respectively.

This systematic review evaluates recent literature from January 2021 to March 2024 on large language model (LLM) applications across diverse medical specialties. Searching PubMed, Web of Science, and Scopus, we included 84 studies. LLMs were applied to tasks such as clinical natural language processing, medical decision support, education, and aiding diagnostic processes. While studies reported benefits such as improved efficiency and, in some specific NLP tasks, high accuracy above 90%, significant challenges persist concerning reliability, ethical implications, and performance consistency, with accuracy in broader diagnostic support applications showing substantial variability, with some as low as 3%. The overall risk of bias in the reviewed literature was considerably low in 72 studies. Key findings highlight a substantial heterogeneity in LLM performance across different medical tasks and contexts, preventing meta-analysis due to a lack of standardized methodologies. Future efforts should prioritize developing domain-specific LLMs using robust medical data and establishing rigorous validation standards to ensure their safe and effective clinical integration. Trial registration: PROSPERO (CRD42024561381).

Virtual machine placement (VMP) optimization is a crucial task in the field of cloud computing. VMP optimization has a substantial impact on the energy efficiency of data centers, as it reduces the number of active physical servers, thereby reducing the power consumption. In this paper, a computational intelligence technique is applied to address the problem of VMP optimization. The problem is formulated as a minimization problem in which the objective is to reduce the number of active hosts and the power consumption. Based on the promising performance of the grey wolf optimization (GWO) technique for combinatorial problems, GWO-VMP is proposed. We propose transforming the VMP optimization problem into binary and discrete problems via two algorithms. The proposed method effectively minimizes the number of active servers that are used to host the virtual machines (VMs). We evaluated the proposed method on various VM sizes in the CloudSIM environment of homogeneous and heterogeneous servers. The experimental results demonstrate the efficiency of the proposed method in reducing energy consumption and the more efficient use of CPU and memory resources.