Baselining Large Language Model Performance in Systems Engineering Using SysEngBench
ABSTRACT In the rapidly evolving field of artificial intelligence (AI), large language model s (LLMs) have demonstrated impressive capabilities in generating natural language. However, their proficiency in specialized domains, particularly in the field of systems engineering (SE), remains less explored and unquantified.
Ryan Bell +3 more
wiley +1 more source
Guided sparse decomposition with anisotropic fusion for medical image enhancement. [PDF]
Bian W, Gu Q, Yang Y.
europepmc +1 more source
Enterprise Network Security Using Few‐Shot Meta‐Learning
This paper involves a few‐shot learning study that uses model‐agnostic meta‐learning. A meta‐dataset was curated by combining six benchmark network intrusion detection datasets by parsing network traffic data from PCAP files. An MAML model performs meta‐training, validation and meta‐testing before and after fine‐tuning.
Sushant Jain +4 more
wiley +1 more source
Implementation and User Evaluation of an On-Premise Large Language Model in a German University Hospital Setting: Cross-Sectional Survey. [PDF]
Grünig A +5 more
europepmc +1 more source
GPU-Accelerated Implementation of Constant-pH Molecular Dynamics in NAMD. [PDF]
Moe S, Chipot C, Roux B.
europepmc +1 more source
Optimized fetal head circumference estimation in 2D ultrasound using EfficientNet-B7 and Adam optimizer. [PDF]
Vimala N +6 more
europepmc +1 more source
Use of a graphics processing unit (GPU) to facilitate real-time 3D graphic presentation of the patient skin-dose distribution during fluoroscopic interventional procedures. [PDF]
Rana V, Rudin S, Bednarek DR.
europepmc +1 more source
Abstract Brain tumour segmentation employing MRI images is important for disease diagnosis, monitoring, and treatment planning. Till now, many encoder‐decoder architectures have been developed for this purpose, with U‐Net being the most extensively utilised. However, these architectures require a lot of parameters to train and have a semantic gap. Some
Muhammad Zeeshan Aslam +3 more
wiley +1 more source
GPU-Accelerated Artificial Intelligence Applications in Cancer Diagnosis, Imaging, and Treatment Planning. [PDF]
Montazer F +12 more
europepmc +1 more source

