A machine learning lung cancer risk prediction model outperformed logistic regression, supporting improved risk assessment and more efficient radiology based lung cancer screening.
Training artificial intelligence models is costly. Researchers estimate that training costs for the largest frontier models ...
Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...
The firm has developed a whole-blood mRNA test intended for use as a screening tool in women at elevated risk of breast cancer.
Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...
A blood-based liquid biopsy test detects early-onset colorectal cancer with strong diagnostic performance and high ...
Background: Acute Pancreatitis-Associated Lung Injury (APALI) is one of the most severe and life-threatening systemic complications in acute pancreatitis patients, with high rates of morbidity and ...
In this paper, we introduce QProteoML, a new quantum machine learning (QML) framework for predicting drug sensitivity in Multiple Myeloma (MM) using high-dimensional proteomic data. MM, an extremely ...
Computational models have made great process toward simulating complex physical phenomena. With this progress has come a significant increase in the computational cost and software complexity of the ...
Abstract: The gunshot event localization and classification have numerous real-time applications. The study is also useful for steering the video camera and guns in the directed direction. This paper ...
Abstract: This work introduces a novel technique for cost-efficient antenna optimization. Our approach employs computational intelligence tools, including machine learning (ML) and bio-inspired ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback