Machine learning helps improve accuracy and efficiency of small-molecule calculations Microsoft researchers used deep learning to create new DFT model by Sam Lemonick, special to C&EN June 20, 2025 ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes ...

The integration of artificial intelligence (AI) and machine learning (ML) in oncology clinical trials is rapidly evolving alongside the broader field. For example, AI-driven adaptive trial designs may ...

The future of data classification isn't just about categorizing data—it's about understanding it in all its complexity and context.

Recent advancements in artificial intelligence (AI) and machine learning (ML) offer a promising alternative by providing data-driven insights that enhance diagnostic accuracy and efficiency. Machine ...

This paper presents a comprehensive machine learning approach for credit score classification, addressing key challenges in financial risk assessment. We propose an optimized CatBoost-based framework ...

In SAR target classification, the lack of target sample data is a common phenomenon, making it imperative to improve SAR target recognition methods with limited samples. Traditional supervised ...

Synthetic aperture radar (SAR) images obtained from multi-sensor systems usually exhibit significant shift in data distribution, known as the domain shift. It is challenging to utilize the relevant ...

Although existing SAR ATR works have primarily utilized machine learning frameworks, particularly neural networks, and made significant efforts in adapting SAR images to network models, SAR images ...

An important question in the application of machine learning models to SAR ATR is the degree to which data is processed prior to training and testing. This work examines performance at two ...