Machine learning
Researchers from Jilin University have made a significant breakthrough in solving the Dirac equation, a fundamental problem in quantum mechanics. Using a deep neural network and an unsupervised machine learning technique, the team was able to accurately solve the equation not only for the ground state but also for low-lying
Machine learning
Researchers at Southern Illinois University have made a significant leap in artificial intelligence for soybean pod number estimation, which is crucial for yield prediction, breeding programs, and precision farming. By developing lightweight and efficient AI models, the team addresses the limitations of existing models, making them suitable for deployment in
Machine learning
Researchers have proposed a direct geometrically constrained SLAM method based on target detection and depth image segmentation, named YGDD-SLAM. This method can work robustly, accurately, and continuously in highly dynamic environments. The system first acquires static and potential dynamic feature points in the current frame through a target detection network,
Machine learning
Current study results on drugs and therapies - personalized medicine have been published, highlighting the potential of machine learning to personalize treatment and improve population health outcomes. Researchers from the Graduate School of Public Health utilized the Shizuoka Kokuho Database to investigate heterogeneity in statin treatment effects, employing a 1:
Machine learning
Research from the School of Accounting has shed light on the relationship between artificial intelligence (AI) and high-quality economic development (HQED) of the manufacturing industry in China. The study, which utilized data from 2010 to 2022, found that AI significantly promotes HQED in the manufacturing industry, particularly in the eastern
Machine learning
Researchers at the Indian Institute of Science (IISc) and the Qatar Science and Technology Research Center (QSRTC) have developed a cutting-edge automated method to assess corrosion in industrial equipment using advanced machine learning and image analysis. This novel technique, published in npj Materials Degradation, can analyze microscope images of corroded
Machine learning
Researchers conducted a study on the limitations of large language models (LLMs), specifically examining their tendency to overestimate their abilities and lack of metacognition. They found that both humans and LLMs were overconfident in their ability to answer trivia questions and complete tasks, but only humans were able to adjust
Machine learning
Malnutrition in children represents a severe global health concern with high prevalence and long-term consequences for growth and development. Researchers from China-Japan Friendship Hospital aimed to understand the contributory factors of child malnutrition, leading to the design of targeted prevention strategies and evidence-based interventions. The study, published in the Journal
Machine learning
A recent patent, filed on October 30, 2022, and published on July 15, 2025, has described a system for inducing variation in user experience parameters in intelligent transportation systems. The system utilizes artificial intelligence and physiological sensed data from the rider to promote an optimized hormonal state and improve rider
Machine learning
Quantitative Systems Pharmacology (QSP) has emerged as a cornerstone of modern drug development, providing a robust framework to integrate data from preclinical and clinical studies and enhance decision-making. Recent advancements in artificial intelligence (AI) and machine learning (ML) are transforming QSP by enabling enhanced data extraction, hybrid mechanistic ML models,
Machine learning
Researchers at Emory University have developed a machine learning-based procedure to identify innovative strategies to target azithromycin treatment to children with watery diarrhea who are most likely to benefit. This approach estimates the child-level expected benefit for a given set of covariates by combining predictions from a library of statistical
Machine learning
Researchers from the Chinese Academy of Sciences have proposed a novel paradigm for evapotranspiration estimation in data-sparse regions through the synergistic integration of data-driven and knowledge-based models. The study, which has been peer-reviewed, utilizes a physics-constrained hybrid model that incorporates Penman-Monteith-derived physical knowledge into a time series transformer. The framework
