Machine learning
Researchers from Anhui University of Chinese Medicine have developed a novel AI-powered prediction model for osteoporosis, a metabolic bone disease affecting millions worldwide. The model, constructed using machine learning and SHAP techniques, demonstrates potential clinical utility in improving the accuracy of clinical diagnosis and treatment experience for older adults. The
Machine learning
Researchers from Cornell University have developed a framework for training and selecting machine learning algorithms to classify cow health status daily using data from automated health monitoring systems and other cow management and performance indicators. The study aimed to identify a single candidate algorithm that could be deployed in a
Machine learning
Researchers at the University of Gondar Comprehensive and Specialized Hospital in Ethiopia have made significant breakthroughs in predicting adverse drug reactions (ADRs) in HIV patients receiving antiretroviral therapy (ART). Through the use of machine learning algorithms, the team was able to identify the top predictors of ADRs, including CD4 count,
Machine learning
Research has highlighted the proliferation of fake news via social media, threatening societal aspects such as politics, economics, and social stability. This review aims to evaluate the effectiveness of machine learning and deep learning algorithms in detecting fake news and identify areas for future research. Key Takeaways: * The research found
Machine learning
Researchers from the SINOPEC Research Institute of Safety Engineering Co. Ltd. have developed a hybrid photonic-plasmonic surface-enhanced Raman spectroscopy (SERS) platform integrated with machine learning algorithms for the ultra-sensitive detection of polycyclic aromatic hydrocarbons (PAHs). The engineered architecture combines Au film-poly(ionic liquid) (PIL) nanobowl-Au nanosphere, exhibiting exceptional detection performance
Periodical publishing
South African researchers have proposed a hybrid RVM-WT-AdaBoostRT-RF framework for predicting power outages in the country's power grid. The framework leverages machine learning algorithms to accurately forecast outages, which is crucial for efficient power grid operations and effective business strategies. The researchers claim that their framework outperforms traditional
Periodical publishing
A recent study published in the Journal of Engineering has showcased the efficacy of a machine learning algorithm in mapping the humus horizon thickness (HHT) in Russian soils. The research, conducted by the Russian Academy of Sciences, utilized the Random Forest algorithm implemented on the Google Earth Engine cloud online
Periodical publishing
Researchers from Xi'an University of Science and Technology have developed a time risk level early warning model that can predict and prevent rock bursts in coal mines. According to the study, the model combines Bayesian optimization algorithm with long-term and short-term memory network, allowing it to accurately warn
Periodical publishing
Researchers from the University of Florida have developed a machine learning framework to classify and identify special nuclear materials using scintillation detectors. This study highlights the challenges in discriminating between different levels of enrichments, weak radiation signals, and complex self-shielding effects. The researchers used supervised learning algorithms, including Random Forest,
Periodical publishing
Investigators from Technical University of Sofia have published a new report on the application of artificial intelligence in high-risk environments, specifically focusing on virtual reality and machine learning. The research aims to help prepare students and professionals with electrical safety, emergency response, and fire safety. The study explores the use
Artificial intelligence
Researchers at Erasmus University Medical Center have made a significant breakthrough in the development of an artificial intelligence (AI)-based algorithm for scoring cystic fibrosis (CF) patients. The study, published in the Journal of Cystic Fibrosis, aimed to validate a newly developed AI-based automated PRAGMA-AI and Mucus Plugging algorithm using
Artificial intelligence
Researchers at the Jordan University of Science and Technology have made significant strides in utilizing artificial intelligence to improve the diagnosis and treatment of macular edema, a prevalent cause of vision loss. By developing and evaluating a deep learning framework, investigators were able to accurately differentiate between diabetic macular edema,
