Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization ...

Rigorous study design and analytical standards are required to generate reliable findings in healthcare from artificial intelligence (AI) research. One crucial but often overlooked aspect is the ...

Rose Yu has drawn on the principles of fluid dynamics to improve deep learning systems that predict traffic, model the ...

A study reveals machine learning algorithms can predict compressive strength in concrete with waste glass powder, enhancing ...

Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises from the lack of robust computational methods for ...

Master data science in 2025. Complete guide to machine learning, big data analytics, Python programming, statistical modeling ...

algorithm and the extreme learning machine (ELM), is proposed in this study, and the stresses of AZ80 magnesium alloy are predicted by the model through a 812-record dataset. The predicting results ...

We collected 52,000 comments from social media and used supervised machine learning algorithms to classify them into positive, negative, and neutral categories. Our methodology included data feature ...

This research employs seven supervised machine learning classification algorithms and follows a comprehensive methodology that includes data balancing and hyperparameter tuning ... and analyses were ...

As machine learning algorithms continue to evolve, their integration into healthcare will become more seamless. Future advancements may include federated learning for secure data sharing across ...