Database optimization has long relied on traditional methods that struggle with the complexities of modern data environments. These methods often fail to efficiently handle large-scale data, complex ...

In the domain of metamaterials, the push toward automated design has been accelerated by advances in generative machine learning. The advent of deep ...

FPMCO decomposes multi-constraint RL into KL-projection sub-problems, achieving higher reward with lower computing than second-order rivals on the new SCIG robotics benchmark.

Solve two Atwood machine problems step by step, including the effects of friction and an inclined plane. Learn how to set up free-body diagrams, apply Newton’s laws, and avoid common mistakes in ...

AI is the broad goal of creating intelligent systems, no matter what technique is used. In comparison, Machine Learning is a specific technique to train intelligent systems by teaching models to learn ...

Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...

Operations research (OR) and machine learning (ML) techniques, when used in isolation, are often inadequate for addressing complex optimization problems. While OR struggles with combinatorial ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

Department of Chemical Engineering, School of Chemical and Materials Engineering, National University of Sciences and Technology, H-12, Islamabad 44000, Pakistan Department of Chemical Engineering, ...

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

Abstract: An increasing number of machine learning algorithms are being applied to multi-objective optimization problems (MOPs), yielding promising results. However, many of these algorithms suffer ...