Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...
A learning algorithm is a mathematical framework or procedure that calculates the best output given a particular set of data. It does this by updating the calculation based on the difference between ...
Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...
How machine intelligence changes the rules of business by Marco Iansiti and Karim R. Lakhani In 2019, just five years after the Ant Financial Services Group was launched, the number of consumers using ...
Abstract: Robust multiobjective optimization problems (RMOPs) widely exist in real-world applications, which introduce a variety of uncertainty in optimization models. While some evolutionary ...
Abstract: The advent of artificial intelligence (AI) has catalyzed transformative changes across diverse domains, establishing itself as a disruptive technology. Traditional methods often fall short ...
Abstract: Delaunay triangulation is an effective way to build a triangulation of a cloud of points, i.e., a partitioning of the points into simplices (triangles in 2D, tetrahedra in 3D, and so on), ...
Abstract: Path planning is a crucial component for robotics and autonomous systems, which facilitate navigation through dynamic and uncertain environments while avoiding obstacles. This review paper ...
Abstract: Deep learning (DL), a branch of machine learning (ML), is the core technology in today’s technological advancements and innovations. Deep learning-based approaches are the state-of-the-art ...
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