Results 201 to 210 of about 25,547 (250)
Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery
The integration of AI/ML into nanomedicine offers a transformative approach to therapeutic design and optimization. Unlike conventional empirical methods, AI/ML models (such as classification, regression, and neural networks) enable the analysis of complex clinical and formulation datasets to predict optimal nanoparticle characteristics and therapeutic
Rohan Chand Sahu +3 more
wiley +1 more source
This study introduces an integrated machine learning framework combining predictive models, a guided multiobjective search strategy, and particle swarm optimization (PSO) to discover novel refractory high‐entropy alloys with exceptional yield strengths (1580–1740 MPa) and fracture strains (23%–27%), overcoming challenges of vast composition spaces and ...
Gang Xu +6 more
wiley +1 more source
Solving olympiad problems using methods from extremal graph theory
Tarigradschi, M., Teleucă, M.
openaire +1 more source
Adaptive rewiring: a general principle for neural network development. [PDF]
Li J +3 more
europepmc +1 more source
Extremal problems in graph theory
AbstractThe aim of this note is to give an account of some recent results and state a number of conjectures concerning extremal properties of graphs.
Béla Bollobás
semanticscholar +4 more sources
On some extremal problems in graph theory
Der Verf. beweist, daß für eine genügend große Konstante \(c\) jeder Graph \(G\) mit \(n\) Punkten und \(cn^{3/2}\) Kanten ein Sechseck \(x_1,x_2,x_3,x_4,x_5,x_6\) enthält und dazu noch einen siebenten Punkt \(y\), der mit \(x_1,x_3\) und \(x_5\) verbunden ist.
P. Erdös
semanticscholar +4 more sources
An extremal problem in graph theory
It is proved that the maximum number of cut-vertices in a connected graph withn vertices andm edges is $$max\left\{ {q:m \leqq (_2^{n - q} ) + q} \right\}$$ All the extremal graphs are determined and the corresponding problem for cut-edges is also solved.
A. Ramachandra Rao
openalex +3 more sources

