Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known Heston option pricing model as a benchmark, researchers show that global ...

Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for clean-energy reactions are screened, identified, and validated across ...

CNN architecture summary: The first dimension in all the layers “?” refers to the batch size. It is left as an unknown or unspecified variable within the network architecture so that it can be chosen ...

Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...

Read more about AI-driven learning analytics struggle to deliver measurable gains in higher education on Devdiscourse ...

India is entering a decisive phase in its artificial intelligence journey. Over the last two years,  conversations around AI have largely revolved around model sizes, GPU access, and sovereign ...

The reason for this shift is simple: data gravity. The core holds the most complete, consistent and authoritative dataset available to the institution. Moving AI decisioning closer to this data ...

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

Over the past year, I've been working on a challenge that faces every organization implementing Zero Trust: how do you manage ...

Electrocatalysis sits at the heart of clean hydrogen production, fuel cells, and carbon dioxide conversion, yet progress toward scalable, ...