In today's competitive market environment, business analysis has developed into a necessity beyond just describing reports to becoming a strategy for data-driven decision making. This research paper offers an integrated approach to applying business analysis to managerial decision analytics for creating a sustainable c...
Mr. R. Nagarajan· International Journal for No...· 0 citations
Source code for CD-BAN, a dual-branch graph neural network with a bilinear cross-attention layer that classifies drug-cyclodextrin inclusion-complex binding affinity (Strong: K > 10,000 M⁻¹ vs. Weak: K < 100 M⁻¹) from 2D SMILES alone, without 3D geometry or handcrafted descriptors. Trained only on the binary extremes,...
With the growing trend in urban development along with advanced intelligent transportation systems, there is a demand for advanced solutions for the management of the transport network. Since transportation networks can easily be modeled using graphs, graph theory is a useful mathematical tool for optimizing complex ne...
K. Jeevalakshmi, M. Gokul· Zenodo (CERN European Organi...· 0 citations
Official code for the paper: HetST-GNN: Relation-Aware Graph Neural Networks with Temporal Conditioning for Back-Order Prediction in Multi-Echelon Supply Chains This release contains the source code used in the paper.
manofseven7· Zenodo (CERN European Organi...· 0 citations
Malware and payment fraud share a structural feature that flat feature vectors throw away. Both leave behind a graph. A binary induces a call graph; a fraud ring induces a web of accounts, devices and shared attributes. Graph neural networks learn on that structure directly, and this review examines what the move has b...
T Ramaprabha· Zenodo (CERN European Organi...· 0 citations
Malware and payment fraud share a structural feature that flat feature vectors throw away. Both leave behind a graph. A binary induces a call graph; a fraud ring induces a web of accounts, devices and shared attributes. Graph neural networks learn on that structure directly, and this review examines what the move has b...
T Ramaprabha· Zenodo (CERN European Organi...· 0 citations
Temporal Graph Neural Networks (T-GNNs) are widely used to model continuous-time dynamic graphs. However, training T-GNNs on large temporal graphs remains computationally prohibitive due to a fundamental challenge: existing systems enforce a stop-line execution paradigm that serializes batch data preparation with GPU c...
Yong-Chun Jiang, Yong-Chao Liu, Kai-Fan Jia et al.· Proceedings of the Internati...· 0 citations
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
MIT News · Artificial Intelligence· news.mit.eduJul 15, 2026
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Microsoft Research Blog· microsoft.comJul 13, 2026
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.