Results 111 to 120 of about 85,695 (269)

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Forest Fire Hazard Assessment using Remote Sensing Data and Machine Learning, Case Study of Jijel, Algeria

Ekológia (Bratislava)
Climate change, particularly in vulnerable areas such as the Mediterranean hotspot, exacerbates the risk of wildfires, turning these regions into true danger zones.
Matougui Zakaria, Zouidi Mohamed
doaj   +1 more source

Explainable DDoS Detection with a CNN-LSTM Hybrid Model and SHAP Interpretation

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
The rising frequency and complexity of Distributed Denial of Service (DDoS) attacks pose a severe threat to network security. This study aims to develop an effective and interpretable DDoS detection framework using a hybrid deep learning approach.
Amali Amali, Anggi Muhammad Rifa'i, Edy Widodo, Ahmad Turmudi Zy, Dhani Ariatmanto   +4 more
doaj   +1 more source

Scalable SHAP-Informed Neural Network

Mathematics
In the pursuit of scalable optimization strategies for neural networks, this study addresses the computational challenges posed by SHAP-informed learning methods introduced in prior work. Specifically, we extend the SHAP-based optimization family by incorporating two existing approximation methods, C-SHAP and FastSHAP, to reduce training time while ...
Jarrod Graham, Victor S. Sheng
openaire   +2 more sources

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Traditional architectural styles in Northwest Henan: a machine learning analysis of influencing factors

Journal of Asian Architecture and Building Engineering
As a significant component of traditional Chinese architectural culture, the architectural styles of northwest Henan embody profound historical, cultural, and artistic values.
Tianxi Lu, Siti Sarah Herman, Nor Atiah Ismail   +2 more
doaj   +1 more source

Early Type 2 diabetes risk prediction using explainable machine learning in a two-stage approach

Frontiers in Digital Health
BackgroundDiabetes is a chronic disease characterized by elevated blood glucose levels. Without early detection and proper management, it can lead to serious complications and increase healthcare costs.
Silas Majyambere, Silas Majyambere, Tony Lindgren, Celestin Twizere, Isaac Ntakirutimana   +4 more
doaj   +1 more source

Linear, efficient and symmetric values for TU-games [PDF]

In this paper, we study values for TU-games which satisfy three classical properties: Linearity, efficiency and symmetry. We give the general analytical form of these values and their relation with the Shapley value and the Egalitarian value.
Célestin Chameni Nembua, Nicolas Gabriel Andjiga   +1 more
core  

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source

Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films

Advanced Intelligent Discovery, EarlyView.
This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley   +1 more source