Results 91 to 100 of about 113,519 (298)

Automatic Determination of Quasicrystalline Patterns from Microscopy Images

Advanced Intelligent Discovery, EarlyView.
This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender, Marco Corrias, Cesare Franchini   +2 more
wiley   +1 more source

Synthesizing Time-Series Gene Expression Data to Enhance Network Inference Performance Using Autoencoder

Applied Sciences
It is a challenge to infer a gene regulatory network from time-series gene expression data in the systems biology field. A lack of gene expression data samples is a factor limiting the performance of the inference methods.
Cao-Tuan Anh, Yung-Keun Kwon
doaj   +1 more source

A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation [PDF]

, 2020
Xiaoxia Liang, Fang Duan, Ian Bennett, David Mba   +3 more
openalex   +1 more source

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

A Scalable Hybrid Autoencoder–Extreme Learning Machine Framework for Adaptive Intrusion Detection in High-Dimensional Networks

Future Internet
The rapid expansion of network environments has introduced significant cybersecurity challenges, particularly in handling high-dimensional traffic and detecting sophisticated threats.
Anubhav Kumar, Rajamani Radhakrishnan, Mani Sumithra, Prabu Kaliyaperumal, Balamurugan Balusamy, Francesco Benedetto   +5 more
doaj   +1 more source

Inverse Design of Alloys via Generative Algorithms: Optimization and Diffusion within Learned Latent Space

Advanced Intelligent Discovery, EarlyView.
This work presents a novel generative artificial intelligence (AI) framework for inverse alloy design through operations (optimization and diffusion) within learned compact latent space from variational autoencoder (VAE). The proposed work addresses challenges of limited data, nonuniqueness solutions, and high‐dimensional spaces.
Mohammad Abu‐Mualla, Ellis Crabtree, Fredrick Michael, Yayue Pan, Jida Huang   +4 more
wiley   +1 more source

Enhancing Visitor Forecasting with Target-Concatenated Autoencoder and Ensemble Learning

Machine Learning and Knowledge Extraction
Accurate forecasting of inbound visitor numbers is crucial for effective planning and resource allocation in the tourism industry. Preceding forecasting algorithms primarily focused on time series analysis, often overlooking influential factors such as ...
Ray-I Chang, Chih-Yung Tsai, Yu-Wei Chang   +2 more
doaj   +1 more source

Monitoring the misalignment of machine tools with autoencoders after they are trained with transfer learning data [PDF]

, 2023
Mustafa Demetgül, Qi Zheng, Ibrahim Tansel, Jürgen Fleischer   +3 more
openalex   +1 more source

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

Federated Learning-Based Service Caching in Multi-Access Edge Computing System

Applied Sciences
Multi-access edge computing (MEC) brings computations closer to mobile users, thereby decreasing service latency and providing location-aware services.
Tuan Phong Tran, Anh Hung Ngoc Tran, Thuan Minh Nguyen, Myungsik Yoo   +3 more
doaj   +1 more source