Results 211 to 220 of about 5,879,357 (336)

Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Advanced Intelligent Discovery, EarlyView.
A multimodal fusion pipeline predicts high‐resolution ion distributions in imaging mass spectrometry by integrating Fourier transform ion cyclotron resonance, time‐of‐flight matrix‐assisted laser desorption/ionization, and time‐of‐flight secondary ion mass spectrometry data.
Md Inzamam Ul Haque, Ramakrishnan Kannan, Jacob D. Hinkle, Sylwia A. Stopka, Anton V. Ievlev, Nathalie Y. R. Agar, Olga S. Ovchinnikova, Debangshu Mukherjee   +7 more
wiley   +1 more source

Pattern Mining of Older Drivers' Driving Behavior Through Telematics-data-driven Unsupervised Learning. [PDF]

IEEE Sens J
Moshfeghi S, Jang J.
europepmc   +1 more source

From Semi-supervised to Almost-unsupervised Speech Recognition with Very-low Resource by Jointly Learning Phonetic Structures from Audio and Text Embeddings [PDF]

, 2019
Yi‐Chen Chen, Sung-Feng Huang, Hung-yi Lee, Lin-shan Lee   +3 more
openalex   +1 more source

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

Advanced Intelligent Discovery, EarlyView.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

Morphology based cell classification : unsupervised machine learning approach

, 2017
Dhananjay Bhaskar
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

EmbedTAD Using Graph Embedding and Unsupervised Learning to Identify TADs from High-Resolution Hi-C Data. [PDF]

Commun Biol
Chowdhury HMAM, Oluwadare O.
europepmc   +1 more source

Toward Environmentally Friendly Hydrogel‐Based Flexible Intelligent Sensor Systems

Advanced Intelligent Discovery, EarlyView.
This review summarizes environmentally and biologically friendly hydrogel‐based flexible sensor systems focusing on physical, chemical, and physiological sensors. Furthermore, device concepts moving forward for the practical application are discussed about wireless integration, the interface between hydrogel and dry electronics, automatic data analysis
Sudipta Kumar Sarkar, Kuniharu Takei
wiley   +1 more source

Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning. [PDF]

J Imaging
Larin I, Karabelsky A.
europepmc   +1 more source

An Enhanced Generative Adversarial Imputation Network With Unsupervised Learning for Random Missing Data Imputation of All Sensors

Xin Xie, Ying Lei, Chunyan Xiang, Yixian Li, Lijun Liu   +4 more
openalex   +1 more source