Results 171 to 180 of about 1,195,838 (307)

clues: An R Package for Nonparametric Clustering Based on Local Shrinking [PDF]

Determining the optimal number of clusters appears to be a persistent and controversial issue in cluster analysis. Most existing R packages targeting clustering require the user to specify the number of clusters in advance.
Ruben H. Zamar, Ross Lazarus, Fang Chang, Weiliang Qiu, Xiaogang Wang   +4 more
core  

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

An optimization framework for hierarchical clustering. [PDF]

Bioinform Adv
Gilad G, Sharan R.
europepmc   +1 more source

“Smelltronics”—From Gas to Smell Sensing

Advanced Materials, EarlyView.
The emerging field of smelltronics, encompassing sensing technologies for complex volatile organic compounds, holds significant potential for extracting valuable chemical information. It facilitates the noninvasive, real‐time monitoring of humans, food, and the environment.
Takeshi Ono, Tsunaki Takahashi, Jiangyang Liu, Haruka Honda, Zeng Jing, Wataru Tanaka, Takuro Hosomi, Takeshi Yanagida   +7 more
wiley   +1 more source

Pulsed Laser‐Assisted Phase Engineering of Multimetallic Colloidal Nanocrystals With Complex Compositions

Advanced Materials, EarlyView.
The engineering of colloidal nanocrystals with complex compositions has emerged as a highly active area of research within nanomaterials science. This review covers key aspects of colloidal solid‐solution nanocrystal formation using pulsed laser irradiation.
Marina T. Candela, Sergio Triviño‐Sánchez, Andrés Guerrero‐Martínez, Luis M. Liz‐Marzán, Guillermo González‐Rubio   +4 more
wiley   +1 more source

Unveiling gene modules at Atlas scale through hierarchical clustering of single-cell data. [PDF]

Nat Commun
Tang F, Zhang Z, Zhou W, Li G, Xu Y, Tian L.   +5 more
europepmc   +1 more source

Monolithic 3D‐Integrated All‐Solid Ion‐Gated Carbon Nanotube Transistors With Tunable Ionic Conductance for Multi‐Timescale Reservoir Computing

Advanced Materials, EarlyView.
A dual‐timescale reservoir based on monolithically 3D (M3D)‐integrated CNT solid ion‐gated transistors is demonstrated. Tunable ionic dynamics and pulse‐engineered operation enable linear and symmetric synaptic updates. The M3D‐integrated array achieves robust temporal encoding and accurate classification of moving MNIST sequences, highlighting its ...
Haksoon Jung, Hanbin Cho, Yongwoo Lee, Seunghun Baek, Hyeongjun Kim, Yun Goo Ro, Hyunhyub Ko, Joonki Suh, Yong‐Young Noh, Jimin Kwon   +9 more
wiley   +1 more source

Integration of hierarchical clustering in hematologic biomarkers prediction for endocrine toxicity during immunotherapy in lung cancer patients. [PDF]

Sci Rep
Coniac S, Ionescu RT, Costache-Outas MC, Antone-Iordache IL, Barbu AM, Bardan VT, Zamfir A, Ionescu AI, Badiu C.   +8 more
europepmc   +1 more source

Phase Engineering of Nanomaterials (PEN): Evolution, Current Challenges, and Future Opportunities

Advanced Materials, EarlyView.
This review summarizes the synthesis, phase transition, advanced characterization spanning ex situ to in situ and operando techniques, and diverse applications of phase engineering of nanomaterials (PEN). It further outlines key challenges and future opportunities, such as phase stability, architecture control, and artificial intelligence (AI)‐driven ...
Ye Chen, Zhanxi Fan, Jingjie Ge, Qiyuan He, Zhuangchai Lai, Jiawei Liu, Chaoliang Tan, Hua Zhang   +7 more
wiley   +1 more source

Scalable non-linear Support Vector Machine using hierarchical clustering

, 2006
This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data sets by using a selective sampling strategy for the training set.
Murty, Narasimha M, Shevade, SK, Asharaf, S   +2 more
core