Results 91 to 100 of about 469,122 (324)

Unveiling Phonon Contributions to Thermal Conductivity and the Applicability of the Wiedemann—Franz Law in Ruthenium and Tungsten Thin Films

Advanced Functional Materials, EarlyView.
Thermal transport in Ru and W thin films is studied using steady‐state thermoreflectance, ultrafast pump–probe spectroscopy, infrared‐visible spectroscopy, and computations. Significant Lorenz number deviations reveal strong phonon contributions, reaching 45% in Ru and 62% in W.
Md. Rafiqul Islam, Pravin Karna, Niraj Bhatt, Sandip Thakur, Helge Heinrich, Daniel M. Hirt, Saman Zare, Christopher Jezewski, Rinus T.P. Lee, Kandabara Tapily, John T. Gaskins, Colin D. Landon, Sean W. King, Ashutosh Giri, Patrick E. Hopkins   +14 more
wiley   +1 more source

Support Vector Machines [PDF]

, 2002
This chapter gives a short introduction to support vector machines, the basic learning method used, extended, and analyzed for text classification throughout this work. Support vector machines [Cortes and Vapnik, 1995][Vapnik, 1998] were developed by Vapnik et al.
openaire   +2 more sources

An Ultra‐Robust Memristor Based on Vertically Aligned Nanocomposite with Highly Defective Vertical Channels for Neuromorphic Computing

Advanced Functional Materials, EarlyView.
An ultra‐robust memristor based on SrTiO3‐CeO2 (S‐C) vertically aligned nanocomposite (VAN) achieving exceptional endurance of 1012 switching cycles via interface engineering. Artificial neural networks (ANNs) integrated with S‐C VAN memristors exhibit high training accuracy across multiple datasets.
Zedong Hu, Hongyi Dou, Jeremy B. Gan, Cun Li, Xuanyu Sheng, Juanjuan Lu, Abhijeet Choudhury, Jialong Huang, Yizhi Zhang, Zhongxia Shang, Ye Cao, Xinghang Zhang, Haiyan Wang   +12 more
wiley   +1 more source

Probabilistic Kernel Support Vector Machines

, 2019
We propose a probabilistic enhancement of standard kernel Support Vector Machines for binary classification, in order to address the case when, along with given data sets, a description of uncertainty (e.g., error bounds) may be available on each datum ...
Chen, Yongxin, Georgiou, Tryphon T., Tannenbaum, Allen R.   +2 more
core  

Functional Materials for Environmental Energy Harvesting in Smart Agriculture via Triboelectric Nanogenerators

Advanced Functional Materials, EarlyView.
This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva, Giulio Fatti, Emanuel Carlos, Guilherme Ferreira, Sumita Goswami, Suman Nandy, Luiz H. C. Mattoso, Elvira Fortunato, Caio G. Otoni, Rodrigo Martins   +9 more
wiley   +1 more source

Facial expression recognition using three-stage support vector machines

Visual Computing for Industry, Biomedicine, and Art, 2019
Herein, a three-stage support vector machine (SVM) for facial expression recognition is proposed. The first stage comprises 21 SVMs, which are all the binary combinations of seven expressions.
Issam Dagher, Elio Dahdah, Morshed Al Shakik   +2 more
doaj   +1 more source

Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination [PDF]

, 2020
Ying Liu, Lihua Huang
openalex   +1 more source

Harnessing Non‐Covalent Protein–Protein Interaction Domains for Production of Biocatalytic Materials Systems

Advanced Functional Materials, EarlyView.
Non‐covalent protein–protein interactions mediated by SH3, PDZ, or GBD domains enable the self‐assembly of stable and biocatalytically active hydrogel materials. These soft materials can be processed into monodisperse foams that, once dried, exhibit enhanced mechanical stability and activity and are easily integrated into microstructured flow ...
Julian S. Hertel, Maria Alessandra Martini, Marius Stoeckle, Annika J. Weber, Kersten S. Rabe, Christof M. Niemeyer   +5 more
wiley   +1 more source

Least Squares Minimum Class Variance Support Vector Machines

Computers
In this paper, we propose a Support Vector Machine (SVM)-type algorithm, which is statistically faster among other common algorithms in the family of SVM algorithms. The new algorithm uses distributional information of each class and, therefore, combines
Michalis Panayides, Andreas Artemiou
doaj   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source