Results 91 to 100 of about 9,169,616 (322)

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Generalized Legendre Polynomials for Support Vector Machines (SVMS) Classification

International Journal of Network Security & Its Applications, 2019
In this paper, we introduce a set of new kernel functions derived from the generalized Legendre polynomials to obtain more robust and higher support vector machine (SVM) classification accuracy. The generalized Legendre kernel functions are suggested to provide a value of how two given vectors are like each other by changing the inner product of these ...
Afifi, Ashraf, E.A.Zanaty
openaire   +1 more source

Regression depth and support vector machine [PDF]

The regression depth method (RDM) proposed by Rousseeuw and Hubert [RH99] plays an important role in the area of robust regression for a continuous response variable.
Christmann, Andreas
core  

Machine Learning‐Assisted KCl‐CaCl2‐LiCl Electrolyte Design for Low‐Temperature, High‐Performance Calcium‐Based Liquid Metal Batteries

Advanced Science, EarlyView.
A machine learning‐assisted framework optimizes the KCl‐CaCl2‐LiCl ternary electrolyte. The optimized 13:35:52 mol% composition enables Ca‐based liquid metal batteries to operate stably at 480 °C, with >99.5% coulombic efficiency, ultralow self‐discharge, and excellent cycling stability, advancing low‐temperature large‐scale energy storage.
Xinglin Zhou, Lei Huang, Yan Zhou, Xiaohui Ning   +3 more
wiley   +1 more source

Benchmarking least squares support vector machine classifiers. [PDF]

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a ( convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function
Suykens, Johan, Vandewalle, Joos, De Moor, Bart, Van Gestel, Tony, Vanthienen, Jan, Baesens, Bart, Dedene, Guido, Viaene, Stijn   +7 more
core  

An Efficient Detection Platform Based on Mesoporous Au@Cr2O3 Particles with Schwarz P Surface for Precise Periodontitis Metabolite Profiling

Advanced Science, EarlyView.
A non‐invasive periodontitis diagnosis platform was developed using Au nanoparticles‐decorated mesoporous Cr2O3 (Au@mCr2O3) particles with Schwarz P surface as matrix for saliva metabolic fingerprinting (SMFs) analysis via MALDI‐MS. With the assistance of machine learning of SMFs, this platform enables efficient diagnosis and the screening of potential
Yue Sun, Yan Wang, Fangying Shi, Wenhe Xie, Yiding Zhang, Heyuhan Zhang, Chunhui Deng, Limin Wu, Meihua Chen, Yonghui Deng   +9 more
wiley   +1 more source

Simple SVM based whole-genome segmentation [PDF]

, 2009
We present a support vector machine (SVM) based framework for DNA segmentation into binary classes. Two applications are explored: transcription start site prediction and transcription factor binding prediction.
Justin Bedo, Izhak Haviv, Geoff Macintyre, Adam Kowalczyk   +3 more
core   +1 more source

Multiscale Spatial Fusion Feature‐Driven Characterization of Gastric Cancer Invasive Margins: A Multicenter Cohort Study for Preoperative Accurate Differentiation Between T4a and T4b Subtypes

Advanced Science, EarlyView.
This study develops a multi‐dimensional vision Transformer‐based model, GAVR, to accurately distinguish gastric cancer T4a/b stages preoperatively. Validated across multi‐center cohorts, it achieves excellent performance and significantly improves radiologists’ diagnostic accuracy, offering a promising tool for clinical decision‐making.
Guoliang Zheng, Xiaomiao Chai, Peng Jin, Xin Xin, Yingjie Li, Chun Yang, Jia Wei, Zishuo Yan, Jingyu Zhang, Qianning Zhao, Yingchen Han, Ning Zhang, Fuze Li, Bo Qiao, Huan Wang, Huachuan Zheng, Yan Li, Xin Zhang, Yan Zhao, Wenjun Mao, Jing Zhang   +20 more
wiley   +1 more source

Klasifikasi Gelombang Otot Lengan Pada Robot Manipulator Menggunakan Support Vector Machine

Rekayasa, 2019
Teknologi robotika semakin berkembang. Banyak orang berinovasi untuk membantu aktivitas mereka, diantaranya membuat robot manipulator untuk mengambil barang di tempat berbahaya atau memindah barang dengan presisi yang sangat tinggi.
Muhammad Ja'far Ubaidillah, Ii Munadhif, Noorman Rinanto   +2 more
doaj   +1 more source

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

, 2020
In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the ...
E. Y. Boateng, Joseph Otoo., D. Abaye
semanticscholar   +1 more source