Results 71 to 80 of about 1,011,171 (338)

Research on Application of Regression Least Squares Support Vector Machine on Performance Prediction of Hydraulic Excavator

Journal of Control Science and Engineering, 2014
In order to improve the performance prediction accuracy of hydraulic excavator, the regression least squares support vector machine is applied. First, the mathematical model of the regression least squares support vector machine is studied, and then the ...
Zhan-bo Chen
doaj   +1 more source

Prediction of Chongqing's grain output based on support vector machine

Frontiers in Sustainable Food Systems, 2023
Scientific prediction of agricultural food production plays an essential role in stabilizing food supply. In order to improve the accuracy of grain yield prediction and reduce the error of grain yield prediction in Chongqing, this paper proposes a new ...
Jia Wang, Guixian Tian, Yongchao Tao, Chengwu Lu   +3 more
doaj   +1 more source

Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events [PDF]

, 2022
Saed Asaly, Lee-Ad Gottlieb, ‪Nimrod Inbar‬‏, Yuval Reuveni   +3 more
openalex   +1 more source

SNUPN‐Related Muscular Dystrophy: Novel Phenotypic, Pathological and Functional Protein Insights

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective SNUPN‐related muscular dystrophy or LGMDR29 is a new entity that covers from a congenital or childhood onset pure muscular dystrophy to more complex phenotypes combining neurodevelopmental features, cataracts, or spinocerebellar ataxia. So far, 12 different variants have been described.
Nuria Muelas, Pablo Iruzubieta, Alberto Damborenea, Laura Pérez‐Fernández, Inmaculada Azorín, Juan Carlos Jiménez García, Ana Töpf, Pilar Martí, Lorena Fores‐Toribio, María Manterola, Rosana Blanco‐Mañez, Oihane Pikatza‐Menoio, Sonia Alonso‐Martín, Volker Straub, Aitziber L. Cortajarena, Adolfo López de Munain, David De Sancho, Lorea Blázquez, Juan J. Vilchez   +18 more
wiley   +1 more source

Large-scale pinball twin support vector machines [PDF]

, 2021
M. Tanveer, Aruna Tiwari, Rahul Choudhary, M. A. Ganaie   +3 more
openalex   +1 more source

CSF Levels of NPTX2 Are Associated With Less Brain Atrophy Over Time in Cognitively Unimpaired Individuals

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Neuronal pentraxin 2 (NPTX2) is a synaptic protein involved in synaptic plasticity and regulation of neuronal excitability. Lower baseline cerebrospinal fluid (CSF) NPTX2 levels have been shown to be associated with an earlier onset of mild cognitive impairment (MCI), a pre‐dementia syndrome, even after CSF Alzheimer's Disease (AD)
Juan P. Vazquez, Corinne Pettigrew, Yuxin Zhu, Claire Anderson, Guray Erus, Christos Davatzikos, Michael Miller, Abhay Moghekar, Sungtaek Oh, Chan‐Hyun Na, Marilyn Albert, Paul Worley, Anja Soldan   +12 more
wiley   +1 more source

Exploring Kernel Machines and Support Vector Machines: Principles, Techniques, and Future Directions

Mathematics
The kernel method is a tool that converts data to a kernel space where operation can be performed. When converted to a high-dimensional feature space by using kernel functions, the data samples are more likely to be linearly separable.
Ke-Lin Du, Bingchun Jiang, Jiabin Lu, Jingyu Hua, M. N. S. Swamy   +4 more
doaj   +1 more source

A two‐stage transformer fault diagnosis method based multi‐filter interactive feature selection integrated adaptive sparrow algorithm optimised support vector machine [PDF]

, 2022
Hanyu Shi, Mingxia Chen
openalex   +1 more source

Suboptimal Solution Path Algorithm for Support Vector Machine [PDF]

, 2011
We consider a suboptimal solution path algorithm for the Support Vector Machine. The solution path algorithm is an effective tool for solving a sequence of a parametrized optimization problems in machine learning.
Karasuyama, Masayuki, Takeuchi, Ichiro
core   +1 more source

Optimized Time–Frequency Analysis for Induction Motor Fault Detection Using Hybrid Differential Evolution and Deep Learning Techniques

International Journal of Adaptive Control and Signal Processing, EarlyView.
Workflow of the parameter optimization process for ITSC fault detection, applying Differential Evolution optimization and the Smooth Pseudo Wigner‐Ville Distribution for signal processing. The optimized parameters are then used in the failure identification pipeline, which combines the signal processing with a YOLO‐based architecture for fault severity
Rafael Martini Silva, Avyner Lorran de Oliveira Vitor, Marcelo Favoretto Castoldi, Alessandro Goedtel, Wesley Angelino Souza   +4 more
wiley   +1 more source