|
|
|
|
|
Vera Afreixo, Ana Helena Tavares, Vera Enes, Miguel Pinheiro, Leonor Rodrigues and Gabriela Moura
In this work, we aimed to establish a stable and accurate procedure with which to perform feature selection in datasets with a much higher number of predictors than individuals, as in genome-wide association studies. Due to the instability of feature sel...
ver más
|
|
|
|
|
|
|
|
|
|
|
Mohamed Shenify, Fokrul Alom Mazarbhuiya and A. S. Wungreiphi
There are many applications of anomaly detection in the Internet of Things domain. IoT technology consists of a large number of interconnecting digital devices not only generating huge data continuously but also making real-time computations. Since IoT d...
ver más
|
|
|
|
|
|
|
|
|
|
|
Iqbal Muhammad Zubair, Yung-Seop Lee and Byunghoon Kim
The selection of group features is a critical aspect in reducing model complexity by choosing the most essential group features, while eliminating the less significant ones. The existing group feature selection methods select a set of important group fea...
ver más
|
|
|
|
|
|
|
|
|
|
|
András Hubai, Sándor Szabó and Bogdán Zaválnij
The principal component analysis is a well-known and widely used technique to determine the essential dimension of a data set. Broadly speaking, it aims to find a low-dimensional linear manifold that retains a large part of the information contained in t...
ver más
|
|
|
|
|
|
|
|
|
|
|
Azal Ahmad Khan, Salman Hussain and Rohitash Chandra
Quantum computing has opened up various opportunities for the enhancement of computational power in the coming decades. We can design algorithms inspired by the principles of quantum computing, without implementing in quantum computing infrastructure. In...
ver más
|
|
|
|
|
|
|
|
|
|
|
Marco Scutari
Bayesian networks (BNs) are a foundational model in machine learning and causal inference. Their graphical structure can handle high-dimensional problems, divide them into a sparse collection of smaller ones, underlies Judea Pearl?s causality, and determ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yifan Wang, Jinglei Xu, Qihao Qin, Ruiqing Guan and Le Cai
In this study, we propose a novel dynamic mode decomposition (DMD) energy sorting criterion that works in conjunction with the conventional DMD amplitude-frequency sorting criterion on the high-dimensional schlieren dataset of the unsteady flow of a spik...
ver más
|
|
|
|
|
|
|
|
|
|
|
Boqian Ji, Jun Huang, Xiaoqiang Lu, Yacong Wu and Jingjiang Liu
The wing aerodynamic shape optimization is a typical high-dimensional problem with numerous independent design variables. Researching methods to reduce the dimensionality of optimization from the perspective of aerodynamic characteristics is necessary. O...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ligang Yuan, Jing Liu, Haiyan Chen, Daoming Fang and Wenlu Chen
Scene taxiing time is an important indicator for assessing the operational efficiency of airports as well as green airports, and it is also a fundamental parameter in flight regularity statistics. The accurate prediction of taxiing time can help decision...
ver más
|
|
|
|
|
|
|
|
|
|
|
Weilong Guang, Peng Wang, Jinshuai Zhang, Linjuan Yuan, Yue Wang, Guang Feng and Ran Tao
Predicting the flow situation of cavitation owing to its high-dimensional nonlinearity has posed great challenges. To address these challenges, this study presents a novel reduced order modeling (ROM) method to accurately analyze and predict cavitation f...
ver más
|
|
|
|
|
|