Atom Decomposition with Adaptive Basis Selection Strategy for Matrix Completion

2016 ◽  
Vol 12 (3) ◽  
pp. 1-25 ◽  
Author(s):  
Yao Hu ◽  
Chen Zhao ◽  
Deng Cai ◽  
Xiaofei He ◽  
Xuelong Li
Keyword(s):  
Matrix Completion ◽  
Selection Strategy ◽  
Adaptive Basis Selection

Sexual selection strategy and sexism in the Greek system: Mating strategies represented in Greek life

2014 ◽  
Author(s):  
Patrick J. Ewell ◽  
C. Veronica Smith ◽  
Rosanna E. Guadagno
Keyword(s):  
Sexual Selection ◽  
Mating Strategies ◽  
Selection Strategy ◽  
Greek Life ◽  
Greek System

Space Operations Center orbit altitude selection strategy

1982 ◽  
Author(s):  
J. INDRIKIS ◽  
H. MYERS
Keyword(s):  
Selection Strategy ◽  
Space Operations ◽  
Operations Center

Optimal Route Selection Strategy in Wireless Mesh Networks

2018 ◽  
Vol 6 (1) ◽  
pp. 238-243
Author(s):  
Pushpender Sarao ◽  
◽  
T. Raghavendra Gupta ◽  
S. Suresh ◽  
◽  
...  
Keyword(s):  
Wireless Mesh Networks ◽  
Mesh Networks ◽  
Selection Strategy ◽  
Route Selection ◽  
Optimal Route ◽  
Wireless Mesh

Distant Supervision for Relation Extraction with Matrix Completion

2014 ◽  
Author(s):  
Miao Fan ◽  
Deli Zhao ◽  
Qiang Zhou ◽  
Zhiyuan Liu ◽  
Thomas Fang Zheng ◽  
...  
Keyword(s):  
Matrix Completion ◽  
Relation Extraction ◽  
Distant Supervision

Identifying the Predictors of Self-Management Behaviors in Patients with Diabetes Based on Ecological Approach: A Systematic Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Mahnaz Davari ◽  
Hamed Rezakhani Moghaddam ◽  
Aghil Habibi Soola
Keyword(s):  
Scientific Information ◽  
The Self ◽  
Self Management ◽  
Ecological Approach ◽  
Selection Strategy ◽  
Diabetic Patients ◽  
Personal Factors ◽  
Management Interventions ◽  
Number Of Patients ◽  
Management Behaviors

Background: Recognizing and promoting the factors that affect the self-management behaviors of diabetes leads to a reduction in the number of patients and an improvement in the quality of care. The ecological approach focuses on the nature of people's interactions with their physical and socio-cultural environments. Objective: The purpose of this study was to identify the predictors of self-management behaviors with a comprehensive approach in these patients. Methods: The Keywords were investigated in the relevant national and international databases, including PubMed, Google Scholar, Science Direct, Scopus, and Scientific Information Database, Magiran, and Iran Medex to obtain the articles published from 2009 to 2019. The search and article selection strategy was developed based on the Prisma checklist and was carried out in three steps. Results: Most studies have shown that personal factors had the highest prediction power for the self-management of diabetes. Then, the interpersonal factors, society and policy-making factors, and group and organization factors were most frequently reported predictors of self-management behaviors in diabetic patients. Conclusion: Self-management of diabetes is necessary for controlling it because 95% of care is done by the patient. When designing self-management interventions, factors based on the individual level that increasing self-management behaviors should be taken into account.

User-Driven Network Selection during Vertical Handover in 4G Networks

2017 ◽  
Vol 10 ◽  
pp. 1
Author(s):  
Abubakar Muhammad Miyim ◽  
Mahamod Ismail ◽  
Rosdiadee Nordin
Keyword(s):  
Blocking Probability ◽  
Service Selection ◽  
Vertical Handover ◽  
Network Selection ◽  
Selection Strategy ◽  
Intelligent Network ◽  
Decision Algorithm ◽  
Radio Access ◽  
Service Application ◽  
Multi Access

The importance of network selection for wireless networks, is to facilitate users with various personal wireless devices to access their desired services via a range of available radio access networks. The inability of these networks to provide broadband data service applications to users poses a serious challenge in the wireless environment. Network Optimization has therefore become necessary, so as to accommodate the increasing number of users’ service application demands while maintaining the required quality of services. To achieve that, the need to incorporate intelligent and fast mechanism as a solution to select the best value network for the user arises. This paper provides an intelligent network selection strategy based on the user- and network-valued metrics to suit their preferences when communicating in multi-access environment. A user-driven network selection strategy that employs Multi-Access Service Selection Vertical Handover Decision Algorithm (MASS-VHDA) via three interfaces; Wi-Fi, WiMAX and LTE-A is proposed, numerically evaluated and simulated. The results from the performance analysis demonstrate some improvement in the QoS and network blocking probability to satisfy user application requests for multiple simultaneous services.

scholarly journals Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

2019 ◽  
Vol 11 (16) ◽  
pp. 1933 ◽  
Author(s):  
Yangyang Li ◽  
Ruoting Xing ◽  
Licheng Jiao ◽  
Yanqiao Chen ◽  
Yingte Chai ◽  
...  
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Sample Selection ◽  
Spatial Relations ◽  
Speckle Noise ◽  
Classification Performance ◽  
Selection Strategy ◽  
Training Set ◽  
Polarimetric Synthetic Aperture Radar ◽  
Unlabeled Sample

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

Brain Storm Optimization Algorithm based on adaptive inertial selection strategy for the RCPSP

2019 ◽  
Author(s):  
Yali Wu ◽  
XiaoPeng Wang ◽  
GuoTing Li ◽  
ATing Lu
Keyword(s):  
Optimization Algorithm ◽  
Selection Strategy ◽  
Brain Storm Optimization
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