scholarly journals Development of a Hybrid Prediction Mechanism using SMA and EXS Methods for GSM Logical Channel Load Variables

2015 ◽  
Vol 109 (1) ◽  
pp. 16-24
Author(s):  
Garba S ◽  
Muazu M.B ◽  
Dajab D.D
Keyword(s):  
Hybrid Prediction ◽  
Logical Channel ◽  
Prediction Mechanism

An online hybrid prediction model for mud pit volume in the complex geological drilling process

2021 ◽  
Vol 111 ◽  
pp. 104793
Author(s):  
Yang Zhou ◽  
Xin Chen ◽  
Edwardo F. Fukushima ◽  
Min Wu ◽  
Weihua Cao ◽  
...  
Keyword(s):  
Prediction Model ◽  
Drilling Process ◽  
Hybrid Prediction

scholarly journals Multi-omics-based prediction of hybrid performance in canola

2021 ◽  
Author(s):  
Dominic Knoch ◽  
Christian R. Werner ◽  
Rhonda C. Meyer ◽  
David Riewe ◽  
Amine Abbadi ◽  
...  
Keyword(s):  
Hilbert Space ◽  
Genetic Markers ◽  
Reproducing Kernel ◽  
Prediction Models ◽  
Agronomic Traits ◽  
Reproducing Kernel Hilbert Space ◽  
Hybrid Performance ◽  
Hybrid Prediction ◽  
Gaussian Kernels ◽  
Breeding Programmes

Abstract Key message Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programmes, including testing of large numbers of experimental hybrids, the prediction of hybrid performance is of utmost interest to plant breeders. In this study, we tested the effectiveness of hybrid prediction models in spring-type oilseed rape (Brassica napus L./canola) employing different omics profiles, individually and in combination. To this end, a population of 950 F1 hybrids was evaluated for seed yield and six other agronomically relevant traits in commercial field trials at several locations throughout Europe. A subset of these hybrids was also evaluated in a climatized glasshouse regarding early biomass production. For each of the 477 parental rapeseed lines, 13,201 single nucleotide polymorphisms (SNPs), 154 primary metabolites, and 19,479 transcripts were determined and used as predictive variables. Both, SNP markers and transcripts, effectively predict hybrid performance using (genomic) best linear unbiased prediction models (gBLUP). Compared to models using pure genetic markers, models incorporating transcriptome data resulted in significantly higher prediction accuracies for five out of seven agronomic traits, indicating that transcripts carry important information beyond genomic data. Notably, reproducing kernel Hilbert space regression based on Gaussian kernels significantly exceeded the predictive abilities of gBLUP models for six of the seven agronomic traits, demonstrating its potential for implementation in future canola breeding programmes.

A novel hybrid prediction algorithm to network traffic

2015 ◽  
Vol 70 (9-10) ◽  
pp. 427-439 ◽  
Author(s):  
Dingde Jiang ◽  
Zhengzheng Xu ◽  
Hongwei Xu
Keyword(s):  
Network Traffic ◽  
Prediction Algorithm ◽  
Hybrid Prediction

AS prediction mechanism for distributed threads systems

2011 ◽  
Vol 71 (10) ◽  
pp. 1367-1376 ◽  
Author(s):  
P.D.M. Plentz ◽  
C. Montez ◽  
R.S. de Oliveira
Keyword(s):  
Prediction Mechanism

scholarly journals Failure Prediction in Commercial Banks with a Hybrid Prediction Model

2014 ◽  
Vol 1 (2) ◽  
pp. 209-220
Author(s):  
Yinhua Li ◽  
Yong Shi ◽  
Anqiang Huang ◽  
Haizhen Yang
Keyword(s):  
Prediction Model ◽  
Commercial Banks ◽  
Failure Prediction ◽  
Hybrid Prediction

scholarly journals Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 27
Author(s):  
Sehrish Malik ◽  
DoHyeun Kim
Keyword(s):  
Smart Factory ◽  
Prediction Algorithm ◽  
Utilization Rate ◽  
Task Management ◽  
Management Mechanism ◽  
Machine Maintenance ◽  
Machine Utilization ◽  
And Performance ◽  
Artificial Neural Network Ann ◽  
Prediction Mechanism

The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate.

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