Performance prediction model for a high-impulse monopropellant propulsion system

1974 ◽  
Vol 11 (11) ◽  
pp. 764-768 ◽  
Author(s):  
H. C. HEARN ◽  
D. L. YOUNG
Keyword(s):  
Prediction Model ◽  
Performance Prediction ◽  
Propulsion System

Project performance prediction model linking agility and flexibility demands to project type

2021 ◽  
Author(s):  
Marco Aurélio Oliveira ◽  
Luiz V. O. Dalla Valentina ◽  
André Hideto Futami ◽  
Osmar Possamai ◽  
Carlos Alberto Flesch
Keyword(s):  
Prediction Model ◽  
Performance Prediction ◽  
Project Performance

scholarly journals Student Performance Prediction with Optimum Multilabel Ensemble Model

2021 ◽  
Vol 30 (1) ◽  
pp. 511-523
Author(s):  
Ephrem Admasu Yekun ◽  
Abrahaley Teklay Haile
Keyword(s):  
High School Students ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Transformation Method ◽  
Classification Task ◽  
Support Vector ◽  
School Students ◽  
K Nearest Neighbors ◽  
Classifier Chains

Abstract One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.

A performance prediction model for BIC-OFDM transmissions with AMC over nonlinear fading channels

2011 ◽  
Author(s):  
Filippo Giannetti ◽  
Ivan Stupia ◽  
Vincenzo Lottici ◽  
Riccardo Andreotti ◽  
Aldo N. D'Andrea ◽  
...  
Keyword(s):  
Prediction Model ◽  
Fading Channels ◽  
Performance Prediction ◽  
A Performance

scholarly journals Mechanical and acoustic performance prediction model for elastomers in different environmental conditions

2018 ◽  
Vol 144 (4) ◽  
pp. 2269-2280 ◽  
Author(s):  
Yunke Huang ◽  
Hong Hou ◽  
Selda Oterkus ◽  
Zhengyu Wei ◽  
Shuai Zhang
Keyword(s):  
Prediction Model ◽  
Performance Prediction ◽  
Environmental Conditions ◽  
Acoustic Performance

Probabilistic performance prediction model for Austrian torrent control infrastructure

2018 ◽  
Vol 15 (2) ◽  
pp. 170-179 ◽  
Author(s):  
Roman Paratscha ◽  
Alfred Strauss ◽  
Roman Smutny ◽  
Thomas Lampalzer ◽  
Hans Peter Rauch ◽  
...  
Keyword(s):  
Prediction Model ◽  
Performance Prediction

scholarly journals Short-term Performance Prediction Model for Aviation Job-shop

2014 ◽  
Vol 7 (7) ◽  
pp. 1442-1447
Author(s):  
Ibrahim Y. Sokar ◽  
Yusoff Jamaluddin ◽  
Mardina Abdullah
Keyword(s):  
Prediction Model ◽  
Performance Prediction ◽  
Job Shop ◽  
Short Term ◽  
Term Performance

scholarly journals MODELO DE PREVISÃO DE DESEMPENHO DE TRIATLETAS COM A UTILIZAÇÃO DA ANÁLISE DISCRIMINANTE (TRIATHLETES PERFORMANCE PREDICTION MODEL USING DISCRIMINANT ANALYSIS)

2017 ◽  
Vol 10 ◽  
pp. 111-118
Author(s):  
Domingos Rodrigues Pandeló Júnior
Keyword(s):  
Discriminant Analysis ◽  
Prediction Model ◽  
Performance Prediction ◽  
A Performance

A previsão do desempenho esportivo é relevante para a identificação de talentos e no estabelecimento de estratégias de treinamento. O objetivo do presente artigo é estabelecer um modelo que tenha a capacidade de predizer o desempenho de triatletas. Para isso foi utilizada a análise discriminante, que é uma técnica de análise multivariada. Trabalhou-se com 21 voluntários, sendo 7 profissionais e 14 amadores, todos do sexo masculino. Foram selecionadas variáveis antropométricas, fisiológicas e de treinamento fáceis de serem mensuradas, sem a necessidade de utilização de laboratórios específicos. O presente estudo mostrou que com a utilização de algumas variáveis pode-se buscar inferir a performance de triatletas. A previsão de performance é de vital importância, quer seja para a detecção de talentos, quer seja para a estruturação do treinamento, o que mostra a importância do desenvolvimento de modelos desse tipo.

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