Exploratory Data Analysis To Identify The Most Important Feature Of University Admission Test Criteria Using Random Forest And Neural Network Algorithm

2021 ◽  
Author(s):  
Acep Irham Gufroni ◽  
Purwanto Purwanto ◽  
Farikhin Farikhin ◽  
Adi Wibowo ◽  
Budi Warsito
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Random Forest ◽  
Exploratory Data Analysis ◽  
University Admission ◽  
Network Algorithm ◽  
Test Criteria ◽  
Exploratory Data ◽  
Neural Network Algorithm ◽  
Admission Test

scholarly journals proABC-2: PRediction Of AntiBody Contacts v2 and its application to information-driven docking

2020 ◽  
Author(s):  
F. Ambrosetti ◽  
T. H. Olsen ◽  
P. P. Olimpieri ◽  
B. Jiménez-García ◽  
E. Milanetti ◽  
...  
Keyword(s):  
Neural Network ◽  
Monoclonal Antibodies ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Rational Design ◽  
Computational Approaches ◽  
Fundamental Step ◽  
Link Type ◽  
Network Algorithm ◽  
Neural Network Algorithm

AbstractMonoclonal antibodies (mAbs) are essential tools in the contemporary therapeutic armoury. Understanding how these recognize their antigen is a fundamental step in their rational design and engineering. The rising amount of publicly available data is catalysing the development of computational approaches able to offer valuable, faster and cheaper alternatives to classical experimental methodologies used for the study of antibody-antigen complexes.Here we present proABC-2, an update of the original random-forest antibody paratope predictor, based on a convolutional neural network algorithm. We also demonstrate how the predictions can be fruitfully used to drive the docking in HADDOCK.The proABC-2 server is freely available at: https://bianca.science.uu.nl/proabc2/.

scholarly journals Sports Action Recognition Based on GB-BP Neural Network and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lidong Wang ◽  
Kai Qiu ◽  
Wang Li
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Data Analysis ◽  
Action Recognition ◽  
Bp Neural Network ◽  
Big Data Analysis ◽  
Classification Model ◽  
Gradient Boosting ◽  
Network Algorithm ◽  
Neural Network Algorithm

In recent years, the application of the gradient boosting-back propagation (GB-BP) neural network algorithm in many industries has brought huge benefits, so how to combine the GB-BP neural network algorithm with sports has become a research hotspot. Based on this, this paper studies the application of the GB-BP neural network algorithm in wrestling, designs the sports athletes action recognition and classification model based on the GB-BP neural network algorithm, first analyzes the research status of wrestling action recognition, and then optimizes and improves the shortcomings of action recognition and big data analysis technology. The GB-BP neural network algorithm can realize the accurate recognition and classification of wrestlers’ training actions and carry out big data mining analysis with known action recognition, so as to achieve accurate classification. The experimental results show that the model can play a good role in wrestling and effectively improve the efficiency of wrestlers in training.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm
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