Stock prediction method based on integrated learning

2020 ◽  
Vol 5 (1) ◽  
pp. 2-4
Author(s):  
Dickson Di Sen Wanga ◽  
Cheng Guob ◽  
Yue Yangc ◽  
Yijian Yangd ◽  
Zihan Wange
Keyword(s):  
Prediction Method ◽  
Integrated Learning ◽  
Stock Prediction

An Integrated Prediction Method for Identifying Protein-Protein Interactions

2020 ◽  
Vol 17 (4) ◽  
pp. 271-286
Author(s):  
Chang Xu ◽  
Limin Jiang ◽  
Zehua Zhang ◽  
Xuyao Yu ◽  
Renhai Chen ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Feature Vector ◽  
Prediction Method ◽  
Feature Representation ◽  
Protein Interaction Networks ◽  
Learning Approach ◽  
Biological Processes ◽  
Integrated Learning ◽  
Protein Protein Interactions ◽  
Training Process

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks. Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, viaMultivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process. Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pyloridataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiaedataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Humandataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.

Prediction of Refracturing Timing of Horizontal Wells in Tight Oil Reservoirs Based on an Integrated Learning Algorithm

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6524
Author(s):  
Xianmin Zhang ◽  
Jiawei Ren ◽  
Qihong Feng ◽  
Xianjun Wang ◽  
Wei Wang
Keyword(s):  
Support Vector Machine ◽  
Learning Algorithm ◽  
Prediction Method ◽  
Horizontal Wells ◽  
Songliao Basin ◽  
Oil Reservoirs ◽  
Support Vector ◽  
Tight Oil ◽  
Integrated Learning ◽  
Tight Oil Reservoirs

Refracturing technology can effectively improve the EUR of horizontal wells in tight reservoirs, and the determination of refracturing time is the key to ensuring the effects of refracturing measures. In view of different types of tight oil reservoirs in the Songliao Basin, a library of 1896 sets of learning samples, with 11 geological and engineering parameters and corresponding refracturing times as characteristic variables, was constructed by combining numerical simulation with field statistics. After a performance comparison and analysis of an artificial neural network, support vector machine and XGBoost algorithm, the support vector machine and XGBoost algorithm were chosen as the base model and fused by the stacking method of integrated learning. Then, a prediction method of refracturing timing of tight oil horizontal wells was established on the basis of an ensemble learning algorithm. Through the prediction and analysis of the refracturing timing corresponding to 257 groups of test data, the prediction results were in good agreement with the real value, and the correlation coefficient R2 was 0.945. The established prediction method can quickly and accurately predict the refracturing time, and effectively guide refracturing practices in the tight oil test area of the Songliao basin.

scholarly journals Research on the B2C Online Marketing Effect Based on the LS-SVM Algorithm and Multimodel Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huiru Liao ◽  
Sang-Bing Tsai
Keyword(s):  
Model Prediction ◽  
Learning Algorithm ◽  
Prediction Method ◽  
Online Marketing ◽  
Learning Method ◽  
Integrated Learning ◽  
Network Marketing ◽  
Marketing Practice ◽  
Correlation Degree ◽  
Sample Set

The comprehensive B2C online marketing is analyzed, and the current situation and shortage of comprehensive B2C online marketing strategies are summarized. Then, based on the relevant theories of consumer behavior and online marketing, the model of influencing factors in the purchasing decision-making process of online consumers is preliminarily constructed, the online purchasing behavior of consumers is studied by means of questionnaire survey, and the model is revised and improved through data collection and verification. Finally, based on the model, the online marketing strategy is discussed from the aspects of comprehensive B2C online marketing construction, product positioning, price strategy, channel construction, website design, and so on. It has important guiding significance to comprehensive B2C online marketing practice. Aiming at the B2C online marketing problem of multimodel fusion with multiobservation samples, a new multimodel fusion B2C online marketing algorithm based on LS-SVM is proposed, which is suitable for multiobservation samples. In each B2C online marketing of multimodel fusion, the mode of B2C online marketing to be multimodel fusion is represented by the multiobservation sample set. Firstly, the label of the multiobservation sample set is assumed, and this assumption condition is taken as the constraint condition of the optimization problem in LS-SVM. Thus, the B2C online marketing error of multimodel fusion is obtained. The category of multiobservation samples was determined by comparing the B2C online marketing errors of multimodel fusion under two assumptions. The B2C network marketing prediction method, stacking integrated learning method based on multimodel fusion, is adopted to build a multimachine learning algorithm embedded into the stacking integrated learning B2C network marketing prediction model. Through verification, it shows that the lower the correlation degree, the better the model prediction effect. Compared with the traditional single-model prediction, the B2C network marketing prediction method based on multimodel fusion stacking integrated learning method has higher prediction accuracy. The model prediction effect is better.

Selection of a leading edge noise prediction method for PNoise

2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Prediction Method ◽  
Leading Edge ◽  
Wind Farms ◽  
Broadband Noise ◽  
Turbulence Energy ◽  
Noise Prediction ◽  
Migration Routes ◽  
Turbulent Inflow

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.

Penerapan Rinfo Sebagai Media Pendukung Untuk Proses Pembelajaran Pada Perguruan Tinggi Raharja

CCIT Journal ◽  
2014 ◽  
Vol 8 (1) ◽  
pp. 101-115
Author(s):  
Untung Rahardja ◽  
Khanna Tiara ◽  
Ray Indra Taufik Wijaya
Keyword(s):  
Learning Process ◽  
Human Life ◽  
Learning Tools ◽  
Integrated Learning ◽  
Civilizing Process ◽  
The World ◽  
The Future ◽  
Two Factors ◽  
Life Education ◽  
New Generation

Education is an important factor in human life. According to Ki Hajar Dewantara, education is a civilizing process that a business gives high values ??to the new generation in a society that is not only maintenance but also with a view to promote and develop the culture of the nobility toward human life. Education is a human investment that can be used now and in the future. One other important factor in supporting human life in addition to education, which is technology. In this globalization era, technology has touched every joint of human life. The combination of these two factors will be a new innovation in the world of education. The innovation has been implemented by Raharja College, namely the use of the method iLearning (Integrated Learning) in the learning process. Where such learning has been online based. ILearning method consists of TPI (Ten Pillars of IT iLearning). Rinfo is one of the ten pillars, where it became an official email used by the whole community’s in Raharja College to communicate with each other. Rinfo is Gmail, which is adapted from the Google platform with typical raharja.info as its domain. This Rinfo is a medium of communication, as well as a tool to support the learning process in Raharja College. Because in addition to integrated with TPi, this Rinfo was connected also support with other learning tools, such as Docs, Drive, Sites, and other supporting tools.

A Prediction Method of Groundwater Table Fluctuations in Landslide Area by Neural Networks.

Landslides ◽  
1995 ◽  
Vol 31 (4) ◽  
pp. 9-15 ◽  
Author(s):  
Hiroyuki YOSHIMATSU ◽  
Akira MUKAI
Keyword(s):  
Neural Networks ◽  
Prediction Method ◽  
Groundwater Table ◽  
Landslide Area
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