Predicting Partner’s Digital Transformation Based on Artificial Intelligence

Partner’s digital transformation is one of the most important metrics for businesses, particularly for businesses in the subscription world. Hence, how to predict partner transformation is a consistent focus in the industry. In this paper, we use an AI (Artificial Intelligence) relevant algorithm to analyze partner’s digital transformation issues and propose a novel method, named the hybrid VKR (VAE, K-means, and random forest) algorithm, to predict partner transformation. We apply our algorithm to partner transformation issues. First, we show the prediction of about 5980 partners from 25689 partners, who are transformed and sorted according to important indicators. Secondly, we recap the tremendous effort that was required by the company to obtain high-quality results for economic change when a partner is transforming along with one or many of the transformation dimensions. Finally, we identify unethical behavior by looking through deal transaction data. Overall, our work sheds light on several potential problems in partner transformation and calls for improved scientific practices in this area.

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Artificial Intelligence Random Forest Algorithm and the Application

2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-16-1726-3_25 ◽

2021 ◽

pp. 205-213

Author(s):

Yan Huang ◽

Xiong Zhou

Keyword(s):

Artificial Intelligence ◽

Random Forest ◽

Random Forest Algorithm

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Rapid identification of high-quality marine shale gas reservoirs based on the oversampling method and random forest algorithm

Artificial Intelligence in Geosciences ◽

10.1016/j.aiig.2021.12.001 ◽

2021 ◽

Author(s):

Linqi Zhu ◽

Xueqing Zhou ◽

Chaomo Zhang

Keyword(s):

Random Forest ◽

Shale Gas ◽

Rapid Identification ◽

Random Forest Algorithm ◽

Gas Reservoirs ◽

High Quality ◽

Marine Shale ◽

Shale Gas Reservoirs

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About digital transformation of the transport industry in Russia

Scientific notes of the Russian academy of entrepreneurship ◽

10.24182/2073-6258-2020-19-3-95-105 ◽

2020 ◽

Vol 19 (3) ◽

pp. 95-105

Author(s):

Natalia V. Vysotskaya ◽

T. V. Kyrbatskaya

Keyword(s):

Artificial Intelligence ◽

Business Model ◽

Data Science ◽

Digital Transformation ◽

Digital Culture ◽

Blockchain Technology ◽

Community Approach ◽

Non Linear ◽

Transport Industry ◽

Artificial Intelligence Systems

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.

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Prediction of Breast Cancer using Decision tree and Random Forest Algorithm

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i2.226229 ◽

2018 ◽

Vol 6 (2) ◽

pp. 226-229

Author(s):

N.Sridevi . ◽

◽

S.Anitha . ◽

Keyword(s):

Breast Cancer ◽

Random Forest ◽

Decision Tree ◽

Random Forest Algorithm

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METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

Informatization and communication ◽

10.34219/2078-8320-2019-10-3-71-76 ◽

2019 ◽

pp. 71-76

Author(s):

A.E. Semenov

Keyword(s):

Random Forest ◽

Develop Model ◽

Random Forest Algorithm ◽

Saint Petersburg ◽

Pedestrian Navigation ◽

Factors Influencing ◽

The City

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

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MetalExplorer, a Bioinformatics Tool for the Improved Prediction of Eight Types of Metal-Binding Sites Using a Random Forest Algorithm with Two- Step Feature Selection

Current Bioinformatics ◽

10.2174/2468422806666160618091522 ◽

2017 ◽

Vol 12 (6) ◽

Cited By ~ 6

Author(s):

Jiangning Song ◽

Chen Li ◽

Cheng Zheng ◽

Jerico Revote ◽

Ziding Zhang ◽

...

Keyword(s):

Feature Selection ◽

Random Forest ◽

Metal Binding ◽

Binding Sites ◽

Random Forest Algorithm ◽

Bioinformatics Tool ◽

Metal Binding Sites

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Prediction model based on the Laplacian eigenmap method combined with a random forest algorithm for rainstorm satellite images during the first annual rainy season in South China

Natural Hazards ◽

10.1007/s11069-021-04585-0 ◽

2021 ◽

Author(s):

Xiao-yan Huang ◽

Li He ◽

Hua-sheng Zhao ◽

Ying Huang ◽

Yu-shuang Wu

Keyword(s):

Random Forest ◽

Prediction Model ◽

South China ◽

Rainy Season ◽

Satellite Images ◽

Random Forest Algorithm ◽

Model Based

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Classification and photometric redshift estimation of quasars in photometric surveys

Proceedings of the International Astronomical Union ◽

10.1017/s1743921320001829 ◽

2020 ◽

Vol 15 (S359) ◽

pp. 40-41

Author(s):

L. M. Izuti Nakazono ◽

C. Mendes de Oliveira ◽

N. S. T. Hirata ◽

S. Jeram ◽

A. Gonzalez ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Nearest Neighbour ◽

Random Forest Algorithm ◽

Photometric Redshift ◽

Using Data

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.

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