Study on Service Mining-Oriented Automobile Customer Behavior Analysis Model

2020 ◽

Vol 13 (5) ◽

pp. 1008-1019

Author(s):

N. Vijayaraj ◽

T. Senthil Murugan

Keyword(s):

Resource Allocation ◽

Behavior Analysis ◽

User Behavior ◽

Low Cost ◽

Cloud Services ◽

Online Auction ◽

Analysis Model ◽

Efficient Resource ◽

User Centric ◽

Cloud Resources

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.

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Stress behavior analysis model of steel fiber reinforced concrete beam in the whole process

Izvestiya vysshikh uchebnykh zavedenii. Fizika ◽

10.17223/00213411/64/4/74 ◽

2021 ◽

pp. 74-84

Author(s):

Luo Dong ◽

Keyword(s):

Reinforced Concrete ◽

Behavior Analysis ◽

Concrete Beam ◽

Steel Fiber ◽

Reinforced Concrete Beam ◽

Fiber Reinforced Concrete ◽

Analysis Model ◽

Steel Fiber Reinforced Concrete ◽

Whole Process ◽

Stress Behavior

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Dynamic Content based Behavior Analysis Model for Users on Social Media

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2018.4056 ◽

2018 ◽

Vol 6 (4) ◽

pp. 314-317

Author(s):

S. Lakshmi

Keyword(s):

Social Media ◽

Behavior Analysis ◽

Analysis Model ◽

Dynamic Content

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Telecom Customer Behavior Analysis Using Naïve Bayes Classifier

10.1109/ccet52649.2021.9544169 ◽

2021 ◽

Author(s):

Md. Golam Rabiul Alam ◽

Sajjad Hussain ◽

Md. Mofaqkhayrul Islam Mim ◽

Md Tarikul Islam

Keyword(s):

Behavior Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Customer Behavior ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier

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Research of Recommended Service of Mobile Terminal Based on User Behavior Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.109.577 ◽

2011 ◽

Vol 109 ◽

pp. 577-581

Author(s):

Wei Liu ◽

Dong Mei Mu ◽

Dao Li Huang ◽

Ji Hao

Keyword(s):

Behavior Analysis ◽

Recommendation System ◽

User Behavior ◽

Mobile Terminal ◽

Network Access ◽

Analysis Model ◽

User Behavior Analysis ◽

User Clustering ◽

Transfer Of Information ◽

User Recommendation

Due to its portability, mobile terminals (mobile phones and similar devices) have become an transfer of information, between people as well as an important tool for network access. Based on user behavior analysis, using the information of data warehouse will be a reasonable quantification of qualitative indicators, draw the user a variety of potential semantic behavior, and user clustering and dimension reduction, the establishment of a recommendation based on user behavior analysis model . This paper based on user behavior analysis, first extract the user factors into the model are data on these factors reduce the dimensions of the conclusion that the targeted user recommendation system, and such user back into the model test to verify the target User's accuracy.

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