scholarly journals Fuzzy-AHP Based Mobile Games Recommendation System Using Bayesian Network

2017 ◽  
Vol 15 (4) ◽  
pp. 461-468
Author(s):  
Jae-Taek Oh ◽  
Sang-Yong Lee
Keyword(s):  
Bayesian Network ◽  
Recommendation System ◽  
Fuzzy Ahp ◽  
Mobile Games

scholarly journals Collapse Risk Analysis of Deep Foundation Pits in Metro Stations Using a Fuzzy Bayesian Network and a Fuzzy AHP

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Guohua Zhang ◽  
Chengtang Wang ◽  
Yuyong Jiao ◽  
Hao Wang ◽  
Weimin Qin ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Bayesian Network ◽  
Fuzzy Ahp ◽  
Fault Tree Analysis ◽  
Expert Elicitation ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Metro Station ◽  
Potential Risk Factors ◽  
Effective Decision Making

Collapse risk analysis is of great significance for ensuring construction safety in foundation pits. This study proposes a comprehensive methodology for dynamic risk analysis of foundation pit collapse during construction based on a fuzzy Bayesian network (FBN) and a fuzzy analytical hierarchy process (FAHP). Firstly, the potential risk factors contributing to foundation pit collapse are identified based on the results of statistical analysis of foundation pit collapse cases, expert inquiry, and fault tree analysis. Then, a FAHP and improved expert elicitation considering a confidence index are adopted to elicit the probability parameters of the BN. On this basis, quantitative risk reasoning and sensitivity analysis of foundation pit collapse are achieved by means of fuzzy Bayesian inference. Finally, an actual deep foundation pit in a metro station was used to illustrate a specific application of this approach, and the results were in accordance with the field observations and numerical simulation results. The proposed approach can provide effective decision-making support for planners and engineers, which is vital to the prevention and control of the occurrence of the foundation pit collapse accidents.

Risk assessment of gas explosion in coal mines based on fuzzy AHP and bayesian network

2020 ◽  
Vol 135 ◽  
pp. 207-218 ◽  
Author(s):  
Min Li ◽  
Hetang Wang ◽  
Deming Wang ◽  
Zhenlu Shao ◽  
Shan He
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Fuzzy Ahp ◽  
Coal Mines ◽  
Gas Explosion

scholarly journals An Efficient Mining for Recommendation System for Academics

2020 ◽  
Vol 8 (5) ◽  
pp. 1619-1626
Keyword(s):  
World Wide Web ◽  
Bayesian Network ◽  
World Wide ◽  
Recommendation System ◽  
Research Articles ◽  
System Research ◽  
Research Papers ◽  
Research Article ◽  
Research Scholar ◽  
Semantic Tree

At present time huge numbers of research articles are available on World Wide Web in any domain. The research scholar explores a research papers to get the appropriate information and it takes time and effort of the researcher. In this scenario, there is the need for a researcher to search a research based on its research article. In the present paper a method of Knowledge ablation from a collection of research articles, is presented to evolve a system research paper recommendation system (RPRS), which would generate the recommendations for research article based on researcher choice. The RPRS accumulate the knowledge ablated from the pertinent research articles in the form of semantic tree. It accumulates all the literal sub parts with their reckoning in nodes. These parts are arranged based on their types in such a way that the leaf nodes stores the words with its prospect, the higher layer gives details about dictum with its reckoning, next to it an abstract. A Bayesian network is applied to construct a verisimilitude model which would quotation the pertinent tidings from the knowledge tree to construct the recommendation and word would be scored through TF-IDF value

scholarly journals Designing Personalisation in a Environmental Recommendation System: Differences of AHP and Fuzzy AHP

2021 ◽  
Author(s):  
Katerina Kabassi
Keyword(s):  
Decision Making ◽  
Recommendation System ◽  
Fuzzy Ahp ◽  
Environmental Awareness ◽  
Multi Criteria Decision Making ◽  
The Past

Different methods have been proposed for designing the personalization process in a recommendation system. In the past, multi-criteria decision making theories have been proposed for the design of stereotypes in a recommendation system for environmental awareness. The main objective of this paper is on presenting the main differences when applying the fuzzy AHP and AHP for designing the weights of criteria in a recommendation system that its personalization process is based on multi-criteria decision making theories.

Development of a new knowledge-based fabric recommendation system by integrating the collaborative design process and multi-criteria decision support

2017 ◽  
Vol 88 (23) ◽  
pp. 2682-2698 ◽  
Author(s):  
Yan Hong ◽  
Xianyi Zeng ◽  
Pascal Bruniaux ◽  
Yan Chen ◽  
Xujing Zhang
Keyword(s):  
Design Process ◽  
Hierarchical Structure ◽  
Collaborative Design ◽  
Recommendation System ◽  
Rating Scale ◽  
Fuzzy Ahp ◽  
Fuzzy Topsis ◽  
Evaluation Procedure ◽  
Knowledge Based ◽  
Solution Algorithms

In this paper, a perception-based fabric recommendation system is proposed to help fashion designers to select the most appropriate fabric in the design process, meeting the perception of the target consumer. The proposed methodology is based on the development of an interactive hierarchical structure, decomposing the decision problem into five levels: goal, criteria (consumers' requirements), sub evaluation criteria (fabric properties), rating scale, and alternatives, which ensures the analysis of requirements of consumers and knowledge sharing among designers. The proposed knowledge-based recommendation system includes a collaborative design process, a commonly used sensory evaluation procedure, and a computational model using the Fuzzy AHP (Analytic Hierarchy Process) and Fuzzy TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) algorithms. The Fuzzy AHP method is used to structure the fabric selection problem and determine the relevant weights of the concerned criteria and corresponding components, while the Fuzzy TOPSIS method is used to evaluate the alternative fabrics based on the criteria obtained from the AHP process and to give the total final ranking of the involved fabric alternatives. Experimental results indicate that, using the proposed interactive hierarchical structure, professional knowledge of the designers can be fully extracted to ensure a high level of consumer satisfaction.

Mobile Information Recommendation Using Multi-Criteria Decision Making with Bayesian Network

2015 ◽  
Vol 14 (02) ◽  
pp. 317-338 ◽  
Author(s):  
Han-Saem Park ◽  
Moon-Hee Park ◽  
Sung-Bae Cho
Keyword(s):  
Decision Making ◽  
Bayesian Network ◽  
Recommendation System ◽  
Multi Criteria Decision Making ◽  
Group Recommendation ◽  
Mobile Environment ◽  
Subjective Test ◽  
Reliable Performance ◽  
Information Recommendation ◽  
Hierarchy Process

The advancement of network technology and the popularization of the Internet lead to increased interest in information recommendation. This paper proposes a group recommendation system that takes the preferences of group users in mobile environment and applies the system to recommendation of restaurants. The proposed system recommends the restaurants by considering various preferences of multiple users. To cope with the uncertainty in mobile environment, we exploit Bayesian network, which provides reliable performance and models individual user's preference. Also, Analytical Hierarchy Process of multi-criteria decision-making method is used to estimate the group users' preference from individual users' preferences. Experiments in 10 different situations provide a comparison of the proposed method with random recommendation, simple rule-based recommendation and neural network recommendation, and confirm that the proposed method is useful with the subjective test.

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