Automatic fuzzy decision making system with learning for competing and connected businesses

2011 ◽  
Vol 38 (12) ◽  
pp. 14574-14584 ◽  
Author(s):  
Festus Oluseyi Oderanti ◽  
Philippe De Wilde
2020 ◽  
Vol 9 (3) ◽  
pp. 42-62
Author(s):  
Karthik S. ◽  
Saroj Kumar Dash ◽  
Punithavelan N.

Farmers are widely applying chemical pesticides to the agricultural lands to kill weeds to reduce crop losses and to prevent diseases created by insects. By applying pesticides to the lands, typically have greater agricultural yield. As pesticides have toxic ingredients, they can create so many health problems to humans and will degrade the environment gradually. Since each pesticide is linked to some health hazards when the composition of the pesticides exceeds its limits, uncertainty arises in determining the human health hazards. Hence, fuzzy logic-based decision-making model is designed to diagnose the human health hazards. In the model, the linguistic terms are used to represent the association between pesticides and human health hazards with the aid of chemists and physicians. Fuzzy numbers are used to represent the values for the linguistic terms. Therefore, the intent of the paper is to analyze the human health hazards induced by applying different pesticides in the agricultural lands through the proposed fuzzy decision-making system.


2013 ◽  
Vol 347-350 ◽  
pp. 3-6
Author(s):  
Jian Xiong Long

In the multi-objective fuzzy decision-making system, the decision-making problems will be encountered in the multi-objective and multi-evaluation process, so that it does not achieve effective decision-making results. How to solve this multi-objective decision making problems, a fuzzy choice function is proposed, that will be proved an effective method of fuzzy evaluation. The multi-objective decision making problem in the selection and evaluation is effectively solved in a decision-making system by using of the fuzzy choice function.


Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1095 ◽  
Author(s):  
Yian Zhu ◽  
Lin Zhang ◽  
Haobin Shi ◽  
Kao-Shing Hwang ◽  
Xianchen Shi ◽  
...  

Routing selection in opportunistic social networks is a complex and challenging issue due to intermittent communication connections among mobile devices and dynamic network topologies. The structural characteristics of opportunistic social networks indicate that the social attributes of mobile nodes play a significant role on data dissemination. To this end, in this paper, we propose an adaptive routing-forwarding control scheme (FPRDM) based on an intelligent fuzzy decision-making system. On the foundation of the conception of fuzzy inference logic, two techniques are used in the proposed routing algorithm. Information fusion of social characteristics of message users and node identification are implemented based on the fuzzy recognition strategy, and the fuzzy decision-making mechanism is applied to control message replication and optimize data transmission. Simulation results demonstrate that, in the best case, the proposed scheme presents an average delivery ratio of 0.8, reduces the average end-to-end delay by nearly 45% as compared with the Epidemic routing protocol, and lowers the network overhead by about 75% as compared to the Spray and Wait routing algorithm.


1998 ◽  
Author(s):  
Ahmed Soliman ◽  
Yong-Yha Kim ◽  
Giorgio Rizzoni ◽  
José Candau

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