Fuzzy Max-Min Composition in Quality Specifications of Multi-Agent System

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In this paper a new technique named Fuzzy Max- Min Composition is used to obtain prioritization of quality specifications that assist quality engineer in achieving the desired level of quality for Multi-agent systems. Intuitionistic fuzzy Set has been used to capture the uncertainties associated with stakeholder’s recommendation.

Science

International Journal of Computing Algorithm, Vol 3(2), June 2014
ISSN(Print):2278-2397
Website: www.ijcoa.com
Fuzzy Max-Min Composition in Quality
Specifications of Multi-Agent System
T.Pathinathan1
, J. Jon Arockiaraj2
, M.Poonthentral2
1
Department of Mathematics, Loyola College, Chennai
2
Department of Mathematics, St. Joseph’s college of Arts and Science, Cuddalore
E-mail: jonarockiaraj@gmail.com
Abstract
In this paper a new technique named Fuzzy Max- Min Composition is used to obtain
prioritization of quality specifications that assist quality engineer in achieving the desired
level of quality for Multi-agent systems. Intuitionistic fuzzy Set has been used to capture the
uncertainties associated with stakeholder’s recommendation

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Bollworms are among the most damaging pests in cotton cultivation, affecting the bolls where the cotton fibers are formed. There are several species of bollworms, each capable of causing significant yield loss and quality degradation if not effectively managed. Here’s a detailed look at the primary bollworm species affecting cotton: Cotton Bollworm (Helicoverpa armigera): Also known as the corn earworm or the Old World bollworm, this pest is found in many regions around the world. It is highly polyphagous (feeds on many different plants) and poses a threat not only to cotton but also to maize, tomatoes, and legumes. The larvae bore into the cotton bolls, feeding on the developing seeds and fibers, which can lead to boll rot. Pink Bollworm (Pectinophora gossypiella): A significant pest of cotton, the pink bollworm larvae infest the cotton bolls, feeding on the seeds and lint. This can severely damage or destroy the bolls. In regions where pink bollworms are prevalent, they have been a major driver for the adoption of genetically engineered Bt cotton, which expresses a bacterium gene toxic to certain insects. Tobacco Budworm (Heliothis virescens): Closely related to the cotton bollworm, the tobacco budworm primarily attacks tobacco but is also a common pest in cotton. It primarily damages the flowers and bolls of the cotton plant. Differentiating between the tobacco budworm and the cotton bollworm based on appearance can be challenging, but it is crucial for effective management. American Bollworm (Helicoverpa zea): Known in some regions as the corn earworm, it is similar in behavior to Helicoverpa armigera and poses a threat to a variety of crops, including cotton. The larvae attack the cotton bolls, leading to direct damage to the cotton lint and seeds. Management Strategies: Cultural Controls: Crop rotation, destruction of crop residues, and deep plowing can help break the pest’s life cycle. Timing of planting can also be adjusted to avoid peak pest infestation. Biological Controls: Natural enemies like Trichogramma wasps, which parasitize bollworm eggs, and predators such as lacewings and ladybugs can be encouraged. Bacillus thuringiensis (Bt) products can also be sprayed, which are particularly effective against young larvae. Chemical Controls: Insecticides may be required when infestation levels exceed economic thresholds. However, resistance management must be considered, alternating modes of action to avoid developing resistance. Genetic Approaches: Bt cotton, genetically modified to express Bacillus thuringiensis toxin, has been highly effective in controlling bollworms and has dramatically reduced the reliance on chemical insecticides. Monitoring and Scouting: Regular field scouting and using pheromone traps to monitor adult populations can help in timely and targeted application of control measures. The effective management of bollworms often requires an integrated approach

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1. International Journal of Computing Algorithm, Vol 3(2), June 2014 ISSN(Print):2278-2397 Website: www.ijcoa.com Fuzzy Max-Min Composition in Quality Specifications of Multi-Agent System T.Pathinathan1 , J. Jon Arockiaraj2 , M.Poonthentral2 1 Department of Mathematics, Loyola College, Chennai 2 Department of Mathematics, St. Joseph’s college of Arts and Science, Cuddalore E-mail: jonarockiaraj@gmail.com Abstract In this paper a new technique named Fuzzy Max- Min Composition is used to obtain prioritization of quality specifications that assist quality engineer in achieving the desired level of quality for Multi-agent systems. Intuitionistic fuzzy Set has been used to capture the uncertainties associated with stakeholder’s recommendation

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