Elham Majd, Vimala Balakrishnan and Vahid Godazgar
This paper aims to enhance the successful interaction between buyers and suppliers who use intelligent agents by presenting a computational model to detect the most reliable…
Abstract
Purpose
This paper aims to enhance the successful interaction between buyers and suppliers who use intelligent agents by presenting a computational model to detect the most reliable supplier agent according to advice of an advisor agent.
Design/methodology/approach
In this case, the authors study the most representative models in agent environments. According to these analysis criteria, a computational model was presented to compute the reliability of supplier agents and then select the most reliable one. To evaluate the proposed method, the experimentation was carried out in two stages. First, the average accuracy of model in computing the reliability was evaluated by comparing a random selection method. Second, the performance of the model in detecting the most reliable supplier was evaluated in an agent environment by applying trust network game as a simulator.
Findings
The experimental results revealed that the proposed method can detect the most reliable supplier accurately in both consistent and oscillating agent environments.
Originality/value
The authors believe that the proposed model will be beneficial to enhance the fulfillment of purchasing between buyers and suppliers.
Details
Keywords
The purpose of this paper is to enhance trust in e-commerce multi-agent systems by presenting a model, called RUU, to select the most trustworthy provider agent based on learning…
Abstract
Purpose
The purpose of this paper is to enhance trust in e-commerce multi-agent systems by presenting a model, called RUU, to select the most trustworthy provider agent based on learning from previous interactions and computing reliability, unreliability and uncertainty.
Design/methodology/approach
The methodology comprises analyzing the most representative existing trust models, while a new concept was proposed and measured as unreliability. To make decision about the agents, RUU integrated reliability, unreliability and uncertainty components and used the TOPSIS multi-criteria decision method to select the most trustworthy provider agent. To evaluate the RUU model, the experimentation was carried out in two stages. First, the average accuracy of the model was investigated by simulating RUU in a multi-agent environment. Second, the performance of the model was compared with other related trust models.
Findings
The experimental results revealed that RUU model outperforms current models in providing accurate credibility measurements and computing an accurate trust mechanism for agents, also presenting a decision-making process to choose the most trustworthy provider agent.
Research limitations/implications
The model presented based on different mathematical computations that take time to be calculated, which is a big limitation of computational models.
Practical implications
RUU enables an agent to make effective and sound decisions in light of the uncertainty that exists in e-commerce multi-agent environments.
Originality/value
This paper is beneficial to enhance the fulfilment of purchasing between provider and requester agents. In fact, the proposed model can ensure critical transactions performed securely in e-commerce multi-agent environments.
Details
Keywords
Elham Majd and Vimala Balakrishnan
The purpose of this paper is to enhance trust in multi-agent systems by presenting a new computational model, named reputation-distribute-conflict (R-D-C), to select the most…
Abstract
Purpose
The purpose of this paper is to enhance trust in multi-agent systems by presenting a new computational model, named reputation-distribute-conflict (R-D-C), to select the most trustworthy provider agent based on computing reputation, disrepute, and conflict of each provider agent.
Design/methodology/approach
R-D-C propose based on three vital components for evaluating trustworthiness of providers as reputation, disrepute, and conflict, where disrepute is a component almost all trust models ignored. The R-D-C model presents a computational method for evaluating to select the most trustworthy provider agent. In order to evaluate the R-D-C model, the experimentation was carried out in two stages, by designing a simulated multi-agent environment. First, the accuracy of the R-D-C model in computing R-D-C was investigated. Second, the performance of the model was compared with other existing trust models. Moreover, comparison of the performance of the R-D-C model with other models demonstrates that the R-D-C model performs significantly better than the other models. Therefore, the R-D-C model is capable of evaluating the trustworthiness of agents more accurately and it can select the most trustworthy provider better than the other models.
Findings
The results show that the R-D-C model works well in different multi-agent environments, even when the number of untrustworthy providers is higher than that of the trustworthy ones.
Originality/value
The R-D-C model is useful for researchers to enhance the safety of online transactions in multi-agent environments, especially if the researchers explore more components; in fact the R-D-C model is capable of adding these new components and selects the most trustworthy provider agent.
Details
Keywords
Elhameh Chehsmazar, Mitra Zarrati, Bahareh Yazdani, Elham Razmpoosh, Agha Fatemeh Hosseini and Farzad Shidfar
Adipose tissue accumulation by trapping vitamin D and reducing its level may cause serious side effects. The purpose of this study is to determine the effects of vitamin D…
Abstract
Purpose
Adipose tissue accumulation by trapping vitamin D and reducing its level may cause serious side effects. The purpose of this study is to determine the effects of vitamin D supplementation on dehydroepiandrosterone (DHEA), paraoxonase 1 (PON 1), insulin, free fatty acid (FFA), apolipoprotein-AI (Apo-AI) and apolipoprotein B (Apo-B) concentration in obese and overweight participants under low-calorie diet (LCD) program.
Design/methodology/approach
Healthy overweight and obese individuals (n = 70) with vitamin D deficiency were randomly assigned into 2 groups to receive either vitamin D supplements (an oral 2,000 IU vitamin D supplement) or placebo for 8 weeks.
Findings
All the participants were given an LCD program during the intervention. Vitamin D supplementation led to a significant increase in the levels of 25(OH)D (vitamin D vs placebo groups: 36.6 ± 9.8 vs 19.9 ± 3.5 ng/mL, p < 0.001), PON 1 levels (vitamin D vs placebo groups: 80 ± 25 vs 58 ± 23.2 ng/mL, p = 0.001), DHEA concentration (vitamin D vs placebo groups: 2.3 ± 0.7 vs 1.5 ± 0.6 ng/mL, p < 0.001) and Apo-AI levels (vitamin D vs placebo groups 3.7 ± 0.5 vs 3 ± 0.5 mg/dL, p < 0.001). Besides, intake of vitamin D supplements led to a significant decrease in FFA (vitamin D vs placebo groups: 3.1 ± 0.75 vs 3.5 ± 0.5 ng/mL, p = 0.001). After adjusting the analyses based on baseline levels, age and baseline body mass index measures, significant changes were observed in the insulin levels (0.03 ± 0.06 vs −1.7 ± 0.6 µIU/ml, p = 0.04). But the authors did not find any significant difference in the concentration of Apo-B between groups (vitamin D vs placebo groups: 71.5 ± 35.5 vs 66.6 ± 28.5 mg/dL, p = 0.05).
Originality/value
Overall vitamin D supplementation for eight weeks among vitamin D-deficient obese and overweight participants had beneficial effects on serum DHEA PON 1 FFA insulin and Apo- AI while it did not affect the Apo-B concentration.