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The aim of this study was to investigate the effect of adding balango seed antioxidant extract on some properties of low-fat stirred yogurt. In this regard, the antioxidant extract of balango seeds was extracted by three traditional methods (immersion) for 24 hours, microwave for 3, 12 and 24 minutes and ultrasound for 30, 60 and 90 minutes and tests such as total phenol, DPPH scavenging activity and iron reducing power were performed on them, and according to the results of this part, the best extract was selected and added to low-fat yogurt in different concentrations (0, 0.2, 0.3, 0.4, and 0.5 g/liter). The results showed that the extract obtained after 30 minutes of ultrasound had the highest amount of total phenol, DPPH scavenging activity, and the power of reducing iron. On the other hand, it was determined that the highest and lowest pH and acidity of the produced yogurts, respectively, belonged to the control sample that did not contain the antioxidant extract of balango seeds, and with the increase of the extract in the samples, the pH decreased but the acidity increased, and the sample without the extract had the lowest amount. Watering (28.52%) was among the samples and with the addition of Balango extract to the formulation of manufactured yogurts, the number of Lactobacillus acidophilus increased. Finally, according to the findings of the general acceptance and properties of low-fat stirred yogurt, it can be said that the addition of 0.2 g/liter of balango seed extract leads to the improvement of the qualitative and sensory properties of low-fat stirred yogurt.
 

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Nowadays, web-based services like E-Commerce and E-Banking make fundamental changes to the ways of using internet and human's life. Web shares direct media with low costs between services of businesses and their customers. Businesses need to record, study and analyze their users' behavior and interests in order to adapt content and interface of their web site with users' interest for targeted marketing and advertising and then complete the process of personalization. For this purpose and for analysis of users’ behavior and making recommendations based on the users’ behavior, web mining approaches can be used. In this paper, a model was developed which can be applied for analyzing and predicting users' behaviors of a specific web site. First, users were clustered with affinity propagation algorithm and then, their behaviors were analyzed using sequential pattern mining algorithm called CM-SPADE. In the next step, for each cluster, Users' profile was created. Then by using these profiles, recommendations can be made for new users. At last, the represented model was evaluated and the final results was acceptable.