ارائه روشی برای تشخیص اعتماد در شبکه های اجتماعی با توجه به ویژگی های فردی و شخصی به کمک روش سیستم استنتاج عصبی-فازی سازگار

نوع مقاله : مقاله کامپیوتر

نویسندگان

1 دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی واحد تهران جنوب

2 گروه مهندسی کامپیوتر

چکیده

در این مقاله به ارائه روشی برای تشخیص اعتماد در شبکه‌های اجتماعی با توجه به ویژگی‌های فردی و شخصی به کمک روش سیستم استنتاج عصبی-فازی سازگار پرداختیم. پژوهش حاضر نیاز به دیتاست داشته که برای این منظور یک پرسشنامه آنلاین طراحی و 1000 رکورد با متغیرهای سن، جنسیت، شغل، ساعات فعالیت در فضای مجازی، نوع استفاده از فضای مجازی و نوع روابط در فضای مجازی جمع‌آوری کردیم. این دیتاست به‌عنوان داده‌ای مرجع برای تحلیل‌های مشابه قابل‌استفاده است و به لحاظ تأمین امنیت داده از سطح بالایی برخوردار است. ابتدا به ارزیابی و تحلیل توصیفی دیتاست پرداخته‌ایم. برای این منظور از نرم‌افزارهای اکسل و اس‌پی‌اس‌اس استفاده نمودیم، با استفاده از شبیه‌سازی در متلب به مدل‌سازی و تحلیل پرداخته‌ایم. برای معرفی حدود تغییرات و رفتار فازی برای متغیرها، پارامترهای دیتاست با استفاده از توابع عضویت بیزین به الگوریتم معرفی شده‌اند. به دلیل عدم مشخص بودن نوع توابع عضویت، پوشش فضای تحت کنترل بیشتر، حجم محاسباتی کمتر، کاهش زمان تحلیل و افزایش دقت، از روش خوشه‌بندی کاهشی استفاده کرده‌ایم و به آموزش شبکه با استفاده از شبکه عصبی پس‌خور پرداخته‌ایم. آموزش شبکه را با داده‌های آموزش تا رسیدن به همگرایی کامل ادامه داده‌ایم. در ادامه، داده‌های تست و چک را وارد نموده‌ایم و با استفاده از تابع عملکرد مربع خطا به این نتیجه رسیده‌ایم که با روش به‌کار رفته شده در این پژوهش می‌توان با خطای زیر 5/1 درصد، اعتماد افراد را به یکدیگر در فضای مجازی پیش‌بینی نمود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Provide a method for recognizing trust in social networks according to individual and personal characteristics using a compatible neural-fuzzy inference system method

نویسندگان [English]

  • Mahsa Farhadi Savadkouhi 1
  • Mahmood Deypir 2
1 Master's student, Technical and Engineering Faculty, Islamic Azad University, South Tehran Branch
2 Computer Department
چکیده [English]

In this article, we presented a method to detect trust in social networks according to individual and personal characteristics with the help of the adaptive neuro-fuzzy inference system method. The current research required a dataset, for this purpose we designed an online questionnaire and collected 1000 records with the variables of age, gender, occupation, hours of activity in the virtual space, the type of use of the virtual space and the type of relationships in the virtual space, this dataset is as reference data can be used for similar analyzes and has a high level of data security. First, we evaluated and descriptively analyzed the data set, for this purpose we used Excel and SPSS software, we modeled and analyzed using MATLAB simulation. To introduce change limits and fuzzy behavior for variables, dataset parameters were introduced to the algorithm using Bayesian membership functions. Due to the uncertainty of the type of membership functions, coverage of the space under control, less computational volume, reduction of analysis time and increase of accuracy, we used the deductive clustering method and trained the network using feedforward neural network and trained the network with data. We continued the training until we reached full convergence. We entered the test and check data and using the squared error performance function, we came to the conclusion that with the method used in this research, it is possible to predict people's trust in each other in virtual space with an error of less than 1.5%.

کلیدواژه‌ها [English]

  • social networks
  • trust
  • fuzzy system
  • trust reversal
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