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ارائه یک روش فازی-تکاملی برای تشخیص خطاهای نرم‌افزار Proposing an evolutionary-fuzzy method for software defects detection 1 1 تشخیص خطاهای نرم‌افزار، یکی از بزرگ‌ترین چالش‌های توسعه نرم‌افزاراست و بیش‌ترین بودجه را در فرآیند توسعه نرم‌افزار به خود اختصاص می­دهد. با توجه به اهمیت تشخیص خطاهای نرم‌افزار، در این مقاله روشی بر مبنای مجموعه‌های فازی و الگوریتم‌های تکاملی ارائه می‌شود. از آن‌جا که ماهیت مجموعه‌داده‌های تشخیص خطای نرم‌افزار نامتوازن است،  از مزایای الگوریتم‌های خوشه‌بندی فازی به‌منظور نمونه‌برداری از داده‌ها و توجه بیشتر به طبقه اقلیت استفاده شده است. روش پیشنهادی در‌واقع یک الگوریتم ترکیبی است که در ابتدا از روش خوشه‌بندی c میانگین فازی به‌منظور نمونه‌برداری بوت‌استراپ وزن­دار استفاده می­شود. وزن داده‌ها همان درجه عضویت آنهاست و درجه عضویت داده‌های طبقه اقلیت افزایش می­یابد. در گام بعدی، از الگوریتم خوشه‌بندی کاهشی برای ایجاد طبقه‌بند استفاده می­شود که توسط داده‌های تولید‌شده در مرحله قبل آموزش می­بیند؛ همچنین از الگوریتم ژنتیک دودویی برای انتخاب ویژگی­های مناسب استفاده می‌شود. نتایج به‌دست‌آمده و هم­چنین مقایسه آنها با چندین روش معروف در این زمینه، کارایی مناسب روش پیشنهادی را نشان می­دهد. برای انجام آزمایش­ها از ده پایگاه داده معروف با گستره وسیعی از اندازه و نرخ عدم توازن، استفاده شده است و برای تأیید نتایج از آزمون آماری تی بهره برده‌ایم.   2 Software defects detection is one of the most important challenges of software development and it is the most prohibitive process in software development. The early detection of fault-prone modules helps software project managers to allocate the limited cost, time, and effort of developers for testing the defect-prone modules more intensively.  In this paper, according to the importance of software defects detection, a method based on fuzzy sets and evolutionary algorithms is proposed. Due to the imbalanced nature of software defect detection datasets, benefits of fuzzy clustering algorithms were used to data sampling and more attention to the minority class. This method is a combined algorithm which, firstly has used fuzzy c-mean clustering as weighted bootstrap sampling. Weight of data (their membership’s degrees) increases for minority class. In the next step, the subtractive clustering algorithm is applied to produce the classifier which was trained by produced data in the previous step. The binary genetic algorithm was utilized to select appropriate features. The results and also comparisons with eight popular methods in software defect detection literature, show an acceptable performance of the proposed method. The experiments were performed on ten real-world datasets with a wide range of data sizes and imbalance rates. Also T-test is used as the statistical significance test for pair wise comparison of our proposed method against the others. The final results of T-test are shown in tables for three performance measures (G-mean, AUC and Balanced) over various datasets. (As the obtained results apparently show our proposed method has the ability to improve three aforementioned performance criteria simultaneously). Some methods just have improved the G-mean measure while the AUC and Balance criteria have lower values than the others. Securing a high level of three performance measures simultaneously illustrates the ability of our proposed algorithm for handling the imbalance problem of software defects detection datasets. 3 16 2017/09/15 1396/6/24 2019/01/9 1397/10/19 مهدی افتخاری Mahdi Eftekhari دانشگاه شهید باهنر کرمان ایران m.eftekhari@uk.ac.ir مریم مجیدی مومن آبادی Maryam Majidi momenabadi دانشگاه شهید باهنر کرمان ایران majena67@yahoo.com مجتبی خمر Mojtaba Khamar دانشگاه شهید باهنر کرمان ایران mojtabakhammar69@gmail.com classification evolutionary algorithm fuzzy logic imbalance datasets software defect detection الگوریتم های تکاملی تشخیص خطای نرم‌افزار طبقه‌بندی مجموعه داده‌های نامتوازن منطق فازی 1] S. Wang and X. Yao, "Using class imbalance learn-ing for software defect prediction," IEEE Trans. Reliab., vol. 62, no. 2, pp. 434-443, 2013.##[2] B.-J. Park, S.-K. Oh, and W. Pedrycz, "The design of polynomial function-based neural network predictors for detection of software defects," Inf. Sci. (Ny)., vol. 229, pp. 40-57, 2013.##[3] P. C. Pendharkar, "Exhaustive and heuristic search approaches for learning a software defect predic-tion model," Eng. Appl. Artif. Intell., vol. 23, no. 1, pp. 34-40, 2010.##[4] J. Zheng, "Cost-sensitive boosting neural networks for software defect prediction," Expert Syst. Appl., vol. 37, no. 6, pp. 4537-4543, 2010.##[5] Mahdizadeh Mahboubeh, Eftekhari Mahdi. "A new fuzzy rules weighting approach based on Genetic Programming for imbalanced classifica-tion" . JSDP., no. 11 (2), pp.111-125, 2015##[6] Z. Yan, X. Chen, and P. Guo, "Software defect prediction using fuzzy support vector regression," Adv. Neural Networks-ISNN 2010, pp. 17-24, 2010.##[7] A. K. Pandey and N. K. Goyal, "A fuzzy model for early software fault prediction using process matur-ity and software metrics," Int. J. Electron. Eng., vol. 1, no. 2, pp. 239-245, 2009.##[8] S. Di Martino, F. Ferrucci, C. Gravino, and F. Sarro, "A genetic algorithm to configure support vector machines for predicting fault-prone com-ponents," in International Conference on Product Focused Software Process Improvement, 2011, pp. 247-261.##[9] P. S. Sandhu, S. Khullar, S. Singh, S. K. Bains, M. Kaur, and G. Singh, "A Study on Early Prediction of Fault Proneness in Software Modules using Genetic Algorithm," World Acad. Sci. Eng. Technol., vol. 72, 2010.##[10] M. M. Rosli, N. H. I. Teo, N. S. M. Yusop, and N. S. Mohammad, "The design of a software fault prone application using evolutionary algorithm," in Open Systems (ICOS), 2011 IEEE Conference on, 2011, pp. 338-343.##[11] M.-Y. Chen, "A hybrid ANFIS model for busi-ness failure prediction utilizing particle swarm optimization and subtractive clustering," Inf. Sci. (Ny)., vol. 220, pp. 180-195, 2013.##[12] M. E. R. Bezerra, A. L. I. Oliveira, and S. R. L. Meira, "A constructive rbf neural network for es-timating the probability of defects in software modules," in Neural Networks, 2007. IJCNN 2007. International Joint Conference on, 2007, pp. 2869-2874.##[13] M. E. R. Bezerra, A. L. I. Oliveiray, and P. J. L. Adeodatoz, "Predicting software defects: A cost-sensitive approach," in Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on, 2011, pp. 2515-2522.##[14] H. A. Al-Jamimi and L. Ghouti, "Efficient prediction of software fault proneness modules using support vector machines and probabilistic neural networks," in Software Engineering (MySEC), 2011 5th Malaysian Conference in, 2011, pp. 251-256.##[15] N. R. Pal, K. Pal, J. M. Keller, and J. C. Bezdek, "A possibilistic fuzzy c-means clustering algor-ithm," IEEE Trans. fuzzy Syst., vol. 13, no. 4, pp. 517-530, 2005.##[16] D. Gray, D. Bowes, N. Davey, Y. Sun, and B. Christianson, "Using the Support Vector Machine as a Classification Method for Software Defect Prediction with Static Code Metrics.," in EANN, 2009, vol. 2009, pp. 223-234.##[17] K. O. Elish and M. O. Elish, "Predicting defect-prone software modules using support vector machines," J. Syst. Softw., vol. 81, no. 5, pp. 649-660, 2008.##[18] S. Wang, A. Mathew, Y. Chen, L. Xi, L. Ma, and J. Lee, "Empirical analysis of support vector machine ensemble classifiers," Expert Syst. Appl., vol. 36, no. 3, pp. 6466-6476, 2009.##[19] I. Gondra, "Applying machine learning to soft-ware fault-proneness prediction," J. Syst. Softw., vol. 81, no. 2, pp. 186-195, 2008.##[20] S. R. Kannan, S. Ramathilagam, and P. C. Chung, "Effective fuzzy c-means clustering algorithms for data clustering problems," Expert Syst. Appl., vol. 39, no. 7, pp. 6292-6300, 2012.##[21] O. T. Yıldız, O. Aslan, and E. Alpaydın, "Mul-tivariate statistical tests for comparing classifica-tion algorithms," Lect Notes Comp Sci, vol. 6683, pp. 1-15, 2011.##1] S. Wang and X. Yao, "Using class imbalance learn-ing for software defect prediction," IEEE Trans. Reliab., vol. 62, no. 2, pp. 434-443, 2013.##[2] B.-J. Park, S.-K. Oh, and W. Pedrycz, "The design of polynomial function-based neural network predictors for detection of software defects," Inf. Sci. (Ny)., vol. 229, pp. 40-57, 2013.##[3] P. C. Pendharkar, "Exhaustive and heuristic search approaches for learning a software defect predic-tion model," Eng. Appl. Artif. Intell., vol. 23, no. 1, pp. 34-40, 2010.##[4] J. Zheng, "Cost-sensitive boosting neural networks for software defect prediction," Expert Syst. Appl., vol. 37, no. 6, pp. 4537-4543, 2010.##[5] مهدی زاده محبوبه، افتخاری مهدی. ارائه ‌روش جدید مبتنی‌بر برنامه‌نویسی ژنتیک برای وزن‌دهی قوانین فازی در طبقه‌بندی نامتوازن. پردازش علائم و داده‌ها. ۱۳۹۳; ۱۱ (۲) :۱۱۱-۱۲۵##[5] Mahdizadeh Mahboubeh, Eftekhari Mahdi. "A new fuzzy rules weighting approach based on Genetic Programming for imbalanced classifica-tion" . JSDP., no. 11 (2), pp.111-125, 2015##[6] Z. Yan, X. Chen, and P. Guo, "Software defect prediction using fuzzy support vector regression," Adv. Neural Networks-ISNN 2010, pp. 17-24, 2010.##[7] A. K. Pandey and N. K. Goyal, "A fuzzy model for early software fault prediction using process matur-ity and software metrics," Int. J. Electron. Eng., vol. 1, no. 2, pp. 239-245, 2009.##[8] S. Di Martino, F. Ferrucci, C. Gravino, and F. Sarro, "A genetic algorithm to configure support vector machines for predicting fault-prone com-ponents," in International Conference on Product Focused Software Process Improvement, 2011, pp. 247-261.##[9] P. S. Sandhu, S. Khullar, S. Singh, S. K. Bains, M. Kaur, and G. Singh, "A Study on Early Prediction of Fault Proneness in Software Modules using Genetic Algorithm," World Acad. Sci. Eng. Technol., vol. 72, 2010.##[10] M. M. Rosli, N. H. I. Teo, N. S. M. Yusop, and N. S. Mohammad, "The design of a software fault prone application using evolutionary algorithm," in Open Systems (ICOS), 2011 IEEE Conference on, 2011, pp. 338-343.##[11] M.-Y. Chen, "A hybrid ANFIS model for busi-ness failure prediction utilizing particle swarm optimization and subtractive clustering," Inf. Sci. (Ny)., vol. 220, pp. 180-195, 2013.##[12] M. E. R. Bezerra, A. L. I. Oliveira, and S. R. L. Meira, "A constructive rbf neural network for es-timating the probability of defects in software modules," in Neural Networks, 2007. IJCNN 2007. International Joint Conference on, 2007, pp. 2869-2874.##[13] M. E. R. Bezerra, A. L. I. Oliveiray, and P. J. L. Adeodatoz, "Predicting software defects: A cost-sensitive approach," in Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on, 2011, pp. 2515-2522.##[14] H. A. Al-Jamimi and L. Ghouti, "Efficient prediction of software fault proneness modules using support vector machines and probabilistic neural networks," in Software Engineering (MySEC), 2011 5th Malaysian Conference in, 2011, pp. 251-256.##[15] N. R. Pal, K. Pal, J. M. Keller, and J. C. Bezdek, "A possibilistic fuzzy c-means clustering algor-ithm," IEEE Trans. fuzzy Syst., vol. 13, no. 4, pp. 517-530, 2005.##[16] D. Gray, D. Bowes, N. Davey, Y. Sun, and B. Christianson, "Using the Support Vector Machine as a Classification Method for Software Defect Prediction with Static Code Metrics.," in EANN, 2009, vol. 2009, pp. 223-234.##[17] K. O. Elish and M. O. Elish, "Predicting defect-prone software modules using support vector machines," J. Syst. Softw., vol. 81, no. 5, pp. 649-660, 2008.##[18] S. Wang, A. Mathew, Y. Chen, L. Xi, L. Ma, and J. Lee, "Empirical analysis of support vector machine ensemble classifiers," Expert Syst. Appl., vol. 36, no. 3, pp. 6466-6476, 2009.##[19] I. Gondra, "Applying machine learning to soft-ware fault-proneness prediction," J. Syst. Softw., vol. 81, no. 2, pp. 186-195, 2008.##[20] S. R. Kannan, S. Ramathilagam, and P. C. Chung, "Effective fuzzy c-means clustering algorithms for data clustering problems," Expert Syst. Appl., vol. 39, no. 7, pp. 6292-6300, 2012.##[21] O. T. Yıldız, O. Aslan, and E. Alpaydın, "Mul-tivariate statistical tests for comparing classifica-tion algorithms," Lect Notes Comp Sci, vol. 6683, pp. 1-15, 2011.## ##

انتخاب اعضای ترکیب در خوشه‌بندی ترکیبی با استفاده از رأی‌گیری Cluster ensemble selection using voting 1 1 خوشه‌­بندی ترکیبی، به ترکیب نتایج حاصل از خوشه­‌بندی­‌های موجود می‌­پردازد. پژوهش‌های دهۀ اخیر نشان می‌­دهد، چنان­چه به جای ترکیب همۀ خوشه­‌بندی‌­ها، تنها دست‌ه­ای از ­­­­­­­آن­ها بر اساس کیفیت و تنوع انتخاب شوند، آن­چه به‌­عنوان خروجی خوشه­بندی ترکیبی حاصل می‌شود، بسیار دقیق­‌تر خواهد بود. این مقاله به ارائه یک روش جدید برای انتخاب خوشه­‌بندی‌­ها بر اساس دو معیار کیفیت و تنوع می‌پردازد. برای رسیدن به این منظور ابتدا خوشه­‌بندی­‌های مختلفی با استفاده از الگوریتم k-means ایجاد می­‌شود که در هر بار اجرا، مقدار k یک عدد تصادفی است. در ادامه خوشه‌بندی­‌هایی که به این نحو تولید شده­اند، با استفاده از الگوریتم جدیدیکه براساس میزان شباهت بین خوشه‌بندی‌­های مختلف عمل می­‌کند، گروه‌­بندی می‌­شوند تا آن­‌دسته از خوشه‌­بندی‌­هایی که به یکدیگر شبیه‌­اند در یک دسته قرار گیرند؛ سپس از هر دسته، با استفاده از یک روش مبتنی بر رأی­‌گیری، با کیفیت‌­ترین عضو آن برای ایجاد خوشه‌­بندی ترکیبی انتخاب می‌شود. در این مقاله از سه تابع HPGA، CSPA و MCLA برای ترکیب خوشه‌­بندی‌­ها استفاده شده است. در انتها برای آزمایش  این روش جدید از  داده­‌های واقعی موجود در پایگاه داده UCI استفاده شده است. نتایج نشان می‌­دهد که روش جدید کارایی بیشتر و دقیق‌تری نسبت به روش‌­های قبلی دارد. 2 Clustering is the process of division of a dataset into subsets that are called clusters, so that objects within a cluster are similar to each other and different from objects of the other clusters. So far, a lot of algorithms in different approaches have been created for the clustering. An effective choice (can combine) two or more of these algorithms for solving the clustering problem. Ensemble clustering combines results of existing clusterings to achieve better performance and higher accuracy. Instead of combining all of existing clusterings, recent decade researchers show, if only a set of clusterings is selected  based on quality and diversity, the result of ensemble clustering would be more accurate. This paper proposes a new method for ensemble clustering based on quality and diversity. For this purpose, firstly first we need a lot of different base clusterings to combine them. Different base clusterings are generated by k-means algorithm with random k in each execution. After the generation of base clusterings, they are put into different groups according to their similarities using a new grouping method. So that clusterings which are similar to each other are put together in one group. In this step, we use normalized mutual information (NMI) or adjusted rand index (ARI) for computing similarities and dissimilarities between the base clustering. Then from each group, a best qualified clustering is selected via a voting based method. In this method, Cluster-validity-indices were used to measure the quality of clustering. So that all members of the group are evaluated by the Cluster-validity-indices. In each group, clustering that optimizes the most number of Cluster-validity-indices is selected.  Finally, consensus functions combine all selected clustering. Consensus function is an algorithm for combining existing clusterings to produce final clusters. In this paper, three consensus functions including CSPA, MCLA, and HGPA have used for combining clustering. To evaluate proposed method, real datasets from UCI repository have used. In experiment section, the proposed method is compared with the well-known and powerful existing methods. Experimental results demonstrate that proposed algorithm has better performance and higher accuracy than previous works.   17 30 2017/09/152016/12/31 1395/10/11 2019/01/92019/01/9 1397/10/19 علیرضا لطیفی پاکدهی Alireza Latifi Pakdehi دانشگاه تربیت دبیر شهید رجایی ایران alireza.latifi@yahoo.com نگین دانشپور Negin Daneshpour دانشگاه تربیت دبیر شهید رجایی ایران ndaneshpour@sru.ac.ir Ensemble clustering select member validity index خوشه‌بندی ترکیبی انتخاب اعضا شاخص‌های ارزیابی کیفیت [1] Fazl Ersi, Ehsan and Kazemi Noghabi, Masoud, "Clustering of Data Based on Key Identifica-ion",Journal of Signals and Data Processing (JSDP); 14 (4): 31-42; 2017.##[2] A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: a review," ACM computing surveys (CSUR), vol. 31, pp. 264-323, 1999.##[3] H.-P. Kriegel, P. Kröger, and A. 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پروتکل کارا برای جمع چندسویه امن با قابلیت تکرار An Efficient and Secure Frequent Multiparty Summation protocol 1 1 در محاسبات چند سویه امن، گروهی از کاربران، نتیجه یک تابع ریاضی را بر روی داده محرمانه خود، با حفظ حریم خصوصی داده­ها محاسبه می­کنند. از موارد پرکاربرد محاسبات چند‌سویه امن، جمع چندسویه امن است که هدف آن انجام عملیات جمع بر روی داده محرمانه کاربران است. در برخی کاربردها ممکن است، هر عضو چندین مقدارِ محرمانه داشته و هدف، محاسبه مجموعِ داده­های متناظر باشد؛ در این صورت لازم است، پروتکلِ جمعِ چندسویه امن، چندین‌بار برای محاسبه مجموع داده­های گروه تکرار شود. در این پژوهش، مسئله جمع چندسویه امن با قابلیت تکرار، بدون افزایش هزینه محاسباتی و ارتباطی، مورد توجه قرار گرفته ­­است؛ در این مسئله هر کاربر چندین مقدار محرمانه دارد و اعضا قصد دارند مجموع داده‌­های محرمانه خود را به‌صورت نظیربه­‌نظیر محاسبه کنند؛ به‌­طوری­که محرمانگی داده­های هر کاربر حفظ شود. در این مقاله یک پروتکل کارا جهت محاسبه جمع چندسویه امن با قابلیت تکرار در مدل شبه‌درست‌کار ارائه شده است. راه‌کار پیشنهادی، بدون نیاز به کانال امن، محرمانگی د‌اد‌ه­های کاربران و نتایج حاصل جمع­ را تأمین کرده و در مقابل تبانی جزئی کاربران تا سطح نفر ایمن و نسبت به روش­‌های موجود، از نظر هزینه محاسبات و ارتباطات بسیار کاراست. 2 In secure multiparty computation (SMC), a group of users jointly and securely computes a mathematical function on their private inputs, such that the privacy of their private inputs will be preserved. One of the widely used applications of SMC is the secure multiparty summation which securely computes the summation value of the users’ private inputs. In this paper, we consider a secure multiparty summation problem where each group member has m private inputs and wants to efficiently and securely computes the summation values of their corresponding inputs; in other words, users compute m summation values where the first value is the summation of users’ first private inputs, the second one is the summation of users’ second private inputs and so on. We propose an efficient and secure protocol in the semi honest model, called frequent-sum, which computes the desired values while preserving the privacy of users’ private inputs as well as the privacy of the summation results. Let  be a set of n users and the private inputs of user  is denoted as . The proposed frequent-sum protocol includes three phases: In the first phase, each user  selects a random number , computes and publishes the vectors  of  components where each component  of  is of  form . After it,  computes the vector , such that each component  is of form. In the second phase, users jointly and securely compute their AV-net (Anonymous Veto network) masks and the Burmester-Desmedt (BD) conference key. To do so, each user  selects two random numbers  and  and publishes  to the group. Then,  computes and sends  to the group. Then, each user is able to compute  and ;  is the AV-net mask of  and  is the conference key. In the third phase, using the AV-net mask and the conference key, group members securely and collaboratively compute the summation of their random numbers , . To achieve this, each user broadcasts  to the group, where  is the AV-net mask of  and  is the ’s portion of the conference key. Multiplying all s results in canceling the AV-net mask and getting the value of . Then each member is able to compute  by the following Eq.: Now each user is able to compute  by subtracting  from each component of : It is shown that the proposed protocol is secure against collusion attack of at most  users. In other words, the frequent-sum protocol is secure against partial collusion attack; only a full collusion (collusion of  users) would break the privacy of the victim user, in this situation there is no reason for the victim user to join to such a group. The performance analysis shows that the proposed protocol is efficient in terms of the computation and communication costs, comparing with previous works. Also, the computation cost of the frequent-sum protocol is in-dependent of the number of inputs of each user  which makes the protocol more efficient than the previous works. Table 1 compares the proposed protocol with previous works. 31 40 2017/09/152016/12/312016/09/2 1395/6/12 2019/01/92019/01/92019/01/9 1397/10/19 شادیه عزیزی shadi azizi دانشگاه اصفهان ایران sh.azizi93@eng.ui.ac.ir مائده عاشوری تلوکی maede Ashouri-Talouki دانشگاه اصفهان ایران m.ashouri@eng.ui.ac.ir حمید ملا hamid mala دانشگاه اصفهان ایران h.mala@eng.ui.ac.ir secure multiparty sum without secure channel partial collusion semi honest model جمع چندسویه امن کانال ناامن تبانی جزئی مدل شبه‌درست‌کار [1] A. C. Yao, "Protocols for Secure Computations", Proceedings of the 23 rd Annual IEEE Symposium on Foundations of Computer Science. Chicago: IEEE . 1982. pp. 160-164.##[2] C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin and M. Y. Zhu, "Tools for Privacy Preserving Distributed Data Mining". ACM SIGKDD Explorations Newslette, volume 4, pp. 28-34. 2002.##[3] M. Ashouri-Talouki and A. Baraani-Dastjerdi, "Anonymous Electronic Voting Protocol with Deniable Authentication for Mobile Ad Hoc Networks". International Journal of Multimedia and Ubiquitous Engineering, vol. 9, pp. 361-368, 2014.##[4] H. Kaur, N. Kumar and S. Batra, "An efficient multi-party scheme for privacy preserving colla-borative filtering for healthcare recommender system", Future Generation Computer Systems, 2018.##[5] M. Ashouri-Talouki, A. Baraani-Dastjerdi and A. A. Selçuk, "GLP: A cryptographic approach for group location privacy", Computer Communi-cations, vol. 35, pp. 1527-1533, 2012.##[6] M. Ashouri-Talouki, A. Baraani-Dastjerdi and A. A. Selçuk, "The Cloaked-Centroid protocol: loca-tion privacy protection for a group of users of location-based services". Knowledge and Informa-tion Systems, vol. 45, pp. 589-615, 2015.##[7] M. Ashouri-Talouki, A. Baraani-Dastjerdi and A. A. Selçuk, "Preserving location privacy for a group of users", Turkish Journal of Electrical Engineering & Computer Sciences, vol. 21, pp. 1857-1870, 2013.##[8] Y. Wu, K. Wang, Z. Zhang, W. Lin, H. Chen and C. Li, "Privacy Preserving Group Nearest Neighbor Search", In Proceedings of the 21st International Conference on Extending Database Technology (EDBT), 2018.##[9] S. Li, K. Xue, Q. Yang and P. Hong, "PPMA: Privacy-preserving multisubset data aggregation in smart grid". IEEE Transactions on Industrial Informatics, vol. 14, pp. 462-471, 2018.##[10] M. Joye, "Cryptanalysis of a privacy-preserving aggregation protocol", IEEE Transactions on Dependable and Secure Computing, vol. 14, pp. 693-694, 2017.##[11] Y. Zhang, Q. Chen and S. Zhong, "Efficient and Privacy-Preserving Min and k-th Min Computations in Mobile Sensing Systems", IEEE Transactions on Dependable and Secure Compu-ting, vol. 14, pp. 9-21, 2017.##[12] Y. Mo and R. M. Murray, "Privacy preserving average consensus". IEEE Transactions on Automatic Control, vol. 62, pp. 753-765, 2017.##[13] R. Sheikh, B. Kumar and D. K. Mishra, "Privacy-Preserving k-Secure Sum Protocol". Interna-tional Journal of Computer Science and Information Security (IJCSIS), vol. 6, pp. 184-188, 2009.##[14] R. Sheikh, B. Kumar and D. K. Mishra, "A Distributed k-Secure Sum Protocol for Secure Multi-Party Computations". Journal of Compu-ting, vol. 2, no. 3. 2010.##[15] R. Sheikh, B. Kumar and D. K. Mishra, "Changing Neighbors k-Secure Sum Protocol for Secure Multi-Party Computation". International Journal of Computer Science and Information Security (IJCSIS), vol. 7, pp. 239-243, 2010.##[16] M. Jangde, M. S. Chandel and M. K. Mishra, "Hybrid Technique For Secure Sum Protocol". World of Computer Science and Information Technology Journal (WCSIT), vol. 1, pp. 198-201, 2011.##[17] I. Jahan, N. N. Sharmy, S. Jahan, F. A. Ebha and N. J. Lisa, "Design of a Secure Sum Protocol using Trusted Third Party System for Secure Multi-Party Computations". 6th International Conference on Information and Communication Systems (ICICS) IEEE, pp. 136-141, 2015.##[18] Z. Youwen, H. Liusheng, Y. Wei and Y. Xing, "Efficient Collusion-Resisting Secure Sum Protocol". Chinese Journal of Electronics, pp. 407-413, 2011.##[19] J. Rautaray and R. Kumar, "Distributed Database RK-Secure Sum Protocol". International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), vol. 2, pp. 559-562, March 2013.##[20] J. Rautaray and R. Kumar, "Distributed RK- Secure Sum Protocol for Privacy Preserving". IOSR Journal of Computer Engineering (IOSR-JCE), vol. 9, pp. 49-52, Feb. 2013.##[21] J. Rautaray, R. Kumar and G. Bajpai, "Modified Distributed Rk Secure Sum Protocol". Interna-tional Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), vol. 2, pp. 734-736, March 2013.##[22] T. Jung and X. Yang Li, "Collusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel", IEEE Transactions on Dependable and secure computing, pp. 45-57, 2015.##[23] M. Ashouri-Talouki and A. Baraani-Dastjerdi, "Cryptographic collusion-resistant protocols for secure sum", International Journal of Electronic Security and Digital Forensics, vol. 9, pp. 19-34, 2017.##[24] S. Mehnaz, G. Bellala and E. Bertino, "A Secure Sum Protocol and Its Application to Privacy-preserving Multi-party Analytics". In Proceed-ings of the 22nd ACM on Symposium on Access Control Models and Technologies, pp. 219-230, 2017.##[25] F. Hao and P. Zielinski, "A 2-Round Anonymous Veto Protocol". In Security Protocols, Springer Berlin Heidelberg, pp. 202-211, 2009.##[26] M. Burmester and Y. Desmedt, "A secure and efficient conference key distribution system". In Advances in Cryptology .Springer-Verla, pp. 275-286, 2006.##[1] A. C. Yao, "Protocols for Secure Computations", Proceedings of the 23 rd Annual IEEE Symposium on Foundations of Computer Science. Chicago: IEEE . 1982. pp. 160-164.##[2] C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin and M. Y. Zhu, "Tools for Privacy Preserving Distributed Data Mining". ACM SIGKDD Explorations Newslette, volume 4, pp. 28-34. 2002.##[3] M. Ashouri-Talouki and A. Baraani-Dastjerdi, "Anonymous Electronic Voting Protocol with Deniable Authentication for Mobile Ad Hoc Networks". International Journal of Multimedia and Ubiquitous Engineering, vol. 9, pp. 361-368, 2014.##[4] H. Kaur, N. Kumar and S. Batra, "An efficient multi-party scheme for privacy preserving colla-borative filtering for healthcare recommender system", Future Generation Computer Systems, 2018.##[5] M. Ashouri-Talouki, A. Baraani-Dastjerdi and A. A. Selçuk, "GLP: A cryptographic approach for group location privacy", Computer Communi-cations, vol. 35, pp. 1527-1533, 2012.##[6] M. Ashouri-Talouki, A. Baraani-Dastjerdi and A. A. Selçuk, "The Cloaked-Centroid protocol: loca-tion privacy protection for a group of users of location-based services". Knowledge and Informa-tion Systems, vol. 45, pp. 589-615, 2015.##[7] M. Ashouri-Talouki, A. Baraani-Dastjerdi and A. A. Selçuk, "Preserving location privacy for a group of users", Turkish Journal of Electrical Engineering & Computer Sciences, vol. 21, pp. 1857-1870, 2013.##[8] Y. Wu, K. Wang, Z. Zhang, W. Lin, H. Chen and C. Li, "Privacy Preserving Group Nearest Neighbor Search", In Proceedings of the 21st International Conference on Extending Database Technology (EDBT), 2018.##[9] S. Li, K. Xue, Q. Yang and P. Hong, "PPMA: Privacy-preserving multisubset data aggregation in smart grid". IEEE Transactions on Industrial Informatics, vol. 14, pp. 462-471, 2018.##[10] M. Joye, "Cryptanalysis of a privacy-preserving aggregation protocol", IEEE Transactions on Dependable and Secure Computing, vol. 14, pp. 693-694, 2017.##[11] Y. Zhang, Q. Chen and S. Zhong, "Efficient and Privacy-Preserving Min and k-th Min Computations in Mobile Sensing Systems", IEEE Transactions on Dependable and Secure Compu-ting, vol. 14, pp. 9-21, 2017.##[12] Y. Mo and R. M. Murray, "Privacy preserving average consensus". IEEE Transactions on Automatic Control, vol. 62, pp. 753-765, 2017.##[13] R. Sheikh, B. Kumar and D. K. Mishra, "Privacy-Preserving k-Secure Sum Protocol". Interna-tional Journal of Computer Science and Information Security (IJCSIS), vol. 6, pp. 184-188, 2009.##[14] R. Sheikh, B. Kumar and D. K. Mishra, "A Distributed k-Secure Sum Protocol for Secure Multi-Party Computations". Journal of Compu-ting, vol. 2, no. 3. 2010.##[15] R. Sheikh, B. Kumar and D. K. Mishra, "Changing Neighbors k-Secure Sum Protocol for Secure Multi-Party Computation". International Journal of Computer Science and Information Security (IJCSIS), vol. 7, pp. 239-243, 2010.##[16] M. Jangde, M. S. Chandel and M. K. Mishra, "Hybrid Technique For Secure Sum Protocol". World of Computer Science and Information Technology Journal (WCSIT), vol. 1, pp. 198-201, 2011.##[17] I. Jahan, N. N. Sharmy, S. Jahan, F. A. Ebha and N. J. Lisa, "Design of a Secure Sum Protocol using Trusted Third Party System for Secure Multi-Party Computations". 6th International Conference on Information and Communication Systems (ICICS) IEEE, pp. 136-141, 2015.##[18] Z. Youwen, H. Liusheng, Y. Wei and Y. Xing, "Efficient Collusion-Resisting Secure Sum Protocol". Chinese Journal of Electronics, pp. 407-413, 2011.##[19] J. Rautaray and R. Kumar, "Distributed Database RK-Secure Sum Protocol". International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), vol. 2, pp. 559-562, March 2013.##[20] J. Rautaray and R. Kumar, "Distributed RK- Secure Sum Protocol for Privacy Preserving". IOSR Journal of Computer Engineering (IOSR-JCE), vol. 9, pp. 49-52, Feb. 2013.##[21] J. Rautaray, R. Kumar and G. Bajpai, "Modified Distributed Rk Secure Sum Protocol". Interna-tional Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), vol. 2, pp. 734-736, March 2013.##[22] T. Jung and X. Yang Li, "Collusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel", IEEE Transactions on Dependable and secure computing, pp. 45-57, 2015.##[23] M. Ashouri-Talouki and A. Baraani-Dastjerdi, "Cryptographic collusion-resistant protocols for secure sum", International Journal of Electronic Security and Digital Forensics, vol. 9, pp. 19-34, 2017.##[24] S. Mehnaz, G. Bellala and E. Bertino, "A Secure Sum Protocol and Its Application to Privacy-preserving Multi-party Analytics". In Proceed-ings of the 22nd ACM on Symposium on Access Control Models and Technologies, pp. 219-230, 2017.##[25] F. Hao and P. Zielinski, "A 2-Round Anonymous Veto Protocol". In Security Protocols, Springer Berlin Heidelberg, pp. 202-211, 2009.##[26] M. Burmester and Y. Desmedt, "A secure and efficient conference key distribution system". In Advances in Cryptology .Springer-Verla, pp. 275-286, 2006.## ##

طراحی و پیاده‌سازی سامانۀ بی‌درنگ آشکارسازی و شناسایی پلاک خودرو در تصاویر ویدئویی Design and Implementation of Real-Time License Plate Recognition System in Video Sequences 1 1 سامانه­های شناسایی خودکار پلاک خودرو (ANPR) کاربردهای بسیاری در سامانه­های نظارت بر ترافیک روزانه و سامانه­‌های کنترل عوارض جاده­ای دارند. در این مقاله، الگوریتمی بی­درنگ برای آشکارسازی و شناسایی پلاک در قاب­های ویدئو (frames) و شناسایی هم­زمان چند پلاک در یک قاب ویدئویی طراحی و پیاده­سازی می­‌کنیم. درقبل در زمینه تشخیص و شناسایی یک پلاک خودرو در یک صحنه، کارهایی صورت گرفته که در بیش­تر آنها به بی­درنگ­بودن الگوریتم، توجه کمی شده است؛ درحالی­که مسألۀ افزایش سرعت شناسایی پلاک­ها به‌همراه آشکارسازی و شناسایی صحیح چند پلاک خودرو در صحنه برای کاربردهای آن، اهمیت بالایی دارد. برخلاف روش­هایی با پیچیدگی محاسباتی بالا، ما روش­های مؤثر و ساده­ای را برای بی­درنگ­بودن به­کار گرفتیم. روش پیشنهادی روی ویدئوهایی از دوربین­های بزرگراه­ها ارزیابی­شده و درصد آشکارسازی % 79/98 حاصل شد. این سامانه به زبان C++ و با استفاده از کتابخانه OpenCV پیاده­سازی شده است. میانگین زمان پردازش هر قاب در مرحلۀ Z آشکارسازی پلاک، 25 میلی­ثانیه و میانگین زمان کلی پردازش هر قاب چهل میلی‌ثانیه است که می­تواند در کاربردهای بی­درنگ استفاده شود. درصد بازشناسی ارقام پلاک نیز % 83/97 به‌دست آمد. سامانۀ بی­درنگ پیشنهادی می­تواند چند پلاک را از انواع مختلف در هر قاب تشخیص داده و شناسایی کند. نتایج آزمایش­ها نشان می­دهد که روش و نحوۀ­ پیاده­سازی ما نسبت به کارهای گذشته، سرعت بالاتر و درصد آشکارسازی و بازشناسی بهتری دارد؛ طوری­که آن را برای کاربردهای بی‌درنگ بسیار مناسب ساخته است.     2 An automatic Number Plate Recognition (ANPR) is a popular topic in the field of image processing and is considered from different aspects, since early 90s. There are many challenges in this field, including; fast moving vehicles, different viewing angles and different distances from camera, complex and unpredictable backgrounds, poor quality images, existence of multiple plates in the scene, variable lighting conditions throughout the day, and so on. ANPR systems have many applications in today’s traffic monitoring and toll-gate systems. In this paper, a real-time algorithm is designed and implemented for simultaneous detection and recognition of multiple number plates in video sequences. Already some papers on plate localization and recognition in still? images have been existed , however, they do not consider real time processing. While for the related applications, real-time detection and recognition of multiple plates on the scene is very important. Unlike methods with high computational complexity, we apply simple and effective techniques for being real-time. At first, background is modeled using Gaussian Mixture Model (GMM) and moving objects are determined. Then, plate candidate regions are found by vertical edge detection and horizontal projection. After that, license plates are localized and extracted by morphological operations and connected components analysis. When plates were are detected, their characters are separated with another algorithm. Finally a neural network is applied for character recognition. This system is implemented in C++ using OpenCV library. The average localization time per frame is 25 ms and total processing time, including localization and recognition, is 40 ms that can be used in real-time applications. The proposed method is evaluated on videos from highway cameras and the detection rate of 98.79% and recognition rate of 97.83% is obtained. Our real-time system can also recognize multiple plates of different types in each frame. Experimental results show that our method have higher speed and better recognition rate than previous works therefore it is suitable for real-time applications.   41 56 2017/09/152016/12/312016/09/22017/10/29 1396/8/7 2019/01/92019/01/92019/01/92019/01/9 1397/10/19 میترا عبداللهی Mitra Abdollahi دانشگاه صنعتی شاهرود ایران abdollahi370@yahoo.com حسین خسروی Hossein Khosravi دانشگاه صنعتی شاهرود ایران hosseinkhosravi@shahroodut.ac.ir Real-time License Plate Recognition System Gaussian Mixture Model Projection Connected Components Analysis Neural Network سامانۀ بی‌درنگ آشکارسازی و شناسایی پلاک خودرو مدل مخلوط گاوسی افکنش تحلیل اجزای متصل به هم شبکه عصبی [1] B.Y. Amirgaliyev, C.A. Kenshimov, K.K. Kuatov, M.Z. Kairanbay, Z.Y. Baibatyr and A.K. Jantassov, "License plate verification method for automatic license plate recognition systems," Twelve International Conference on Electronics Computer and Computation (ICECCO), pp. 1-3, 2015.##[2] Y. Li and H. Wu, "Design and Implementation of the License Plate Positioning System Based on the DSP," International Conference on Computer Sciences and Applications (CSA), pp. 635-638, 2013.##[3] Y.K. Wang, C.T. Fan and J.F. Chen, "Traffic Camera Anomaly Detection," 22nd International Conference on Pattern Recognition (ICPR), pp. 4642-4647, 2014.##[4] G. A. Montazer and M. Shayestehfar, "Iranian license plate identification with fuzzy support vector machine," Journal of Signal and Data Processing (JSDP), vol 12, no. 1, pp. 47-56, 2015.##[5] S. Du, M. Ibrahim, M. Shehata and W. Badawy, "Automatic license plate recognition (ALPR): A state-of-the-art review," IEEE Transactions on circuits System video Technology, vol. 23, no. 2, pp. 311-325, 2013.##[6] M.M.I. Chacon and S.A. Zimmerman, "License plate location dynamic PCNN scheme," International Joint Conference Neural Network, 2003.##[7] B. Chenaghlou and M. Rahmati, "Online license plate detection in complex background images using fuzzy math morphology," in 5th Conference of the Machine Vision and Image Processing, Tabriz, Iran, 2008.##[8] V. Abolghasemi and A. Ahmadifard, "An edge-based color-aided method for license plate detection," Image and Vision Computing, vol. 27, pp. 1134-1142, 2009.##[9] T. Duan, T. Hong Du, T. Phuoc and N. Hoang, "Building an automatic vehicle license plate recognition system," in International Conference Computer Science, Can Tho, Vietnam, 2005.##[10] Y. Wang, W. Lin and S. Horng, "A sliding window technique for efficient license plate localization based on discrete wavelet transform," Expert Systems with Applications, vol. 38, pp. 3142-3146, 2011.##[11] J. Li and M. Xie, "A color and texture feature based approach to license plate location," in international conference on computational intelligence and security, 2007.##[12] S. Rastegar, R. Ghaderi, G. R. Ardeshir and Nima Asadi, "An intelligent control system using an efficient License Plate Location and Recognition Approach," International Journal of Image Processing, vol. 3, no. 5, pp. 252-264, 2009.##[13] L. Yu, "Research on Edge Detection in License Plate Recognition," in Proceedings of International Conference on Computer Application and System Modeling, 2012.##[14] A. George and V.J. Pillai, "VNPR system using artificial neural network," International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1-6, 2016.##[15] L. Fuliang and G. Shuangxi, "Character Recognition System Based on Back Propagation Neural Network," in International Conference on Machine Vision and Human-Machine Interface (MVHI), 2010.##[16] A. Nagare, "License plate character recognition system using neural network," International Journal of Computer Applications, vol. 25, no. 10, July 2011.##[17] M. Nejati and H. Poorghasem, " Identification of license plate characters using the mixing structure of experts," Journal of electronics Industries, vol. 3, no. 2, pp. 41-60, 2012.##[18] D.K. Yadav, "Efficient method for moving object detection in cluttered background using Gaussian Mixture Model," IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 943-948, 2014.##[19] C. Stauffer, W. Eric and L. Grimson, "Learning patterns of activity using real-time tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 747-757, 2000.##[20] C. Stauffer and W.E.L. Grimson, "Adaptive background mixture models for real-time tracking," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA, 23-25 June 1999.##[21] H. Scharr, "Optimal Operators in Digital Image Processing," Dissertation (in German), 2000.##[22] L.L. Chun and Y.S. Chung, "Modified unsharp masking detection using Otsu thresholding and Gray code," IEEE International Conference on Industrial Technology (ICIT), pp. 787-791, 2016.##[23] M. Sarfraz, M. Ahmed and S. Ghazi, "Saudi Arabian license plate recognition system," in Proceedings of International Conference on Geom. Model. Graph., 2003.##[24] V. Abolghasemi and A. Ahmadifard, "Application of IFT transformation in license plate recognition system," in Third Conference on Information and Knowledge Technology, Mashhad, Iran, 2007.##[25] H. Zhang, W. Jia, X. He and Q. Wu, "Learning-based license plate detection using global and local features," Pattern Recognition, pp. 1102-1105, 2006.##[26] T. Duan, D. Duc and T. Du, "Combining Hough transform and contour algorithm for detecting vehicles' license-plates," in Proceedings of International Symposium on Intelligent Multimedia Video Speech Processing, 2004.##[27] K. Deb and K. Jo, "A vehicle license plate detection method for intelligent transportation system applications," Cybernetic System International Journal, vol. 40, no. 8, pp. 689-705, 2009.##[28] C. Anagnostopoulos, T. Alexandropoulos, V. Loumos and E. Kayafas, "Intelligent traffic management through MPEG-7 vehicle flow surveillance," in Proceedings of IEEE International Symposium Modern Computing, 2006.##[29] S. Wang and H. Lee, "A cascade framework for a real-time statistical plate recognition system," IEEE Transactions on Information Forensics Security, vol. 2, no. 2, pp. 267-282, June 2007.##[30] X. Shi, W. Zhao and Y. Shen, "Automatic license plate recognition system based on color image processing," Lecture Notes computer science, vol. 3483, pp. 1159-1168, 2005.##[31] H. Lee, S. Chen and S. Wang, "Extraction and recognition of license plates of motorcycles and vehicles on highways," in Proceedings of International Conference on Pattern Recognition, 2004.##[32] M. S. Sarfraz, A. Shahzad, M. A. Elahi, M. Fraz, I. Zafar and E. A. Edirisinghe, "Real-time automatic license plate recognition for CCTV forensic applications," Journal of Real-Time Image Processing, vol. 8, pp. 285-295, 2013.##[33] M. Wang, Y. Liu, B. Liao, Y. Lin and M. Horng, "A vehicle license plate recognition system based on spatial/frequency domain filtering and neural networks," in Proceedings of Computing Collective Intelligence Technology Application, LNCS 6423, 2010.##[34] S. Chang, L. Chen, Y. Chung and S. Chen, "Automatic License Plate Recognition," IEEE Transaction on Intelligent Transportation Systems, vol. 5, no. 1, pp. 42-53, 2004.##[35] P. Comelli, P. Ferragina, M. Granieri and F. Stabile, "Optical recognition of motor vehicle license plates," IEEE Transaction on Vehicles Technology, vol. 44, no. 4, pp. 790-799, November 1995.##[36] T. Naito, T. Tsukada, K. Yamada, K. Kozuka and S. Yamamoto, "Robust license-plate recognition method for passing vehicles under outside environment," IEEE Transaction on Vehicles Technology, vol. 49, no. 6, pp. 2309-2319, November 2000.##[37] H. Khosravi, "A Sliding and Classifying Approach Towards Real Time Persian License Plate Recognition," International Journal of Engineering (IJE), vol. 28, no. 1, pp. 74-80, January 2015.##[1] B.Y. Amirgaliyev, C.A. Kenshimov, K.K. Kuatov, M.Z. Kairanbay, Z.Y. Baibatyr and A.K. Jantassov, "License plate verification method for automatic license plate recognition systems," Twelve International Conference on Electronics Computer and Computation (ICECCO), pp. 1-3, 2015.##[2] Y. Li and H. Wu, "Design and Implementation of the License Plate Positioning System Based on the DSP," International Conference on Computer Sciences and Applications (CSA), pp. 635-638, 2013.##[3] Y.K. Wang, C.T. Fan and J.F. Chen, "Traffic Camera Anomaly Detection," 22nd International Conference on Pattern Recognition (ICPR), pp. 4642-4647, 2014.##[4] غ. منتظر و م. شایسته فر، "شناسایی پلاک خودروهای ایرانی با روش جایابی فازی پلاک"، فصل¬نامه پردازش علائم و داده¬ها، دوره 12، شماره 1، صفحات 47-56، 1394.##[4] G. A. Montazer and M. Shayestehfar, "Iranian license plate identification with fuzzy support vector machine," Journal of Signal and Data Processing (JSDP), vol 12, no. 1, pp. 47-56, 2015.##[5] S. Du, M. Ibrahim, M. Shehata and W. Badawy, "Automatic license plate recognition (ALPR): A state-of-the-art review," IEEE Transactions on circuits System video Technology, vol. 23, no. 2, pp. 311-325, 2013.##[6] M.M.I. Chacon and S.A. Zimmerman, "License plate location dynamic PCNN scheme," International Joint Conference Neural Network, 2003.##[7] ب. چناقلو و م. رحمتی، "تشخیص بر خط مکان پلاک خودرو در تصاویر با پس زمینه پیچیده با استفاده از مورفولوژی ریاضی فازی"، در پنجمین کنفرانس ماشین بینایی و پردازش تصویر، تبریز، ایران، 1387.##[7] B. Chenaghlou and M. Rahmati, "Online license plate detection in complex background images using fuzzy math morphology," in 5th Conference of the Machine Vision and Image Processing, Tabriz, Iran, 2008.##[8] V. Abolghasemi and A. Ahmadifard, "An edge-based color-aided method for license plate detection," Image and Vision Computing, vol. 27, pp. 1134-1142, 2009.##[9] T. Duan, T. Hong Du, T. Phuoc and N. Hoang, "Building an automatic vehicle license plate recognition system," in International Conference Computer Science, Can Tho, Vietnam, 2005.##[10] Y. Wang, W. Lin and S. Horng, "A sliding window technique for efficient license plate localization based on discrete wavelet transform," Expert Systems with Applications, vol. 38, pp. 3142-3146, 2011.##[11] J. Li and M. Xie, "A color and texture feature based approach to license plate location," in international conference on computational intelligence and security, 2007.##[12] S. Rastegar, R. Ghaderi, G. R. Ardeshir and Nima Asadi, "An intelligent control system using an efficient License Plate Location and Recognition Approach," International Journal of Image Processing, vol. 3, no. 5, pp. 252-264, 2009.##[13] L. Yu, "Research on Edge Detection in License Plate Recognition," in Proceedings of International Conference on Computer Application and System Modeling, 2012.##[14] A. George and V.J. Pillai, "VNPR system using artificial neural network," International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1-6, 2016.##[15] L. Fuliang and G. Shuangxi, "Character Recognition System Based on Back Propagation Neural Network," in International Conference on Machine Vision and Human-Machine Interface (MVHI), 2010.##[16] A. Nagare, "License plate character recognition system using neural network," International Journal of Computer Applications, vol. 25, no. 10, July 2011.##[17] م. نجاتی و ح. پورقاسم، "بازشناسی کاراکترهای پلاک خودرو با استفاده از ساختار اختلاط خبره ها"، فصل¬نامه صنایع الکترونیک، جلد 3، شماره 2، صفحات 41-60، 1391.##[17] M. Nejati and H. Poorghasem, " Identification of license plate characters using the mixing structure of experts," Journal of electronics Industries, vol. 3, no. 2, pp. 41-60, 2012.##[18] D.K. Yadav, "Efficient method for moving object detection in cluttered background using Gaussian Mixture Model," IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 943-948, 2014.##[19] C. Stauffer, W. Eric and L. Grimson, "Learning patterns of activity using real-time tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 747-757, 2000.##[20] C. Stauffer and W.E.L. Grimson, "Adaptive background mixture models for real-time tracking," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA, 23-25 June 1999.##[21] H. Scharr, "Optimal Operators in Digital Image Processing," Dissertation (in German), 2000.##[22] L.L. Chun and Y.S. Chung, "Modified unsharp masking detection using Otsu thresholding and Gray code," IEEE International Conference on Industrial Technology (ICIT), pp. 787-791, 2016.##[23] M. Sarfraz, M. Ahmed and S. Ghazi, "Saudi Arabian license plate recognition system," in Proceedings of International Conference on Geom. Model. Graph., 2003.##[24] و. ابوالقاسمی و ع. احمدی فرد، "کاربرد تبدیل IFT در سیستم شناسایی پلاک خودرو"، در سومین کنفرانس اطلاعات و دانش، مشهد، ایران، 1386.##[24] V. Abolghasemi and A. Ahmadifard, "Application of IFT transformation in license plate recognition system," in Third Conference on Information and Knowledge Technology, Mashhad, Iran, 2007.##[25] H. Zhang, W. Jia, X. He and Q. Wu, "Learning-based license plate detection using global and local features," Pattern Recognition, pp. 1102-1105, 2006.##[26] T. Duan, D. Duc and T. Du, "Combining Hough transform and contour algorithm for detecting vehicles' license-plates," in Proceedings of International Symposium on Intelligent Multimedia Video Speech Processing, 2004.##[27] K. Deb and K. Jo, "A vehicle license plate detection method for intelligent transportation system applications," Cybernetic System International Journal, vol. 40, no. 8, pp. 689-705, 2009.##[28] C. Anagnostopoulos, T. Alexandropoulos, V. Loumos and E. Kayafas, "Intelligent traffic management through MPEG-7 vehicle flow surveillance," in Proceedings of IEEE International Symposium Modern Computing, 2006.##[29] S. Wang and H. Lee, "A cascade framework for a real-time statistical plate recognition system," IEEE Transactions on Information Forensics Security, vol. 2, no. 2, pp. 267-282, June 2007.##[30] X. Shi, W. Zhao and Y. Shen, "Automatic license plate recognition system based on color image processing," Lecture Notes computer science, vol. 3483, pp. 1159-1168, 2005.##[31] H. Lee, S. Chen and S. Wang, "Extraction and recognition of license plates of motorcycles and vehicles on highways," in Proceedings of International Conference on Pattern Recognition, 2004.##[32] M. S. Sarfraz, A. Shahzad, M. A. Elahi, M. Fraz, I. Zafar and E. A. Edirisinghe, "Real-time automatic license plate recognition for CCTV forensic applications," Journal of Real-Time Image Processing, vol. 8, pp. 285-295, 2013.##[33] M. Wang, Y. Liu, B. Liao, Y. Lin and M. Horng, "A vehicle license plate recognition system based on spatial/frequency domain filtering and neural networks," in Proceedings of Computing Collective Intelligence Technology Application, LNCS 6423, 2010.##[34] S. Chang, L. Chen, Y. Chung and S. Chen, "Automatic License Plate Recognition," IEEE Transaction on Intelligent Transportation Systems, vol. 5, no. 1, pp. 42-53, 2004.##[35] P. Comelli, P. Ferragina, M. Granieri and F. Stabile, "Optical recognition of motor vehicle license plates," IEEE Transaction on Vehicles Technology, vol. 44, no. 4, pp. 790-799, November 1995.##[36] T. Naito, T. Tsukada, K. Yamada, K. Kozuka and S. Yamamoto, "Robust license-plate recognition method for passing vehicles under outside environment," IEEE Transaction on Vehicles Technology, vol. 49, no. 6, pp. 2309-2319, November 2000.##[37] H. Khosravi, "A Sliding and Classifying Approach Towards Real Time Persian License Plate Recognition," International Journal of Engineering (IJE), vol. 28, no. 1, pp. 74-80, January 2015.## ##

یک مدل موضوعی احتمالاتی مبتنی بر روابط محلّی واژگان در پنجره‌های هم‌پوشان A Probabilistic Topic Model based on Local Word Relationships in Overlapped Windows 1 1  بسیاری از مدل‌های موضوعی مانند LDA که مبتنی بر هم‌رخدادی واژگان در سطح یک سند هستند قادر به بهره‌گیری از روابط محلی واژگان نیستند. برخی از مدل‌های موضوعی مانند BTM سعی کرده‌اند با ترکیب موضوعات و مدل‌های زبانی n-gram، این مشکل را حل کنند. امّا BTM مبتنی بر ترتیب دقیق واژگان است؛ بنابراین با مشکل تُنُکی روبه­روست. در این مقاله یک مدل موضوعی احتمالاتی جدید معرفی شده که قادر به مدل­کردن روابط محلی واژگان با استفاده از پنجره‌های هم‌پوشان است. بر اساس فرضیه هم‌رخدادی، رخداد هم­زمان واژگان در پنجره‌های کوتاه­تر، گواه محکم­تری بر ارتباط معنایی آنهاست. در مدل پیشنهادی، هر سند، مجموعه‌ای از پنجره‌های هم‌پوشان فرض می‌شود، که هریک متناظر با یکی از واژگان متن است. موضوعات بر مبنای هم‌رخدادی واژگان در این پنجره‌های هم‌پوشان استخراج می‌شوند. به‌عبارت دیگر، مدل پیشنهادی، روابط محلی واژگان را بدون وابستگی به ترتیب دقیق آنها مدل می‌کند. آزمایش­های ما نشان می‌دهد که روش پیشنهادی، موضوعات منسجم‌تری را تولید و در کاربرد خوشه‌بندی اسناد، دقیق‌تر از دو مدل LDA و BTM   عمل می‌کند.   2 A probabilistic topic model assumes that documents are generated through a process involving topics and then tries to reverse this process, given the documents and extract topics. A topic is usually assumed to be a distribution over words. LDA is one of the first and most popular topic models introduced so far. In the document generation process assumed by LDA, each document is a distribution over topics and each word in the document is sampled from a chosen topic of that distribution. It assumes that a document is a bag of words and ignores the order of the words. Probabilistic topic models such as LDA which extract the topics based on documents-level word co-occurrences are not equipped to benefit from local word relationships. This problem is addressed by combining topics and n-grams, in models like Bigram Topic Model (BTM). BTM modifies the document generation process slightly by assuming that there are several different distributions of words for each topic, each of which correspond to a vocabulary word. Each word in a document is sampled from one of the distributions of its selected topic. The distribution is determined by its previous word. So BTM relies on exact word orders to extract local word relationships and thus is challenged by sparseness. Another way to solve the problem is to break each document into smaller parts for example paragraphs and use LDA on these parts to extract more local word relationships in these small parts. Again, we will be faced with sparseness and it is well-known that LDA does not work well on small documents. In this paper, a new probabilistic topic model is introduced which assumes a document is a set of overlapping windows but does not break the document into those parts and assumes the whole document as a single distribution over topics. Each window corresponds to a fixed number of words in the document. In the assumed generation process, we walk through windows and decide on the topic of their corresponding words. Topics are extracted based on words co-occurrences in the overlapping windows and the overlapping windows affect the process of document generation because; the topic of a word is considered in all the other windows overlapping on the word. On the other words, the proposed model encodes local word relationships without relying on exact word order or breaking the document into smaller parts. The model, however, takes the word order into account implicitly by assuming the windows are overlapped. The topics are still considered as distributions over words. The proposed model is evaluated based on its ability to extract coherent topics and its clustering performance on the 20 newsgroups dataset. The results show that the proposed model extracts more coherent topics and outperforms LDA and BTM in the application of document clustering.   57 70 2017/09/152016/12/312016/09/22017/10/292017/11/24 1396/9/3 2019/01/92019/01/92019/01/92019/01/92019/01/26 1397/11/6 مرضیه رحیمی Marziea Rahimi دانشگاه صنعتی شاهرود ایران mr_ir26@yahoo.com مرتضی زاهدی Morteza Zahedi دانشگاه صنعتی شاهرود ایران zahedi@shahroodut.ac.ir هدی مشایخی Hoda Mashayekhi دانشگاه صنعتی شاهرود ایران hmashayekhi@shahroodut.ac.ir probabilistic topic models Gibbs sampling co-occurrence graphical models مدل‌های موضوعی احتمالاتی نمونه‌برداری گیبس هم‌رخدادی مدل‌های گرافیکی خوشه‌بندی متن [1] Faili, H., H. Ghader, and M. Morteza Analoui, "A Bayesian Model for Supervised Grammar Induc-tion," Signal and Data Processing, 2012. 9(1), pp. 19-34.##[2] D., et al. Wang, "Multi-document summarization using sentence-based topic models," 2009. Association for Computational Linguistics.##[3] S. S. Sadegi and B, vazir nejad, "Extractive summarization based on cognitive aspects of human mind for narrative text," Signal and Data Processing, vol.12(2), pp. 87-96, 2015##[4] H. Zhang and G. Zhong, "Improving short text classification by learning vector representations of both words and hidden topics," Knowledge-Based Systems, 2016. 102: pp. 76-86.##[5] D.M. Blei, A.Y. Ng, and M.I. Jordan, "Latent dirichlet allocation," Journal of machine Learning research, pp. 993-1022, 2003.##[6] H.M. Wallch, "Topic modeling: beyond bag-of-words," ACM, 2006.##[7] C.D. Manning, et al., "Introduction to Information Retrieval," Cambridge University Press, pp. 496, 2008.##[8] im Walde, S.S. and A. Melinger, "An in-depth look into the co-occurrence distribution of semantic associates," Italian Journal of Linguistics, Special Issue on From Context to Meaning: Distributional Models of the Lexicon in Linguistics and Cognitive Science, 2008.##[9] N. Barbieri, et al., "Probabilistic topic models for sequence data," Machine learning, vol.93(1), pp. 5-29, 2013.##[10] T.L. Griffiths, M. Steyvers, and J.B. Tenenbaum, "Topics in semantic representation." Psycho-logical review, vol.114(2), pp. 211, 2007.##[11] X. Wang, A. McCallum, and X. Wei. "Topical n-grams: Phrase and topic discovery, with an application to information retrieval," IEEE, 2007.##[12] G. Yang, et al., "A novel contextual topic model for multi-document summarization, "Expert Sys-tems with Applications, vol. 42(3), pp. 1340-1352, 2015.##[13] S. Jameel, W. Lam, and L. Bing, "Supervised topic models with word order structure for document classification and retrieval learning," Information Retrieval Journal, vol.18(4), pp. 283-330, 2015.##[14] Y.W. The, "A hierarchical Bayesian language model based on Pitman-Yor processes," Associa-tion for Computational Linguistics, 2006.##[15] H. Noji, D. Mochihashi, and Y. Miyao. "Improvements to the Bayesian Topic N-Gram Models," in EMNLP, 2013.##[16] I. Sato and H. Nakagawa. "Topic models with power-law using Pitman-Yor process," ACM, 2010.##[17] Y.-S. Jeong and H.-J. Choi, "Overlapped latent Dirichlet allocation for efficient image segmenta-tion," Soft Computing, vol. 19(4), pp. 829-838.##[18] Y. Zue, J. Zhao, and K. Xu, "Word network topic model: a simple but general solution for short and imbalanced texts," Knowledge and Information Systems, pp. 1-20, 2014.##[19] W. Ou, Z. Xie, and Z. Lv. "Spatially Regularized Latent topic Model for Simultaneous object discovery and segmentation," in Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on. 2015. IEEE.##[20] T.L. Griffiths and M. Steyvers, "Finding scientific topics," in Proceedings of the National academy of Sciences, 2004. 101(suppl 1), pp. 5228-5235.##[21] T. Minka and J. Lafferty. "Expectation-propagation for the generative aspect model," Morgan Kaufmann Publishers In, 2002.##[22] J. Rennie, 20 Newsgroups. Available from: http://qwone.com/~jason/20Newsgroups/20news-18828.tar.gz##[23] G. Heinrich, "Parameter estimation for text analy-sis," University of Leipzig, Tech. Rep, 2008.##[24] D. Newman, et al. "Automatic evaluation of topic coherence," in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Com-putational Linguistics. 2010. Association for Computational Linguistics.##[25] D. O'Callaghan, et al., "An analysis of the coherence of descriptors in topic modeling," Expert Systems with Applications, vol. 42(13), pp. 5645-5657, 2013.##[26] D. Mimno , et al. "Optimizing semantic coherence in topic models," Association for Computational Linguistics, 2011.##[27] M. Meilă, "Comparing clusterings by the variation of information, in Learning theory and kernel machines," Springer, 2003, pp. 173-187.##[1]فیلی هشام، قادر حمیدرضا، آنالویی مرتضی. یک مدل بیزی برای استخراج باناظر گرامر زبان طبیعی. پردازش علائم و داده‌ها. ۱۳۹۱; ۹ (۱) :۱۹-۳۴##[1] Faili, H., H. Ghader, and M. Morteza Analoui, "A Bayesian Model for Supervised Grammar Induc-tion," Signal and Data Processing, 2012. 9(1), pp. 19-34.##[2] D., et al. Wang, "Multi-document summarization using sentence-based topic models," 2009. Association for Computational Linguistics.##[3] صادقی سیده ساره، وزیرنژاد بهرام. خلاصه‌ساز متون روایی مبتنی بر جنبه‌های شناختی ذهن انسان. پردازش علائم و داده‌ها. ۱۳۹۴; ۱۲ (۲) :۸۷-۹۶##[3] S. S. Sadegi and B, vazir nejad, "Extractive summarization based on cognitive aspects of human mind for narrative text," Signal and Data Processing, vol.12(2), pp. 87-96, 2015##[4] H. Zhang and G. Zhong, "Improving short text classification by learning vector representations of both words and hidden topics," Knowledge-Based Systems, 2016. 102: pp. 76-86.##[5] D.M. Blei, A.Y. Ng, and M.I. Jordan, "Latent dirichlet allocation," Journal of machine Learning research, pp. 993-1022, 2003.##[6] H.M. Wallch, "Topic modeling: beyond bag-of-words," ACM, 2006.##[7] C.D. Manning, et al., "Introduction to Information Retrieval," Cambridge University Press, pp. 496, 2008.##[8] im Walde, S.S. and A. Melinger, "An in-depth look into the co-occurrence distribution of semantic associates," Italian Journal of Linguistics, Special Issue on From Context to Meaning: Distributional Models of the Lexicon in Linguistics and Cognitive Science, 2008.##[9] N. Barbieri, et al., "Probabilistic topic models for sequence data," Machine learning, vol.93(1), pp. 5-29, 2013.##[10] T.L. Griffiths, M. Steyvers, and J.B. Tenenbaum, "Topics in semantic representation." Psycho-logical review, vol.114(2), pp. 211, 2007.##[11] X. Wang, A. McCallum, and X. Wei. "Topical n-grams: Phrase and topic discovery, with an application to information retrieval," IEEE, 2007.##[12] G. Yang, et al., "A novel contextual topic model for multi-document summarization, "Expert Sys-tems with Applications, vol. 42(3), pp. 1340-1352, 2015.##[13] S. Jameel, W. Lam, and L. Bing, "Supervised topic models with word order structure for document classification and retrieval learning," Information Retrieval Journal, vol.18(4), pp. 283-330, 2015.##[14] Y.W. The, "A hierarchical Bayesian language model based on Pitman-Yor processes," Associa-tion for Computational Linguistics, 2006.##[15] H. Noji, D. Mochihashi, and Y. Miyao. "Improvements to the Bayesian Topic N-Gram Models," in EMNLP, 2013.##[16] I. Sato and H. Nakagawa. "Topic models with power-law using Pitman-Yor process," ACM, 2010.##[17] Y.-S. Jeong and H.-J. Choi, "Overlapped latent Dirichlet allocation for efficient image segmenta-tion," Soft Computing, vol. 19(4), pp. 829-838.##[18] Y. Zue, J. Zhao, and K. Xu, "Word network topic model: a simple but general solution for short and imbalanced texts," Knowledge and Information Systems, pp. 1-20, 2014.##[19] W. Ou, Z. Xie, and Z. Lv. "Spatially Regularized Latent topic Model for Simultaneous object discovery and segmentation," in Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on. 2015. IEEE.##[20] T.L. Griffiths and M. Steyvers, "Finding scientific topics," in Proceedings of the National academy of Sciences, 2004. 101(suppl 1), pp. 5228-5235.##[21] T. Minka and J. Lafferty. "Expectation-propagation for the generative aspect model," Morgan Kaufmann Publishers In, 2002.##[22] J. Rennie, 20 Newsgroups. Available from: http://qwone.com/~jason/20Newsgroups/20news-18828.tar.gz##[23] G. Heinrich, "Parameter estimation for text analy-sis," University of Leipzig, Tech. Rep, 2008.##[24] D. Newman, et al. "Automatic evaluation of topic coherence," in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Com-putational Linguistics. 2010. Association for Computational Linguistics.##[25] D. O'Callaghan, et al., "An analysis of the coherence of descriptors in topic modeling," Expert Systems with Applications, vol. 42(13), pp. 5645-5657, 2013.##[26] D. Mimno , et al. "Optimizing semantic coherence in topic models," Association for Computational Linguistics, 2011.##[27] M. Meilă, "Comparing clusterings by the variation of information, in Learning theory and kernel machines," Springer, 2003, pp. 173-187.## ##

مدل جدیدی برای جستجوی عبارت بر اساس کمینه جابه‌جایی وزن‌دار A novel model for phrase searching based-on Minimum Weighted Relocation Model 1 1 بر اساس پژوهش­های انجام‌شده روی موتورهای جستجو،‌ بیش­تر پرس‌وجوهای کاربران بیش از یک واژه است. برای پرس‌وجوهای با بیش از یک واژه دو مدل می‌توان ارائه داد. در مدل نخست فرض می‌شود واژگان پرس‌وجو مستقل از یکدیگر هستند و در مدل دوم محل و ترتیب واژگان وابسته فرض می‌شود. آزمایش‌ها نشان می‌دهد که در بیش­تر پرس‌وجوها بین واژگان وابستگی وجود دارد. یکی از پارامترهایی که می‌تواند وابستگی بین واژگان پرس‌وجو را مشخص کند، فاصلۀ بین واژگان پرس‌وجو در سند است. در این مقاله تعریف جدیدی از فاصله بر اساس کمینه جابه­جایی وزن‌دار[1] واژگان سند به­منظور تطبیق بر پرس‌وجو ارائه می‌شود. هم‌چنین با توجه به این‌که بیش­تر الگوریتم‌های رتبه‌بندی از فرکانس رخداد یک واژه در سند[2] برای امتیاز‌دهی به اسناد استفاده می‌کنند و برای پرس‌وجو با بیش از یک واژه تعریف روشنی از این پارامتر وجود ندارد. در این مقاله پارامترهای ‌فرکانس رخداد یک عبارت[3]  و معکوس فرکانس سند[4] با توجه به مفهوم جدید فاصله تعریف‌شده و الگوریتم‌هایی برای محاسبه آن‌ها ارائه شده است. همچنین نتایج الگوریتم پیشنهادی با چند الگوریتم مقایسه شده است که افزایش خوبی را در میانگین دقّت نشان می‌دهد. [1] MWRM [2] Term Frequency   [3] Phrase Frequency [4] Inverted Document Frequency 2 Finding high-quality web pages is one of the most important tasks of search engines. The relevance between the documents found and the query searched depends on the user observation and increases the complexity of ranking algorithms. The other issue is that users often explore just the first 10 to 20 results while millions of pages related to a query may exist. So search engines have to use suitable algorithms with high performance to find the most relevant pages. The ranking section is an important part of search engines. Ranking is a process in which the web page quality is estimated by the search engine. There are two main methods for ranking web pages. In the first method, ranking is done based on the documents’ content (traditional rankings). Models, such as Boolean model, probability model and vector space model are used to rank documents based on their contents. In the second method, based on the graph, web connections and the importance of web pages, ranking process is performed. Based on researches on search engines, the majority of user queries is more than one term. For queries with more than one term, two models can be used. The first model assumes that query terms are independent of each other while the second model considers a location and order dependency between query terms. Experiments show that in the majority of queries there are dependencies between terms. One of the parameters that can specify dependencies between query terms is the distance between query terms in the document. In this paper, a new definition of distance based on Minimum Weighted Displacement Model (MWDM) of document terms to accommodate the query terms is presented. In the Minimum Weighted Displacement Model (MWDM), we call the minimum number of words moving a text to match the query term by space. In addition, because most of the ranking algorithms use the TF (Term Frequency) to score documents and for queries more than one term, there is no clear definition of these parameters; in this paper, according to the new distance concept, Phrase Frequency and Inverted Document Frequency are defined. Also, algorithms to calculate them are presented. The results of the proposed algorithm compared with multiple corresponding algorithms shows a favorable increase in average precision.   71 84 2017/09/152016/12/312016/09/22017/10/292017/11/242017/11/16 1396/8/25 2019/01/92019/01/92019/01/92019/01/92019/01/262019/01/9 1397/10/19 جواد پاک سیما javad paksima دانشگاه پیام‌نور یزد ایران paksima@stu.yazd.ac.ir Search engine Ranking Distance Phrase Frequency موتور جستجو رتبه‌بندی فاصله وابستگی واژگان فرکانس عبارت (PF) [1] A. Z. Bidoki, "Effective Web Ranking and Crawling(in persian)," University of Tehran, 2009.##[2] R. Baeza-Yates and B. Ribeiro-Neto, "Modern information retrieval," New York, vol. 9, p. 513, 1999.##[3] G. Salton and C. Buckley, "Term-weighting approaches in automatic text retrieval," Informa-tion Processing and Management, vol. 24, no. 5, pp. 513-523, 1988.##[4] S. E. Robertson, Overview of the Okapi projects, vol. 53, no. 1. MCB UP Ltd, 1997, pp. 3-7.##[5] Y. Zhang and A. Moffat, "Some Observations on User Search Behaviour.," Austr. J. Intelligent Information Processing Systems, vol. 9, no. 2, pp. 1-8, 2006.##[6] D. Bahle, H. Williams, and J. Zobel, "Compaction techniques for nextword indexes," in String Processing and Information Retrieval, Interna-tional Symposium on, 2001, p. 33.##[7] H. E. Williams, J. Zobel, and D. Bahle, "Fast phrase querying with combined indexes," ACM Transactions on Information Systems (TOIS), vol. 22, no. 4, pp. 573-594, 2004.##[8] A. Doucet and H. Ahonen-Myka, "An efficient any language approach for the integration of phrases in document retrieval," Language resources and evaluation, vol. 44, no. 1-2, pp. 159-180, 2010.##[9] I. H. Witten, A. Moffat, and T. C. Bell, Managing gigabytes: compressing and indexing documents and images. Morgan Kaufmann, 1999.##[10] D. Bahle, "Efficient Phrase Querying," School of Computer Science and Information Technology, Royal Melbourne Institute of Technology, 2003.##[11] A. Fellinghaug, "Phrase searching in text index-es," no. June, p. 137, 2008.##[12] C. J. van Rijsbergen, "A theoretical basis for the use of co-occurrence data in information retrie-val," Journal of documentation, vol. 33, no. 2, pp. 106-119, 1977.##[13] R. Nallapati and J. Allan, "Capturing term dependencies using a language model based on sentence trees," in Proceedings of the eleventh international conference on Information and knowledge management, 2002, pp. 383-390.##[14] E. M. Keen, "The use of term position devices in ranked output experiments," Journal of Documentation, vol. 47, no. 1, pp. 1-22, 1991.##[15] W. B. Croft, H. R. Turtle, and D. D. Lewis, "The use of phrases and structured queries in information retrieval," in Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval, 1991, pp. 32-45.##[16] D. Metzler and W. B. Croft, "A Markov random field model for term dependencies," in Proceed-ings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, 2005, pp. 472-479.##[17] E. K. F. Dang, R. W. P. Luk, and J. Allan, "A context-dependent relevance model," Journal of the Association for Information Science and Technology, 2015.##[18] F. Song and W. B. Croft, "A general language model for information retrieval," in Proceedings of the eighth international conference on In-formation and knowledge management, 1999, pp. 316-321.##[19] J. Gao, J.-Y. Nie, G. Wu, and G. Cao, "Dependence language model for information retrieval," in Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrie-val, 2004, pp. 170-177.##[20] B. He, J. X. Huang, and X. Zhou, "Modeling term proximity for probabilistic information retrieval models," Information Sciences, vol. 181, no. 14, pp. 3017-3031, 2011.##[21] Y. Rasolofo and J. Savoy, Term proximity scoring for keyword-based retrieval systems. Springer, 2003.##[22] C. Eickhoff, A. P. de Vries, and T. Hofmann, "Modelling Term Dependence with Copulas," in Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015, pp. 783-786.##[23] S. Büttcher, C. L. A. Clarke, and B. Lushman, "Term proximity scoring for ad-hoc retrieval on very large text collections," in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006, pp. 621-622.##[24] T. Tao and C. Zhai, "An exploration of proximity measures in information retrieval," in Proceed-ings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007, pp. 295-302.##[25] J. Zhao and Y. Yun, "A proximity language model for information retrieval," in Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 2009, pp. 291-298.##[26] J. Zhao, J. X. Huang, and B. He, "CRTER: using cross terms to enhance probabilistic information retrieval," in Proceedings of the 34th inter-national ACM SIGIR conference on Research and development in Information Retrieval, 2011, pp. 155-164.##[27] J. Zhao, J. X. Huang, and Z. Ye, "Modeling term associations for probabilistic information retrieval," ACM Transactions on Information Systems (TOIS), vol. 32, no. 2, p. 7, 2014.##[28] J. Miao, J. X. Huang, and Z. Ye, "Proximity-based rocchio's model for pseudo relevance," in Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, 2012, pp. 535-544.##[29] C. L. A. Clarke, G. V. Cormack, and E. A. Tudhope, "Relevance ranking for one to three term queries," Information Processing & Management, vol. 36, no. 2, pp. 291-311, 2000.##[30] J. Klekota, F. P. Roth, and S. L. Schreiber, "Query Chem: a Google-powered web search combining text and chemical structures," Bioin-formatics, vol. 22, no. 13, pp. 1670-1673, 2006.##[31] K. Sadakane and H. Imai, "Text Retrieval by using k-word Proximity Search," in Database Applications in Non-Traditional Environments, 1999.(DANTE'99) Proceedings. 1999 Inter-national Symposium on, 1999, pp. 183-188.##[32] X. Lu, A. Moffat, and J. S. Culpepper, "On the cost of extracting proximity features for term-dependency models," in CIKM 2015, 2015, pp. 293-302.##[33] M. Blum, R. W. Floyd, V. Pratt, R. L. Rivest, and R. E. Tarjan, "Time bounds for selection," Journal of computer and system sciences, vol. 7, no. 4, pp. 448-461, 1973.##[34] R. Courant, Differential and integral calculus, vol. 2. John Wiley & Sons, 2011.##[35] S. E. Robertson and S. Walker, "Some for Simple Effective Approximations to the 2 - Poisson Model Probabilistic Weighted Retrieval," Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 232-241, 1994.##[36] H. Zaragoza, N. Craswell, M. J. Taylor, S. Saria, and S. E. Robertson, "Microsoft Cambridge at TREC 13: Web and Hard Tracks.," in TREC, 2004, vol. 4, p. 1.##[37] R. Duda O., P. Hart E., and D. Stork G., Pattern Classification. 2000.##[38] S. Robertson and H. Zaragoza, The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc, 2009.##[39] J. Zhao and J. X. Huang, "An enhanced context-sensitive proximity model for probabilistic information retrieval," in Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, 2014, pp. 1131-1134.##[1] A. Z. Bidoki, "Effective Web Ranking and Crawling(in persian)," University of Tehran, 2009.##[2] R. Baeza-Yates and B. Ribeiro-Neto, "Modern information retrieval," New York, vol. 9, p. 513, 1999.##[3] G. Salton and C. Buckley, "Term-weighting approaches in automatic text retrieval," Informa-tion Processing and Management, vol. 24, no. 5, pp. 513-523, 1988.##[4] S. E. Robertson, Overview of the Okapi projects, vol. 53, no. 1. MCB UP Ltd, 1997, pp. 3-7.##[5] Y. Zhang and A. Moffat, "Some Observations on User Search Behaviour.," Austr. J. Intelligent Information Processing Systems, vol. 9, no. 2, pp. 1-8, 2006.##[6] D. Bahle, H. Williams, and J. Zobel, "Compaction techniques for nextword indexes," in String Processing and Information Retrieval, Interna-tional Symposium on, 2001, p. 33.##[7] H. E. Williams, J. Zobel, and D. Bahle, "Fast phrase querying with combined indexes," ACM Transactions on Information Systems (TOIS), vol. 22, no. 4, pp. 573-594, 2004.##[8] A. Doucet and H. Ahonen-Myka, "An efficient any language approach for the integration of phrases in document retrieval," Language resources and evaluation, vol. 44, no. 1-2, pp. 159-180, 2010.##[9] I. H. Witten, A. Moffat, and T. C. Bell, Managing gigabytes: compressing and indexing documents and images. Morgan Kaufmann, 1999.##[10] D. Bahle, "Efficient Phrase Querying," School of Computer Science and Information Technology, Royal Melbourne Institute of Technology, 2003.##[11] A. Fellinghaug, "Phrase searching in text index-es," no. June, p. 137, 2008.##[12] C. J. van Rijsbergen, "A theoretical basis for the use of co-occurrence data in information retrie-val," Journal of documentation, vol. 33, no. 2, pp. 106-119, 1977.##[13] R. Nallapati and J. Allan, "Capturing term dependencies using a language model based on sentence trees," in Proceedings of the eleventh international conference on Information and knowledge management, 2002, pp. 383-390.##[14] E. M. Keen, "The use of term position devices in ranked output experiments," Journal of Documentation, vol. 47, no. 1, pp. 1-22, 1991.##[15] W. B. Croft, H. R. Turtle, and D. D. Lewis, "The use of phrases and structured queries in information retrieval," in Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval, 1991, pp. 32-45.##[16] D. Metzler and W. B. Croft, "A Markov random field model for term dependencies," in Proceed-ings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, 2005, pp. 472-479.##[17] E. K. F. Dang, R. W. P. Luk, and J. Allan, "A context-dependent relevance model," Journal of the Association for Information Science and Technology, 2015.##[18] F. Song and W. B. Croft, "A general language model for information retrieval," in Proceedings of the eighth international conference on In-formation and knowledge management, 1999, pp. 316-321.##[19] J. Gao, J.-Y. Nie, G. Wu, and G. Cao, "Dependence language model for information retrieval," in Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrie-val, 2004, pp. 170-177.##[20] B. He, J. X. Huang, and X. Zhou, "Modeling term proximity for probabilistic information retrieval models," Information Sciences, vol. 181, no. 14, pp. 3017-3031, 2011.##[21] Y. Rasolofo and J. Savoy, Term proximity scoring for keyword-based retrieval systems. Springer, 2003.##[22] C. Eickhoff, A. P. de Vries, and T. Hofmann, "Modelling Term Dependence with Copulas," in Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015, pp. 783-786.##[23] S. Büttcher, C. L. A. Clarke, and B. Lushman, "Term proximity scoring for ad-hoc retrieval on very large text collections," in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006, pp. 621-622.##[24] T. Tao and C. Zhai, "An exploration of proximity measures in information retrieval," in Proceed-ings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007, pp. 295-302.##[25] J. Zhao and Y. 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پیش‌بینی و تعیین عوامل مؤثر بر بقای پنج‌سالۀ کلیۀ پیوندی در داده‌های نامتوازن با رویکرد فراابتکاری و یادگیری ماشین Prediction and determining the effective factors on the survival transplanted kidney for five-year in imbalanced data by the meta-heuristic approach and machine learning 1 1 در مرحلۀ نهاییِ نارسایی کلیه، پیوند کلیه می­تواند عمر بیماران را طولانی کند و کیفیت زندگی بیمار را بسیار بهبود بخشد. بعد از عمل پیوند کلیه، بررسی میزان یا پیش­بینی بقای کلیۀ پیوندی اهمیت زیادی دارد. این مطالعه بر روی بیماران کلیۀ پیوندی بیمارستان‌­هایامام رضا(ع) و چهارمین شهید محراب کرمانشاه در سال­های 2012- 2001 انجام شده است. از آن­جایی‌که داده­های نامتوازن باعث ناکارامدی مدل­های یادگیری ماشین می­شوند، ابتدا داده­های نامتوازن با دو روش بیش‌­نمونه‌­برداری و زیر­نمونه­برداری متوازن شدند؛ سپس عوامل اثرگذار بر بقای پیوند کلیه به­کمک الگوریتم فراابتکاری ژنتیک شناسایی شده و مدل یادگیر طبقه­بند نزدیک­ترین همسایه برای پیش­بینی بقای پنج سالۀ کلیۀ پیوندی به‌کار گرفته شد. بقای کلیۀ پیوندی در روش بیش­نمونه­برداری با دقّت 8/96 درصد و زیر­نمونه­برداری با دقّت 2/89 درصد پیش­بینی شد. هم‌چنین، ویژگی­های وزن، سنِّ دهنده و گیرنده، اورۀ قبل پیوند، کراتین قبل پیوند، هموگلوبین قبل و بعد پیوند، جنسیتِ دهنده، RH دهنده و گیرنده، بیماری اولیه، سنِّ دهندۀ بالای سی و سنِّ گیرندۀ بالای چهل، به­عنوان ویژگی­های تأثیرگذا­ر ­در بقای کلیه پیوندی شناسایی شد. مقایسه نتایج به‌دست­آمده از این پژوهش با مطالعات پیشین، برتری مدل پیشنهادی را از نقطه­نظر دقّت مدل نشان می­دهد. به­عبارتی متوازن­سازی داده­ها همراه با انتخاب ویژگی­ بهینه منجر به ارائه مدل پیش­بینی دقیق­تری می‌شود.   2 Chronic kidney failure is one of the most widespread diseases in Iran and the world. In general, the disease is common in high health indexes societies due to increased longevity. Treatment for chronic kidney failure is dialysis and kidney transplantation. Kidney transplantation is an appropriate and effective strategy for patients with End-Stage Renal Disease (ESRD), and it provides a better life and reduces mortality risk for patients. In contrast to many benefits that kidney transplantation has in terms of improving physical and mental health and the life’s quality in kidney transplantation patients, it may be rejected because of host's immune response to the received kidney, and it consequences the need for another transplantation, or even death will have to. In fact, a patient that can survive for years with dialysis, he may lose his life with an inappropriate transplantation or be forced into high-risk surgical procedures.  According to the above, the study of predicting the survival of kidney transplantation, its effective factors and providing a model for purposing of high prediction accuracy is essential. Studies in the field of survival of kidney transplantation include statistical studies, artificial intelligence and machine learning. In all of the studies in this feild, researchers have sought to identify a more effective set of features in survival of transplantation and the design of predictive models with higher accuracy and lower error rate. This study carried out on 756 kidney transplant patients with 21 features of Imam Reza and Fourth Shahid Merab hospital in Kermanshah from 2001 to 2012. Some features set to binary value and other features have real continuous values. Due to data are unbalance, which led to convergence of classification model to majority class, so over sampling and under sampling techniques has been used for achieving higher accuracy. To identify the more effective features on the survival of the kidney transplantation, the genetic meta-heuristic algorithm is used. For this purpose binary coding for each chromosome has been used; it is combining three single-point, two-point, and uniform operators to make better generations, better convergence and achieve higher accuracy rate. The genetic search algorithm plays a vital role in searching for such a space in a reasonable time because data search space is exponential. In fact, in balanced data, genetic algorithm determines the effective factors and the K-nearest neighbor model with precision of classification as the evaluator function was used to predict the five-year survival of the kidney transplantation. Based on the results of this study, in comparison to similar studies for prediction of survival transplanted kidney, the five-year survival rate of transplanted kidney was appropriate in these models. Also the effective factors in over sampling and under sampling methods with a precision of 96.8% and 89.2% are obtained respectively. in addition weight, donor and recipient age, pre-transplantation urea, pre-transplantation creatinine, hemoglobin before and after transplantation, donor gender, donor and recipient RH, primary illness, donor age up 30 and receipt age up 40 were identified as the effective features on kidney transplantation survival. Comparing the results of this study with previous studies shows the superiority of the proposed model from the point of view of the models' precision. In particular, balancing the data along the selection of optimal features leads to a high precision predictive model.   85 94 2017/09/152016/12/312016/09/22017/10/292017/11/242017/11/162017/12/25 1396/10/4 2019/01/92019/01/92019/01/92019/01/92019/01/262019/01/92018/10/6 1397/7/14 نسیبه امامی nasibeh emami دانشگاه کوثر بجنورد ایران nasibeh.emami@kub.ac.ir زینب حسنی zeinab hassani دانشگاه کوثر بجنورد ایران hassani@kub.ac.ir Kidney Transplantation imbalance data Genetic Algorithm K- nearest neighbors پیوند کلیه داده‌های نامتوازن الگوریتم ژنتیک نزدیک‌ترین همسایگی [1] M. Mirzaei, and M. Firooz Abadi, "The impact of data mining on prediction of renal transplantation survival and identifying the effective factors on the transplanted kidney," Journal Of Health and Biomedical Informatics, Medical Informatics Re-search Center, vol. 3, pp. 1-9, 2016.##[2] N. Javanroh Givi, R. Alimi, H. Esmaily, M. T. Shakeri, and A. Shamsa, "Assessment of effective factors on renal transplantation estimation of rejection hazard for transplanted in Mashhad Qaem hospital," Journal of North Khorasan University of Medical Sciences, vol. 5, pp. 315-321, 2013.##[3] M. Ashrafi, and et. al, "Application of artificial neural network to predict graft survival after kidney transplantation: reports of 22 years follow up of 316 patients in Isfahan," Tehran University Medical Journal, vol. 67, pp. 353-359, 2009.##[4] A. Almasi Hashiani, A. Rajaeefard, J . Hassanzade, and H. Salahi, "Survival analysis of renal transplantation and its relationship with age and sex," Koomesh, vol. 11, pp. 302-307, 2010.##[5] T. D. Noia, and et al, "An end stage kidney disease predic-tor based on an artificial neural networks ensemble," Expert Systems with App-lications, vol. 40, pp. 4438-4445, 2013.##[6] G. Santori, I. Fontana, and U. Valente, "Applica-tion of an artificial neural network model to predict delayed decrease of serum creatinine in pediatric patients after kidney transplantation," Transplant Proc, vol. 39, pp. 1813-1819, 2007.##[7] T. S. Brown, and et al, "Bayesian modeling of pre transplant variables accurately predicts kidney graft survival," Am J Nephrol, vol. 6, pp. 561-569, 2012.##[8] J. Lasserre, S. Arnold, M. Vingron, P. Reinke, and C. P. Hinrichs, "Predicting the outcome of renal transplantation," J Am Med Inform Assoc, vol. 19, pp. 255-262, 2012.##[9] A. H. Hashemian, B. beiranvand, M. Rezaei, A. Bardideh, and E. Zand-Karimi, "Comparison of artificial neural network of kidney transplant survival," International Journal of Advanced Bio-logical and biomedical Research, vol. 1, pp. 1204-1212, 2013.##[10] R. J. Oskouei and B. S. Bigham, "Over-sampling via under-sampling in strongly Imbalanced data," International Journal of Advanced Intelli-gence Paradigms, 2015.##[11] M. M. Rahman, and D. N Davis, "Addressing the class imbalance problem in medical datasets," Int J Machine Learning and Compute, vol. 2, pp. 224-228, 2013.##[12] N. V Chawla, "Data mining for imbalanced datasets: an overview," Data Mining Knowledge Discovery Handbook, 2005.##[13] Y. Sun, A. K. C. Wong, and M. S Kamel, "Classification of imbalanced data: a review," Int j patt Recogn Artif Intell, vol. 4, pp. 687-719, 2009.##[14] D. C. Li, C. W. Liu, and S. C. Hu, "A learning method for the class imbalance problem with medical datasets," J comput Bio Medi, vol. 5, pp. 509-518, 2010.##[15] F. Hoseinkhani, and B. Naser Sharif, " Two methods of converting feature based on genetic algorithms to reduce the classification error of support vector machine," Journal Of signs and data Processing, vol. 24, pp. 23-39, 2015.##[16] H. Hoglund, " Tax payment default prediction using genetic algorithm-based variable sele-ction," Expert Syst Appl, vol. 88, pp. 368-375, 2017.##[17] S. Nagpal, S. Arora, S. Dey and Shreya, " Feature selection using gravitational search algorithm for biomedical data," Procedia Comput Sci, vol. 115, pp. 258-265, 2017.##[18] X. We and et. al, "Top 10 algorithms in data mining," knowl Inf Syst, vol. 14, pp. 1-37, 2008.##[19] J. H. Holland, " Adaptation in natural and artifi-cial systems," University of Michigan Press, 1975.##[20] D. E. Goldberg, "Genetic algorithms in search optimization and mechine learning," Addison-Wesley Publishing, INC. Reading. Mass, 1989.##[21] S. Olariu and A. Y. Zomaya, "Handbook of bioinspired algorithms and applications," Taylor & Francis Group, LLC Press, 2006.##[1] میرزایی، محترم، و فیروز آبادی، سید محمد، "کاربرد داده¬کاوی در پیش¬بینی بقای پیوند کلیه و شناسایی متغیر¬های تاثیر¬گذار در بقای کلیه پیوندی"، مجله انفورماتیک سلامت و زیست پزشکی، مرکز تحقیقات انفورماتیک پزشکی، دوره 3، شماره 1، صفحات 1-9، 1395.##[1] M. Mirzaei, and M. Firooz Abadi, "The impact of data mining on prediction of renal transplantation survival and identifying the effective factors on the transplanted kidney," Journal Of Health and Biomedical Informatics, Medical Informatics Re-search Center, vol. 3, pp. 1-9, 2016.##[2] جوانروح گیوی، نیلوفر، علیمی، رسول، اسماعیلی، حبیب ا...، شاکری، محمد¬تقی، و شمسا، علی، "عوامل مؤثر بر بقای پیوند کلیه و برآورد خطر رد پیوند برای پیوند شدگان مراجعه کننده به بیمارستان قائم مشهد"، مجله دانشگاه علوم پزشکی خراسان شمالی، دوره 5، شماره 2، صفحات 315-321، 1392.##[2] N. Javanroh Givi, R. Alimi, H. Esmaily, M. T. Shakeri, and A. Shamsa, "Assessment of effective factors on renal transplantation estimation of rejection hazard for transplanted in Mashhad Qaem hospital," Journal of North Khorasan University of Medical Sciences, vol. 5, pp. 315-321, 2013.##[3] اشرفی، مهدی، و همکاران،" پیش¬بینی بقای پنج ساله پیوند کلیه با استفاده از مدل شبکه عصبی مصنوعی: گزارش 22 سال پی¬گیری از 316 بیمار در اصفهان"، مجله دانشکده پزشکی، دانشگاه علوم پزشکی تهران، دوره 67، شماره 5، صفحات 353-359، 1388.##[3] M. Ashrafi, and et. al, "Application of artificial neural network to predict graft survival after kidney transplantation: reports of 22 years follow up of 316 patients in Isfahan," Tehran University Medical Journal, vol. 67, pp. 353-359, 2009.##[4] الماسی حشیانی، امیر، رجایی¬فرد، عبدالرضا، حسن¬زاده، جعفر، وصلاحی، حشمت¬ا...،" تحلیل بقا پیوند کلیه و ارتباط آن با سن و جنس دهنده و گیرنده عضو بین بیماران پیوند شده"، مجله علمی دانشگاه علوم پزشکی سمنان، جلد 11، شماره 4، صفحات 302-307، 1389.##[4] A. Almasi Hashiani, A. Rajaeefard, J . Hassanzade, and H. Salahi, "Survival analysis of renal transplantation and its relationship with age and sex," Koomesh, vol. 11, pp. 302-307, 2010.##[5] T. D. Noia, and et al, "An end stage kidney disease predic-tor based on an artificial neural networks ensemble," Expert Systems with App-lications, vol. 40, pp. 4438-4445, 2013.##[6] G. Santori, I. Fontana, and U. Valente, "Applica-tion of an artificial neural network model to predict delayed decrease of serum creatinine in pediatric patients after kidney transplantation," Transplant Proc, vol. 39, pp. 1813-1819, 2007.##[7] T. S. Brown, and et al, "Bayesian modeling of pre transplant variables accurately predicts kidney graft survival," Am J Nephrol, vol. 6, pp. 561-569, 2012.##[8] J. Lasserre, S. Arnold, M. Vingron, P. Reinke, and C. P. Hinrichs, "Predicting the outcome of renal transplantation," J Am Med Inform Assoc, vol. 19, pp. 255-262, 2012.##[9] A. H. Hashemian, B. beiranvand, M. Rezaei, A. Bardideh, and E. Zand-Karimi, "Comparison of artificial neural network of kidney transplant survival," International Journal of Advanced Bio-logical and biomedical Research, vol. 1, pp. 1204-1212, 2013.##[10] R. J. Oskouei and B. S. Bigham, "Over-sampling via under-sampling in strongly Imbalanced data," International Journal of Advanced Intelli-gence Paradigms, 2015.##[11] M. M. Rahman, and D. N Davis, "Addressing the class imbalance problem in medical datasets," Int J Machine Learning and Compute, vol. 2, pp. 224-228, 2013.##[12] N. V Chawla, "Data mining for imbalanced datasets: an overview," Data Mining Knowledge Discovery Handbook, 2005.##[13] Y. Sun, A. K. C. Wong, and M. S Kamel, "Classification of imbalanced data: a review," Int j patt Recogn Artif Intell, vol. 4, pp. 687-719, 2009.##[14] D. C. Li, C. W. Liu, and S. C. Hu, "A learning method for the class imbalance problem with medical datasets," J comput Bio Medi, vol. 5, pp. 509-518, 2010.##[15] حسین¬خانی، فاطمه و ناصر¬شریف، بابک،" دو روش تبدیل ویژگی مبتنی بر الگوریتم¬های ژنتیک برای کاهش خطای دسته¬بندی ماشین بردار پشتیبان"، مجله پردازش علائم و داده¬ها، جلد 24، شماره 2، صفحات 23-39، 1394.##[15] F. Hoseinkhani, and B. Naser Sharif, " Two methods of converting feature based on genetic algorithms to reduce the classification error of support vector machine," Journal Of signs and data Processing, vol. 24, pp. 23-39, 2015.##[16] H. Hoglund, " Tax payment default prediction using genetic algorithm-based variable sele-ction," Expert Syst Appl, vol. 88, pp. 368-375, 2017.##[17] S. Nagpal, S. Arora, S. Dey and Shreya, " Feature selection using gravitational search algorithm for biomedical data," Procedia Comput Sci, vol. 115, pp. 258-265, 2017.##[18] X. We and et. al, "Top 10 algorithms in data mining," knowl Inf Syst, vol. 14, pp. 1-37, 2008.##[19] J. H. Holland, " Adaptation in natural and artifi-cial systems," University of Michigan Press, 1975.##[20] D. E. Goldberg, "Genetic algorithms in search optimization and mechine learning," Addison-Wesley Publishing, INC. Reading. Mass, 1989.##[21] S. Olariu and A. Y. Zomaya, "Handbook of bioinspired algorithms and applications," Taylor & Francis Group, LLC Press, 2006.## ##

رویکردی با ناظر در استخراج واژگان کلیدی اسناد فارسی با استفاده از زنجیره‌های لغوی Supervised approach for keyword extraction from Persian documents using lexical chains 1 1 واژگان کلیدی، واژگان اصلی و کانونی یک متن و مضمون اصلی مطلب هستند. تهیه این واژگان به روش سنّتی نیازمند صرف زمان و هم‌چنین دانش تخصّصی راجع به موضوع متن است. از آن‌جا که واژگان کلیدی کاربردهای فراوانی در به‌کارگیری مستندات الکترونیکی دارند، شناسایی روش‌های خودکار و بهبودیافته برای استخراج این دسته از واژگان همیشه مورد توجه بوده است. رویکرد پژوهش حاضر یک روش باناظر برای استخراج واژگان کلیدی است که در آن با استفاده از زنجیره‌های لغوی واژگان متن، ویژگی‌های جدیدی برای هر واژه استخراج شده ‌است. در ایجاد زنجیره‌های لغوی سعی بر شکل‌گیری روابط بین معنای واژگان بوده‌ایم، از‌این‌رو در مدل ارائه‌شده « فارس‌نت»  نقش مهمی در ایجاد آنها ایفا می‌کند. داده‌ها‌ی مورد ارزیابی در این پژوهش مقالات علمی پژوهشی نشریات فارسی هستند. نتایج به‌دست‌آمده نشان می‌دهد که استفاده از روابط معنایی بین واژگان در کنار ویژگی‌های آماری، عملکرد مناسبی را در استخراج واژگان کلیدی از مقالات نتیجه می‌دهد.   2 Keywords are the main focal points of interest within a text, which intends to represent the principal concepts outlined in the document. Determining the keywords using traditional methods is a time consuming process and requires specialized knowledge of the subject. For the purposes of indexing the vast expanse of electronic documents, it is important to automate the keyword extraction task. Since keywords structure is coherent, we focus on the relation between words. Most of previous methods in Persian are based on statistical relation between words and didn’t consider the sense relations. However, by existing ambiguity in the meaning, using these statistic methods couldn’t help in determining relations between words. Our method for extracting keywords is a supervised method which by using lexical chain of words, new features are extracted for each word. Using these features beside of statistic features could be more effective in a supervised system. We have tried to map the relations amongst word senses by using lexical chains. Therefore, in the proposed model, “FarsNet” plays a key role in constructing the lexical chains. Lexical chain is created by using Galley and McKeown's algorithm that of course, some changes have been made to the algorithm. We used java version of hazm library to determine candidate words in the text. These words were identified by using POS tagging and Noun phrase chunking. Ten features are considered for each candidate word. Four features related to frequency and position of word in the text and the rest related to lexical chain of the word. After extracting the keywords by the classifier, post-processing performs for determining Two-word key phrases that were not obtained in the previous step. The dataset used in this research was chosen from among Persian scientific papers. We only used the title and abstract of these papers. The results depicted that using semantic relations, besides statistical features, would improve the overall performance of keyword extraction for papers. Also, the Naive Bayes classifier gives the best result among the investigated classifiers, of course, eliminating some of the features of the lexical chain improved its performance.   95 110 2017/09/152016/12/312016/09/22017/10/292017/11/242017/11/162017/12/252017/12/3 1396/9/12 2019/01/92019/01/92019/01/92019/01/92019/01/262019/01/92018/10/62018/05/16 1397/2/26 عطیه شریفی Atieh Sharifi دانشگاه بین‌المللی امام خمینی ایران a.sharifi@edu.ikiu.ac.ir محمد امین مهدوی M.Amin Mahdavi دانشگاه بین‌المللی امام خمینی ایران mahdavi@eng.ikiu.ac.ir Keyword Extraction Persian Document Supervised Learning Lexical Chain FarsNet استخراج واژگان کلیدی اسناد فارسی یادگیری باناظر زنجیره لغوی فارس‌نت [1]J. Wang, J. Liu and C. Wang, "Keyword Extraction Based on PageRank," in Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2007.##[2]X. Li and F. Song, "Keyphrase Extraction and Grouping Based on Association Rules," in FLAIRS Conference, Hollywood, Florida, 2015.##[3] B. Lott, "Survey of keyword extraction techniques," UNM Education, 2012.##[4] R. Nelken and S. M. Shieber, "Lexical chaining and word-sense-disambiguation," School of Engineering and Applied Sciences, Harvard University, Cambridge ,Technical Report TR-06-07, MA, 2007.##[5] G. Ercan, "Automated text summarization and keyphrase extraction," M.S. thesis, bilkent univer-sity, Ankara, Turkey, 2006.##[6] M. Shamsfard, "Towards Semi Automatic Construction of a Lexical Ontology for Persian," in sixth International Conference on Language Resources and Evaluation, Morocco, 2008.##[7] M. Galley and K. McKeown, "Improving word sense disambiguation in lexical chaining," IJCAI, vol. 3, pp. 1486-1488, 2003.##[8] k. Hasan and v. Ng, "Automatic Keyphrase Extraction: A Survey of the State of the Art," in ACL, 2014.##[9] C. Wu, M. Marchese and J. Jiang, "Machine Learning-Based Keywords Extraction for Scien-tific Literature," Journal of Universal Computer Science, vol. 13, no. 10, pp. 1471-1483, 2007.##[10] S. Beliga, "Keyword extraction: a review of methods and approaches," University of Rijeka, Department of Informatics, Rijeka, 2014.##[11] S. beliga, A. Mestrovic and S. Martincic, "An overview of graph-based keyword extraction methods and approaches," Journal of information and organizational sciences, vol. 39, no. 1, pp. 1-20, 2015.##[12] T. Pay and S. Lucci, "Automatic Keyword Extraction: An Ensemble Method," in 2017 IEEE International Conference on Big Data, Boston, 2017.##[13] M. Johansson and P. Lindstrom, "Keyword Extraction using Machine Learning," M.S. thesis, Gothenburg University, Gothenburg, Sweden, 2010.##[14] A. Hulth, "Combining machine learning and natural language processing for automatic key-word extraction," Ph.D. dissertation, Stockholm University, Stockholms, Sweden, 2004.##[15] Y. HaCohen-kerner, Z. Gross and A. Masa, "Automatic extraction and learning of keyphrases from scientific articles," in International Con-ference on Intelligent Text Processing and Computational Linguistics. Springer Berlin Heidelberg, 2005.##[16] O. Medelyan and I. H. Witten, "Thesaurus based automatic keyphrase indexing," in Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries. ACM, 2006.##[17] C. Zhang, H. WANG, Y. LIU, D. WU, Y. LIAO and B. WANG, "Automatic Keyword Extraction from Documents Using Conditional Random Fields," Computational Information Systems, vol. 4, no. 3, pp. 1169-1180, 2008.##[18] M. Krapivin, A. Autayeu, M. Ma, E. Blanzieri and N. Segata, "Keyphrases extraction from scientific documents: improving machine learning approa-ches with natural language processing," in International Conference on Asian Digital Lib-raries. Springer Berlin Heidelberg, 2010.##[19] C. Caragea and F. Bulgarov, "Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach," in Empirical Methods in Natural Language Processing (EMNLP), Doha, 2014.##[20] O. Alqaryouti, T. A. Farouk, A. R. Nabhan and K. Shaalan, "Graph-Based Keyword Extraction," in Intelligent Natural Language Processing: Trends and Applications, Springer, Cham, 2018, pp. 159-172.##[21] Z. Liu and P. Liu, "Clustering to Find Exemplar Terms for Keyphrase Extraction," in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 2009.##[22] S. Arabi Narei, M.Vahidi Asl and B.Minaei Bidgoli, "Keyword extraction for persian text classification,"in First Iran Data Mining Conf-erence, Amir kabir university ,2007.##[23]M. Mohammadi Janghara and M.Analouei , " keyword extraction from persian documents", in 13th Annual Conference of Computer Society of Iran, kish island- computer society, Sharif Univer-sity of Technology, 2008.##[24] A. Ahmadi and T. Hoseinikhah, "Keyword Extraction from a text using Neural Network," in Tenth international industrial engineering con-ference, Amirkabir University, 2014.##[25]F. Rad, H. Parvin, A. Dehbashi, B. Minaei, "A New Method for Automatic Indexing and Extract-ing Keywords for Information Retrieval and Clustering of Texts", Journal of Signal Processing and Data, Volume 13, No. 1, page 87-100, 2017.##[26]H. G. Silber and K. F. McCoy, "Efficiently computed lexical chains as an intermediate representation for automatic text summarization," Association for Computational Linguistics, vol. 28, no. 4, pp. 487-496, 2002.##[27]M. Enss, "An investigation of word sense disambiguation for improving lexical chaining," M.S. thesis, Waterloo University, Waterloo, Canada, 2006.##[28]X. Li, "Keyphrase Extraction and Grouping Based on Association Rules," M.S. thesis, Guelph University, Guelph, Canada, 2014.##[29]B. Lott, "Survey of keyword extraction tech-niques," December, 2012.##[30]S. Beliga, "Keyword extraction: a review of me-thods and approaches," unpublished, 2014.##[1]J. Wang, J. Liu and C. Wang, "Keyword Extraction Based on PageRank," in Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2007.##[2]X. Li and F. Song, "Keyphrase Extraction and Grouping Based on Association Rules," in FLAIRS Conference, Hollywood, Florida, 2015.##[3] B. Lott, "Survey of keyword extraction techniques," UNM Education, 2012.##[4] R. Nelken and S. M. Shieber, "Lexical chaining and word-sense-disambiguation," School of Engineering and Applied Sciences, Harvard University, Cambridge ,Technical Report TR-06-07, MA, 2007.##[5] G. Ercan, "Automated text summarization and keyphrase extraction," M.S. thesis, bilkent univer-sity, Ankara, Turkey, 2006.##[6] M. Shamsfard, "Towards Semi Automatic Construction of a Lexical Ontology for Persian," in sixth International Conference on Language Resources and Evaluation, Morocco, 2008.##[7] M. Galley and K. McKeown, "Improving word sense disambiguation in lexical chaining," IJCAI, vol. 3, pp. 1486-1488, 2003.##[8] k. Hasan and v. Ng, "Automatic Keyphrase Extraction: A Survey of the State of the Art," in ACL, 2014.##[9] C. Wu, M. Marchese and J. Jiang, "Machine Learning-Based Keywords Extraction for Scien-tific Literature," Journal of Universal Computer Science, vol. 13, no. 10, pp. 1471-1483, 2007.##[10] S. Beliga, "Keyword extraction: a review of methods and approaches," University of Rijeka, Department of Informatics, Rijeka, 2014.##[11] S. beliga, A. Mestrovic and S. Martincic, "An overview of graph-based keyword extraction methods and approaches," Journal of information and organizational sciences, vol. 39, no. 1, pp. 1-20, 2015.##[12] T. Pay and S. Lucci, "Automatic Keyword Extraction: An Ensemble Method," in 2017 IEEE International Conference on Big Data, Boston, 2017.##[13] M. Johansson and P. Lindstrom, "Keyword Extraction using Machine Learning," M.S. thesis, Gothenburg University, Gothenburg, Sweden, 2010.##[14] A. Hulth, "Combining machine learning and natural language processing for automatic key-word extraction," Ph.D. dissertation, Stockholm University, Stockholms, Sweden, 2004.##[15] Y. HaCohen-kerner, Z. Gross and A. Masa, "Automatic extraction and learning of keyphrases from scientific articles," in International Con-ference on Intelligent Text Processing and Computational Linguistics. Springer Berlin Heidelberg, 2005.##[16] O. Medelyan and I. H. Witten, "Thesaurus based automatic keyphrase indexing," in Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries. ACM, 2006.##[17] C. Zhang, H. WANG, Y. LIU, D. WU, Y. LIAO and B. WANG, "Automatic Keyword Extraction from Documents Using Conditional Random Fields," Computational Information Systems, vol. 4, no. 3, pp. 1169-1180, 2008.##[18] M. Krapivin, A. Autayeu, M. Ma, E. Blanzieri and N. Segata, "Keyphrases extraction from scientific documents: improving machine learning approa-ches with natural language processing," in International Conference on Asian Digital Lib-raries. Springer Berlin Heidelberg, 2010.##[19] C. Caragea and F. Bulgarov, "Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach," in Empirical Methods in Natural Language Processing (EMNLP), Doha, 2014.##[20] O. Alqaryouti, T. A. Farouk, A. R. Nabhan and K. Shaalan, "Graph-Based Keyword Extraction," in Intelligent Natural Language Processing: Trends and Applications, Springer, Cham, 2018, pp. 159-172.##[21] Z. Liu and P. Liu, "Clustering to Find Exemplar Terms for Keyphrase Extraction," in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 2009.##[22]س. عربی نرئی, م. وحیدی اصل و ب. مینایی بیدگلی, "استخراج واژگان کلیدی جهت طبقه‌بندی متون فارسی," در اولین کنفرانس داده‌کاوی ایران, دانشگاه صنعتی امیرکبیر, 1386.##[22] S. Arabi Narei, M.Vahidi Asl and B.Minaei Bidgoli, "Keyword extraction for persian text classification,"in First Iran Data Mining Conf-erence, Amir kabir university ,2007.##[23] م. محمدی جنقرا و م. آنالویی, "استخراج واژگان کلیدی اسناد فارسی," در سیزدهمین کنفرانس سالانه انجمن کامپیوتر ایران, جزیره کیش - انجمن کامپیوتر, دانشگاه صنعتی شریف, 1386.##[23]M. Mohammadi Janghara and M.Analouei , " keyword extraction from persian documents", in 13th Annual Conference of Computer Society of Iran, kish island- computer society, Sharif Univer-sity of Technology, 2008.##[24]ع. احمدی و ط. حسینی خواه, "استخراج واژگان کلیدی یک متن با استفاده از شبکه‌های عصبی," در دهمین کنفرانس بین المللی مهندسی صنایع, دانشگاه امیرکبیر, 1392.##[24] A. Ahmadi and T. Hoseinikhah, "Keyword Extraction from a text using Neural Network," in Tenth international industrial engineering con-ference, Amirkabir University, 2014.##[25] ف. راد, ح. پروین, آ. دهباشی و ب. مینایی, "ارائه روشی جدید برای شاخص‌گذاری خودکار و استخراج واژگان کلیدی برای بازیابی اطلاعات و خوشه‌بندی متون," نشریه پردازش علائم و داده‌ها, جلد 13, شماره 1, صفحه 100-87 ,1395.##[25]F. Rad, H. Parvin, A. Dehbashi, B. Minaei, "A New Method for Automatic Indexing and Extract-ing Keywords for Information Retrieval and Clustering of Texts", Journal of Signal Processing and Data, Volume 13, No. 1, page 87-100, 2017.##[26]H. G. Silber and K. F. McCoy, "Efficiently computed lexical chains as an intermediate representation for automatic text summarization," Association for Computational Linguistics, vol. 28, no. 4, pp. 487-496, 2002.##[27]M. Enss, "An investigation of word sense disambiguation for improving lexical chaining," M.S. thesis, Waterloo University, Waterloo, Canada, 2006.##[28]X. Li, "Keyphrase Extraction and Grouping Based on Association Rules," M.S. thesis, Guelph University, Guelph, Canada, 2014.##[29]B. Lott, "Survey of keyword extraction tech-niques," December, 2012.##[30]S. Beliga, "Keyword extraction: a review of me-thods and approaches," unpublished, 2014.## ##

ارائه طرح احراز اصالت سبک با قابلیت گمنامی و اعتماد در اینترنت اشیا The Lightweight Authentication Scheme with Capabilities of Anonymity and Trust in Internet of Things (IoT) 1 1 اینترنت اشیا مفهوم جدیدی است که باعث حضور حس‌گرها در زندگی انسان شده است؛ به­طوری‌که تمامی اطلاعات توسط همین حس‌گرها جمع‌آوری، پردازش و منتقل می‌شوند. برای برقراری یک ارتباط امن، با افزایش تعداد حس‌گرها، نخستین چالش، احراز اصالت بین آنها است. گمنامی، سبک‌وزنی و قابلیت اعتماد نیز از جمله مواردی هستند که باید مد نظر قرار گیرند. در این پژوهش پروتکل‌های احراز اصالت در حوزه اینترنت اشیا بررسی شده و محدودیت­ها و آسیب‌پذیری‌های امنیتی آنها مورد تحلیل واقع شده‌اند. هم‌چنین پروتکل احراز اصالت جدیدی پیشنهاد می‌شود که گمنامی به‌عنوان یک پارامتر مهم، در آن لحاظ می­شود. از طرفی تابع چکیده‌ساز و عمل‌گرهای منطقی نیز مورد استفاده قرار می­گیرند تا هم پروتکل سبک باشد و هم حس‌گر‌ها بتوانند به­عنوان موجودیت‌هایی محدود از لحاظ محاسباتی، از آنها استفاده کند. در این پروتکل نیازمندی‌های امنیتی از قبیل قابلیت عدم ردیابی، مقیاس­پذیری، دسترس­پذیری و غیره لحاظ شده‌اند و پروتکل در مقابل حملات مختلف از جمله حمله جعل هویت، تکرار، مرد میانی و ... مقاوم است. 2 The Internet of Things (IoT), is a new concept that its emergence has caused ubiquity of sensors in the human life. All data are collected, processed, and transmitted by these sensors. As the number of sensors increases,   the first challenge in establishing a secure connection is authentication between sensors. Anonymity, lightweight, and trust between entities are other main issues that should be considered. However, this challenge also requires some features so that the authentication is done properly. Anonymity, light weight and trust between entities are among the issues that need to be considered. In this study, we have evaluated the authentication protocols concerning the Internet of Things and analyzed the security vulnerabilities and limitations found in them. A new authentication protocol is also proposed using the hash function and logical operators, so that the sensors can use them as computationally limited entities. This protocol is performed in two phases and supports two types of intra-cluster and inter-cluster communication. The analysis of proposed protocol shows that security requirements have been met and the protocol is resistant against various attacks. In the end, confidentiality and authentication of the protocol are proved applying AVISPA tool and the veracity of the protocol using the BAN logic. Focusing on this issue, in this paper, we have evaluated the authentication protocols in the Internet of Things and analyzed their limitations and security vulnerabilities. Moreover, a new authentication protocol is presented which the anonymity is its main target. The hash function and logical operators are used not only to make the protocol lightweight but also to provide some computational resources for sensors. In compiling this protocol, we tried to take into account three main approaches to covering the true identifier, generating the session key, and the update process after the authentication process. As with most authentication protocols, this protocol is composed of two phases of registration and authentication that initially register entities in a trusted entity to be evaluated and authenticated at a later stage by the same entity. It is assumed that in the proposed protocol we have two types of entities; a weak entity and a strong entity. The poor availability of SNs has low computing power and strong entities of CH and HIoTS that can withstand high computational overhead and carry out heavy processing. We also consider strong entities in the proposed protocol as reliable entities since the main focus of this research is the relationship between SNs. On the other hand, given the authenticity of the sensors and the transfer of the key between them through these trusted entities, the authenticity of the sensors is confirmed, and the relationship between them is also reliable. This protocol supports two types of intra-cluster and inter-cluster communication. The analysis of the proposed protocol shows that security requirements such as untraceability, scalability, availability, etc. have been met and it is resistant against the various attacks like replay attack, eavesdropping attack. 111 122 2017/09/152016/12/312016/09/22017/10/292017/11/242017/11/162017/12/252017/12/32017/10/6 1396/7/14 2019/01/92019/01/92019/01/92019/01/92019/01/262019/01/92018/10/62018/05/162018/08/6 1397/5/15 شادی جانبابایی Shadi Janbabaei دانشگاه شاهد تهران ایران gharaee@gmail.com حسین قرائی Hossein Gharaee پژوهشگاه ارتباطات و فناوری اطلاعات (مرکز تحقیقات مخابرات ایران) ایران gharaee@itrc.ac.ir ناصر محمد زاده Naser Mohammadzadeh دانشگاه شاهد تهران ایران mohammadzadeh@shahed.ac.ir Internet of things Authentication Anonymity Lightweight اینترنت اشیا احراز اصالت گمنامی سبک وزنی و اعتماد [1] D. Miorandi, S. Sicari, F. De Pellegrini, and I. Chlamtac, "Internet of things: Vision, applications and research challenges," Ad Hoc Networks, vol. 10, pp. 1497-1516, 2012.##[2] K. Ashton, "That 'internet of things' thing," RFiD Journal, vol. 22, pp. 97-114, 2009.##[3] M. Abomhara and G. M. Koien, "Security and privacy in the Internet of Things: Current status and open issues," in Privacy and Security in Mobile Systems (PRISMS), 2014 International Conference on, 2014, pp. 1-8.##[4] D. Bandyopadhyay and J. Sen, "Internet of things: Applications and challenges in technology and standardization," Wireless Personal Communica-tions, vol. 58, pp. 49-69, 2011.##[5] R. Roman, J. Zhou, and J. Lopez, "On the features and challenges of security and privacy in distribut-ed internet of things," Computer Networks, vol. 57, pp. 2266-2279, 2013.##[6] E. Vasilomanolakis, J. Daubert, M. Luthra, V. Gazis, A. Wiesmaier, and P. Kikiras, "On the Security and Privacy of Internet of Things Archit-ectures and Systems," in Secure Internet of Things (SIoT), 2015 International Workshop on, 2015, pp. 49-57.##[7] J. M. Kizza, "Computer Network Security Protocols," in Guide to Computer Network Secu-rity, ed: Springer, 2015, pp. 357-386.##[8] M. R. Kanjee, K. Divi, and H. Liu, "A physiological authentication scheme in secure healthcare sensor networks," in Sensor Mesh and Ad Hoc Communications and Networks (SECON), 2010 7th Annual IEEE Communications Society Conference on, 2010, pp. 1-3.##[9] T. Kothmayr, C. Schmitt, W. Hu, M. Brunig, and G. Carle, "A DTLS based end-to-end security architecture for the Internet of Things with two-way authentication," in Local Computer Networks Workshops (LCN Workshops), 2012 IEEE 37th Conference on, 2012, pp. 956-963.##[10] M. Hernandez-Goya and P. Caballero-Gil, "Analysis of Lightweight Cryptographic Solu-tions for Authentication in IoT," in Inter-national Conference on Computer Aided Systems Theory, 2013, pp. 373-380.##[11] S. Janbabaei, H. Gharaee, and N. Mohammad-zadeh, "Lightweight, anonymous and mutual authentication in IoT infrastructure," in Tele-communications (IST), 2016 8th Interna-tional Symposium on, 2016, pp. 162-166.##[12] D. A. Ha, K. T. Nguyen, and J. K. Zao, "Efficient authentication of resource-constrained IoT devices based on ECQV implicit certificates and datagram transport layer security protocol," in Proceedings of the Seventh Symposium on Info-rmation and Communication Technology, 2016, pp. 173-179.##[13] P. Porambage, C. Schmitt, P. Kumar, A. Gurtov, and M. Ylianttila, "Two-phase authentication protocol for wireless sensor networks in dis-tributed IoT applications," in Wireless Comm-unications and Networking Conference (WCNC), 2014 IEEE, 2014, pp. 2728-2733.##[14] P. Porambage, C. Schmitt, P. Kumar, A. Gurtov, and M. Ylianttila, "Pauthkey: A pervasive authentication protocol and key establishment scheme for wireless sensor networks in distri-buted iot applications," International Journal of Distributed Sensor Networks, vol. 2014, 2014.##[15] K. Srivastava, A. K. Awasthi, S. D. Kaul, and R. Mittal, "A hash based mutual RFID tag authen-tication protocol in telecare medicine info-rmation system," Journal of medical systems, vol. 39, p. 153, 2015.##[16] P. Gope and T. Hwang, "Untraceable sensor movement in distributed IoT infrastructure," Sensors Journal, IEEE, vol. 15, pp. 5340-5348, 2015.##[17] M.-C. Chuang and J.-F. Lee, "TEAM: Trust-extended authentication mechanism for vehicular ad hoc networks," Systems Journal, IEEE, vol. 8, pp. 749-758, 2014.##[18] D. He and S. Zeadally, "An analysis of RFID authentication schemes for internet of things in healthcare environment using elliptic curve cryptography," IEEE Internet of Things Journal, vol. 2, pp. 72-83, 2015.##[1] D. Miorandi, S. Sicari, F. De Pellegrini, and I. Chlamtac, "Internet of things: Vision, applications and research challenges," Ad Hoc Networks, vol. 10, pp. 1497-1516, 2012.##[2] K. Ashton, "That 'internet of things' thing," RFiD Journal, vol. 22, pp. 97-114, 2009.##[3] M. Abomhara and G. M. Koien, "Security and privacy in the Internet of Things: Current status and open issues," in Privacy and Security in Mobile Systems (PRISMS), 2014 International Conference on, 2014, pp. 1-8.##[4] D. Bandyopadhyay and J. Sen, "Internet of things: Applications and challenges in technology and standardization," Wireless Personal Communica-tions, vol. 58, pp. 49-69, 2011.##[5] R. Roman, J. Zhou, and J. Lopez, "On the features and challenges of security and privacy in distribut-ed internet of things," Computer Networks, vol. 57, pp. 2266-2279, 2013.##[6] E. Vasilomanolakis, J. Daubert, M. Luthra, V. Gazis, A. Wiesmaier, and P. Kikiras, "On the Security and Privacy of Internet of Things Archit-ectures and Systems," in Secure Internet of Things (SIoT), 2015 International Workshop on, 2015, pp. 49-57.##[7] J. M. Kizza, "Computer Network Security Protocols," in Guide to Computer Network Secu-rity, ed: Springer, 2015, pp. 357-386.##[8] M. R. Kanjee, K. Divi, and H. Liu, "A physiological authentication scheme in secure healthcare sensor networks," in Sensor Mesh and Ad Hoc Communications and Networks (SECON), 2010 7th Annual IEEE Communications Society Conference on, 2010, pp. 1-3.##[9] T. Kothmayr, C. Schmitt, W. Hu, M. Brunig, and G. Carle, "A DTLS based end-to-end security architecture for the Internet of Things with two-way authentication," in Local Computer Networks Workshops (LCN Workshops), 2012 IEEE 37th Conference on, 2012, pp. 956-963.##[10] M. Hernandez-Goya and P. Caballero-Gil, "Analysis of Lightweight Cryptographic Solu-tions for Authentication in IoT," in Inter-national Conference on Computer Aided Systems Theory, 2013, pp. 373-380.##[11] S. Janbabaei, H. Gharaee, and N. Mohammad-zadeh, "Lightweight, anonymous and mutual authentication in IoT infrastructure," in Tele-communications (IST), 2016 8th Interna-tional Symposium on, 2016, pp. 162-166.##[12] D. A. Ha, K. T. Nguyen, and J. K. Zao, "Efficient authentication of resource-constrained IoT devices based on ECQV implicit certificates and datagram transport layer security protocol," in Proceedings of the Seventh Symposium on Info-rmation and Communication Technology, 2016, pp. 173-179.##[13] P. Porambage, C. Schmitt, P. Kumar, A. Gurtov, and M. Ylianttila, "Two-phase authentication protocol for wireless sensor networks in dis-tributed IoT applications," in Wireless Comm-unications and Networking Conference (WCNC), 2014 IEEE, 2014, pp. 2728-2733.##[14] P. Porambage, C. Schmitt, P. Kumar, A. Gurtov, and M. Ylianttila, "Pauthkey: A pervasive authentication protocol and key establishment scheme for wireless sensor networks in distri-buted iot applications," International Journal of Distributed Sensor Networks, vol. 2014, 2014.##[15] K. Srivastava, A. K. Awasthi, S. D. Kaul, and R. Mittal, "A hash based mutual RFID tag authen-tication protocol in telecare medicine info-rmation system," Journal of medical systems, vol. 39, p. 153, 2015.##[16] P. Gope and T. Hwang, "Untraceable sensor movement in distributed IoT infrastructure," Sensors Journal, IEEE, vol. 15, pp. 5340-5348, 2015.##[17] M.-C. Chuang and J.-F. Lee, "TEAM: Trust-extended authentication mechanism for vehicular ad hoc networks," Systems Journal, IEEE, vol. 8, pp. 749-758, 2014.##[18] D. He and S. Zeadally, "An analysis of RFID authentication schemes for internet of things in healthcare environment using elliptic curve cryptography," IEEE Internet of Things Journal, vol. 2, pp. 72-83, 2015.## ##

برچسب‌گذاری ادات سخن زبان فارسی با استفاده از مدل شبکۀ فازی Part Of Speech Tagging of Persian Language using Fuzzy Network Model 1 1 برچسب‌گذاری ادات سخن یکی از مسائل مطرح در حوزۀ پردازش زبان‌های طبیعی است. هدف در این مسئله تعیین نقش واژگان در جمله است. برحسب این برچسب‌گذاری ویژگی‌های دستوری و نحوی واژگان نیز مشخص می‌شود. در این مقاله یک روش مبتنی بر آماری برای ادات سخن فارسی پیشنهاد شده است. در این روش محدودیت‌های روش‌های آماری با استفاده از معرّفی یک مدل شبکه فازی کاهش پیدا کرده است؛ به­طوری­که در­صورت وجود تعداد کمی دادۀ آموزشی، مدل فازی پارامترهای قابل اطمینان‌تری را تخمین می‌زند. در این روش ابتدا هنجار‌سازی به‌عنوان پیش‌پردازش صورت گرفته و سپس فراوانی هر واژه با توجه به برچسب مربوطه به‌صورت یک تابع فازی تخمین زده و سپس مدل شبکه فازی  تشکیل ­شده و درجۀ هر یال در این شبکه با استفاده از یک شبکۀ عصبی و تابع عضویت مشخص می‌شود. درنهایت بعد از این­که مدل شبکۀ فازی برای یک جمله ساخته شد، از الگوریتم ویتربی برای تعیین محتمل‌ترین مسیر در این شبکه استفاده شده است. نتایج آزمایش روی پیکرۀ بی‌جن‌خان کارایی این روش را تأیید کرده و نشان می‌دهد که روش پیشنهادی در شرایطی که داده‌های آموزشی کم­تری در اختیار باشد، از روش‌های مشابه، مثل مدل مخفی مارکوف عملکرد بهتری دارد. 2 Part of speech tagging (POS tagging) is an ongoing research in natural language processing (NLP) applications. The process of classifying words into their parts of speech and labeling them accordingly is known as part-of-speech tagging, POS-tagging, or simply tagging. Parts of speech are also known as word classes or lexical categories. The purpose of POS tagging is determining the grammatical category of the words in a sentence. Grammatical and syntactical features of words are determined based on these tags. The function of existing tagging methods depends on the corpus. As if the educational and test data are extracted from a corpus, the methods are well-functioning, or if the number of educational data is low, especially in probabilistic methods, the accuracy level also decreases. The words used in sentences are often vague. For example, the word 'Mahrami' can be a noun or an adjective. Existing ambiguity can be eliminated by using neighbor words and an appropriate tagging method. Methods in this domain are divided into several categories such as:based on memory [2], rule based methods [5], statistical [6], and neural network [7]. The precision of more of these methods is an average of 95% [1]. In the paper [13], using the TnT probabilistic tagging and smoothing and variations on the estimation of the three-words likelihood function, a tagging model has been created that has reached 96.7% in total on the Penn Treebank and NEGRA entities. [14] Using the representation of the dependency network and extensive use of lexical features, such as the conditional continuity of the sequence of words, as well as the effective use of the foreground in the linear models of linear logarithms and fine-grained modeling of the unknown words, on the Penn Treebank WSJ model, 97.24% accuracy is achieved. The first work in Farsi that has used the word neighborhoods and the similarity distribution between them. The accuracy of the system is 57.5%. In [19], a Persian open source tagger called HunPoS was proposed. This tag uses the same TnT method based on the Hidden Markov model and a triple sequence of words, and 96.9% has reached on the ''Bi Jen Khan'' corpus. In this paper a statistical based method is proposed for Persian POS tagging. The limitations of statistical methods are reduced by introducing a fuzzy network model, such that the model is able to estimate more reliable parameters with a small set of training data. In this method, normalization is done as a preprocessing step and then the frequency of each word is estimated as a fuzzy function with respect to the corresponding tag. Then the fuzzy network model is formed and the weight of each edge is determined by means of a neural network and a membership function. Eventually, after the construction of a fuzzy network model for a sentence, the Viterbi algorithm as s subset of Hidden Markov Model (HMM) algorithms is used to specify the most probable path in the network. The goal of this paper is to solve a challenge of probabilistic methods when the data is low and estimation made by these models  is mistaken. The results of testing this method on ``Bi Jen Khan'' corpus verified that the proposed method has better performance than similar methods, like hidden Markov model, when fewer training examples are available. In this experiment, several times the data is divided into two groups of training and test with different sizes ascending. On the other hand, in the initial experiments, we reduced the train data size and, in subsequent experiments, increased its size and compared with the HMM algorithm. As shown in figure 4, the train and test set and are directly related to each other, as the error rate decreases with increasing the training set and vice versa. In tests, three criteria involving precision, recall and F1 have been used. In Table 4, the implementation of HMM models and a fuzzy network is compared with each other and the results are shown.   123 130 2017/09/152016/12/312016/09/22017/10/292017/11/242017/11/162017/12/252017/12/32017/10/62016/12/21 1395/10/1 2019/01/92019/01/92019/01/92019/01/92019/01/262019/01/92018/10/62018/05/162018/08/62019/01/9 1397/10/19 محمد بادپیما mohammad badpeima دانشگاه مالک‌اشتر ایران badpeima.mohammad@chmail.ir فاطمه حورعلی Fatemeh hourali مجتمع آموزش عالی اسفراین ایران hourali@esfarayen.ac.ir مریم حورعلی Maryam hourali دانشگاه مالک‌اشتر ایران maryam_hourali@yahoo.com : Natural language processing Part of speech (POS) tagging Persian language Fuzzy Neural network پردازش زبان‌های طبیعی برچسب‌زنی اجزای سخن زبان فارسی فازی شبکۀ عصبی [1] M. R. Feizi Derakhshi, F. Firozi, M. Rahimi, "Comparison of Works Performed on the Persian Part-of-Speech Tagging," Computational Linguis-tics, 3rd National Conference on Computer Linguistics, Sharif University of Technology, 2014.##[2] M. Hosseini, "Automatic labeling system and automatic disambiguation of the components of the word for the textual form of Persian language," MA, Iran University of Science And Technology, Tehran, 2008.##[3] M. BijanKhan, "The Role of the Corpus in Writing a Grammar: An Introduction to a Software", Iranian Journal of Linguistics, 19(2), 2004.##[4] G. D. Forney, "The Viterbi algorithm," Proceedings of the IEEE, pp. 268-278, 1973.##[5] E. Brill, "A simple rule-based part of speech tagger", In Proceedings of the 3rd Conference on Applied Natural Language Process-ing(ANLP-92), pp. 153-155, 1992.##[6] K. W. Church, "A stochastic PARTS program and noun phrase parser for unrestricted text", In Proceedings of Applied Natural Language Pro-cessing, pp. 136-143, 1988.##[7] J. Benello, A. W. Mackie , and J. A. Anderson , "Syntactic category disambiguation with neural networks," Computer Speech and Language, vol.3, pp.203-217, 1989.##[8] H. Hidekiyo and Y. Nishkawa, "Fuzzy network technique for technological forecast-ing", Fuzzy Sets and Systems, pp. 99-113, 1984.##[9] H. Kawamura, "Fuzzy network for decision support systems", Fuzzy Sets and Systems, pp. 59-72, 1993.##[10] S. Chanas and, W. Kolodziejczyk, "Maximum flow in a network with fuzzy arc capacities", Fuzzy Sets and Systems, pp. 165-173, 1982.##[11] R. Sedgewick, "Algorithms in C," Addison-Wes-ley Publishing Company, 1990.##[12] H.-J. Zimmermann , "Fuzzy Set Theory and Its Applications, " Kluwer-Nijhoff Publishing, pp. 61-82, 1985.##[13] T. Brants, "TnT - a statistical partof-speech tagger," In Proceedings of the 6th Conference on Applied Natural Language Processing, 2000, pages 224-231.##[14] K. Toutanova, D. Klein, Ch. D. Manning and Y. Singer, "Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network", 2003,##[15] J. Giménez, and L. Màrquez, "A general pos tagger generator based on support vector machines, " In Proceedings of the 4th Interna-tional Conference on Language Resources and Evaluation (LREC 2004), Lisbon, Portugal.##[16] H. Tseng, D. Jurafsky, and Ch. Manning. "Morphological features help POS tagg-ing of unknown words across language varieties, " Fourth SIGHAN Work-shop on Chinese Language Processing, 2005, pp. 32-39.##[17] P. Hal acsy, A. Kornai, and C. Oravecz, "HunPos - an open source trigram tagger, ", In Proceedings of the 45th Annual Meeting of the Association for Com-putational Linguistics, Posters Prague, Czech Republic, 2007.##[18] S. Mostafa ASSI and M. Haji Abdolhosseini, "Grammatical Tagging of a Persian Corpus," Institute for Humanities and Cultural Studies, 2000.##[19] S. Mojgan, "A Statistical Part-of-Speech Tagger for Persian," Department of Linguistics and Philology, NODALIDA 2011, Riga, Latvia, May 11-13, 2011.##[20] K. Jae-Hoon, and G. Chang Kim, "Fuzzy network model for part-of-speech tagging under small training data," Natural Language En-gineering 2.02 (1996), pp. 95-110.##[1] محمدرضا فیضی درخشی، فرهنگ فیروزی، مهدی رحیمی،"مقایسه کارهای انجام‌شده برای برچسب‌گذاری ادات سخن زبان فارسی"، زبان‌شناسی رایانشی، سومین همایش ملی زبان‌شناسی رایانشی، دانشگاه صنعتی شریف، ۱۳۹۳.##[1] M. R. Feizi Derakhshi, F. Firozi, M. Rahimi, "Comparison of Works Performed on the Persian Part-of-Speech Tagging," Computational Linguis-tics, 3rd National Conference on Computer Linguistics, Sharif University of Technology, 2014.##[2] مهدی حسینی، سیستم برچسب‌گذاری و ابهام‌زدایی خودکار اجزای کلام برای پیکره متنی زبان فارسی، کارشناسی ارشد، علم و صنعت، تهران، ۱۳۸۷.##[2] M. Hosseini, "Automatic labeling system and automatic disambiguation of the components of the word for the textual form of Persian language," MA, Iran University of Science And Technology, Tehran, 2008.##[3] M. BijanKhan, "The Role of the Corpus in Writing a Grammar: An Introduction to a Software", Iranian Journal of Linguistics, 19(2), 2004.##[4] G. D. Forney, "The Viterbi algorithm," Proceedings of the IEEE, pp. 268-278, 1973.##[5] E. Brill, "A simple rule-based part of speech tagger", In Proceedings of the 3rd Conference on Applied Natural Language Process-ing(ANLP-92), pp. 153-155, 1992.##[6] K. W. Church, "A stochastic PARTS program and noun phrase parser for unrestricted text", In Proceedings of Applied Natural Language Pro-cessing, pp. 136-143, 1988.##[7] J. Benello, A. W. Mackie , and J. A. Anderson , "Syntactic category disambiguation with neural networks," Computer Speech and Language, vol.3, pp.203-217, 1989.##[8] H. Hidekiyo and Y. Nishkawa, "Fuzzy network technique for technological forecast-ing", Fuzzy Sets and Systems, pp. 99-113, 1984.##[9] H. Kawamura, "Fuzzy network for decision support systems", Fuzzy Sets and Systems, pp. 59-72, 1993.##[10] S. Chanas and, W. Kolodziejczyk, "Maximum flow in a network with fuzzy arc capacities", Fuzzy Sets and Systems, pp. 165-173, 1982.##[11] R. Sedgewick, "Algorithms in C," Addison-Wes-ley Publishing Company, 1990.##[12] H.-J. Zimmermann , "Fuzzy Set Theory and Its Applications, " Kluwer-Nijhoff Publishing, pp. 61-82, 1985.##[13] T. Brants, "TnT - a statistical partof-speech tagger," In Proceedings of the 6th Conference on Applied Natural Language Processing, 2000, pages 224-231.##[14] K. Toutanova, D. Klein, Ch. D. Manning and Y. Singer, "Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network", 2003,##[15] J. Giménez, and L. Màrquez, "A general pos tagger generator based on support vector machines, " In Proceedings of the 4th Interna-tional Conference on Language Resources and Evaluation (LREC 2004), Lisbon, Portugal.##[16] H. Tseng, D. Jurafsky, and Ch. Manning. "Morphological features help POS tagg-ing of unknown words across language varieties, " Fourth SIGHAN Work-shop on Chinese Language Processing, 2005, pp. 32-39.##[17] P. Hal acsy, A. Kornai, and C. Oravecz, "HunPos - an open source trigram tagger, ", In Proceedings of the 45th Annual Meeting of the Association for Com-putational Linguistics, Posters Prague, Czech Republic, 2007.##[18] S. Mostafa ASSI and M. Haji Abdolhosseini, "Grammatical Tagging of a Persian Corpus," Institute for Humanities and Cultural Studies, 2000.##[19] S. Mojgan, "A Statistical Part-of-Speech Tagger for Persian," Department of Linguistics and Philology, NODALIDA 2011, Riga, Latvia, May 11-13, 2011.##[20] K. Jae-Hoon, and G. Chang Kim, "Fuzzy network model for part-of-speech tagging under small training data," Natural Language En-gineering 2.02 (1996), pp. 95-110.## ##