ارائه یک رویکرد فازی برای بهینه‌سازی پیش‌بینی سری زمانی با مرتبه بالا Presenting a Fuzzy Approach to Optimize Predicting High Order Time Series 1 1 در این پژوهش، یک روش بهینه‌سازی پیشبینیِ فازیِ جدید مبتنی بر سریهای زمانی فازی مرتبه بالا ارائه می‌شود که درآن از تابع بهینه‌سازی ازدحام ذرات برای بهینه‌‌کردن الگوریتم محاسبه ویژگی استفاده شده است. هدف روشِ پیشنهادی، پیشبینی سری زمانی فازی مرتبه بالا است و عملکردِ بهتری را برای رفع مشکلات پیش‌بینی سری‌های زمانی فازی مرتبه بالا، ارائه می‌دهد؛ بدین‌منظور روش این پژوهش بدین صورت است که پس از فازی‌سازیِ سری زمانی و ایجاد روابط منطقی فازی، با استفاده از حدِ پایینِ بازه عنصرِ موردِ پیشبینی و بازه پس از آن و اختلاف حاصل از عناصر متوالی، محاسبات خاصی را انجام داده و مجموعهای از ویژگیها به‌دست می‌آید؛ سپس با استفاده از تابع بهینه‌سازی ازدحام ذرات بهترین پارامترها انتخاب می‌شود. در همین راستا تابع شایستگی در روش پیشنهادی دو بخش دارد: یک بخش به‌صورت کلی (میانگین تمام مرتبه‌ها) و یک بخش به‌صورت جزئی (تک‌تک ستون مرتبه‌ها) است. یافته‌ها و نتایج تجربی حاکی از این است که: ویژگی‌های به‌دست‌آمده توسط روش پیشنهادی، داده‌های پرت و زائد کمتری دارد که این خود سبب پیش‌بینی نزدیک‌تر، با خطای کمتر می‌شود و در نهایت غیرفازی انجام می‌شود. عدد حاصل، مقدار صحیح پیش‌بینی‌شده عنصر مورد نظر است. روش پیشنهادی با استفاده از داده‌های سری زمانی ثبتنام دانشگاه آلاباما که شامل تعداد ثبت‌نام سالانه در این دانشگاه - از سال 1971 تا سال 1992 میلادی- انجام شده و با سایر روش‌ها، توسط میانگین مجذور خطا و میانگین خطا، برای تعیین نرخ دقت پیش‌بینی، مورد مقایسه قرار گرفت؛ به‌‌گونه‌ای که در مقایسه با سایر روش‌ها، شاهد خطای کمتری بودیم. 2 It is difficult to apply the real world’s conceptions due to their uncertainty. Generally, time series are known to be non-linear or non-stationary. Regarding these two features, a system should be sensitive enough to apply the unity of time series and repeat this sensitiveness in the prediction. A predict system can exactly scrutinize the hidden features of time series and also can have high predicting runs. Lots of statistical tools such as regression analysis, gradient average, exponential gradient average and auto regression gradient average are used in traditional predictions. One of the biggest challenges of these approaches is the necessity of greater observations and the avoidance of linguistic variables or subjective experts’ ideas. Also these methods are limited to linear being assumptions. In order to dominate the limitations of traditional methods, many researchers have utilized soft computations like fuzzy logic, fuzzy neural networks, evolutionary algorithms and etc. In this paper, we proposed a new fuzzy prediction novel based on the high order fuzzy time series. Our proposed model is based on the higher order fuzzy time series prediction computational approach. In this method a group of features are evaluated, by adding the value of the preceding element of predicting element to the result of the series’ differences. At that, particle swarm optimization is used to optimize Calculation algorithm features, which renders a better performance in order to solve the problems of higher order fuzzy time series. Then by choosing the best features, a result can be inferred as the predicting value. The performance of the approach is presented in which after the fuzzification of time series and creating the logical fuzzy relations, by using the lower limit of the predicting element’s range and its consecutive range, and the resulted difference of sequential elements, some specific computations are done and a set of features are gained. Then, using the particle swarm optimization function, the best parameter is selected. The fitness function in the proposed method has two parts: a general section (the average of all orders) and a partial (Every columns orders). In general section, the overall average of error is shown. In Every columns orders section each column individually considered. For the second to tenth order (9 PSO separate) the answer is checked. The method is as follow; we used two parameters b and d for the feature calculation algorithm. The amount of d was manually and randomly between 3 – 1000, but PSO find the amount of b. Properties obtained by this method, have less outliers data and waste, which it causes predicted closer, with less error. Finally, defuzzification is performed. The yielded score is the predicted integer value of considered element. In order to decide the precision of the prediction’s rate, we compare the proposed model to other methods using the mean square error and the average error. In order to show the efficiency of the proposed approach, we have implemented this method on the Alabama University’s enrollment database. It can be observed that the suggested method provides better results compared to the other methods and also renders a lower error. 3 16 2016/10/26 1395/8/5 2017/06/10 1396/3/20 حسام عمرانپور Hesam Omranpour دانشگاه صنعتی نوشیروانی بابل ایران h.omranpour@nit.ac.ir فهیمه آزادیان Fahime Azadian دانشگاه صنعتی نوشیروانی بابل ایران f.azadian196@yahoo.com Predict Time series Optimization Fuzzy logic High-order fuzzy time series Fuzzification Defuzzification پیش‌بینی سـری زمـانی بهیـنه‌سازی منطق فازی سـری زمانی فازی مرتبه بالا فازی‌سازی غیرفازی‌سازی [1] Hung-Wen Peng, Shen-Fu Wu, Chia-Ching Wei, Shie-Jue Lee. (2015). Time series forecasting with a neuro-fuzzy modeling scheme. Elsevier Applied Soft Computing. Pages 481–493.##[2] Mrinalini Shah. (2012). Fuzzy based trend mapping and forecasting for time series data. Elsevier Expert Systems with Applications. Pages 6351–6358Knowledge-Based Systems.##[3] S. Askari , N. Montazerin. (2015). A high-order multi-variable Fuzzy Time Series forecasting algorithm based on fuzzy clustering. Elsevier. Expert Systems with Applications (2015) 2121–2135.##[4] Ramezani Mouziraji Farhad , Yaghoobi Mehdi , Ghanghermeh Abdolazim. (2011). Caspian Sea Level Prediction Based On Fuzzy Regressor System. Scientific Information Database (SID) Journal: WATER AND WASTEWATER; Page(s) 90 To 98.##[5] Gholamali Heydari, MohammadAli Vali , Ali Akbar Gharaveisi.(2016). Chaotic time series prediction via artificial neural square fuzzy inference Elsevier Expert Systems with Applications , Pages 461–468.##[6] Sapankevych, N.I. ; Sankar, Ravi. (2009). Time Series Prediction Using Support Vector Machines: A Survey. IEEE Computational Intelligence Society. 1556-603X.##[7] Vasilii A. Gromov, Artem N. Shulga. (2012). Chaotic time series prediction with employment of ant colony optimization. Elsevier, Expert Systems with Applications 39. 8474–8478.##[8] Mu-Yen Chen. (2014). A high-order fuzzy time series forecasting model for internet stock trading. Elsevier Future Generation Computer Systems.##[9] Ozge Cagcag Yolcu, Ufuk Yolcu, Erol Egrioglu, C. Hakan Aladag. (2016). High order fuzzy time series forecasting method based on an intersection operation. Elsevier Applied Mathematical Modelling.##[10] P. Singh. (2016). Chapter 2 Fuzzy Time Series Modeling Approaches : A‌Review. Springer. Applications of Soft Computing.##[11] Erol Egrioglu, Eren Bas, Cagdas Hakan Aladag, Ufuk Yolcu. (2016). Probabilistic Fuzzy Time Series Method Based on Artificial Neural Network. American Journal of Intelligent Systems. P-ISSN: 2165-8978 , E-ISSN: 2165-8994.##[12] Tak-chung Fu. (2011). A review on time series data mining. Elsevier, Engineering Applications of Artificial Intelligence 24, 164-181.##[13] Ahmed Kattan, Shaheen Fatima, Muhammad Arif. (2015). Time-series event-based prediction: An unsupervised learning framework based on genetic programming.Elsevier. Information Sciences 301 99–123.##[14] Brad S. chisson. (1994). Forcasting Enrollmet With Fuzzy Time Series pII.. Elsevier.. Science. 0165-114(93)E0211-A.##[15] Singh, S. R. (2009). A computational methd of forecasting based on high-order fuzzy time series. Elsevier international Journal of applied Expert Systems with Applications 36 , 10551-10559.##[16] Aghili Setare. Omranpour Hesam. Motameni Homayun. (2014). Application of a Fuzzy method for predicting based on high-order time series. IEEE, 978-1-4799-3351-8/14/$31.00.##[17] Omolbanin Yazdanbakhsh. Scott Dick. (2017). Forecasting of Multivariate Time Series via Complex Fuzzy Logic. IEEE, 2168-2216.##[18] Ping Jiang, Qingli Dong. Peizhi Li, Lanlan Lian. (2017). A novel high-order weighted fuzzy time series model and itsapplication in nonlinear time series prediction. Elsevier Applied Soft Computing.##[1] Hung-Wen Peng, Shen-Fu Wu, Chia-Ching Wei, Shie-Jue Lee. (2015). Time series forecasting with a neuro-fuzzy modeling scheme. Elsevier Applied Soft Computing. Pages 481–493.##[2] Mrinalini Shah. (2012). Fuzzy based trend mapping and forecasting for time series data. Elsevier Expert Systems with Applications. Pages 6351–6358Knowledge-Based Systems.##[3] S. Askari , N. Montazerin. (2015). A high-order multi-variable Fuzzy Time Series forecasting algorithm based on fuzzy clustering. Elsevier. Expert Systems with Applications (2015) 2121–2135.##[4] Ramezani Mouziraji Farhad , Yaghoobi Mehdi , Ghanghermeh Abdolazim. (2011). Caspian Sea Level Prediction Based On Fuzzy Regressor System. Scientific Information Database (SID) Journal: WATER AND WASTEWATER; Page(s) 90 To 98.##[5] Gholamali Heydari, MohammadAli Vali , Ali Akbar Gharaveisi.(2016). Chaotic time series prediction via artificial neural square fuzzy inference Elsevier Expert Systems with Applications , Pages 461–468.##[6] Sapankevych, N.I. ; Sankar, Ravi. (2009). Time Series Prediction Using Support Vector Machines: A Survey. IEEE Computational Intelligence Society. 1556-603X.##[7] Vasilii A. Gromov, Artem N. Shulga. (2012). Chaotic time series prediction with employment of ant colony optimization. Elsevier, Expert Systems with Applications 39. 8474–8478.##[8] Mu-Yen Chen. (2014). A high-order fuzzy time series forecasting model for internet stock trading. Elsevier Future Generation Computer Systems.##[9] Ozge Cagcag Yolcu, Ufuk Yolcu, Erol Egrioglu, C. Hakan Aladag. (2016). High order fuzzy time series forecasting method based on an intersection operation. Elsevier Applied Mathematical Modelling.##[10] P. Singh. (2016). Chapter 2 Fuzzy Time Series Modeling Approaches : A‌Review. Springer. Applications of Soft Computing.##[11] Erol Egrioglu, Eren Bas, Cagdas Hakan Aladag, Ufuk Yolcu. (2016). Probabilistic Fuzzy Time Series Method Based on Artificial Neural Network. American Journal of Intelligent Systems. P-ISSN: 2165-8978 , E-ISSN: 2165-8994.##[12] Tak-chung Fu. (2011). A review on time series data mining. Elsevier, Engineering Applications of Artificial Intelligence 24, 164-181.##[13] Ahmed Kattan, Shaheen Fatima, Muhammad Arif. (2015). Time-series event-based prediction: An unsupervised learning framework based on genetic programming.Elsevier. Information Sciences 301 99–123.##[14] Brad S. chisson. (1994). Forcasting Enrollmet With Fuzzy Time Series pII.. Elsevier.. Science. 0165-114(93)E0211-A.##[15] Singh, S. R. (2009). A computational methd of forecasting based on high-order fuzzy time series. Elsevier international Journal of applied Expert Systems with Applications 36 , 10551-10559.##[16] Aghili Setare. Omranpour Hesam. Motameni Homayun. (2014). Application of a Fuzzy method for predicting based on high-order time series. IEEE, 978-1-4799-3351-8/14/$31.00.##[17] Omolbanin Yazdanbakhsh. Scott Dick. (2017). Forecasting of Multivariate Time Series via Complex Fuzzy Logic. IEEE, 2168-2216.##[18] Ping Jiang, Qingli Dong. Peizhi Li, Lanlan Lian. (2017). A novel high-order weighted fuzzy time series model and itsapplication in nonlinear time series prediction. Elsevier Applied Soft Computing. ##

رفع نوفه ویدئو توسط تبدیل قیچک قطعه‌ای Video Denoising Using block Shearlet Transform 1 1 از روش‌های معمول در بهره‌برداری از نظام‌مندی‌ها و ویژگی‌های هندسی در سیگنال‌های چندبعدی می‌توان به استفاده از اتساع ناهمسان‌گرد[1] و مقیاس‌بندی سهموی[2] اشاره کرد که اساس تبدیلاتی همانند قیچک[3] و پیچک[4] را شکل می‌دهند. در چنین تبدیل‌هایی تحلیل کاملی از سیگنال ورودی صورت می‌پذیرد که با رشد تعداد ابعاد[5] داده، افزونگی آن به‌صورت نمایی زیاد شده و امکان پیاده‌سازی و استفاده عملی از آن‌ها را به‌شدت محدود می‌کند. در مقابل تبدیل‌های جدایی‌پذیر هر بعد داده ورودی را جداگانه مورد پردازش قرار می‌دهند که منجر به نادیده‌گرفته‌شدن نظام‌مندی‌های چندبعدی آن خواهد شد. با توجه به برتری نسبی تبدیل قیچک در مواجهه با داده‌های گسسته و برای چیره‌شدن بر مشکلات پیچیدگی زمانی و افزونگی[6] تبدیل قیچک کلاسیک در تحلیل داده‌های چندبعدی، در این مقاله ویرایش جدیدی از تبدیل قیچک گسسته با قابلیت کنترل افزونگی ارائه می‌شود. به‌بیان‌دیگر با رویکرد جدید، به‌دنبال امکان برقراری مصالحه بهتر بین افزونگی و پیچیدگی زمانی تبدیل از یک‌سو با میزان کامل‌بودن تحلیل و بهره‌برداری آن از نظام‌مندی‌های ورودی از سوی دیگر هستیم. در این راستا ماتریس اتساع به‌صورت قطری قطعه‌ای کاهش داده می‌شود که به معنای عملکرد مستقل تحلیل حاصل در زیرفضاهای متناظر با قطعه‌های مجزا خواهد بود. بدین ترتیب، شیوه تجزیه ماتریس اتساع به زیرقطعه‌ها، ابزار کنترلی مناسبی برای میزان افزونگی و پیچیدگی محاسباتی تبدیل حاصل به‌دست می‌دهد. به‌عنوان یک نمونه از کاربرد عملی رویکرد پیشنهادی، در این مقاله روشی برای رفع نوفه[7] ویدئو با استفاده از تبدیل قیچک قطعه‌ای ارائه‌شده و با تبدیل قیچک کلاسیک دو و سه‌بعدی مقایسه می‌شود. نتایج حاکی از آن است که رویکرد پیشنهادی با مصرف جزئی از زمان و حافظه تبدیل سه‌بعدی افزایش کیفیت قابل‌توجهی نسبت به تبدیل دوبعدی می‌تواند ارائه کند. * نویسنده عهده‌دار مکاتبات * Corresponding author [1] Anisotropic Dilations [2] Parabolic [3] Shearlets [4] Curvelets [5] Dimensions [6] Redundancy [7] Denoising 2 Parabolic scaling and anisotropic dilation form the core of famous multi-resolution transformations such as curvelet and shearlet, which are widely used in signal processing applications like denoising. These non-adaptive geometrical wavelets are commonly used to extract structures and geometrical features of multi-dimensional signals and preserve them in noise removal treatments. In discrete setups, it is shown that shearlets can outperform other rivals since in addition to scaling, they are formed by shear operator which can fully remain on integer grid. However, the redundancy of multidimensional shearlet transform exponentially grows with respect to the number of dimensions which in turn leads to the exponential computational and space complexity. This, seriously limits the applicability of shearlet transform in higher dimensions. In contrast, separable transforms process each dimension of data independent of other dimensions which result in missing the informative relations among different dimensions of the data. Therefore, in this paper a modified discrete shearlet transform is proposed which can overcome the redundancy and complexity issues of the classical transform. It makes a better tradeoff between completeness of the analysis achieved by processing full relations among dimensions on one hand and the redundancy and computational complexity of the resulting transform on the other hand. In fact, how dilation matrix is decomposed and block diagonalized, gives a tuning parameter for the amount of inter dimension analysis which may be used to control computation complexity and also redundancy of the resultant transform. In the context of video denoising, three different decompositions are proposed for 3x3 dilation matrix. In each block diagonalization of this dilation matrix, one dimension is separated and the other two constitute a 2D shearlet transform. The three block shearlet transforms are computed for the input data up to three levels and the resultant coefficients are treated with automatically adjusted thresholds. The output is obtained via an aggregation mechanism which combine the result of reconstruction of these three transforms. Using experiments on standard set of videos at different levels of noise, we show that the proposed approach can get very near to the quality of full 3D shearlet analysis while it keeps the computational complexity (time and space) comparable to the 2D shearlet transform. 17 30 2016/10/262017/10/6 1396/7/14 2017/06/102018/05/16 1397/2/26 حجت باقرزاده Hojjat Bagherzadeh دانشگاه فردوسی مشهد ایران hojjat.bagherzadehhosseinabad@stu.um.ac.ir احد هراتی Ahad Harati دانشگاه فردوسی مشهد ایران a.harati@um.ac.ir زهرا امیری Zahra Amiri دانشگاه فردوسی مشهد ایران za_am10@stu.um.ac.ir رجبعلی کامیابی گل RajabAli KamyabiGol دانشگاه فردوسی مشهد ایران kamyabi@um.ac.ir anisotropic dilation matrix curvelet transform multidimensional shearlet transform block diagonal dilation matrix video denoising ماتریس اتساع ناهمسانگرد تبدیل پیچک تبدیل قیچک چندبعدی ماتریس اتساع قطری قطعه‌ای رفع نویز ویدئو [1] N. Kingsbury, "Adaptive Wavelet Thresholding for Image Denoising and Compression," IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 9, no. 9, pp. 1532-1546, 2000.##[2] G. Gao, "Image denoising by non-subsampled shearlet domain multivariate model and its method noise thresholding," Optik, vol. 124, no. 22, pp. 5756-5760, 2013.##[3] S. Hauser and G. Steidl, "Convex Multiclass Segmentation with Shearlet Regularization," International Journal of Computer Mathe-matics, vol. 90, no. 1, pp. 62-81, 2013.##[4] S. Liu, S. Hu and Y. Xiao, "Image separation using wavelet-complex shearlet dictionary," Journal of Systems Engineering and Electronics, vol. 25, no. 2, pp. 314-321, 2014.##[5] G. Easley, D. Labate and W. Q. Lim, "Sparse Directional Image Representations using the Discrete Shearlet Transform," Applied and Computational Harmonic Analysis, vol. 25, no. 1, pp. 25-46, 2008.##[6] P. S. Negi and D. Labate, "3-D Discrete Shearlet Transform and Video Processing," IEEE Trans-action on Image Processing, vol. 21, no. 6, pp. 2944-2954, 2012.##[7] D. L. Donoho and M. R. Duncan, "Digital Curvelet Transform: Strategy, Implementation and Experiments," Proc. SPIE 4056, Wavelet Applications VII, pp. 12-29, 2000.##[8] E. J. Candes and D. L. Donoho, "New tight frames of curvelets and optimal representation of objects with piecewise C^2 singularities," Comm. Pure and Appl. Math., vol. 56, pp. 216-266, 2004.##[9] E. J. Candes, L. Demanet, D. L. Donoho and L. Ying, "Fast Discrete Curvelet Transforms," SIAM Multiscale Model, vol. 5, no. 3, pp. 861-899, 2006.##[10] A. Lisowska, Geometrical Multiresolution Adaptive Transforms: Theory and Applications, Springer International Publishing, 2014.##[11] J. L. Strack, F. Murtagh and J. M. Fadili, Sparse Image and Signal Processing, Cambridge Uni-versity Press, 2010.##[12] M. N. Do and M. Vetterli, "The finite ridgelet transform for image representation," IEEE Transactions on Image Processing, vol. 12, no. 1, pp. 16-28, 2003.##[13] J. Ma and G. Plonka, "A review of curvelets and recent applications," IEEE Signal Processing Magazine, 2009.##[14] D. Labate, W. Q. Lim, G. Kutyniok and G. Weiss, "Sparse multidimensional representation using shearlets," Wavelets XI, Proceedings of the SPIE, pp. 254-262, 2005.##[15] S. Yi, D. Labate, G. R. Easley and H. Krim, "A Shearlet Approach to Edge Analysis and Detection," IEEE Transaction on Image Proce-ssing, vol. 18, no. 5, pp. 929 - 941, 2009.##[16] S. Dahlke and G. Teschke, "The continuous shearlet transform in higher dimensions: varia-tions of a theme," Group Theory: Classes, Repr-esentation and Connections, and Appli-cations, vol. 1, pp. 167-175, 2010.##[17] P. Grohs and G. Kutyniok, "Parabolic mol-ecules," Foundations of Computational Math-ematics, vol. 14, no. 2, pp. 229-337, 2013.##[18] P. Grohs, S. Keiper, G. Kutyniok and M. Schafer, "α-Molecules," in Seminar for Applied Mathemetics, 2014.##[1] N. Kingsbury, "Adaptive Wavelet Thresholding for Image Denoising and Compression," IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 9, no. 9, pp. 1532-1546, 2000.##[2] G. Gao, "Image denoising by non-subsampled shearlet domain multivariate model and its method noise thresholding," Optik, vol. 124, no. 22, pp. 5756-5760, 2013.##[3] S. Hauser and G. Steidl, "Convex Multiclass Segmentation with Shearlet Regularization," International Journal of Computer Mathe-matics, vol. 90, no. 1, pp. 62-81, 2013.##[4] S. Liu, S. Hu and Y. Xiao, "Image separation using wavelet-complex shearlet dictionary," Journal of Systems Engineering and Electronics, vol. 25, no. 2, pp. 314-321, 2014.##[5] G. Easley, D. Labate and W. Q. Lim, "Sparse Directional Image Representations using the Discrete Shearlet Transform," Applied and Computational Harmonic Analysis, vol. 25, no. 1, pp. 25-46, 2008.##[6] P. S. Negi and D. Labate, "3-D Discrete Shearlet Transform and Video Processing," IEEE Trans-action on Image Processing, vol. 21, no. 6, pp. 2944-2954, 2012.##[7] D. L. Donoho and M. R. Duncan, "Digital Curvelet Transform: Strategy, Implementation and Experiments," Proc. SPIE 4056, Wavelet Applications VII, pp. 12-29, 2000.##[8] E. J. Candes and D. L. Donoho, "New tight frames of curvelets and optimal representation of objects with piecewise C^2 singularities," Comm. Pure and Appl. Math., vol. 56, pp. 216-266, 2004.##[9] E. J. Candes, L. Demanet, D. L. Donoho and L. Ying, "Fast Discrete Curvelet Transforms," SIAM Multiscale Model, vol. 5, no. 3, pp. 861-899, 2006.##[10] A. Lisowska, Geometrical Multiresolution Adaptive Transforms: Theory and Applications, Springer International Publishing, 2014.##[11] J. L. Strack, F. Murtagh and J. M. Fadili, Sparse Image and Signal Processing, Cambridge Uni-versity Press, 2010.##[12] M. N. Do and M. Vetterli, "The finite ridgelet transform for image representation," IEEE Transactions on Image Processing, vol. 12, no. 1, pp. 16-28, 2003.##[13] J. Ma and G. Plonka, "A review of curvelets and recent applications," IEEE Signal Processing Magazine, 2009.##[14] D. Labate, W. Q. Lim, G. Kutyniok and G. Weiss, "Sparse multidimensional representation using shearlets," Wavelets XI, Proceedings of the SPIE, pp. 254-262, 2005.##[15] S. Yi, D. Labate, G. R. Easley and H. Krim, "A Shearlet Approach to Edge Analysis and Detection," IEEE Transaction on Image Proce-ssing, vol. 18, no. 5, pp. 929 - 941, 2009.##[16] S. Dahlke and G. Teschke, "The continuous shearlet transform in higher dimensions: varia-tions of a theme," Group Theory: Classes, Repr-esentation and Connections, and Appli-cations, vol. 1, pp. 167-175, 2010.##[17] P. Grohs and G. Kutyniok, "Parabolic mol-ecules," Foundations of Computational Math-ematics, vol. 14, no. 2, pp. 229-337, 2013.##[18] P. Grohs, S. Keiper, G. Kutyniok and M. Schafer, "α-Molecules," in Seminar for Applied Mathemetics, 2014.## ##

بهبود به‌روزرسانی پایگاه داده تحلیلی نیمه‌آنی Improving Near Real Time Data Warehouse Refreshment 1 1 امروزه تصمیم‌گیری سریع، اهمیت زیادی در محیط کسب و کار دارد. بنابراین مدیران سعی دارند تا از داده‌های موجود در پایگاه داده تحلیلی برای پیش‌بینی و تصمیم‌گیری درست استفاده کنند. برای داشتن داده‌های مناسب، باید تغییرات ایجاد‌شده در منابع، با کم‌ترین تأخیر در پایگاه داده تحلیلی اعمال شوند. برای رسیدن به این هدف، الگوریتم‌های متعددی ارایه شده است که از آن جمله به الگوریتم X-HYBRIDJOIN می‌توان اشاره کرد. در این الگوریتم برای انتخاب پارتیشنی از لوح سخت که در حافظه اصلی بارگزاری می‌شود از روش مناسبی استفاده نشده است. در این مقاله الگوریتم جدیدی ارائه می‌شود که در آن تغییراتی در نحوه انتخاب پارتیشن یادشده، ایجاد شده است. بدین صورت که برای هر پارتیشنی از R که بر روی لوح سخت قرار دارد، تعداد رکوردهای موجود از آن پارتیشن در حافظه اصلی، شمارش شده و در آرایه‌ای ثبت می‎شود. با استفاده از آرایه به‌دست آمده، هر بار پارتیشنی را می‌توان انتخاب کرد که شامل بیشترین رکورد برای پیوست است. برای شمارش تعداد رکوردهای هر پارتیشن، در هنگام ورود جریان داده، بررسی می‌شود که جریان داده ورودی مربوط به کدام پارتیشن است. نتایج حاصل از اجرای الگوریتم جدید نشان می‌دهد که زمان پیوست و فضای مصرفی کاهش یافته است. 2 Near-real time data warehouse gives the end users the essential information to achieve appropriate decisions. Whatever the data are fresher in it, the decision would have a better result either. To achieve a fresh and up-to-date data, the changes happened in the side of source must be added to the data warehouse with little delay. For this reason, they should be transformed in to the data warehouse format. One of the famous algorithms in this area is called X-HYBRIDJOIN. In this algorithm the data characteristics of real word have been used to speed up the join operation. This algorithm keeps some partitions, which have more uses, in the main memory. In the proposed algorithm in this paper, disk-based relation is joined with input data stream. The aim of such join is to enrich stream. The proposed algorithm uses clustered index for disk-based relation and join attribute. Moreover, it is assumed that the join attribute is exclusive throughout the relation. This algorithm has improved the mentioned algorithm in two stages. At the first stage, some records of source table which are frequently accessible are detected. Detection of such records is carried out during the algorithm implementation. The mechanism is in the way that each record access is counted by a counter and if it becomes more than the determined threshold, then it is considered as the frequently used record and placed in the hash table. The hash table is used to keep the frequently used records in the main memory. When the stream is going to enter in to join area, it is searched in this table. At the second stage, the choice method of the partition which is going to load in the main memory has been changed. One dimensional array is used to choose the mentioned partition. This array helps to select a partition of source table with highest number of records for the join among all partitions of source table. Using this array in each iteration, always leads to choose the best partition loading in memory. To compare the usefulness of the suggested algorithm some experiments have been done. Experimental results show that the service rate acquired in suggested algorithm is more than the existing algorithms. Service rate is the number of joined records in a time unit. Increasing service rate causes the effectiveness of the algorithm. 31 44 2016/10/262017/10/62017/06/24 1396/4/3 2017/06/102018/05/162018/04/29 1397/2/9 عیسی حضرتی Isa Hazrati دانشگاه آزاد اسلامی، میاندوآب ایران i.hazrati@srttu.edu نگین دانشپور Negin Daneshpour دانشگاه تربیت دبیر شهید رجایی ایران ndaneshpour@srttu.edu Near Real Time Data Warehouse Join Data Stream Decision Making پایگاه داده تحلیلی نیمه‌آنی پیوست جریان داده تصمیم‌گیری [1] I. Hazrati, N. Daneshpour, "RX-HYBRIDJOIN: improved algorithm for near-real-time data warehouse," presented at the 10th Symposium on the Advancement of Science and Technology, Mashhad, Khavaran Higher Education Institution, 2015.##[2] I. Hazrati, N. Daneshpour, "IX-HYBRIDJOIN: improved algorithm for near-real-time data warehouse," presented at the 21th National Computer Conference of Iran, Tehran, Institute of Basic Sciences, 2015.##[3] A. Nguyen and A. Tjoa, "Zero-latency data warehousing for heterogeneous data sources and continuous data streams," Paper presented at the 5th International Conference on Information Integration and Web-based Applications Services, Austrian, 2003, pp. 55–64.##[4] A. Gupta, F. Yang, J. Govig, A. Kirsch, K. Chan, K. Lai, S. Wu, S. G. Dhoot, A. R. Kumar, A. Agiwal, S. Bhansali, M. Hong, J. Cameron, M. Siddiqi, D. Jones, J. Shute, A. Gubarev, S. Venka--taraman, and D. Agrawal, "Mesa: geo-replicated, near real-time, scalable data warehouse-ing," presented at the 40th International Conf-erence on Very Large Data Bases, China, 2014, pp. 1259-1270.##[5] A. Karakasidis, P. Vassiliadis, and E. Pitoura, " ETL queues for active data warehousing," presented at the 2th International Workshop on Information Quality in Information Systems, New York, 2005, pp. 28–39.##[6] C. Anderson, The Long Tail: Why the Future of Business is Selling Less of More, Hyperion, 2009.##[7] F. Dehne, Q. Kong, A. Rau-Chaplin, H. Zaboli, and R. Zhou, "Scalable real-time OLAP on cloud architectures," Journal of Parallel and Distributed Computing, vol. 79-80, pp. 31-41, 2015.##[8] F. Dehne, Q. Kong, A. Rau-Chaplin, H. Zaboli, and R. Zhou, "Distributed Tree Data Structure For Real-Time OLAP On Cloud Architectures," presented at the International Conference on Big Data, Silicon Valley, 2013, pp. 499-505.##[9] F. Majeed and S. Mahmood, "Efficient data streams processing in the real time data ware-house," presented at the 3rd IEEE Interna-tional Conference on Computer Science and Infor-mation Technology, Chengdu, 2010, pp. 57-61.##[10] F. Majeed, S. Mahmood, S. Ubaid, N. Khalil, S. Siddiqi, and F. Ashraf, "A burst resolution technique for data streams management in the real-time data warehouse," presented at the 7th Internat-ional Conference on Emerging Technologies, Islamabad, 2011, pp. 1-5.##[11] H. Zhou, D. Yang, and Y. Xu, "An ETL strategy for real-time data warehouse," presented at the International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, 2011, pp. 329–336.##[12] H. Alzeini, SH. Hameed, and M. Habaebi, "A framework for developing real-time OLAP algorithm using multi-core processing and GPU: heterogeneous computing," presented at the 5th International Conference on Mechatronics, Kuala Lumpur. 2013.##[13] L. Golab, T. Johnson, J. S. Seidel, and V. Shkapenyuk, "Stream warehousing with data depot," presented at the 35th SIGMOD Interna-tional Conference on Management of Data, Rhode Island, 2009, pp. 847–854.##[14] L. Chen, W. Rahayu, and D. Taniar, "Towards near real-time data warehousing," presented at the 24th IEEE International Conference on Advanced Information Networking and Applications, Perth, 2011, pp. 1150-1157.##[15] M. Obal, B. Dursun, Z. Erdem, and A. Kadir, "A real-time data warehouse approach for data processing," presented at the Signal Processing and Communications Applications Conference, Haspolat, 2013, pp. 1-4.##[16] M. A. Naeem, G. Dobbie, and G. Weber, "X-HYBRIDJOIN for near-real-time data warehousing," presented at the 28th British National Conference on Databases, Manchester, 2011, pp. 33–47.##[17] M. A. Naeem, G. Dobbie, and G. Weber, "A lightweight stream-based join with limited resource consumption" presented at the 14th International Conference DaWaK, Vienna, 2011, pp. 431-442.##[18] M. A. Naeem, G. Dobbie, and G. Weber, "Hybridjoin for near-real-time data warehousing," International Journal of Data Warehousing and Mining, vol. 7, no. 4, pp. 21-42, 2011.##[19] M. A. Naeem, G. Dobbie, and G. Weber, "An event-based near real-time data integration archite-cture," presented at the Enterprise Distributed Object Computing Conference Workshops, Munich, 2008, pp. 401–404.##[20] M. A. Naeem and N. Jamil, "An efficient stream-based join to procees end user transactions in real-time data warehousing," Journal of Digital Infor-mation Management, vol. 3, pp. 201-215, 2014.##[21] M. Thiele and W. Lehner, "Evaluation of load scheduling strategies for real-time data warehouse environments," presented at the 35th International Conference on Very Large Databases, Lyon, 2009, pp. 84-99.##[22] N. Polyzotis, S. Skiadopoulos, P. Vassiliadis, A. Simitsis, and N. Frantzell, "Meshing Streaming Updates with Persistent Data in an Active Data Warehouse," IEEE Transactions on Knowledge and Data Engineering, vol. 20, issue. 7, pp. 976-991, 2008.##[23] R. Abrahiem, "A new generation of middleware solutions for a near-real-time data warehousing architecture," presented at the 2007 IEEE International Conference on Electro/Information Technology, Chicago, 2007, pp. 192-197.##[24] S. Sudha and S. Manikandan, "M-hybridjoin- an adaptive approach for stream based near real-time data warehousing," International Journal of Ad-vanced Engineering Technology, vol. 7, issue 1, pp. 321-326, 2016.##[25] T. Jorg, and S. Dessloch, "Near real-time data warehousing using state-of-the-art ETL tools," presented at the 35th International Conference on Very Large Databases, Lyon. 2009.##[26] W. J. Labio, J. L. Wiener, H. Garcia, and V. Gorelik, "Efficient resumption of interrupted ware-house loads," SIGMOD Rec. vol. 29, no. 2, pp. 46–57, 2000.##[27] W. J. Labio, J. Yang, Y. Cui, H. Garcia, and J. Widom, "Performance issues in incremental warehouse maintenance," presented at the 26th International Conference on Very Large Data Bases, San Francisco, 2000, pp.461–472.##[28] ] M. A. Naeem, G. Dobbie, and G. Weber, "Efficient usage of memory resources in near-real-time data warehousing," presented at the Emerging Trends and Applications in Information Communi-cation Technologies, Pakistan, 2012, pp. 326-337.##[29] M. A. Naeem, G. Dobbie, and G. Weber, "Optimised X-HYBRIDJOIN for near-real-time data warehousing" presented at the 23th Austra-lasian Database Conference, Melbourne, 2012, pp. 21-30.##[1] حضرتی آغبلاغ، عیسی و دانشپور، نگین، "RX-HYBRIDJOIN: الگوریتمی بهبود یافته برای پایگاه داده تحلیلی نیمه‌آنی،" دهمین سمپوزیوم پیشرفت علوم و تکنولوژی، مشهد، موسسه آموزش عالی خاوران. 1394.##[1] I. Hazrati, N. Daneshpour, "RX-HYBRIDJOIN: improved algorithm for near-real-time data warehouse," presented at the 10th Symposium on the Advancement of Science and Technology, Mashhad, Khavaran Higher Education Institution, 2015.##[2] حضرتی آغبلاغ، عیسی و دانشپور، نگین، "IX-HYBRIDJOIN: الگوریتمی بهبود یافته برای پایگاه داده تحلیلی نیمه‌آنی،" مقاله منتشر شده در بیست و یکمین کنفرانس ملی کامپیوتر ایران، تهران، پژوهشکده دانش‌های بنیادین. 1394.##[2] I. Hazrati, N. Daneshpour, "IX-HYBRIDJOIN: improved algorithm for near-real-time data warehouse," presented at the 21th National Computer Conference of Iran, Tehran, Institute of Basic Sciences, 2015.##[3] A. Nguyen and A. Tjoa, "Zero-latency data warehousing for heterogeneous data sources and continuous data streams," Paper presented at the 5th International Conference on Information Integration and Web-based Applications Services, Austrian, 2003, pp. 55–64.##[4] A. Gupta, F. Yang, J. Govig, A. Kirsch, K. Chan, K. Lai, S. Wu, S. G. Dhoot, A. R. Kumar, A. Agiwal, S. Bhansali, M. Hong, J. Cameron, M. Siddiqi, D. Jones, J. Shute, A. Gubarev, S. Venka--taraman, and D. Agrawal, "Mesa: geo-replicated, near real-time, scalable data warehouse-ing," presented at the 40th International Conf-erence on Very Large Data Bases, China, 2014, pp. 1259-1270.##[5] A. Karakasidis, P. Vassiliadis, and E. Pitoura, " ETL queues for active data warehousing," presented at the 2th International Workshop on Information Quality in Information Systems, New York, 2005, pp. 28–39.##[6] C. Anderson, The Long Tail: Why the Future of Business is Selling Less of More, Hyperion, 2009.##[7] F. Dehne, Q. Kong, A. Rau-Chaplin, H. Zaboli, and R. Zhou, "Scalable real-time OLAP on cloud architectures," Journal of Parallel and Distributed Computing, vol. 79-80, pp. 31-41, 2015.##[8] F. Dehne, Q. Kong, A. Rau-Chaplin, H. Zaboli, and R. Zhou, "Distributed Tree Data Structure For Real-Time OLAP On Cloud Architectures," presented at the International Conference on Big Data, Silicon Valley, 2013, pp. 499-505.##[9] F. Majeed and S. Mahmood, "Efficient data streams processing in the real time data ware-house," presented at the 3rd IEEE Interna-tional Conference on Computer Science and Infor-mation Technology, Chengdu, 2010, pp. 57-61.##[10] F. Majeed, S. Mahmood, S. Ubaid, N. Khalil, S. Siddiqi, and F. Ashraf, "A burst resolution technique for data streams management in the real-time data warehouse," presented at the 7th Internat-ional Conference on Emerging Technologies, Islamabad, 2011, pp. 1-5.##[11] H. Zhou, D. Yang, and Y. Xu, "An ETL strategy for real-time data warehouse," presented at the International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, 2011, pp. 329–336.##[12] H. Alzeini, SH. Hameed, and M. Habaebi, "A framework for developing real-time OLAP algorithm using multi-core processing and GPU: heterogeneous computing," presented at the 5th International Conference on Mechatronics, Kuala Lumpur. 2013.##[13] L. Golab, T. Johnson, J. S. Seidel, and V. Shkapenyuk, "Stream warehousing with data depot," presented at the 35th SIGMOD Interna-tional Conference on Management of Data, Rhode Island, 2009, pp. 847–854.##[14] L. Chen, W. Rahayu, and D. Taniar, "Towards near real-time data warehousing," presented at the 24th IEEE International Conference on Advanced Information Networking and Applications, Perth, 2011, pp. 1150-1157.##[15] M. Obal, B. Dursun, Z. Erdem, and A. Kadir, "A real-time data warehouse approach for data processing," presented at the Signal Processing and Communications Applications Conference, Haspolat, 2013, pp. 1-4.##[16] M. A. Naeem, G. Dobbie, and G. Weber, "X-HYBRIDJOIN for near-real-time data warehousing," presented at the 28th British National Conference on Databases, Manchester, 2011, pp. 33–47.##[17] M. A. Naeem, G. Dobbie, and G. Weber, "A lightweight stream-based join with limited resource consumption" presented at the 14th International Conference DaWaK, Vienna, 2011, pp. 431-442.##[18] M. A. Naeem, G. Dobbie, and G. Weber, "Hybridjoin for near-real-time data warehousing," International Journal of Data Warehousing and Mining, vol. 7, no. 4, pp. 21-42, 2011.##[19] M. A. Naeem, G. Dobbie, and G. Weber, "An event-based near real-time data integration archite-cture," presented at the Enterprise Distributed Object Computing Conference Workshops, Munich, 2008, pp. 401–404.##[20] M. A. Naeem and N. Jamil, "An efficient stream-based join to procees end user transactions in real-time data warehousing," Journal of Digital Infor-mation Management, vol. 3, pp. 201-215, 2014.##[21] M. Thiele and W. Lehner, "Evaluation of load scheduling strategies for real-time data warehouse environments," presented at the 35th International Conference on Very Large Databases, Lyon, 2009, pp. 84-99.##[22] N. Polyzotis, S. Skiadopoulos, P. Vassiliadis, A. Simitsis, and N. Frantzell, "Meshing Streaming Updates with Persistent Data in an Active Data Warehouse," IEEE Transactions on Knowledge and Data Engineering, vol. 20, issue. 7, pp. 976-991, 2008.##[23] R. Abrahiem, "A new generation of middleware solutions for a near-real-time data warehousing architecture," presented at the 2007 IEEE International Conference on Electro/Information Technology, Chicago, 2007, pp. 192-197.##[24] S. Sudha and S. Manikandan, "M-hybridjoin- an adaptive approach for stream based near real-time data warehousing," International Journal of Ad-vanced Engineering Technology, vol. 7, issue 1, pp. 321-326, 2016.##[25] T. Jorg, and S. Dessloch, "Near real-time data warehousing using state-of-the-art ETL tools," presented at the 35th International Conference on Very Large Databases, Lyon. 2009.##[26] W. J. Labio, J. L. Wiener, H. Garcia, and V. Gorelik, "Efficient resumption of interrupted ware-house loads," SIGMOD Rec. vol. 29, no. 2, pp. 46–57, 2000.##[27] W. J. Labio, J. Yang, Y. Cui, H. Garcia, and J. Widom, "Performance issues in incremental warehouse maintenance," presented at the 26th International Conference on Very Large Data Bases, San Francisco, 2000, pp.461–472.##[28] ] M. A. Naeem, G. Dobbie, and G. Weber, "Efficient usage of memory resources in near-real-time data warehousing," presented at the Emerging Trends and Applications in Information Communi-cation Technologies, Pakistan, 2012, pp. 326-337.##[29] M. A. Naeem, G. Dobbie, and G. Weber, "Optimised X-HYBRIDJOIN for near-real-time data warehousing" presented at the 23th Austra-lasian Database Conference, Melbourne, 2012, pp. 21-30.## ##

الگوریتم بهینه تقسیم‌بندی تصاویر میکروسکوپی خون برای تشخیص سلول‌های لوسمی حاد لنفوبلاست با استفاده از الگوریتم FCM و بهینه‌سازی ژنتیک An Optimal Algorithm for Dividing Microscopic Images of Blood for the Diagnosis of Acute Pulmonary Lymphoblastic Cell Using the FCM Algorithm and Genetic Optimization 1 1 سرطان‌ها گروهی از بیماری‌ها هستند که به‌صورت رشد بی‌رویه و خارج از کنترل تعدادی از سلول‌ها، ایجاد می‌شوند و حدود دویست نوع مختلف دارند. سرطان لوسمی (خون) یکی از انواع‌ این سرطان‌هاست. تشخیص سرطان خون در بیمارستان‌ها و مراکز درمانی با تهیه لام از بافت خون و قراردادن زیر میکروسکوپ و توسط یک‌ متخصص پاتولوژی صورت می‌گیرد. پاتولوژیست‌ها با توجه به شکل و تعداد گلبول‌های موجود در خون نوع بیماری را مشخص می‌کنند. هدف از این مقاله ارائه مدلی هوشمند با استفاده از الگوریتم FCM [1] به‌منظور خوشه‌بندی و شبکه عصبی برای انتخاب ویژگی‌هاست؛ همچنین در آن از الگوریتم ژنتیک در مرحله بهبود الگوهای تشخیصی استفاده شده است. با استفاده از این مدل به تشخیص زود‌هنگام سرطان لوسمی حاد لنفوبلاست و سپس دسته‌بندی ALL[2] به سه زیر شاخه مورفولوژیکی (L1، L2 وL3) می‌توان اقدام کرد. در این پژوهش نمونه‌هایی از 38 بیمار سرطانی لوسمی حاد لنفوییدی تهیه شد. این مطالعه بر روی 68 تصویر میکروسکوپی و با در‌نظر‌گرفتن پانزده ویژگی هندسی و آماری انجام شد که نتیجه آن حاکی از حساسیت، ویژگی و دقت بالاتر برای ده ویژگی نسبت به سایر ویژگی‌ها بود. بر اساس ویژگی‌های استخراج‌شده، این روش با سه روش مشابه اخیر مقایسه شد. ارزیابی‌ها نشان داد که روش پیشنهادی به‌طور میانگین پارامترهای حساسیت، ویژگی و دقت را به میزان 15/85%، 17/98% و 53/96% به‌دست آورد. 1 Fuzzy C-means Clustering 2 Acute Lymphoblastic Leukemia 2 Cancer is type of disease caused by irregular, uncontrollable growth of blood cells in bone marrow. The process of generating three main blood cells including pallets, red and white blood cells, is started from a progenitor cell called as blast. Blast generates a considerable number of immature cells which are developed affected by differentiation factors. If any interruption occurs during this process, leukemia may be initiated. Diagnosis of leukemia is performed at hospitals or medical centers by examination of the blood tissue smeared across a slide and under a microscope by a pathologist. Processing the digital images of blood cells, in order to improve the quality of the image or highlighting the malicious segments of the image, is important in early stages of the disease. There are four types of leukemia consisting acute or chronic and myeloid or lymphocytic. Acute lymphocytic (or lymphoblastic) leukemia (ALL) is concentrated in this study. ALL is caused by continuous generation of immature, malignant lymphocytes in bone marrow which are speeded by blood circulation to other organs. In this research, fuzzy C-means (FCM) algorithm is applied to blood digital images for clustering purpose, neural networks for feature selection and Genetic Algorithm (GA) for optimization. This model diagnoses ALL at early stages and categorizes it into three morphological subcategories (i.e., L1, L2, and L3). For performance evaluation of the proposed method, 38 samples of patients with ALL were collected. It was performed on 68 microscopic images in terms of 15 features and yielded to higher percentage of sensitivity, specificity, and accuracy for 10 out of 15 features. The proposed method was compared to three recent methods. The evaluations showed that the sensitivity, specificity and accuracy reached to 85.15%, 98.17% and 96.53%, respectively. 45 54 2016/10/262017/10/62017/06/242017/08/3 1396/5/12 2017/06/102018/05/162018/04/292018/05/16 1397/2/26 عباس کریمی Abbas Karimi دانشگاه آزاد اسلامی اراک ایران akarimi@iau-arak.ac.ir لیلا سادات حسینی Leila Sadat Hoseini دانشگاه آزاد اسلامی اراک ایران rahenorayaneh@yahoo.com leukemia FCM algorithm neural network genetic algorithm clustering سرطان خون الگوریتم FCM شبکه عصبی الگوریتم ژنتیک خوشه‌بندی [1] Amin MM, Kermani S, Talebi A, Oqhli MG. Recognition of Acute Lymphoblastic Leukemia Cells in Microscopic Images Using K-Means Clustering and Support Vector Machine Classi-fier J Med Signals Sens. 2015 Jan-Mar;5(1):49-58.##[2] Dan L. Longo, Anthony S. Fauci, Dennis L. Kasper, Stephen L. Hauser, Larry Jameson, Joseph Loscalzo. Harrison's Principles of Inter-nal Medicine, 18th Edition, Chapter 110. Malig-nancies of Lymphoid Cells. Clinical Features, Treatment, and Prognosis of Specific Lymphoid Malignancies. 2015.##[3] Collier, J.A.B Oxford Handbook of Clinical Specialties, Third Edition. Oxford. 1991; pp. 810. ISBN 0-19-262116-5.##[4] ACS : How Is Acute Lymphocytic Leukemia Classified?". 2016. [Available from:http:// www. Cancer. org/docroot/CRI/ content/ CR-I_2_4_3X_ How_ Is_ Acute_ Lymphocytic_ Leukemia_ Classified. asp? rnav= cri].##[5] Hoffbrand AV, Moss PAH and Pettit JE. "Essential Haematology", Blackwell, 5th ed., 2006.##[6] Messinger YH, Gaynon PS, Sposto R, van der Giessen J, Eckroth E, Malvar J, et al. Therapeutic Advances in Childhood Leukemia & Lymphoma (TACL) Consortium. "Bortezomib with chemo-therapy is highly active in advanced B-precursor acute lymphoblastic leukemia: Thera-peutic Advances in Childhood Leukemia & Lymphoma (TACL) Study". Blood. 2012; 120 (2): 285–90.##[7] Lambrou GI, Papadimitriou L, Chrousos GP, Vlahopoulos SA. "Glucocorticoid and pro-teasome inhibitor impact on the leukemic lymphoblast: multiple, diverse signals converg-ing on a few key downstream regulators". Mol Cell Endocrinol. 2012; 351 (2): 142–51.##[8] Halim NH, Mashor MY, Hassan R. Automatic blasts counting for acute leukemia based on blood samples. Int J Res Rev Comput Sci 2:971, 2011.##[9] Heydari H, Souratgar A.A, Rashidi I, Malekpoor N, Parvizi A. Diagnosis of leukemia [a particular type of acute lymphoblastic leukemia by using artificial neural networks. Iranian Student Conference on Electrical Engineering, Tarbiat Modarres University. 24- 26 September 1389.##[10] Luque-Baena RM, Urda D, Subirats JL, Franco L, Jerez JM. Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data. Theoretical Biology and Medical Modelling 2014, 11(Suppl 1):S7.##[11] Soltanzadeh R, Rabbani H. Talebi A. Classification of Three Types of Red Blood Cells in Peripheral Blood Smear in Proc. IEEE Int. Conf. on Signal Processing, pp. 707 – 710, China, 2010.##[12] Moradi P, Ahmadian S, Akhalghian F. An effective trust-based recommendation method using a novel graph clustering algorithm. Statis-tical Mechanics and its Applications. Volume 436, 15 October 2015, Pages 462–481##[13] ZohourParvaz F, Fatemizadeh E, Behnam H. Speed improvement in graph-cuts-based registra-tion for non-rigid image registration of brain magnetic resonance images. JSDP. 2017; 13 (4) :79-92##[1] Amin MM, Kermani S, Talebi A, Oqhli MG. Recognition of Acute Lymphoblastic Leukemia Cells in Microscopic Images Using K-Means Clustering and Support Vector Machine Classi-fier J Med Signals Sens. 2015 Jan-Mar;5(1):49-58.##[2] Dan L. Longo, Anthony S. Fauci, Dennis L. Kasper, Stephen L. Hauser, Larry Jameson, Joseph Loscalzo. Harrison's Principles of Inter-nal Medicine, 18th Edition, Chapter 110. Malig-nancies of Lymphoid Cells. Clinical Features, Treatment, and Prognosis of Specific Lymphoid Malignancies. 2015.##[3] Collier, J.A.B Oxford Handbook of Clinical Specialties, Third Edition. Oxford. 1991; pp. 810. ISBN 0-19-262116-5.##[4] ACS : How Is Acute Lymphocytic Leukemia Classified?". 2016. [Available from:http:// www. Cancer. org/docroot/CRI/ content/ CR-I_2_4_3X_ How_ Is_ Acute_ Lymphocytic_ Leukemia_ Classified. asp? rnav= cri].##[5] Hoffbrand AV, Moss PAH and Pettit JE. "Essential Haematology", Blackwell, 5th ed., 2006.##[6] Messinger YH, Gaynon PS, Sposto R, van der Giessen J, Eckroth E, Malvar J, et al. Therapeutic Advances in Childhood Leukemia & Lymphoma (TACL) Consortium. "Bortezomib with chemo-therapy is highly active in advanced B-precursor acute lymphoblastic leukemia: Thera-peutic Advances in Childhood Leukemia & Lymphoma (TACL) Study". Blood. 2012; 120 (2): 285–90.##[7] Lambrou GI, Papadimitriou L, Chrousos GP, Vlahopoulos SA. "Glucocorticoid and pro-teasome inhibitor impact on the leukemic lymphoblast: multiple, diverse signals converg-ing on a few key downstream regulators". Mol Cell Endocrinol. 2012; 351 (2): 142–51.##[8] Halim NH, Mashor MY, Hassan R. Automatic blasts counting for acute leukemia based on blood samples. Int J Res Rev Comput Sci 2:971, 2011.##[9] Heydari H, Souratgar A.A, Rashidi I, Malekpoor N, Parvizi A. Diagnosis of leukemia [a particular type of acute lymphoblastic leukemia by using artificial neural networks. Iranian Student Conference on Electrical Engineering, Tarbiat Modarres University. 24- 26 September 1389.##[10] Luque-Baena RM, Urda D, Subirats JL, Franco L, Jerez JM. Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data. Theoretical Biology and Medical Modelling 2014, 11(Suppl 1):S7.##[11] Soltanzadeh R, Rabbani H. Talebi A. Classification of Three Types of Red Blood Cells in Peripheral Blood Smear in Proc. IEEE Int. Conf. on Signal Processing, pp. 707 – 710, China, 2010.##[12] Moradi P, Ahmadian S, Akhalghian F. An effective trust-based recommendation method using a novel graph clustering algorithm. Statis-tical Mechanics and its Applications. Volume 436, 15 October 2015, Pages 462–481##[13] ZohourParvaz F, Fatemizadeh E, Behnam H. Speed improvement in graph-cuts-based registra-tion for non-rigid image registration of brain magnetic resonance images. JSDP. 2017; 13 (4) :79-92## ##

تشخیص و فیلترینگ هوشمند تصاویر نامتعارف به‌کمک شبکه‌های عصبی عمیق Intelligent Identifications and Filtering of Unconventional Images Based on Deep Neural Networks 1 1 با پیشرفت روزافزون اینترنت و رسانه‌های تحت وب، توزیع و اشتراک منابع اطلاعاتی نظیر تصویر در حال افزایش است. اشتراک این منابع علاوه بر مزایای بسیار، خطرات و مشکلاتی نظیر دسترسی به تصاویر نامتعارف دارد که به نوبه خود تهدیدی برای فرهنگ جوامع مختلف، به‌خصوص نوجوانان و جوانان است. امروزه بسیاری از افراد، عضو سایت‌های اجتماعی از جمله اینستاگرام و فیسبوک هستند. به‌دلیل عدم وجود فیلترینگ هوشمند مناسب، حتی وجود درصدی اندک از تصاویر نامتعارف، فیلتر‌شدن کلیِ سایت‌های اجتماعی را به همراه دارد که برای کاربران، احساس نارضایتی را به ارمغان می‌آورد. به همین منظور، در این مقاله به تحلیل و بررسی روشی برای دسته‌بندی تصاویر نامتعارف و فیلترینگ هوشمند آن‌ها پرداخته شده است. یکی از مشکلات این نوع از سامانه‌ها، حجم بالای داده‌های موجود در شبکه‌های تحت وب و استخراج ویژگی‌های معنادار در این حجم از داده‌ها است. در این راستا، در این مقاله روشی جدید، بر پایه شبکه‌های عصبی عمیق به منظور تشخیص هوشمند تصاویر نامتعارف ارائه شده است. این نوع از شبکه‌ها، مفاهیم سطح بالا را از روی‌ ویژگی‌های سطح پایین استخراج می‌کنند و با این استخراج مفاهیم، به دقت مناسبی در دسته‌بندی اطلاعات دست‌ می‎یابند. در این پژوهش، معماری جدیدی برای شناسایی تصاویر نامتعارف پیشنهاد شده است. نتایج به‌دست‌آمده بر روی مجموعه داده به‌نسبت بزرگ آزمایش شده است. این آزمایش‌ها نشان می‎دهد که روش پیشنهادی دو درصد دقت بیشتری نسبت به روش‌های جدید مطرح‌شده در شناسایی تصاویر نامتعارف دارد. 2 Currently vast improvement of internet access and significant growth of web based broadcasters have resulted in distribution and sharing of informative resources such as images worldwide. Although this kind of sharing may bring many advantages, there are certain risks such as access of kids to porn images which should not be neglected. In fact, access to these images can be a threat to the culture of any society where kids and adults are included. However, many of internet users are members of social websites including Facebook or Instagram and without an appropriate intelligent filtering system, presence of few unconventional images may result in total filtering of these websites causing unpleasant feeling of members. In this paper, an attempt was made to propose an approach for classification and intelligent filtering of unconventional images. One of the major issues on these occasions is the analysis of a large scale of data available in the websites which might be a very time consuming task. A deep neural network might be a good option to resolve this issue and provide a good accuracy in dealing with huge databases. In this research, a new architecture for identifying unconventional images is proposed. In the proposed approach, the new architecture is presented with a combination of AlexNet and LeNet architecture that uses convolutional, polling and fully-connected layers. The activation function used in this architecture, is the Rectified Linear Unit (ReLU) function. The reason of using this activation function is the high speed of convergence in deep convolution networks and simplicity in implementation. The proposed architecture consists of several parts. The first two parts consist of convolutional layers, ReLUs and pooling. In this section, convolution is applied to the input image with different dimensions and filters. In the next section, the convolutional layer with ReLU is used without pooling. The next section, like the first two parts, includes convolutional layers, ReLU and pooling. Finally, the last three parts include the fully-connected layers with ReLU. The output of the last layer is the two classes, which specifies the degree of belonging of each input to the class of unconventional and conventional images. The results are tested on a large-scale dataset. These tests show that the proposed method is more accurate than the other methods recently developed for identifying unconventional images. 55 68 2016/10/262017/10/62017/06/242017/08/32016/10/8 1395/7/17 2017/06/102018/05/162018/04/292018/05/162017/03/5 1395/12/15 علی قنبری سرخی ali ghanbari sorkhi دانشگاه صنعتی شاهرود ایران ali.ghanbari289@gmail.com منصور فاتح Mansour Fateh دانشگاه صنعتی شاهرود ایران mansoor_fateh@yahoo.com حمید حسن‌پور Hamid Hassanpour دانشگاه صنعتی شاهرود ایران h.hassanpour@shahroodut.ac.ir Intelligent filtering system unconventional images deep neural network conventional neural network فیلترینگ هوشمند تصاویر نامتعارف شبکه عصبی عمیق شبکه ‌عصبی کانولوشن [1] Richmond, R., Facebook's new way to combat child pornography. New York Times, 2011##[2] http://www.dailyinfographic.com/the-stats-on-internet-pornography-nfographic,accessed 2017/1/19.##[3] Malamuth, N.M., T. Addison, and M. Koss, Pornography and sexual aggression: Are there reliable effects and can we understand them? Annual review of sex research, 2000. 11(1): p. 26-91.##[4] Malamuth, N.M., Criminal and noncriminal sexual aggressors. Annals of the New York Academy of Sciences, 2003. 989(1): p. 33-58.##[5] Alexy, E.M., A.W. Burgess, and R.A. Prentky, Pornography use as a risk marker for an aggressive pattern ofbehavior among sexually reactive children and adolescents. Journal of the American Psychiatric Nurses Association, 2009. 14(6): p. 442-453##[6] Carter, D.L., R.A. Prentky, R.A. Knight, P.L. Vanderveer, and R.J. Boucher, Use of pornography in the criminaland developmental histories of sexual offenders. Journal of Interpersonal Violence, 1987. 2(2): p. 196-211.##[7] Lin, Y.-C., H.-W. Tseng, and C.-S. Fuh. Pornography detection using support vector machine. in 16th IPPR Conference on Computer Vision, Graphicsand Image Processing (CVGIP 2003). 2003.##[8] Zuo, H., W. Hu, and O. Wu. Patch-based skin color detection and its application to pornography image filtering. in Proceedings of the 19th international conference on World wide web. 2010. ACM.##[9] Largillier, T., G. Peyronnet, and S. Peyronnet, Efficient filtering of adult content using textual information. Murdock et al.[7], 2016: p. 14-17.##[10] Rowley, H.A., Y. Jing, and S. Baluja. Large scale image-based adult-content filtering. in VISAPP (1). 2006. Citeseer.##[11] Krizhevsky, A., I. Sutskever, and G.E. Hinton. Imagenet classification with deep convolutional neural networks. in Advances in neural information processing systems. 2012.##[12] Forsyth, D.A. and M.M. Fleck. Identifying nude pictures. in Applications of Computer Vision, 1996. WACV'96., Proceedings 3rd IEEE Workshop on. 1996. IEEE.##[13] Fleck, M.M., D.A. Forsyth, and C. Bregler. Finding naked people. in European Conference on Computer Vision. 1996. Springer.##[14] Hu, W., O. Wu, Z. Chen, Z. Fu, and S. Maybank, Recognition of pornographic web pages by classifying texts and images. IEEE transactions on pattern analysis and machine intelligence, 2007. 29(6): p. 1019-1034.##[15] Zhuo, L., J. Zhang, Y. Zhao, and S. Zhao, Compressed domain based pornographic imagerecognition using multi-cost sensitive decision trees. Signal Processing, 2013. 93(8): p. 2126-2139.##[16] Wang, M. and X.-S. Hua, Active learning in multimedia annotation and retrieval: A survey. ACM Transactions on Intelligent Systems and Technology (TIST), 2011. 2(2): p. 10.##[17] Li, F.-f., S.-w. Luo, X.-y. Liu, and B.-j. Zou, Bag-of-visual-words model for artificial pornographic images recognition. Journal of Central South University, 2016. 23(6): p. 1383-1389.##[18] Baeza-Yates, R. and B. Ribeiro-Neto, Modern information retrieval. Vol. 463. 1999: ACM press New York.##[19] Zhang, J., L. 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Journal of Interpersonal Violence, 1987. 2(2): p. 196-211.##[7] Lin, Y.-C., H.-W. Tseng, and C.-S. Fuh. Pornography detection using support vector machine. in 16th IPPR Conference on Computer Vision, Graphicsand Image Processing (CVGIP 2003). 2003.##[8] Zuo, H., W. Hu, and O. Wu. Patch-based skin color detection and its application to pornography image filtering. in Proceedings of the 19th international conference on World wide web. 2010. ACM.##[9] Largillier, T., G. Peyronnet, and S. Peyronnet, Efficient filtering of adult content using textual information. Murdock et al.[7], 2016: p. 14-17.##[10] Rowley, H.A., Y. Jing, and S. Baluja. Large scale image-based adult-content filtering. in VISAPP (1). 2006. Citeseer.##[11] Krizhevsky, A., I. Sutskever, and G.E. Hinton. Imagenet classification with deep convolutional neural networks. in Advances in neural information processing systems. 2012.##[12] Forsyth, D.A. and M.M. Fleck. Identifying nude pictures. in Applications of Computer Vision, 1996. WACV'96., Proceedings 3rd IEEE Workshop on. 1996. IEEE.##[13] Fleck, M.M., D.A. Forsyth, and C. Bregler. Finding naked people. in European Conference on Computer Vision. 1996. Springer.##[14] Hu, W., O. Wu, Z. Chen, Z. Fu, and S. Maybank, Recognition of pornographic web pages by classifying texts and images. IEEE transactions on pattern analysis and machine intelligence, 2007. 29(6): p. 1019-1034.##[15] Zhuo, L., J. Zhang, Y. Zhao, and S. Zhao, Compressed domain based pornographic imagerecognition using multi-cost sensitive decision trees. Signal Processing, 2013. 93(8): p. 2126-2139.##[16] Wang, M. and X.-S. Hua, Active learning in multimedia annotation and retrieval: A survey. ACM Transactions on Intelligent Systems and Technology (TIST), 2011. 2(2): p. 10.##[17] Li, F.-f., S.-w. Luo, X.-y. Liu, and B.-j. Zou, Bag-of-visual-words model for artificial pornographic images recognition. 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آشکارسازی حالات لبخند و خنده چهره افراد بر پایه نقاط کلیدی محلی کمینه Smile and Laugh Expressions Detection Based on Local Minimum Key Points 1 1 در این مقاله، آشکارسازی حالات لبخند و خنده چهره با رویکرد توصیف و کاهش بُعد نقاط کلیدی ارائه شده‌است. اساس کار در این پژوهش بر مبنای دو هدف استخراج نقاط محلی کلیدی و ویژگی ظاهری آن‎ها، و همچنین کاهش وابستگی سامانه به آموزش نهاده شده‌است. برای تحقق این اهداف سه سناریوی مختلف استخراج ویژگی‌ ارائه شده است. ابتدا اجزای یک صورت توسط الگوریتم الگوی دودویی محلی آشکار می‌شود؛ سپس در سناریوی نخست، با توجه به تغییرات همبستگی پیکسل‌های مجاور بافت محدوده لب، مجموعه نقاط کلیدی محلی بر پایه گوشه‌یاب هریس استخراج می‌شود. در سناریوی دوم، کاهش بعد نقاط مستخرج سناریوی نخست با بهبود الگوریتم تحلیل مؤلفه‌های اصلی انجام می‌شود؛ و در سناریوی آخر با مقایسه مختصات نقاط مستخرج از سناریوی نخست و توصیف‌گر بریسک مجموعه نقاط بحرانی استخراج می‌شود. در ادامه بدون آموزش سامانه، با مقایسه شکل و فاصله هندسی نقاط محلی محدوده لب حالات چهره آشکار می‌شود. برای ارزیابی روش پیشنهادی، از پایگاه داده‌های استاندارد و شناخته‌شده Cohn-Kaonde،CAFE، JAFFE و Yale استفاده شده‌است. نتایج به‌دست‌آمده از سناریوهای مختلف به‌ترتیب بیان‌گر بهبود 33/6 و 46/16 درصدی متوسط نرخ دقت بازشناسی سناریوی دوم نسبت به نخست و سناریوی سوم نسبت به دوم است. همچنین نتایج کلی آزمایش‌ها، کارایی قابل قبول بالای 90 درصد روش پیشنهادی را نشان می‌دهد. 2 In this paper, a smile and laugh facial expression is presented based on dimension reduction and description process of the key points. The paper has two main objectives; the first is to extract the local critical points in terms of their apparent features, and the second is to reduce the system’s dependence on training inputs. To achieve these objectives, three different scenarios on extracting the features are proposed. First of all, the discrete parts of a face are detected by local binary pattern method that is used to extract a set of global feature vectors for texture classification considering various regions of an input-image face. Then, in the first scenario and with respect to the correlation changes of adjacent pixels on the texture of a mouth area, a set of local key points are extracted using the Harris corner detector. In the second scenario, the dimension reduction of the extracted points of first scenario provided by principal component analysis algorithm leading to reduction in computational costs and overall complexity without loss of performance and flexibility; and in the final scenario, a set of critical points is extracted through comparing the extracted points’ coordinates of the first scenario and the BRISK Descriptor, which is utilized a neighborhood sampling strategy of directions for a key-point. In the following, without training the system, facial expressions are detected by comparing the shape and the geometric distance of the extracted local points of the mouth area. The well-known standard Cohn-Kaonde, CAFÉ, JAFFE and Yale benchmark dataset are applied to evaluate the proposed approach. The results shows an overall enhancement of 6.33% and 16.46% for second scenario compared with first scenario and third scenario compared with second scenario. The experimental results indicate the power efficiency of the proposed approach in recognizing images more than 90 % across all the datasets. 69 88 2016/10/262017/10/62017/06/242017/08/32016/10/82017/08/2 1396/5/11 2017/06/102018/05/162018/04/292018/05/162017/03/52018/05/16 1397/2/26 مینا محمدی دشتی Mina Mohammadi Dashti دانشگاه آزاد اسلامی واحد نجف آباد، دانشکده مهندسی کامپیوتر ایران m.mohammadi96@yahoo.com مجید هارونی Majid Harouni دانشگاه آزاد اسلامی واحد دولت آباد، دانشکده مهندسی کامپیوتر ایران m.harouni@iauda.ac.ir Local key points extraction facial expression detection corner detector descriptor algorithm dimension reduction استخراج نقاط کلیدی محلی آشکارسازی حالات چهره گوشه‌یابی الگوریتم‌ توصیف‌گر کاهش بُعد [1] J. L. Lakin, "Automatic cognitive processes and nonverbal communication," The Sage handbook of nonverbal communication, pp. 59-77, 2006.##[2] M. Harouni, D. Mohamad, and A. 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یک چارچوب نیمه‌نظارتی مبتنی بر لغت‌نامه وفقی خودساخت جهت تحلیل نظرات فارسی A Semi-supervised Framework Based on Self-constructed Adaptive Lexicon for Persian Sentiment Analysis 1 1 با معرفی وب 2.0 و 3.0 تعاملات کاربران در فضای مجازی، منجر به ایجاد انبوهی از نظرات ارزشمند شده است. با توجه به‌دشواری یا عدم امکان تحلیل و بررسی دستی این نظرات، تحلیل احساس متن و یا نظرکاوی به‌عنوان یکی از زیرمجموعه‌های پردازش زبان طبیعی مطرح شد. تلاش‌های محدودی در نظرکاوی فارسی نسبت به سایر زبان‌ها صورت گرفته است. در این مقاله برای نخستین بار، یک چارچوب نیمه‌نظارتی برای نظرکاوی فارسی ارائه شده است. درضمن، ازآنجاکه یکی از آخرین پیشرفت‌های علمی در نظرکاوی زبان فارسی الگوریتمی بر اساس استخراج الگوهای حسی وفقی (حساس به مجموعه‌داده) مبتنی بر خبره انسانی است، در این پژوهش ضمن ارتقای الگوریتم یادشده، تعیین برچسب‌های حاوی احساس به کمک یک لغت‌نامه خودساخت (بدون نیاز به خبره انسانی) وفقی انجام می‌گیرد؛ همچنین کاربرد دسته‌بند مدل مخفی مارکوف خودناظر بر روی خصیصه‌های یادشده در کنار قوانین مبتنی بر معیار شباهت برای فرآیند نظرکاوی بررسی ‌شده‌است. در راستای خودآموزسازی هوشمند، روشی برای ارزیابی قابلیت اطمینان بالای خروجی، ارائه ‌شده‌است که خودآموزی به‌شرط وجود آن انجام می‌پذیرد. روش پیشنهادی با اجرا بر روی دادگان مبنا نرخ صحت نود درصد (باوجود عدم نیاز به خبره انسانی) را که در مقایسه با روش‌های نظارتی و نیمه‌نظارتی مستقل از خبره موجود برتری قابل‌ملاحظه‌ای دارد، خروجی می‌دهد؛ همچنین این الگوریتم نیمه‌نظارتی هنگام استفاده از مجموعه آموزش کوچک با نسبت مجموعه دادگان آموزش/آزمون ده به نود نیز بررسی و با نرخ صحت 80% قابلیت اطمینان آن به اثبات رسید. 2 With the appearance of Web 2.0 and 3.0, users’ contribution to WWW has created a huge amount of valuable expressed opinions. Considering the difficulty or impossibility of manually analyzing such big data, sentiment analysis, as a branch of natural language processing, has been highly considered. Despite the other (popular) languages, a limited number of research studies have been conducted in Persian sentiment analysis. In this study, for the first time, a semi-supervised framework is proposed for Persian sentiment analysis. Moreover, considering that one of the most recent studies in Persian, is an algorithm based on extracting adaptive (dataset-sensitive) expert-based emotional patterns. In this research, extraction of the same state-of-the-art emotional patterns is proposed to be performed automatically. Moreover, application of the HMM classifier, by utilizing the mentioned features (as its states) is analyzed; and additionally, HMM-based sentiment analysis is upgraded by being combined with a rule-based classifier for the opinion assignment process. In addition, toward intelligent self-training, a criterion for evaluating, the high reliability of output is presented by which (assuming satisfaction of the criterion) the self-training process is performed in “lexicon-extraction” and “classifier,” as learning systems. The proposed method, by being applied on the basis dataset, provides 90% of accuracy (despite its expert-independent lexicon generation nature), which in comparison with the supervised and semi-supervised methods in the state-of-the-art has a considerable superiority. Moreover, this semi-supervised method is evaluated by a 10/90 ratio of train/ test and its reliability is demonstrated by providing 80% of accuracy. 89 102 2016/10/262017/10/62017/06/242017/08/32016/10/82017/08/22017/04/10 1396/1/21 2017/06/102018/05/162018/04/292018/05/162017/03/52018/05/162017/10/25 1396/8/3 محسن نجف‌زاده Mohsen Najafzadeh دانشگاه آزاد اسلامی، واحد مشهد ایران mohsen.najafzadeh@mshdiau.ac.ir سعید راحتی قوچانی Saeed Rahati Quchan دانشگاه آزاد اسلامی، واحد مشهد ایران rahati@mshdiau.ac.ir رضا قائمی Reza Ghaemi دانشگاه آزاد اسلامی، واحد قوچان ایران r.ghaemi@iauq.ac.ir Opinion Mining Self-training Self-constructed Lexicon Hidden Markov Model Adaptive Dictionary نظرکاوی یادگیری خودناظر لغت‌نامه خودساخت مدل مخفی مارکوف لغت‌نامه وفقی [1] S.M. Asghari N, M. Kahani, and E. Askarian, "Opinion Mining by means of syntactic and semantic labels, and discovering emotional relations in Persian sentences". Computer Society of Iran (20th) Computer Conference (CSICC 2015). Ferdowsi University of Mashhad. 2015##[2] S. Borhani Z., A.A. Niknafs, and M. Mohammadi, "Opinion mining in product reviews, using emotional vocabulary network" 2nd National Conference on Indurtrial Engineering & Systems (NIESC 2014). Najafabad branch, Islamic Azad University. 2014##[3] H. Sotudeh and Z. Honarjooyan, "A review on Persian challenges in digital paradigms, and their effect on efficiency of automatic text processing and information retrieval," Library and Informa-tion Science, 15 (4), Astan Quds Razavi. 2013##[4] S. Alimardani and A. Aghaei, "Opinion Mining in Persian Language Using Supervised Algorithms," 2015.##[5] A. Azimizadeh, M. M. Arab, and S. R. Quchani, "Persian part of speech tagger based on Hidden Markov Model," 9th JADT, 2008.##[6] M. E. Basiri, A. R. Naghsh-Nilchi, and N. Ghassem-Aghaee, "A Framework for Sentiment Analysis in Persian," 2014.##[7] J. Bollen, H. Mao, and X. Zeng, "Twitter mood predicts the stock market," J. Comput. Sci., vol. 2, no. 1, pp. 1–8, 2011.##[8] I. Dehdarbehbahani, A. Shakery, and H. Faili, "Semi-supervised word polarity identification in resource-lean languages," Neural Networks, vol. 58, pp. 50-59, 2014.##[9] M. Gamon, "Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis," in Proceedings of the 20th international conference on Computa-tional Linguistics, 2004, p. 841.##[10] V. Gupta and G. S. Lehal, "A survey of text mining techniques and applications," J. Emerg. Technol. web Intell., vol. 1, no. 1, pp. 60–76, 2009.##[11] A. K. Jain and Y. Pandey, "Analysis and implementation of sentiment classification using lexical POS markers," Int. J., vol. 2, no. 1, 2013.##[12] B. Liu, "Sentiment analysis and opinion mining," Synth. Lect. Hum. Lang. Technol., vol. 5, no. 1, pp. 1–167, 2012.##[13] B. Liu, "Sentiment analysis: Mining opinions, sentiments, and emotions": Cambridge Univer-sity Press, 2015.##[14] J. Liu, Y. Cao, C.-Y. Lin, Y. Huang, and M. Zhou, "Low-Quality Product Review Detection in Opinion Summarization," in EMNLP-CoNLL, 2007, pp. 334–342.##[15] R. Kumar and R. Vadlamani, "A survey on opinion mining and sentiment analysis: tasks, approaches and applications," Knowledge-Based Syst., vol. 89, pp. 14–46, 2015.##[16] E. Sadikov, A. Parameswaran, and P. Venetis, "Blogs as predictors of movie success," 2009##[17] M. Saraee and A. Bagheri, "Feature selection methods in Persian sentiment analysis," in Natural Language Processing and Information Systems, Springer, 2013, pp. 303–308.##[18] M. Shams, A. Shakery, and H. Faili, "A non-parametric LDA-based induction method for sentiment analysis," in Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on, 2012, pp. 216–221.##[19] S. M. Thede and M. P. Harper, "A second-order hidden Markov model for part-of-speech tagging," in Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, 1999, pp. 175–182##[20] A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe, "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment," ICWSM, vol. 10, pp. 178–185, 2010.##[21] H. Scudder, "Probability of error of some adaptive pattern-recognition machines," IEEE Transactions on Information Theory, vol. 11, pp. 363-371, 1965.##[22] N. F. F. da Silva, L. F. Coletta, E. R. Hruschka, and E. R. Hruschka Jr, "Using unsupervised information to improve semi-supervised tweet sentiment classification," Information Sciences, vol. 355, pp. 348-365, 2016.##[23] L. R. Welch, "Hidden Markov models and the Baum-Welch algorithm," IEEE Information Theory Society Newsletter, vol. 53, pp. 10-13, 2003.##[24] M. Kang, J. Ahn, and K. Lee, "Opinion mining using ensemble text hidden Markov models for text classification." 2017.##[25] N. F. F. D. Silva, L. F.Coletta, & E. R.Hruschka, "A survey and comparative study of tweet sentiment analysis via semi-supervised learning." ACM Computing Surveys (CSUR), 49(1), 15. 2016##[26] D. Rao, and D. Ravichandran, "Semi-supervised polarity lexicon induction" in Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (pp. 675-682). Association for Computational Linguistics. 2009.##[27] L. Becker, G. Erhart, D. Skiba, and V. Matula, "AVAYA: Sentiment Analysis on Twitter with Self-Training and Polarity Lexicon Expansion". SemEval@ NAACL-HLT, pp. 333-340, 2013.##[28] S.Liu, F.Li, F.Li, X.Cheng, & H.Shen, "Adaptive co-training SVM for sentiment classification on tweets". In Proceedings of the 22nd International Conference on World Wide Web Information & Knowledge Management (pp. 2079-2088). ACM. 2013.##[29] S.Liu, W.Zhu, N.Xu, F.Li, X. Q.Cheng, Y.Liu, & Y.Wang, "Co-training and visualizing sentim-ent evolvement for tweet events". In Proceedings of the 22nd International Confer-ence on World Wide Web (pp. 105-106). ACM. 2013##[1] سید محمد اصغری نکاح، محسن کاهانی و احسان. عسگریان. «نظرکاوی با استفاده از برچسب‌های صرفی و معنایی و کشف روابط حسی جملات فارسی». بیستمین کنفرانس ملی سالانه انجمن کامپیوتر ایران. دانشگاه فردوسی مشهد. 1394.##[1] S.M. Asghari N, M. Kahani, and E. Askarian, "Opinion Mining by means of syntactic and semantic labels, and discovering emotional relations in Persian sentences". Computer Society of Iran (20th) Computer Conference (CSICC 2015). Ferdowsi University of Mashhad. 2015##[2] برهانی زرندی، سمیه، علی اکبر نیک نفس، و مجید محمدی. "عقیده کاوی در نقد کالا با استفاده از شبکه واژگان احساسی"، دومین کنفرانس ملی مهندسی صنایع و سیستم ها، نجف آباد، دانشگاه آزاد اسلامی واحد نجف آباد، گروه مهندسی صنایع، 1392.##[2] S. Borhani Z., A.A. Niknafs, and M. Mohammadi, "Opinion mining in product reviews, using emotional vocabulary network" 2nd National Conference on Indurtrial Engineering & Systems (NIESC 2014). Najafabad branch, Islamic Azad University. 2014##[3] هاجر ستوده, زهره هنرجویان. "مروری بر دشواری‌های زبان فارسی در محیط دیجیتال و تاثیرات آنها بر اثر بخشی پردازش خودکار متن و بازیابی اطلاعات". فصلنامه علمی و پژوهشی کتابداری و اطلاع رسانی - آستان قدس رضوی، 1391.##[3] H. Sotudeh and Z. Honarjooyan, "A review on Persian challenges in digital paradigms, and their effect on efficiency of automatic text processing and information retrieval," Library and Informa-tion Science, 15 (4), Astan Quds Razavi. 2013##[4] S. Alimardani and A. Aghaei, "Opinion Mining in Persian Language Using Supervised Algorithms," 2015.##[5] A. Azimizadeh, M. M. Arab, and S. R. Quchani, "Persian part of speech tagger based on Hidden Markov Model," 9th JADT, 2008.##[6] M. E. Basiri, A. R. Naghsh-Nilchi, and N. Ghassem-Aghaee, "A Framework for Sentiment Analysis in Persian," 2014.##[7] J. Bollen, H. Mao, and X. Zeng, "Twitter mood predicts the stock market," J. Comput. Sci., vol. 2, no. 1, pp. 1–8, 2011.##[8] I. Dehdarbehbahani, A. Shakery, and H. Faili, "Semi-supervised word polarity identification in resource-lean languages," Neural Networks, vol. 58, pp. 50-59, 2014.##[9] M. Gamon, "Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis," in Proceedings of the 20th international conference on Computa-tional Linguistics, 2004, p. 841.##[10] V. Gupta and G. S. Lehal, "A survey of text mining techniques and applications," J. Emerg. Technol. web Intell., vol. 1, no. 1, pp. 60–76, 2009.##[11] A. K. Jain and Y. Pandey, "Analysis and implementation of sentiment classification using lexical POS markers," Int. J., vol. 2, no. 1, 2013.##[12] B. Liu, "Sentiment analysis and opinion mining," Synth. Lect. Hum. Lang. Technol., vol. 5, no. 1, pp. 1–167, 2012.##[13] B. Liu, "Sentiment analysis: Mining opinions, sentiments, and emotions": Cambridge Univer-sity Press, 2015.##[14] J. Liu, Y. Cao, C.-Y. Lin, Y. Huang, and M. Zhou, "Low-Quality Product Review Detection in Opinion Summarization," in EMNLP-CoNLL, 2007, pp. 334–342.##[15] R. Kumar and R. Vadlamani, "A survey on opinion mining and sentiment analysis: tasks, approaches and applications," Knowledge-Based Syst., vol. 89, pp. 14–46, 2015.##[16] E. Sadikov, A. Parameswaran, and P. Venetis, "Blogs as predictors of movie success," 2009##[17] M. Saraee and A. Bagheri, "Feature selection methods in Persian sentiment analysis," in Natural Language Processing and Information Systems, Springer, 2013, pp. 303–308.##[18] M. Shams, A. Shakery, and H. Faili, "A non-parametric LDA-based induction method for sentiment analysis," in Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on, 2012, pp. 216–221.##[19] S. M. Thede and M. P. Harper, "A second-order hidden Markov model for part-of-speech tagging," in Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, 1999, pp. 175–182##[20] A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe, "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment," ICWSM, vol. 10, pp. 178–185, 2010.##[21] H. Scudder, "Probability of error of some adaptive pattern-recognition machines," IEEE Transactions on Information Theory, vol. 11, pp. 363-371, 1965.##[22] N. F. F. da Silva, L. F. Coletta, E. R. Hruschka, and E. R. Hruschka Jr, "Using unsupervised information to improve semi-supervised tweet sentiment classification," Information Sciences, vol. 355, pp. 348-365, 2016.##[23] L. R. Welch, "Hidden Markov models and the Baum-Welch algorithm," IEEE Information Theory Society Newsletter, vol. 53, pp. 10-13, 2003.##[24] M. Kang, J. Ahn, and K. Lee, "Opinion mining using ensemble text hidden Markov models for text classification." 2017.##[25] N. F. F. D. Silva, L. F.Coletta, & E. R.Hruschka, "A survey and comparative study of tweet sentiment analysis via semi-supervised learning." ACM Computing Surveys (CSUR), 49(1), 15. 2016##[26] D. Rao, and D. Ravichandran, "Semi-supervised polarity lexicon induction" in Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (pp. 675-682). Association for Computational Linguistics. 2009.##[27] L. Becker, G. Erhart, D. Skiba, and V. Matula, "AVAYA: Sentiment Analysis on Twitter with Self-Training and Polarity Lexicon Expansion". SemEval@ NAACL-HLT, pp. 333-340, 2013.##[28] S.Liu, F.Li, F.Li, X.Cheng, & H.Shen, "Adaptive co-training SVM for sentiment classification on tweets". In Proceedings of the 22nd International Conference on World Wide Web Information & Knowledge Management (pp. 2079-2088). ACM. 2013.##[29] S.Liu, W.Zhu, N.Xu, F.Li, X. Q.Cheng, Y.Liu, & Y.Wang, "Co-training and visualizing sentim-ent evolvement for tweet events". In Proceedings of the 22nd International Confer-ence on World Wide Web (pp. 105-106). ACM. 2013## ##

ارائه الگوریتم پویا برای تنظیم هم‌روندی فرایندهای کسب‌وکار A Dynamic Programing Algorithm for Tuning Concurrency of Business Processes 1 1 سامانههای مدیریت فرایندهای کسبوکار(BPMS)، سامانههای پیچیده اطلاعاتی هستند که جهت رقابت در بازار جهانی و افزایش بهره‌وری اقتصادی، استفاده از آنها در هر سازمانی، امری حیاتی و ضروری است. ایجاد تعادل بارِکاری منابع در BPMS، یکی از چالشهایی است که از دیرباز مورد مطالعه و بررسی پژوهش‌گران قرار گرفته است. تعادل بارِکاری منابع، باعث افزایش پایداری سامانه، افزایش کارایی منابع و افزایش کیفیت محصولات میشود. در این مقاله، مسئله تنظیم همروندی در BPMS به‌عنوان یک مسئله کاربردی در جهت بهبود تعادل بارکاری منابع و یک‌نواختی در بارکاری هر منبع معرفی میشود و برای حل این مسئله، در ابتدای هر فرایند یک عنصر تأخیردهنده در نظر گرفته میشود و هدف مسئله تنظیم مقدار تأخیر در ابتدای هر فرایند است. برای این منظور یک الگوریتم بهینه‌سازی پویا ارائه و سرعت اجرای الگوریتم پویای پیشنهادشده نسبت به الگوریتم جستجوی فضای حالت و الگوریتم تکاملی PSO مقایسه میشود. مقایسه انجام‌شده نشان میدهد سرعت الگوریتم پیشنهادی نسبت به الگوریتم جستجوی فضای حالت به‌صورت 37 ساعت به 8/5 سال است؛ درحالی‎که الگوریتم POS همین مسئله را درسه دقیقه حل میکند. آزمایش انجام‌شده روی یک پایگاه داده واقعی 64/21 درصد بهبود را در عملکرد الگوریتم پیشنهادی نشان میدهد. 2 Business process management systems (BPMS) are vital complex information systems to compete in the global market and to increase economic productivity. Workload balancing of resources in BPMS is one of the challenges have been long studied by researchers. Workload balancing of resources increases the system stability, improves the efficiency of the resources and enhances the quality of their products. Workload balancing of resources in BPMS is considered as an important factor of the performance and the stability in systems. Setting the workload of each source at a certain level increases the efficiency of the resources. The main objectives of this research are the concept of resource workload balance and uniformity of the workload for each source at a specified level. To optimize the balance workload and uniformity of each source, the setting multi-process concurrency was offered and studied. Also, the regulation of multi-process concurrency was mentioned as an optimization problem. In this paper, tuning concurrency of the business process is introduced as a problem in BPMS, which is an application issue to improve at workload balance of resources and uniformity in the workload of each resource. To solve this problem, a delay vector is defined, each element of delay vector makes the synthetic delay at the first of each business process, then a dynamic optimization algorithm is presented to compute delay vector and the speed of the proposed algorithms is compared with and state-space search algorithm and evolutionary algorithm of PSO. The comparison shows that the speed of the proposed algorithm is 37 hours to 5.8 years compared to the state-space search algorithm, while the POS algorithm solves the same problem in just 3 minutes. The experimental results on a real dataset show 21.64 percent improvement in the performance of the proposed algorithm. 103 118 2016/10/262017/10/62017/06/242017/08/32016/10/82017/08/22017/04/102016/12/19 1395/9/29 2017/06/102018/05/162018/04/292018/05/162017/03/52018/05/162017/10/252017/08/20 1396/5/29 مهدی یعقوبی Mehdi Yaghoubi دانشگاه صنعتی شاهرود ایران mehdi.yaghoubi@gmail.com مرتضی زاهدی Morteza Zahedi دانشگاه صنعتی شاهرود ایران zahedi@ganjineh.co.ir علیرضا احمدی‌فرد Alireza Ahmadyfard دانشگاه صنعتی شاهرود ایران ahmadyfard@shahroodut.ac.ir Business process management systems tuning concurrency of business processes workload balancing dynamic optimization time complexity سامانه مدیریت فرایندهای کسب‌وکار تنظیم هم‌روندی فرایندها تعادل بارِکاری بهینه‌سازی پویا پیچیدگی زمانی [1] M. Hammer, The agenda: What every business must do to dominate the decade: Crown Pub, 2003.##[2] M. Hammer and J. Champy, Reengineering the Corporation: Manifesto for Business Revolution, A: Zondervan, 2009.##[3] H. Smith and P. Fingar, Business process management: the third wave vol. 1: Meghan-Kiffer Press Tampa, 2003.##[4] W. M. Van Der Aalst, A. H. Ter Hofstede, and M. Weske, "Business process management: A survey," in International conference on business process management, 2003, pp. 1-12.##[5] B.-H. Ha, J. Bae, and S.-H. Kang, "Workload balancing on agents for business process efficiency based on stochastic model," in Business Process Management, ed: Springer, 2004, pp. 195-210.##[6] B.-H. Ha, J. Bae, Y. T. Park, and S.-H. Kang, "Development of process execution rules for workload balancing on agents," Data & Knowledge Engineering, vol. 56, pp. 64-84, 2006.##[7] Y. Xie, C.-F. Chien, and R.-Z. Tang, "A dynamic task assignment approach based on individual worklists for minimizing the cycle time of business processes," Computers & Industrial Engineering, vol. 99, pp. 401-414, September 2015.##[8] X. Liu, J. Chen, Y. Ji, and Y. Yu, "Q-learning Algorithm for Task Allocation Based on Social Relation," Process-Aware Systems, pp. 49-58, 2015.##[9] D. E. Culler, J. P. Singh, and A. Gupta, Parallel computer architecture: a hardware/software approach: Gulf Professional Publishing, 1999.##[10] D. Grosu and A. T. Chronopoulos, "Algorithmic mechanism design for load balancing in distributed systems," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 34, pp. 77-84, 2004.##[11] E. Rahm, "Dynamic load balancing in parallel database systems," in European Conference on Parallel Processing, 1996, pp. 37-52.##[12] L.-j. Jin, F. Casati, M. Sayal, and M.-C. Shan, "Load balancing in distributed workflow management system," in Proceedings of the 2001 ACM symposium on Applied computing, 2001, pp. 522-530.##[13] W. Zhao, L. Yang, H. Liu, and R. Wu, "The Optimization of Resource Allocation Based on Process Mining," in Advanced Intelligent Computing Theories and Applications, ed: Springer, 2015, pp. 341-353.##[14] M. Zur Muehlen, "Organizational management in workflow applications–issues and perspectives," Information Technology and Management, vol. 5, pp. 271-291, 2004.##[15] J. Xu, C. Liu, and X. Zhao, "Resource allocation vs. business process improvement: How they impact on each other," in BPM, 2008, pp. 228-243.##[16] A. S. Nisafani, A. Wibisono, S. Kim, and H. Bae, "Bayesian Selection Rule for Human-Resource Selection in Business Process Management Systems," Journal of Society for e-Business Studies, vol. 17, 2014.##[17] A. Wibisono, A. S. Nisafani, H. Bae, and Y.-J. Park, "On-the-Fly Performance-Aware Human Resource Allocation in the Business Process Management Systems Environment Using Naïve Bayes," in Asia Pacific Business Process Management, ed: Springer, 2015, pp. 70-80.##[18] S. Rhee, H. Bae, D. Ahn, and Y. Seo, "Efficient workflow management through the introduction of TOC concepts," in Proceedings of the 8th annual international conference on industrial engineering theory, applications and practice (IJIE2003), 2003.##[19] M. Shen, G.-H. Tzeng, and D.-R. Liu, "Multi-criteria task assignment in workflow management systems," in System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on, 2003.##[20] K. Georgoulakos, K. Vergidis, G. Tsakalidis, and N. Samaras, "Evolutionary Multi-Objective Optimization of business process designs with pre-processing," in Evolutionary Computation (CEC), 2017 IEEE Congress on, 2017, pp. 897-904.##[21] M. Wibig, "Dynamic Programming and Genetic Algorithm for Business Processes Optimisation," International Journal of Intelligent Systems and Applications, vol. 5, p. 44, 2012.##[22] M. Wibig and C. Polska, "NSGA II algorithm application within the dynamic programming approach to business process optimisation," Journal of Applied Computer Science, vol. 21, pp. 195-207, 2013.##[23] E. A. Alluisi and B. B. Morgan Jr, "Engineering psychology and human performance," Annual review of psychology, vol. 27, pp. 305-330, 1976.##[24] S. Dreyfus, "Richard Bellman on the birth of dynamic programming," Operations Research, vol. 50, pp. 48-51, 2002.##[25] P. Diban, M. K. A. Aziz, D. C. Foo, X. Jia, Z. Li, and R. R. Tan, "Optimal biomass plantation replanting policy using dynamic programming," Journal of Cleaner Production, vol. 126, pp. 409-418, 2016.##[26] R. Jia, S. J. Mellon, S. Hansjee, A. Monk, D. Murray, and J. A. Noble, "Automatic bone segmentation in ultrasound images using local phase features and dynamic programming," in Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, 2016, pp. 1005-1008.##[27] M. Roozegar, M. Mahjoob, and M. Jahromi, "Optimal motion planning and control of a nonholonomic spherical robot using dynamic programming approach: simulation and experimental results," Mechatronics, 2016.##[28] C. Finck and R. Li, "Operational load shaping of office buildings connected to thermal energy storage using dynamic programming," 2016.##[29] M.Fatehi Hassan Abaad, H.Ghanee and A.M. Latif, "A novel method for suitable selection of watermark strength in digital image watermarking based on imperialist competitive algorithm" JSDP, vol. 10 no. 1, pp. 56-43, 2013.##[30] J. Kennedy, "Particle swarm optimization," in Encyclopedia of machine learning, ed: Springer, 2011, pp. 760-766.##[31] Z. Liu, P. Zhu, W. Chen, and R.-J. Yang, "Improved particle swarm optimization algorithm using design of experiment and data mining techniques," Structural and Multidisciplinary Optimization, vol. 52, pp. 813-826, 2015.##[32] C. Ou-Yang, H.-J. Cheng, and Y.-C. Juan, "An Integrated mining approach to discover business process models with parallel structures: towards fitness improvement," International Journal of Production Research, vol. 53, pp. 3888-3916, 2015.##[33] H.-J. Cheng, C. Ou-Yang, and Y.-C. Juan, "A hybrid approach to extract business process models with high fitness and precision," Journal of Industrial and Production Engineering, vol. 32, pp. 351-359, 2015.##[34] F. Ahmadizar, Kh.B. Soltanian and F. Akhlaghian, " Construction and Training of Artificial Neural Networks using Evolution Strategy with Parallel Populations", JSDP. vol. 13, no. 1, pp. 101-114, 2016.##[35] W. M. van der Aalst and A. H. Ter Hofstede, "YAWL: yet another workflow language," Information systems, vol. 30, pp. 245-275, 2005.##[1] M. Hammer, The agenda: What every business must do to dominate the decade: Crown Pub, 2003.##[2] M. Hammer and J. Champy, Reengineering the Corporation: Manifesto for Business Revolution, A: Zondervan, 2009.##[3] H. Smith and P. Fingar, Business process management: the third wave vol. 1: Meghan-Kiffer Press Tampa, 2003.##[4] W. M. Van Der Aalst, A. H. Ter Hofstede, and M. Weske, "Business process management: A survey," in International conference on business process management, 2003, pp. 1-12.##[5] B.-H. Ha, J. Bae, and S.-H. Kang, "Workload balancing on agents for business process efficiency based on stochastic model," in Business Process Management, ed: Springer, 2004, pp. 195-210.##[6] B.-H. Ha, J. Bae, Y. T. Park, and S.-H. Kang, "Development of process execution rules for workload balancing on agents," Data & Knowledge Engineering, vol. 56, pp. 64-84, 2006.##[7] Y. Xie, C.-F. Chien, and R.-Z. Tang, "A dynamic task assignment approach based on individual worklists for minimizing the cycle time of business processes," Computers & Industrial Engineering, vol. 99, pp. 401-414, September 2015.##[8] X. Liu, J. Chen, Y. Ji, and Y. Yu, "Q-learning Algorithm for Task Allocation Based on Social Relation," Process-Aware Systems, pp. 49-58, 2015.##[9] D. E. Culler, J. P. Singh, and A. Gupta, Parallel computer architecture: a hardware/software approach: Gulf Professional Publishing, 1999.##[10] D. Grosu and A. T. Chronopoulos, "Algorithmic mechanism design for load balancing in distributed systems," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 34, pp. 77-84, 2004.##[11] E. Rahm, "Dynamic load balancing in parallel database systems," in European Conference on Parallel Processing, 1996, pp. 37-52.##[12] L.-j. Jin, F. Casati, M. Sayal, and M.-C. Shan, "Load balancing in distributed workflow management system," in Proceedings of the 2001 ACM symposium on Applied computing, 2001, pp. 522-530.##[13] W. Zhao, L. Yang, H. Liu, and R. Wu, "The Optimization of Resource Allocation Based on Process Mining," in Advanced Intelligent Computing Theories and Applications, ed: Springer, 2015, pp. 341-353.##[14] M. Zur Muehlen, "Organizational management in workflow applications–issues and perspectives," Information Technology and Management, vol. 5, pp. 271-291, 2004.##[15] J. Xu, C. Liu, and X. Zhao, "Resource allocation vs. business process improvement: How they impact on each other," in BPM, 2008, pp. 228-243.##[16] A. S. Nisafani, A. Wibisono, S. Kim, and H. Bae, "Bayesian Selection Rule for Human-Resource Selection in Business Process Management Systems," Journal of Society for e-Business Studies, vol. 17, 2014.##[17] A. Wibisono, A. S. Nisafani, H. Bae, and Y.-J. Park, "On-the-Fly Performance-Aware Human Resource Allocation in the Business Process Management Systems Environment Using Naïve Bayes," in Asia Pacific Business Process Management, ed: Springer, 2015, pp. 70-80.##[18] S. Rhee, H. Bae, D. Ahn, and Y. Seo, "Efficient workflow management through the introduction of TOC concepts," in Proceedings of the 8th annual international conference on industrial engineering theory, applications and practice (IJIE2003), 2003.##[19] M. Shen, G.-H. Tzeng, and D.-R. Liu, "Multi-criteria task assignment in workflow management systems," in System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on, 2003.##[20] K. Georgoulakos, K. Vergidis, G. Tsakalidis, and N. Samaras, "Evolutionary Multi-Objective Optimization of business process designs with pre-processing," in Evolutionary Computation (CEC), 2017 IEEE Congress on, 2017, pp. 897-904.##[21] M. Wibig, "Dynamic Programming and Genetic Algorithm for Business Processes Optimisation," International Journal of Intelligent Systems and Applications, vol. 5, p. 44, 2012.##[22] M. Wibig and C. Polska, "NSGA II algorithm application within the dynamic programming approach to business process optimisation," Journal of Applied Computer Science, vol. 21, pp. 195-207, 2013.##[23] E. A. Alluisi and B. B. Morgan Jr, "Engineering psychology and human performance," Annual review of psychology, vol. 27, pp. 305-330, 1976.##[24] S. Dreyfus, "Richard Bellman on the birth of dynamic programming," Operations Research, vol. 50, pp. 48-51, 2002.##[25] P. Diban, M. K. A. Aziz, D. C. Foo, X. Jia, Z. Li, and R. R. Tan, "Optimal biomass plantation replanting policy using dynamic programming," Journal of Cleaner Production, vol. 126, pp. 409-418, 2016.##[26] R. Jia, S. J. Mellon, S. Hansjee, A. Monk, D. Murray, and J. A. Noble, "Automatic bone segmentation in ultrasound images using local phase features and dynamic programming," in Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, 2016, pp. 1005-1008.##[27] M. Roozegar, M. Mahjoob, and M. Jahromi, "Optimal motion planning and control of a nonholonomic spherical robot using dynamic programming approach: simulation and experimental results," Mechatronics, 2016.##[28] C. Finck and R. Li, "Operational load shaping of office buildings connected to thermal energy storage using dynamic programming," 2016.##[29]م. فتاحی حسن آباد، ح. قانعی یخدان و ع. م. لطیف، "ارائه یک روش نوین جهت تعیین قوت واترمارک با استفاده از الگوریتم رقابت استعماری"، پردازش علائم و داده‌ها، شماره 10، صفحات 43-56، 1392.##[29] M.Fatehi Hassan Abaad, H.Ghanee and A.M. Latif, "A novel method for suitable selection of watermark strength in digital image watermarking based on imperialist competitive algorithm" JSDP, vol. 10 no. 1, pp. 56-43, 2013.##[30] J. Kennedy, "Particle swarm optimization," in Encyclopedia of machine learning, ed: Springer, 2011, pp. 760-766.##[31] Z. Liu, P. Zhu, W. Chen, and R.-J. Yang, "Improved particle swarm optimization algorithm using design of experiment and data mining techniques," Structural and Multidisciplinary Optimization, vol. 52, pp. 813-826, 2015.##[32] C. Ou-Yang, H.-J. Cheng, and Y.-C. Juan, "An Integrated mining approach to discover business process models with parallel structures: towards fitness improvement," International Journal of Production Research, vol. 53, pp. 3888-3916, 2015.##[33] H.-J. Cheng, C. Ou-Yang, and Y.-C. Juan, "A hybrid approach to extract business process models with high fitness and precision," Journal of Industrial and Production Engineering, vol. 32, pp. 351-359, 2015.##[34]ف. احمدی زر، خ. ب. سلطانیان و ف. اخلاقیان‌ طابف "طراحی و آموزش شبکه‌های عصبی مصنوعی به وسیله استراتژی تکاملی با جمعیت‌‌های موازی"، پردازش علائم و داده‌ها، شماره 13، صفحات 101-114، 1395.##[34] F. Ahmadizar, Kh.B. Soltanian and F. Akhlaghian, " Construction and Training of Artificial Neural Networks using Evolution Strategy with Parallel Populations", JSDP. vol. 13, no. 1, pp. 101-114, 2016.##[35] W. M. van der Aalst and A. H. Ter Hofstede, "YAWL: yet another workflow language," Information systems, vol. 30, pp. 245-275, 2005.## ##

یک سامانه توصیه‎گر ترکیبی با استفاده از اعتماد و خوشه‎بندی دوجهته به‎منظور افزایش کارایی پالایش‎گروهی A hybrid recommender system using trust and bi-clustering in order to increase the efficiency of collaborative filtering 1 1 موفقیت سامانه‎های تجارت‎ الکترونیکی و تراکنش‎های کسب‎وکار برخط، به‎طور قابل توجهی وابسته به طراحی مؤثر سازوکار توصیه محصولات است. فراهم‎کردن توصیه‎های باکیفیت برای سامانه‎های تجارت ‎الکترونیکی بسیار مهم است تا بدین‎ترتیب به کاربران در تصمیم‎گیری مؤثر میان انتخاب‎های متعدد کمک کند. پالایش‎گروهی یک روش برای تولید توصیه‎ها بر اساس رتبه‎های کاربران مشابه است که به‎صورت وسیعی مورد قبول واقع شده است. این روش دارای چندین مشکل ذاتی مانند کم‎پشتی داده‎ها، شروع سرد و مقیاس‎پذیری است. حل این مشکلات و بهبود کارایی پالایش‎گروهی از چالش‎های مطرح در این زمینه است. در این مقاله یک سامانه ترکیبی جدید که شبکه اعتماد و خوشه‎بندی دوجهته را برای افزایش کارایی پالایش‎گروهی به‌کار می‎بندد، پیشنهاد شده است. نتایج تجربی بر روی زیرمجموعه‎ای از مجموعه‎داده‎های epinions، اثربخشی و کارایی سامانه پیشنهادی در مقابل روش‎های پالایش‎ گروهی مبتنی بر کاربر و پالایش‎ گروهی ترکیبی با اعتماد را تأیید می‎کند. 2 In the present era, the amount of information grows exponentially. So, finding the required information among the mass of information has become a major challenge. The success of e-commerce systems and online business transactions depend greatly on the effective design of products recommender mechanism. Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Recommender systems have been developed in order to respond this problem in order to customize the required information for users. So far, several types of recommender systems have been developed such as collaborative filtering recommender systems, content-based recommender systems and knowledge-based recommender systems. Each of these systems has advantages and disadvantages. Most of the recommender systems are based on collaborative filtering; Collaborative filtering is a widely accepted technique to generate recommendations based on the ratings of like-minded users. In fact, the main idea of this technique is to benefit from the past behavior or existing beliefs of the user community to predict products that are likely to be liked by the current user of the system. In collaborative filtering, we use the similarity between users or items to recommend products. However, this technique has several inherent problems such as cold start, sparsity and scalability. Since the collaborative filtering system is considered to be the most widely used recommender system, solving these problems and improving the effectiveness of collaborative filtering is one of the challenges raised in this context. None of the proposed hybrid systems have ever been able to resolve all of the collaborative filtering problems in a single and desirable manner; in this paper, we proposed a new hybrid recommender system that applies trust network as well as bi-clustering to improve the effectiveness of collaborative filtering. Therefore, the objectives of this research can be summarized as follows: sparsity reduction, increasing the speed of producing recommendations and increasing the accuracy of recommendations. In the proposed system, the trust between users is used to fill the user-item matrix which is a sparse matrix to solve the existing problem of sparsity. Then using bi-clustering, the user-item matrix is subdivided into matrices to solve the problem of scalability of the collaborative filtering and then the collaborative filtering is implemented for each sub matrix and the results from the implementation of the collaborative filtering for the sub-matrices are combined and recommendations are made for the users. The experimental results on a subset of the extended Epinions dataset verify the effectiveness and efficiency of our proposed system over user-based collaborative filtering and hybrid collaborative filtering with trust techniques. Improve sparsity problem Experimental results showed that our proposed system solves some of the sparsity problems which is due to the using the trust in the hybrid recommender system. By using trust, we can predict many uncertain ratings. Thus, transforming the user-item sparsity matrix into a half-full matrix. Improve scalability problem The results show that the proposed system has a higher speed compared with the user-based collaborative filtering algorithm and hybrid collaborative filtering with trust, and increasing the volume of data has little effect on increase online computing time. The reason can be summarized as a using of bi-clustering. Bi-directional clusters are made offline and break down the matrix of rankings into smaller subsets. Implementing the collaborative filtering on these smaller sets has led to increased computing speed. Improve the new user problem This system can provide accurate results for the new users due to the use of trust, because product collections viewed by new user can increase with the trust between the users. This system can predict the similarity between the new user and other users. So, the results are more accurate than the results of the user-based collaborative filtering and hybrid collaborative filtering with trust. 119 132 2016/10/262017/10/62017/06/242017/08/32016/10/82017/08/22017/04/102016/12/192017/06/24 1396/4/3 2017/06/102018/05/162018/04/292018/05/162017/03/52018/05/162017/10/252017/08/202018/05/16 1397/2/26 منیره حسینی Monireh Hosseini دانشگاه صنعتی خواجه نصیرالدین طوسی ایران hosseini@kntu.ac.ir مقصود نصرالهی Maghsood Nasrollahi دانشگاه صنعتی خواجه نصیرالدین طوسی ایران Maghsod68@gmail.com علی بقائی Ali Baghaei دانشگاه صنعتی خواجه نصیرالدین طوسی ایران a.baghaei@mail.kntu.ac.ir Recommender systems Collaborative filtering Trust Bi-clustering Hybrid recommender systems سامانه‎های توصیه‎گر پالایش‎گروهی اعتماد خوشه‎بندی دوجهته سامانه توصیه‎گر ترکیبی [1] H. Alizadeh, M. Moshki, H. Parvin, B. Minaei Bidgoli, "Clustering Ensemble based on combination of subset of primary clusters", JSDP; 7 (1):19-32, 2010.##[2] G. Adomavicius, and A. Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions", IEEE transactions on knowledge and data engineering, 17(6):734-49, 2005 Jun,##[3] C. Basu, H. Hirsh, and W. Cohen, "Recommenda-tion as classification: Using social and content-based information in recommenda-tion", InAaai/iaai, pp. 714-720, 1998 Jul 1.##[4] J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, "Recommender systems survey", Knowledge-Based Systems, 46:109-32, 2013.##[5] J. S. Breese, D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering". InProceedings of the Fourteenth conference on Uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc, pp. 43-52, 1998 Jul 24.##[6] D. U. Yongping, and L. HUANG, "Improve the Collaborative Filtering Recommender System Performance by Trust Network Construction", Chinese Journal of Electronics, 25(3):418-23, 2016 May.##[7] A. Felfering, G. Friedrich, and L. S. Thieme, "Recommender Systems", Intelligent Systems IEEE, 22, pp. 18-22, 2007.##[8] L. Gao, and C. Li, "Hybrid personalized recommended model based on genetic algorithm", In2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, IEEE, 12, pp. 1-4, 2008 Oct.##[9] T. George, and S. Merugu, "A scalable collaborative filtering framework based on co-clustering", InFifth IEEE International Confer-ence on Data Mining (ICDM'05), IEEE, pp. 4-pp, 2005 Nov 27.##[10] F. Gorunescu, "Data Mining: Concepts, models and techniques", Springer Science & Business Media, Vol. 12, 2011.##_4##_3##_5##_1##_2##_6##[11] G. Guo, J. Zhang, and D. Thalmann, "Merging trust in collaborative filtering to alleviate data sparsity and cold start", Knowledge-Based Systems, 57:57-68, 2014.##[12] J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, "Evaluating collaborative filtering recommender systems", ACM Transactions on Information Systems (TOIS), 22(1):5-3, 2004 Jan 1.##[13] Y. Ho, D. Fong, and Z. Yan, "A Hybrid GA-based Collaborative Filtering Model for Online Recommenders", InIce-b, pp. 200-203, 2007.##[14] H. Ingoo, J. O. Kyong, and H. R. Tae, "The collaborative filtering recommendation based on SOM cluster-indexing CBR", Expert Systems with Applications, 25(3):413-23, 2003.##[15] D. Jannach, and M. Zanker, A. Felfering, and G. Friedrich, "Recommender Systems an introduce-tion", Cambridge University Press, New York, 2013.##[16] R. Katarya, and O. P. Verma, "A collaborative recommender system enhanced with particle swarm optimization technique", Multimedia Tools and Applications, 1-5, 2016.##[17] K. J. Kim, and H. Ahn, "A recommender system using GA K-means clustering in an online shopping market", Expert systems with applications, 34(2):1200-9, 2008 Feb 29.##[18] S. C. Kim, C. S. Park, and S. K. Kim, "A Hybrid Recommendation System Using Trust Scores in a Social Network", Embedded and Multimedia Computing Technology and Service, 107-112, 2012.##[19] U. Kużelewska, and K. Wichowski, "A Modified Clustering Algorithm DBSCAN Used in a Collaborative Filtering Recommender System for Music Recommendation", InTheory and Engineering of Complex Systems and Depend-ability, Springer International Publish-ing, pp. 245-254, 2015.##[20] N. Lathia, S. Hailes, and L. Capra, "Trust-based collaborative filtering", InIFIP International Conference on Trust Management 2008 Jun 18, Springer US, pp. 119-134, 2008.##[21] T. Q. Lee, Y. Park, and Y. T. Park, "A time-based approach to effective recommender systems using implicit feedback", Expert systems with applications; 34(4):3055-62, 2008 May 31##[22] Y. M. Li, C. T. Wu, and C. Y. Lai, "A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship", Decision Support Systems, 55(3):740-52, 2013 Jun 30.##[23] X. Luo, Y. Xia, and Q. Zhu, "Incremental collaborative filtering recommender based on regularized matrix factorization", Knowledge-Based Systems, 27:271-80, 2012 Mar 31.##[24] N. Manouselis, and K. Verbert, "Layered evaluation of multi-criteria collaborative filtering for scientific paper recommendation", Procedia Computer Science, 18:1189-97, 2013 Dec 31.##[25] L. Martinez, R. M. Rodriguez, and M. Espinilla, "Reja: a georeferenced hybrid recommender system for restaurants", InProceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, IEEE Computer Society, Volume 03, pp. 187-190, 2009 Sep 15.##[26] T. M. Murali, and S. Kasif, "Extracting conserved gene expression motifs from gene expression data", InPacific symposium on Biocomputing, Vol. 8, pp. 77-88, 2003.##[27] M. Nilashi, O. Ibrahim, N. Ithnin, and N. H. Sarmin, "A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS", Electronic Commerce Research and Applications, 14(6):542-62, 2015 Nov 30.##[28] M. H. Park, J. H. Hong, and S. B. Cho, "Location-based recommendation system using bayesian user's preference model in mobile devices", InInternational Conference on Ubiquitous Intelligence and Computing, Springer Berlin Heidelberg, pp. 1130-1139, 2007 Jul 1.##[29] A. Prelić, S. Bleuler, P. Zimmermann, A. Wille, P. Bühlmann, W. Gruissem, L. Hennig, L. Thiele, and E. Zitzler, "A systematic comparison and evaluation of biclustering methods for gene expression data", Bioinformatics, 22(9):1122-9, 2006 May 1.##[30] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommenda-tion algorithms", InProceedings of the 10th international conference on World Wide Web, ACM, pp. 285-295, 2001 Apr 1.##[31] J. B. Schafer, J. Konstan, and J. Riedl, "Recommender systems in e-commerce", InProceedings of the 1st ACM conference on Electronic commerce, ACM, pp. 158-166, 1999 Nov 1.##[32] A. A. Shabalin, V. J. Weigman, C. M. Perou, and A. B. Nobel, "Finding large average submatrices in high dimensional data", The Annals of Applied Statistics, 985-1012, 2009 Sep 1.##[33] X. Shen, H. Long, and C. Ma, "Incorporating trust relationships in collaborative filtering recom-mender system", InSoftware Engineering, Arti-ficial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2015 16th IEEE/ACIS International Conference, pp. 1-8, 2015 Jun 1.##[34] T. H. Soliman, S. A. Mohamed, and A. A. Sewisy, "Developing a mobile location-based collabora-tive Recommender System for GIS applica-tions", InComputer Engineering & Systems (ICCES), 2015 Tenth International Conference, IEEE, pp. 267-273, 2015 Dec 23.##[35] P. Symeonidis, A. Nanopoulos, A. N. Papadopoulos, and Y. Manolopoulos, "Nearest-biclusters collaborative filtering based on constant and coherent values", Information retrieval, 11(1):51-75, 2008 Feb 1;##[36] P. Victor, M. De Cock, and C. Cornelis, "Trust and recommendations", InRecommender syst-ems handbook 2011, Springer US, pp. 645-675, 2011.##[37] B. Xu, J. Bu, C. Chen, and D. Cai, "An exploration of improving collaborative recomm-ender systems via user-item subgroups", InPro-ceedings of the 21st international conference on World Wide Web, ACM, pp. 21-30, 2012 Apr 16.##[38] R. R. Yager, "Fuzzy logic methods in recomm-ender systems", Fuzzy Sets and Systems, 136(2):133-49, 2003 Jun 1.##[39] S. Yan, "A Collaborative Filtering Recommender Approach by Investigating Interactions of Interest and Trust", InKnowledge Engineering and Management, Springer Berlin Heidelberg, pp. 173-188, 2014.##[40] X. Yang, Y. Guo, Y. Liu, and H. Steck, "A survey of collaborative filtering based social recomm-ender systems", Computer Communica-tions, 41:1-0, 2014 Mar 15.##[41] W. Yuan, D. Guan, Y. K. Lee, S. Lee, and S. J. Hur, "Improved trust-aware recommender system using small-worldness of trust networks", Knowledge-Based Systems, 23(3):232-8, 2010 Apr 30.##[42] N. Zheng, and Q. Li, "A recommender system based on tag and time information for social tagging systems", Expert Systems with Applications, 38(4):4575-87, 2011 Apr 30.##[43] R. Zhu, and S. Gong, "Analyzing of collaborative filtering using clustering technology", In Computing, Communication, Control, and Management. CCCM 2009, ISECS International Colloquium on Vol. 4, pp. 57-59, IEEE, 2009.##[1] علیزاده، حسین، مشکی، محسن، پروین، حمید، مینایی بیدگلی، بهروز، "خوشه‌بندی ترکیبی مبتنی بر زیرمجموعه‌ای از خوشه‌های اولیه"، پردازش علائم و داده‌ها، ۷ (۱) :۱۹-۳۲، ۱۳۸۹.##[1] H. Alizadeh, M. Moshki, H. Parvin, B. Minaei Bidgoli, "Clustering Ensemble based on combination of subset of primary clusters", JSDP; 7 (1):19-32, 2010.##[2] G. Adomavicius, and A. Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions", IEEE transactions on knowledge and data engineering, 17(6):734-49, 2005 Jun,##[3] C. Basu, H. Hirsh, and W. Cohen, "Recommenda-tion as classification: Using social and content-based information in recommenda-tion", InAaai/iaai, pp. 714-720, 1998 Jul 1.##[4] J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, "Recommender systems survey", Knowledge-Based Systems, 46:109-32, 2013.##[5] J. S. Breese, D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering". InProceedings of the Fourteenth conference on Uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc, pp. 43-52, 1998 Jul 24.##[6] D. U. Yongping, and L. HUANG, "Improve the Collaborative Filtering Recommender System Performance by Trust Network Construction", Chinese Journal of Electronics, 25(3):418-23, 2016 May.##[7] A. Felfering, G. Friedrich, and L. S. Thieme, "Recommender Systems", Intelligent Systems IEEE, 22, pp. 18-22, 2007.##[8] L. Gao, and C. Li, "Hybrid personalized recommended model based on genetic algorithm", In2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, IEEE, 12, pp. 1-4, 2008 Oct.##[9] T. George, and S. Merugu, "A scalable collaborative filtering framework based on co-clustering", InFifth IEEE International Confer-ence on Data Mining (ICDM'05), IEEE, pp. 4-pp, 2005 Nov 27.##[10] F. Gorunescu, "Data Mining: Concepts, models and techniques", Springer Science & Business Media, Vol. 12, 2011.##_4##_3##_5##_1##_2##_6##[11] G. Guo, J. Zhang, and D. Thalmann, "Merging trust in collaborative filtering to alleviate data sparsity and cold start", Knowledge-Based Systems, 57:57-68, 2014.##[12] J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, "Evaluating collaborative filtering recommender systems", ACM Transactions on Information Systems (TOIS), 22(1):5-3, 2004 Jan 1.##[13] Y. Ho, D. Fong, and Z. Yan, "A Hybrid GA-based Collaborative Filtering Model for Online Recommenders", InIce-b, pp. 200-203, 2007.##[14] H. Ingoo, J. O. Kyong, and H. R. Tae, "The collaborative filtering recommendation based on SOM cluster-indexing CBR", Expert Systems with Applications, 25(3):413-23, 2003.##[15] D. Jannach, and M. Zanker, A. Felfering, and G. Friedrich, "Recommender Systems an introduce-tion", Cambridge University Press, New York, 2013.##[16] R. Katarya, and O. P. Verma, "A collaborative recommender system enhanced with particle swarm optimization technique", Multimedia Tools and Applications, 1-5, 2016.##[17] K. J. Kim, and H. Ahn, "A recommender system using GA K-means clustering in an online shopping market", Expert systems with applications, 34(2):1200-9, 2008 Feb 29.##[18] S. C. Kim, C. S. Park, and S. K. Kim, "A Hybrid Recommendation System Using Trust Scores in a Social Network", Embedded and Multimedia Computing Technology and Service, 107-112, 2012.##[19] U. Kużelewska, and K. Wichowski, "A Modified Clustering Algorithm DBSCAN Used in a Collaborative Filtering Recommender System for Music Recommendation", InTheory and Engineering of Complex Systems and Depend-ability, Springer International Publish-ing, pp. 245-254, 2015.##[20] N. Lathia, S. Hailes, and L. Capra, "Trust-based collaborative filtering", InIFIP International Conference on Trust Management 2008 Jun 18, Springer US, pp. 119-134, 2008.##[21] T. Q. Lee, Y. Park, and Y. T. Park, "A time-based approach to effective recommender systems using implicit feedback", Expert systems with applications; 34(4):3055-62, 2008 May 31##[22] Y. M. Li, C. T. Wu, and C. Y. Lai, "A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship", Decision Support Systems, 55(3):740-52, 2013 Jun 30.##[23] X. Luo, Y. Xia, and Q. Zhu, "Incremental collaborative filtering recommender based on regularized matrix factorization", Knowledge-Based Systems, 27:271-80, 2012 Mar 31.##[24] N. Manouselis, and K. Verbert, "Layered evaluation of multi-criteria collaborative filtering for scientific paper recommendation", Procedia Computer Science, 18:1189-97, 2013 Dec 31.##[25] L. Martinez, R. M. Rodriguez, and M. Espinilla, "Reja: a georeferenced hybrid recommender system for restaurants", InProceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, IEEE Computer Society, Volume 03, pp. 187-190, 2009 Sep 15.##[26] T. M. Murali, and S. Kasif, "Extracting conserved gene expression motifs from gene expression data", InPacific symposium on Biocomputing, Vol. 8, pp. 77-88, 2003.##[27] M. Nilashi, O. Ibrahim, N. Ithnin, and N. H. Sarmin, "A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS", Electronic Commerce Research and Applications, 14(6):542-62, 2015 Nov 30.##[28] M. H. Park, J. H. Hong, and S. B. Cho, "Location-based recommendation system using bayesian user's preference model in mobile devices", InInternational Conference on Ubiquitous Intelligence and Computing, Springer Berlin Heidelberg, pp. 1130-1139, 2007 Jul 1.##[29] A. Prelić, S. Bleuler, P. Zimmermann, A. Wille, P. Bühlmann, W. Gruissem, L. Hennig, L. Thiele, and E. Zitzler, "A systematic comparison and evaluation of biclustering methods for gene expression data", Bioinformatics, 22(9):1122-9, 2006 May 1.##[30] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommenda-tion algorithms", InProceedings of the 10th international conference on World Wide Web, ACM, pp. 285-295, 2001 Apr 1.##[31] J. B. Schafer, J. Konstan, and J. Riedl, "Recommender systems in e-commerce", InProceedings of the 1st ACM conference on Electronic commerce, ACM, pp. 158-166, 1999 Nov 1.##[32] A. A. Shabalin, V. J. Weigman, C. M. Perou, and A. B. Nobel, "Finding large average submatrices in high dimensional data", The Annals of Applied Statistics, 985-1012, 2009 Sep 1.##[33] X. Shen, H. Long, and C. Ma, "Incorporating trust relationships in collaborative filtering recom-mender system", InSoftware Engineering, Arti-ficial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2015 16th IEEE/ACIS International Conference, pp. 1-8, 2015 Jun 1.##[34] T. H. Soliman, S. A. Mohamed, and A. A. Sewisy, "Developing a mobile location-based collabora-tive Recommender System for GIS applica-tions", InComputer Engineering & Systems (ICCES), 2015 Tenth International Conference, IEEE, pp. 267-273, 2015 Dec 23.##[35] P. Symeonidis, A. Nanopoulos, A. N. Papadopoulos, and Y. Manolopoulos, "Nearest-biclusters collaborative filtering based on constant and coherent values", Information retrieval, 11(1):51-75, 2008 Feb 1;##[36] P. Victor, M. De Cock, and C. Cornelis, "Trust and recommendations", InRecommender syst-ems handbook 2011, Springer US, pp. 645-675, 2011.##[37] B. Xu, J. Bu, C. Chen, and D. Cai, "An exploration of improving collaborative recomm-ender systems via user-item subgroups", InPro-ceedings of the 21st international conference on World Wide Web, ACM, pp. 21-30, 2012 Apr 16.##[38] R. R. Yager, "Fuzzy logic methods in recomm-ender systems", Fuzzy Sets and Systems, 136(2):133-49, 2003 Jun 1.##[39] S. Yan, "A Collaborative Filtering Recommender Approach by Investigating Interactions of Interest and Trust", InKnowledge Engineering and Management, Springer Berlin Heidelberg, pp. 173-188, 2014.##[40] X. Yang, Y. Guo, Y. Liu, and H. Steck, "A survey of collaborative filtering based social recomm-ender systems", Computer Communica-tions, 41:1-0, 2014 Mar 15.##[41] W. Yuan, D. Guan, Y. K. Lee, S. Lee, and S. J. Hur, "Improved trust-aware recommender system using small-worldness of trust networks", Knowledge-Based Systems, 23(3):232-8, 2010 Apr 30.##[42] N. Zheng, and Q. Li, "A recommender system based on tag and time information for social tagging systems", Expert Systems with Applications, 38(4):4575-87, 2011 Apr 30.##[43] R. Zhu, and S. Gong, "Analyzing of collaborative filtering using clustering technology", In Computing, Communication, Control, and Management. CCCM 2009, ISECS International Colloquium on Vol. 4, pp. 57-59, IEEE, 2009.## ##

روشی نوین در کاهش نوفه رایسین از مقدار بزرگی سیگنال دیفیوژن در تصویربرداری تشدید مغناطیسی (MRI) An Improved Rician Noise Correction Technique from the Magnitude of Diffusion MR Images 1 1 شدت واقعی سیگنال استخراج‌شده از تصاویر MRI به دلیل اصلاح نوفه در محاسبات مربوط به مقدار بزرگی(magnitude) سیگنال در پیکسل‌های کم‌شدت، همواره با نوفۀ رایسین همراه است. این مسئله به‌ویژه برای تصویربرداری‌های MRI با نسبت سیگنال به نوفۀ بسیار کم (SNR<3.0) مانند دیفیوژن MRI، در مقادیر بزرگِ مشهور به b- value (حاصل کاربرد گرادیآن‌های مغناطیسی پرقدرت در زمآن‌های طولانی) نیز صادق است. در این پژوهش روشی برای حذف نوفۀ رایسین از پیکسل‌های تصاویر حاصل از مقدار بزرگی در MRI ارائه و یک معادلۀ خطی با عبارت تصحیح نوفه پیشنهاد شده که می‌تواند نوفۀ بایاس‌شده را به پیکسل‌ها بطور منفرد حذف کند. در صورت مشخّص بودن مقدار متوسط و واریانس تابع دانسیتهء شدت هر پیکسل، این تصحیح نوفه مطلوب و کامل است، اما در صورت عدم دسترسی دقیق به این اطلاعات برای هر تک‌پیکسل، میانگین‌گیری از نزدیک‌ترین پیکسل‌های مجاور در تصویر معادلۀ تصحیح نوفه به‌کارگرفته‌می‌شود. انتخاب تعداد پیکسل‌های مجاور با رساندن خطا به کمترین مقدار تقریبی انجام می‌شود. طبق محاسبه در این روش، به‌کارگیری دست‌کم 9 پیکسل همسایه برای سیگنال به نوفه معادل0/1 SNR=1.0)) مقدار تقریبی تا زیر 10 % خطا می‌دهد. روش کاهش نوفۀ رایسین ارائه‌شده در این مقاله برتری قابل ملاحظه‌ای را در حذف نوفه از سیگنال دیفیوژن MRI در مقادیر بزرگ b-نسبت به روش‌های ارائه‌شده تا زمان حاضر نشان می‌دهد. 2 The true MR signal intensity extracted from noisy MR magnitude images is biased with the Rician noise caused by noise rectification in the magnitude calculation for low intensity pixels. This noise is more problematic when a quantitative analysis is performed based on the magnitude images with low SNR(<3.0). In such cases, the received signal for both the real and imaginary components will fluctuate around a low level (e.g. zero) often producing negative values. The magnitude calculation on such signals will rectify all negative values to produce only positive magnitudes, thereby artificially raising the average level of these pixels. The signal thus will be biased by the rectified noise. Diffusion MRI using high b-values (using strong magnetic gradients) is one the most important cases of biased Rician noise. A technique for removing this bias from individual pixels of magnitude MR images is presented in this study. This method provides a bias correction for individual pixels using a linear equation with the correction term separated from the term to be corrected (i.e. the pixel intensity). The correction is exact when the mean and variance of the pixel intensity probability density functions are known. When accurate mean values are not available, a nearest neighbor average is used to approximate the mean in the calculation of the linear correction term. With a nine pixel nearest neighbor average (i.e. one layer of nearest neighbors) the bias correction for individual pixel intensities is accurate to within 10% error for signal to noise ratios SNR=1.0. Several different noise correction schemes from the literature are presented and compared. The new Rician bias correction presented in this work represents a significant improvement over previously published techniques. The proposed approach substantially removes the Rician noise bias from diffusion MR signal decay over an extended range of b-values from zero to very high b-values. 133 147 2016/10/262017/10/62017/06/242017/08/32016/10/82017/08/22017/04/102016/12/192017/06/242017/08/17 1396/5/26 2017/06/102018/05/162018/04/292018/05/162017/03/52018/05/162017/10/252017/08/202018/05/162018/05/15 1397/2/25 مرضیه نظام‌زاده Marzieh Nezamzadeh دانشگاه تربیت مدرس ایران m.nezamzadeh@modares.ac.ir magnitude signal Diffusion MRI probability distribution function Rician bias تصویر رزنانس مغناطیسی توزیع احتمال نوفه رایسین سیگنال دیفیوژن MRI [1] Whittall KP, MacKay AL, Graeb DA, Nugent RA, Li DKB, Paty DW. (1997)." In vivo measurement of T2 distributions and water contents in normal human brain." Magn Reson Med. 37, pp. 34-43.##[2] Assaf Y, Freidlin RZ, Rohde GK, Basser PJ.(2004)."New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter." 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Technol. 27, pp. 248–264##[1] Whittall KP, MacKay AL, Graeb DA, Nugent RA, Li DKB, Paty DW. (1997)." In vivo measurement of T2 distributions and water contents in normal human brain." Magn Reson Med. 37, pp. 34-43.##[2] Assaf Y, Freidlin RZ, Rohde GK, Basser PJ.(2004)."New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter." Magn Reson Med. 52, pp. 965-978.##[3] Le Bihan D. (2007)."The 'wet mind': water and functional neuroimaging." Phys Med Biol. 52, pp. R57-R90.##[4] Østergaard L.(2004)."Cerebral perfusion imaging by bolus tracking." Magn Reson Imaging. 15, pp. 3-9.##[5] Chen CN, Hoult DI.(1989). "Biomedical magnetic resonance technology." Bristol and New York: Adam Hilger. pp.117-176.##[6] Hoult DI, Lauterbur PC.(1979). "The sensitivity of the zeugmatographic experiment involving human samples." J Magn Reson. 34, pp. 425-433.##[7] Rice SO. (1944). "Mathematical analysis of random noise. Bell System Technological Journal. 23, pp. 282-332.##[8] Wang Y, Lei T.(1994). "Statistical analysis of MR imaging and its applications in image modeling." In: Proceedings of the IEEE International Conference on Image Processing and Neural Networks. 1, pp. 866-870.##[9] Cárdenas-Blanco A, Tejos C, Irarrazaval P, Cameron IG. (2008). "Noise in magnitude magnetic resonance images." Concepts in Magnetic Resonance Part A. 32A(6), pp. 409–416.##[10] Bernstein MA, Thomasson DM, Perman WH. (1989). "Improved detectability in low signal-to-noise ratio magnetic resonance images by means of a phase–corrected real reconstruction." Med Phys. 16, pp. 813-817.##[11] Henkelman RM.(1986). "Measurement of signal intensities in the presence of noise in MR images." Med Phys. 12, pp. 232-233.##[12] Gudbjartsson H, Patz S.(1995). "The Rician distribution of noisy MRI data." Magn Reson Med. 34, pp. 910-914.##[13] Miller AJ, Joseph PM.(1993). "The use of power images to perform quantitative analysis on low SNR MR images." Magn Reson Imag. 11, pp. 1051-1056.##[14] McGibney G, Smith MR. (1973). "An unbiased signal-to-noise ratio measure for magnetic resonance images." Med Phys. 20, pp. 1077-1078.##[15] Koay CG, Basser PJ. (2006). "Analytically exact correction scheme for signal extraction from noisy magnitude MR signals." J Magn Reson. 179, pp. 317-322.##[16] Cárdenas-Blanco A, Nezamzadeh M, Foottit C, Cameron I.(2007). "Accurate noise bias correction applied to individual pixels." In: Proceedings of the 15th Annual Meeting of ISMRM-ESMRMB, Berlin, Germany. pp. 657.##[17] Sijbers J, den Dekker AJ, Scheunders P, Van Dyck D. (1998). "Maximum-likelihood estimation of Rician distribution parameters." IEEE Trans Med Imaging. 17, pp. 357-361.##[18] Kingsley, PB. (2005). "Improved formulas to correct noisy low-intensity DTI data." In: Proceedings of the ISMRM Workshop on Methods for Quantitative Diffusion MRI of Human Brain. poster 52.##[19] Nezamzadeh M, Cameron IG.(2006). "A new Rician noise bias correction." In: Proceedings of the 14th Annual Meeting of ISMRM, Seattle, Washington, USA, pp. 346.##[20] Jones DK, Basser PJ. (2004). "Squashing peanuts and smashing pumpkins: How noise distorts diffusion weighted MR data." Magn Reson Med. 52, pp. 979-993.##[21] Olariu E, Cárdenas-Blanco A, Cameron, IG. (2007). "Analysis of Noise Corrected Diffusion Decay of Human Brain." In: Proceedings of the 15th Annual Meeting of ISMRM-ESMRMB, Berlin, Germany. pp. 3301.##[22] Gilbert G, Simard D, Beaudoin G. (2007). "Impact of improved combination of signals from array coils in diffusion tensor imaging." IEEE Trans Med Imaging. 26, 1428-1436.##[23] Constantinides CD, Atalar E, McVeigh ER.(1997). "Signal-to-noise measurements in magnitude images from NMR phased arrays." Magn Reson Med. 38, pp. 852-857.##[24] Sijbers J, Poot D, Dekker AJ, Pintjens W. (2007). "Automatic estimation of the noise variance from the histogram of a magnetic resonance image." Phys Med and Biol. 52, pp. 1335-1348.##[25] Andersen AH.(1996). "On the Rician distribution of noisy MRI data." Magn Reson Med. 36, pp. 331-333.##[26] Niendorf T., Dijkhuizen R., Norris D.G., van Lookeren, Campagne M., Nicolay K. (1996). "Biexponential diffusion attenuation in various states of brain tissue: implications for diffusion weighted imagin.", Magn. Reson. Med. 36, pp. 847- 857.##[27] Haldar PJ.,Wedeen VJ., Nezamzadeh M., Dai G. Weiner MW., Schuff N., Liang ZP.(2013) "Improved diffusion imaging through SNR-enhancing joint reconstruction."Magn Reson Med. 69(1), pp.227-289.##[28] Liu RW, Shi L, Yu SC, Wang D.(2015). "A two-step optimization approach for nonlocal total variation-based Rician noise reduction in magnetic resonance images." Med Phys. 42(9), pp. 5167-87.##[29] R. Riji • Jeny Rajan • Jan Sijbers • Madhu S. Nair. (2015). "Iterative bilateral filter for Rician noise reduction in MR images." Signal, Image and Video Processing. 9(7), pp. 1543.##[30] Baselice F., Ferraioli G., Pascazio V. (2017)."A 3D MRI denoising algorithm based on Bayesian theory.", BioMed Eng OnLine, 16: 25, pp.1-19.##[31] Pal Ch., Das P., Chakrabarti A., Ghosh R.(2017). "Rician noise removal in magnitude MRI images using efficient anisotropic diffusion filtering." Int.J.Imaging Syst. Technol. 27, pp. 248–264## ##