ترکیب تصاویر با کمک ترکیب فازی، موجک و آنتروپی تصویر

•   آرشیو : نسخه تابستان 1398
•   موضوع : مهندسی نرم افزار
• نویسنده/گان : سعید امیری، احمد مصلی نژاد، احسان امیری
•   چکیده :
  الگوریتم ترکیب بر پایه ی تشخیص تمرکز می تواند سطح کیفی تصویر ترکیبی را افزایش دهد. در روش معرفی شده ابتدا، منبع تصویر به سه بخش تقسیم می شود. سپس یک قانون جدید تشخیص تمرکز مبتنی بر موجک و آنتروپی فرمول بندی می شود. درنهایت پیکسل های نواحی هموار، متناسب با طبقه بندی فازی صورت گرفته بر روی آن، ترکیب می شوند. با توجه به آنکه خروجی موجک سیگنال می باشد، معکوس جریان تبدیل موجک جهت بدست آوردن تصویر نهایی اعمال می شود. خاصیت این الگوریتم، بهبود دقت تشخیص تمرکز به اندازه 12.0 نسبت به مدل موجک و حذف اثرات بلوکی در تصاویر ترکیب شده می باشد. نتایج آزمایش ها نشان داده اند که الگوریتم ترکیب ارائه شده، در مقایسه با الگوریتم موجک در حالت ماکزیمم و میانگین و منطق فازی با توجه به معیارهای PSNR و MAE امتیاز بهتری کسب می کند. واژگان کليدي: ترکیب تصویر، فوکوس، موجک، آنتروپی. اسپم ها یکی از متداول ترین جنبه های منفی ایمیل ها می باشند که امکان حذف کامل آنها وجود ندارد، ولی می توان با استفاده از برخی روش ها تعداد آنها را کاهش داد. برای کاهش تعداد ایمیل های اسپم، روش های متعددی پیشنهاد شده است. یک روش رایج، دسته بندی ایمیل ها به ایمیل های اسپم و ایمیل های غیر اسپم است. ابعاد بالای ویژگی ها، از مشکلات عمده این روش می باشد که مستقیماً بر روی کیفیت فرآیند یادگیری تاثیر می¬گذارد. استفاده از روش های استخراج ویژگی، قبل از فرآیند یادگیری، تأثیر چشمگیری در بهبود کارایی دسته بندی دارد. در زمینۀ استخراج ویژگی، الگوریتم های فراابتکاری به دلیل بهره گیری از استراتژی های اکتشاف و بهره برداری، کاربرد فراوانی دارند. برای افزایش دقت و بهبود کارایی الگوریتم های فراابتکاری، می توان مراحل این الگوریتم ها را با هم ترکیب نمود. در این پژوهش، الگوریتم کرم شب تاب با استفاده از الگوریتم عنکبوت اجتماعی بهبود داده شده و در مسأله¬ی استخراج ویژگی برای تشخیص ایمیل های اسپم استفاده شده است. همچنین در این پژوهش، به منظور بهبود هرچه بیشتر جمعیت الگوریتم کرم شب تاب و ایجاد تعادل بین استراتژی های اکتشاف و بهره برداری آن، از پارامتر P استفاده شده است که جابجایی بین الگوریتم ها را کنترل می کند. روش پیشنهادی در محیط متلب پیاده سازی شده و بر روی مجموعه داده spambase اجرا گردیده است. در انتها، الگوریتم پیشنهادی با الگوریتم های ژنتیک، مورچگان، عنکبوت اجتماعی و کرم شب تاب مقایسه شده است. نتایج مقایسه ها نشان می دهد که از لحاظ صحت و نرخ خطای دسته بندی، الگوریتم پیشنهادی نسبت به سایر الگوریتم ها بهبود چشمگیری دارد.
•   کلید واژه : اسپم، ایمیل های اسپم، الگوریتم عنکبوت اجتماعی، الگوریتم کرم شب تاب و ماشین بردار پشتیبان
•   Title : Hybrid Social Spider Algorithm with Firefly Algorithm for feature selection in spam email detection
•   Abstract :
  Spams are one of the most common negative aspects of emails that we cannot remove them completely, but we can reduce their number by using some methods. Several methods have been proposed to reduce the number of spam emails. A common method is classifying emails into spam and non-spam emails. The high dimensions of the features are one of the main problems that directly affect the quality of the learning process. The use of features extraction methods, prior to the learning process, has a significant impact on the classification performance improvement. In feature extraction, metahuristic algorithms are widely used due to the use of exploration and exploitation strategies. In order to increase the accuracy and improve the efficiency of metahuristic algorithms, steps of these algorithms can be combined together. In this thesis, the firefly algorithm has been improved using the social spider algorithm and has been used to extract features in spam emails detection. Also in this thesis, in order to further improve the population of the firefly algorithm and to create a balance between its exploration and exploitation strategies, parameter P is used that controls the switching between algorithms. The proposed method is simulated in the MATLAB environment and has been implemented on Spambase dataset. At the end, the proposed method is compared with genetic, ant, social spider and firefly algorithms. The results of the comparisons show that, in terms of accuracy and error rate of classification, the proposed algorithm is significantly improved compared to other algorithms.
•   مراجع :
  [01] Heydari, A., Tavakoli, M. a., & Heydari, Z. (2014, December 31). Detection of review spam:A survey. Expert Systems With Applications, 1- 9. doi:10.1016/j.eswa.2014.12.029
  [02] Kaya, Y., & Ertugrul, O. F. (2016, Junuary 11). A novel approach for spam email detection based on shifted binary patterns. wiley online library, 1216-1225. doi:10.1002/sec.1412
  [03] Idris, I., & Selamat, A. (2014). Improved email spam detection model with nagative selection algorithm and particle swarm optimization. Applied Soft Computing, 1 - 17. doi:10.1016j.asoc.2014.05.002
  [04] Bhowmick, A., & Hazarika, S. M. (2016, Jun 3). Machine Learning for E-mail spsm Filtering:Review,Techniques and Trends. manuscript No., 1 - 27.
  [05] Klaibar, S. M., & Razavi, S. N. (2014). Spam filtering by using Genetic based Feature Selection. International Jornal of Computer Applications Technology and Research, 3(12, 839 - 843, 2014, ISSN:- 2319 - 8656), 839 - 843. Retrieved from www.ijcat.com
  [06] Bayati, E., Sadeghzadeh, M., & Kumarci, F. (2014, January 1). Improving the Methods of Email Classification through the Fuzzy Decition Tree. Academic and Applied Studies, 4(1), 38 - 50. Retrieved from www.academians.org
  [07] Prilepok, M., Jezowicz, T., Platos, J., & Snacel, V. (2012). Spam detection using Compression and PSO. Fourth International on Computational Aspects of Social Networks(CASoN) (pp. 263 - 270). IEEE.
  [08] Saad, O., Darwish, A., & Faraj, R. (2012, Febuary). A survey of machine learning techniques for Spam filtering. IJCSNS International Jornal of Computer and Network Security, 12 No.2, 66- 72.
  [09] Almeida, T. A., Almeida, J., & Yamakami, A. (2010, December 2). Spam filtering:how the dimensionality reduction affects the accuracy of Naive Bayes classifiers. The Brazilian Computer Society, 183 - 200. doi:10.1007/s13174-010-0014-7
  [10] Su, M.-C., Lo, H.-H., & Hsu, F.-H. (2010). A neural tree and its application to spam detection. Expert Systems With Applications, 7966 - 7985. doi:10.1016/j.eswa.2010.04.038
  [11] Upasana, *. C. (2010). A Survey of Text Classification Techniques for E-mail Filtering. Second International Conference on Machine Learning and Computing (pp. 32 - 36). IEEE computer socity. doi:10.1109/ICMLC.2010.61
  [12] Blanzieri, E., & Bryl, A. (2009, July 10). A survey of learning -based techniques of email spam filtering.
  [13] Metsis, V., Androutsopoulos, I., & Paliouras, G. (2006). Spam Filtering with Naive Bayes - Which Naive Bayes? Third Conference on Email and Anti-Spam, (pp. 1 - 9). California USA. Retrieved July 27 - 28, 2006
  [14] Lee, S. M., Kim, D. S., Kim, J. H., & Park, J. S. (2010). Spam Detection Using Feature Selection and Parameters Optimization. International Conference on Complex,Intelligent and Software Intensive Systems (pp. 883 - 885). IEEE computer society. doi:10.1109/CISIS.2010.116
  [15]Geerthik.s. (2013). Survey on Internet Spam: Classification and Analysis. Int.J.Computer Technology & Applications, 4(3),384-391, 384 - 391. Retrieved from www.ijcta.com
  [16]M.Basavaraju, D. (n.d.). A Novel Method of Spam Mail Detection using Text Based Clustering Approach. International Journal of Computer Applications (0975 - 8887), 5- no.4,August 2010.
  [17]Rekha, S. N. (2014, may). A Review on Different Spam Detection Approaches. International Journal of Trends and Technology (IJETT), 11 Number 6, 315 - 318. Retrieved from www.ijettjournal.org
  [18]Savita Teli, S. B. (2014). Effective Spam Detection Method for Email. International Conference on Advances in Engineering & Technology (ICAET-2014), (pp. 68 - 72). Retrieved from www.iosrjournals.org
  [19]Anjali Sharma, D. J. (2015, may). Data Pre-Processing in Spam Detection. IJSTE - International Journal of Science Technology & Engineering, 1(11/ 007), 33 - 37. Retrieved from www.ijste.org
  [20]Isabella Miszalsaka, W. Z. (2007, February 20-24). Selected Methods of Spam Filtering in Email. Department of Microelectronics and Computer Science,Technical University of Lopez, aj,Politechniki 507 - 513. Retrieved from wojtekz@dmcs.pl
  [21]Ge Song, L. S. (n.d.).(2008) Email Spam Detection. 1 - 16.
  [22]Benjamin Markines, C. C. (2009, April 21). Social Spam Detection. AIRweb, 1 - 7. Retrieved from bmarkine@cs.indiana.edu
  [23]Sadegh Kharazmi, A. F. (2007, Nov 20 - 21). Spam Email detection Using Bayesian Spanning Tree. IDMC'07 ,Amir Kabir University, 1 - 6.
  [24]Zahrasadat Torabi, M. N. (2012). New Approach for Spam Detection with Suport Vector Machine And Feature Selection . First National Conference of Technology Developments on Elecronical , Electronics and Computer Engineering, (pp. 1 - 12).
  [27] G.Brown, T. M. (2008). Social networks and context-aware spam. In ACM Conference on Supporttive Work.
  [29] Gao, Y. (Dec. 2010). A Comprehansive Approach to Image Spam Detection: From Server to Client Solution, Information Forensics and Security. IEEE Transations.
  [30] Gao, Y. (2008). Image spam hunter, Acoustics, Speech and Signal Processing. ICASSP 2008. IEEE International Conference on April 2008.
  [31] N. Immorlica, K. J. (Redmond, 2006). Click Fraud Resistant Methods for Learning Click- Through Rates. Technical report, Microsoft Research.
  [32] L.Page, S. B. (1998). The page rank citation ranking: Bringing order to the web.
  [33] T. Fawcet. December (2003) Achallenge problem for data mining. KDD Explorations, in vivo spam filtering.
  [34] Veloso, A. (Oct. 2006). Lazy Associative Classification for Content-based Spam Detection. Web Congress, 2006, IEEE conference publication.
  [35] Z. Gyongyi, H. G.-M. (2005). Web Spam technology. In First International Workshop on Adversarial Information Retrieval on the Web(AIR Web).
  [36] K. Chellapilla, A. M. (Banff, Canada, 2007). A taxonomy of javascript redirection spam. In Proceedings of the 3rd international workshop on Adversarial information retrieval on the web, AIR Web 07.
  [37] Azam, N. (((2014))). Comparative Study of Features Space Reduction Techniques for Spam Detection. Department of Computer Engineering College of Electrical and Mechanical National University of Sciences and Technology.
  [38] M. Basavaraju, D. R. (n.d.). A Novel Method of Spam Mail Detection using Text BasedClustering Approach. International Journal of Computer Applications (0975 - 8887) Volume 5-No.4, August 2010.
  [39] Thamarai Subramaniam, H. A. (4 October, 2010). Overview of textual anti-spam filtering techniques. International Jornal of the Physical Sciences Vol. 5(12), pp. 1869-1882.
  [40] P. Cunningham, N. N. (Retrieved December 28, 2011). A Case-Based Approach to Spam Filtering that Can Track Concept Drift. Retrieved from https:/www.cstcd.ie/publications/tech-reports/reports.03/TCD-CS-2003-16.pdf
  [41] F. Roli, G. F. (Retrieved December 28, 2011). The emerging role of visual pattern recognition in spam filtering: challenge and opportunity for IAPR researchers. Retrieved from http://www.iapr.org/members/newaletter/newsletter07-92/index_files/page465.htm
  [42] G. Ashokkumar, s. D. (Anna University, Channai-6000 025, India). Spam Filtering. Department of Computer Science and Engineering.
  [43] Erik Cuevas, M. C.-C. ((2013)). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications 40 , 6374–6384.
  [44] Sang Min Lee، Dong Seong Kim، Ji Ho Kim، و Jong Sou Park. (2010) Spam Detection Using Feature Selection and Parameters Optimization. International Conference on Complex,Intelligent and Software Intensive Systems (883-885) IEEE computer society. doi:101109/CISIS.2010.116
  [45] Enayat Bayati, Mehdi Sadeghzadeh, Farshad Kumarci. 1) January( .(2014) Improving the Methods of Email Classification through the Fuzzy Decition Tree. Academic and Applied Studies 38-40. www.academians.org
  [46] Sorayya Mirzapour Kalaibar, S. N. (((2014)), Volume 3– Issue 12, 2014, ISSN:- 2319–8656). Spam filtering by using Genetic based Feature Selection. International Journal of Computer Applications Technology and Research, 839 - 843.
  [47] Ngnotchouye, S. L. ((2017)). Firefly Algorithm for Discrete Optimization Problems: A Survey. KSCE Journal of Civil Engineering 21(2), Received September 5, 2016/Revised October 21, 2016/Accepted November 2, 2016, 535-545.
  [48] Mohd Herwan Sulaiman, H. D. ((2012)). Modified Firefly Algorithm in Solving Economic Dispatch Problems with Practical Constraints. 2012 IEEE International Conference on Power and Energy (PECon), 2-5 December 2012, Kota Kinabalu Sabah, Malaysia.
  [49] Yilmaz Kaya , Omer Faruk Ertugrul. ( 11 Junuary, ( 2016 .) A novel approach for spam email detection based on shifted binary patterns. wiley online library , 1216 - 1225 . doi:10.1002/sec.1412
  [51] Seyedeh Zahra Mirjalili, S. S. ((11 February 2015)). Designing evolutionary feedforward neural networks using A Hybrid Firefly Algorithm and social spider optimization algorithm. Neural Comput & Applic.
  [52] Mohamed A. Tawhid, A. F. ((Springer-Verlag Berlin Heidelberg 2016)). A hybrid social spider optimization and genetic algorithm for minimizing molecular potential energy function. Soft Comput, DOI 10.1007/–00500-016-2208-9.
  [53] Yongquan Zhou, Y. Z.-B. ((2017)). A simplex method-based social spider optimization algorithm for clustering analysis. Engineering Applications of Artificial Intelligence 64, 67–82.
  [54] Harley Vera-Olivera, J. L.-A.-R. (n.d.). Social Spider Algorithm Approach for Clustering. National University of San Antonio Abad del Cusco.
  [55] Salima Ouadfel, A. T.-A. ((2016)). Social spiders optimization and flower pollination algorithm for multilevel image thresholding: A performance study. Expert Systems With Applications 55 , 566–584.
  [56] K. B. Gavali, A. (ISSN Print: 0976-6340 and ISSN Online: 0976-6359, ((October 2017)). job shop scheduling with the aid of hybrid social spider optimization and gray wolf optimization with industrial scheduling case study. International Journal of Mechanical Engineering and Technology (IJMET), Volume 8, Issue 10, Article ID: IJMET_08_10_031, 274–284.
  [57] W.T. Elsayed, Y. H.-b. ((2016)). Modified social spider algorithm for solving the economic dispatch problem. Engineering Science and Technology, an International Journal, 2215-0986/, 2016 Karabuk University. Publishing services by Elsevier B.V., http://dx.doi.org/10.1016/j.jestch.2016.09.002.
  [58] Arora, S. G. (S. Berretti et al. (eds.), Intelligent Systems Technologies and Applications,Advances in Intelligent Systems and Computing 384,((2016)). A Hybrid Firefly Algorithm and Social Spider. Computer Science Department, DAV University, Jalandhar, Punjab, India,Springer International Publishing Switzerland 2016, DOI: 10.1007/978-3-319-23036-8_2.
  [59] Mohamed Abu ElSoud, A. M. ((2016)). Computational Intelligence Optimization Algorithm Based on Meta-heuristic Social-Spider: Case Study on CT Liver Tumor Diagnosis. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 4,.
  [60] Danillo R. Pereira, M. A. ((2014)). A Social-Spider Optimization Approach for Support Vector Machines Parameters Tuning. IEEE, 978-1-4799-4458-3/14/2014 IEEE.
  [61] Urvashi Prakash Shukla, S. J. ((2016)). Parallel social spider clustering algorithm for high dimensional datasets. Engineering ApplicationsofArtificial Intelligence56, 75–90.
  [62] Hossam M. Zawbaa, E. E. ((2015)). A wrapper approach for feature selection based on swarm optimization algorithm inspired from the behavior of social-spiders. 978-1-4673-9360-7/15 2015 IEEE
  [63] Yu, J. J. ((2015 IEEE)). Parameter Sensitivity Analysis of Social Spider Algorithm. Student Member, IEEE and Victor O.K. Li, Fellow, IEEE Department of Electrical and Electronic Engineering, The University of Hong Kong, 978-1-4799-7492-4/15.
  [64] Dr.R.Prabhakar M.Basavaraju. (2010) A Novel Method of Spam Mail Detection using Text Based Clustering Approach. International Journal of Computer Applications no5. 4,August 2010.
  [65] Qiang Zhang, H. L. ((2013)). A New Extreme Learning Machine Optimized by Firefly Algorithm. 2013 Sixth International Symposium on Computational Intelligence and Design.
  [66] Gandomi AH, Y. X. ((2011)). Mixed variable structural optimization using firefly algorithm. Comput Struct 2011,89(23–24), 2325–36.
  [67] Yang, X. ((2010)). Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons, USA.
  [68] Hui Wang, W. W. ((2016)). Adaptive Firefly Algorithm with Alternative Search. 2016 IEEE Congress on Evolutionary Computation (CEC), 978-1-5090-0623-6/16 2016 IEEE.
  [69] XS, Y. ((2010)). Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons.
  [70] Hassanzadeh and Meybody.(2012) A survey of learning -based techniques of email spam filtering. doi:10/1007/ s-9109-009-104626.
  [71] Vangelis Metsis، Ion Androutsopoulos، و Goorgios Paliouras.(2006) Spam Filtering with Naive Bayes - Which Naive Bayes? Third Conference on Email and Anti-Spam، (1-9) California USA.27-28 July, 2006
  [72] X.S. Yang. ((2008)). Nature-Inspired Metaheuristic Algorithms. Luniver Press.
  [73] J. Senthilnath, S. O. ((2011)). Clustering using firefly algorithm: Performance study. Swarm and Evolutionary Computation 1 , 164–171.
  [74] Yang, X. ((vol. 5792, 2009)). Firefly algorithms for multimodal optimization. SAGA, Lecture Notes in Computer,Stochastic Algorithms:Foundationsand Applications Sciences, 169–178.
  [75] Yang, X. S. ((2010)). Firefly algorithm، stochastic Test Functions and Design ptimization. Int. J. bio-inspired computation.
  [76] Tahereh Hassanzadeh, M. R. ((2012)). A New Hybrid Approach for Data Clustering using Firefly Algorithm and K-means. The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), 978-1-4673-1479-4/12 2012 IEEE.
  [77] Adorio, E. D. ((2005)). Mvf-multivariate test functions library in c for unconstrained global optimization.
  [78] Yang, X.-S. ((2014)). Swarm Intelligence Based Algorithms: A Critical Analysis. Evolutionary Intelligence 7)1), 17–28 .
  [79] Apostolopoulos, T. V. ((2010)). Application of the firefly algorithm for solving the economic emissions load dispatch problem. International Journal of Combinatorics 2011 .
  [80] Horng, M.-H. ((2012)). Vector quantization using the firefly algorithm for image compression. Expert Systems with Applications 39(1), 1078–1091 .
  [81] Khadwilard, A. e. ((2011)). Application of firefly algorithm and its parameter setting for job shop scheduling. First Symposius on Hands-On Research and Development, 89–97 .
  [82] Sayadi, M. R.-N. ((2010)). A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. International Journal of Industrial Engineering Computations 1(1), 1–10.
  [83] Senthilnath, J. O. ((2011)). Clustering using firefly algorithm: performance study. Swarm and Evolutionary Computation 1(3), 164–171 .
  [84] James J.Q, V. O. ((The University of Hong Kong , October 2013)). A Social Spider Algorithm for Global Algorithm. Technical Report No. TR 2013-004.
  [85] Palit, S. S. (15–17 September 2011, India, ((2011)). A cryptanalytic attack on the knapsack cryptosystem using binary Firefly algorithm. 2nd Int. Conference on Computer and Communication Technology (ICCCT), 428–432 .
  [86] Lukasik, S. z. (Springer, Heidelberg ((2009)). Firefly algorithm for continuous constrained optimization tasks. Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, 97–106.
  [87] Senthilnath, J. O. ((2011)). Clustering using firefly algorithm: performance study. Swarm and Evolutionary Computation 1(3), 164–171 .
  [88] Sayadi, M. R.-N. ((2010)). A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. International Journal of Industrial Engineering Computations 1(1), 1–10.
  [89] Zeng B, L. M. ((2014)). Research on assembly sequence planning based on improved firefly algorithm. Comput Integr Manuf Syst 20(4), In Chinese, 799–806.
  [90] Arora, S. G. (S. Berretti et al. (eds.), Intelligent Systems Technologies and Applications,Advances in Intelligent Systems and Computing 384,((2016)). A Hybrid Firefly Algorithm and Social Spider. Computer Science Department, DAV University, Jalandhar, Punjab, India,Springer International Publishing Switzerland 2016, DOI: 10.1007/978-3-319-23036-8_2.
  [91] Mohamed Abu ElSoud, A. M. ((2016)). Computational Intelligence Optimization Algorithm Based on Meta-heuristic Social-Spider: Case Study on CT Liver Tumor Diagnosis. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 4,.
  [92] Yu, J. J. ((2015 IEEE)). Parameter Sensitivity Analysis of Social Spider Algorithm. Student Member, IEEE and Victor O.K. Li, Fellow, IEEE Department of Electrical and Electronic Engineering, The University of Hong Kong, 978-1-4799-7492-4/15.
  [93] Erik Cuevas, M. C. ((2015)). A Computational Intelligence Optimization Algorithm Based on the Behavior of the Social-Spider. Springer International Publishing Switzerland 2015, A.T. Azar and Vaidyanathan (eds.), Computational Intelligence Applications, in Modeling and Control, Studies in Computational Intelligence 575, DOI 10.1007/978-3-319-11017-2_6.
  [94] Mohamed Abd El Aziz, A. E. ((18 December 2016)). An improved social spider optimization algorithm based on rough sets for solving minimum number attribute reduction problem. Neural Comput & Applic, DOI 10.1007/s00521-016-2804-8.
  
  [97] Yongquan Zhou, Y. Z.-B. ((2017)). A simplex method-based social spider optimization algorithm for clustering analysis. Engineering Applications of Artificial Intelligence 64 , 67–82.
  [98] Harley Vera-Olivera, J. L.-A.-R. (n.d.). Social Spider Algorithm Approach for Clustering. National University of San Antonio Abad del Cusco.
  [99] Salima Ouadfel, A. T.-A. ((2016)). Social spiders optimization and flower pollination algorithm for multilevel image thresholding: A performance study. Expert Systems With Applications 55 , 566–584.
  [100] K. B. Gavali, A. (ISSN Print: 0976-6340 and ISSN Online: 0976-6359, ((October 2017)). JOB SHOP SCHEDULING WITH THE AID OF HYBRID SOCIAL SPIDER OPTIMIZATION AND GRAY WOLF OPTIMIZATION WITH INDUSTRIAL SCHEDULING CASE STUDY. International Journal of Mechanical Engineering and Technology (IJMET), Volume 8, Issue 10, Article ID: IJMET_08_10_031, 274–284.
  [101] W.T. Elsayed, Y. H.-b. ((2016)). Modified social spider algorithm for solving the economic dispatch problem. Engineering Science and Technology, an International Journal, 2215-0986/, 2016 Karabuk University. Publishing services by Elsevier B.V., http://dx.doi.org/10.1016/j.jestch.2016.09.002.
  [102] T.Ravi Chandran, A. B. ((2016)). A Social Spider Optimization Approach for Clustering Text Documents. International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB16), 978-1-4673-9745-2 ©2016 IEEE.
  [103] Danillo R. Pereira, M. A. ((2014)). A Social-Spider Optimization Approach for Support Vector Machines Parameters Tuning. IEEE, 978-1-4799-4458-3/14/2014 IEEE.
  [104] Urvashi Prakash Shukla, S. J. ((2016)). Parallel social spider clustering algorithm for high dimensional datasets. Engineering ApplicationsofArtificial Intelligence56, 75–90.
  [105] Erik Cuevas, M. C.-C. ((2013)). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications 40 , 6374–6384.
  [107] Eva Tuba, L. M. ((2016)). Support Vector Machine Parameter Tuning using Firefly Algorithm. 26th Conference Radioelektronika 2016, April 19-20, Kosice, Slovak Republic, 978-1-5090-1674-7/16/2016 IEEE.
  [110] A.H. Gandomi, X.-S. Y. ((2013)). Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simulat18 , http://dx.doi.org/10.1016/j.cnsns.2012.06.009, 89–98.
  [111] Daniela Sanchez, P. M. ((5 June 2017)). Optimization of modular granular neural networks using a firefly algorithm for human recognition. Engineering Applications of Artificial Intelligence 64 , http://dx.doi.org/10.1016/j.engappai.2017.06.007 , 172–186.
  [112] Mohammed Alweshah, S. A. ((2015 )). Hybridizing firefly algorithms with a probabilistic neural network for forsolving classification problems. Applied Soft Computing, 2015 Published by Elsevier B.V., http://dx.doi.org/10.1016/j.asoc.2015.06.018, 1568-4946.
  [113] A. Rahmani, S. M. ((2014)). A hybrid Firefly-Genetic Algorithm for the capacitated facility location problem. Information Sciences (2014), http://dx.doi.org/10.1016/j.ins.2014.06.002, 0020-0255 /2014 Elsevier Inc. All rights reserved.
  [114] Majdi M. Mafarja, S. M. ((22 April 2017)). Hybrid Whale Optimization Algorithm with Simulated Annealing for Feature Selection. Neurocomputing, S0925-2312(17)30807-X, DOI:10.1016/j.neucom.2017.04.053, , NEUCOM 18405.
  [115] Mingfu Li, Y. Z. ((17 June 2015)). The modified firefly algorithm considering fireflies’ visual range and its application in assembly sequences planning. Int J Adv Manuf Technol, DOI:10.1007/s001700157457-8.
  [116] Nadia Nekouie, M. Y. ((18 February 2016)). A new method in multimodal optimization based A new method in multimodal optimization based. Artif Intell Rev, DOI 10.1007/s10462-016-9463-0, Springer Science+Business Media Dordrecht 2016.
  [117] Heydari, A., Tavakoli, M. a., & Heydari, Z. (2014, December 31). Detection of review spam:A survey. Expert Systems With Applications, 1- 9. doi:10.1016/j.eswa.2014.12.029
  [119] Ismaila Idris, Ali Selamat. (2014) Improved email spam detection model with nagative selection algorithm and particle swarm optimization. Applied Soft Computing،pp1-17.
  [120] Alexy Bhowmick , Shyamanta M. Hazarika. ( 3 Jun, ( 2016 .) Machine Learning for E-mail spsm Filtering: Review,Techniques and Trends. manuscript No. pp 1 - 27 .
  [121] Sorayya Mirzapour Klaibar , Seyed Naser Razavi. ( 2014 .) Spam filtering by using Genetic based Feature Selection. International Jornal of Computer Applications Technology and Research 3(839( pp 839 - 843 .www.ijcat.com.
  
  [123] Michal Prilepok , Tomas Jezowicz , Jan Platos , Vaclav Snacel. ( 2012 .) Spam detection using Compression and PSO. Fourth International on Computational Aspects of Social Networks(CASoN) (pp 263 -270 .) IEEE.
  [124] Omar Saad , Ashraf Darwish , Ramadan Faraj.( Febuary, ( 2012 .) A survey of machine learning techniques for Spam filtering. IJCSNS International Jornal of Computer and Network Security , 12 No.2 pp 66 - 72 .
  [127] S. Chakravarty Upasana. ( 2010 .) A Survey of Text Classification Techniques for E-mail Filtering. Second International Conference on Machine Learning and Computing (pp32 - 36.) IEEE computer socity.
  [128] Enrico Blanzieri, Anton Bryl. ( 10 July), ( 2009 .) A survey of learning -based techniques of email spam filtering. 63 - 92 . doi:10.1007/s10462 - 009 - 9109 - 6
  [129] Vangelis Metsis , Ion Androutsopoulos , Goorgios Paliouras. ( 2006 .) Spam Filtering with Naive Bayes - Which Naive Bayes? Third Conference on Email and Anti-Spam (pp 1 - 9 .) California USA. . 27 -28 July,2006
  [130] Sebastiani, F. (2002). Machine learning in automated text categorization. ACM computing surveys (CSUR), 34(1), 1-47.
  [131] Geerthik.s.(2013) Survey on Internet Spam: Classification and Analysis. Int.J.Computer Technology Applications،-.www.ijcta.com
  [132] Dr.R.Prabhakar M.Basavaraju.(2010) A Novel Method of Spam Mail Detection using Text Based Clustering Approach. International Journal of Computer Applications (0975 – 8887( (5 - no.4,August 2010(
  [133] Sandeep Negi Rekha. (may, (2014) A Review on Different Spam Detection Approaches. International Journal of Trends and Technology (IJETT)، 11 Number ,315-318, www.ijettjournal.org
  [134] Santoshkumar Biradar Savita Teli. (2004) Effective Spam Detection Method for Email. International Conference on Advances in Engineering & Technology (ICAET-2014)، . (pp 68-72) www.iosrjournals.org
  [135] Dr.Manisha,Manisha,Dr.Rekha Jain Anjali Sharma. (may, (2015). Data Pre-Processing in Spam Detection. IJSTE - International Journal of Science Technology & Engineerin, pp37-33 www.ijste.org
  [136] Wojciech Zabierrowski,Anderzej Napieralski Isabella Miszalsaka.) 20-24 February( , (2007). Selected Methods of Spam Filtering in Email. Department of Microelectronics and Computer Science,Technical University of Lopez, aj,Politechniki 11, 513-507,924-90.wojtekz@dmcs.pl
  [137] Lauren Steimle Ge Song (2010). Email Spam Detection. pp1-6.
  [138] Ciro Cattuto,Filippo Menczer Benjamin Markines. (  April),(2009). Social Spam Detection. AIRweb، pp1-7, bmarkine@cs.indiana.edu
  [139] Ali Farahmandnejhad,Shahab Behjati Sadegh Kharazmi  Nov), (2007). Spam Email detection Using Bayesian Spanning Tree. IDMC' ,Amir Kabir University، pp1-6.
  [140] MohammadHosein Nadimi,Akbar NabiollahiZahrasadat Torabi. (2012) New Approach for Spam Detection with Suport Vector Machine And Feature Selection . First National Conference of Technology Developments on Elecronical , Electronics and Computer Engineering، (pp1-12).
  [141] Harris Interactive Public relation research. (2008) A study of social networks scams.
  [142] S.Moyer and N.Hamiel. (2008) satan is on my friend list: Attacking social networks. http://www.blackhat.com/html/bh-usa-08/bh-usa-08-archeve.html
  [143] T.Howe, M. Ihbe, A. Prakash, and K. Borders G.Brown. (2008) Social networks and context-aware spam. In ACM Conference on Supporttive Work.
  [144] Rajabioun, R. ((2011)). Cuckoo optimization algorithm. Appl. Soft Comput. 11, 5508–5518 (2011).
  [145] Yan Gao. (Dec. (2010) A Comprehansive Approach to Image Spam Detection: From Server to Client Solution, Information Forensics and Security. IEEE Transations.
  [146] Yan Gao. (2008) Image spam hunter, Acoustics, Speech and Signal Processing. ICASSP 2008. IEEE International Conference on April 2008
  [147] K. Jain, M. Mahdian, and K. Talwar N. Immorlica. (Redmond, (2006) Click Fraud Resistant Methods for Learning Click- Through Rates. Technical report, Microsoft Research.
  [148] S. Brin, R.Motwani, and T. Winograd. L.Page.(1998) The page rank citation ranking: Bringing order to the web.
  [149] T. Fawcet. December. (2003) Achallenge problem for data mining. KDD Explorations, in vivo spam filtering.
  [150] Adriano Veloso. Oct. (2006) Lazy Associative Classification for Content-based Spam Detection. Web Congress, 2006, IEEE conference publication.
  [151] H. Garcia-Molina Z. Gyongyi. (2005) Web Spam technology. In First International Workshop on Adversarial Information Retrieval on the Web(AIR Web).
  [152] A. Maykov K. Chellapilla. (Banff, Canada, (2007) A taxonomy of javascript redirection spam. In Proceedings of the 3rd international workshop on Adversarial information retrieval on the web, AIR Web 07
  [153] Nouman Azam. (2014) Comparative Study of Features Space Reduction Techniques for Spam Detection. Department of Computer Engineering College of Electrical and Mechanical National University of Sciences and Technology.
  [154] Dr. R. Prabhakar M. Basavaraju. (2010) A Novel Method of Spam Mail Detection using Text BasedClustering Approach. International Journal of Computer Applications (0975-8887) Volume 5-No.4, August 2010
  [155] Hamid A. jalab and Alaa Y. Taqa , Thamarai Subramaniam (4 October), (2010) Overview of textual anti-spam filtering techniques. International Jornal of the Physical Sciences Vol5-(12).pp 1882-1869
  [156] N. Nowlan P. Cunningham. (Retrieved December 28, (2011) A Case-Based Approach to Spam Filtering that Can Track Concept Drift.https:/www.cstcd.ie/publications/tech-reports/reports.03/TCD-CS-16-2002.pdf
  [157] G. Fumera F. Roli. (Retrieved December 28), (2011) The emerging role of visual pattern recognition in spam filtering: challenge and opportunity for IAPR researchers. http://www.iapr.org/members/newaletter/newsletter/index_files/page.htm
  [158] S. Dhineskumar G. Ashokkumar. (Anna University, Channai, India). Spam Filtering. Department of Computer Science and Engineering.
  [159] B. Schoelkopf, N. Cristianini. (2010) Support vector machines and kernel methods, the new generation of learning machines. Artificial Intelligence Magazine.
  [160] E. Frank I. Witten. (2000) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann.
  [161] V. Vapnik. (1998) The Nature of Statistical Learning Theory. Springer; 2 edition (December 14, 1998).
  [162] S. Wu S. Amari. (1999) Improving support vector machine classifiers by modifying kernel functions. Neural Networks, ,pp 783-789
  [163] R. O. Duda، P. Hart، و David G. Stork. (2010) Pattern Classification 2nd edition. Wiley.
  [164] C. Miller. (Retrieved December 28, (2011). Neural Network-based Antispam Heuristics. Symantec Enterprise Securit. www.symantec.com
  [165] C. Wu. (2009). Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks. Expert Syst.
  [166] Ron Kohavi. (1996). Scaling up the Accuracy of Naive Bayes Classifier: a Decition tree Hybrid. Data mining and visualization, Silicin Graphics, Inc.
  [167] L. A. Zadeh. (1975) The Concept of a Linguistic Variable and Its Application To Approximate Reasoning-I. Information Science، pp 199-249.
  [168] M. El-Alfy, Feares S. Al-Qunaieer EL-sayed (2008). A Fuzzy Similarity Approach for Automated Spam Filtering. Procs of the IEEE/ACS.
  [169] Y. Hao et al. (Eds.) Jong-Wan Kim and Sin-Jae Kang. (2005). Feature selection by Fuzzy Inference and its Application to Spam mail filtering. CIS 05, Part I, pp.361-363, Springer-Verlag Berlin Heidelberg.
  [170] Deb, Hossain Fuad. (2005) A Trainable Fuzzy Spam Detection System. PDCN 2005, Innsbruck, Austria.
  [171] S. Forrest, M.R. Glickman J. Balthrop. (2002) Revisiting LISYS: parameters and normal behavior. Proceedings of the 2002 Congress on Evolutionary Computing.
  [172] et al. F Gonzalez.(2003). An evolutionary approach to generate fuzzy anomaly (attack) signatures. IEEE Systems, Man and Cybernetics Society Information Assurance Workshop .
  [173] Uyar M, Tekin R, Yildirim S. Kaya Y. (2014) 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. . Applied Mathematics and Computation. pp219-243.
  [174] Michal Prilepok, T. J. (((2012)) Fourth International Conference, IEEE). Spam Detection using compression and PSO. computational Aspects of Social Networks (CASoN), 263-270.
  [175] Enrico Blanzieri, A. B. ((2009)). A Survey of learning-based Techniques of email spam filtering. Springer Science+Business Media B.V 2009, 63-92, DOI 10.1007/s 10462-009-9109-6.
  [176] Ismaila Idris, A. S. (((2014))). Improved email spam detection model with negative selectionalgorithm and particle swarm optimization. Contents lists available at ScienceDirect, 2014 Published by Elsevier B.V., 1568-4946, http://dx.doi.org/10.1016/j.asoc.2014.05.002.
  [177] Sang Min Lee, D. S. (((2010))). Spam Detection Using Feature Selection and Parameters Optimization. International Conference on Complex, Intelligent and Software Intensive Systems.
  [178] Enayat Bayati, M. S. (((2014))- Vol. 4(1) January 2014). Improving the Methods of Email Classification. Journal of Academic and Applied Studies (Special Issue on Applied Sciences).
  [180] Omar Saad، Ashraf Darwish، و Ramadan Faraj. (Febuary, (2012) A survey of machine learning techniques for Spam filtering. IJCSNS International Jornal of Computer and Network Security، No2. pp72-76.
  [181] Guzella, T. S. (2009). A review of machine learning approaches to spam filtering. Expert Systems with Applications 36(7), 10206-10222.
  [ 182] Ertuğrul, Y. K. (((2016)),Security Comm. Networks 2016; 9:1216–1225). A novel approach for spam email detection based on shifted binary patterns. SECURITY AND COMMUNICATION NETWORKS, Published online 11 January 2016 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/sec.1412.
  [183] Kim, K. I. (2003). Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. Pattern Analysis and Machine Intelligence,IEEE Transactions on 25(12), 1631-1639.
  [184] Savita Teli, S. B. (2014). Effective Spam Detection Method for Email. International Conference on Advances in Engineering & Technology (ICAET-2014), (pp. 68 - 72). Retrieved from www.iosrjournals.org
  [185] Temitayo, F. S. (2012). Hybrid GA-SVM for Efficient Feature Selection in E-mail Classification. Computer Engineering and Intelligent Systems, Vol. 3, No .3.
  [186] Mu-Chun Su، HSU-Hsun Lo، و Fu-Hau Hsu (2010). A neural tree and its application to spam detection. Expert Systems With Applications،pp7955-7986.
  [187] Yip, C. &. ((2008)). Cooperative capture of large prey solves scaling challenge faced by spider societies. Proceedings of the National Academy of Sciences of the United States of America, 11818–11822.
  [188] Bonabeau, E. D. ((1999)). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York.
  [189] Kassabalidis, I. E.-S. ((2001)). Swarm intelligence for routing in communication networks. Global Telecommunications Conference, GLOBECOM’01, 6, IEEE, 3613–3617.
  [190] Gordon, D. ((2003)). The organization of work in social insect colonies. Complexity 8(1), 43–46.
  [191] Lubin, T. ((2007)). The evolution of sociality in spiders. Brockmann, H.J. (ed.) Advances in the Study of Behavior, vol. 37, Academic Press, Burlington , 83–145.
  [192] Uetz, G. ((1997)). Colonial web-building spiders: balancing the costs and benefits of group-living. Choe, E.J., Crespi, B. (eds.) The Evolution of Social Behavior in Insects and Arachnids, Cambridge University Press, Cambridge , 458–475.
  [193] Aviles, L. ((1986)). Sex-ratio bias and possible group selection in the social spider Anelosimus eximius. Am. Nat. 128(1), 1–12.
  [194] Burgess, J. ( (1982)). Social spacing strategies in spiders. Rovner, P.N. (ed.) Spider communication: mechanisms and ecological significance, Princeton University Press, Princeton, 317–351.
  [195] Maxence, S. ((2010)). Social organization of the colonial spider Leucauge sp. in the Neotropics:vertical stratification within colonies. J. Arachnol. 38, 446–451.
  [196] Subbulakshmi CV, D. S. ((2015)). Medical dataset classification: a machine learning paradigm integrating particle swarm optimization with extreme learning machine classifier. Sci World, J doi:10.1155/2015/418060.
  [197] Hui Wanga, b. W. (June 13, 2017). A Hybrid Multi-Objective Firefly Algorithm for Big Data Optimization. Preprint submitted to Applied Soft Computing, http://dx.doi.org/doi:10.1016/j.asoc.2017.06.029.
  [198]Anjali Sharma, D. J. (2015, may). Data Pre-Processing in Spam Detection. IJSTE - International Journal of Science Technology & Engineering, 1(11/ 007), 33 - 37. Retrieved from www.ijste.org
  [199] C.Xiao-li, L. P.-y.-f. (2009). A method of spam filtering based on weighted sipport vector machines. IEEE International Symposium on IT in Medicine&Education,1, 947-950.
  [200] B. Baharudin, L. H. (2010). A review of machine learning algorithms for text-documents classification. JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY, Vol. 1,N. 1, 4-20.
  [201] LIU, H. F. (2010). A hybrid algorithm for text classification problem.
  [202] X. Wu, V. J. (2007). Top 10 Algorithma in Data Mining. Knowledge and Information Systems, Vol. 14, N. 1, 1-37.
  [203] Awad, W. E. (2011). Mashine Learning methods for E-mail Classification. International Journal of Computer Applications, Vol. 16, No.1.
  [204] Yuguo, L. Z. (2011). A word sequence kernels used in spam-filtering. Scientific Research and Essays Vol. 6, 1275-1280.
  [205] Drucker, H. S. (1999). Support vector machines for spam categorization. Neural Networks, IEEE Transactions on 10(5), 1048-1054.
  
  [206] ندیمی,. ز( اسفند 1391). مروری بر ارزیابی ماشین بردار پشتیبان در تشخیص و طبقه بندی ایمیل اسپم. همایش ملی کاربرد سیستم های هوشمند (محاسبات نرم) در علوم و صنایع، دانشگاه آزاد اسلامی واحد قوچان
•  صفحات : 58-68
•   دانلود فایل ( 627 KB )