jist-202605211820260521182303CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagJournal of Information Systems and Telecommunication (JIST) jist2322-143711122019725MahdiehGhazviniKamalJamshidiNaserMovahedinia11122019122210.7508/jist.2019.01.002http://jist.ir/en/Article/15161http://jist.ir/en/Article/Download/15161http://jist.ir/en/Article/Download/15161http://jist.ir/en/Article/Download/15161http://jist.ir/en/Article/Download/15161http://jist.ir/en/Article/Download/15161http://jist.ir/en/Article/Download/15161http://jist.ir/en/Article/Download/15161[1] A. Malik, J. Qadir, B. Ahmad, K.-L. A. Yau, and U. Ullah, "QoS in IEEE 802.11-based wireless networks: a contemporary review," Journal of Network and Computer Applications, vol. 55, pp. 24-46, 2015. [2] H. I. Zawia, R. Hassan, and D. P. Dahnil, "A survey of medium access mechanisms for providing robust audio video streaming in ieee 802.11 aa standard," IEEE Access, vol. 6, pp. 27690-27705, 2018. [3] A. Banchs and P. Serrano, "Analysis and Configuration of IEEE 802.11e," in Medium Access Control in Wireless Networks: Nova Science Publishers, 2008. [4] H. Ghazanfar, R. Taheri, and S. Nejatian, "Application of Learning Methods for QoS Provisioning of Multimedia Traffic in IEEE802. 11e," in Fundamental Research in Electrical Engineering: Springer, 2019, pp. 369-383. [5] L. Li, M. Pal, and Y. R. Yang, "Proportional fairness in multi-rate wireless LANs," in IEEE INFOCOM 2008-The 27th Conference on Computer Communications, 2008: IEEE, pp. 1004-1012. [6] M. Ghazvini, N. Movahedinia, and K. Jamshidi, "GTXOP: A game theoretic approach for QoS provisioning using transmission opportunity tuning," PloS one, vol. 8, no. 5, p. e62925, 2013. [7] N. Guo, C. Chen, and C. Pei, "Dynamic TXOP Assignment for Fairness (DTAF) in IEEE 802.11 e WLAN under Heavy Load Conditions," in Parallel and Distributed Computing, Applications and Technologies, 2006. PDCAT'06. Seventh International Conference on, 2006: IEEE, pp. 80-85. [8] E. K. Kim and Y. J. Suh, "ATXOP: an adaptive TXOP based on the data rate to guarantee fairness for IEEE 802.11 e wireless LANs," in 60th Vehicular Technology Conference, 2004. VTC2004-Fall. , 2005, vol. 4: IEEE, pp. 2678-2682. [9] E. Kim and Y. J. Suh, "A Rate Adaptive Transmission Opportunity for Fairness over IEEE 802.11 e Wireless LANs," in IEEE International Conference on Communications, ICC '07 2007: IEEE, pp. 4523-4528. [10] K. Ju, D. Lee, and K. Chung, "Dynamic TXOP allocation to support QoS based on channel conditions in wireless networks," in 8th International Conference on Computing Technology and Information Management (ICCM), 2012, vol. 2: IEEE, pp. 721-724. [11] M. Yazdani, M. Kamali, N. Moghim, and M. Ghazvini, "A fair access mechanism based on TXOP in IEEE 802.11 e wireless networks," International Journal of Communication Networks and Information Security (IJCNIS), vol. 8, no. 1, 2016. [12] G. Min, J. Hu, and M. E. Woodward, "A dynamic IEEE 802.11e txop scheme in wlans under self-similar traffic: Performance enhancement and analysis," in International Conference on Communications, ICC '08, 2008: IEEE, pp. 2632-2636. [13] L. Romdhani and C. Bonnet, "Performance analysis and optimization of the 802.11 e EDCA transmission opportunity (TXOP) mechanism," in Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMOB 2007., 2007: IEEE, pp. 68-75. [14] Y. P. Fallah and H. M. Alnuweiri, "Modeling and Performance Evaluation of Frame Bursting in Wireless LANs," in IWCMC ’06: Proceeding of the 2006 International Conference on Communications and Mobile Computing, New York, NY, USA, 2006, pp. 869-874. [15] J. Hu, G. Min, and M. E. Woodward, "Analysis and Comparison of Burst Transmission Schemes in Unsaturated 802.11e WLANs," in Global Telecommunications Conference (Globecom), Washington, DC, USA, 2007, pp. 5133–5137. [16] F. Peng, H. M. Alnuweiri, and V. C. M. Leung, "Analysis of burst transmission in IEEE 802.11 e wireless LANs," in Communications, 2006. ICC'06. IEEE International Conference on, 2006, vol. 2: IEEE, pp. 535-539. [17] S. Selvakennedy, "The Influence of MAC Buffer on the Contention Based Access Scheme with Bursting Option for IEEE 802.11e Wireless Networks," Journal of Engineering Science and Technology (JESTEC),, vol. 1, no. 2, pp. 119–138, 2006. [18] Rashwand S. and J. Mišic, "Stable operation of IEEE 802.11e EDCA: Interaction between offered load and MAC parameters," Ad Hoc Networks, vol. 10, pp. 162-173, 2012. [19] S. Rashwand and J. Misic, "IEEE 802.11e EDCA under Bursty Traffic-How Much TXOP Can Improve Performance," IEEE Transactions on Vehicular Technology, vol. 60, no. 3, pp. 1099-1115, 2011. [20] Z. Feng, G. Wen, Z. Zou, and F. Gao, "RED-TXOP scheme for video transmission in IEEE802. 11E EDCA WLAN," in Communications Technology and Applications, 2009. ICCTA'09. IEEE International Conference on, 2009: IEEE, pp. 371-375. [21] J. Majkowski and F. C. Palacio, "Dynamic TXOP configuration for Qos enhancement in IEEE 802.11 e wireless LAN," in International Conference on Software in Telecommunications and Computer Networks, SoftCOM'06. , Barcelona 2006: IEEE, pp. 66-70. [22] H. Liu and Y. Zhao, "Adaptive EDCA algorithm using video prediction for multimedia IEEE 802.11 e WLAN," in Wireless and Mobile Communications, 2006. ICWMC'06. International Conference on, 2006: IEEE, pp. 10-10. [23] K. Ju and K. Chung, "Dynamic TXOP allocation for multimedia QoS providing over wireless networks," in Information Networking (ICOIN), 2013 International Conference on, 2013: IEEE, pp. 397-401. [24] M. A Al-Maqri, M. Othman, B. Mohd Ali, and Z. Mohd Hanapi, "Providing Dynamic TXOP for QoS Support of Video Transmission in IEEE 802.11 e WLANs," Journal of Networks, vol. 10, no. 9, pp. 501-511, 2015. [25] N. Cranley, T. Debnath, and M. Davis, "An Experimental Investigation of Parallel Multimedia Streams Over IEEE 802.11e WLAN Networks Using TXOP,” in IEEE International Conference on " in International Conference on Communications,ICC'07, Glasgow, Scotland, 2007, pp. 1740–1746. [26] A. Ksentini, A. Nafaa, A. Gueroui, and M. Naimi, "ETXOP: A resource allocation protocol for QoS-sensitive services provisioning in 802.11 networks," ELSEVIER’s Performance Evaluation (PEVA), vol. 64, no. 5, pp. 419-443, 2007. [27] J. Y. Lee, H. Y. Hwang, J. Shin, and S. Valaee, "Distributed optimal TXOP control for throughput requirements in IEEE 802.11 e wireless LAN," in 22nd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), 2011: IEEE, pp. 935-939. [28] M. A. Togou and G.-M. Muntean, "A Dynamic Transmission Opportunity Allocation Scheme to Improve Service Quality of Vehicle-to-Vehicle Non-Safety Applications," in 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), 2018: IEEE, pp. 1-5. [29] Z. Zhu, F. Cao, and Z. Fan, "WLAN throughput management: A game theoretic TXOP scheduling approach," in Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), 2015 IEEE 20th International Workshop on, 2015: IEEE, pp. 161-164. [30] C.-H. Lin, C.-K. Shieh, W.-S. Hwang, and W.-T. Huang, "Proportional bandwidth allocation with consideration of delay constraint over IEEE 802.11 e-based wireless mesh networks," Wireless Networks, vol. 24, no. 5, pp. 1575-1592, 2018. [31] J. Lee, H. Yoon, and I. Yeom, "Distributed fair scheduling for wireless mesh networks using IEEE 802.11," IEEE transactions on vehicular technology, vol. 59, no. 9, pp. 4467-4475, 2010. [32] M. Namazi, N. Moghim, M. Ghazvini, and A. Askarian, "Dynamic TXOP Assignment in IEEE802.11e Multi-hop Wireless Networks Based on an Admission Control Method," Wireless Personal Communications, vol. 97, no. 1, pp. 749–772, 2017. [33] S. Szott and J. Konorski, "Traffic remapping attacks in ad hoc networks," IEEE Communications Magazine, vol. 56, no. 4, pp. 218-224, 2018. [34] I. Ahmad, Z. Kaleem, R. Narmeen, L. D. Nguyen, and D.-B. Ha, "Quality-of-service aware game theory-based uplink power control for 5G heterogeneous networks," Mobile Networks and Applications, vol. 24, no. 2, pp. 556-563, 2019. [35] Y. Jiang, H. Ge, M. Bennis, F.-C. Zheng, and X. You, "Power control via Stackelberg game for small-cell networks," Wireless Communications and Mobile Computing, vol. 2019, 2019. [36] H. Jang, S.-Y. Yun, J. Shin, and Y. Yi, "Game theoretic perspective of optimal CSMA," IEEE Transactions on Wireless Communications, vol. 17, no. 1, pp. 194-209, 2018. [37] J. Hu, G. Min, W. Jia, and M. E. Woodward, "Comprehensive QoS Analysis of Enhanced Distributed Channel Access in Wireless Local Area Networks," Information Sciences, pp. 20–34, 2012. [38] J. Hu, G. Min, and M. E. Woodward, "Performance analysis of the TXOP burst transmission scheme in single-hop ad hoc networks with unbalanced stations," Computer Communications, vol. 34, no. 13, pp. 1593-1603, 2011. [39] G. Min, J. Hu, W. Jia, and M. E. Woodward, "Performance analysis of the TXOP scheme in IEEE 802.11 e WLANs with bursty error channels," in Wireless Communications and Networking Conference, 2009. WCNC 2009. IEEE, 2009: IEEE, pp. 1-6. [40] J. Hu, G. Min, M. E. Woodward, and W. Jia, "A comprehensive analytical model for IEEE 802.11 e QoS differentiation schemes under unsaturated traffic loads," in Communications, 2008. ICC'08. IEEE International Conference on, 2008: IEEE, pp. 241-245. [41] F. Kelly, "Charging and rate control for elastic traffic," European transactions on telecommunications, vol. 8, no. 1, pp. 33-37, 1997.Mohammad EbrahimKhademiMohammadFakhredaneshSeyed MojtabaHoseini11122019233310.7508/jist.2019.01.003http://jist.ir/en/Article/15222http://jist.ir/en/Article/Download/15222http://jist.ir/en/Article/Download/15222http://jist.ir/en/Article/Download/15222http://jist.ir/en/Article/Download/15222http://jist.ir/en/Article/Download/15222http://jist.ir/en/Article/Download/15222http://jist.ir/en/Article/Download/15222[1] P. B. Baxendale, “Machine-made index for technical literature—an experiment,” IBM J. Res. Dev., vol. 2, no. 4, pp. 354–361, 1958. [2] H. P. Edmundson, “New methods in automatic extracting,” J. ACM JACM, vol. 16, no. 2, pp. 264–285, 1969. [3] H. P. Luhn, “The automatic creation of literature abstracts,” IBM J. Res. Dev., vol. 2, no. 2, pp. 159–165, 1958. [4] H. Khanpour, “Sentence extraction for summarization and notetaking,” University of Malaya, 2009. [5] W. Song, L. C. Choi, S. C. Park, and X. F. Ding, “Fuzzy evolutionary optimization modeling and its applications to unsupervised categorization and extractive summarization,” Expert Syst. Appl., vol. 38, no. 8, pp. 9112–9121, 2011. [6] F. Jin, M. Huang, and X. Zhu, “A comparative study on ranking and selection strategies for multi-document summarization,” in Proceedings of the 23rd International Conference on Computational Linguistics: Posters, 2010, pp. 525–533. [7] G. A. Miller, “WordNet: a lexical database for English,” Commun. ACM, vol. 38, no. 11, pp. 39–41, 1995. [8] M. Shamsfard, “Developing FarsNet: A lexical ontology for Persian,” in 4th Global WordNet Conference, Szeged, Hungary, 2008. [9] M. Shamsfard et al., “Semi automatic development of farsnet; the persian wordnet,” in Proceedings of 5th global WordNet conference, Mumbai, India, 2010, vol. 29. [10] G. E. Hinton, J. L. Mcclelland, and D. E. Rumelhart, Distributed representations, Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations. MIT Press, Cambridge, MA, 1986. [11] Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin, “A neural probabilistic language model,” J. Mach. Learn. Res., vol. 3, no. Feb, pp. 1137–1155, 2003. [12] P. D. Turney and P. Pantel, “From frequency to meaning: Vector space models of semantics,” J. Artif. Intell. Res., vol. 37, pp. 141–188, 2010. [13] S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” science, vol. 290, no. 5500, pp. 2323–2326, 2000. [14] J. B. Tenenbaum, V. De Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” science, vol. 290, no. 5500, pp. 2319–2323, 2000. [15] H. Schwenk, “Continuous space language models,” Comput. Speech Lang., vol. 21, no. 3, pp. 492–518, 2007. [16] R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, “Natural language processing (almost) from scratch,” J. Mach. Learn. Res., vol. 12, no. Aug, pp. 2493–2537, 2011. [17] R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in Proceedings of the 25th international conference on Machine learning, 2008, pp. 160–167. [18] J. Devlin, R. Zbib, Z. Huang, T. Lamar, R. M. Schwartz, and J. Makhoul, “Fast and Robust Neural Network Joint Models for Statistical Machine Translation.,” in ACL (1), 2014, pp. 1370–1380. [19] I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in neural information processing systems, 2014, pp. 3104–3112. [20] Z. Chen et al., “Revisiting Word Embedding for Contrasting Meaning.,” in ACL (1), 2015, pp. 106–115. [21] T. Mikolov, A. Deoras, S. Kombrink, L. Burget, and J. Černockỳ, “Empirical evaluation and combination of advanced language modeling techniques,” in Twelfth Annual Conference of the International Speech Communication Association, 2011. [22] M. Hassel and N. Mazdak, “FarsiSum: a Persian text summarizer,” in Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages, 2004, pp. 82–84. [23] A. Zamanifar, B. Minaei-Bidgoli, and M. Sharifi, “A new hybrid farsi text summarization technique based on term co-occurrence and conceptual property of the text,” in Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008.SNPD’08. Ninth ACIS International Conference on, 2008, pp. 635–639. [24] M. Shamsfard, T. Akhavan, and M. E. Joorabchi, “Persian document summarization by PARSUMIST,” World Appl. Sci. J., vol. 7, pp. 199–205, 2009. [25] A. Zamanifar and O. Kashefi, “AZOM: a Persian structured text summarizer,” Nat. Lang. Process. Inf. Syst., pp. 234–237, 2011. [26] F. Shafiee and M. Shamsfard, “Similarity versus relatedness: A novel approach in extractive Persian document summarisation,” J. Inf. Sci., p. 0165551517693537, 2017. [27] H. Shakeri, S. Gholamrezazadeh, M. A. Salehi, and F. Ghadamyari, “A new graph-based algorithm for Persian text summarization,” in Computer science and convergence, Springer, 2012, pp. 21–30. [28] T. Hosseinikhah, A. Ahmadi, and A. Mohebi, “A new Persian Text Summarization Approach based on Natural Language Processing and Graph Similarity,” Iran. J. Inf. Process. Manag., vol. 33, no. 2, pp. 885–914, 2018. [29] F. Kiyomarsi and F. R. Esfahani, “Optimizing persian text summarization based on fuzzy logic approach,” in 2011 International Conference on Intelligent Building and Management, 2011. [30] M. Tofighy, O. Kashefi, A. Zamanifar, and H. H. S. Javadi, “Persian text summarization using fractal theory,” in International Conference on Informatics Engineering and Information Science, 2011, pp. 651–662. [31] M. Bazghandi, G. T. Tabrizi, M. V. Jahan, and I. Mashahd, “Extractive Summarization Of Farsi Documents Based On PSO Clustering,” jiA, vol. 1, p. 1, 2012. [32] S. M. Tofighy, R. G. Raj, and H. H. S. Javad, “AHP techniques for Persian text summarization,” Malays. J. Comput. Sci., vol. 26, no. 1, pp. 1–8, 2013. [33] P. Asef, K. Mohsen, T. S. Ahmad, E. Ahmad, and Q. Hadi, “IJAZ: AN OPERATIONAL SYSTEM FOR SINGLE-DOCUMENT SUMMARIZATION OF PERSIAN NEWS TEXTS,” vol. 0, no. 121, pp. 33–48, Jan. 2014. [34] T. Strutz, Data fitting and uncertainty: A practical introduction to weighted least squares and beyond. Vieweg and Teubner, 2010. [35] B. B. Moghaddas, M. Kahani, S. A. Toosi, A. Pourmasoumi, and A. Estiri, “Pasokh: A standard corpus for the evaluation of Persian text summarizers,” in Computer and Knowledge Engineering (ICCKE), 2013 3th International eConference on, 2013, pp. 471–475. [36] S. Farzi and S. Kianian, “Katibeh: A Persian news summarizer using the novel semi-supervised approach,” Digit. Scholarsh. Humanit., vol. 34, no. 2, pp. 277–289, 2018. [37] M. A. Honarpisheh, G. Ghassem-Sani, and S. A. Mirroshandel, “A Multi-Document Multi-Lingual Automatic Summarization System.,” in IJCNLP, 2008, pp. 733–738. [38] A. Joshi, E. Fidalgo, E. Alegre, and L. Fernández-Robles, “SummCoder: An unsupervised framework for extractive text summarization based on deep auto-encoders,” Expert Syst. Appl., vol. 129, pp. 200–215, 2019. [39] R. Nallapati, F. Zhai, and B. Zhou, “Summarunner: A recurrent neural network based sequence model for extractive summarization of documents,” in Thirty-First AAAI Conference on Artificial Intelligence, 2017. [40] A. AleAhmad, H. Amiri, E. Darrudi, M. Rahgozar, and F. Oroumchian, “Hamshahri: A standard Persian text collection,” Knowl.-Based Syst., vol. 22, no. 5, pp. 382–387, 2009. [41] hazm: Python library for digesting Persian text. Sobhe, 2017. [42] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” in Advances in Neural Information Processing Systems 26, C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Eds. 2013, pp. 3111–3119. [43] J. Leskovec, A. Rajaraman, and J. D. Ullman, Mining of massive datasets. Cambridge university press, 2014. [44] C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text summarization branches out: Proceedings of the ACL-04 workshop, 2004, vol. 8. [45] ROUGE-2.0: Java implementation of ROUGE for evaluation of summarization tasks. Stemming, stopwords and unicode support. 2017. [46] L. Page, S. Brin, R. Motwani, and T. Winograd, “The PageRank citation ranking: Bringing order to the web.,” Stanford InfoLab, 1999. [47] J. Carbonell and J. Goldstein, “The use of MMR, diversity-based reranking for reordering documents and producing summaries,” in Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, 1998, pp. 335–336.AbbasMirzaeiAmirRahimi11122019506410.7508/jist.2019.01.005http://jist.ir/en/Article/15223http://jist.ir/en/Article/Download/15223http://jist.ir/en/Article/Download/15223http://jist.ir/en/Article/Download/15223http://jist.ir/en/Article/Download/15223http://jist.ir/en/Article/Download/15223http://jist.ir/en/Article/Download/15223http://jist.ir/en/Article/Download/15223[1] A. Imran, A. Zoha and A. Abu-Dayya, “Challenges in 5G: how to empower SON with big data for enabling 5G,” IEEE Network, Vol. 28, No. 2, 2014, pp. 27–33. [2] N. Baldo, L. Giupponi and J. Mangues-Bafalluy, "Big Data Empowered Self Organized Networks," European Wireless 2014; 20th European Wireless Conference, 2014, pp. 1-8. [3] R. Murugeswari, S. Radhakrishnan, and D. Devaraj, “A multi-objective evolutionary algorithm based QoS routing in wireless mesh networks,” Applied Soft Computing, Vol. 40, No C, 2016, pp. 517–525. [4] Jun, Ersin Uzun, and Jose J. Garcia-luna-aceves. "NETWORK CODING FOR CONTENT-CENTRIC NETWORK." U.S. Patent 20,160,065,685, issued March 3, 2016. [5] H. Zhang, C. Jiang, R. Q. Hu and Y. Qian, "Self-organization in disaster-resilient heterogeneous small cell networks," IEEE Network, vol. 30, No. 2, 2016, pp. 116-121. [6] S. Berger, M. Simsek, A. Fehske, P. Zanier, I. Viering, and G. Fettweis, “Joint Downlink and Uplink Tilt-Based Self-Organization of Coverage and Capacity Under Sparse System Knowledge,” IEEE Transactions on Vehicular Technology, Vol. 65, No. 4, 2016, pp. 2259–2273. [7] M. Huang and XU. Zhang, “Enhanced automatic neighbor relation function for 5G cellular systems with massive MIMO,” 2017 IEEE International Conference on Communications (ICC), 2017, PP 1-6. 2017. [8] K.-L. Yap and Y.-W. Chong, “Software-Defined Networking Techniques to Improve Mobile Network Connectivity: Technical Review,” IETE Technical Review, 2017, pp.1-13. [9] Gebremichail and C. Beard, “Fade Duration Based Neighbor Cell List Optimization for Handover in Femtocell Networks,” International Journal of Interdisciplinary Telecommunications and Networking, Vol. 9, No. 2, 2017, pp. 1–15. [10] T. Han and N. Ansari, “Network Utility Aware Traffic Load Balancing in Backhaul-Constrained Cache-Enabled Small Cell Networks with Hybrid Power Supplies,” IEEE Transactions on Mobile Computing, Vol. 16, No. 10, 2017, pp. 2819–2832. [11] Guita, José, Luis M. Correia, and Marco Serrazina. "Balancing the Load in LTE Urban Networks via Inter-Frequency Handovers." M.S. thesis, Instituto Superior Técnico ,University of Lisbon, Lisbon, Portugal, 2016. [12] K. Zheng, Z. Yang, K. Zhang, P. Chatzimisios, K. Yang, and W. Xiang, “Big data-driven optimization for mobile networks toward 5G,” IEEE Network, Vol. 30, No. 1, 2016, pp. 44–51. [13] S, L, B. Iwan, R. Nicolas, Q. Ripault, J.R. Andrade, S. Han, H. Kim, "Self-optimization of plasmonic nanoantennas in strong femtosecond fields." Optica, Vol.4, No.9, 2017, pp. 1038-1043. [14] Z. Dawy, W. Saad, A. Ghosh, J. G. Andrews, and E. Yaacoub, “Toward Massive Machine Type Cellular Communications,” IEEE Wireless Communications, Vol. 24, No. 1, 2017, pp. 120–128,. [15] Gillot, David, and John Yue Jun Jiang. "Method and system for providing roaming intelligence (RI) to a host network operator for its roaming traffic." U.S. Patent 9,338,663, 2016. [16] S. Fong, C. Fang, N. Tian, R. Wong, B. W. Yap, "Self-Adaptive Parameters Optimization for Incremental Classification in Big Data Using Neural Network." In Big Data Applications and Use Cases Springer International Publishing, 2016. [17] Agrawal, Himanshu, and Krishna Asawa. "New Architecture for Dynamic Spectrum Allocation in Cognitive Heterogeneous Network using Self Organizing Map." arXiv preprint arXiv, 2016. [18] G. Foster, S. Vahid, and R. Tafazolli, “SON Evolution for 5G Mobile Networks,” Fundamentals of 5G Mobile Networks, USA, Wiley, 2015. [19] H. Goudarzi and M. Pedram, “Hierarchical SLA-Driven Resource Management for Peak Power-Aware and Energy-Efficient Operation of a Cloud Datacenter,” IEEE Transactions on Cloud Computing, Vol. 4, No. 2, 2016, pp. 222–236. [20] C. Segura, C. Coello, G. Valladares, C. Leon, " Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization." Annals of Operations Research, Vol.240, No.1, 2016, pp. 217-250. [21] Z. M. Fadlullah, D. M. Quan, N. Kato, and I. Stojmenovic, “GTES: An Optimized Game-Theoretic Demand-Side Management Scheme for Smart Grid,” IEEE Systems Journal, Vol. 8, No. 2, 2014, pp. 588–597,. [22] Y. Liu, C. Yuen, S. Huang, N. Ul Hassan, X. Wang and S. Xie, "Peak-to-Average Ratio Constrained Demand-Side Management With Consumer's Preference in Residential Smart Grid," IEEE Journal of Selected Topics in Signal Processing, Vol. 8, No. 6, 2014, pp. 1084-1097. [23] A. De Waegenaere, J. L. Wielhouwer," A breakpoint search approach for convex resource allocation problems with bounded variables."Optimization Letters ,Vol.6, No.4, 2012, pp. 629-640. [24] A. Galindo-Serrano, “Self-organized Femto-cells: A Time Difference Learning Approach,” Ph.D. thesis, Universitat Politecnica de Catalunya (UPC), Barcelona, Spain, 2013. [25] Y. Jiang, Q. Liu, F. Zheng, X. Gao and X. You, Energy-efficient joint resource allocation and power control for communications, IEEE Transactions on Vehicular Technology 65(8) (2016), 6119–6127.حبیبراثیMaryamShirzadian Gilan1112201911110.7508/jist.2019.01.001http://jist.ir/en/Article/15242http://jist.ir/en/Article/Download/15242http://jist.ir/en/Article/Download/15242http://jist.ir/en/Article/Download/15242http://jist.ir/en/Article/Download/15242http://jist.ir/en/Article/Download/15242http://jist.ir/en/Article/Download/15242http://jist.ir/en/Article/Download/15242[1] Palmer J, Palumbo S, Cao V, et al. A new illuminator of opportunity bistatic radar research project at DSTO. Defence Science and Technology Organisation Edinburgh (Australia) Electronic Warfare and Radar Division. 2009. [2] H. Ma, M. Antoniou, D. Pastina, F. Santi, F. Pieralice, M. Bucciarelli and M. Cherniakov, “Maritime moving target indication using passive GNSS-based bistatic radars,” IEEE Transactions on Aerospace and Electronic Systems. 54(1):115-130, 2018. [3] J. Palmer, S. Palumbo, A. Summers, D. Merrett, S. Searle and S. Howard, “An overview of an illuminator of opportunity passive radar research project and its signal processing research directions,” Digital Signal Processing. 21(5):593-599, 2011. [4] M. Malanowski and K. Kulpa, “Two Methods for Target Localization in Multistatic Passive Radar,” IEEE Transactions on Aerospace and Electronic Systems. 48(1):572-580, 2012. [5] S. Wu, D. Xu, J. Tan, et al. “Two base station location techniques with adjusted measurements in circular scattering environments,” International Journal of Communication Systems. 29(6):1073-1083, 2016. [6] N. H. Nguyen, “Multistatic Target Tracking and Localization: Waveform Adaptation, Geometry Optimization, and Pseudolinear Estimation,” University of South Australia. 2016. [7] A. Ali, G. Shah, M. Aslam, “Model for autonomous agents in machin-to-machine navigation networks,” International Journal of Communication Systems. 31(4): e3491, 2018. [8] K.C. Ho and W. Xu, “An accurate algebraic solution for moving source location using TDOA and FDOA measurements,” IEEE Transactions on Signal Processing. 52(9):2453-2463, 2004. [9] B. Xu, W. D. Qi, L. Wei and P. Liu, “Turbo-TSWLS: enhanced two-step weighted least squares estimator for TDOA-based localization,” Electronics Letters. 48(25): 1597-1598, 2012. [10] H. Yang and J. Chun, “An improved algebraic solution for moving target localization in noncoherent MIMO radar systems,” IEEE Transactions on Signal Processing. 64(1):258-270, 2016. [11] R. Amiri, F. Behnia and M.A.M Sadr, “Efficient positioning in MIMO radars with widely separated antennas,” IEEE Communications Letters. 21(7):1569-1572, 2017. [12] R. Amiri, F. Behnia and M.A.M Sadr, “Positioning in MIMO radars based on constrained least squares estimation,” IEEE Communications Letters. 21(10):2222-2225, 2017. [13] B. Huang, L. Xie and Z. Yang, “TDOA-based source localization with distance-dependent noises,” IEEE Transactions on Wireless Communications. 14(1):468-480, 2015. [14] K. C. Ho, “Bias reduction for an explicit solution of source localization using TDOA,” IEEE Transactions on Signal Processing. 60(5): 2101-2114, 2012. [15] S. Stein, “Algorithms for ambiguity function processing,” IEEE Transactions on Acoustics, Speech, and Signal Processing. 29(3):588-599, 1981. [16] M. Cherniakov, “Bistatic radars: emerging technology,” John Wiley & Sons. 2008. [17] Y. T. Chan and K. C. Ho, “A simple and efficient estimator for hyperbolic location,” IEEE Transactions on signal processing,” 42(8):1905-1915, 1994.Mohadese EshaghiFarbodRazzaziAlirezaBehrad11122019748710.7508/jist.2019.01.007http://jist.ir/en/Article/15253http://jist.ir/en/Article/Download/15253http://jist.ir/en/Article/Download/15253http://jist.ir/en/Article/Download/15253http://jist.ir/en/Article/Download/15253http://jist.ir/en/Article/Download/15253http://jist.ir/en/Article/Download/15253http://jist.ir/en/Article/Download/15253[1] M. Graciarena, A. Alwan, D. Ellis, H. Franco, L. Ferrer, J. H. L. Hansen, A. Janin, B. S. Lee, Y. Lei, V. Mitra, N. Morgan, S.O. Sadjadi, T.J. Tsai, N. Scheffer, L.N. Tan and B. Williams, “All for one: feature combination for highly channel-degraded speech activity detection,” in ISCA Interspeech, pp.709–713, 2013. [2] M. Eshaghi, and M. R. Karami Mollaei, “Voice activity detection based on using wavelet packet,” in Digital Signal Processing, vol. 20, No. 4, pp. 1102-1115, 2010. [3] Y. Datao, H. Jiqing, Z. Guibin and Z. Tieran, “Sparse power spectrum based robust voice activity detector,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, pp. 289-292, 2012. [4] W Hongzhi, X Yuchao and L Meijing, “Study on the MFCC similarity-based voice activity detection algorithm,” in International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), Dengleng, 2011. [5] S.G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, No.7, pp. 674-693,1989. [6] Nima Mesgarani, and Shihab Shamma, “Denoising in the Domain of Spectro-temporal Modulations”, in EURASIP Journal on Audio, Speech, and Music Processing , ID. 42357, 8 pages, doi:10.1155/2007/42357 ,2007. [7] Weifeng Li, Yicong Zhou, Norman Poh, Fei Zhou, and Qingmin Liao, “Feature Denoising Using Joint Sparse Representation for In-car Speech Recognition”, in IEEE Transactions on audio, speech, and language processing, vol.20, No.7, pp. 681-684, 2013. [8] N. Mesgarani, S. David, and S.A. Shamma, “Representation of phoneme in primary auditory cortex: how the brain analyzes speech,” in IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), Hawaii, April, 2007. [9] Majid Mirbagheri, Nima Mesgarani, and Shihab Shamma, “Nonlinear filtering of spectrotemporal modulation in speech enhancement,” in IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), pp. 5478-81, 2010. [10] C. Kim, K. Kumar and R. M. Stern, “Binaural sound source seperation motivated by auditory processing,” in Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP2011, Prague, 2011. [11] C. Mart´ınez, J. Goddardb, D. Milone, and H. Rufiner, “ Bio inspired sparse spectro-temporal representation of speech for robust classification,”in Computer Speech and Language, vol.26, No.5, pp. 336-345, 2012. [12] Jort Florent Gemmeke, Hugo Van Hamme, Bert Cranen, and Lou Boves, “Compressive Sensing for Missing Data Imputation in Noise Robust Speech Recognition”, in IEEE Journal of selected topics in signal processing, vol.4,No.2, pp. 272-287, 2010. [13] B. K. Natarajan, “Sparse approximate solutions to linear systems,” in Society for Industrial and Applied Mathematics (SIAM J). Computer, vol.24,No.2, pp.227–234, 1995. [14] Mohadese Eshaghi, Farbod Razzazi, and Alireza Behrad, “A voice activity detection algorithm in spectro-temporal domain using sparse representation,” in International Journal of Machine Learning and Cybernetics, 2018, DOI: 10.1007/s13042-018-0856-z. [15] G. Hosein Mohimani, Massoud Babaie-Zadeh, and Christian Jutten, “A fast approach for overcomplete sparse decomposition based on smoothed L0 norm,” in IEEE Transactions on Signal Processing, vol.57, No.1, pp.289-301, 2009. [16] K. Kreutz-Delgado, J.F. Murray, B.D. Rao, K. Engan, T. Lee, and T.J. Sejnowski, “Dictionary learning algorithms for sparse representation,” in Neural Computer, vol.15, No.2, pp.349–396,2003. [17] P. O. Hoyer, “Non-negative matrix factorization with sparseness constraints,” in Journal of Machine Learning Research, vol.5, No. 9, pp.1457–1469, 2004. [18] R. Zdunek, and A. Cichocki, “Non-negative matrix factorization with quadratic programming,” in Neurocomputing, vol.71, No.10-12, pp. 2309-2320, 2007. [19] M. Aharon, M. Elad, A. Bruckstein, “K-SVD: An algorithm for designing over complete dictionaries for sparse representation,” in IEEE Transactions on Signal Processing, vol.54, No.11, pp.4311–4322, 2006. [20] W. M. Fisher, G. R. Doddington, M. Goudie and M. Kathleen, “The DARPA speech recognition research database: specifications and status”, in DARPA Workshop on Speech Recognition, 1986. [21] A. Varga, H. J. M. Steeneken, M. Tomlinson and D. Jones, “The NOISEX-92 study the effect of additive noise on automatic speech recognition”, Documentation included in the NOISEX-92 CD-ROMs, 1992. [22] B. Raj, T. Virtanen, S. Chaudhure, and R. Singh, “Non-negative matrix factorization based compensation of music for automatic speech recognition,” International Conference on Speech and Language Processing, 2010. [23] N. Mesgarani, S. Shamma, and M. Slaney, “Speech discrimination based on multiscale spectro-temporal modulations,” in IEEE International Conference on Acoustics, Speech and Signal Processing, vol4, No.1, pp.601–604, 2004. [24] Ian Vince McLoughlin, “Super-Audible Voice Activity Detection,” in IEEE Transactions on Speech and Audio Processing, vol. 22, No.9, pp.1424-1433, 2014. [25] L. N. Tan, B. J. Borgstrom, and A. Alwan, “Voice activity detection using harmonic frequency components in likelihood ratio test,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2010. [26] J. Ramirez, J.C. Segura, C. Benitez, A. de la Torre and A. Rubio, “Efficient voice activity detection algorithms using long-term speech information,” in Speech Communication, vol.42, No.3-4, pp.271–287, 2004. [27] M. Yanna and A. Nishihara, “Efficient voice activity detection algorithm using long-term spectral flatness measure,” in EURASIP Journal on Audio, Speech and Music Processing, 2013, DOI: 10.1186/1687-4722-2013-21. [28] Xu-Kui Yang, Liang He, Dan Qu1 and Wei-Qiang Zhang, “Voice activity detection algorithm based on long-term pitch information,” in EURASIP Journal on Audio, Speech, and Music Processing, 2016, DOI: 10.1186/s13636-016-0092-y.SoodehBakhshandehazmiazmiMohammadTeshnehlab11122019354910.7508/jist.2019.01.004http://jist.ir/en/Article/15265http://jist.ir/en/Article/Download/15265http://jist.ir/en/Article/Download/15265http://jist.ir/en/Article/Download/15265http://jist.ir/en/Article/Download/15265http://jist.ir/en/Article/Download/15265http://jist.ir/en/Article/Download/15265http://jist.ir/en/Article/Download/15265A. Salimi, M. Ziaii, A. Amiri, M. H. Zadeh, S. Karimpouli, and M. Moradkhani, “Using a feature subset selection method and support vector machine to address curse of dimensionality and redundancy in hyperion hyperspectral data classification,” The Egyptian Journal of Remote Sensing and Space Science, vol. 21, no. 1, pp. 27–36, 2018. [2] T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri et al., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring,” science, vol. 286, no. 5439, pp. 531–537, 1999. [3] H. Liu and H. Motoda, Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series). Chapman and Hall, 2007. [4] A. Jovi´c, K. Brki´c, and N. Bogunovi´c, “A review of feature selection methods with applications,” in 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE, 2015, pp. 1200–1205. [5] B.Liao,Y.Jiang,W.Liang,W.Zhu,L.Cai,andZ.Cao, “Gene selection using locality sensitive laplacian score,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 11, no. 6, pp. 1146–1156, Nov 2014. [6] S. Theodoridis and K. Koutroumbas, Pattern Recognition, fourth edition. Academic Press, Oxford, 2008. [7] R. Cai, Z. Hao, X. Yang, and W. Wen, “An efficient gene selection algorithm based on mutual information,” Neurocomputing, vol. 72, no. 4, pp. 991 – 999, 2009. [8] L. E. Raileanu and K. Stoffel, “Theoretical comparison between the gini index and information gain criteria,” Annals of Mathematics and Artificial Intelligence, vol. 41, no. 1, pp. 77–93, May 2004. [9] H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information criteria of max-dependency, max relevance, and min-redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 1226– 1238, Aug 2005. [10] L. Yu and H. Liu, “Efficient feature selection via analysis of relevance and redundancy,” Journal of Machine Learning Research, vol. 5, pp. 1205–1224, 2004. [11] A. J. Ferreira and M. A. Figueiredo, “Efficient feature selection filters for high-dimensional data,” Pattern Recognition Letters, vol. 33, no. 13, pp. 1794 – 1804, 2012. [12] S. Tabakhi, P. Moradi, and F. Akhlaghian, “An unsupervised feature selection algorithm based on ant colony optimization,” Engineering Applications of Artificial Intelligence, vol. 32, no. Supplement C, pp. 112 – 123, 2014. [13] C. Lai, M. J. Reinders, and L. Wessels, “Random subspace method for multivariate feature selection,” Pattern Recognition Letters, vol. 27, no. 10, pp. 1067 – 1076, 2006. [14] R. J. Manoj, M. A. Praveena, and K. Vijayakumar, “An aco– ann based feature selection algorithm for big data,” Cluster Computing, pp. 1–8, 2018. [15] I. Jain, V. K. Jain, and R. Jain, “Correlation feature selection based improved-binary particle swarm optimization for gene selection and cancer classification,” Applied Soft Computing, vol. 62, pp. 203–215, 2018. [16] S. Maldonado, R. Weber, and J. Basak, “Simultaneous feature selection and classification using kernel-penalized support vector machines,” Information Sciences, vol. 181, no. 1, pp. 115 – 128, 2011. [17] G. Wang, Q. Song, B. Xu, and Y. Zhou, “Selecting feature subset for high dimensional data via the propositional foil rules,” Pattern Recognition, vol. 46, no. 1, pp. 199 – 214, 2013. [18] J. Canul-Reich, L. O. Hall, D. Goldgof, J. N. Korecki, and S. Eschrich, “Iterative feature perturbation as a gene selector for microarray data,” vol. 26, 08 2012. [19] P. A. Mundra and J. C. Rajapakse, “Svm-rfe with mrmr filter for gene selection,” IEEE Transactions on NanoBioscience, vol. 9, no. 1, pp. 31–37, March 2010. [20] L.-Y. Chuang, C.-H. Yang, K.-C. Wu, and C.-H. Yang, “A hybrid feature selection method for dna microarray data,” Computers in Biology and Medicine, vol. 41, no. 4, pp. 228 – 237, 2011. [21] W. Zhao, G. Wang, H. Wang, H. Chen, H. Dong, and Z. Zhao, “A novel framework for gene selection,” vol. 3, pp. 184–191, 04 2011. [22] G. Forman, “An extensive empirical study of feature selection metrics for text classification,” J. Mach. Learn. Res., vol. 3, pp. 1289–1305, march 2003. [23] K. Kira and L. A. Rendell, “The feature selection problem: Traditional methods and a new algorithm,” in Proceedings of the Tenth National Conference on Artificial Intelligence, ser. AAAI’92, 1992, pp. 129–134. [24] I. Kononenko, Estimating attributes: Analysis and extensions of RELIEF. [25] L. Yu and H. Liu, “Feature selection for high-dimensional data: A fast correlation-based filter solution,” in Proceedings, Twentieth International Conference on Machine Learning, vol. 2, 01 2003, pp. 856–863. [26] R. Battiti, “Using mutual information for selecting features in supervised neural net learning,” IEEE Transactions on Neural Networks, vol. 5, no. 4, pp. 537–550, July 1994. [27] Q. Song, J. Ni, and G. Wang, “A fast clustering-based feature subset selection algorithm for high-dimensional data,” IEEE Trans. on Knowl. and Data Eng., vol. 25, pp. 1–14, jan 2013. [28] M. Mandal and A. Mukhopadhyay, Unsupervised Nonredundant Feature Selection: A Graph-Theoretic Approach. Springer Berlin Heidelberg, 2013, pp. 373–380. [29] S. Bandyopadhyay, T. Bhadra, P. Mitra, and U. Maulik, “Integration of dense subgraph finding with feature clustering for unsupervised feature selection,” Pattern Recognition Letters, vol. 40, no. Supplement C, pp. 104 – 112, 2014. [30] S. Tabakhi, A. Najafi, R. Ranjbar, and P. Moradi, “Gene selection for microarray data classification using a novel ant colony optimization,” Neurocomputing, vol. 168, no. Supplement C, pp. 1024 – 1036, 2015. [31] P. Moradi and M. Rostami, “Integration of graph clustering with ant colony optimization for feature selection,” Knowledge-Based Systems, vol. 84, no. Supplement C, pp. 144 – 161, 2015. [32] A. Pino Angulo, “Gene selection for microarray cancer data classification by a novel rule-based algorithm,” Information, vol. 9, no. 1, p. 6, 2018. [33] S. S. Kannan and N. Ramaraj, “A novel hybrid feature selection via symmetrical uncertainty ranking based local memetic search algorithm,” Knowledge-Based Systems, vol. 23, no. 6, pp. 580–585, 2010. [34] K. Zheng and X. Wang, “Feature selection method with joint maximal information entropy between features and class,” Pattern Recognition, vol. 77, pp. 20–29, 2018. [35] U. M. Fayyad and K. B. Irani, “Multi-interval discretization of continuous-valued attributes for classification learning,” in IJCAI, 1993, pp. 1022–1029. [36] C. Shi, Y. Cai, D. Fu, Y. Dong, and B. Wu, “A link clustering based overlapping community detection algorithm,” Data and Knowledge Engineering, vol. 87, no. Supplement C, pp. 394 – 404, 2013. [37] E. Le Martelot and C. Hankin, “Fast multi-scale detection of relevant communities in large-scale networks,” The Computer Journal, vol. 56, no. 9, pp. 1136–1150, 2013. [38] V. D. Blondel, J. loup Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,” pp. 1–12, 2008. [39] C. Gao, D. Wei, Y. Hu, S. Mahadevan, and Y. Deng, “A modified evidential methodology of identifying influential nodes in weighted networks,”Physica A:Statistical Mechanics and its Applications, vol. 392, no. 21, pp. 5490–5500, 2013. [40] M. R. G. R. . V. L. V. Nicosia, R. Criado, “Controlling centrality in complex networks,” Scientific Reports 2, no. 218, pp. 218–223, 2012. [41] M. Campiteli, A. Holanda, L. Soares, P. Soles, and O. Kinouchi, “Lobby index as a network centrality measure,” Physica A: Statistical Mechanics and its Applications, vol. 392, pp. 5511 – 5515, 2013. [42] Y. Du, C. Gao, Y. Hu, S. Mahadevan, and Y. Deng, “A new method of identifying influential nodes in complex networks based on topsis,” Physica A: Statistical Mechanics and its Applications, vol. 399, no. Supplement C, pp. 57 – 69, 2014. [43] P. Mitra, C. A. Murthy, and S. K. Pal, “Unsupervised feature selection using feature similarity,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp. 301–312, March 2002. [44] M. P. V. S. Kirkpatrick, C. D. Gelatt Jr., “Optimization by simulated annealing,” American Association for the Advancement of Science, vol. 220, no. 4598, pp. 671–680, may 1983. [45] K. R. B. D. S. Repository, kent ridge bio-medical dataset, http://datam.i2r.a-star.edu.sg/datasets/krbd/. [46] B. institute, Cancer Program Data Sets, 2014, http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi. [47] I. T. G. A. Statnikov, C.F. Aliferis, Gene Expression Model Selector, 2005, http://www.gems-system.org. [48] M. Haindl, P. Somol, D. Ververidis, and C. Kotropoulos, Feature Selection Based on Mutual Correlation. Springer Berlin Heidelberg, 2006, pp. 569–577. [49] P. Moradi and M. Rostami, “A graph theoretic approach for unsupervised feature selection,” Engineering Applications of Artificial Intelligence, vol. 44, no. Supplement C, pp. 33 – 45, 2015. [50] A. K. Das, S. Goswami, A. Chakrabarti, and B. Chakraborty, “A new hybrid feature selection approach using feature association map for supervised and unsupervised classification,” Expert Systems with Applications, vol. 88, no. Supplement C, pp. 81 – 94, 2017. [51] A. Brankovic, M. Hosseini, and L. Piroddi, “A distributed feature selection algorithm based on distance correlation with an application to microarrays,” IEEE/ACM transactions on computational biology and bioinformatics, 2018. [52] L. Sun, X. Kong, J. Xu, R. Zhai, S. Zhang et al., “A hybrid gene selection method based on relieff and ant colony optimization algorithm for tumor classification,” Scientific Reports, vol. 9, no. 1, p. 8978, 2019. [53] M. Moradkhani, A. Amiri, M. Javaherian, and H. Safari, “A hybrid algorithm for feature subset selection in high dimensional datasets using fica and iwssr algorithm,” Applied Soft Computing, vol. 35, pp. 123–135, 2015. [54] P. Bermejo, J. A. Gamez, and J. M. Puerta, “A grasp algorithm for fast hybrid (filter-wrapper) feature subset selection in high dimensional datasets,” Pattern Recognition Letters, vol. 32, no. 5, pp. 701–711, 2011. [55] X. Huang, L. Zhang, B. Wang, F. Li, and Z. Zhang, “Feature clustering based support vector machine recursive feature elimination for gene selection,” Applied Intelligence, vol. 48, no. 3, pp. 594–607, 2018. [56] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, pp. 389–422, Jan 2002. [57] J. R. Quinlan, “Induction of decision trees,” Mach. Learn., vol. 1, March 1986. [58] D. W. Aha, D. Kibler, and M. K. Albert, “Instance-based learning algorithms,” Machine Learning, vol. 6, pp. 37–66, Jan 1991. [59] Hamdani and R. Wardoyo, “The complexity calculation for group decision making using topsis algorithm,” AIP Conference Proceedings, vol. 1755, pp. 0700071 – 0700077, 2016. [60] T. J. Cleophas and A. H. Zwinderman, “Quantile-quantile plots, a good start for looking at your medical data (50 cholesterol measurements and 58 patients),” in Machine Learning in Medicine-a Complete Overview. Springer, 2015, pp. 253– 259.AlirezaKamanghadGholamrezaHashemzadeMohammadaliAfshar kazemiNosratollahShadnoosh11122019657310.7508/jist.2019.01.006http://jist.ir/en/Article/15266http://jist.ir/en/Article/Download/15266http://jist.ir/en/Article/Download/15266http://jist.ir/en/Article/Download/15266http://jist.ir/en/Article/Download/15266http://jist.ir/en/Article/Download/15266http://jist.ir/en/Article/Download/15266http://jist.ir/en/Article/Download/15266[1] A. Mishra, and D. Mishra, "Customer Relationship Management: Implementation Process Perspective", Acta Polytechnica Hungaric, Vol. 6, No. 4, 2009, pp. 83- 99. [2] A. Jafarnejad, C. Loox and A. Monshi, "Towards Electronic Customer Relationship Management: An CRM Solutions Development Mythology”, Iranian Journal of Management Studies (IJMS), Vol. 1, No.1, 2007, pp. 73-89. [3] P. Forrouhiyehpour, "Assessing the Readiness for implementing CRM in B2B Markets Using AHP Method", M.S. Thesis, Lulea University of Technology, Sweden, 2008. [4] R. Shaikh, "Mobile CRM A Case Study of Mobile CRM Strategies", M.S Thesis, Information and Communication Technology, KTH Royal Institute of Technology, Stockholm, Sweden, 2015. [5] A. Jafarnejad, S.R. Safavi and M. Ajali "E-Readiness assessment of Gas National company of Iran based on AHP and fuzzy logic", 2th international conference of OR, Babolsar, Iran, 2009. [6] P. Hanafizadeh, M. Hanafizadeh, and M. Khodabakhshi, "Taxonomy of E-Readiness assessment measures", International Journal of Information Management, Vol. 29, 2009, pp. 189-195. [7] D. Dada, "E-Readiness for developing countries: Moving the focus from the environment to the users", EJISDC, Vol. 27, No. 6, 2006, pp. 1-14. [8] K. Anderson, and C. Kerr, "Customer Relationship Management", McGraw Hill Companies, Inc., 2002. [9] S. Grandhi and R. Chugh. "Strategic Value of Mobile CRM Application: A Review of Mobile CRM at Dow Corning and Direct TV", International Conference on Innovation and Information Management (ICIM 2012), IPCSIT Vol. 36, 2012, pp. 388-393. [10] I. Mahdavi, N. Cho, B. Shirazi and N. Sahebjamnia, "Designing evolving user profile in e-CRM with dynamic clustering of web documents." Data & Knowledge Engineering, Vol 65, 2008, pp. 355-372. [11] R. Lin, R. Chen and K. Shun Chiu, "Customer relationship management and innovation capability: an empirical study", Industrial Management & Data Systems, Vol. 110, No. 1, 2010, pp. 111-133. [12] I. Dalla Pozza, O. Goetz and J.M. Sahut, "Implementation effects in the relationship between CRM and its performance", Journal of Business Research, Vol. 89, 2018, pp. 391-403. [13] M. Wang, "Measuring CRM service quality in the library context: a preliminary study", The Electronic Library, Vol. 26, No. 6, 2007, pp. 896-911. [14] A. Albadvi and M. Enayat tabar, "Customer Relationship Management in electronic environment". 4th international conference of management, Tehran, Iran, 2006. [15] S. Kaur, S. Saini and R. Vashisht, "Mobile Computing", Advance in Electronic and Electric Engineering, Vol. 3, No. 6, 2013, pp. 675-682. [16] A. Turban et al. (2008). Information Technology for Management: Transforming Organizations in the Digital Economy, 6th ed., John Wiley & Sons, 2008. [17] T. Fan-Chen, P.T.T. Linh, T.C.E. Cheng, and T. Ching, "Enhancing customer loyalty to mobile instant messaging: Perspectives of network effect and self-determination theories", Telematics and Informatics, Vol. 35, 2018, pp. 1133-1143. [18] S. Klaous, The Fourth Industrial Revolution. Penguin Random House, UK, 2017. [19] Word Economic Forum. "Deep Shift Technology Tipping Points and Societal Impact", 2015, Retrieved from http://www3.weforum.org/docs/WEF_GAC15_Technological_Tipping_Points_report_2015.pdf. [20] F. Gu, J. Niu, Z. Qi and M. Atiquzzaman, "Partitioning and Offloading in Smart Devices for Mobile Cloud Computing: State of the Art and Future Directions", Journal of Network and Computer Applications. Accepted 19 June 2018. [21] G. Componovo, Y. Pigneur, A. Rangone and F. Renga, "Mobile Customer Relationship management: An Explorative Investigation of the Italian Consumer Market", Vol 10, No. 1109, ICMB.2005.63. pp. 42-48. [22] D. Verma and D.S. Verma, "Managing Customer Relationship through Mobile CRM in Organized retail outlets", International Journal of Engineering Trends and Technology (IJETT), Vol. 4, No. 5, 2015, pp. 1697-1701. [23] S. San-Martin, N.H. Jimenez, and B. Lopez-Catalan, "The firm’s benefits of mobile CRM from relationship marketing approach and the TOE model", Spanish Journal of Marketing- ESIC 20, 2016, pp. 18-29. [24] M. Rodriguez and K. Trainor, "A conceptual model of the drivers and outcomes of mobile CRM application adoption", Journal of Research in Interactive Marketing. Vol. 10, No.1, 2016, pp. 67-84. [25] S.M. Mutula, and P. Brakel, "An evaluation of E-Readiness assessment tools with respect to information access: Towards an integrated information rich tool". International Journal of Information Management, Vol. 26, 2006, pp. 212–223. [26] M. Hourali, M. Fathian, A. Montazeri, and M. Hourali, "A model for E-Readiness assessment of Iranian small and medium enterprises". Journal of Faculty of Engineering, Vol. 41, No. 7, 2008, pp. 969-985. [27] Economist Intelligence Unit, “E-Readiness rankings”, 2008 Retrieved from: http://graphics.eiu.com/upload/ibm_ereadiness_2008.pdf. [28] Bridges. org., "E-Ready for What E-Readiness in Developing Countries: Current Status and Prospects toward the Millennium Development Goals", 2005, Retrieved from:http://www. bridges.org/en/Publication.3.html. [29] S. Toufani, "E-Readiness assessment in Iranian B2C Enterprises (case: Iranian Book Publishing Companies)”, Master Thesis, Lulea University of Technology, Sweden, 2009. [30] K. Ruikar, C.J. Anumba and P.M. Carrillo, "VERDICT-An e-readiness assessment application for construction companies", Automation in Construction, Vol. 15, 2006, pp. 98-110. [31] K.S. AL-Osami, "Mathematical Models for E-Readiness Assessment of Organizations with Intranets", Mater Thesis, King Saud University, Saudi Arabia, 2007. [32] J. C. Injazz and K. Popovich (2003). "Understanding customer relationship management (CRM) people, process and technology", Business Process Management Journal, Vol. 9, 2003, pp. 672-688.