jist-202605192320260519232702CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagJournal of Information Systems and Telecommunication (JIST) jist2322-14371282018622MuharramMansoorizadeh12820189510510.7508/jist.2018.02.005http://jist.ir/en/Article/15018http://jist.ir/en/Article/Download/15018http://jist.ir/en/Article/Download/15018http://jist.ir/en/Article/Download/15018http://jist.ir/en/Article/Download/15018http://jist.ir/en/Article/Download/15018http://jist.ir/en/Article/Download/15018http://jist.ir/en/Article/Download/15018[1] J. Baliga, K. Hinton and R. S. Tucker, Energy consumption of the Internet, University of Melbourne, Department of Electrical and Electronic Engineering, 2011. [2] C. Fang, F. R. Yu, T. Huang, J. Liu and Y. Liu, "A Survey of Green Information-Centric Networking: Research Issues and Challenges.," IEEE Communications Surveys and Tutorials, vol. 17, pp. 1455-1472, 2015. [3] I. F. Akyildiz, A. Lee, P. Wang, M. Luo and W. Chou, "A roadmap for traffic engineering in SDN-OpenFlow networks," Computer Networks, vol. 71, pp. 1-30, 2014. [4] A. G. Cervero, M. Chincoli, L. Dittmann, A. Fischer, A. E. Garcia, J. Gal{\'a}n-Jim{\'e}nez, L. Lefevre, H. d. Meer, T. Monteil, P. Monti and others, "Green wired networks," Large-Scale Distributed Systems and Energy Efficiency: A holistic view, pp. 41-80, 2015. [5] M. Baldi and Y. Ofek, "Time for a" greener" internet," in Communications Workshops, 2009. ICC Workshops 2009. IEEE International Conference on, 2009. [6] R. Bolla, R. Bruschi, F. Davoli and F. Cucchietti, "Energy efficiency in the future internet: a survey of existing approaches and trends in energy-aware fixed network infrastructures," Communications Surveys \& Tutorials, IEEE, vol. 13, pp. 223-244, 2011. [7] G. Leduc, H. Abrahamsson, S. Balon, S. Bessler, M. D'Arienzo, O. Delcourt, J. Domingo-Pascual, S. Cerav-Erbas, I. Gojmerac, X. Masip and others, "An open source traffic engineering toolbox," Computer Communications, vol. 29, pp. 593-610, 2006. [8] F. Blanchy, L. M{\'e}lon and G. Leduc, "A Preemption-aware on-line routing algorithm for MPLS networks," Telecommunication Systems, vol. 24, pp. 187-206, 2003. [9] P. Mahadevan, P. Sharma, S. Banerjee and P. Ranganathan, "A power benchmarking framework for network devices," in NETWORKING 2009, Springer, 2009, pp. 795-808. [10] K. Cengiz and T. Dag, "A review on the recent energy-efficient approaches for the Internet protocol stack," EURASIP Journal on Wireless Communications and Networking, vol. 2015, pp. 1-22, 2015. [11] A. P. Bianzino, C. Chaudet, D. Rossi and J. L. Rougier, "A Survey of Green Networking Research," IEEE Communications Surveys Tutorials, vol. 14, pp. 3-20, 2012. [12] M. Xia, M. Tornatore, Y. Zhang, P. Chowdhury, C. U. Martel and B. Mukherjee, "Greening the Optical Backbone Network: A Traffic Engineering Approach.," in ICC, 2010. [13] G. Lin, S. Soh, M. Lazarescu and K.-W. Chin, "Energy-aware two link-disjoint paths routing," in High Performance Switching and Routing (HPSR), 2013 IEEE 14th International Conference on, 2013. [14] B. Addis, A. Capone, G. Carello, L. G. Gianoli and B. Sanso, "Energy-aware multiperiod traffic engineering with flow-based routing," in Communications (ICC), 2012 IEEE International Conference on, 2012. [15] W. Fisher, M. Suchara and J. Rexford, "Greening backbone networks: reducing energy consumption by shutting off cables in bundled links," in Proceedings of the first ACM SIGCOMM workshop on Green networking, 2010. [16] E. Amaldi, A. Capone and L. G. Gianoli, "Energy-aware IP traffic engineering with shortest path routing," Computer Networks, vol. 57, pp. 1503-1517, 2013. [17] A. Ruiz-Rivera, K.-W. Chin, S. Soh and R. Raad, "On the performance of online and offline green path establishment techniques," EURASIP Journal on Wireless Communications and Networking, vol. 2015, pp. 1-17, 2015. [18] B. Fortz, "Applications of meta-heuristics to traffic engineering in IP networks," International transactions in operational research, vol. 18, pp. 131-147, 2011. [19] E. Bonetto, L. Chiaraviglio, F. Idzikowski and E. Le Rouzic, "Algorithms for the multi-period power-aware logical topology design with reconfiguration costs," Journal of Optical Communications and Networking, vol. 5, pp. 394-410, 2013. [20] R. Samadi, M. Nassiri and M. Mansoorizadeh, "Energy-aware traffic engineering in IP networks using non-dominated sorting genetic II algorithm," International Journal of Advanced Intelligence Paradigms, vol. 1, 2019. [21] R. Hassan, B. Cohanim, O. De Weck and G. Venter, "A comparison of particle swarm optimization and the genetic algorithm," in 46th AIAA/ ASME/ ASCE/ AHS/ ASC structures, structural dynamics and materials conference, 2005. [22] O. Okonor, N. Wang, S. Georgoulas and Z. Sun, "Green Link Weights for Disruption-Free Energy-Aware Traffic Engineering," IEEE Systems Journal, vol. 11, pp. 661-672, 2017. [23] R. Wang, S. Gao, W. Yang and Z. Jiang, "Energy aware routing with link disjoint backup paths," Computer Networks, vol. 115, pp. 42-53, 2017. [24] T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, Introduction to algorithms third edition, MIT Press, 2009. [25] A. P. Engelbrecht, Computational intelligence: an introduction, John Wiley & Sons, 2007. [26] S. Orlowski, M. Pi{\'o}ro, A. Tomaszewski and R. Wess{\"a}ly, "SNDlib 1.0--Survivable Network Design Library," in Proceedings of the 3rd International Network Optimization Conference (INOC 2007), Spa, Belgium, 2007.AliNaghash AsadiMohammad Abdollahi Azgomi 1282018768710.7508/jist.2018.02.003http://jist.ir/en/Article/15092http://jist.ir/en/Article/Download/15092http://jist.ir/en/Article/Download/15092http://jist.ir/en/Article/Download/15092http://jist.ir/en/Article/Download/15092http://jist.ir/en/Article/Download/15092http://jist.ir/en/Article/Download/15092http://jist.ir/en/Article/Download/15092[1] G. Zipf, "Human behavior and the principle of least effort," The Economical Journal, vol. 60, no. 3, pp. 808-810, 1950. [2] A. I. Saichev, Y. Malevergne and D. Sornette, Theory of Zipf's Law and Beyond, Springer-Verlag Berlin Heidelberg, 2010. [3] S. Arshad, S. Hu and B. N. Ashraf, "Zipf's law and city size distribution: A survey of the literature and future research agenda," Statistical Mechanics and its Applications, vol. 492, no. 15, pp. 75-92, 2018. [4] S. Arshad, S. Hu and B. N. Ashraf, "Zipf’s law, the coherence of the urban system and city size distribution: Evidence from Pakistan," Physica A (2018), https://doi.org/10.1016/j.physa.2018.08.065. [5] D. Wang, H. Cheng, P. Wang and G. Jian, "Zipf's Law in Passwords," IEEE Transactions on Information Forensics and Security, vol. 12, no. 11, pp. 2776-2791, 2017. [6] A. Liu, V. Lau and G. Caire, "Capacity scaling of wireless device-to-device caching networks under the physical model," in IEEE International Symposium on Information Theory, Germany, 2017. [7] A. Iorliam, A. T. Ho, N. Poh, S. Tirunagari and P. Bours, "Data forensic techniques using Benford's law and Zipf's law for keystroke dynamics," in International Workshop on Biometrics and Forensics, Norway, 2015. [8] M. Jauhari, A. Saxena and J. Gautom, "Zipf’s Law and Number of hits on the World Wide Web," Annals of Library and Information Studies, vol. 54, no. 2, pp. 81-84, 2007. [9] L. Adamic and B. Huberman, "Zipf’s Law and the Internet," in Glottometrics, 2007. [10] B. R. Chang and H. F. Tsai, "Improving network traffic analysis by foreseeing data packet- flow with hybrid fuzzy-based model prediction," Expert Systems with Applications, vol. 36, no. 3, pp. 6960-6965, 2009. [11] J. Sommers and P. Barford, "Self-Configuring Network Traffic Generation," in the 4th ACM SIGCOMM conference on Internet measurement, Italy, 2004. [12] A. Botta, A. Dainotti and A. Pescape, "A tool for the generation of realistic network workload for emerging networking scenarios," Computer Networks, vol. 56, no. 1, pp. 3531-3547, 2012. [13] "TCPReplay," [Online]. Available: http://tcpreplay.synfin.net/wiki. [Accessed 23 08 2018]. [14] "Network, devices & services testing-Spirent," [Online]. Available: http://www.spirent.com/. [Accessed 23 08 2018]. [15] W. M. Shbair, A. R. Bashandy and S. I. Shaheen, "A New Security Mechanism to Perform Traffic," in International Conference on Computational Science and Engineering, 2004. [16] F. Sally and P. Vern, "Difficulties in simulating the internet," IEEE/ACM Transactions on Networking, vol. 9, no. 4, pp. 392-403, 2001. [17] V. Paxon, "Strategies for sound internet measurement," in the 4th ACM SIGCOMM conference on Internet measurement, Italy, 2004. [18] V. Chandola, A. Banerjee and V. Kumar, "Anomaly detection: a survey," ACM Computing Surveys, vol. 41, no. 3, pp. 1-58, 2009. [19] A. Patcha and J. M. Park, "An overview of anomaly detection techniques: existing solutions and latest technological trends," Computer Networks, vol. 51, no. 12, pp. 3448-3470, 2009. [20] P. Barford, J. Kline, D. Plonka and A. Ron, "A signal analysis of network traffic anomalies," in the 2nd ACM SIGCOMM Workshop on Internet measurement, France, 2002. [21] S. Luo and G. A. Marin, "Generating Realistic Network Traffic for Security Experiments," in IEEE SoutheastCon, USA, 2004. [22] E. Garsva, N. Paulauskas, G. Grazulevicius and L. Gulbinovic, "Packet Inter-arrival Time Distribution in Academic Computer Network," ELEKTRONIKA IR ELEKTROTECHNIKA, vol. 20, no. 3, pp. 87-90, 2014. [23] M. Fras, J. Mohorko and Z. Cucej, "Packet Size Process Modeling of Measured Self-similar Network Traffic with Defragmentation Method," in 15th International Conference on Systems, Signals and Image Processing, Slovakia, 2008. [24] W. E. Leland, M. S. Taqqu, W. Willinger and D. V. Wilson, "the self-similar nature of Ethernet traffic (extended version)," IEEE/ACM Transactions on Networking, vol. 2, no. 1, pp. 1-15, 1994. [25] X. An and L. Qu, "A Study Based on Self-Similar Network Traffic Model," in Sixth International Conference on Intelligent Systems Design and Engineering Applications, China, 2015. [26] A. Pashko and V. Tretynyk, "Methods of the statistical simulation of the self-similar traffic," Advances in Intelligent Systems and Computing, vol. 754, no. 1, pp. 54-64, 2018. [27] V. I. Strelkovskaya, T. I. Grygoryeva and I. N. Solovskaya, "Self-similar traffic in G/M/1 queue defined by the Weibull distribution," Radioelectronics and Communications Systems, vol. 61, no. 3, pp. 128-134, 2018. [28] M. A. Arfeen, K. Pawlikowski, D. McNickle and A. Willig, "The Role of the Weibull Distribution in Internet Traffic Modeling," in 25th International Conference on Teletraffic Congress, China, 2013. [29] L. Arshadi and A. H. Jahangir, "An empirical study on TCP flow interarrival time distribution for normal and anomalous traffic," International Journal of Communication Systems, vol. 30, no. 1, pp. 1-19, 2017. [30] T. K. Bandhopadhya, M. Saxena and A. Tiwari, "Jitter’s Alpha-Stable Distribution Behavior," Computer Technology and Electronics Engineering, vol. 3, no. 1, pp. 13-16, 2013. [31] G. J. Fernandes, J. P. Rodrigues, L. F. Carvalho, J. F. Al-Muhtadi and M. J. Proença, "A comprehensive survey on network anomaly detection," Telecommunication Systems (2018), https://doi.org/10.1007/s11235-018-0475-8. [32] "IDS Distribution," [Online]. Available: http://cs.fit.edu/~mmahoney/dist/. [Accessed 23 08 2018]. [33] L. Arshadi and A. H. Jahangir, "Benford's law behavior of Internet traffic," Journal of Network and Computer Applications, vol. 40, no. 1, pp. 194-205, 2014. [34] A. N. Asadi, "An approach for detecting anomalies by assessing the inter-arrival time of UDP packets and flows using Benford’s law," in 2nd International Conference on Knowledge-Based Engineering and Innovation, Tehran, 2015. [35] Y. Gu, A. McCallum and D. Towsley, "Detecting Anomalies in Network Traffic Using Maximum Entropy Estimation," in the 5th ACM SIGCOMM conference on Internet measurement, USA, 2005. [36] S. Honda, T. Nakashima and S. Oshima, "Entropy Based Analysis of Anomaly Access of IP Packets," in 3rd International Conference on Innovative Computing Information and Control, China, 2008. [37] L. I. Han, "Research of K-MEANS Algorithm based on Information Entropy in Anomaly Detection," in Fourth International Conference on Multimedia Information Networking and Security, China, 2012. [38] S. K. Gautam and H. Om, "Anomaly detection system using entropy based technique," in First International Conference on Next Generation Computing Technologies, India, 2015. [39] A. A. Waskita, H. Suhartanto and L. T. Handoko, "A performance study of anomaly detection using entropy method," in International Conference on Computer, Control, Informatics and its Applications, Indonesia, 2016. [40] D. Hong, D. Zhao and Y. Zhang, "The Entropy and PCA Based Anomaly Prediction in Data Streams," Procedia Computer Science, vol. 96, no. 1, pp. 139-146, 2016. [41] "NUST," [Online]. Available: http://wisnet.seecs.nust.edu.pk/downloads.php. [Accessed 13 06 2013]. [42] "MAWI Working Group Traffic Archive," [Online]. Available: http://mawi.wide.ad.jp/mawi/. [Accessed 13 10 2016]. [43] A. E. Kossovsky, Benford's Law: Theory, the General Law of Relative Quantities, and Forensic Fraud Detection Applications, NewYork: WorldScientific, 2014. [44] C. E. Shannon, "A mathematical theory of communication," Bell System Technical Journal, vol. 27, no. 4, pp. 623-656, 1948. YasHosseini TehraniSeyed MojtabaAtarodiZIbaFazel1282018606610.7508/jist.2018.02.001http://jist.ir/en/Article/15099http://jist.ir/en/Article/Download/15099http://jist.ir/en/Article/Download/15099http://jist.ir/en/Article/Download/15099http://jist.ir/en/Article/Download/15099http://jist.ir/en/Article/Download/15099http://jist.ir/en/Article/Download/15099http://jist.ir/en/Article/Download/15099[1] R. Sinha, Y. Wei, and S. Hwang; “A survey on LPWA technology: LoRa and NB-IoT,” Elsevier, ICT Express 3, 14–21, 2017. [2] Semtech, AN 120022, LoRa Modulation Basics, May 2015. Available: http://www.semtech.com/images/datasheet/an1200.22pdf. [3] Link Labs, “NB-IoT vs. LoRa vs. Sigfox,” Home Blog, January 23, 2017. [4] H. Wang and A. O. Fapojuwo, “A Survey of Enabling Technologies of Low Power and Long Range Machine-to-Machine Communications,” IEEE Communications Surveys & Tutorials, DOI 10.1109/COMST.2017.2721379. [5] INGENU, “RPMA Technology for the Internet of Things,” 14 March 2017. Available: http://theinternetofthings.report/Resources/Whitepapers/4cbc5e5e-6ef8-4455-b8cd-f6e3888624cb_RPMA%20Technology.pdf. [6] Nokia, “LTE evolution for IoT connectivity,” Nokia, 2016. Available: http://resources.alcatel-lucent.com/asset/200178. [7] R. Ratasuk, N. Mangalvedhe, Y. Zhang, M. Robert and J. P. Koskinen, “Overview of narrowband IoT in LTE Rel-13,” 2016 IEEE Conference on Standards for Communications and Networking (CSCN), Berlin, 2016. [8] Nicolas Ducrot et al., Olivier Hersent et al.; “LoRa Device Developer Guide,” Orange Connected Objects & Partnerships plus Activity, April, 2016. [9] LoRa Alliance, “A technical overview of LoRa and LoRaWAN,” Technical Marketing Workgroup 1.0, 2016. [10] D. Rohde, J. Schwarz, Narrowband Internet of Things, Aug., 2016.Available: https://www.rohdeschwarz.com/us/applications/narrowba d-Internet-of-things-application-note 56280-314242.html. [11] F. Samie, L. Bauer, and J. Henkel. "IoT Technologies for Embedded Computing: A Survey." Proceedings of the Eleventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis. CODES ’16. 2016, 8:1–8:10. [12] S. Andreev et al. “Understanding the IoT connectivity landscape: a contemporary M2M radio technology roadmap” IEEE Communications Magazine, 2015, pp. 32–40. [13] M. Elkhodr, S. Shahrestani, and H. Cheung. "Emerging Wireless Technologies in the Internet of Things: A Comparative Study." International Journal of Wireless & Mobile Networks (IJWMN), 2016. [14] U. Raza, P. Kulkarni, and M. Sooriyabandara. "Low Power Wide Area Networks: An Overview," IEEE Communications Surveys Tutorials, 2017. [15] R. Sanchez-Iborra and M. Cano. "State of the Art in LP-WAN Solutions for Industrial IoT Services." Sensors, 2016. [16] A. Ali et al. "Technologies and challenges in developing Machine-to-Machine applications: A survey." Journal of Network and Computer Applications, 2017, pp. 124 –139. [17] B. Moyer. "Low Power, Wide Area: A Survey of Longer-Range IoT Wireless Protocols." EE Journal, 2015. [18] Q. Song, L. Nuaymi, and X. Lagrange. "Survey of radio resource management issues and proposals for energy efficient cellular networks that will cover billions of machines." EURASIP Journal on Wireless Communications and Networking, 2016. [19] W. Guibene, K. E. Nolan, and M. Y. Kelly. "Survey on Clean Slate Cellular-IoT Standard Proposals." 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015. [20] K. Mikhaylov, J. Petaejaejaervi, and T. Haenninen, “Analysis of Capacity and Scalability of the LoRa Low Power Wide Area Network Technology,” European Wireless, May 2016, pp. 1–6. [21] N. Mangalvedhe, R. Ratasuk, and A. Ghosh, “NB-IoT Deployment Study for Low Power Wide Area Cellular IoT,” PIMRC, 2016. [22] M. Lauridsen, H, Nguyen, B. Vejlgaard, I. Kovacs, and P. Mogensen, “comparison of GPRS, NB-IoT, LoRa, and SigFox in a 7900km^2 area”, Vehicular Technology Conference (VTC Spring), 2017 IEEE 85th, Nov. 2017. [23] L.Angrisani, P.Arpaia, F. Bonavolonta, M. Conti, and A. Liccardo, “LoRa Protocol Performance Assessment in Critical Noise Conditions,” Research and Technologies for Society and Industry (RTSI), 2017 IEEE International Forum on, Sept. 2017. [24] C.Orfanidis, L.Feeney, M. Jacobsson, and P. Gunningberg, “Investigating interference between LoRa and IEEE 802.15.4g networks”, 2017 IEEE 13th international Conference on Wireless and Mobile Computing, Network and Communications (WiMOB), 2017. [25] L. Gregora, L. Vojtech, and M. Neruda, “Indoor Signal Propagation of LoRa Technology,” 2016 17th International Conference on Mechatronics Mechatronika (ME), Prague, Czech Republic, 2016, pp. 1–4. [26] A. Wixted, P. Kinnaird, A. Tait, A. Ahmadinia, and N. Strachan, “Evaluation of LoRa and LoRaWAN for Wireless Sensor Networks,” 2016 IEEE SENSORS, October 2016, pp. 1–3. [27] M. Bor, J. Vidler, and U. Roedig, “LoRa for the Internet of Things,” Proceedings of the 2016 International Conference on Embedded Wireless Systems and Networks, Graz, Austria, February 2016, pp. 361–366. [28] A. Zanella and M. Zorzi, “Long-Range Communications in Unlicensed Bands: The Rising Stars in the IoT and Smart City Scenarios,” IEEE Wireless Communications, vol. 23, no. 5, pp. 60–67, October 2016. [29] P. Neumann, J. Montavont, and T. No¨el, “Indoor Deployment of Low-Power Wide Area Networks (LPWAN): a LoRaWAN case study”, 2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), New York, NY, USA, October 2016, pp. 1–8. [30] M. Aref and A. Sikora, “Free-space range measurements with Semtech LoRaTM technology,” 2014 2nd International Symposium on Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing System (IDAACS-SWS 2014), Offenburg, Germany, September 2014, pp. 19–23. [31] Lukas, W. A. Tanumihardja, and E. Gunawan, “On the application of IoT: Monitoring of troughs water level using WSN,” 2015 IEEE Conference on Wireless Sensors, ICWiSE 2015, Melaka, Malaysia, August 2016, pp. 58–62. [32] J. Pet¨aj¨aj¨arvi, K. Mikhaylov, A. Roivainen, T. H¨anninen, and M. Pettissalo, “On the coverage of LPWANs: Range evaluation and channel attenuation model for LoRa technology,” 2015 14th International Conference on ITS Telecommunications (ITST 2015), Copenhagen, Denmark, December 2016, pp. 55–59. [33] J. Pet¨aj¨aj¨arvi, K. Mikhaylov, M. H¨am¨al¨ainen, and J. Iinatti, “Evaluation of LoRa LPWAN technology for remote health and wellbeing monitoring,” International Symposium on Medical Information and Communication Technology (ISMICT), Worcester, MA, USA, March 2016, pp. 1–5. [34] T. Wendt, F. Volk, and E. Mackensen, “A benchmark survey of Long Range (LoRa TM) Spread Spectrum-Communication at 2.45 GHz for safety applications”, Wireless and Microwave Technology Conference (WAMICON), 2015 IEEE 16th Annual, Cocoa Beach, FL, USA, April 2015, pp. 1–4. [35] A. Augustin, J. Yi, T. Clausen, and W. Townsley, “A Study of LoRa: Long Range & Low Power Networks for the Internet of Things,” Sensors, vol. 16, no. 9, 2016. [36] S. persia, C. Carciofi, and M. Faccili, “NB-IoT and LoRa Connectivity analysis for M2M/IoT Smart Grid Applications,” 2017 AEIT International Annual Conference, Dec. 2017. [37] M.Pennacchiono, M. Gabriella, T, Oercorella, C. Carrlini, “NB-IoT System Deployment for Smart Metering: Evaluation of Coverage and Capacity Performances,” 2017 AEIT International Annual Conference, Dec. 2017.maryamnajimiSajjad Nankhoshki128201810611110.7508/jist.2018.02.006http://jist.ir/en/Article/15101http://jist.ir/en/Article/Download/15101http://jist.ir/en/Article/Download/15101http://jist.ir/en/Article/Download/15101http://jist.ir/en/Article/Download/15101http://jist.ir/en/Article/Download/15101http://jist.ir/en/Article/Download/15101http://jist.ir/en/Article/Download/15101[1] W. B. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy Efficient Communication Protocol for Wireless Microsensor Networks", in Proceedings Sciences, 2000, pp.1-10. [2] L. Xiang, J. Luo, and A. Vasilakos, " Compressed Data Aggregation for Energy Efficient Wireless Sensor Networks", in 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Adhoc Communications and Networks (SECON), 2011, pp. 46–54. [3] X. Y. Liu, Y. Zhu , L. Kong, C. Liu, Y. Gu, A. V. Vasilakos, and M.-Y. Wu, "CDC: Compressive Data Collection for Wireless Sensor Networks", IEEE Transactions on Parallel and Distributed Systems, Vol.26, No.8, 2015, pp. 2188–2197. [4] X. Xu, R. Ansari, A. Khokhar, and A.V. Vasilakos, "Hierarchical Data Aggregation Using Compressive Sensing (HDACS) in WSNs", ACM Transactions on Sensor Networks (TOSN), Vol.11, No.3, 2015, pp.45-45. [5] W. B. Heinzelman, A.P. Chandrakasan, and H. Balakrishnan, "An Application Specific Protocol Architecture for Wireless Microsensor Networks", IEEE Transactions on Wireless Communications, Vol.1, No.4,2002, pp.660–670. [6] N. M. A. Latiff, C. C. Tsimenidis, and B. S. Sharif, "Energy-Aware Clustering for Wireless Sensor Networks Using Particle Swarm Optimization", in Proceedings of 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications,2007, pp. 1–5. [7] B. Singh, and D.K. Lobiyal, "A Novel Energy-Aware Cluster Head Selection Based on Particle Swarm Optimization for Wireless Sensor Networks", Human-Centric Computing and Information Sciences Journal, Vo.l2, No.1, 2012 , pp.2–13. [8] J. Roselin, P. Latha and S. Benitta, "Maximizing the Wireless Sensor Networks Lifetime through Energy Efficient Connected Coverage", Elsevier Adhoc Networks Journal, Vol.62, 2017, pp.1-10. [9] S. Arjunan and P. Sujatha, "Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol", Applied Intelligence springer Journal, Vol.48, No.8, 2018, pp. 2229–2246. [10] S. Jabbar, M. Ahmad, K.R. Malik, Sh. Khalid, J. Chaudhry and O. Aldabbas, "Designing an energy-aware mechanism for lifetime improvement of wireless sensor networks: a comprehensive study", Mobile Networks and Applications Springer Journal, Vol. 23, No.3, 2018, pp. 432–445. [11] W. Y. Poe and J. B. Schmitt, "Minimizing the Maximum Delay in Wireless Sensor Networks by intelligent sink placement", Tech. Rep. 362/07, Distributed Computer Systems Lab, University of Kaiserslautern, Kaiserslautern, Germany, 2007. [12] J. Pan, L. Cai, Y. T. Hou, Y. Shi and S. X. Shen, "Optimal Base Station Locations in Two-Tiered Wireless Sensor Networks", IEEE Transactions on Mobile Computing, Vol. 4, No. 5, 2005, pp. 458-473. [13] M. 1. Showkat, B. Paul, M. A. Matin, and M. S Alam, "Optimal Sink Location in Wireless Sensor Networks Using Particle Swarm Optimization", in Proc. IEEE Interntional Conference on Antennas, Propagation and Systems (I A009), .Ihor Bahru, Malaysia, 2009, pp. 5445 – 5450. [14] A. Ebrahimzadeh, M.Najimi, S. M. Hosseni Andargoli, and A. Fallahi, "Sensor Selection and Optimal Energy Detection Threshold for Efficient Cooperative Spectrum Sensing", IEEE Transaction on Vehicular Technology Journal, Vol.64, No.4, 2015, pp. 1565 – 1577. [15] N. Aslam, and W. Phillips, W. Robertson and Sh. Sivakumar, "A Multi- Criterion Optimization Technique for Energy Efficient Cluster Formation in Wireless Sensor Networks", Information Fusion Journal in Press, Elsevier, Vol. 12. No.3, 2011, pp.202-212. [16] M. Najimi, A. Ebrahimzadeh, S.M. Hosseni Andargoli, and A. Fallahi, "Lifetime Maximization in Cognitive Sensor Networks Based on the Node Selection", IEEE sensors Journal, Vol. 14, No. 7, 2014, pp.2376-2383. [17] J. Kennedy, and R. Eberhart, "Particle Swarm Optimization", IEEE International Conference on Neural Networks, 1995, pp. 1942–1948. [18] M. Azharuddin and P.K. Jana, "Particle Swarm Optimization for Maximizing Lifetime of Wireless Sensor Networks", Computers and Electrical Engineering Journal, Elsevier, Vol.51, 2016, pp.26-42. [19] R.V. Kulkarni, and G.K. Venayagamoorthy, "Particle Swarm Optimization in Wireless Sensor Networks: A Brief Survey", IEEE Transactions on Systems, Vol.41, No.2, 2011, pp. 262-267. [20] I.S. Akila, R. Venkatesan, and R. Abinaya, "A PSO Based Energy Efficient Clustering Approach for Wireless Sensor Networks", in IEEE International Conference on Computation of Power, Energy Information and Communication (ICCPEIC), 2016, pp.259-264. [21] S. Maleki, A. Pandharipande, and G. Leus, "Energy-efficient distributed spectrum sensing for cognitive sensor networks", in Proceedings of 35th Annual Conference IEEE Industrial Electronics, 2009, pp. 2642–2646. [22] S. Maleki, A. Pandharipande, and G. Leus, "Energy efficient distributed spectrum sensing with convex optimization", in Proceedings of 3rd International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2009, pp. 396–399. [23] M. Najimi, A. Ebrahimzadeh, S.M. Hosseni Andargoli, and A. Fallahi, "A Novel Sensing Nodes and Decision Node Selection Method for Energy Efficiency of Cooperative Spectrum Sensing in Cognitive Sensor Networks", IEEE Sensors Journal, Vol.13, No.5, 2013, pp.1610-1621. Nosratali Ashrafi PayamanMohammadrezaKangavari1282018677510.7508/jist.2018.02.002http://jist.ir/en/Article/15105http://jist.ir/en/Article/Download/15105http://jist.ir/en/Article/Download/15105http://jist.ir/en/Article/Download/15105http://jist.ir/en/Article/Download/15105http://jist.ir/en/Article/Download/15105http://jist.ir/en/Article/Download/15105http://jist.ir/en/Article/Download/15105[1] U. Kang, “Mining Tera-Scale Graphs: Theory, Engineering and Discoveries,” 2012. [2] “Facebook active users.” [Online]. Available: https://www.yahoo.com/news/number-active-users-facebook-over-230449748.html. [3] S. Navlakha, R. Rastogi, and N. Shrivastava, “Graph summarization with bounded error,” Proc. 2008 ACM SIGMOD Int. Conf. Manag. data - SIGMOD ’08, p. 419, 2008. [4] K. LeFevre and E. Terzi, “GraSS: Graph Structure Summarization,” Proc. 2010 SIAM Int. Conf. Data Min., pp. 454–465, 2010. [5] N. Zhang, Y. Tian, and J. M. Patel, “Discovery-driven graph summarization,” 2010 IEEE 26th Int. Conf. Data Eng. (ICDE 2010), pp. 880–891, 2010. [6] M. A. Beg, M. Ahmad, A. Zaman, and I. Khan, “Scalable approximation algorithm for graph summarization,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10939 LNAI, pp. 502–514, 2018. [7] Y. Liu, T. Safavi, N. Shah, and D. Koutra, “Reducing large graphs to small supergraphs: a unified approach,” Soc. Netw. Anal. Min., vol. 8, no. 1, 2018. [8] Y. Tian, R. A. Hankins, and J. M. Patel, “Efficient Aggregation for Graph Summarization,” pp. 567–579. [9] Y. Bei, Z. Lin, and D. Chen, “Summarizing scale-free networks based on virtual and real links,” Phys. A Stat. Mech. its Appl., vol. 444, no. 2, pp. 360–372, 2016. [10] H. Cheng, Y. Zhou, and J. X. Yu, “Clustering Large Attributed Graphs: A Balance between Structural and Attribute Similarities,” ACM Trans. Knowl. Discov. Data, vol. 5, no. 2, pp. 1–33, 2011. [11] M. Riondato, D. García-Soriano, and F. Bonchi, “Graph summarization with quality guarantees,” Data Min. Knowl. Discov., vol. 31, no. 2, pp. 314–349, 2017. [12] X. Liu, Y. Tian, Q. He, W.-C. Lee, and J. McPherson, “Distributed Graph Summarization,” Proc. 23rd ACM Int. Conf. Conf. Inf. Knowl. Manag. - CIKM ’14, pp. 799–808, 2014. [13] C. Chen, C. X. Lin, M. Fredrikson, M. Christodorescu, X. Yan, and J. Han, “Mining graph patterns efficiently via randomized summaries,” Proc. VLDB Endow., vol. 2, no. 1, pp. 742–753, 2009. [14] S. Hosseini, H. Yin, M. Zhang, Y. Elovici, and X. Zhou, “Mining Subgraphs From Propagation Networks Through Temporal Dynamic Analysis,” in 2018 19th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2018. [15] P. Pourashraf, N. Tomuro, S. B. Shouraki, “From Windows to Logos: Analyzing Outdoor Images to Aid Flyer Classification”, International Conference Image Analysis and Recognition, Springer, Cham, pp. 175-184, 2018. [16] U. Von Luxburg, “A Tutorial on Spectral Clustering,” Stat. Comput., vol. 17, no. March, pp. 395–416, 2007. [17] I. Dhillon, Y. Guan, and B. Kulis, “A unified view of kernel k-means, spectral clustering and graph cuts”, Technical Report, Computer Science Department, University of Texas at Austin, pp. 1–20, 2004. [18] B. Auffarth, “Spectral Graph Clustering,” Univ. Barcelona course Rep. Tech. Av. Aprendizaj Univ. Politec. Catalunya, pp. 1–12, 2007. [19] S. Uw, A. Y. Ng, M. I. Jordan, and Y. Weiss, “On spectral clustering: Analysis and an algorithm,” Adv. Neural Inf. Process. Syst. 14, pp. 849–856, 2002. [20] D. Zhou and C. J. C. Burges, “Spectral clustering and transductive learning with multiple views,” Proc. 24th Int. Conf. Mach. Learn. - ICML ’07, pp. 1159–1166, 2007. [21] S. Smyth and S. White, “A spectral clustering approach to finding communities in graphs,” Proc. 5th SIAM Int. Conf. Data Min., pp. 76–84, 2005. [22] J. Liu, C. Wang, M. Danilevsky, and J. Han, “Large-scale spectral clustering on graphs,” IJCAI Int. Jt. Conf. Artif. Intell., pp. 1486–1492, 2013. [23] C.-D. Wang, J.-H. Lai, and P. S. Yu, “Dynamic Community Detection in Weighted Graph Streams,” Proc. 2013 SIAM Int. Conf. Data Min., pp. 151–161, 2013. [24] G. T. Prabavathi and V. Thiagarasu, “Overlapping Community Detection Algorithms in Dynamic Networks: An Overview,” Int. J. Emerg. Technol. Comput. Appl. Sci., pp. 299–303, 2013. [25] P. Ben Sheldon, “Community Detection Algorithms: a comparative evaluation on artificial and real-world networks D . Phil student report,” Other, pp. 1–27, 2010. [26] W. Wang and W. N. Street, “A novel algorithm for community detection and influence ranking in social networks,” ASONAM 2014 - Proc. 2014 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Min., no. Asonam, pp. 555–560, 2014. [27] O. Benyahia et al., “Community detection in dynamic graphs with missing edges To cite this version: HAL Id: hal-01590597 Community Detection in Dynamic Graphs with Missing Edges,” 2017. [28] C. Chen, X. Yan, F. Zhu, J. Han, and P. S. Yu, “Graph OLAP - Towards Online Analytical Processing on Graphs,” Data Mining, 2008. ICDM’08. Eighth IEEE Int. Conf., pp. 103–112, 2008. [29] Y. Wu, Z. Zhong, W. Xiong, and N. Jing, “Graph summarization for attributed graphs,” Proc. - 2014 Int. Conf. Inf. Sci. Electron. Electr. Eng. ISEEE 2014, vol. 1, pp. 503–507, 2014. [30] S. Grimm, P. Hitzler, and A. Abecker, “Knowledge Representation and Ontologies Logic, Ontologies and Semantic Web Languages,” Semant. Web Serv., pp. 51–105, 2007. [31] N. Ashrafi Payaman, M. R. Kangavari, “GSSC: Graph Summarization based on both Structure and Concepts”, International Journal of Information & Communication Technology Research, vol. 9, no. 1, pp. 33-44, 2017. [32] N. Ashrafi Payaman, M. R. Kangavari. “Graph Hybrid Summarization”, Journal of AI and Data Mining, vol. 6, no. 2 pp. 335-340, 2018.Azadeh RoustakianiNedaAbdolvandSaeidehRajaei Harandi1282018889410.7508/jist.2018.02.004http://jist.ir/en/Article/15117http://jist.ir/en/Article/Download/15117http://jist.ir/en/Article/Download/15117http://jist.ir/en/Article/Download/15117http://jist.ir/en/Article/Download/15117http://jist.ir/en/Article/Download/15117http://jist.ir/en/Article/Download/15117http://jist.ir/en/Article/Download/15117[1] J. Fletcher and J. Patrick, “Evaluating the Utility of Appraisal Hierarchies as a Method for Sentiment Classification”. Proceedings of the Australasian Language Technology Workshop 2005, (December 2005), pp. 134–142. [2] B. Pang, and L. Lee, “Opinion Mining and Sentiment Analysis”. Foundations and Trends in Information Retrieval, Vol. 1, No. 2, 2006, pp. 91–231. [3] P. Korenek, and M. Šimko, “Sentiment analysis on microblog utilizing appraisal theory.” World Wide Web, Vol. 17, No. 4, 2014, 847–867. [4] M. A. Kanade, M. A. Deshmukh, M. S. Surwase, M.A. Kulkarni, and A. Zore, “Implementation on Intelligent Sentiment Review Analysis with Short Form Words and False Negative Comment Consideration”. International Journal of Research in Engineering, Technology and Science, Vol. 3, No. 4, April 2018. [5] J. Brooke, “A Semantic Approach to Automated Text Sentiment Analysis, 118.” Retrieved from http://www.sfu.ca/~mtaboada/docs/Julian_Brooke_MA.2009. [6] L. Zhang, and B. Liu, “Sentiment analysis and opinion mining. In Encyclopedia of Machine Learning and Data Mining” (pp. 1152-1161). Springer, Boston, MA. ‏2017. [7] A. Pak, and P. Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining”. In Proceedings of the Seventh Conference on International Language Resources and Evaluation, 2010, pp. 1320–1326. [8] B. Pang, and L. Lee, “A Sentimental Education: Sentiment Analysis using Subjectivity Summation based on Minimum Cuts”. ACL ’04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, 2004, p. 271. [9] T. Wilson, J. Wiebe, and P. Hoffman, “Recognizing contextual polarity in phrase level sentiment analysis”. Acl, Vol. 7, No. 5, 2005, pp. 12–21. [10] C. Whitelaw, C. Whitelaw, N. Garg, N. Garg, S. Argamon and S. Argamon, S. “Using appraisal groups for sentiment analysis”. Proceedings of the 14th ACM International Conference on Information and Knowledge Management - CIKM ’05, 2005, p. 625. [11] C. S.-G. Khoo, A. Nourbakhsh, A., and N. Jin-Cheon, N. “Sentiment analysis of online news text: a case study of appraisal theory”. Online Information Review, Vol. 36, No. 6, 2012, pp. 858–878. [12] K. Bloom, K. Sentiment analysis based on appraisal theory and functional local grammars. Illinois Institute of Technology.‏ ProQuest Dissertations Publishing, 2011. 3504518. [13] M. Dragoni, A. G. Tettamanzi, and C. da Costa Pereira, (2014, May). “A fuzzy system for concept-level sentiment analysis.” In Semantic web evaluation challenge (pp. 21-27). Springer, Cham.‏ [14] B. Keith, E. Fuentes, and C. Meneses, “A Hybrid Approach for Sentiment Analysis Applied to Paper”.‏ In Proceedings of ACM SIGKDD Conference, Halifax, Nova Scotia, Canada, August 2017 (KDD’17), 2017, P. 10 . [15] S. Moghaddam, “Beyond sentiment analysis: mining defects and improvements from customer feedback.” In European Conference on Information Retrieval (pp. 400-410). Springer, Cham.‏ 2015, March. [16] H. Li, J. Ding, D. Nie, and L. Tang, “Accurate Recommendation Based on Opinion Mining”. Advances in Intelligent Systems and Computing, Vol. 329, 2015, pp. 325–333. [17] K. Denecke and Y. Deng, “Sentiment analysis in medical settings: New opportunities and challenges”. Artificial Intelligence in Medicine, Vol 64, No. 1, 2015, pp. 17–27. [18] A. Andreevskaia, and S. Bergler, “Mining WordNet for fuzzy sentiment: Sentiment tag extraction from WordNet glosses”. Proceedings of EACL, 6, 2006, pp. 209–216. [19] T. Rahman, “Sentiment Analysis by Using Fuzzy Logic”, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No. 1, 2014, pp. 33–48. [20] D. K. Tayal, and S. K. Yadav, “Word level sentiment analysis using fuzzy sets”. Analysis, Vol. 54, 2015, p. 59.‏ [21] B. V. Krishna, A. K. Pandey, and A. S. Kumar, “Feature Based Opinion Mining and Sentiment Analysis Using Fuzzy Logic”. In Cognitive Science and Artificial Intelligence (pp. 79-89). Springer, Singapore.‏ 2018. [22] O. Appel, F. Chiclana, J. Carter, and H. Fujita, “A hybrid approach to the sentiment analysis problem at the sentence level”. Knowledge-Based Systems, Vol. 108, 2016, pp. 110-124.‏ [23] P. Haseena Rahmath. “Fuzzy based Sentiment Analysis of Online Product Reviews using Machine Learning Techniques.” International Journal of Computer Applications (0975 – 8887). Vol. 99, No. 17, 2014, pp. 9-16. [24] A. Alamsyah, W. Rahmah, and H. Irawan, “Sentiment analysis based on appraisal theory for marketing intelligence in Indonesia?” Mobile phone market. Journal of Theoretical and Applied Information Technology, Vol. 82, No. 2, 2015, pp. 335–340. [25] A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning Word Vectors for Sentiment Analysis”. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011. pp. 142–150.KhaterehGhasvarian Jahromi VidaGhasvarian Jahromi128201811211810.7508/jist.2018.02.007http://jist.ir/en/Article/15125http://jist.ir/en/Article/Download/15125http://jist.ir/en/Article/Download/15125http://jist.ir/en/Article/Download/15125http://jist.ir/en/Article/Download/15125http://jist.ir/en/Article/Download/15125http://jist.ir/en/Article/Download/15125http://jist.ir/en/Article/Download/15125[1] F. L. Chu, “Using a logistic growth regression model to forecast the demand for tourism in Las Vegas,” Tour. Manag. Perspect., vol. 12, pp. 62–67, 2014. [2] “Strategic Plan for the Development of Tourism in Mozambique (2004-2013),” 2013. [3] U. Gunter and I. Önder, “Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data,” Tour. Manag., vol. 46, pp. 123–135, 2015. [4] P. F. Pai, K. C. Hung, and K. P. Lin, “Tourism demand forecasting using novel hybrid system,” Expert Syst. Appl., vol. 41, no. 8, pp. 3691–3702, 2014. [5] B. Peng, H. Song, and G. I. Crouch, “A meta-analysis of international tourism demand forecasting and implications for practice,” Tour. Manag., vol. 45, pp. 181–193, 2014. [6] H. Song and G. Li, “Tourism demand modelling and forecasting-A review of recent research,” Tour. Manag., vol. 29, no. 2, pp. 203–220, 2008. [7] M. Akin, “A novel approach to model selection in tourism demand modeling,” Tour. Manag., vol. 48, pp. 64–72, 2015. [8] F. L. Chu, “Analyzing and forecasting tourism demand with ARAR algorithm,” Tour. Manag., vol. 29, no. 6, pp. 1185–1196, 2008. [9] A. Guizzardi and A. Stacchini, “Real-time forecasting regional tourism with business sentiment surveys,” Tour. Manag., vol. 47, pp. 213–223, 2015. [10] H. Song, G. Li, S. Witt, and B. Fei, “Tourism demand modelling and forecasting: how should demand be measured?,” Tour. Econ., vol. 16, no. 1, pp. 63–81, 2010. [11] A. Palmer, J. José Montaño, and A. Sesé, “Designing an artificial neural network for forecasting tourism time series,” Tour. Manag., vol. 27, no. 5, pp. 781–790, 2006. [12] E. Hadavandi, A. Ghanbari, K. Shahanaghi, and S. Abbasian-Naghneh, “Tourist arrival forecasting by evolutionary fuzzy systems,” Tour. Manag., vol. 32, no. 5, pp. 1196–1203, 2011. [13] X. Sun, W. Sun, J. Wang, Y. Zhang, and Y. Gao, “Using a Grey-Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China,” Tour. Manag., vol. 52, pp. 369–379, 2016. [14] H. Hassani, A. Webster, E. S. Silva, and S. Heravi, “Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis,” Tour. Manag., vol. 46, pp. 322–335, 2015. [15] C. H. Wang, “Predicting tourism demand using fuzzy time series and hybrid grey theory,” Tour. Manag., vol. 25, no. 3, pp. 367–374, 2004. [16] S. Shen, G. Li, and H. Song, “Combination forecasts of International tourism demand,” Ann. Tour. Res., vol. 38, no. 1, pp. 72–89, 2011. [17] K. Y. Chen, “Combining linear and nonlinear model in forecasting tourism demand,” Expert Syst. Appl., vol. 38, no. 8, pp. 10368–10376, 2011. [18] D. Cheng and L. Bin Liu, “Forecasting of tourism demand for Guilin based on combined model,” in 7th International Joint Conference on Computational Sciences and Optimization, CSO 2014, 2014, no. 1, pp. 100–103. [19] X. Wang, H. Zhang, and X. Guo, “Demand Forecasting Models of Tourism Based on ELM,” in 7th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2015, 2015, pp. 326–330. [20] Y. H. Liang, “Using the combined model for forecasting the tourism demand,” in International Conference on Machine Learning and Cybernetics, 2016, vol. 2, pp. 612–615. [21] S. Sadatiseyedmahalleh, N. H. Bateni, and N. H. Bateni, “Prediction of Tourism Demand in Iran by Using Artificial Neural Network (ANN) and Supporting Vector Machine (SVR),” Int. J. Multicult. Multireligious Underst., vol. 3, no. 1, pp. 37–44, 2016. [22] J. Rosselló and A. Sansó, “Yearly, monthly and weekly seasonality of tourism demand: A decomposition analysis,” Tour. Manag., vol. 60, pp. 379–389, 2017. [23] A. N. Kazak, “Analysis of Dynamics of Demand, Revenue and Ergonomic Aspects of Tourism,” in Third International Conference on Human Factors in Complex Technical Systems and Environments (ERGO)s and Environments (ERGO), 2018, pp. 13–15. [24] Y. Li and H. Cao, “Prediction for Tourism Flow based on LSTM Neural Network,” Procedia Comput. Sci., vol. 129, pp. 277–283, 2018. [25] Y. Yao et al., “A paired neural network model for tourist arrival forecasting,” Expert Syst. Appl., vol. 114, pp. 588–614, 2018. [26] S. Sun, Y. Wei, K. L. Tsui, and S. Wang, “Forecasting tourist arrivals with machine learning and internet search index,” Tour. Manag., vol. 70, pp. 1–10, 2019. [27] J. Kang, C. Feng, Q. Shao, and H. Hu, “Prediction of Chatter in Machining Process Based on Hybrid SOM-DHMM Architecture,” in ICIC 2007, LNAI 4682, 2007, pp. 1004–1013. [28] L. R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” in Proceedings of the IEEE, 1989, vol. 77, no. 2. [29] A. Asrari, D. Seyed Javan, M. Hossein Javidi, and M. Monfared, “Application of Gray-Fuzzy-Markov Chain Method for Day-Ahead Electric Load Forecasting,” Przegląd Elektrotechniczny Electr. Rev., no. 3, pp. 228–237, 2012. [30] N. Amjady, F. Keynia, and H. Zareipour, “Wind Power Prediction by a New Forecast Engine Composed of Modi fi ed Hybrid Neural Network and Enhanced Particle Swarm Optimization,” IEEE Trans. Sustain. Energy, vol. 2, no. 3, pp. 265–276, 2011.