Farzanegan Pub Farzanegan DOI info@joce.ir mEDRA 20240328 DOI Registration 01
06 10.22034/zagros.2019.1008.1727 https://rcsj.ir/user/articles/73 Farzanegan Pub mEDRA <TitleType>01</TitleType> <TitleText>استگانوگرافی، واترمارکينگ و نقش آن در امنیت اطلاعات درفضای مجازی</TitleText> <TitleType>05</TitleType> <TitleText>استگانوگرافی، واترمارکينگ و نقش آن در امنیت اطلاعات درفضای مجازی</TitleText> 01 01 73 zagros Publisher IR 01 73 JB 17 1 4 01 202403 7 10 <TitleType>01</TitleType> <TitleText>استگانوگرافی، واترمارکينگ و نقش آن در امنیت اطلاعات درفضای مجازی</TitleText> 01 s:13896:"Blanco, R., & Lioma, C. (2012). Graph-based term weighting for information retrieval. Information retrieval, Vol 15, No. 1, pp. 54-92. Parveen, D., & Strube, M. (2015). Integrating importance, non-redundancy and coherence in graph-based extractive summarization. in proc the twenty-fourth international joint conference on artificial intelligence (IJCAI). pp. 1298–1304. Zhang, R. (2011). Sentence ordering driven by local and global coherence for summary generation. In proc the ACL-HLT 2011 student session 6–11. Celikyilmaz, A., & Hakkani-Tur, D. (2011), Discovery of topically coherent sentences for extractive summarization. In proc the 49th annual meeting of the association for computational linguistics, 491–499. Ferreira, T. C., Krahmer, E., & Wubben, S. (2016). Towards more variation in text generation: Developing and evaluating variation models for choice of referential form. In proc of the 54th annual meeting of the association for computational linguistics.1, 568-577. Fox, H. J. (2002). Phrasal cohesion and statistical machine translation. In proc the conference on empirical methods in natural language processing (EMNLP), 304-311. Lin, Z., Liu, C., Ng., H. T., & Kan, M., (2012). Combining coherence models and machine translation evaluation metrics for summarization evaluation. In proc the 50th annual meeting of the association for computational linguistics, 1, 1006–1014. Xiong, D., Ding, Y., Zhang, M., & Tan, C. L. (2013). Lexical chain-based cohesion models for document-level statistical machine translation. In proc 2013 conference on empirical methods in natural language processing, 1563–1573. Zhang, M., Feng. V. W., Qin, B., Hirst, G., Liu, T., & Huang, J. (2015). Encoding world knowledge in the evaluation of local coherence. in proc of the 2015 conference of the North American chapter of the association for computational linguistics: Human language technologies. 1087-1096. [10] پورمعصومی، آ. صدوقی یزدی، ه. قائمی، ه و دلخسته. ز (۱۳۹۵). آنالیز حس اسناد فارسی با طراحی حوزه تبدیل بهینه. نشریه مهندسی برق و مهندسی کامپیوتر ایران. (۱۴)۲. [11] گلپر رابوکی، ع. ضرغامی فر، س، و رضایی نور، ج (۱۳۹۵). استخراج ویژگی ها و بسط لغت نامه در اندیشه کاوی مورد استفاده در متون فارسی. نشریه مهندسی برق و مهندسی کامپیوتر ایران، (۱۴)۳. [12] نوری هفت چشمه، ک. خدادادی، ر. اکبری، ی . رضوی، س. م، و احمدی ترشیزی. ح (۱۳۹۵). تشخیص جنسیت نویسنده مستقل از متن و زبان نوشتاری با استفاده از پالایش پویای نمادین مبتنی بر تبدیل رادان. نشریه مهندسی برق و مهندسی کامپیوتر ایران، (۱۴)۴. [13] عباسی، ف. سهرابی، ب. مائیان، ا. و خدیور، آ (۱۳۹۶). ارائه مدلی جهت دسته بند احساسات خریداران کتاب با استفاده از رویکرد ترکیبی. فصلنامه مطالعات مدیریت کسب و کار هوشمند، (۶)۲۱، ۶۵- ۹۲. [14] کشاورزیان، س. و براردخت، ح (۱۳۹۶). جایگاه کتاب و کتابخوانی در سایت تبیان با رویکرد متن کاوی و تحلیل شبکه‌های اجتماعی. فصلنامه مطالعات مدیریت کسب و کار هوشمند، (۶)۲۱ْ ۱۶۹- ۱۸۸. [15] امیری٬ م. ختن لو٬ ح. (۱۳۹۲). خوشه بندی اسناد مبتنی بر آنتولوژی و رویکرد فازی. فصلنامه علمی ‍پژوهشی فنآوری اطلاعات و ارتباطات ایران. (۵)۱۷،۱۸ [16] میردامادی، م. م. زارع بیدکی، ع. م،و رضاییان، م (۱۳۹۳). قطعه بندی عبارات متون فارسی با استفاده از شبکه های عصبی. نشریه مهندسی برق و مهندسی کامپیوتر ایران، (۱۱)۲. [17] برنجيان شاپوررضا، دستغيب محمد باقر، جستجوگر واژه‌هاي مصوب فرهنگستان زبان و ادب فارسي و واژه‌هاي معادل و رايج آنها در سيستم‌هاي بازيابي اطلاعات، مجله علمی پژوهش در علوم رایانه، شماره 12، زمستان 1397، ص25-38. Higgins, D., Burstin, J., Marcu, D., & Gentile, C. (2004). Evaluating multiple aspects of coherence in student essays. In proc. NAACL-HLT. 185- 192. Burstein, J., Tetreault, J., & Andreyev, S. (2010). Using entity-based features to model coherence in student essays. In proc NAACL-HLT. 681-684. Yannakoudakis, H., & Briscoe, T. (2012). Modeling coherence in ESOL learner texts. In proc the seventh workshop on building educational applications using NLP, 33-43. Huang, G., Tan, M., Huang, S., Mo, R., & Zhou, Y. (2017). A discourse coherence model for analyzing Chinese students' essay. In progress in informatics and computing (PIC).430-434: IEEE. Arunsirot, S. (2013). An analysis of textual metafunction in Thai EFL students' writing. Novitas-ROYAL (Research on Youth and Language), (7)2, 160-174. Luhn, H. P. (1958). A business intelligence system. IBM journal of research and development, Vol. 2, No. 4, pp. 314-319. Halliday, M. A. K., & Hasan, R. (1976). Cohesion in English, ed: London: longman. Iyyer, M., Manjunatha, V., Boyd-Graber, J., & Daume, H. (2015). Deep unordered composition rivals’ syntactic methods for text classification. In proc of the 53rd annual meeting of the association for computational linguistics.1, 1681-1691. Wieting, J., Bansal, M., Gimpel, K., & Livescu, K. (2016). Embedding words and sentences via character n-grams. in proc of the 2016 conference on empirical methods in natural language processing, Austin. 1504–1515 Abdolahi, M., & Zahedi, M. (2017). Sentence matrix normalization using most likely n-grams vector. presented at the 2017 IEEE 4th international conference on knowledge-based engineering and innovation (KBEI), Tehran, Iran. Foltz, P. W., Kintsch, W., & Landauer, T. K. (1998). The measurement of textual coherence with latent semantic analysis. Discourse processes, Vol. 25, No. 2, pp. 285-307. Barzilay, R., & Lapata, M. (2008). Modeling local coherence: An entity-based approach, Computational linguistics, Vol. 34, No. 1, pp. 1-34. Putra, G. V. G., & Tokunaga, T. (2017). Evaluating text coherence based on semantic similarity graph. In proc of TextGraphs-11: the workshop on graph-based methods for natural language processing. 76-85. Xu, F., Du, S., Li, M., & Wang, M. (2017). An entity-driven recursive neural network model for chinese discourse coherence modeling. arXiv preprint arXiv:1704.04336. Lioma, C., Tarissan, F., Simonsen, J. G., Petersen, G., & Larsen, B. (2016). Exploiting the bipartite structure of entity grids for document coherence and retrieval. In proc of the 2016 ACM international conference on the theory of information retrieval. 11-20. Guinaudeau, C., & Strube, M. (2013), Graph-based local coherence modeling. In proc of the 51st annual meeting of the association for computational linguistics, 1, 93-103. Petersen, C., Lioma, C., Simonsen, J. G., & Larsen. B. (2015). Entropy and graph-based modeling of document coherence using discourse entities: An application to IR. In proc of the 2015 international conference on the theory of information retrieval.191-200: ACM. Mesgar, M., & Strube, M. (2015). Graph-based coherence modeling for assessing readability. In proc of the fourth joint conference on lexical and computational semantics. 309- 318. [36] عبدالهی، م، و زاهدی، م (۱۳۹۶). بهبود روش‌های ارزیابی انسجام متن با ترکیب مزایای سه رویکرد مبتنی بر موجودیت، گراف و آنتروپی. سومین کنفرانس بین المللی بازشناسی الگو و تحلیل تصویر ایران. Xiong, D., Zhang, M., & Wang, X. (2015). Topic-based coherence modeling for statistical machine translation. IEEE/ACM transactions on audio, speech and language processing (TASLP), Vol. 23, No. 3, pp. 483-493. Somasundaran, S., Burstein, J., & Chodorow, M. (2014). Lexical chaining for measuring discourse coherence quality in test-taker essays. in proceedings of COLING 2014, the 25th international conference on computational linguistics: Technical papers. 950- 961. Nguyen, D. T., & Joty, S. (2017). A neural local coherence model. In proc of the 55th annual meeting of the association for computational linguistics. 1, 1320-1330. Logeswaran, L., Lee, H., & Radev, D. (2016). Sentence ordering using recurrent neural networks, arXiv preprint arXiv:1611.02654. Logeswaran, L., Lee, H., & Radev, D. (2018). Sentence Ordering and Coherence Modeling using Recurrent Neural Networks. arXiv:1611.02654 [cs.CL].5285-5292. Li, J., & Jurafsky, D. (2016). Neural net models for open-domain discourse coherence. arXiv preprint arXiv:1606.01545. Kiddon, C., Zettlemoyer, L., & Choi, Y. (2016). Globally coherent text generation with neural checklist models. In proc of the 2016 conference on empirical methods in natural language processing. 329- 339. Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882. Zhang, X., & LeCun, Y. (2015). Text understanding from scratch, arXiv preprint arXiv:1502.01710. Christensen, J., Soderland, S., & Etzioni, O. (2013). Towards coherent multi-document summarization. In proc of the 2013 conference of the North American chapter of the association for computational linguistics: Human language technologies. 1163- 1173. Zhang, R., Li, W., Liu, N., & Gao, D. (2016). Coherent narrative summarization with a cognitive model. Computer speech & language, Vol. 35, pp. 134-160. Gambhir, M., &Gupta, V. (2017). Recent automatic text summarization techniques: a survey. Artificial intelligence review, Vol. 47, No. 1, pp. 1-66. Liu, P. J. (2018). Generating Wikipedia by summarizing long sequences, arXiv preprint arXiv:1801.10198. [50] کيومرثی فرشاد، بختياري شقایق، هادي پور مریم، خلاصه سازي خودکار متن با استفاده از کلونی زنبورعسل، مجله علمی پژوهش در علوم رایانه، شماره 6، زمستان 1396، ص46-59. [51] محسنی علیرضا، ونوس مرضی، جدیدي نژاد امیر حسین، خلاصه سازي تک سندي متون فارسی به کمک یادگيري عميق ماشينی، مجله علمی پژوهش در علوم رایانه، شماره 12، زمستان 1392، ص1-16. Han, A. F., & Wong, D. F. (2016). Machine translation evaluation: A survey, arXiv preprint arXiv:1605.04515. Sim Smith, K. (2018). Coherence in Machine Translation. Doctor of Philosophy thesis. University of Sheffield. Smith, K.S., Aziz, W., & Specia, L. (2015). A proposal for a coherence corpus in machine translation. in proc of the second workshop on discourse in machine translation. 52-58. Smith, K. S., Aziz, W., & Specia. L. (2016). The trouble with machine translation coherence. in proc of the 19th annual conference of the European association for machine translation. 178- 189. Zhang, Y., Gan, Z., Fan, K., Chen, Z., Henao, R., Shen, D., & Carin, L. (2017). Adversarial feature matching for text generation. arXiv preprint arXiv:1706.03850. Siddharthan, A. (2014). A survey of research on text simplification. ITL-International journal of applied linguistics, Vol. 165, No. 2, pp. 259-298. Song, W., Fu, R., Liu, L., & Liu, T. (2015). Discourse element identification in student essays based on global and local cohesion. In proc of the 2015 conference on empirical methods in natural language processing. 2255-2261. Kusner, M., Sun, Y., Kolkin, N., & Weinberger, K. (2015). From word embeddings to document distances. in international conference on machine learning. 957- 966. Lee, G. H., & Lee, K. G. (2017). Automatic text summarization using reinforcement learning with embedding features. In proc of the eighth international joint conference on natural language processing. 2, 193-197. Severyn, A., & Moschitti, A. (2015). Learning to rank short text pairs with convolutional deep neural networks. in proc of the 38th international ACM SIGIR conference on research and development in information retrieva: ACM. Severyn, A., & Moschitti, A.(2016). Modeling relational information in question-answer pairs with convolutional neural networks, arXiv preprint arXiv:1604.01178. Vijayarani, S., Ilamathi, M. G., & Nithya, M. (2015). Preprocessing techniques for text mining-an overview. International journal of computer science & communication networks, Vol. 5, No. 1, pp. 7- 16. Denny, M. J., & Spirling, A. (2018). Text preprocessing for unsupervised learning: why it matters, when it misleads, and what to do about it. Political analysis, Vol. 26, No. 2, pp. 168- 189. Simard, M. (1998). Automatic insertion of accents in French text. in proc of the third conference on empirical methods for natural language processing. 27-35. https://developer.syn.co.in/tutorial/bot/oscova/pretrained-vectors.html Mikolov, T., & Sutskever, I. (2013). Distributed representations of words and phrases and their compositionality. in proc. NIPS 2013. 3111–3119. Rosenfeld, R. (1996). A maximum entropy approach to adaptive statistical language modeling. Computer speech & language, Vol. 10, No. 3, pp. 187-228. Ermakova, L., Mothe, J., & Firsov, A. (2017). A Metric for sentence ordering assessment based on topic-comment structure. in proc of the 40th international ACM SIGIR conference on research and development in information retrieval. 1061-1064: ACM."; 1 A01 افسون سروقد ، پویا روزبه جوان ، عرفانه نوروزی افسون سروقد ، پویا روزبه جوان ، عرفانه نوروزی C. افسون سروقد ، پویا روزبه جوان ، عرفانه نوروزی 01 eng 202403 2024 C. zagros 06 10.22034/zagros.2019.1008.1727 https://rcsj.ir/user/articles/75 Farzanegan Pub mEDRA <TitleType>01</TitleType> <TitleText>مقایسه دو الگوریتم ژنتیک آموزش مبتنی بر پرسش و خودسازگار مبتنی بر LDA در ساخت کاتالوگ های موبایل گرا در تجارت سیار</TitleText> <TitleType>05</TitleType> <TitleText>مقایسه دو الگوریتم ژنتیک آموزش مبتنی بر پرسش و خودسازگار مبتنی بر LDA در ساخت کاتالوگ های موبایل گرا در تجارت سیار</TitleText> 01 01 75 zagros Publisher IR 01 75 JB 17 1 4 01 202403 7 10 <TitleType>01</TitleType> <TitleText>مقایسه دو الگوریتم ژنتیک آموزش مبتنی بر پرسش و خودسازگار مبتنی بر LDA در ساخت کاتالوگ های موبایل گرا در تجارت سیار</TitleText> 01 1. Darwish, A., Hassanien, A. E., Elhoseny, M., Sangaiah, A. K., & Muhammad, K. (2019). The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: Opportunities, challenges, and open problems. Journal of Ambient Intelligence and Humanized Computing, 10(10), 4151-4166. 2. Palanisamy, V., & Thirunavukarasu, R. (2019). Implications of big data analytics in developing healthcare frameworks–A review. Journal of King Saud University-Computer and Information Sciences, 31(4), 415-425. 3. Albahri, O. S., Zaidan, A. A., Zaidan, B. B., Hashim, M., Albahri, A. S., & Alsalem, M. A. (2018). Real-time remote health-monitoring Systems in a Medical Centre: A review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects. Journal of medical systems, 42(9), 164. 4. L Gu, J Stankovic, Radio-triggered wake-up capability for sensor networks, in Proceedings of the 10th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2004, Toronto, Canada, pp. 27–36 (May 2014). 5. R Falk, H-J Hof, Fighting insomnia: a secure wake-up scheme for wireless sensor networks, in Third International Conference on Emerging Security Information, Systems and Technologies, SECURWARE ‘09, Athens/Glyfada,Greece, pp. 191–196 (2009). 6. N Pletcher, JM Rabaey, Ultra-low power wake-up receivers for wireless sensor networks. Ph.D. Dissertation, EECS Department, University of California, Berkeley (May 2018) 7. W Ye, J Heidemann, D Estrin, An energy-efficient MAC protocol for wireless sensor networks, in Proceedings Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2002, vol. 3. New York, NY, USA, pp. 1567–1576 (2012) 8. T van Dam, K Langendoen, An adaptive energy-efficient MAC protocol for wireless sensor networks, in Proceedings of the First ACM Conference on Embedded Networked Sensor Systems, Los Angeles, CA, USA, pp. 171–180 (November 2003) 9. A El-Hoiydi, J-D Decotignie, WiseMAC: an ultra low power MAC protocol for the downlink of infrastructure wireless sensor networks, in Proceedings Ninth International Symposium on Computers and Communications, ISCC’04, vol. 1.Alexandria, EGYPT, pp. 244–251 (July 2014) 10. J Polastre, J Hill, D Culler, Versatile low power media access for wireless sensor networks, in Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, ser. SenSys ‘04, New York, NY, USA, ACM, pp. 95–107 (2014) 11.M Buettner, GV Yee, E Anderson, R Han, X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks, in Proceedings of the 4th International Conference on Embedded Networked Sensor Systems, ser. SenSys ‘06, New York, NY, USA, ACM, pp. 307–320 (2016) 12. S Marinkovic, E Popovici, C Spagnol, S Faul, W Marnane, Energy-efficient low duty cycle MAC protocol for wireless body area networks. IEEE Trans Inf Technol Biomed. 13(6), 915–925 (2009) 13.M Miller, N Vaidya, A MAC protocol to reduce sensor network energy consumption using a wakeup radio. IEEE Trans Mobile Comput. 4(3), 228–242 (2015) 14. I Demirkol, C Ersoy, E Onur, Wake-up receivers for wireless sensor networks: benefits and challenges. IEEE Wirel Commun. 16(4), 88–96 (2009) 15. P Le-Huy, S Roy, Low-power 2.4 GHz wake-up radio for wireless sensor networks, in IEEE International Conference on Wireless and Mobile Computing Networking and Communications, 2008. WIMOB ‘08, Avignon, France, pp. 13–18 (October 2018) 16. J Jung, K Ha, J Lee, Y Kim, D Kim, Wireless body area network in a ubiquitous healthcare system for physiological signal monitoring and health consulting. Int J Signal Process Pattern Recogn. 1, 47–54 (2018) 17. C Ding, X Wu, Z Lv, Design and implementation of the Zigbee-based body sensor network system, in 5th International Conference on Wireless Communications, Networking and Mobile Computing, WiCom ‘09, Beijing, China, pp. 1–4 (September 2009) 18. H Cao, X Liang, I Balasingham, VCM Leung, Performance analysis of ZigBee technology for wireless body area sensor networks, in Ad Hoc Networks, ser. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 28. (Springer, Berlin, 2010), pp.747–761. doi:10.1007/978-3-642-11723-7_51 19. G Fang, E Dutkiewicz, BodyMAC: energy efficient TDMA-based MAC protocol for wireless body area networks, in 9th International Symposium on Communications and Information Technology, ISCIT 2009, Incheon, Korea, pp. 1455–1459 (September 2009) 20. N Timmons, W Scanlon, An adaptive energy efficient MAC protocol for the medical body area network, in 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace Electronic Systems Technology, Wireless VITAE 2009, Aalborg, Denmark, pp.587–593 (May 2009) 21. JY Khan, MR Yuce, F Karami, Performance evaluation of a wireless body area sensor network for remote patient monitoring, in 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008, Vancouver, Canada, pp. 1266–1269 (August 2018) 22. HC Keong, MR Yuce, Analysis of a multi-access scheme and asynchronous transmit-only UWB for wireless body area networks, in 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’09), Minnesota, USA, pp. 6906–6909 (September 2009) 23. J Ansari, D Pankin, P Mhnen, Radio-triggered wake-ups with addressing capabilities for extremely low power sensor network applications. Int J Wirel Inf Netw. 16, 118–130 (2009). doi:10.1007/s10776-009-0100- 24. BV d Doorn, W Kavelaars, K Langendoen, A prototype low cost wakeup radio for the 868 MHz band. Int J Senor Netw. 5, 22–32 (2009). doi:10.1504/ IJSNET.2009.023313. 25. AL AMEEN, Moshaddique, et al. A power efficient MAC protocol for wireless body area networks. EURASIP Journal on Wireless Communications and Networking, 2012, vol. 2012, no 1, p. 33. 26. CAVALLARI, Riccardo, et al. A survey on wireless body area networks: Technologies and design challenges. IEEE Communications Surveys & Tutorials, 2014, vol. 16, no 3, p. 1635-1657. 27. 5. R Falk, H-J Hof, Fighting insomnia: a secure wake-up scheme for wireless sensor networks, in Third International Conference on Emerging Security Information, Systems and Technologies, SECURWARE ‘09, Athens/Glyfada,Greece, pp. 191–196 (2009). 28. N Pletcher, JM Rabaey, Ultra-low power wake-up receivers for wireless sensor networks. Ph.D. Dissertation, EECS Department, University of California, Berkeley (May 2018) 29. A El-Hoiydi, J-D Decotignie, WiseMAC: an ultra low power MAC protocol for the downlink of infrastructure wireless sensor networks, in Proceedings Ninth International Symposium on Computers and Communications, ISCC’04, vol. 1.Alexandria, EGYPT, pp. 244–251 (July 2014). 30. Graber, M. L., Siegal, D., Riah, H., Johnston, D., & Kenyon, K. (2019). Electronic health record–related events in medical malpractice claims. Journal of patient safety, 15(2), 77-85. 31. Howe, J. L., Adams, K. T., Hettinger, A. Z., & Ratwani, R. M. (2018). Electronic health record usability issues and potential contribution to patient harm. Jama, 319(12), 1276-1278. 32. Meeks, D. W., Smith, M. W., Taylor, L., Sittig, D. F., Scott, J. M., & Singh, H. (2014). An analysis of electronic health record-related patient safety concerns. Journal of the American Medical Informatics Association, 21(6), 1053-1059. 33. Graber, M. L., Bailey, R., & Johnston, D. (2016). Goals and Priorities for Health Care Organizations to Improve Safety Using Health IT. 34. Xiao, C., Choi, E., & Sun, J. (2018). Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. Journal of the American Medical Informatics Association, 25(10), 1419-1428. 35. Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., ... & Sundberg, P. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, 1(1), 18. 36. Curtis, J. R., Sathitratanacheewin, S., Starks, H., Lee, R. Y., Kross, E. K., Downey, L., ... & Lindvall, C. (2018). Using electronic health records for quality measurement and accountability in care of the seriously ill: opportunities and challenges. Journal of palliative medicine, 21(S2), S-52. 1 A01 سمانه رحیمی، احمد مصلی نژاد سمانه رحیمی، احمد مصلی نژاد C. سمانه رحیمی، احمد مصلی نژاد 01 eng 202403 2024 C. zagros 06 10.22034/zagros.2019.1008.1727 https://rcsj.ir/user/articles/76 Farzanegan Pub mEDRA <TitleType>01</TitleType> <TitleText>کنترل فرکانس- بار ریزشبکه ها با استفاده از کنترل کننده فازی نوع-2 مرتبه کسری</TitleText> <TitleType>05</TitleType> <TitleText>کنترل فرکانس- بار ریزشبکه ها با استفاده از کنترل کننده فازی نوع-2 مرتبه کسری</TitleText> 01 01 76 zagros Publisher IR 01 76 JB 17 1 4 01 202403 7 10 <TitleType>01</TitleType> <TitleText>کنترل فرکانس- بار ریزشبکه ها با استفاده از کنترل کننده فازی نوع-2 مرتبه کسری</TitleText> 01 [1] Demirkol I. , Ersoy C. , Onur E. 2009. "Wake-up receivers for wireless sensor networks: benefits and challenges", IEEE wireless Communications, [1536-1284], 16(4):88-96. [2] Le-Huy P. , Roy S. 2008. "Low-power 2.4 GHz wake-up radio for wireless sensor networks". 2008 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications: IEEE:13-18. [3] Darwish A. , Hassanien A. E. , Elhoseny M. , Sangaiah A. K. , Muhammad K. 2019. "The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: Opportunities, challenges, and open problems", Journal of Ambient Intelligence and Humanized Computing, [1868-5137], 10(10):4151-4166. [4] Ansari J. , Pankin D. , Mähönen P. 2009. "Radio-triggered wake-ups with addressing capabilities for extremely low power sensor network applications", International Journal of Wireless Information Networks, [1068-9605], 16(3):118. [5] Albahri O. , Zaidan A. , Zaidan B. , Hashim M. , Albahri A. , Alsalem M. 2018. "Real-time remote health-monitoring Systems in a Medical Centre: A review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects", Journal of medical systems, [0148-5598], 42(9):164. [6] Gu L. , Stankovic J. A. 2004. "Radio-Triggered Wake-Up Capability for Sensor Networks". IEEE Real-Time and Embedded Technology and Applications Symposium: Citeseer:27-37. [7] Palanisamy V. , Thirunavukarasu R. 2019. "Implications of big data analytics in developing healthcare frameworks–A review", Journal of King Saud University-Computer and Information Sciences, [1319-1578], 31(4):415-425. [8] Miller M. J. , Vaidya N. H. 2005. "A MAC protocol to reduce sensor network energy consumption using a wakeup radio", IEEE Transactions on mobile Computing, [1536-1233], 4(3):228-242. [9] Khan J. Y. , Yuce M. R. , Karami F. 2008. "Performance evaluation of a wireless body area sensor network for remote patient monitoring". 2008 30th annual international conference of the IEEE engineering in medicine and biology society: IEEE:1266-1269. [10] Al Ameen M. , Ullah N. , Chowdhury M. S. , Islam S. R. , Kwak K. 2012. "A power efficient MAC protocol for wireless body area networks", EURASIP Journal on Wireless Communications and Networking, [1687-1499], 2012(1):33. [11] Van der Doorn B. , Kavelaars W. , Langendoen K. 2009. "A prototype low-cost wakeup radio for the 868 MHz band", International Journal of Sensor Networks, [1748-1279], 5(1):22-32. [12] Pletcher N. M. , Rabaey J. M. 2008. Ultra-low Power Wake-up Receivers for Wireless Snesor Networks: University of California, Berkeley. [13] Cao H. , Liang X. , Balasingham I. , Leung V. C. 2009. "Performance analysis of ZigBee technology for wireless body area sensor networks". International Conference on Ad Hoc Networks: Springer:747-761. [14] Fang G. , Dutkiewicz E. 2009. "BodyMAC: Energy efficient TDMA-based MAC protocol for wireless body area networks". 2009 9th international symposium on communications and information technology: IEEE:1455-1459. [15] Timmons N. F. , Scanlon W. G. 2009. "An adaptive energy efficient MAC protocol for the medical body area network". 2009 1st international conference on wireless communication, vehicular technology, information theory and aerospace & electronic systems technology: IEEE:587-593. [16] Cavallari R. , Martelli F. , Rosini R. , Buratti C. , Verdone R. 2014. "A survey on wireless body area networks: Technologies and design challenges", IEEE Communications Surveys & Tutorials, [1553-877X], 16(3):1635-1657. [17] Ye W. , Heidemann J. , Estrin D. 2002. "An energy-efficient MAC protocol for wireless sensor networks". Proceedings. Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies: IEEE, 3:1567-1576. [18] Van Dam T. , Langendoen K. 2003. "An adaptive energy-efficient MAC protocol for wireless sensor networks". Proceedings of the 1st international conference on Embedded networked sensor systems: ACM:171-180. [19] El-Hoiydi A. , Decotignie J.-D. 2004. "WiseMAC: an ultra low power MAC protocol for the downlink of infrastructure wireless sensor networks". Proceedings. ISCC 2004. Ninth International Symposium on Computers And Communications (IEEE Cat. No. 04TH8769): IEEE, 1:244-251. [20] Polastre J. , Hill J. , Culler D. 2004. "Versatile low power media access for wireless sensor networks". Proceedings of the 2nd international conference on Embedded networked sensor systems:95-107. [21] Buettner M. , Yee G. V. , Anderson E. , Han R. 2006. "X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks". Proceedings of the 4th international conference on Embedded networked sensor systems:307-320. [22] Marinkovic S. J. , Popovici E. M. , Spagnol C. , Faul S. , Marnane W. P. 2009. "Energy-efficient low duty cycle MAC protocol for wireless body area networks", IEEE Transactions on Information Technology in Biomedicine, [1089-7771], 13(6):915-925. [23] Jung J. , Ha K. , Lee J. , Kim Y. , Kim D. 2008. "Wireless body area network in a ubiquitous healthcare system for physiological signal monitoring and health consulting", International Journal of Signal Processing, Image Processing and Pattern Recognition, [2005-4254], 1(1):47-54. [24] Ding C. , Wu X. , Lv Z. 2009. "Design and implementation of the Zigbee-based body sensor network system". 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing: IEEE:1-4. [25] Falk R. , Hof H.-J. 2009. "Fighting insomnia: A secure wake-up scheme for wireless sensor networks". 2009 Third International Conference on Emerging Security Information, Systems and Technologies: IEEE:191-196. [26] Graber M. L. , Bailey R. , Johnston D. 2016. "Goals and Priorities for Health Care Organizations to Improve Safety Using Health IT". [27] Graber M. L. , Siegal D. , Riah H. , Johnston D. , Kenyon K. 2019. "Electronic health record–related events in medical malpractice claims", Journal of patient safety, 15(2):77. [28] Howe J. L. , Adams K. T. , Hettinger A. Z. , Ratwani R. M. 2018. "Electronic health record usability issues and potential contribution to patient harm", Jama, [0098-7484], 319(12):1276-1278. [29] Meeks D. W. , Smith M. W. , Taylor L. , Sittig D. F. , Scott J. M. , Singh H. 2014. "An analysis of electronic health record-related patient safety concerns", Journal of the American Medical Informatics Association, [1067-5027], 21(6):1053-1059. [30] Curtis J. R. , Sathitratanacheewin S. , Starks H. , Lee R. Y. , Kross E. K. , Downey L. , Sibley J. , Lober W. , Loggers E. T. , Fausto J. A. 2018. "Using electronic health records for quality measurement and accountability in care of the seriously ill: opportunities and challenges", Journal of palliative medicine, [1096-6218], 21(S2):S-52-S-60. [31] Rajkomar A. , Oren E. , Chen K. , Dai A. M. , Hajaj N. , Hardt M. , Liu P. J. , Liu X. , Marcus J. , Sun M. 2018. "Scalable and accurate deep learning with electronic health records", NPJ Digital Medicine, [2398-6352], 1(1):18. [32] Xiao C. , Choi E. , Sun J. 2018. "Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review", Journal of the American Medical Informatics Association, [1067-5027], 25(10):1419-1428. 1 A01 مهدی سخندانی ، محمدحسن خوبان مهدی سخندانی ، محمدحسن خوبان C. مهدی سخندانی ، محمدحسن خوبان 01 eng 202403 2024 C. zagros 06 10.22034/zagros.2019.1008.1727 https://rcsj.ir/user/articles/77 Farzanegan Pub mEDRA <TitleType>01</TitleType> <TitleText>تعیین روش درمانی بیماران مبتلا به سرطان پستان به کمک روش های هوش مصنوعی</TitleText> <TitleType>05</TitleType> <TitleText>تعیین روش درمانی بیماران مبتلا به سرطان پستان به کمک روش های هوش مصنوعی</TitleText> 01 01 77 zagros Publisher IR 01 77 JB 17 1 4 01 202403 7 10 <TitleType>01</TitleType> <TitleText>تعیین روش درمانی بیماران مبتلا به سرطان پستان به کمک روش های هوش مصنوعی</TitleText> 01 [1] Hung-Jen Liao, Chun-Hung Richard Lin, Ying-Chih Lin, Kuang-Yuan Tung " Intrusion detection system: A comprehensive review." Journal of Network and Computer Applications Volume 36, Issue 1, January 2013. [2] Suhair H Amer, Jr John A Hamilton " Input Data Processing Techniques in Intrusion Detection Systems" 2015 Global Journal of Computer Science and Technology , 2015. [3] Liguo Chen, Yuedong Zhang, Qi Zhaob, Guanggang Gengb, ZhiWei Yan, “Detection of DNS DDoS Attacks with Random Forest Algorithm on Spark”, The 2nd International Workshop on Big Data and Networks Technologies, 2018. [4] K. Ishibashi, T. Toyono, K. Toyama, M. Ishino, H. Ohshima, I. Mizukoshi, Detecting mass mailing worm infected hosts by mining dns traffic data, in: Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data, ACM, pp.159–164, 2005. [5] H. Choi, H. Lee, H. Lee, H. Kim, Botnet detection by monitoring group activities in dns traffic, in: Computer and Information Technology, 2007. CIT 2007. 7th IEEE International Conference on, IEEE, pp. 715–720, 2007. [6] J. Zhang, R. Perdisci, W. Lee, X. Luo, U. Sarfraz, Building a scalable system for stealthy p2p-botnet detection, IEEE transactions on information forensics and security 9 (1), 27–38, 2014. [7] W. Ruan, Y. Liu, R. Zhao, Pattern discovery in dns query traffic, Procedia Computer Science 17, 80 – 87, 2013. doi:http://dx.doi.org/10.1016/j.procs.2013.05.012. [8] J. Zhang, Y. Zhang, P. Liu, J. He, A spark-based ddos attack detection model in cloud services, in: International Conference onInformation Security Practice and Experience, Springer, pp. 48–64, 2016. [9] R. Begleiter, Y. Elovici, Y. Hollander, O. Mendelson, L. Rokach, R. Saltzman, A fast and scalable method for threat detection in large-scale dns logs, 2013 IEEE International Conference on, IEEE, pp. 738–741, 2013. [10] Guyon, I.; “An Introduction to Variable and Feature Selection”; Journal of Machine Learning Research, 2003, Vol.3, pp.1157-1182. [11] S. Mirjalili, M. Mirjalili.,” Grey Wolf Optimizer”, Eelsevier, Advances in Engineering Software, vol. 69, pp. 46-61, (2014). [12] Manjula C. Belavagi, and Balachandra Muniyal,” Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection”, Twelfth International Multi-Conference on Information Processing-2016 (IMCIP-2016), 89, 117 – 123, 2016. 1 A01 ستایش صادقی، عباس رضایی، امین گلاب پور ستایش صادقی، عباس رضایی، امین گلاب پور C. ستایش صادقی، عباس رضایی، امین گلاب پور 01 eng 202403 2024 C. zagros