﻿<?xml version="1.0" encoding="utf-8"?><doi_batch xmlns="http://www.crossref.org/schema/4.3.7" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.3.7 http://www.crossref.org/schema/deposit/crossref4.3.7.xsd"><head><doi_batch_id>jict-2026052800</doi_batch_id><timestamp>20260528004706</timestamp><depositor><depositor_name>CMV Verlag</depositor_name><email_address>khoffmann@cmv-verlag.com</email_address></depositor><registrant>CMV Verlag</registrant></head><body><journal><journal_metadata language="fa"><full_title>Journal of Information and Communication Technology</full_title><abbrev_title>jict</abbrev_title><issn media_type="electronic">2717-0411</issn></journal_metadata><journal_issue><publication_date media_type="online"><month>10</month><day>3</day><year>2020</year></publication_date><journal_volume><volume>11</volume></journal_volume><issue>41</issue></journal_issue><journal_article publication_type="full_text"><titles><title>New Method to Improve Illumination Variations in Adult Images Based on Fuzzy Deep Neural Network</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Sasan</given_name><surname>Karamizadeh</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>abouzar</given_name><surname>arabsorkhi</surname></person_name></contributors><publication_date media_type="online"><month>10</month><day>3</day><year>2020</year></publication_date><pages><first_page>1</first_page><last_page>13</last_page></pages><doi_data><doi>10.66224/jict.8676.11.41.1</doi><resource>http://jour.aicti.ir/en/Article/8676</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/8676</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/8676</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/8676</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/8676</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/8676</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/8676</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/8676</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>1.  Wang, H., and Fan, A.: ‘Pornographic information of Internet views detection method based on the connected areas’, in Editor (Ed.)^(Eds.): ‘Book Pornographic information of Internet views detection method based on the connected areas’ (International Society for Optics and Photonics, 2017, edn.), pp. 1032228</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
2.Wang, Y., Jin, X., and Tan, X.: ‘Pornographic image recognition by strongly-supervised deep multiple instance learning’, in Editor (Ed.)^(Eds.): ‘Book Pornographic image recognition by strongly-supervised deep multiple instance learning’ (IEEE, 2016, edn.), pp. 4418-4422</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
3.Adnan, A., and Nawaz, M.: ‘RGB and hue color in pornography detection’: ‘Information Technology: New Generations’ (Springer, 2016), pp. 1041-1050</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
4.Nian, F., Li, T., Wang, Y., Xu, M., and Wu, J.: ‘Pornographic image detection utilizing deep convolutional neural networks’, Neurocomputing, 2016, 210, pp. 283-293</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
5.Karamizadeh, S., Abdullah, S.M., Zamani, M., Shayan, J., and Nooralishahi, P.: ‘Face recognition via taxonomy of illumination normalization’: ‘Multimedia Forensics and Security’ (Springer, 2017), pp. 139-160</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
6.Brancati, N., De Pietro, G., Frucci, M., and Gallo, L.: ‘Dynamic Colour Clustering for Skin Detection Under Different Lighting Conditions’, in Editor (Ed.)^(Eds.): ‘Book Dynamic Colour 
Clustering for Skin Detection Under Different Lighting Conditions’, in Editor (Ed.)^(Eds.): ‘Book Dynamic Colour Clustering for Skin Detection Under Different Lighting Conditions’ (Springer, 2016, edn.), pp. 27-35</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
7.Karamizadeha, S., Mabdullahb, S., Randjbaranc, E., and Rajabid, M.J.: ‘A review on techniques of illumination in face recognition’, Technology, 2015, 3, (02), pp. 79-83</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
8.Surinta, O., and Khamket, T.: ‘Recognizing pornographic images using deep convolutional neural networks’, in Editor (Ed.)^(Eds.): ‘Book Recognizing pornographic images using deep convolutional neural networks’ (IEEE, 2019, edn.), pp. 150-154</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
9.Noh, Y., Koo, D., Kang, Y.-M., Park, D., and Lee, D.: ‘Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering’, in Editor (Ed.)^(Eds.): ‘Book Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering’ (IEEE, 2017, edn.), pp. 877-880</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
10.Md Noor, S.S., Ren, J., Marshall, S., and Michael, K.: ‘Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries’, Sensors, 2017, 17, (11), pp. 2644</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
11.Gross, R., Baker, S., Matthews, I., and Kanade, T.: ‘Face recognition across pose and illumination’: ‘Handbook of face recognition’ (Springer, 2005), pp. 193-216</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
12. .Chen, Z., Liu, C., Chang, F., Han, X., and Wang, K.: ‘Illumination processing in face recognition’, International Journal of Pattern Recognition and Artificial Intelligence, 2014, 28, (05), pp. 1456011</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
13.Basilio, J.A.M., Torres, G.A., Pérez, G.S., Medina, L.K.T., and Meana, H.M.P.: ‘Explicit image detection using YCbCr space color model as skin detection’, 
Clustering for Skin Detection Under Different Lighting Conditions’, in Editor (Ed.)^(Eds.): ‘Book Dynamic Colour Clustering for Skin Detection Under Different Lighting Conditions’ (Springer, 2016, edn.), pp. 27-35</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
7.Karamizadeha, S., Mabdullahb, S., Randjbaranc, E., and Rajabid, M.J.: ‘A review on techniques of illumination in face recognition’, Technology, 2015, 3, (02), pp. 79-83</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
8.Surinta, O., and Khamket, T.: ‘Recognizing pornographic images using deep convolutional neural networks’, in Editor (Ed.)^(Eds.): ‘Book Recognizing pornographic images using deep convolutional neural networks’ (IEEE, 2019, edn.), pp. 150-154</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
9.Noh, Y., Koo, D., Kang, Y.-M., Park, D., and Lee, D.: ‘Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering’, in Editor (Ed.)^(Eds.): ‘Book Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering’ (IEEE, 2017, edn.), pp. 877-880</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
10.Md Noor, S.S., Ren, J., Marshall, S., and Michael, K.: ‘Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries’, Sensors, 2017, 17, (11), pp. 2644</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
11.Gross, R., Baker, S., Matthews, I., and Kanade, T.: ‘Face recognition across pose and illumination’: ‘Handbook of face recognition’ (Springer, 2005), pp. 193-216</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
12.Chen, Z., Liu, C., Chang, F., Han, X., and Wang, K.: ‘Illumination processing in face recognition’, International Journal of Pattern Recognition and Artificial Intelligence, 2014, 28, (05), pp. 1456011</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
13.Basilio, J.A.M., Torres, G.A., Pérez, G.S., Medina, L.K.T., and Meana, H.M.P.: ‘Explicit image detection using YCbCr space color model as skin detection’, Applications of Mathematics and Computer Engineering, 2011, pp. 123-128</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
14.Karamizadeh, S., and Arabsorkhi, A.: ‘Methods of pornography detection’, in Editor (Ed.)^(Eds.): ‘Book Methods of pornography detection’ (2018, edn.), pp. 33-38</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
15.Sufyanu, Z., Mohamad, F.S., Yusuf, A.A., and Mamat, M.B.: ‘Enhanced Face Recognition Using Discrete Cosine Transform’, Engineering Letters, 2016, 24, (1)</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
16.Liu, Z., Zhao, H., Pu, J., and Wang, H.: ‘Face recognition under varying illumination’, Neural Computing and Applications, 2013, 23, (1), pp. 133-139</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
17.Anagha, K., and Ram, A.R.: ‘Pose Tolerant Face Recognition: A Review’, in Editor (Ed.)^(Eds.): ‘Book Pose Tolerant Face Recognition: A Review’ (IEEE, 2020, edn.), pp. 0147-0152</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
18.Jin, X., Wang, Y., and Tan, X.: ‘Pornographic Image Recognition via Weighted Multiple Instance Learning’, IEEE transactions on cybernetics, 2018, 49, (12), pp. 4412-4420</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
19.Lopes, A.P., de Avila, S.E., Peixoto, A.N., Oliveira, R.S., and Araújo, A.d.A.: ‘A bag-of-features approach based on hue-sift descriptor for nude detection’, in Editor (Ed.)^(Eds.): ‘Book A bag-of-features approach based on hue-sift descriptor for nude detection’ (IEEE, 2009, edn.), pp. 1552-1556</unstructured_citation></citation><citation key="ref27"><unstructured_citation>
20.Moustafa, M.: ‘Applying deep learning to classify pornographic images and videos’, arXiv preprint arXiv:1511.08899, 2015</unstructured_citation></citation><citation key="ref28"><unstructured_citation>
21.Ding, X., Li, B., Li, Y., Guo, W., Liu, Y., Xiong, W., and Hu, W.: ‘Web Objectionable Video Recognition Based on Deep Multi Instance Learning with Representative Prototypes Selection’, IEEE Transactions on Circuits and Systems for Video Technology, 2020</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>A model information technology adoption in academic research projects in the filed ICT based on Information Technology Adoption Integrated Modeling (ITAIM)</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Shahram</given_name><surname>Aliyari</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>masoud</given_name><surname>movahedi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>sirous</given_name><surname>kazemian</surname></person_name></contributors><publication_date media_type="online"><month>10</month><day>3</day><year>2020</year></publication_date><pages><first_page>13</first_page><last_page>32</last_page></pages><doi_data><doi>10.66224/jict.8677.11.41.13</doi><resource>http://jour.aicti.ir/en/Article/8677</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/8677</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/8677</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/8677</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/8677</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/8677</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/8677</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/8677</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>1.	باقری، محمد، (1388)،پذیرش بانکداری اینترنتی در ایران: بسط مدل پذیرش فناوری، فصل‌نامه علوم فناوری اطلاعات، دوره 24، شماره 13، بهار 1388، ص 5-34</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
2.	بدیع، علی، دستجردی، علی(1395)، ارایه یک مدل مفهومی پذیرش فناوری در بانکداری الکترونیکی بر اساس مدل های TRA, TAM. TAMII, TPM همایش بین المللی تجارت و اقتصاد الکترونیکی</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
3.	جهانگیر، غلام؛ دیانی، محمدحسین؛ نو کاریزی، محسن؛ توسعه مدل پذیرش فناوری اطلاعات دیویس(TAM) از طریق سنجش تأثیر باورهای خودکار آمد اعضای خودکارآمدی دانشگاه علوم پزشکی مشهد بر پذیرش سامانه‌های اطلاعاتی، پژوهش‌نامه کتابداری و اطلاع‌رسانی،1394، 5(2)، 319-339</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
4.	حسینی، نگین(1392)، پذیرش و استفاده از پایگاه‌های اطلاعاتی پیوسته لاتین توسط اعضای هیئت‌علمی دانشگاه‌های شهر کرمانشاه بر اساس نظریه یکپارچه پذیرش و استفاده از فناوریUTAUT، فصل‌نامه دانش سیاسی، سال هفتم، شماره27 </unstructured_citation></citation><citation key="ref5"><unstructured_citation>
5.	حسین پور، جعفر، (1386)، نقش فناوری اطلاعات در تحول ساختار سازمان‌ها، اطلاعات سیاسی اقتصادی، شماره 237 از 182 تا 195، خرداد و تیر 1386</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
6.	حق‌شناس، اصغر، دلوی، محمدرضا، شفیعیه، مسعود(1386)؛ نقش سرمایه‌های اجتماعی در توسعه،تدبیر، دی ماه، شماره 188 </unstructured_citation></citation><citation key="ref7"><unstructured_citation>
7.	خلعتبری، احمد، (1390)، ارائه یک مدل جدید برای پذیرش سرویس‌های دولت الکترونیک توسط کاربران، پایان‌نامه دانشگاه خواجه‌نصیرالدین طوسی، دانشکده مهندسی صنایع، 1390</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
8.	داور پناه، محمدرضا، (1381)، موانع زیر ساختی بهره-گیری از فناوری اطلاعات در کتابخانه های دانشگاهی ایران، کتابداری و اطلاع رسانی، دوره2، شماره 5، صفحه 1 تا 23</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
9.	داوری، علی، (1392)، مدل‌سازی معادلات ساختاری با نرم‌افزار pls، تهران: سازمان انتشارات جهاد دانشگاهی </unstructured_citation></citation><citation key="ref10"><unstructured_citation>
10.	درودی، فریبرز، (1389)، برنامه‌ریزی راهبردی فناوری اطلاعات، کتاب ماه علوم و فنون، اردیبهشت 1389، شماره 125 از 20 تا 35</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
11.	رضایی، مسعود، (1388)، نظریه‌های رایج درباره‌ی پذیرش فناوری اطلاعات و ارتباطات، پژوهش‌های ارتباطی (پژوهش و سنجش)، دوره 16 ، شماره4، زمستان 1388</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
12.	رستمی، مسلم، (1391)، نقش عوامل موثر بر پذیرش و بکارگیری فناوری اطلاعات و ارتباطات در بین جوانان روستایی بر اساس نظریه نشر نوآوری راجرز- مطالعه موردی: روستاهای اورامانات استان کرمانشاه، کتابداری و اطلاع رسانی، دوره 16، شماره 2، از صفحه 119 تا 138</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
13.	علی یاری، شهرام، (1394)، نقش عوامل فردی و سازمانی و مدیریتی مؤثر بر پذیرش فناوری اطلاعات در سازمان‌های دولتی ایران]، فصل‌نامه پژوهش‌های مدیریت منابع انسانی، سال هفتم، شماره 30</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
14.	علی یاری، شهرام، (1393)، ارائه مدل پذیرش فناوری اطلاعات در سازمان‌های دولتی ایران، رساله دکترا، گروه مهندسی صنایع دانشکده فنی و مهندسی دانشگاه جامع امام حسین (ع)، 1393</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
15.	فهامی، ریحانه، زارع، حسین، (1392)، عوامل موثر بر پذیرش فناوری های جدید در آموزش از راه دور با استفاده از مدل پذیرش فناوری ( مطالعه مورد دانشگاه پیام نور اصفهان)، رهیافتی نو در مدیریت، دوره 4، شماره 1، از 67 تا 79</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
16.	قلی پور، رحمت‌الله، (1383)، تأثیر فناوری اطلاعات بر ساختار سازمانی و نیروی کار، مدریت فرهنگ‌سازمانی، شماره 7 از 127 تا 125</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
17.	کیهان، جواد، محمدمرادی نقده، سارا، (1379)، بررسی عوامل مؤثر بر استفاده و پذیرش فنآوری رایانه توسط معلمان در امر تدریس با استفاده از نقشه های شناختی فازی، پژوهش در نظام های آموزشی، شماره 43 از 233 تا 249</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
18.	کاهویی، مهدی (1392)، عوامل موثر بر پذیرش فناوری اطلاعات در محیط بالینی از دیدگاه پرستاران، پياورد سلامت، دوره 7، شماره 4، از صفحه 262 تا صفحه 277</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
19.	 مشایخی، علینقی و همکاران، بررسی عوامل کلیدی موثر بر کاربرد فناوری اطلاعات در سازمان های دولتی ایران: کاربرد روش دلفی، مجله مدرس علوم انسانی، دوره 9، شماره 3، پاییز 1384</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
20.	مانیان، امیر و همکاران، (1386)، استفاده از مدل معادلات ساختاری در ارائه مدلی برای موفقیت برنامه‌ریزی استراتژیک فناوری اطلاعات، فصلنامه دانش مدیریت دانشگاه تهران، شماره74، 1386، ص 117 تا 138</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
21.	محمدی، علی(1392)، شناسایی و تبیین عوامل مؤثر بر پذیرش نوآوری فناوری اطلاعات در سازمان‌های دولتی با رویکرد معادلات ساختاری، مدریت فناوری اطلاعات، دوره 5، شماره 4، از صفحه 195 تا 218</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
22.	محترمی، امیر، خداداد حسینی، سید حمید، الهی، شعبان (1392)، بررسی عوامل موثر بر فناوری های اطلاعاتی در سازمان ها، فصلنامه مدیریت توسعه فناوری، شماره 3، ص 97 تا ص 122</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
23.	نادری، بنی، محمود و همکاران (1394)، عوامل موثر بر پذیرش فناوری اطلاعات در هتل های شهر شیراز، مطالعات مدیریت گردشگری، سال نهم، شماره 29</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
24.Anderson J. (2007). Social, ethical and legal barriers to e-health. International Journal of Medical Informatics; 76(5): 480-3
25.Akbari, M., &amp; Alipour Pijani, A. (2013). ICT َAdoption: A Case Study of SMEs in Tehran (Iran). The International Journal of Humanities, 20(3), 93-121.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
26.Beglaryan, M., Petrosyan, V., &amp; Bunker, E. (2017). Development of a tripolar model of technology acceptance: Hospital-based physicians’ perspective on EHR. International Journal of Medical Informatics, 102, 50-61.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
27.Cimperman, M., Brenčič, M. M., &amp; Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior—applying an Extended UTAUT model. International journal of medical informatics, 90, 22-31.</unstructured_citation></citation><citation key="ref27"><unstructured_citation> 
28.Chang, K.M &amp; Cheung, W. (2001). Determianants of intention to use internet/ www.at work: a confirmatory study. Information and management, 39(1), 1-14</unstructured_citation></citation><citation key="ref28"><unstructured_citation>
29.Chen,C.D; Fan, Y.W &amp;Farn, C.K (2007) Predicting Electronic toll collection service Adoption: An Integration of the technology Acceptance model and theory of planned Behavior. Transportation Research, part c, 15, 300-311</unstructured_citation></citation><citation key="ref29"><unstructured_citation>
30.Chen, K., &amp; Chan, A. H. S. (2014). Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics, 57(5), 635-652.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>
31.Damanpour, F. (1991). Organizational Innovation: A Meta-analysis of Effects of Determinants and Moderators. Academy of Management Journal, 34 (3), pp. 555-590</unstructured_citation></citation><citation key="ref31"><unstructured_citation>
32.Dickman Portz, J. et al . (2019) ,Using the  Technology Acceptance Model to Explore User Experience, Intent to Use, and Use Behavior of a Patient Portal Among Older Adults With Multiple Chronic Conditions: Descriptive Qualitative Study. Journal of Medical Internet Research. Vol 21. , 1-12</unstructured_citation></citation><citation key="ref32"><unstructured_citation>
33.Fischer, S. H., David, D., Crotty, B. H., Dierks, M., &amp; Safran, C. (2014). Acceptance and use of health information technology by community-dwelling elders. International journal of medical informatics, 83(9), 624-635.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>
34.Farzandipour,M. Nadi-Ravandi,S. Gilasi,H. Soleimani.N. (2019). Iranian Health Information Technology Acceptance Model (IHITAM) from Users’ Views. ACTA Inform MED. 27(4): 245-252</unstructured_citation></citation><citation key="ref34"><unstructured_citation>
35.Heltzol, pual. (2019). IT manager's survival gide: 11 ways to thrive in the years ahead. CIO publisher.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>
36.Handayani, P. W., Hidayanto, A. N., Pinem, A. A., Hapsari, I. C., Sandhyaduhita, P. I., &amp; Budi, I. (2017). Acceptance model of a Hospital Information System. International journal of medical informatics, 99, 11-28.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>
37.Hoque, R., &amp; Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75-84.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>
38.Hsieh, P. J. (2015). Physicians’ acceptance of electronic medical records exchange: An extension of the decomposed TPB model with institutional trust and perceived risk. International journal of medical informatics, 84(1), 1-14.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>
39.IT Project management and reporting Guidelines, October, (2018). Information technology services university of California office of the president.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>
40.Jack T. Marchewka. (2015). Information Technology Project Management: Providing Measurable Organizational Value. 5th Edition: Wiley Publishing 
41.King, W.R., He, J (2006).” A meta-analysis of the Teachnology acceptance model”. Information &amp; management vol, 43, pp : 740-755</unstructured_citation></citation><citation key="ref40"><unstructured_citation>
42.Kwon, T.H. &amp; Zumad, R.W. (1987) .Unifying the Fragmented Models of Information  systems Implementation. In Boland, J.R. and Hirschheim R. (edition). Critical Issues in information systems Research. New York, Wiley.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>
43.Koh. Ch. Prybutok, V.R., Ryan. Sh &amp; Ibragimova, B .(2006). The impotance of strategic in an emerging e-government environment. Business Process Management Jornal, 12(1):22-33</unstructured_citation></citation><citation key="ref42"><unstructured_citation>
44.Kanda.M.J&amp; Ogollah Kennedy, (2013), " Factors influencing adoption and use of information and communication technology at the ethics and anticorruption of Kenya". Journal of Business Administration and Management Sciences Research Vol. 2(11), pp. 224-309</unstructured_citation></citation><citation key="ref43"><unstructured_citation>
45.Lee, J.D. &amp; Jongsu Lee, J.(2009). E-government adoption in ASEAN: the case of Cambodia Sinawong Sang. Internet Research, 19(5): 517-534</unstructured_citation></citation><citation key="ref44"><unstructured_citation>
46.Mishra, D., Akman, I., &amp; Mishra, A. (2014). Theory of reasoned action application for green information technology acceptance. Computers in human behavior, 36, 29-40.</unstructured_citation></citation><citation key="ref45"><unstructured_citation>
46.Oreg S. Personality, (2006) context ,and resistance to organizational change. European Journal of Work andOrganizational Psychology; 15(1): 73-101.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>
47.Pan, C.C., &amp; Brophy, J. (2003). Students attitude equation modeling inquiry. Jornal of Educational media and Library 41(2), 181-194</unstructured_citation></citation><citation key="ref47"><unstructured_citation>
48.Rogers, Everettm. ; Chafee, Steven, H. (1983). Communication as an Academic Discipline: A Dialogue. Journal of Communication Vol 33 no 3, pp: 18-30 </unstructured_citation></citation><citation key="ref48"><unstructured_citation>
49.Sun,H.&amp;Zhang, P. (2006).The role of moderating factors in user technology acceptance. Human- Computer studies, 64: 53-78.</unstructured_citation></citation><citation key="ref49"><unstructured_citation>
50.Schaper, Louise K., and Graham P. Pervan. (2007).” ICT and OTC: Amodel of Information and Communication technology acceptance and utilization by occupational therapists.” International journal of medical informatics 76: s 212-s221.</unstructured_citation></citation><citation key="ref50"><unstructured_citation>
51.Sindhu Yoga,I. Dyah Permatha Korry. N,    Dhian Rani Yulianti.N. (2019). ”Information Technology Adoption on Digital Marketing Communication Channel” International Journal of Social Sciences and Humanities
Vol. 3 No. 2. pages: 95~104</unstructured_citation></citation><citation key="ref51"><unstructured_citation>
52.Tsiknakis M, Kouroubali A. (2009) Organizational factors affecting successful adoption of innovativeeHealth services: A case study employing the FITT framework. International Journal of MedicalInformatics; 78(1): 39-52.</unstructured_citation></citation><citation key="ref52"><unstructured_citation>
53.Turban .E. , Linder .D., Mclen E., Wetherbe. J., (2002) "Information technology for management " 6 THED , 720 pages</unstructured_citation></citation><citation key="ref53"><unstructured_citation>
54.Thong, J. Y. L., and Yap, C.S., (1995)." CEO characteristics, organizational characteristics and information technology adoption in the small business. , omega 23(4), 429-442</unstructured_citation></citation><citation key="ref54"><unstructured_citation>
55.Tung,F.C.Chang S.C. &amp;Chou, C.M.(2008). An Extension of Trust and TAM Model with IDT in the Adoption of the electronic logistics information system in HIS in the medical Industry. In.J. Medical informatics, 77. 324-335</unstructured_citation></citation><citation key="ref55"><unstructured_citation>
56.Venketash, V. and Davis, F.D. (2006). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management science, 46(2), 186-204</unstructured_citation></citation><citation key="ref56"><unstructured_citation>
57.Venkatesh, V. Bala. H., (2008), " technology acceptance model3 and research agenda on interventions". Decision Science Volume 39, Number2
 </unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Comparing A Hybridization of Fuzzy Inference System and Particle Swarm Optimization Algorithm with Deep Learning to Predict Stock Prices </title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Majid</given_name><surname>Abdolrazzagh-Nezhad</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>mahdi</given_name><surname>kherad</surname></person_name></contributors><publication_date media_type="online"><month>10</month><day>3</day><year>2020</year></publication_date><pages><first_page>33</first_page><last_page>56</last_page></pages><doi_data><doi>10.66224/jict.8678.11.41.33</doi><resource>http://jour.aicti.ir/en/Article/8678</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/8678</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/8678</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/8678</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/8678</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/8678</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/8678</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/8678</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>Farmer, J.D. and A.W. Lo, Frontiers of         finance: Evolution and efficient markets. Proceedings of the National Academy of Sciences, 1999. 96(18): p. 9991-9992.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
	2. Vachhani, H., et al. Machine learning based stock market analysis: A short survey. in International Conference on Innovative Data Communication Technologies and Application. 2019. Springer.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
3. Jain, V.R., M. Gupta, and R.M. Singh, Analysis and Prediction of Individual </unstructured_citation></citation><citation key="ref4"><unstructured_citation>



4. جهانتیغ, ف., د.پ. تلگردویی, and صفورا, وقفه های زمانی بهینه در پیش بینی قیمت نفت توسط شبکه عصبی پویا اصلاح‌شده با الگوریتم ژنتیک. فصلنامه مطالعات اقتصاد انرژی, 2018. 14(56): p. 115-143.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
5. قربانی, et al., پیش بینی سیگنال معاملات سهام با استفاده از شبکه های پتری رنگی و الگوریتم ژنتیک (مطالعه موردی: بازار بورس تهران). پژوهشنامه مدیریت اجرایی, 2019. 11(21): p. 205-227.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
6.	نژاد, ف. and مینایی, پیش‌بینی رفتار بازار سهام بر اساس شبکه‌های عصبی مصنوعی با رویکرد یادگیری جمعی هوشمند. مدیریت صنعتی, 2018. 10(2): p. 315-334.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
7.	رمضانی and عاملی, پیش بینی قیمت سهام با استفاده از شبکه عصبی فازی مبتنی برالگوریتم ژنتیک و مقایسه با شبکه عصبی فازی. تحقیقات مدلسازی اقتصادی, 2016. 6(22): p. 61-91.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
8.	باباجانی, et al., پیش بینی قیمت سهام در بورس تهران با استفاده از شبکه عصبی بازگشتی بهینه شده با الگوریتم کلونی زنبور عسل مصنوعی. راهبرد مدیریت مالی, 2019. 7(2): p. 195-228.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
9.Kim, T. and H.Y. Kim, Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data. PloS one, 2019. 14(2).</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
10.monajemi, abzari, and rayati, Stock price prediction in stock exchange stock exchange using fuzzy neural network and genetic algorithm and comparing it with artificial neural network. Quarterly Journal of Economics, 2010. 3(6): p. 1-26.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
11.Hájek, P., V. Olej, and R. Myskova, Forecasting stock prices using sentiment information in annual reports: A neural network and support vector regression approach. WSEAS Transactions on Business and Economics, 2013. 10(4): p. 293-305.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
12.Hadavandi, E., H. Shavandi, and A. Ghanbari, Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowledge-Based Systems, 2010. 23(8): p. 800-808.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
13.Chen, Y., et al., Hybrid methods for stock index modeling. Fuzzy Systems and Knowledge Discovery, 2005: p. 490-490.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
14.Wang, S., et al., Stock price prediction based on chaotic hybrid particle swarm optimisation-RBF neural network. International Journal of Applied Decision Sciences, 2017. 10(2): p. 89-100.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
15.Khuat, T.T. and M.H. Le, An Application of Artificial Neural Networks Stock Prices of Financial Sector Companies in NIFTY50. International Journal of Information Engineering and Electronic Business, 2018. 11(2): p. 33.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
16.Ghasemiyeh, R., R. Moghdani, and S.S. Sana, A Hybrid Artificial Neural Network with Metaheuristic Algorithms for Predicting Stock Price. Cybernetics and Systems, 2017. 48(4): p. 365-392.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
18.Rajihy, Y., K. Nermend, and A. Alsakaa, Back-propagation artificial neural networks in stock market forecasting. An application to the Warsaw Stock Exchange WIG20. Aestimatio, 2017(15): p. 88.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
	20. موسوی, س. علیرضا, and غلامی, استفاده از الگوریتم ترکیبی عصبی کرم شب‌تاب و روش رگولاسیون بیزین جهت پیش‌بینی قیمت سهام. مهندسی مالی و مدیریت اوراق بهادار, 1397. 9(36): p. 295-321.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
21.Fischer, T. and C. Krauss, Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 2018. 270(2): p. 654-669.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
22.Long, W., Z. Lu, and L. Cui, Deep learning-based feature engineering for stock price movement prediction. Knowledge-Based Systems, 2019. 164: p. 163-173.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
23.Kelotra, A. and P. Pandey, Stock market prediction using optimized deep-convlstm model. Big Data, 2020. 8(1): p. 5-24.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
24.Xiao, C., W. Xia, and J. Jiang, Stock price forecast based on combined model of ARI-MA-LS-SVM. Neural Computing and Applications, 2020: p. 1-10.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
25.Lee, M.-C., Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, 2009. 36(8): p. 10896-10904.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
26.Chen, Y. and Y. Hao, A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications, 2017. 80: p. 340-355.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
27.Nair, B.B., V. Mohandas, and N. Sakthivel, A decision tree—rough set hybrid system for stock market trend prediction. International Journal of Computer Applications, 2010. 6(9): p. 1-6.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
28.Qiu, W., X. Liu, and L. Wang, Forecasting shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees. Expert Systems with Applications, 2012. 39(9): p. 7680-7689.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>
29.Basak, S., et al., Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 2019. 47: p. 552-567.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>
30.Khaidem, L., S. Saha, and S.R. Dey, Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003, 2016.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>
31.Sharma, N. and A. Juneja. Combining of random forest estimates using LSboost for stock market index prediction. in 2017 2nd International Conference for Convergence in Technology (I2CT). 2017. IEEE.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>
32.الهام, غ. and د. سيدمحمدرضا, پيش بيني روند قيمت در بازار سهام با استفاده از الگوريتم جنگل تصادفي. فصلنامه مهندسی مالی و مدیریت اوراق بهادار 1397. 9(35): p. 301-322.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>
33.Alkhatib, K., et al., Stock price prediction using k-nearest neighbor (kNN) algorithm. International Journal of Business, Humanities and Technology, 2013. 3(3): p. 32-44.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>
34.زاده, et al., پیش‌بینی قیمت سهام با استفاده از روش خود رگرسیون با وقفه توزیعی (ARDL). تحقیقات مالی, 1386. 9(23): p. 49-60.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>
35.قلی زاده, م.ح., et al., پیش بینی قیمت سهام با روش رگرسیون فازی. پژوهشنامه اقتصاد کلان, 1390. 6(12): p. 107-128.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>
36.Kita, E., M. Harada, and T. Mizuno, Application of Bayesian Network to stock price prediction. Artif. Intell. Research, 2012. 1(2): p. 171-184.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>
37.Sun, Q., W.-G. Che, and H.-L. Wang, Bayesian regularization BP neural network model for the stock price prediction, in Foundations and applications of intelligent systems. 2014, Springer. p. 521-531.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>
38.Wang, L., et al., Stock market trend prediction using dynamical Bayesian factor graph. Expert Systems with Applications, 2015. 42(15-16): p. 6267-6275.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>
39.Hassan, M.R., et al., A HMM-based adaptive fuzzy inference system for stock market forecasting. Neurocomputing, 2013. 104: p. 10-25.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>
40.Chang, P.-C. and C.-H. Liu, A TSK type fuzzy rule based system for stock price prediction. Expert Systems with applications, 2008. 34(1): p. 135-144.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>
41.Lincy, G.R.M. and C.J. John, A multiple fuzzy inference systems framework for daily stock trading with application to NASDAQ stock exchange. Expert Systems with Applications: An International Journal, 2016. 44(C): p. 13-21.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>
42.Chandar, S.K., Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction. Journal of Ambient Intelligence and Humanized Computing, 2019: p. 1-9.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>
43.Feylizadeh, M.R., M.H. Keshavarz, and A. Hendalianpour, Presenting a model for predicting the Tehran Stock Exchange Index using ANFIS and fuzzy regression. Journal of New Researches in Mathematics, 2019.</unstructured_citation></citation><citation key="ref42"><unstructured_citation>
44.Nhu, H.N., S. Nitsuwat, and M. Sodanil. Prediction of stock price using an adaptive Neuro-Fuzzy Inference System trained by Firefly Algorithm. in 2013 International Computer Science and Engineering Conference (ICSEC). 2013. IEEE.</unstructured_citation></citation><citation key="ref43"><unstructured_citation>
45.Dash, R. and P. Dash, Efficient stock price prediction using a self evolving recurrent neuro-fuzzy inference system optimized through a modified differential harmony search technique. Expert Systems with Applications, 2016. 52: p. 75-90.</unstructured_citation></citation><citation key="ref44"><unstructured_citation>
46.Wei, L.-Y., A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX. Economic Modelling, 2013. 33: p. 893-899.</unstructured_citation></citation><citation key="ref45"><unstructured_citation>
47.Bagheri, A., H.M. Peyhani, and M. Akbari, Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization. Expert Systems with Applications, 2014. 41(14): p. 6235-6250.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>
48.Han, J., J. Pei, and M. Kamber, Data mining: concepts and techniques. 2011: Elsevier.</unstructured_citation></citation><citation key="ref47"><unstructured_citation>
49.Bova, S., et al. A logical analysis of Mamdani-type fuzzy inference, I theoretical bases. in International Conference on Fuzzy Systems. 2010. IEEE.</unstructured_citation></citation><citation key="ref48"><unstructured_citation>
50.Kennedy, J. and R. Eberhart, Particle Sswarm Ooptimization. IEEE, 1995: p. 1942-1948.</unstructured_citation></citation><citation key="ref49"><unstructured_citation>
51.Engelbrecht, A.P., Computational intelligence: an introduction. 2 ed. 2007, England: John Wiley &amp; Sons. 597.</unstructured_citation></citation><citation key="ref50"><unstructured_citation>
52.Werbos, P.J., Beyond Regression: New Tools for Prediction and Analysis in the Behavioural Sciences. 1974, Harvard University: Boston, USA.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Introducing a genetic algorithm based Method for Community person's stance Detection in social media and news</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>mehdi</given_name><surname>salkhordeh haghighi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Seyyed Mohammad </given_name><surname>ebrahimi</surname></person_name></contributors><publication_date media_type="online"><month>10</month><day>3</day><year>2020</year></publication_date><pages><first_page>57</first_page><last_page>74</last_page></pages><doi_data><doi>10.66224/jict.8681.11.41.57</doi><resource>http://jour.aicti.ir/en/Article/8681</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/8681</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/8681</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/8681</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/8681</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/8681</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/8681</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/8681</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>1.A. M. Kaplan و M. Haenlein, “Users of the world, unite! The challenges and opportunities of social media,” Business Horizons, جلد 53, شماره 1, pp. 59-68, 2010. </unstructured_citation></citation><citation key="ref2"><unstructured_citation> 20148. [درون خطي2. 
3.A. Mislove , “Online social networks: measurement, analysis, and applications to distributed information systems,” 2009.</unstructured_citation></citation><citation key="ref3"><unstructured_citation> 
4.M. Sachan , D. Contractor, T. Faruquie و L. Subramaniam , “Using content and interactions for disdiscovering communities in social networks,” در Paper presented at the proceedings of the 21st international conference on world wide web, 2012. </unstructured_citation></citation><citation key="ref4"><unstructured_citation>
5.D. Ganley و C. Lampe, “The ties that bind: social network principles in online communities,” Decision Support Systems, جلد 47, شماره 3, pp. 266-274, 2009. </unstructured_citation></citation><citation key="ref5"><unstructured_citation>
6.M. Kuramochi و G. Karypis , “Finding frequent patterns in a large sparse graph,” Data Min Knowl Discov , جلد 11, شماره 3, p. 243–271. </unstructured_citation></citation><citation key="ref6"><unstructured_citation>
7.X. Yan و J. Han, “gspan: graph-based substructure pattern mining,” در Paper presented at the Proceedings of the IEEE international conference on data mining (ICDM 2002), 2002. </unstructured_citation></citation><citation key="ref7"><unstructured_citation>
8.H. Cai, V. W. Zheng و K. C.-C. Chang, “A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications,” IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, جلد 30, شماره 9, 2017. </unstructured_citation></citation><citation key="ref8"><unstructured_citation>
9.Z.-Y. Chen و C. C. Chen, “SCIFNET: Stance community identification of topic persons using friendship network analysis,” Knowledge-Based Systems, جلد 110, p. 30–48, 2016. </unstructured_citation></citation><citation key="ref9"><unstructured_citation>
10.B. Akhgar, G. B. Saathoff, H. R. Arabnia, R. Hill, A. Staniforth و P. Saskia Bayerl, تدوين كنندگان, “Mining Social Media:Architecture, Tools, and Approaches to Detect Criminal Activity,” در Application of Big Data for Application of Big Data for National Security, Elsevier, 2015, pp. 155-172.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
11.G.-J. Qi , C. Aggarwal و T. Huang, “Community detection with edge content in social media networks,” در Paper presented at the 2012 IEEE 28th international conference on data engineering</unstructured_citation></citation><citation key="ref11"><unstructured_citation>, 2012. 
12.S. Borgatti و M. Everett , “Graph colorings and power in experimental exchange networks,” Soc Netw, جلد 14, شماره 3, p. 287–308, 1992. # 
13.S. Nijssen و J. Kok , “A quickstart in frequent structure mining can make a difference,” در Paper presented at the Proceedings of the tenthACMSIGKDD international conference on knowledge discovery and data mining, 2004. </unstructured_citation></citation><citation key="ref12"><unstructured_citation>
14.C. Troussas , M. Virvou, J. Caro و K. Espinosa , “Mining relationships among user clusters in Facebook for language learning,” در Paper presented at the international conference on computer, information and telecommunication systems (CITS), 2013. </unstructured_citation></citation><citation key="ref13"><unstructured_citation>
15.C. Chen, Z.-Y. Chen و C.-Y. Wu , “An unsupervised approach for person name bipolarization using principal component analysis,” IEEE Trans. Knowl. Data Eng., جلد 24, pp. 1963-1976, 2012. </unstructured_citation></citation><citation key="ref14"><unstructured_citation>
16.C. Chen و C.-Y. Wu , “Bipolar person name identification of topic documents using principal component analysis,” در Proceeding of the 23rd International Conference on Computational Linguistics, 2010. </unstructured_citation></citation><citation key="ref15"><unstructured_citation>
17.C. D. Manning, P. Raghavan and H. Schütze, "An Introduction to Information Retrieval," in Cambridge University Press, New York, 2009. </unstructured_citation></citation><citation key="ref16"><unstructured_citation>
18.S. B. Yudhoatmojo و M. A. Samuar, “Community Detection On Citation Network Of DBLP Data Sample Set Using LinkRank Algorithm,” در 4th Information Systems International Conference 2017, ISICO 2017, Bali, Indonesia, 2017. </unstructured_citation></citation><citation key="ref17"><unstructured_citation>
19.B. Viswanath, M. Bashir , M. Crovella , S. Guha, K. Gummadi, B. Krishnamurthy و A. Mislove, “Towards detecting anomalous user behavior in online social networks.,” در Proceedings of the 23rd USENIX security symposium (USENIX Security, 2014. </unstructured_citation></citation><citation key="ref18"><unstructured_citation>
20.M. Girvan و M. Newman, “Community structure in social and biological networks,” Proc Natl Acad Sci, جلد 99(12), p. 7821–6, 2002. </unstructured_citation></citation><citation key="ref19"><unstructured_citation>
21.J. Gao, F. Liang , W. Fan, C. Wang , Y. Sun و J. Han, “On community outliers and their efficient detection in information networks,” در Proceedings of the 16th ACM SIGKDD international conference Proceedings of the 16th ACM SIGKDD international conference, 2010. </unstructured_citation></citation><citation key="ref20"><unstructured_citation>
22.X. Chen and J. Li, "Community detection in complex networks using edge-deleting with restrictions," Physica A: Statistical Mechanics and its Applications, vol. 519, pp. 181-194, 4 2019. </unstructured_citation></citation><citation key="ref21"><unstructured_citation>
23.M. Rezvani, W. Liang, . C. Liu و . J. Xu Yu , “Efficient Detection of Overlapping Communities Using Asymmetric Triangle Cuts,” IEEE Transactions on Knowledge and Data Engineering, جلد 30, شماره 11, 2018. </unstructured_citation></citation><citation key="ref22"><unstructured_citation>
24.Z. Liu, B. Xiang, W. Guo , Y. Chen, K. Guo و . J. Zheng , “Overlapping Community Detection Algorithm Based on Coarsening and Local Overlapping Modularity,” IEEE Access, جلد 7, 2019. </unstructured_citation></citation><citation key="ref23"><unstructured_citation>
25. T. Meng , L. Cai , . T. He , L. Chen و . Z. Deng, “Local Higher-Order Community Detection Based on Fuzzy Membership Functions,” IEEE Access, جلد 7, 2019. </unstructured_citation></citation><citation key="ref24"><unstructured_citation>
26.I. Messaoudi و N. Kamel, “A multi-objective bat algorithm for community detection on dynamic social networks,” Applied Intelligence, pp. 1-18, 1 2019.</unstructured_citation></citation><citation key="ref25"><unstructured_citation> 
27.W. Luo, N. Lu, L. Ni, W. Zhu و W. Ding, “Local community detection by the nearest nodes with greater centrality,” Information Sciences, جلد 517, pp. 377-392, 2020. </unstructured_citation></citation><citation key="ref26"><unstructured_citation>
28.W. Luo, . D. Zhang , . H. Jiang , L. Ni و . Y. Hu , “Local Community Detection With the Dynamic Membership Function,” IEEE Transactions on Fuzzy Systems, جلد 26, شماره 5, 2018. </unstructured_citation></citation><citation key="ref27"><unstructured_citation>
29.X. Wan, X. Zuo و F. Song, “Solving dynamic overlapping community detection problem by a multiobjective evolutionary algorithm based on decomposition,” Swarm and Evolutionary Computation, 2020</unstructured_citation></citation><citation key="ref28"><unstructured_citation> 
30.Y. Zhang, Y. Liu, J. Li, J. Zhu, C. Yang, W. Yang و C. Wenc, “WOCDA: A whale optimization based,” Physica A: Statistical Mechanics and its Applications, جلد 539, 2020. </unstructured_citation></citation><citation key="ref29"><unstructured_citation>
31.X. Pan, . G. Xu , . B. Wang و . T. Zhang , “A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks,” IEEE Access , جلد 7, 2019. </unstructured_citation></citation><citation key="ref30"><unstructured_citation>
32.Z. Harris, “Distributional structure,” Word , جلد 10, p. 146–162, 1954.</unstructured_citation></citation><citation key="ref31"><unstructured_citation> 
33.H. Kanayama و T. Nasukawa, “Fully automatic lexicon expansion for domain-ori- ented sentiment analysis,” در Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2006. </unstructured_citation></citation><citation key="ref32"><unstructured_citation>
34.G. Keller , “Statistics for Management and Economics,” Cengage Learning, 2008</unstructured_citation></citation><citation key="ref33"><unstructured_citation>. 
35.P. Turney و M. Littman , “Measuring praise and criticism: inference of semantic orientation from association,” ACM Trans. Inf. Syst., جلد 21, p. 315–346, 2003. # 
36.M. Newman, “Fast algorithm for detecting community structure in networks,” Phys. Rev., جلد 74, 2004. 
37.X. Xu, N. Yuruk, Z. Feng و T. Schweiger, “SCAN: a structural clustering algo- rithm for networks,” در Proceedings of the 13th ACM SIGKDD International 
Conference on Knowledge Discovery and Data Mining, 2007.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>
38.B. Yang, W. Cheung و J. Liu, “Community mining from signed social networks,” IEEE Trans. Knowl. Data Eng, جلد 19, p. 1333–1348, 2007. </unstructured_citation></citation><citation key="ref35"><unstructured_citation>
39.P. Anchuri و M. Magdon-Ismai, “Communities and balance in signed networks: a spectral approach,” در Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, 2012. </unstructured_citation></citation><citation key="ref36"><unstructured_citation>
40.T. Mitchel, Machine Learning, McGrawHill, 1997. </unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>context-aware travel recommender system exploiting from Geo-tagged photos</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>rezvan</given_name><surname>mohamadrezaei larki</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Reza</given_name><surname>Ravanmehr</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>milad </given_name><surname>amrolahi</surname></person_name></contributors><publication_date media_type="online"><month>10</month><day>3</day><year>2020</year></publication_date><pages><first_page>75</first_page><last_page>96</last_page></pages><doi_data><doi>10.66224/jict.8682.11.41.75</doi><resource>http://jour.aicti.ir/en/Article/8682</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/8682</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/8682</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/8682</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/8682</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/8682</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/8682</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/8682</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>1.J. Bobadilla, F. Ortega, A. Hernando and A. Gutiérrez, “Recommender systems survey,” Know.-Based Syst., vol. 46, pp.109-132, 2013.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
2. بهشتی نژاد، راحله. سمیع، محمد ابراهیم. حمزه، علی. (1398). «بهبود سیستم های توصیه گر با کمک وب معنایی»، نشریه فناوری اطلاعات و ارتباطات ایران ۹، شماره ۳۱ (۱۳۹۸): ۴۵-۵۶.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
3. M. Slehat,“Evaluation of potential tourism resources for developing different forms of tourism : case study of Iraq Al-Amir and its surrounding areas – Jordan,” PhD thesis, Catholic University of Eichstätt-Ingolstadt.2018.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
4. منتظر، غلامعلی. فتحی، وحید. (1394). «تخصیص بهینه درس پار به کمک الگوریتم بهینه سازی گروه ذرات»، نشریه فناوری اطلاعات و ارتباطات ایران ۶، شماره ۲۱ (۱۳۹۴): ۱۵-۲۶. </unstructured_citation></citation><citation key="ref5"><unstructured_citation>
5. N. Henze, P. Dolog and N. Wolfgang, “Reasoning and Ontologies for Personalized Elearning in the Semantic Web,” Educational Technology &amp; Society, Vol. 7, 82–97.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
6. صابری، نفیسه. منتظر، غلامعلی. (1389). «شخصي سازي محيط يادگيري الكترونيكي به كمك توصيه گر فازي مبتني برتلفيق سبك يادگيري و سبك شناختي» نشریه فناوری اطلاعات و ارتباطات ایران 2، شماره 3 (1389): 91-109.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
7. K. Choi, D. Yoo, G. Kim and Y. Suh, “A hybrid online-product recommendation system: Combining implicit Rating based collaborative filtering and sequential pattern analysis,” Electronic Commerce Research and Applications, Vol.11, pp. 309-317, 2012. </unstructured_citation></citation><citation key="ref8"><unstructured_citation>
8. G. Adomavicius and A. Tuzhilin, “Context-Aware Recommender Systems,” In: Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, Ed. Springer, Boston, MA, Springer US, pp. 217-253.‏ 2011.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
9. K. Verbert, N. Manouselis, X. Ochoa, M.  Wolpers, H. Drachsler, I. Bosnic and E. Duval, “Context-aware recommender systems for learning: a survey and future challenges,” IEEE Transactions on Learning Technologies, Vol. 5, no. 4, pp. 318-335, 2012.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
10. F. Ricci, “Mobile recommender systems,” Information Technology &amp; Tourism journal, vol. 12, no. 3, pp.205-231., 2010.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
11. R. Beale and P. Lonsdale, “Mobile context aware systems: The intelligence to support tasks and effectively utilise resources,” In: Mobile Human-Computer Interaction-Mobile HCI 2004. S. Brewster, M. Dunlop, Ed. Springer Berlin Heidelberg. pp. 240-251.‏ 2004.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
12. D. Weib, M. Duchon, F. Fuchs and C. Linnhoff-Popien, “Context-aware personalization for mobile multimedia services,” Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia. ACM, 2008.‏</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
13. N. Manouselis, H. Drachsler, R. Vuorikari, H. Hummel and R. Koper, “Recommender systems in technology enhanced learning,” Recommender systems handbook. Springer US. pp. 387-415. 2011.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
14. A. K. Dey, A. Gregory and D. Salber, “A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications,” International Journal of Human-Computer Studies, Vol. 16, no. 2, pp. 97-166.‏ 2001.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
15. P. Brusilovsky and E. Millán, “User models for adaptive hypermedia and adaptive educational systems,” The adaptive web. Springer-Verlag, 2007.‏</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
16. K. Xu, Clustering. In: Dubitzky W., Wolkenhauer O., Cho KH., Yokota H. (eds) Encyclopedia of Systems Biology. 2013, Springer, New York, NY.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
17. S. Chen, Z. Xu and Y. Tang, “A Hybrid Clustering Algorithm Based on Fuzzy c-Means and Improved Particle Swarm Optimization,” Arabian Journal for Science and Engineering, Vol. 39, pp.8875–8887, 2014.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
18. KP. Soman, S. Diwakar and V. Ajay, “Data mining: theory and practice,” PHI Learning Pvt. Ltd.; 2006.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
19. J. Nayak, B. Naik and HS. Behera, “Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014,” In Computational intelligence in data mining-Vol. 2, pp. 133-149. 2015, Springer, New Delhi.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
20. A. Stetco, X. Zeng and J. Keane, “Fuzzy C-means++: Fuzzy C-means with effective seeding initialization,” Expert Systems with Applications, Vol. 42, no. 21, pp. 7541–7548, 2015.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
21. A. Gargari and E. Lucas, “Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition,” IEEE Congress on Evolutionary Computation, pp. 4661-4667, 2007.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
22. N.M. Villegas, C. Sanchez, J. Dõaz-Cely and G. Tamura, “Characterizing Context -Aware Recommender Systems: A Systematic Literature Review,” Knowledge-Based Systems. Vol. 140, pp. 173-200, 2017.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
23. G. Chen and L. Chen, “Augmenting service recommender systems by incorporating contextual opinions from user reviews,” User Modeling and User-Adapted Interaction, Vol. 25, no. 3, pp. 295–329, 2015.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
24. Z. Xu, C. Ling and C. Gencai, “Topic based context-aware travel recommendation method exploiting geotagged photos,” Neurocomputing, Vol. 155, pp. 99-107, 2015.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
25. Shafaqat, W. and Byun, Y.C., 2020. A Recommendation Mechanism For Under-Emphasized Tourist Spots Using Topic Modeling And Sentiment Analysis. Sustainability, 12(1), p.320.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
26. Alrasheed, H., Alzeer, A., Alhowimel, A. and Althyabi, A., 2020. A Multi-Level Tourism Destination Recommender System. Procedia Computer Science, 170, pp.333-340.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>
27. H. Khallouki, A. Abatal and M. Bahaj,  “An ontology-based context awareness for smart tourism recommendation system,” In:  Proceedings of the Inter-national Conference on Learning and Optimization Algorithms:  Theory and  Applications,  LOPAL  2018,  Rabat,  Morocco,  May  2-5,  2018,  pp43:1–43:5.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>
28. E. Pantano, C-V. Priporas, N. Stylos and C. Dennis, “Facilitating tourists' decision making through open data analyses: A novel recommender system,” Tourism Management Perspectives, Vol. 31. pp. 323-331, 2019.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>
29. B. Kaya, “A hotel recommendation system based on customer location: a link prediction approach,” Multimedia Tools and Applications, Vol. 79, pp. 1745–1758, 2020.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>
30. L. Esmaeili , S. Mardani , A. Hashemi Golpayegani and Z. Zanganeh Madar. “A Novel Tourism Recommender System in the Context of Social Commerce,” Expert Systems With Applications, Vol.149, 113301, July 2020.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>
31. BM. Veloso, F. Leal, B. Malheiro and JC. Burguillo, “On-line guest profiling and hotel recommendation,” Electronic Commerce Research and Applications, Vol. 34, 100832, 2019.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>
32. Z. Xu, L. Chen, H. Guo, M. Lv and G. Chen, “User similarity-based gender-aware travel location recommendation by mining geotagged photos,” International Journal of Embedded Systems, Vol. 10, no. 5, 356, 2018.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>
33. A. Majid, L. Chen, G. Chen, H. Mirza, I. Hussain and J. Woodwaard, “A context-aware personalized travel recommendation system based on Geotagged social media data mining,” International Journal of Geographical Information Science, Vol. 27, no. 4, pp. 662-684, 2013.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>
34. I. Memon, L. Chen, A. Majid, M. Lv, I. Hussain and G. Chen, “Travel recommendation using geo-tagged photos in social media for tourist,” Wireless Personal Communications. vol. 80, no. 4, pp. 1347-1362, 2015.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>
35. Z. Xu, L. Chen, Y. Dai and G. Chen. “A Dynamic Topic Model and Matrix Factorization based Travel Recommendation Method Exploiting Ubiquitous Data,” IEEE Transactions on Multimedia. Vol. 19, no. 8, pp. 1933-1945, 2017.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>
36. S. Ojagh, M. Malek, S. Saeedi, and S. Liag, “A location-based orientation-aware recommender system using IoT smart devices and Social Networks,” Future Generation Computer Systems, Vol. 108, pp. 97-118, 2020.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>
37. G. Zhao, P. Lou, X. Qian and X. Hou, “Personalized location recommendation by fusing sentimental and spatial context,” Knowledge-Based Systems, Vol. 196, 2020.
38. M. Memarzadeh and A. Kamandi, “Model-Based Location Recommender System Using Geotagged Photos On Instagram,” 2020 6th International Conference on Web Research (ICWR), Tehran, Iran, 2020, pp. 203-208.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>
39. L.W. Dietz, A. Sen, R. Roy and W. Worndl, “Mining trips from location-based social networks for clustering travelers and destinations,” Information Technology &amp; Tourism, Vol. 22, pp. 131–166, 2020.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>
40. D. Lyu, L. Chen, Z. Xu and S. Yu, “Weighted multi-information constrained matrix factorization for personalized travel location recommendation based on geo-tagged photos,” Applied Intelligence, Vol. 50, pp. 924–938, 2020.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>
41. A. Chaghari and M. Feizi-Derakhshi,  “Automatic Clustering Using Improved Imperialist Competitive Algorithm,” JSDP. 2017; 14 (2) :159-169.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>
42. امیری، مریم. ختن لو، حسن. (1392).« خوشه بندی اسناد، مبتنی بر‌آنتولوژی و‌ رویکرد فازی» نشریه فناوری اطلاعات و ارتباطات ایران 5، شماره 17 (1392): 73-96.</unstructured_citation></citation><citation key="ref42"><unstructured_citation>
43. D. L. Davies and D. W. Bouldin, “A cluster separation measure,” IEEE Transactions on Pattern Analysis and Machine Intelligence., Vol. 1, no. 2, pp. 224–227, 1979</unstructured_citation></citation><citation key="ref43"><unstructured_citation>
44. H. Chou, M. C. Su, and E. Lai, “A new cluster validity measure and its application to image compression,” Pattern Analysis and Applicationsvol. 7, no. 2, pp. 205–220, Jul. 2004.</unstructured_citation></citation><citation key="ref44"><unstructured_citation>
45. “Flickr 10K Dataset” https:// www.kaggle.com, [accessed: Sep 2020]
46. Del Olmo, F.H. and Gaudioso, E., 2008. Evaluation of recommender systems: A new approach. Expert Systems with Applications, 35(3), pp.790-804.</unstructured_citation></citation><citation key="ref45"><unstructured_citation>
47. E. Rendón, I. M. Abundez, C. Gutierrez, S. D. Zagal, A. Arizmendi, E. M. Quiroz and H. E. Arzate, “ A comparison of internal and external cluster validation indexes,” In Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications (AMERICAN-MATH’11/CEA’11). World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, USA, 158–163, 2011.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>
48. A. Majid, L. Chen, G. Chen, H. T. Mirza and  I. Hussain, “GoThere: Travel suggestions using geotagged photos,” In WWW 2012 companion, April 16–20, 2012. Lyon, France: ACM.</unstructured_citation></citation><citation key="ref47"><unstructured_citation>
49. Y. Zheng, L. Zhang, X. Xie and W.Y. Ma, “Mining interesting locations and travel sequences from GPS trajectories,” in: Proceedings of the 18th International Conference on World Wide Web, WWW '09, ACM, New York, NY, USA, 2009, pp. 791–800.</unstructured_citation></citation><citation key="ref48"><unstructured_citation>
50. Z. Yin, L. Cao, J. Han, J. Luo and T.S. Huang, “Diversified trajectory pattern ranking in geo-tagged social media,” in: Proceedings of SIAM International Conference on Data Mining, 2011, pp. 980–991.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Polar Diagram of Points with Moving Pole</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Bahram</given_name><surname>Sadeghi Bigham</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>fateme</given_name><surname>rabani</surname></person_name></contributors><publication_date media_type="online"><month>10</month><day>3</day><year>2020</year></publication_date><pages><first_page>97</first_page><last_page>104</last_page></pages><doi_data><doi>10.66224/jict.8683.11.41.97</doi><resource>http://jour.aicti.ir/en/Article/8683</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/8683</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/8683</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/8683</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/8683</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/8683</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/8683</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/8683</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>1.Grima, CI, Márquez, A, and Ortega, L. A locus approach to angle Problems in computational geometry. In Proc. of 14th European Workshop in Computational Geometry, Barcelona, 1998.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
2.Grima, CI, Márquez, A, and Ortega, L. Polar diagrams of geometric objects. In 15th European Workshop in Computational Geometry, 1999.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
3.Grima, C, Márquez, A, and Ortega, L. Motion Planning and visibility Problems using Polar diagrams. In Annual conference of the European association for computer graphics, EG. Citeseer, 2003.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
4.Bigham, B Sadeghi and Mohades, Ali. The dual of Polar diagrams and its extraction. In International Conference of Computational Methods in Sciences and Engineering ICCMSE, vol. 7, PP. 451–454, 2006.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
5.Ortega, Lidia M, Rueda, Antonio J, and Feito, Francisco R. A solution to the Path Planning Problem using angle Pre-Processing. Robotics and Autonomous Systems, 58(1):27–36, 2010.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
6.Ortega, Lidia and Robles-Ortega, M Dolores. Visibility resolution with Polar diagrams. APPl. Math, 7(5):1651–1669, 2013.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
7.Bigham, B Sadeghi and Mohades, Ali. Polar diagram with respect to a near Pole. In 23rd European Workshop on Computational Geometry EWCG07, Austria, PP. 206–209. Citeseer, 2007.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
8.Bigham, Bahram Sadeghi, Eskandari, Marzieh, and Tahmasbi, Maryam. Near-Pole Polar diagram of objects and duality. Journal of Computational Science, 3(3):127–131, 2012.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
9.Sadeghi Bigham, Bahram, Mohades, Ali, and Ortega, Lidia. Dynamic Polar diagram. Information Processing Letters, 109(2):142–146, 2008.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
10.Ehsanfar, Ebrahim, Bigham, Bahram 
Sadeghi, and Madadi, Najmeh. An optimal solution for dynamic Polar diagram. in CCCG, PP. 51–54, 2010.e</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
11.Beygi, Mojtaba Nouri and Ghodsi, Mohammad. Polar diagram of moving objects. In 20th Annual Canadian Conference on Computational Geometry, P. 51. Citeseer, 2008.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
12.بزاز، زینب، کاربرد دیاگرام ورونوی حساس به زاویه در مسایل بینایی، پایان نامه کارشناسی ارشد، دانشگاه صنعتی امیرکبیر، 1389</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
13.Aronov, Boris, Edelsbrunner, Herbert, Guibas, Leonidas J., and Sharir, Micha. The number of edges of many faces in a line segment arrangement. Combinatorica, 12(3):261–274, 1992.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
14.De Berg, Mark, Cheong, Otfried, van Kreveld, Marc, and Overmars, Mark. Computational geometry. SPringer Berlin Heidelberg, Berlin, third ed., 2008.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
15.KirkPatrick, David. Optimal search in planar subdivisions. SIAM Journal on Computing, 12(1):28–35, 1983.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
16.Chazelle, Bernard and Dobkin, David P. Intersection of convex objects in two and three dimensions. Journal of the ACM (JACM), 34(1):1–27, 1987.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
17.ربانی، فاطمه، دیاگرام قطبی با قطب متحرک. پایان نامه کارشناسی ارشد، دانشگاه تحصیلات تکمیلی علوم پایه زنجان، 1391</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
18.Sun, Qinbo, et al. "Tacking Control of an Autonomous Sailboat Based on Force Polar Diagram." 2018 13th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2018.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
19.The magnetotelluric (MT) method is commonly used to estimate the subsurface conductivity structure. </unstructured_citation></citation><citation key="ref20"><unstructured_citation>
20.Pranata, Erick, Selvi Misnia Irawati, and Sintia Windhi Niasari. "Magnetotelluric Data Analysis using Swift Skew, Bahr Skew, Polar Diagram, and Phase Tensor: a Case Study in Yellowstone, US."</unstructured_citation></citation></citation_list></journal_article></journal></body></doi_batch>