Auswahl der wissenschaftlichen Literatur zum Thema „Popularity detection“
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Zeitschriftenartikel zum Thema "Popularity detection"
Zhang, Xiaoming, Xiaoming Chen, Yan Chen, Senzhang Wang, Zhoujun Li und Jiali Xia. „Event detection and popularity prediction in microblogging“. Neurocomputing 149 (Februar 2015): 1469–80. http://dx.doi.org/10.1016/j.neucom.2014.08.045.
Der volle Inhalt der QuelleNN, Mrs Deepti. „D-SCAN : DEPRESSION DETECTION“. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, Nr. 04 (23.04.2024): 1–5. http://dx.doi.org/10.55041/ijsrem31462.
Der volle Inhalt der QuelleMiao, Zhongchen, Kai Chen, Yi Fang, Jianhua He, Yi Zhou, Wenjun Zhang und Hongyuan Zha. „Cost-Effective Online Trending Topic Detection and Popularity Prediction in Microblogging“. ACM Transactions on Information Systems 35, Nr. 3 (09.06.2017): 1–36. http://dx.doi.org/10.1145/3001833.
Der volle Inhalt der QuelleWolcott, M. J. „Advances in nucleic acid-based detection methods.“ Clinical Microbiology Reviews 5, Nr. 4 (Oktober 1992): 370–86. http://dx.doi.org/10.1128/cmr.5.4.370.
Der volle Inhalt der QuelleHao, Yaojun, Peng Zhang und Fuzhi Zhang. „Multiview Ensemble Method for Detecting Shilling Attacks in Collaborative Recommender Systems“. Security and Communication Networks 2018 (11.10.2018): 1–33. http://dx.doi.org/10.1155/2018/8174603.
Der volle Inhalt der QuelleSkaperas, Sotiris, Lefteris Mamatas und Arsenia Chorti. „Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis“. IEEE Access 7 (2019): 142246–60. http://dx.doi.org/10.1109/access.2019.2940816.
Der volle Inhalt der QuelleSingha, Subroto, und Burchan Aydin. „Automated Drone Detection Using YOLOv4“. Drones 5, Nr. 3 (11.09.2021): 95. http://dx.doi.org/10.3390/drones5030095.
Der volle Inhalt der QuelleMadana Mohana, R., Paramjeet Singh, Vishal Kumar und Sohail Shariff. „Brutality detection and rendering of brutal frames“. MATEC Web of Conferences 392 (2024): 01072. http://dx.doi.org/10.1051/matecconf/202439201072.
Der volle Inhalt der QuelleSatwik, Pallerla. „Hate Speech Detection“. International Journal for Research in Applied Science and Engineering Technology 12, Nr. 3 (31.03.2024): 1646–49. http://dx.doi.org/10.22214/ijraset.2024.59053.
Der volle Inhalt der QuellePatil, Vaibhavi, Sakshi Patil, Krishna Ganjegi und Pallavi Chandratre. „Face and Eye Detection for Interpreting Malpractices in Examination Hall“. International Journal for Research in Applied Science and Engineering Technology 10, Nr. 4 (30.04.2022): 1119–23. http://dx.doi.org/10.22214/ijraset.2022.41456.
Der volle Inhalt der QuelleDissertationen zum Thema "Popularity detection"
Hsu, Yu-Song, und 許煜松. „A Fast Detection Algorithm on Popularity Modeling“. Thesis, 2007. http://ndltd.ncl.edu.tw/handle/03362414636695668535.
Der volle Inhalt der Quelle國立清華大學
資訊工程學系
95
Popularity of publications, such as CDs, books, and movies, is critical to circulations and incomes. However, an erroneous prediction of popularity of publications causes unnecessary costs, or lost due to underproduction. Hence, the analysis of popularity of products has become an important issue. Our purpose in this research was to detect the trend before a publication becomes popular. Generally, the time series of popularity of a product can be divided into three phases – the slow-start phase, the fast-growing phase, and the slow-end phase. We proposed a two-stages detecting algorithm, which monitored the popularity with a CUSUM mechanism, verified the monitoring by comparing the distributions of past and future data to find the outbreak point, predicted the future trend of popularity, and then detected the time that the growth slows down. Thus, the data series was divided into the three mentioned phases. Through some simulation results with real data, the rate of accuracy on detecting outbreak points was over 90%, and over 80% on detecting cool-down points. This exhibits that the proposed algorithm improves efficiency and accuracy while predicting popularity of publications.
„Social Media Analytics for Crisis Response“. Doctoral diss., 2015. http://hdl.handle.net/2286/R.I.29691.
Der volle Inhalt der QuelleDissertation/Thesis
Doctoral Dissertation Computer Science 2015
Bücher zum Thema "Popularity detection"
Copyright Paperback Collection (Library of Congress), Hrsg. Play it again. New York: Volo, 2001.
Den vollen Inhalt der Quelle findenIdentität ermitteln: Ethnische und postkoloniale Kriminalromane zwischen Popularität und Subversion. Würzburg: Königshausen & Neumann, 2011.
Den vollen Inhalt der Quelle findenRevenge of the homecoming queen. New York: Berkley Jam, 2007.
Den vollen Inhalt der Quelle findenGildersleeve, Jessica, und Kate Cantrell. Screening the Gothic in Australia and New Zealand. Nieuwe Prinsengracht 89 1018 VR Amsterdam Nederland: Amsterdam University Press, 2022. http://dx.doi.org/10.5117/9789463721141.
Der volle Inhalt der QuelleHenderson, Lauren. Nụ hôn thần chết. Hà Nội: NXB Văn hóa thông tin, 2009.
Den vollen Inhalt der Quelle findenScripted. New York, NY: G. P. Putnam's Sons, an imprint of Penguin Group (USA), 2015.
Den vollen Inhalt der Quelle findenHenderson, Lauren. Kiss me kill me. New York: Delacorte Press, 2008.
Den vollen Inhalt der Quelle findenHenderson, Lauren. Kiss Me Kill Me. New York: Random House Children's Books, 2009.
Den vollen Inhalt der Quelle findenStine, R. L. The dare. London: Pocket Books, 1994.
Den vollen Inhalt der Quelle findenStine, R. L. The dare. New York: Archway Paperbacks, 1994.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Popularity detection"
Schedl, Markus, Peter Knees und Gerhard Widmer. „Improving Prototypical Artist Detection by Penalizing Exorbitant Popularity“. In Computer Music Modeling and Retrieval, 196–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11751069_18.
Der volle Inhalt der QuelleSahoo, Somya Ranjan, und B. B. Gupta. „Popularity-Based Detection of Malicious Content in Facebook Using Machine Learning Approach“. In First International Conference on Sustainable Technologies for Computational Intelligence, 163–76. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0029-9_13.
Der volle Inhalt der QuelleTolosana, Ruben, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales und Javier Ortega-Garcia. „An Introduction to Digital Face Manipulation“. In Handbook of Digital Face Manipulation and Detection, 3–26. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_1.
Der volle Inhalt der QuelleDevaraj, Jayanthi. „A Comparative Analysis of Deep Learning Models for Fake News Detection and Popularity Prediction of Articles“. In Intelligent Systems and Sustainable Computational Models, 246–65. Boca Raton: Auerbach Publications, 2024. http://dx.doi.org/10.1201/9781003407959-16.
Der volle Inhalt der QuelleChu, Quanquan, Zhenhao Cao, Xiaofeng Gao, Peng He, Qianni Deng und Guihai Chen. „Cease with Bass: A Framework for Real-Time Topic Detection and Popularity Prediction Based on Long-Text Contents“. In Computational Data and Social Networks, 53–65. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04648-4_5.
Der volle Inhalt der QuelleZhu, Chengang, Guang Cheng und Kun Wang. „Program Popularity Prediction Approach for Internet TV Based on Trend Detecting“. In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 142–54. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74176-5_14.
Der volle Inhalt der QuelleNarayan, Shaifali, und Brij B. Gupta. „Study of Smartcards Technology“. In Handbook of Research on Intrusion Detection Systems, 341–56. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2242-4.ch017.
Der volle Inhalt der QuelleMartinez, Marcos E., Francisco López-Orozco, Karla Olmos-Sánchez und Julia Patricia Sánchez-Solís. „Mispronunciation Detection and Diagnosis Through a Chatbot“. In Handbook of Research on Natural Language Processing and Smart Service Systems, 31–45. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4730-4.ch002.
Der volle Inhalt der QuelleGautam, Shikha, und Anand Singh Jalal. „An Image Forgery Detection Approach Based on Camera's Intrinsic Noise Properties“. In Cyber Warfare and Terrorism, 712–22. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2466-4.ch044.
Der volle Inhalt der QuelleGautam, Shikha, und Anand Singh Jalal. „An Image Forgery Detection Approach Based on Camera's Intrinsic Noise Properties“. In Digital Forensics and Forensic Investigations, 92–102. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3025-2.ch008.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Popularity detection"
Abbink, Jasper, und Christian Doerr. „Popularity-based Detection of Domain Generation Algorithms“. In ARES '17: International Conference on Availability, Reliability and Security. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3098954.3107008.
Der volle Inhalt der Quellezhu, xinyi, und yu zhang. „An auxiliary edge cache strategy based on content popularity in NDN“. In Ninth Symposium on Novel Photoelectronic Detection Technology and Applications (NDTA2022), herausgegeben von Wenqing Liu, Hongxing Xu und Junhao Chu. SPIE, 2023. http://dx.doi.org/10.1117/12.2664524.
Der volle Inhalt der QuelleSkaperas, Sotiris, Lefteris Mamatas und Arsenia Chorti. „Early Video Content Popularity Detection with Change Point Analysis“. In GLOBECOM 2018 - 2018 IEEE Global Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/glocom.2018.8648121.
Der volle Inhalt der QuelleYang, Tianbao, Prakash Mandaym Comar und Linli Xu. „Community detection by popularity based models for authored networked data“. In ASONAM '13: Advances in Social Networks Analysis and Mining 2013. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2492517.2492520.
Der volle Inhalt der QuelleYang, Tianbao, Yun Chi, Shenghuo Zhu, Yihong Gong und Rong Jin. „Directed Network Community Detection: A Popularity and Productivity Link Model“. In Proceedings of the 2010 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2010. http://dx.doi.org/10.1137/1.9781611972801.65.
Der volle Inhalt der QuelleSteuber, Florian, Sinclair Schneider, João A. G. Schneider und Gabi Dreo Rodosek. „Real-Time Anomaly Detection and Popularity Prediction for Emerging Events on Twitter“. In ASONAM '23: International Conference on Advances in Social Networks Analysis and Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3625007.3627517.
Der volle Inhalt der QuelleZhang, Weifeng, Ting Zhong, Ce Li, Kunpeng Zhang und Fan Zhou. „CausalRD: A Causal View of Rumor Detection via Eliminating Popularity and Conformity Biases“. In IEEE INFOCOM 2022 - IEEE Conference on Computer Communications. IEEE, 2022. http://dx.doi.org/10.1109/infocom48880.2022.9796678.
Der volle Inhalt der QuelleSparks, Kevin A., Roger G. Li, Gautam S. Thakur, Robert N. Stewart und Marie L. Urban. „Facility detection and popularity assessment from text classification of social media and crowdsourced data“. In SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/3003464.3003466.
Der volle Inhalt der QuelleIlić, Velibor, und Milovan Medojević. „DETECTION OF ANOMALIES ON THE SURFACE OF WORKPIECES PRODUCED ON CNC MACHINES“. In 19th International Scientific Conference on Industrial Systems. Faculty of Technical Sciences, 2023. http://dx.doi.org/10.24867/is-2023-t3.1-2_11141.
Der volle Inhalt der QuelleS. B, Abilash, und Sujitha R. „Instagram Fake and Automated Account Detection: A Review“. In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/dpaz6258/ngcesi23p29.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Popularity detection"
Bielinskyi, Andrii, Vladimir Soloviev, Serhiy Semerikov und Viktoria Solovieva. Detecting Stock Crashes Using Levy Distribution. [б. в.], August 2019. http://dx.doi.org/10.31812/123456789/3210.
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