Literatura académica sobre el tema "Emerging trend detection"
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Artículos de revistas sobre el tema "Emerging trend detection"
Hsu, Ming-Hung, Yu-Hui Chang y Hsin-Hsi Chen. "Temporal Correlation between Social Tags and Emerging Long-Term Trend Detection". Proceedings of the International AAAI Conference on Web and Social Media 4, n.º 1 (16 de mayo de 2010): 255–58. http://dx.doi.org/10.1609/icwsm.v4i1.14049.
Texto completoVALENCIA, MARIA, CODRINA LAUTH y ERNESTINA MENASALVAS. "EMERGING USER INTENTIONS: MATCHING USER QUERIES WITH TOPIC EVOLUTION IN NEWS TEXT STREAMS". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, supp01 (agosto de 2009): 59–80. http://dx.doi.org/10.1142/s0218488509006030.
Texto completoLackner, Bettina C., Andrea K. Steiner, Gabriele C. Hegerl y Gottfried Kirchengast. "Atmospheric Climate Change Detection by Radio Occultation Data Using a Fingerprinting Method". Journal of Climate 24, n.º 20 (15 de octubre de 2011): 5275–91. http://dx.doi.org/10.1175/2011jcli3966.1.
Texto completoWu, Yuqi, Yuhan Deng, Longgang Zhang, Qidong Zhang y Ran Bao. "Research on the Development of Unmanned Underwater System Detection Technology". Journal of Physics: Conference Series 2218, n.º 1 (1 de marzo de 2022): 012079. http://dx.doi.org/10.1088/1742-6596/2218/1/012079.
Texto completoSUCHIT K. RAI, SUNIL KUMAR y MANOJ CHAUDHARY. "Detection of annual and seasonal temperature variability and change using non-parametric test- A case study of Bundelkhand region of central India". Journal of Agrometeorology 23, n.º 4 (11 de noviembre de 2021): 402–8. http://dx.doi.org/10.54386/jam.v23i4.144.
Texto completoParlina, Anne, Kalamullah Ramli y Hendri Murfi. "Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means". Sustainability 13, n.º 5 (7 de marzo de 2021): 2876. http://dx.doi.org/10.3390/su13052876.
Texto completoPerisic, Marija Majda, Mario Štorga y John S. Gero. "COMPUTATIONAL STUDY ON DESIGN SPACE EXPANSION DURING TEAMWORK". Proceedings of the Design Society 1 (27 de julio de 2021): 691–700. http://dx.doi.org/10.1017/pds.2021.69.
Texto completoSalunkhe, Uma R. y Suresh N. Mali. "Security Enrichment in Intrusion Detection System Using Classifier Ensemble". Journal of Electrical and Computer Engineering 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/1794849.
Texto completoKatsurai, Marie y Shunsuke Ono. "TrendNets: mapping emerging research trends from dynamic co-word networks via sparse representation". Scientometrics 121, n.º 3 (18 de octubre de 2019): 1583–98. http://dx.doi.org/10.1007/s11192-019-03241-6.
Texto completoSchaffhauser, Andreas, Wojciech Mazurczyk, Luca Caviglione, Marco Zuppelli y Julio Hernandez-Castro. "Efficient Detection and Recovery of Malicious PowerShell Scripts Embedded into Digital Images". Security and Communication Networks 2022 (29 de junio de 2022): 1–12. http://dx.doi.org/10.1155/2022/4477317.
Texto completoTesis sobre el tema "Emerging trend detection"
Nguyen, Nhu Khoa. "Emerging Trend Detection in News Articles". Electronic Thesis or Diss., La Rochelle, 2023. http://www.theses.fr/2023LAROS003.
Texto completoIn the financial domain, information plays an utmost important role in making investment/business decisions as good knowledge can lead to crafting correct approaches in how to invest or if the investment is worth it. Moreover, being able to identify potential emerging themes/topics is an integral part of this field, since it can help get a head start over other investors, thus gaining a huge competitive advantage. To deduce topics that can be emerging in the future, data such as annual financial reports, stock market, and management meeting summaries are usually considered for review by professional financial experts. Reliable sources of information coming from reputable news publishers, can also be utilized for the purpose of detecting emerging themes. Unlike social media, articles from these publishers have high credibility and quality, thus when analyzed in large sums, it is likely to discover dormant/hidden information about trends or what can become future trends. However, due to the vast amount of information generated each day, it has become more demanding and difficult to analyze the data manually for the purpose of trend identification. Our research explores and analyzes data from different quality sources, such as scientific publication abstracts and a provided news article dataset from Bloomberg called Event-Driven Feed (EDF) to experiment on Emerging Trend Detection. Due to the enormous amount of available data spread over extended time periods, it encourages the use of contrastive approaches to measuring the divergence between past and present surrounding context of extracted words and phrases, thus comparing the similarity between unique vector representations of each interval to discover movement in word usage that can lead to the discovery of new trend. Experimental results reveal that the assessment of context change through time of selected terms is able to detect critical emerging trends and points of emergence. It is also discovered that assessing the evolution of context over a long time span is better than just contrasting the two most recent points in time
Redyuk, Sergey. "Finding early signals of emerging trends in text through topic modeling and anomaly detection". Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15507.
Texto completoPetit, Eva. "Modélisation de données de surveillance épidémiologique de la faune sauvage en vue de la détection de problèmes sanitaires inhabituels". Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENS006/document.
Texto completoRecent studies have shown that amongst emerging infectious disease events in humans, about 40% were zoonoses linked to wildlife. Disease surveillance of wildlife should help to improve health protection of these animals and also of domestic animals and humans that are exposed to these pathogenic agents. Our aim was to develop tools capable of detecting unusual disease events in free ranging wildlife, by adopting a syndromic approach, as it is used for human health surveillance, with pathological profiles as early unspecific health indicators. We used the information registered by a national network monitoring causes of death in wildlife in France since 1986, called SAGIR. More than 50.000 cases of mortality in wildlife were recorded up to 2007, representing 244 species of terrestrial mammals and birds, and were attributed to 220 different causes of death. The network was first evaluated for its capacity to detect early unusual events. Syndromic classes were then defined by a statistical typology of the lesions observed on the carcasses. Syndrome time series were analyzed, using two complimentary methods of detection, one robust detection algorithm developed by Farrington and another generalized linear model with periodic terms. Historical trends of occurrence of these syndromes and greater-than-expected counts (signals) were identified. Reporting of unusual mortality events in the network bulletin was used to interpret these signals. The study analyses the relevance of the use of syndromic surveillance on this type of data and gives elements for future improvements
Junior, José Sergio Bleckmann Reis. "Métodos e softwares para análise da produção científica e detecção de frentes emergentes de pesquisa". Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/85/85133/tde-14102016-131850/.
Texto completoThe progress of previous projects pointed out the need to face some problems of software for detecting emerging research and development trends from databases of scientific publications. It became evident the lack of efficient computing applications dedicated to this purpose that are artifacts of great usefulness to better planning research and development programs in institutions. A review of the currently available software was performed, in order to clearly delineate the opportunity to develop new tools. As a result, a software called Citesnake was implemented, designed particularly to help the detection and study of emerging trends from the analysis of networks of several types extracted from the scientific databases. Using this robust and effective computational tool, analyzes of emerging research and development trends were performed in the field of Generation IV Nuclear Power Generation Systems, in such a way to point out, among the most promising reactor types selected by the GIF - Generation IV International Forum, those that have better evolved over the past ten years and seem to be currently the most capable of fulfilling the promises made on their innovative concepts.
Decker, Sheron Levar. "Detection of bursty and emerging trends towards identification of researchers at the early stage of trends". 2007. http://purl.galileo.usg.edu/uga%5Fetd/decker%5Fsheron%5Fl%5F200708%5Fms.
Texto completoLibros sobre el tema "Emerging trend detection"
Freedman, Jeri. Lymphoma: Current and emerging trends in detection and treatment. New York: Rosen Pub. Group, 2006.
Buscar texto completoFreedman, Jeri. Brain cancer: Current and emerging trends in detection and treatment. New York: Rosen, 2008.
Buscar texto completoEsophageal cancer: Current and emerging trends in detection and treatment. New York: Rosen Pub., 2012.
Buscar texto completoCasil, Amy Sterling. Pancreatic cancer: Current and emerging trends in detection and treatment. New York: Rosen Pub., 2008.
Buscar texto completoFreedman, Jeri. Ovarian cancer: Current and emerging trends in detection and treatment. New York: Rosen Pub. Group, 2009.
Buscar texto completoHussain, Chaudhery. Smartphone-Based Detection Devices: Emerging Trends in Analytical Techniques. Elsevier, 2021.
Buscar texto completoHussain, Chaudhery Mustansar. Smartphone-Based Detection Devices: Emerging Trends in Analytical Techniques. Elsevier, 2021.
Buscar texto completoHarmon, Daniel E. Leukemia Current and Emerging Trends in Detection and Treatment. Rosen Publishing Group, 2011.
Buscar texto completoHasan, Heather. Testicular Cancer Current and Emerging Trends in Detection and Treatment. Rosen Publishing Group, 2011.
Buscar texto completoBreast Cancer: Current and Emerging Trends in Detection and Treatment. Rosen Publishing Group, 2005.
Buscar texto completoCapítulos de libros sobre el tema "Emerging trend detection"
Kontostathis, April, Leon M. Galitsky, William M. Pottenger, Soma Roy y Daniel J. Phelps. "A Survey of Emerging Trend Detection in Textual Data Mining". En Survey of Text Mining, 185–224. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4305-0_9.
Texto completoThomas Rincy, N. y Roopam Gupta. "A Survey of Network Intrusion Detection Using Machine Learning Techniques". En Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, 81–122. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66288-2_4.
Texto completoPatra, Santanu, Rashmi Madhuri y Prashant K. Sharma. "Role of Nanomaterials as an Emerging Trend Towards the Detection of Winged Contaminants". En Nanotechnology in Oil and Gas Industries, 245–89. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60630-9_9.
Texto completoPopoola, Segun I., Ruth Ande, Kassim B. Fatai y Bamidele Adebisi. "Deep Bidirectional Gated Recurrent Unit for Botnet Detection in Smart Homes". En Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, 29–55. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66288-2_2.
Texto completoThomas Rincy, N. y Roopam Gupta. "Correction to: A Survey of Network Intrusion Detection Using Machine Learning Techniques". En Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, C1. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66288-2_13.
Texto completoAdewole, Kayode S., Muiz O. Raheem, Oluwakemi C. Abikoye, Adeleke R. Ajiboye, Tinuke O. Oladele, Muhammed K. Jimoh y Dayo R. Aremu. "Malicious Uniform Resource Locator Detection Using Wolf Optimization Algorithm and Random Forest Classifier". En Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, 177–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66288-2_7.
Texto completoZeng, Li, Yang Li y Zili Li. "Research Hotspots, Emerging Trend and Front of Fraud Detection Research: A Scientometric Analysis (1984–2021)". En Data Mining and Big Data, 91–102. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8991-9_8.
Texto completoKhan, Summaiyya, Akrema, Rizwan Arif, Shama Yasmeen y Rahisuddin. "Recent Advancement in Nanostructured-Based Electrochemical Genosensors for Pathogen Detection". En Emerging Trends in Nanotechnology, 339–58. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9904-0_12.
Texto completoSuresh, S., M. Mohan, C. Thyagarajan y R. Kedar. "Detection of Ransomware in Emails Through Anomaly Based Detection". En Emerging Trends in Computing and Expert Technology, 604–13. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32150-5_59.
Texto completoDash, Rajesh Kumar, Manojit Samanta y Debi Prasanna Kanungo. "Debris Flow Hazard in India: Current Status, Research Trends, and Emerging Challenges". En Landslides: Detection, Prediction and Monitoring, 211–31. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23859-8_10.
Texto completoActas de conferencias sobre el tema "Emerging trend detection"
Nguyen, Nhu Khoa, Emanuela Boros, Gaël Lejeune, Antoine Doucet y Thierry Delahaut. "Utilizing Keywords Evolution in Context for Emerging Trend Detection in Scientific Publications". En SoICT 2022: The 11th International Symposium on Information and Communication Technology. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3568562.3568640.
Texto completoAmeerali, Aaron, Nadine Sangster y Gerard Ragbir. "AUTONOMOUS DETECTION OF VEHICULAR WHEEL ALIGNMENT PARAMETERS". En International Conference on Emerging Trends in Engineering & Technology (IConETech-2020). Faculty of Engineering, The University of the West Indies, St. Augustine, 2020. http://dx.doi.org/10.47412/boqw8777.
Texto completoAkasaki, Satoshi, Naoki Yoshinaga y Masashi Toyoda. "Early Discovery of Emerging Entities in Microblogs". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/678.
Texto completoTucker, Conrad S. y Harrison M. Kim. "Trending Mining for Predictive Product Design". En ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28364.
Texto completoHuang, Jihua y Han-Shue Tan. "Cooperative Collision Detection Based on Future-Trajectory Prediction". En ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14543.
Texto completoBrennan, Feargal y Bart de Leeuw. "The Use of Inspection and Monitoring Reliability Information in Criticality and Defect Assessments of Ship and Offshore Structures". En ASME 2008 27th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/omae2008-57934.
Texto completoSledge, Isaac J., James M. Keller y Gregory L. Alexander. "Emergent trend detection in diurnal activity". En 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4650040.
Texto completoZoveidavianpoor, Mansoor, Eadie Azahar Rosland, Pasi Laakkonen, Saman Aryana, Mohd Zaidi Jaafar, Jamal Mohamad Ibrahim, Hoshang Kolivand et al. "The Concept of Need for a Downhole Scale Inspection Tool: An Appraisal for an Emerging Technology in Scale Management". En International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21265-ms.
Texto completoUgoyah, Joy y Anita Mary Igbine. "Applications of AI and Data-Driven Modeling in Energy Production and Marketing Processes". En SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/207153-ms.
Texto completoLiu, Zhiming y Xiangyu Liu. "Future design outlook of wearable devices". En 10th International Conference on Human Interaction and Emerging Technologies (IHIET 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004053.
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