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Relevant bibliographies by topics / Emerging trend detection

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Journal articles

Academic literature on the topic 'Emerging trend detection'

Author: Grafiati

Published: 21 September 2023

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Journal articles on the topic "Emerging trend detection"

1

Hsu, Ming-Hung, Yu-Hui Chang, and 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, no. 1 (2010): 255–58. http://dx.doi.org/10.1609/icwsm.v4i1.14049.

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Social annotation has become a popular manner for web users to manage and share their information and interests. While users' interests vary with time, tag correlation also changes from users' perspectives. In this work, we explore four methods for estimating temporal correlation between social tags and detect if a long-term trend emerges from the history of temporal correlation between two tags. Three types of trends are specified: steadily-shifting, stabilizing, and cyclic. To compare the results of the four estimation methods, an indirect evaluation is realized by applying detected trends t

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2

VALENCIA, MARIA, CODRINA LAUTH, and 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 (2009): 59–80. http://dx.doi.org/10.1142/s0218488509006030.

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Trend detection analysis from unstructured data poses a huge challenge to current advanced, web-enabled knowledge-based systems (KBS). Consolidated studies in topic and trend detection from text streams have concentrated so far mainly on identifying and visualizing dynamically evolving text patterns. From the knowledge modeling perspective identifying and defining new, relevant features that are able to synchronize the emergent user intentions to the dynamicity of the system's structure is a need. Additionally the advanced KBS have to remain highly sensitive to the content change, marked by ev

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Lackner, Bettina C., Andrea K. Steiner, Gabriele C. Hegerl, and Gottfried Kirchengast. "Atmospheric Climate Change Detection by Radio Occultation Data Using a Fingerprinting Method." Journal of Climate 24, no. 20 (2011): 5275–91. http://dx.doi.org/10.1175/2011jcli3966.1.

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Abstract The detection of climate change signals in rather short satellite datasets is a challenging task in climate research and requires high-quality data with good error characterization. Global Navigation Satellite System (GNSS) radio occultation (RO) provides a novel record of high-quality measurements of atmospheric parameters of the upper-troposphere–lower-stratosphere (UTLS) region. Because of characteristics such as long-term stability, self calibration, and a very good height resolution, RO data are well suited to investigate atmospheric climate change. This study describes the signa

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Wu, Yuqi, Yuhan Deng, Longgang Zhang, Qidong Zhang, and Ran Bao. "Research on the Development of Unmanned Underwater System Detection Technology." Journal of Physics: Conference Series 2218, no. 1 (2022): 012079. http://dx.doi.org/10.1088/1742-6596/2218/1/012079.

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Abstract In recent years, the number of unmanned systems in the world has been increasing. The emerging underwater platforms such as UUV are large in number, small in size, low in speed and low in noise. In the future, a variety of ocean combat missions will use a large number of UUV platforms. The existing methods for detecting underwater vehicles mainly include sonar, magnetometer. Based on this, by referring to a large number of literatures, a review of the research progress of underwater exploration at home and abroad in recent years. And compared with the United States, Russia and other c

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SUCHIT K. RAI, SUNIL KUMAR, and 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, no. 4 (2021): 402–8. http://dx.doi.org/10.54386/jam.v23i4.144.

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Consequences of global warming and climate change are major threat to humans and their socio-economic activities. Agriculture of Bundelkhand region is supposed to be more vulnerable due to emerging scenario of climate change and poor socio-economic status of farming community. Many studies carried out elsewhere have shown evidence of regional temperature variability along with global climate changes. This study focuses on the temporal variability and trend in annual and seasonal temperature (1901-2012) at six locations of Bundelkhand region. The results of the analysis reveal that the annual m

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Parlina, Anne, Kalamullah Ramli, and Hendri Murfi. "Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means." Sustainability 13, no. 5 (2021): 2876. http://dx.doi.org/10.3390/su13052876.

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The literature discussing the concepts, technologies, and ICT-based urban innovation approaches of smart cities has been growing, along with initiatives from cities all over the world that are competing to improve their services and become smart and sustainable. However, current studies that provide a comprehensive understanding and reveal smart and sustainable city research trends and characteristics are still lacking. Meanwhile, policymakers and practitioners alike need to pursue progressive development. In response to this shortcoming, this research offers content analysis studies based on

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Perisic, Marija Majda, Mario Štorga, and John S. Gero. "COMPUTATIONAL STUDY ON DESIGN SPACE EXPANSION DURING TEAMWORK." Proceedings of the Design Society 1 (July 27, 2021): 691–700. http://dx.doi.org/10.1017/pds.2021.69.

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AbstractWhen observing a design space expansion during teamwork, several studies found that cumulative solution-related issues' occurrence follows a linear trend. Such findings contradict the hypothesis of solution-related issues being characteristic for the later design stages. This work relies on agent-based simulations to explore the emerging patterns in design solution space expansion during teamwork. The results demonstrate trends that accord with the empirical findings, suggesting that a cognitive effort in solution space expansion remains constant throughout a design session. The collec

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Salunkhe, Uma R., and 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.

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In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines

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Katsurai, Marie, and Shunsuke Ono. "TrendNets: mapping emerging research trends from dynamic co-word networks via sparse representation." Scientometrics 121, no. 3 (2019): 1583–98. http://dx.doi.org/10.1007/s11192-019-03241-6.

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Abstract Mapping the knowledge structure from word co-occurrences in a collection of academic papers has been widely used to provide insight into the topic evolution in an arbitrary research field. In a traditional approach, the paper collection is first divided into temporal subsets, and then a co-word network is independently depicted in a 2D map to characterize each period’s trend. To effectively map emerging research trends from such a time-series of co-word networks, this paper presents TrendNets, a novel visualization methodology that highlights the rapid changes in edge weights over tim

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Schaffhauser, Andreas, Wojciech Mazurczyk, Luca Caviglione, Marco Zuppelli, and Julio Hernandez-Castro. "Efficient Detection and Recovery of Malicious PowerShell Scripts Embedded into Digital Images." Security and Communication Networks 2022 (June 29, 2022): 1–12. http://dx.doi.org/10.1155/2022/4477317.

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Due to steady improvements in defensive systems, malware developers are turning their attention to mechanisms for cloaking attacks as long as possible. A recent trend exploits techniques like Invoke-PSImage, which allows embedding a malicious script within an innocent-looking image, for example, to smuggle data into compromised devices. To address such a class of emerging threats, new mechanisms are needed, since standard tools fail in their detection or offer poor performance. To this aim, this work introduces Mavis, an efficient and highly accurate method for detecting hidden payloads, retri

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