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Letteratura scientifica selezionata sul tema "Emerging trend detection"

Autore: Grafiati

Pubblicato: 21 settembre 2023

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Articoli di riviste sul tema "Emerging trend detection"

Hsu, Ming-Hung, Yu-Hui Chang e 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. 1 (16 maggio 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 to tag recommendation.

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VALENCIA, MARIA, CODRINA LAUTH e 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 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 evolution of trends in topics extracted from text streams. In this paper, we are describing a three-layered approach called the "user-system-content method" that is helping us to identify the most relevant knowledge mapping features derived from the USER, SYSTEM and CONTENT perspectives into an overall "context model", that will enable the advanced KBS to automatically streamline the query enrichment process in a much more user-centered, dynamical and flexible way. After a general introduction to our three-layered approach, we will describe into detail the necessary process steps for the implementation of our method and will present a case study for its integration on a real multimedia web-content portal using news streams as major source of unstructured information.

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Lackner, Bettina C., Andrea K. Steiner, Gabriele C. Hegerl e Gottfried Kirchengast. "Atmospheric Climate Change Detection by Radio Occultation Data Using a Fingerprinting Method". Journal of Climate 24, n. 20 (15 ottobre 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 signals of ENSO and the quasi-biennial oscillation (QBO) in the data and investigates whether the data already show evidence of a forced climate change signal, using an optimal-fingerprint technique. RO refractivity, geopotential height, and temperature within two trend periods (1995–2010 intermittently and 2001–10 continuously) are investigated. The data show that an emerging climate change signal consistent with the projections of three global climate models from the Coupled Model Intercomparison Project cycle 3 (CMIP3) archive is detected for geopotential height of pressure levels at a 90% confidence level both for the intermittent and continuous period, for the latter so far in a broad 50°S–50°N band only. Such UTLS geopotential height changes reflect an overall tropospheric warming. 90% confidence is not achieved for the temperature record when only large-scale aspects of the pattern are resolved. When resolving smaller-scale aspects, RO temperature trends appear stronger than GCM-projected trends, the difference stemming mainly from the tropical lower stratosphere, allowing for climate change detection at a 95% confidence level. Overall, an emerging trend signal is thus detected in the RO climate record, which is expected to increase further in significance as the record grows over the coming years. Small natural changes during the period suggest that the detected change is mainly caused by anthropogenic influence on climate.

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Wu, Yuqi, Yuhan Deng, Longgang Zhang, Qidong Zhang e Ran Bao. "Research on the Development of Unmanned Underwater System Detection Technology". Journal of Physics: Conference Series 2218, n. 1 (1 marzo 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 countries unmanned system detection technology and equipment, analysis of their applicable conditions and advantages and disadvantages. This paper summarizes the technical difficulties of underwater detection methods and proposes corresponding solutions. At the same time, we forecast the development trend of underwater detection technology in the future.

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SUCHIT K. RAI, SUNIL KUMAR e 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. 4 (11 novembre 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 maximum (TMax) and minimum (TMin) temperature has significantly increasing trend in all the locations in the range of 0.5 to 2.0oC 100 year-1 and 0.5 to 1.1 oC 100 year-1, respectively. Seasonal analysis revealed warming trend in both TMax (0.6-2.6oC100 year-1) and TMin (0.9 to 2.3 oC 100 year-1) during post-monsoon and winter season in all the locations. Majority of the locations showed cooling trend (0.3-1.0 oC 100 year-1), in the mean maximum and minimum temperature during monsoon season except at two locations i.e Jhansi and Banda. However, a significant positive trends (2.9 oC) in the TMin was found for the period of hundred years at Banda district during monsoon season.

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Parlina, Anne, Kalamullah Ramli e Hendri Murfi. "Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means". Sustainability 13, n. 5 (7 marzo 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 topic modeling approaches to capture the evolution and characteristics of topics in the scientific literature on smart and sustainable city research. More importantly, a novel topic-detecting algorithm based on the deep learning and clustering techniques, namely deep autoencoders-based fuzzy C-means (DFCM), is introduced for analyzing the research topic trend. The topics generated by this proposed algorithm have relatively higher coherence values than those generated by previously used topic detection methods, namely non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and eigenspace-based fuzzy C-means (EFCM). The 30 main topics that appeared in topic modeling with the DFCM algorithm were classified into six groups (technology, energy, environment, transportation, e-governance, and human capital and welfare) that characterize the six dimensions of smart, sustainable city research.

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Perisic, Marija Majda, Mario Štorga e John S. Gero. "COMPUTATIONAL STUDY ON DESIGN SPACE EXPANSION DURING TEAMWORK". Proceedings of the Design Society 1 (27 luglio 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 collected data on agents' cognitive processes and solution space properties enabled additional insights, which led to the detection of four distinct regimes of design solution space expansion.

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Salunkhe, Uma R., e 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 data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.

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Katsurai, Marie, e Shunsuke Ono. "TrendNets: mapping emerging research trends from dynamic co-word networks via sparse representation". Scientometrics 121, n. 3 (18 ottobre 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 time. Specifically, we formulated a new convex optimization framework that decomposes the matrix constructed from dynamic co-word networks into a smooth part and a sparse part: the former represents stationary research topics, while the latter corresponds to bursty research topics. Simulation results on synthetic data demonstrated that our matrix decomposition approach achieved the best burst detection performance over four baseline methods. In experiments conducted using papers published in the past 16 years at three conferences in different fields, we showed the effectiveness of TrendNets compared to the traditional co-word representation. We have made our codes available on the Web to encourage scientific mapping in all research fields.

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Schaffhauser, Andreas, Wojciech Mazurczyk, Luca Caviglione, Marco Zuppelli e Julio Hernandez-Castro. "Efficient Detection and Recovery of Malicious PowerShell Scripts Embedded into Digital Images". Security and Communication Networks 2022 (29 giugno 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, retrieving the embedded information, and estimating its size. Experimental results collected by considering real-world malicious PowerShell scripts showcase that Mavis can detect attacks with a high accuracy (100%) while keeping the rate of false positives and false negatives very low (0.01% and 0%, respectively). The proposed approach outperforms other solutions available in the literature or commercially through “as a service” model.

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Tesi sul 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.

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Dans le domaine de la finance, l'information joue un rôle extrêmement important dans la prise de décisions en matière d'investissement. En effet, une meilleure connaissance du contexte peut conduire à l'élaboration d'approches plus appropriées quant à la manière d'investir et à la valeur de l'investissement. En outre, être capable d'identifier les thématiques émergentes fait partie intégrante de ce domaine, car ceci peut aider à prendre de l'avance sur les autres investisseurs, et donc à obtenir des avantages concurrentiels considérables. Pour identifier les thèmes susceptibles d'émerger à l'avenir, des sources telles que les rapports financiers annuels, les données des marchés boursiers ou encore les résumés des réunions de la direction sont examinés par des experts financiers professionnels. Des sources d'information fiables provenant d'éditeurs de presse réputés peuvent également être utilisées pour détecter les thèmes émergents. Contrairement aux médias sociaux, les articles de ces éditeurs jouissent d'une crédibilité et d'une qualité élevées. Ainsi, lorsqu'ils sont analysés en grande quantité, il est probable que l'on découvre des informations dormantes/cachées sur les tendances ou ce qui peut devenir des tendances futures. Cependant, en raison de la grande quantité d'informations générées chaque jour, il est devenu plus exigeant et difficile d'analyser les données manuellement tout en détectant les tendances au plus vite. Notre recherche explore et analyse des données de différentes sources de qualité, telles que des résumés de publications scientifiques et un ensemble de données d'articles d'actualité fournis par Bloomberg, appelé Event-Driven Feed (EDF), afin d'expérimenter la détection des tendances émergentes. En raison de l'énorme quantité de données disponibles réparties sur de longues périodes de temps, elle encourage l'utilisation d'une approche contrastive pour mesurer la divergence entre le contexte environnant, passé et présent des mots et des phrases extraits, comparant ainsi la similarité entre les représentations vectorielles uniques de chaque intervalle pour découvrir des évolutions dans l'utilisation des termes qui peuvent conduire à la découverte d'une nouvelle tendance émergente. Les résultats expérimentaux révèlent que l'évaluation de l'évolution dans le temps du contexte des termes est susceptible de détecter les tendances critiques et les points d'émergence. On découvre également que l'évaluation de l'évolution du contexte sur une longue période est préférable à la simple comparaison des deux points les plus proches dans le temps
In 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

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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.

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Trend prediction has become an extremely popular practice in many industrial sectors and academia. It is beneficial for strategic planning and decision making, and facilitates exploring new research directions that are not yet matured. To anticipate future trends in academic environment, a researcher needs to analyze an extensive amount of literature and scientific publications, and gain expertise in the particular research domain. This approach is time-consuming and extremely complicated due to abundance of data and its diversity. Modern machine learning tools, on the other hand, are capable of processing tremendous volumes of data, reaching the real-time human-level performance for various applications. Achieving high performance in unsupervised prediction of emerging trends in text can indicate promising directions for future research and potentially lead to breakthrough discoveries in any field of science. This thesis addresses the problem of emerging trend prediction in text in two main steps: it utilizes HDP topic model to represent latent topic space of a given temporal collection of documents, DBSCAN clustering algorithm to detect groups with high-density regions in the document space potentially leading to emerging trends, and applies KLdivergence in order to capture deviating text which might indicate birth of a new not-yet-seen phenomenon. In order to empirically evaluate the effectiveness of the proposed framework and estimate its predictive capability, both synthetically generated corpora and real-world text collections from arXiv.org, an open-access electronic archive of scientific publications (category: Computer Science), and NIPS publications are used. For synthetic data, a text generator is designed which provides ground truth to evaluate the performance of anomaly detection algorithms. This work contributes to the body of knowledge in the area of emerging trend prediction in several ways. First of all, the method of incorporating topic modeling and anomaly detection algorithms for emerging trend prediction is a novel approach and highlights new perspectives in the subject area. Secondly, the three-level word-document-topic topology of anomalies is formalized in order to detect anomalies in temporal text collections which might lead to emerging trends. Finally, a framework for unsupervised detection of early signals of emerging trends in text is designed. The framework captures new vocabulary, documents with deviating word/topic distribution, and drifts in latent topic space as three main indicators of a novel phenomenon to occur, in accordance with the three-level topology of anomalies. The framework is not limited by particular sources of data and can be applied to any temporal text collections in combination with any online methods for soft clustering.

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Petit, 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.

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Des études récentes ont montré que parmi les infections émergentes chez l'homme, env. 40% étaient des zoonoses liées à la faune sauvage. La surveillance sanitaire de ces animaux devrait contribuer à améliorer la protection de leur santé et aussi celle des animaux domestiques et des hommes. Notre objectif était de développer des outils de détection de problèmes sanitaires inhabituels dans la faune sauvage, en adoptant une approche syndromique, utilisée en santé humaine, avec des profils pathologiques comme indicateurs de santé non spécifiques. Un réseau national de surveillance des causes de mortalité dans la faune sauvage, appelé SAGIR, a fourni les données. Entre 1986 et 2007, plus de 50.000 cas ont été enregistrés, représentant 244 espèces de mammifères terrestres et d'oiseaux, et attribués à 220 différentes causes de mort. Le réseau a d'abord été évalué pour sa capacité à détecter précocement des événements inhabituels. Des classes syndromiques ont ensuite été définies par une typologie statistique des lésions observées sur les cadavres. Les séries temporelles des syndromes ont été analysées en utilisant deux méthodes complémentaires de détection : un algorithme robuste développé par Farrington et un modèle linéaire généralisé avec des termes périodiques. Les tendances séculaires de ces syndromes et des signaux correspondent a des excès de cas ont été identifiés. Les signalements de problèmes de mortalité inhabituelle dans le bulletin du réseau ont été utilisés pour interpréter ces signaux. L'étude analyse la pertinence de l'utilisation de la surveillance syndromique sur ce type de données et donne des éléments pour des améliorations futures
Recent 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

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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/.

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O progresso de projetos anteriores salientou a necessidade de tratar o problema dos softwares para detecção, a partir de bases de dados de publicações científicas, de tendências emergentes de pesquisa e desenvolvimento. Evidenciou-se a carência de aplicações computacionais eficientes dedicadas a este propósito, que são artigos de grande utilidade para um melhor planejamento de programas de pesquisa e desenvolvimento em instituições. Foi realizada, então, uma revisão dos softwares atualmente disponíveis, para poder-se delinear claramente a oportunidade de desenvolver novas ferramentas. Como resultado, implementou-se um aplicativo chamado Citesnake, projetado especialmente para auxiliar a detecção e o estudo de tendências emergentes a partir da análise de redes de vários tipos, extraídas das bases de dados científicas. Através desta ferramenta computacional robusta e eficaz, foram conduzidas análises de frentes emergentes de pesquisa e desenvolvimento na área de Sistemas Geradores de Energia Nuclear de Geração IV, de forma que se pudesse evidenciar, dentre os tipos de reatores selecionados como os mais promissores pelo GIF - Generation IV International Forum, aqueles que mais se desenvolveram nos últimos dez anos e que se apresentam, atualmente, como os mais capazes de cumprir as promessas realizadas sobre os seus conceitos inovadores.
The 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.

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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.

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Libri sul tema "Emerging trend detection"

Freedman, Jeri. Lymphoma: Current and emerging trends in detection and treatment. New York: Rosen Pub. Group, 2006.

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Freedman, Jeri. Brain cancer: Current and emerging trends in detection and treatment. New York: Rosen, 2008.

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Esophageal cancer: Current and emerging trends in detection and treatment. New York: Rosen Pub., 2012.

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Casil, Amy Sterling. Pancreatic cancer: Current and emerging trends in detection and treatment. New York: Rosen Pub., 2008.

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Freedman, Jeri. Ovarian cancer: Current and emerging trends in detection and treatment. New York: Rosen Pub. Group, 2009.

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Hussain, Chaudhery. Smartphone-Based Detection Devices: Emerging Trends in Analytical Techniques. Elsevier, 2021.

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Hussain, Chaudhery Mustansar. Smartphone-Based Detection Devices: Emerging Trends in Analytical Techniques. Elsevier, 2021.

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Harmon, Daniel E. Leukemia Current and Emerging Trends in Detection and Treatment. Rosen Publishing Group, 2011.

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Hasan, Heather. Testicular Cancer Current and Emerging Trends in Detection and Treatment. Rosen Publishing Group, 2011.

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Breast Cancer: Current and Emerging Trends in Detection and Treatment. Rosen Publishing Group, 2005.

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Capitoli di libri sul tema "Emerging trend detection"

Kontostathis, April, Leon M. Galitsky, William M. Pottenger, Soma Roy e Daniel J. Phelps. "A Survey of Emerging Trend Detection in Textual Data Mining". In 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.

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Thomas Rincy, N., e Roopam Gupta. "A Survey of Network Intrusion Detection Using Machine Learning Techniques". In 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.

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Patra, Santanu, Rashmi Madhuri e Prashant K. Sharma. "Role of Nanomaterials as an Emerging Trend Towards the Detection of Winged Contaminants". In 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.

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Popoola, Segun I., Ruth Ande, Kassim B. Fatai e Bamidele Adebisi. "Deep Bidirectional Gated Recurrent Unit for Botnet Detection in Smart Homes". In 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.

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Thomas Rincy, N., e Roopam Gupta. "Correction to: A Survey of Network Intrusion Detection Using Machine Learning Techniques". In 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.

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Adewole, Kayode S., Muiz O. Raheem, Oluwakemi C. Abikoye, Adeleke R. Ajiboye, Tinuke O. Oladele, Muhammed K. Jimoh e Dayo R. Aremu. "Malicious Uniform Resource Locator Detection Using Wolf Optimization Algorithm and Random Forest Classifier". In 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.

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Zeng, Li, Yang Li e Zili Li. "Research Hotspots, Emerging Trend and Front of Fraud Detection Research: A Scientometric Analysis (1984–2021)". In Data Mining and Big Data, 91–102. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8991-9_8.

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Khan, Summaiyya, Akrema, Rizwan Arif, Shama Yasmeen e Rahisuddin. "Recent Advancement in Nanostructured-Based Electrochemical Genosensors for Pathogen Detection". In Emerging Trends in Nanotechnology, 339–58. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9904-0_12.

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Suresh, S., M. Mohan, C. Thyagarajan e R. Kedar. "Detection of Ransomware in Emails Through Anomaly Based Detection". In 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.

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Dash, Rajesh Kumar, Manojit Samanta e Debi Prasanna Kanungo. "Debris Flow Hazard in India: Current Status, Research Trends, and Emerging Challenges". In Landslides: Detection, Prediction and Monitoring, 211–31. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23859-8_10.

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Atti di convegni sul tema "Emerging trend detection"

Nguyen, Nhu Khoa, Emanuela Boros, Gaël Lejeune, Antoine Doucet e Thierry Delahaut. "Utilizing Keywords Evolution in Context for Emerging Trend Detection in Scientific Publications". In 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.

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Ameerali, Aaron, Nadine Sangster e Gerard Ragbir. "AUTONOMOUS DETECTION OF VEHICULAR WHEEL ALIGNMENT PARAMETERS". In 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.

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Vehicular technology has improved tremendously in the last few decades. Drivers and passengers are now being made more aware of their surroundings as well as the state of their cars, ergo becoming increasingly capable of making better decisions. These 'smart-vehicles' are directed by microcontrollers and microprocessors where a network of sensors and actuators provide contextual feedback for the user. Some of these features include parking and reverse assistance, collision avoidance and cruise control. In the coming years, this trend will undergo unprecedented growth as the technologies become cheaper to manufacture and implement. In fact, more advanced systems now alert the driver to realtime critical failures and problematic conditions while the simpler ones do so upon start-up. This paper provides a tested framework for a potential sensing system to alert the driver when the vehicle alignment is off. Vehicle misalignment can become an issue quickly as the following can result: Increased tire tread wear leading to reduced traction with the road's surface and ultimately higher chances of accidents as well as more frequent replacement of the tires becoming necessary. Uneven friction at contact between the road and tire can increase the resistance resulting in higher fuel consumption by the engine. Strain on multiple components within the braking system and suspension as misalignment can cause drift while in motion and additionally uneven braking. A damaged suspension is quite expensive to repair or replace. Early detection of the extent of misalignment can lead to decreased expenditure in the areas of maintenance and fuel consumption, contributing to an increase in reliability. Since many drivers, however experienced they are, may at times be ignorant of the degree of misalignment their vehicle possesses, adding this technology can serve as a potential remedy ultimately improving the user experience and vehicle longevity.

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Akasaki, Satoshi, Naoki Yoshinaga e Masashi Toyoda. "Early Discovery of Emerging Entities in Microblogs". In 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.

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Keeping up to date on emerging entities that appear every day is indispensable for various applications, such as social-trend analysis and marketing research. Previous studies have attempted to detect unseen entities that are not registered in a particular knowledge base as emerging entities and consequently find non-emerging entities since the absence of entities in knowledge bases does not guarantee their emergence. We therefore introduce a novel task of discovering truly emerging entities when they have just been introduced to the public through microblogs and propose an effective method based on time-sensitive distant supervision, which exploits distinctive early-stage contexts of emerging entities. Experimental results with a large-scale Twitter archive show that the proposed method achieves 83.2% precision of the top 500 discovered emerging entities, which outperforms baselines based on unseen entity recognition with burst detection. Besides notable emerging entities, our method can discover massive long-tail and homographic emerging entities. An evaluation of relative recall shows that the method detects 80.4% emerging entities newly registered in Wikipedia; 92.8% of them are discovered earlier than their registration in Wikipedia, and the average lead-time is more than one year (578 days).

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Tucker, Conrad S., e Harrison M. Kim. "Trending Mining for Predictive Product Design". In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28364.

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The Preference Trend Mining (PTM) algorithm that we propose in this work aims to address some fundamental challenges of current demand modeling techniques being employed in the product design community. The first contribution is a multistage predictive modeling approach that captures changes in consumer preferences (as they relate to product design) over time, hereby enabling design engineers to anticipate next generation product features before they become mainstream/unimportant. Because consumer preferences may exhibit monotonically increasing or decreasing, seasonal or unobservable trends, we proposed employing a statistical trend detection technique to help detect time series attribute patterns. A time series exponential smoothing technique is then used to forecast future attribute trend patterns and generate a demand model that reflects emerging product preferences over time. The second contribution of this work is a novel classification scheme for attributes that have low predictive power and hence may be omitted from a predictive model. We propose classifying such attributes as either obsolete, nonstandard or standard, with the appropriate classification given based on the time series entropy values that an attribute exhibits. By modeling attribute irrelevance, design engineers can determine when to retire certain product features (deemed obsolete) or incorporate others into the actual product architecture (standard) while developing modules for those attributes exhibiting inconsistent patterns throughout time (nonstandard). A cell phone example containing 12 time stamped data sets (January 2009-December 2009) is used to validate the proposed Preference Trend Mining model and compare it to traditional demand modeling techniques for predictive accuracy and ease of model generation.

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Huang, Jihua, e Han-Shue Tan. "Cooperative Collision Detection Based on Future-Trajectory Prediction". In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14543.

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Collision Warning Systems (CWSs) are becoming an important part of vehicle active safety. Commercial CWSs using information measured by the subject vehicle have been available on trucks, buses and passenger cars. Another emerging trend in the development of CWS is based on the cooperative driving concept, where vehicles are equipped with absolute-positioning systems and inter-vehicle communications. The absolute positions, together with the rich information from vehicle motion sensors, facilitate the prediction of vehicle future trajectories. With the current positions and the predicted trajectories of the surrounding vehicles, each vehicle in principle can establish a comprehensive understanding and anticipation of its driving environment. To leverage these potential advantages of the cooperative driving concept, this paper proposes, designs and experiments a collision warning system based on vehicle future-trajectory prediction. A systematic approach with probability measures is designed to detect potential trajectory conflict between the predicted trajectories. Collision detection is then based on the potential trajectory conflicts with examination of their persistency and urgency. Experimental results are provided to verify the feasibility of the design.

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Brennan, Feargal, e Bart de Leeuw. "The Use of Inspection and Monitoring Reliability Information in Criticality and Defect Assessments of Ship and Offshore Structures". In ASME 2008 27th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/omae2008-57934.

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This paper describes the use of inspection reliability information in fitness-for-service and criticality assessments for ship and offshore structures. Assessments of components that have never been inspected should assume a defect distribution from manufacturing quality assurance reports taking into account any propagation of damage that might have occurred. By understanding how to incorporate Probability of Detection (POD) and Probability of Sizing (POS) information with associated confidence measures into damage modelling, operators can appreciate the benefit of conducting inspections and the resulting implications for quantitative risk assessments particularly where no defects are found. The paper illustrates the use of POD and confidence levels for predicting remaining life due to corrosion and fatigue and also how to incorporate sizing statistical performance characteristics of the inspection system into remaining life assessments. In addition, the paper addresses the emerging trend towards monitoring with inspection and how operators and designers can benefit from future trends in structural health monitoring.

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Sledge, Isaac J., James M. Keller e Gregory L. Alexander. "Emergent trend detection in diurnal activity". In 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.

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Zoveidavianpoor, 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". In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21265-ms.

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Abstract Monitoring techniques in oilfield scale management are expensive, susceptible to error, are not conducted in real-time, and they are non-in situ. Most scale prediction tools (i.e., water analysis and computer-based algorithms) have their deficiency and the need for accurately correlate calculated scaling tendencies with actual field data is evident. Lack of info about type, severity and location of scale deposits can lead to the failure of well intervention jobs. This work aims to serve as an opportunity to provide fertile ground and basis for utilizing new emerging technology for scale management in downhole application. Research into utilizing sensors along with an advanced computerized imaging procedure in the downhole application has not been explored to the same extent as other applications, such as scale monitoring in pipelines and surface facilities. Downhole Scale Inspection Tool (DSIT) is a new emerging technology which promises to enhance considerably our ability to detect deposits and scale with the aim of sensors and tomography technology. DSIT has enormous potential for application in downhole condition as it uses slickline unit alongside with routine well intervention jobs. The acquired data by DSIT such as temperature, pressure, depth, deposition thickness and permittivity are utilized for downhole scale analysis, monitoring and detection. When the type of scale is known, it is easier to take the correct steps in preventive maintenance or a cleaning process. Using DSIT, the trend of deposition thickness can be detected and immediately known if it is growing or shrinking. This will help to optimize any chemical feed and also generate substantial savings over time. This paper gives an overview of developing cutting-edge technology in downhole applications for scale management and possible barriers to new technology implementation. Using DSIT can lead to better data acquisition from downhole and contribute to a higher success rate of scale removal in downhole. This technique offers many benefits for scale treatment, monitoring and prediction when filed data is necessary for validation of scaling tendencies.

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Ugoyah, Joy, e Anita Mary Igbine. "Applications of AI and Data-Driven Modeling in Energy Production and Marketing Processes". In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/207153-ms.

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Abstract Faster and more accurate decisions are what the Oil and Gas industry needs with the world's fast-evolving energy needs and economy. The area of Artificial intelligence and Data-driven modelling is relatively new and has not found popular application in the industry. AI is an emerging technology that can be used to predict event outcomes and automate anomaly-detection processes. The various applications of AI in different industries were researched into. This paper highlighted important processes that can be improved with the application of Artificial Intelligence through data-driven modelling. It also highlights areas in the various industries where AI intelligence is already being applied and ways it can be improved. AI and data-driven modelling has the potential to improve exploration accuracy, reduce production down-time, reduce cost of maintenance, and reduce health and safety risks. This body of information can serve as a guideline for adopting AI in the oil and gas industry. A trend of industry-tailored intelligence solutions would be more effective in the evolving energy industry.

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Liu, Zhiming, e Xiangyu Liu. "Future design outlook of wearable devices". In 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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Wearable fitness devices have become increasingly popular among fitness enthusiasts, athletes, and health-conscious individuals seeking to improve their overall wellness. These devices have transformed how people approach fitness by providing real-time feedback and personalized insights into physical activity, sleep patterns, and other vital signs.In recent years, a significant focus has been on designing wearable fitness devices that can accurately track and monitor performance metrics. This has reshaped sophisticated sensors and algorithms to capture and analyze real-time data. However, the current generation of wearable fitness devices primarily focuses on tracking past performance and providing basic analytics.In the future, wearable fitness devices are expected to become more sophisticated and accurate in predicting future performance. This will be possible by incorporating advanced sensors and machine learning algorithms to analyze real-time data and provide personalized recommendations.One of the critical challenges associated with designing wearable fitness devices is the selection of appropriate sensors. Sensors must be able to capture accurate data that can be analyzed to provide meaningful insights into a user's physical activity, sleep patterns, heart rate, and other vital signs. Some commonly used sensors in wearable fitness devices include accelerometers, gyroscopes, heart rate monitors, and GPS. However, integrating these technologies into wearable devices contributes to a limitation in the space of such devices, which may result in reduced user comfort while wearing them. However, there is a possibility that the monitors and integrated chips can be made softer in the future. This is also in line with the development trend of wearable devices, which are expected to become more comfortable and user-friendly over time.In addition to sensors, wearable fitness devices must incorporate advanced algorithms to analyze data and provide accurate predictions. Machine learning algorithms are particularly well-suited for this task, as they can analyze large volumes of data and identify patterns that are difficult to detect using traditional statistical methods. These algorithms can predict future performance based on past activity, identify areas for improvement, and provide personalized recommendations for training and recovery. Real-time data monitoring is essential for timely interventions in the early stages of health issues, facilitating early detection and intervention, and maintaining long-term health prevention.Another important consideration when designing wearable fitness devices is user experience. Wearable devices must be easy to use, comfortable, and visually appealing. A well-designed interactive interface can significantly enhance users' interest in wearable devices and their overall experience while using them, ultimately helping them to achieve their health goals more effectively.Besides, they must also integrate seamlessly with other devices and platforms, such as smartphones and fitness apps.In the future, wearable fitness devices can potentially revolutionize how people approach fitness and wellness. Real-time performance prediction can give users a more accurate and personalized view of their physical activity, allowing them to optimize their training and recovery. Customized coaching and recommendations can help users achieve their fitness goals more efficiently and effectively.Overall, the design of wearable fitness devices and their ability to predict future performance is an exciting area of research and development. As technology evolves and improves, we expect to see more sophisticated and accurate devices that provide users with a comprehensive view of their health and wellness.

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