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Journal articles on the topic 'Contextual anomalies'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Yu, Xiang, Hui Lu, Xianfei Yang, et al. "An adaptive method based on contextual anomaly detection in Internet of Things through wireless sensor networks." International Journal of Distributed Sensor Networks 16, no. 5 (2020): 155014772092047. http://dx.doi.org/10.1177/1550147720920478.

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With the widespread propagation of Internet of Things through wireless sensor networks, massive amounts of sensor data are being generated at an unprecedented rate, resulting in very large quantities of explicit or implicit information. When analyzing such sensor data, it is of particular importance to detect accurately and efficiently not only individual anomalous behaviors but also anomalous events (i.e. patterns of behaviors). However, most previous work has focused only on detecting anomalies while generally ignoring the correlations between them. Even in approaches that take into account

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Filik, Ruth. "Contextual override of pragmatic anomalies: Evidence from eye movements." Cognition 106, no. 2 (2008): 1038–46. http://dx.doi.org/10.1016/j.cognition.2007.04.006.

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Ngueilbaye, Alladoumbaye, Hongzhi Wang, Daouda Ahmat Mahamat, Ibrahim A. Elgendy, and Sahalu B. Junaidu. "Methods for detecting and correcting contextual data quality problems." Intelligent Data Analysis 25, no. 4 (2021): 763–87. http://dx.doi.org/10.3233/ida-205282.

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Knowledge extraction, data mining, e-learning or web applications platforms use heterogeneous and distributed data. The proliferation of these multifaceted platforms faces many challenges such as high scalability, the coexistence of complex similarity metrics, and the requirement of data quality evaluation. In this study, an extended complete formal taxonomy and some algorithms that utilize in achieving the detection and correction of contextual data quality anomalies were developed and implemented on structured data. Our methods were effective in detecting and correcting more data anomalies t

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Clausen, Henry, Gudmund Grov, and David Aspinall. "CBAM: A Contextual Model for Network Anomaly Detection." Computers 10, no. 6 (2021): 79. http://dx.doi.org/10.3390/computers10060079.

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Anomaly-based intrusion detection methods aim to combat the increasing rate of zero-day attacks, however, their success is currently restricted to the detection of high-volume attacks using aggregated traffic features. Recent evaluations show that the current anomaly-based network intrusion detection methods fail to reliably detect remote access attacks. These are smaller in volume and often only stand out when compared to their surroundings. Currently, anomaly methods try to detect access attack events mainly as point anomalies and neglect the context they appear in. We present and examine a

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Efanov, V. N., and N. S. Ivanova. "DIAGNOSTICS OF THE STATE OF COMPLEX TECHNICAL OBJECTS ON THE BASIS OF DETECTION OF ANOMALIES OF TIME SEQUENCES." Kontrol'. Diagnostika, no. 308 (February 2024): 56–62. http://dx.doi.org/10.14489/td.2024.02.pp.056-062.

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The problem of estimating the state of complex technical objects based on the analysis of time sequence anomalies is considered. The purpose of this research is to develop a method of detecting contextual anomalies of time sequences, which allows to determine the degree of development of degradation processes that lead to failures. The study of modern intellectual means of analyzing time sequences of high dimensionality is carried out. It is shown that contextual anomalies are of the greatest interest from the point of view of technical objects state estimation. We propose a spectral method fo

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Seymour, Deni J. "Contextual Incongruities, Statistical Outliers, and Anomalies: Targeting Inconspicuous Occupational Events." American Antiquity 75, no. 1 (2010): 158–76. http://dx.doi.org/10.7183/0002-7316.75.1.158.

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New methodologies are needed to address multiple componentcy and short reuse episodes that are characteristic of mobile group residential and logistical strategies. Chronometric results are often misinterpreted when evaluated within a framework suited to long-term sedentary occupations. The standard practices of age-averaging, eliminating apparent "anomalous" results, and relying on high profile diagnostic tools and vessels and the most visible features—along with the expectation for "contextual congruence"—mask multi-componentcy and episodic reuse. High incidences of site reuse have been dete

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Dou, Shaoyu, Kai Yang, and H. Vincent Poor. "PC2A: Predicting Collective Contextual Anomalies via LSTM With Deep Generative Model." IEEE Internet of Things Journal 6, no. 6 (2019): 9645–55. http://dx.doi.org/10.1109/jiot.2019.2930202.

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Wei, Ji Dong, and Ge Guo. "Multi-Sensor Stockline Tracking within a Blast Furnace." Applied Mechanics and Materials 701-702 (December 2014): 522–27. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.522.

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The paper presents a synergistic approach for height tracking within a blast furnace (BF). The Frequency Modulated Continuous Wave (FMCW) radar has been employed to measure the height and surface profile of the burden surface. However the radar signal is easily disturbed, by the radar anomalies, during the process of continuous measurement. The data from rotating chute and charging switch provide information on contextual relevance with radar anomalies. An anomaly detection models has been developed to increase the measurement accuracy by utilizing contextual information. The approach has been

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Al-Gabalawy, Mostafa. "Detecting anomalies within Unmanned Aerial Vehicle (UAV) video based on contextual saliency." Applied Soft Computing 96 (November 2020): 106715. http://dx.doi.org/10.1016/j.asoc.2020.106715.

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Frolova, E. A. "Typology of speech anomalies in the present-day advertising language." Russian language at school 84, no. 3 (2023): 68–76. http://dx.doi.org/10.30515/0131-6141-2023-84-3-68-76.

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The article considers different groups of speech anomalies found in modern advertising texts. The aim is threefold: to comprehensively analysis speech deviations in advertising texts, to develop a typology of speech anomalies and to determine their role in ensuring the communicative effectiveness of an advertising message. The study used analy sis (lexical, componential, word-formation, classification and differentiation), comparative and integrative methods, as well as the contextual-semantic analysis technique. These procedures enable identification of the content and semantic load of advert

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Zhao, Bo, Xiang Li, Jiayue Li, Jianwen Zou, and Yifan Liu. "An Area-Context-Based Credibility Detection for Big Data in IoT." Mobile Information Systems 2020 (January 25, 2020): 1–12. http://dx.doi.org/10.1155/2020/5068731.

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In order to improve the credibility of big data analysis platform’s results in IoT, it is necessary to improve the quality of IoT data. Many detection methods have been proposed to filter out incredible data, but there are certain deficiencies that performance is not high, detection is not comprehensive, and process is not credible. So this paper proposes an area-context-based credibility detection method for IoT data, which can effectively detect point anomalies, behavioral anomalies, and contextual anomalies. The performance of the context determination and the data credibility detection of

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Garcia, Olivia W., and James C. Brown. "LEVERAGING CONTEXT DISCOVERY FOR EFFECTIVE ANOMALY DETECTION IN COMPLEX SYSTEMS." Pinnacle Research Journal of Scientific and Management Sciences 2, no. 4 (2025): 1–7. https://doi.org/10.55640/tprjsms-v02i04-01.

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Anomaly detection is a fundamental task in various domains, such as cybersecurity, finance, healthcare, and sensor networks. Traditional methods often struggle to distinguish between normal and anomalous behaviors when contextual information is not properly considered. This paper explores context discovery as a key strategy for enhancing anomaly detection. By identifying and utilizing relevant contextual information, anomaly detection systems can more effectively differentiate between benign and anomalous patterns, improving both the accuracy and robustness of detection. We present an approach

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Boukela, Lynda, Gongxuan Zhang, Meziane Yacoub, Samia Bouzefrane, and Sajjad Bagheri Baba Ahmadi. "An approach for unsupervised contextual anomaly detection and characterization." Intelligent Data Analysis 26, no. 5 (2022): 1185–209. http://dx.doi.org/10.3233/ida-215906.

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Outlier detection has been widely explored and applied to different real-world problems. However, outlier characterization that consists in finding and understanding the outlying aspects of the anomalous observations is still challenging. In this paper, we present a new approach to simultaneously detect subspace outliers and characterize them. We introduce the Dimension-wise Local Outlier Factor (DLOF) function to quantify the degree of outlierness of the data points in each feature dimension. The obtained DLOFs are used in an outlier ensemble so as to detect and rank the anomalous points. Sub

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Dias, Maurício Araújo, Giovanna Carreira Marinho, Rogério Galante Negri, Wallace Casaca, Ignácio Bravo Muñoz, and Danilo Medeiros Eler. "A Machine Learning Strategy Based on Kittler’s Taxonomy to Detect Anomalies and Recognize Contexts Applied to Monitor Water Bodies in Environments." Remote Sensing 14, no. 9 (2022): 2222. http://dx.doi.org/10.3390/rs14092222.

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Environmental monitoring, such as analyses of water bodies to detect anomalies, is recognized worldwide as a task necessary to reduce the impacts arising from pollution. However, the large number of data available to be analyzed in different contexts, such as in an image time series acquired by satellites, still pose challenges for the detection of anomalies, even when using computers. This study describes a machine learning strategy based on Kittler’s taxonomy to detect anomalies related to water pollution in an image time series. We propose this strategy to monitor environments, detecting un

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Efanov, V. N., N. S. Ivanova, and V. G. Razumov. "Intelligent technology for assessing the remai¬ning useful life of complex technical systems." Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 24, no. 3 (2024): 54–66. http://dx.doi.org/10.14529/ctcr240305.

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The task of estimating the residual life of complex technical systems has recently become increasingly important. For the pre-trustworthy estimation of this indicator it is required to process large arrays of data on the current state of the system under study. At the same time the task of reconstruction of the model of degradation processes development leading to the occurrence of failures requires solving a number of problems. In this regard, there is a need to use intelligent methods of data processing, which include methods of time series anomaly analysis. Purpose of the study: development

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Chang, Ruohan, Xiaohong Yang, and Yufang Yang. "Processing good-fit anomalies is modulated by contextual accessibility during discourse comprehension: ERP evidence." Language, Cognition and Neuroscience 35, no. 10 (2020): 1423–34. http://dx.doi.org/10.1080/23273798.2020.1784448.

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Aleo, P. D., A. W. Engel, G. Narayan, et al. "Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams." Astrophysical Journal 974, no. 2 (2024): 172. http://dx.doi.org/10.3847/1538-4357/ad6869.

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Abstract We present Lightcurve Anomaly Identification and Similarity Search (LAISS), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly Zwicky Transient Facility (ZTF) Alert Stream via the ANTARES broker, identifying a manageable ∼1–5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host galaxy features within a random forest classifier, tagging transients of rare classes (spectroscopic anomalies), of uncommo

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Xu, Liyan, Kang Xu, Yinchuan Qin, et al. "TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data." Applied Sciences 12, no. 16 (2022): 8085. http://dx.doi.org/10.3390/app12168085.

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Anomaly detection on time series data has been successfully used in power grid operation and maintenance, flow detection, fault diagnosis, and other applications. However, anomalies in time series often lack strict definitions and labels, and existing methods often suffer from the need for rigid hypotheses, the inability to handle high-dimensional data, and highly time-consuming calculation costs. Generative Adversarial Networks (GANs) can learn the distribution pattern of normal data, detecting anomalies by comparing the reconstructed normal data with the original data. However, it is difficu

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Suryadi, Taufik, Kumalasari Kumalasari, and Kulsum Kulsum. "Ethical and Medicolegal Considerations in the Termination of Pregnancy Due to Lethal Congenital Anomalies in Banda Aceh, Indonesia." Open Access Macedonian Journal of Medical Sciences 8, no. C (2020): 167–71. http://dx.doi.org/10.3889/oamjms.2020.5085.

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BACKGROUND: The aim of the following paper is to present the case termination of pregnancy indicated by lethal congenital anomalies based on ethical and medicolegal consideration. The method used to resolve this ethical dilemma is based on clinical ethics theory with systematic consideration of medical indications, patient preferences, quality of life, and contextual features. Medicolegal considerations were also take-into account based on Indonesian Law number 36 of 2009. CASE REPORT: This case report shows the termination of pregnancy in a 26-year-old patient with 25–26 weeks’ gestatio

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Tu, Yuanpeng, Yuxi Li, Boshen Zhang, et al. "Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 21637–45. http://dx.doi.org/10.1609/aaai.v38i19.30162.

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Robust autonomous driving requires agents to accurately identify unexpected areas (anomalies) in urban scenes. To this end, some critical issues remain open: how to design advisable metric to measure anomalies, and how to properly generate training samples of anomaly data? Classical effort in anomaly detection usually resorts to pixel-wise uncertainty or sample synthesis, which ignores the contextual information and sometimes requires auxiliary data with fine-grained annotations. On the contrary, in this paper, we exploit the strong context-dependent nature of segmentation task and design an e

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Serrano-Guerrero, Xavier, Guillermo Escrivá-Escrivá, Santiago Luna-Romero, and Jean-Michel Clairand. "A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles." Energies 13, no. 5 (2020): 1046. http://dx.doi.org/10.3390/en13051046.

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Electricity consumption patterns reveal energy demand behaviors and enable strategY implementation to increase efficiency using monitoring systems. However, incorrect patterns can be obtained when the time-series components of electricity demand are not considered. Hence, this research proposes a new method for handling time-series components that significantly improves the ability to obtain patterns and detect anomalies in electrical consumption profiles. Patterns are found using the proposed method and two widespread methods for handling the time-series components, in order to compare the re

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Moraes, Anaelena Bragança de, Roselaine Ruviaro Zanini, João Riboldi, and Elsa Regina Justo Giugliani. "Risk factors for low birth weight in Rio Grande do Sul State, Brazil: classical and multilevel analysis." Cadernos de Saúde Pública 28, no. 12 (2012): 2293–305. http://dx.doi.org/10.1590/s0102-311x2012001400008.

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The objective of this study was to identify risk factors for low birth weight in singleton live born infants in Rio Grande do Sul State, Brazil, in 2003, based on data from the Information System on Live Births. The study used both classical multivariate and multilevel logistic regression. Risk factors were evaluated at two levels: individual (live births) and contextual (micro-regions). At the individual level the two models showed a significant association between low birth weight and prematurity, number of prenatal visits, congenital anomalies, place of delivery, parity, sex, maternal age,

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Tao, Fenfang, Guo-Sen Xie, Fang Zhao, and Xiangbo Shu. "Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 7 (2025): 7347–55. https://doi.org/10.1609/aaai.v39i7.32790.

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Few-shot anomaly detection (FSAD) aims to detect unseen anomaly regions with the guidance of very few normal support images from the same class. Existing FSAD methods usually find anomalies by directly designing complex text prompts to align them with visual features under the prevailing large vision-language model paradigm. However, these methods, almost always, neglect intrinsic contextual information in visual features, e.g., the interaction relationships between different vision layers, which is an important clue for detecting anomalies comprehensively. To this end, we propose a kernel-awa

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Kisters, Philipp, Hanno Schreiber, and Janick Edinger. "Categorization of crowd-sensing streaming data for contextual characteristic detection." Journal of Smart Cities and Society 2, no. 2 (2023): 55–75. http://dx.doi.org/10.3233/scs-230013.

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The growing reliance on large wireless sensor networks, potentially consisting of hundreds of nodes, to monitor real-world phenomena inevitably results in large, complex datasets that become increasingly difficult to process using traditional methods. The inadvertent inclusion of anomalies in the dataset, resulting from the inherent characteristics of these networks, makes it difficult to isolate interesting events from erroneous measurements. Simultaneously, improvements in data science methods, as well as increased accessibility to powerful computers, lead to these techniques becoming more a

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Koroliova, Valeria, and Iryna Popova. "Destruction of Communicative Pragmatics in Contemporary Absurdist Dramaturgic Texts." PSYCHOLINGUISTICS 27, no. 2 (2020): 195–212. http://dx.doi.org/10.31470/2309-1797-2020-27-2-195-212.

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The aim of the article is characteristics of mechanisms of pragmatics distraction in communication of active participants of modern Ukrainian plays with features of the theatre of the absurdity. Structural and contextual mechanisms of dialogic speech depragmatization are singled out on factual material. In a dramatic dialogue absurdity is explained as a purposeful instruction to convey the thought about illogicalness and chaotic nature of reality, the aimlessness of a human being. The main methods of the study are descriptive, context-interpreting and presuppositional. Study results. One

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Mouton, Alice. "Nommer les dieux hittites : au sujet de quelques épithètes divines." Archiv für Religionsgeschichte 21-22, no. 1 (2020): 225–43. http://dx.doi.org/10.1515/arege-2020-0012.

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AbstractAfter a short overview of Hittite divine epithets (Anatolia of the second half of the second millennium BCE), this paper explores the attestations of two particular divine names, namely “the bloody god U.GUR” and “the vengeful nakkiu-/nakkiwa‐s.” These entities are studied in context in order to determine their identity and functions. Through this contextual analysis, it appears that these supernatural entities are held responsible for various anomalies in the context of Luwian rituals probably coming from the Lower Land (south-central Anatolia).

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Montgomery, Jacob M., Santiago Olivella, Joshua D. Potter, and Brian F. Crisp. "An Informed Forensics Approach to Detecting Vote Irregularities." Political Analysis 23, no. 4 (2015): 488–505. http://dx.doi.org/10.1093/pan/mpv023.

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Electoral forensics involves examining election results for anomalies to efficiently identify patterns indicative of electoral irregularities. However, there is disagreement about which, if any, forensics tool is most effective at identifying fraud, and there is no method for integrating multiple tools. Moreover, forensic efforts have failed to systematically take advantage of country-specific details that might aid in diagnosing fraud. We deploy a Bayesian additive regression trees (BART) model–a machine-learning technique–on a large cross-national data set to explore the dense network of pot

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Nandikotkur, Achyuth, Issa Traore, and Mohammad Mamun. "SeniorSentry: Correlation and Mutual Information-Based Contextual Anomaly Detection for Aging in Place." Sensors 23, no. 15 (2023): 6752. http://dx.doi.org/10.3390/s23156752.

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With the ever-growing reliance on IoT-enabled sensors to age in place, a need arises to protect them from malicious actors and detect malfunctions. In an IoT smart home, it is reasonable to hypothesize that sensors near one another can exhibit linear or nonlinear correlations. If substantiated, this property can be beneficial for constructing relationship trends between the sensors and, consequently, detecting attacks or other anomalies by measuring the deviation of their readings against these trends. In this work, we confirm the presence of correlations between co-located sensors by statisti

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Reshadi, MohammadHossein, Wen Li, Wenjie Xu, et al. "Deep-Shallow Metaclassifier with Synthetic Minority Oversampling for Anomaly Detection in a Time Series." Algorithms 17, no. 3 (2024): 114. http://dx.doi.org/10.3390/a17030114.

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Anomaly detection in data streams (and particularly time series) is today a vitally important task. Machine learning algorithms are a common design for achieving this goal. In particular, deep learning has, in the last decade, proven to be substantially more accurate than shallow learning in a wide variety of machine learning problems, and deep anomaly detection is very effective for point anomalies. However, deep semi-supervised contextual anomaly detection (in which anomalies within a time series are rare and none at all occur in the algorithm’s training data) is a more difficult problem. Hy

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Dias, Maurício Araújo, Erivaldo Antônio da Silva, Samara Calçado de Azevedo, Wallace Casaca, Thiago Statella, and Rogério Galante Negri. "An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing." Remote Sensing 12, no. 1 (2019): 43. http://dx.doi.org/10.3390/rs12010043.

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The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on

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Bolshibayeva, Aigerim, Sabina Rakhmetulayeva, Baubek Ukibassov, and Zhandos Zhanabekov. "Advancing real-time echocardiographic diagnosis with a hybrid deep learning model." Eastern-European Journal of Enterprise Technologies 6, no. 9 (132) (2024): 60–70. https://doi.org/10.15587/1729-4061.2024.314845.

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This research focuses on developing a novel hybrid deep learning architecture designed for real-time analysis of ultrasound heart images. The object of the study is the diagnostic accuracy and efficiency in detecting heart pathologies such as atrial septal defect (ASD) and aortic stenosis (AS) from ultrasound data. The problem is the insufficient accuracy and generalizability of existing models in real-time cardiac image analysis, which limits their practical clinical application. To solve this, the convolutional neural networks (CNNs), combining local feature extraction was integrated with gl

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Markov, G. A. "Application of a Neocortex Model to Identify Contextual Anomalies in the Industrial Internet of Things Network Traffic." Automatic Control and Computer Sciences 57, no. 8 (2023): 1018–24. http://dx.doi.org/10.3103/s0146411623080163.

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Karadayı, Yıldız, Mehmet N. Aydin, and A. Selçuk Öğrenci. "A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data." Applied Sciences 10, no. 15 (2020): 5191. http://dx.doi.org/10.3390/app10155191.

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Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in mu

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Baroni, Laís, Lucas Scoralick, Augusto Reis, et al. "A contextual-compositional approach to discover associations between health determinants and health indicators for neonatal mortality rate monitoring in situations of anomalies." PLOS ONE 19, no. 12 (2024): e0310413. https://doi.org/10.1371/journal.pone.0310413.

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Introduction Epidemiology is considered both a field of research and a methodological approach within the broader health sciences. It aims to understand health-related events’ causes and effects and provide the evidence necessary to prevent disease and implement effective control and prevention strategies. One of the main focuses of epidemiology is identifying the determinant factors in the health situation of populations since health-related anomalies are not randomly distributed among people. This understanding brings up the necessity of considering each place’s particularities and observing

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Hally, Bryan, Luke Wallace, Karin Reinke, and Simon Jones. "A New Spatio-Temporal Selection Method for Estimating Upwelling Medium-Wave Radiation." Remote Sensing 15, no. 14 (2023): 3521. http://dx.doi.org/10.3390/rs15143521.

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Accurate estimates of the unperturbed state of upwelling radiation from the earth’s surface are vital to the detection and classification of anomalous radiation values. Determining radiative anomalies in the landscape is critical for isolating change, a key application being wildfire detection, which is reliant upon knowledge of a location’s radiation budget sans fire. Most techniques for deriving the unperturbed background state of a location use that location’s spatial context, that is, the pixels immediately surrounding the target. Spatial contextual estimation can be subject to error due t

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Badri Babadi, Alireza, Ahmad Reza Askari, and Amir Nadri. "Designing a development physical training model for students of Iranian medical sciences universities." Journal of Multidisciplinary Care 11, no. 4 (2022): 196–205. http://dx.doi.org/10.34172/jmdc.2022.1150.

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Background and aims: The importance of developing physical training and the need to expand it among students increases when the social harms and anomalies observed in this space are carefully analyzed. Knowing the pattern and model governing this matter helps prevent social anomalies. Since no study was done to discover this model, the present research was conducted to investigate the design of the development model of physical training for students of Iranian medical sciences universities. Methods: This study was conducted with an exploratory-fundamental nature, a qualitative approach, and th

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Liang, Jiayu, Jiayan Fan, Zhen Feng, and Jing Xin. "Anomaly Detection in Tax Filing Documents Using Natural Language Processing Techniques." Applied and Computational Engineering 144, no. 1 (2025): 80–89. https://doi.org/10.54254/2755-2721/2025.21736.

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This paper introduces a novel approach to tax fraud detection utilizing natural language processing techniques for identifying anomalies in tax filing documents. The methodology integrates tax-domain specific BERT embeddings with bidirectional LSTM networks to capture contextual relationships within tax documents that traditional numerical analysis might overlook. We present a multi-component ensemble framework that processes both structured and unstructured components of tax filings, extracting semantic relationships between financial entities while maintaining sensitivity to numerical incons

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Bressan, Paola, Luigi Garlaschelli, and Monica Barracano. "Antigravity Hills are Visual Illusions." Psychological Science 14, no. 5 (2003): 441–49. https://doi.org/10.1111/1467-9280.02451.

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Antigravity hills, also known as spook hills or magnetic hills, are natural places where cars put into neutral are seen to move uphill on a slightly sloping road, apparently defying the law of gravity. We show that these effects, popularly attributed to gravitational anomalies, are in fact visual illusions. We re-created all the known types of antigravity spots in our laboratory using tabletop models; the number of visible stretches of road, their slant, and the height of the visible horizon were systematically varied in four experiments. We conclude that antigravity-hill effects follow from a

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Brett, C. M. C., E. P. Peters, L. C. Johns, P. Tabraham, L. R. Valmaggia, and P. K. Mcguire. "Appraisals of Anomalous Experiences Interview (AANEX): a multidimensional measure of psychological responses to anomalies associated with psychosis." British Journal of Psychiatry 191, S51 (2007): s23—s30. http://dx.doi.org/10.1192/bjp.191.51.s23.

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BackgroundCognitive models of psychosis suggest that whether anomalous experiences lead to clinically relevant psychotic symptoms depends on how they are appraised, the context in which they occur and the individual's emotional responseAimsTo develop and validate a semi-structured interview (the Appraisals of Anomalous Experiences Interview; AANEX) to assess (a) anomalous experiences and (b) appraisal, contextual and response variablesMethodFollowing initial piloting, construct validity was tested via cross-sectional comparison of data from clinical and non-clinical samples with anomalous expe

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Thakur, Nirmalya, and Chia Y. Han. "An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments." Information 12, no. 2 (2021): 81. http://dx.doi.org/10.3390/info12020081.

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This framework for human behavior monitoring aims to take a holistic approach to study, track, monitor, and analyze human behavior during activities of daily living (ADLs). The framework consists of two novel functionalities. First, it can perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs to identify a list of distinct behavioral patterns associated with different complex activities. Second, it consists of an intelligent decision-making algorithm that can analyze these behavioral patterns and their relationships with the dynamic contextual and

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Zou, Fumin, Yue Xing, Qiang Ren, Feng Guo, Zhaoyi Zhou, and Zihan Ye. "Dynamic Anomaly Detection in Gantry Transactions Using Graph Convolutional Network-Gate Recurrent Unit with Adaptive Attention." Applied Sciences 13, no. 19 (2023): 11068. http://dx.doi.org/10.3390/app131911068.

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With the wide application of Electronic Toll Collection (ETC) systems, the effectiveness of the operation and maintenance of gantry equipment still need to be improved. This paper proposes a dynamic anomaly detection method for gantry transactions, utilizing the contextual attention mechanism and Graph Convolutional Network-Gate Recurrent Unit (GCN-GRU) dynamic anomaly detection method for gantry transactions. In this paper, four different classes of gantry anomalies are defined and modeled, representing gantries as nodes and the connectivity between gantries as edges. First, the spatial distr

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Fariha, Nudrat, Md Nazmuddin Moin Khan, Md Iqbal Hossain, et al. "Advanced fraud detection using machine learning models: ‎enhancing financial transaction security." International Journal of Accounting and Economics Studies 12, no. 2 (2025): 85–104. https://doi.org/10.14419/c73kcb17.

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The rise of digital payments has accelerated the need for intelligent and scalable systems to detect fraud. This research presents an end-‎to-end, feature-rich machine learning framework for detecting credit card transaction anomalies and fraud using real-world data. The ‎study begins by merging transactional, cardholder, merchant, and merchant category datasets from a relational database to create a unified analytical view. Through the feature engineering process, we extract behavioural signals such as average spending, deviation from ‎historical patterns, transaction timing irregularities, a

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Ojebode, Ayokunmi O., and Idowu O. Odebode. "Onomastics, Medicine and Politics in Femi Osofisan’s The Engagement." Theory and Practice in Language Studies 9, no. 5 (2019): 494. http://dx.doi.org/10.17507/tpls.0905.02.

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Onomastics, medicine and politics in this study are a pragmatic way of depicting the psychosocial condition of Nigeria as an underdeveloped nation. The study explores Femi Osofisan’s The Engagement from a literary onomastic standpoint with the aim of exposing socio-political anomalies in Nigeria. Nigerian leaders commit flaws of egoistical and individualistic interests which often go against the consciences of the led. On this premise, the study explores the characters’ names in The Engagement with a view to gaining insight into Nigeria’s sociocultural and political contexts. Furthermore, Post

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Imashev, S. A., and S. V. Parov. "Modified Seasonal Decomposition Variations of Earth Magnetic Field Induction Module." Informacionnye Tehnologii 30, no. 2 (2024): 59–67. http://dx.doi.org/10.17587/it.30.59-67.

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In this paper, we present a modification of the classic method of seasonal decomposition of the time series, in particular its application for the analysis of geomagnetic data. Seasonal decomposition is a powerful tool for time series analysis, but its classic implementation does not always provide accurate results when the time series contains amplitude outliers and prolonged gaps. We propose a modified approach to solve this task of seasonal decomposition, by applying an average daily profile. This ensures the extraction of various anomalies in the residual component of the decomposition, in

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Ranadheer Reddy Charabuddi. "The Future of Human-AI Collaboration in Manufacturing Finance: Streamlining Cost Management and Vendor Payments." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 525–32. https://doi.org/10.32996/jcsts.2025.7.3.59.

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The integration of artificial intelligence in manufacturing finance represents a transformative approach to vendor management, procurement, and cost control processes. This article explores how AI-driven systems are revolutionizing traditional financial workflows by automating routine transactions, detecting anomalies, and accelerating approval processes. Rather than displacing human expertise, these technological advancements emphasize the critical importance of human-AI collaboration, where machine learning handles data-intensive tasks while human professionals provide strategic oversight, r

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Chen, Xiaolong, Hongfeng Zhang, Cora Un In Wong, and Zhengchun Song. "Context-Aware Markov Sensors and Finite Mixture Models for Adaptive Stochastic Dynamics Analysis of Tourist Behavior." Mathematics 13, no. 12 (2025): 2028. https://doi.org/10.3390/math13122028.

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We propose a novel framework for adaptive stochastic dynamics analysis of tourist behavior by integrating context-aware Markov models with finite mixture models (FMMs). Conventional Markov models often fail to capture abrupt changes induced by external shocks, such as event announcements or weather disruptions, leading to inaccurate predictions. The proposed method addresses this limitation by introducing virtual sensors that dynamically detect contextual anomalies and trigger regime switches in real-time. These sensors process streaming data to identify shocks, which are then used to reweight

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Sufi, Fahim. "A New Social Media-Driven Cyber Threat Intelligence." Electronics 12, no. 5 (2023): 1242. http://dx.doi.org/10.3390/electronics12051242.

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Cyber threats are projected to cause USD 10.5 trillion in damage to the global economy in 2025. Comprehending the level of threat is core to adjusting cyber posture at the personal, organizational, and national levels. However, representing the threat level with a single score is a daunting task if the scores are generated from big and complex data sources such as social media. This paper harnesses the modern technological advancements in artificial intelligence (AI) and natural language processing (NLP) to comprehend the contextual information of social media posts related to cyber-attacks an

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Zhang, Huijie, Ke Ren, Yiming Lin, Dezhan Qu, and Zhenxin Li. "AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data." Sustainability 11, no. 10 (2019): 2944. http://dx.doi.org/10.3390/su11102944.

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Nowadays, huge volume of air quality data provides unprecedented opportunities for analyzing pollution. However, due to the high complexity, most traditional analytical methods focus on abstracting data, so these techniques discard the original structure and limit the understanding of the results. Visual analysis is a powerful technique for exploring unknown patterns since it retains the details of the original data and gives visual feedback to users. In this paper, we focus on air quality data and propose the AirInsight design, an interactive visual analytic system for recognizing, exploring,

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Al-Gabalawy, Mostafa. "Removal notice to: “Detecting Anomalies within Unmanned Aerial Vehicle (UAV) Video Based on Contextual Saliency” [Appl. Soft Comput. 96 (2020) 106715]." Applied Soft Computing 110 (October 2021): 107833. http://dx.doi.org/10.1016/j.asoc.2021.107833.

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Wang, Bingxing, Yuxin Dong, Jianhua Yao, Honglin Qin, and Jiajing Wang. "Exploring Anomaly Detection and Risk Assessment in Financial Markets Using Deep Neural Networks." International Journal of Innovative Research in Computer Science and Technology 12, no. 4 (2024): 92–98. http://dx.doi.org/10.55524/ijircst.2024.12.4.15.

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In this paper, deep learning technology, along with a Gated Recurrent Unit (GRU) combined with an attention mechanism, is used to enhance the recognition ability and risk assessment accuracy of abnormal trading behavior in financial markets. The GRU effectively solves the problem of gradient vanishing in traditional recurrent neural networks through its unique gated structure, allowing the model to learn more stable and effective feature representations in long sequence data. On this basis, the contextual attention (CA) module in the attention mechanism is introduced, enabling the model to aut

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