Relevant bibliographies by topics / Spam detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spam detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "Spam detection"

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Kim, So Yeon, and Kyung-Ah Sohn. "Graph-Based Spam Image Detection for Mobile Phone Spam Image Filtering." International Journal of Software Innovation 3, no. 4 (October 2015): 72–86. http://dx.doi.org/10.4018/ijsi.2015100106.

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Spam images in mobile phones have increasingly appeared these days. As the spam filtering systems become more sophisticated, spams are being more intelligent. Although detection of email-spams has been quite successful, there have not been effective solutions for detecting mobile phone spams yet, especially, spam images. In addition to the expensive image processing time, insufficient spam image data in mobile phones makes it challenging to train a general model. To address this issue, the authors propose a graph-based approach that utilizes graph structure in abundant e-mail spam dataset. The authors employ different clustering algorithms to find a subset of e-mail spam images similar to phone spam images. Furthermore, the performance behavior with respect to different image descriptors of Pyramid Histogram of Visual Words (PHOW) and RGB histogram is extensively investigated. The authors' results highlight that the proposed idea is fairly meaningful in increasing training data size, thus effectively improving image spam detection performance.
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Hemalatha, M., Sriharsha Katta, R. Sai Santosh, and Priyanka Priyanka. "E-MAIL SPAM DETECTION." International Journal of Computer Science and Mobile Computing 11, no. 1 (January 30, 2022): 36–44. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.006.

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E-mail is the most important form of communication. Used for a wide range of people including individuals and organizations. But these people using this e-mail they find it difficult to use because of spam mail. These spam emails are also called unsolicited bulk mail or junk mail. Spam emails are available randomly sent messages to people by anonymous users. Sites are trying to steal yours personal, electronic and financial information. An increase in spam emails leads to crime of theft of sensitive information, reduced productivity. Spam detection is dirty. The line between spam and non-spam messages is blurred, and the condition changes over time. From various attempts to automate spam detection, machine learning has so far proven to be the most effective and popular method of email providers. While we still see spam emails, a quick look at the trash folder will show how many spam is removed from our inbox daily due to machine learning algorithms. It is estimated that 40% of emails are spam mail. These spam wastes time, storage the space and width of the communication band. There are a few ways to receive spam emails but spam senders make it difficult for you to send users from a random sender address or by adding special characters at the beginning or end of the email. There are several machine learning methods for filtering spam emails including Naïve Bayes classifier, Vector support equipment, Neural Networks, Close Neighbour, Rough Sets and Random Forests. In this project we use the Naïve Bayes classifier to identify spam mail. The vast majority of people depend on what is available email or messages sent by a stranger. Possibly anyone can leave an email or message provide gold the opportunity for spam senders to write a spam message about us different interests. Spam fills in the inbox with a number of funny things mails. Slow down our internet speed. Theft useful information such as our details on our contact list. Identifying these people who post spam and spam content can be a a hot topic for research and strenuous activities. Email Spam is functionality of mass mailings. From the cost of Spam is heavily censored by the recipient, it is a successful post proper advertising. Spam email is a form of commercial advertising economically viable because email can be costly effective sender method. With this proposed model some message may be declared spam or not use Bayes' theorem and Naive Bayes’ Classifier and IP addresses of sender is usually found.
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Liu, Xiaoxu, Haoye Lu, and Amiya Nayak. "A Spam Transformer Model for SMS Spam Detection." IEEE Access 9 (2021): 80253–63. http://dx.doi.org/10.1109/access.2021.3081479.

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Wang, Junzhang, Diwen Xue, and Karen Shi. "An Ensemble Framework for Spam Detection on Social Media Platforms." International Journal of Machine Learning and Computing 11, no. 1 (January 2021): 77–84. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1017.

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As various review sites grow in popularity and begin to hold more sway in consumer preferences, spam detection has become a burgeoning field of research. While there have been various attempts to resolve the issue of spam on the open web, specifically as it relates to reviews, there does not yet exist an adaptive and robust framework out there today. We attempt to address this issue in a domain-specific manner, choosing to apply it to Yelp.com first. We believe that while certain processes do exist to filter out spam reviews for Yelp, we have a comprehensive framework that can be extended to other applications of spam detection as well. Furthermore, our framework exhibited a robust performance even when trained on small datasets, providing an approach for practitioners to conduct spam detection when the available data is inadequate. To the best of our knowledge, our framework uses the most number of extracted features and models in order to finely tune our results. In this paper, we will show how various sets of online review features add value to the final performance of our proposed framework, as well as how different machine learning models perform regarding detecting spam reviews.
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Kumar D, Mr Girish. "Spam Detection in Twitter." International Journal for Research in Applied Science and Engineering Technology 8, no. 7 (July 31, 2020): 783–87. http://dx.doi.org/10.22214/ijraset.2020.30337.

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Heron, Simon. "Technologies for spam detection." Network Security 2009, no. 1 (January 2009): 11–15. http://dx.doi.org/10.1016/s1353-4858(09)70007-8.

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Panwar, Manish, Jayesh Rajesh Jogi, Mahesh Vijay Mankar, Mohamed Alhassan, and Shreyas Kulkarni. "Detection of Spam Email." American Journal of Innovation in Science and Engineering 1, no. 1 (December 30, 2022): 18–21. http://dx.doi.org/10.54536/ajise.v1i1.996.

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Spam, often known as unsolicited email, has grown to be a major worry for every email user. Nowadays, it is quite challenging to filter spam emails since they are made, created, or written in such a unique way that anti-spam filters cannot recognize them. In order to predict or categorize emails as spam, this paper compares and reviews the performance metrics of a few categories of supervised machine learning techniques, including Svm (Support Vector Machine), Random Forest, Decision Tree, Cnn, (Convolutional Neural Network), Knn(K Nearest Neighbor), Mlp(Multi-Layer Perceptron), Adaboost (AdaptiveBoosting), and Nave Bayes algorithm. Thegoal of this study is to analyze the specificsor content of the emails, discover a limited dataset, and create a classification model that can predict or categorize whether spam is present in an email. Transformers’ Bidirectional Encoder Representations) has been optimized to perform the duty of separating spam emails from legitimate emails (Ham). To put the text’s context into perspective, Bert uses attention layers. Results are contrasted with a baseline Dnn (deep neural network) modelthat consists of two stacked Dense layers and a Bilstm (bidirectional Long Short-Term Memory) layer. Results are also contrasted with a group of traditional classifiers, including k- Nn (k-nearest neighbours) and Nb (Naive Bayes). The model is tested for robustness andpersistence using two open-source data sets, one of which is utilized to train the model.
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V, Shoba, Ravi Shankar, Ramya Shree, Dhanush H, and Manjunath L. "Spam Detection Using Machine Learning." International Research Journal of Computer Science 10, no. 05 (June 23, 2023): 130–34. http://dx.doi.org/10.26562/irjcs.2023.v1005.05.

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Deep learning has emerged as a powerful technique for spam detection due to its ability to automatically learn relevant features from raw data. This abstract presents an overview of deep learning approaches for spam detection and highlights their effectiveness in combating the ever-evolving landscape of spam. The challenges of spam detection, includes the use of sophisticated techniques by spammers to evade traditional rule-based filters. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown promise in addressing these challenges by automatically extracting high-level features from spam messages. The Spam Detection Using Machine Learning in this paper employs the Deep Learning model Bi-GRU to detect the spam messages. The advantages of this approach, is that it has the ability to handle large-scale datasets and their potential for transfer learning across different spam detection tasks. It highlights the role of deep learning in enhancing feature representation, model generalization, and the overall accuracy of spam detection systems
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Douzi, Samira, Feda A. AlShahwan, Mouad Lemoudden, and Bouabid El Ouahidi. "Hybrid Email Spam Detection Model Using Artificial Intelligence." International Journal of Machine Learning and Computing 10, no. 2 (February 2020): 316–22. http://dx.doi.org/10.18178/ijmlc.2020.10.2.937.

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Shweta B., Dand. "Survey on Spam Review Detection Using Spam Filtering Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 10, 2021): 535–38. http://dx.doi.org/10.22214/ijraset.2021.36333.

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Generally the people trust on product on the basis of that product reviews and rating. Reviews can affect an organization or profile of a brand. The corporation has to assess market reactions towards its goods. However, it is not straightforward to track and organize popular reviews. Many public views are hard to manually process in social media. A methodology is then required to categories positive or negative public assessments automatically. Online feedback will provide customers with an insight into the consistency, efficiency and advice of the product; this provides prospective buyers with a better understanding of the product. One such unrealized opportunity is the usability of web assessments from suppliers in order to fulfill client requirements by evaluating beneficial feedback. Good and negative reviews play a major role in assessing customer needs and in quicker collection of product input from consumers. Sentiment Analysis is a computer study that extracts contextual data from the text. In this study a vast number of online mobile telephone ratings are analyzed. We classify the text as positive and negative, but we also included feelings of frustration, expectation, disgust, apprehension, happiness, regret, surprise and confidence for spam review detection. This delimited grouping of feedback helps to holistically assess the product, allowing buyers to decide better.
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Dissertations / Theses on the topic "Spam detection"

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Hao, Shuang. "Early detection of spam-related activity." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53091.

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Spam, the distribution of unsolicited bulk email, is a big security threat on the Internet. Recent studies show approximately 70-90% of the worldwide email traffic—about 70 billion messages a day—is spam. Spam consumes resources on the network and at mail servers, and it is also used to launch other attacks on users, such as distributing malware or phishing. Spammers have increased their virulence and resilience by sending spam from large collections of compromised machines (“botnets”). Spammers also make heavy use of URLs and domains to direct victims to point-of-sale Web sites, and miscreants register large number of domains to evade blacklisting efforts. To mitigate the threat of spam, users and network administrators need proactive techniques to distinguish spammers from legitimate senders and to take down online spam-advertised sites. In this dissertation, we focus on characterizing spam-related activities and developing systems to detect them early. Our work builds on the observation that spammers need to acquire attack agility to be profitable, which presents differences in how spammers and legitimate users interact with Internet services and exposes detectable during early period of attack. We examine several important components across the spam life cycle, including spam dissemination that aims to reach users' inboxes, the hosting process during which spammers set DNS servers and Web servers, and the naming process to acquire domain names via registration services. We first develop a new spam-detection system based on network-level features of spamming bots. These lightweight features allow the system to scale better and to be more robust. Next, we analyze DNS resource records and lookups from top-level domain servers during the initial stage after domain registrations, which provides a global view across the Internet to characterize spam hosting infrastructure. We further examine the domain registration process and present the unique registration behavior of spammers. Finally, we build an early-warning system to identify spammer domains at time-of-registration rather than later at time-of-use. We have demonstrated that our detection systems are effective by using real-world datasets. Our work has also had practical impact. Some of the network-level features that we identified have since been incorporated into spam filtering products at Yahoo! and McAfee, and our work on detecting spammer domains at time-of-registration has directly influenced new projects at Verisign to investigate domain registrations.
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Sheikhalishahi, Mina. "Spam campaign detection, analysis, and formalization." Doctoral thesis, Université Laval, 2016. http://hdl.handle.net/20.500.11794/26935.

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Tableau d'honneur de la Faculté des études supérieures et postdoctorales, 2016-2017
Les courriels Spams (courriels indésirables ou pourriels) imposent des coûts annuels extrêmement lourds en termes de temps, d’espace de stockage et d’argent aux utilisateurs privés et aux entreprises. Afin de lutter efficacement contre le problème des spams, il ne suffit pas d’arrêter les messages de spam qui sont livrés à la boîte de réception de l’utilisateur. Il est obligatoire, soit d’essayer de trouver et de persécuter les spammeurs qui, généralement, se cachent derrière des réseaux complexes de dispositifs infectés, ou d’analyser le comportement des spammeurs afin de trouver des stratégies de défense appropriées. Cependant, une telle tâche est difficile en raison des techniques de camouflage, ce qui nécessite une analyse manuelle des spams corrélés pour trouver les spammeurs. Pour faciliter une telle analyse, qui doit être effectuée sur de grandes quantités des courriels non classés, nous proposons une méthodologie de regroupement catégorique, nommé CCTree, permettant de diviser un grand volume de spams en des campagnes, et ce, en se basant sur leur similarité structurale. Nous montrons l’efficacité et l’efficience de notre algorithme de clustering proposé par plusieurs expériences. Ensuite, une approche d’auto-apprentissage est proposée pour étiqueter les campagnes de spam en se basant sur le but des spammeur, par exemple, phishing. Les campagnes de spam marquées sont utilisées afin de former un classificateur, qui peut être appliqué dans la classification des nouveaux courriels de spam. En outre, les campagnes marquées, avec un ensemble de quatre autres critères de classement, sont ordonnées selon les priorités des enquêteurs. Finalement, une structure basée sur le semiring est proposée pour la représentation abstraite de CCTree. Le schéma abstrait de CCTree, nommé CCTree terme, est appliqué pour formaliser la parallélisation du CCTree. Grâce à un certain nombre d’analyses mathématiques et de résultats expérimentaux, nous montrons l’efficience et l’efficacité du cadre proposé.
Spam emails yearly impose extremely heavy costs in terms of time, storage space, and money to both private users and companies. To effectively fight the problem of spam emails, it is not enough to stop spam messages to be delivered to end user inbox or be collected in spam box. It is mandatory either to try to find and persecute the spammers, generally hiding behind complex networks of infected devices, which send spam emails against their user will, i.e. botnets; or analyze the spammer behavior to find appropriate strategies against it. However, such a task is difficult due to the camouflage techniques, which makes necessary a manual analysis of correlated spam emails to find the spammers. To facilitate such an analysis, which should be performed on large amounts of unclassified raw emails, we propose a categorical clustering methodology, named CCTree, to divide large amount of spam emails into spam campaigns by structural similarity. We show the effectiveness and efficiency of our proposed clustering algorithm through several experiments. Afterwards, a self-learning approach is proposed to label spam campaigns based on the goal of spammer, e.g. phishing. The labeled spam campaigns are used to train a classifier, which can be applied in classifying new spam emails. Furthermore, the labeled campaigns, with the set of four more ranking features, are ordered according to investigators priorities. A semiring-based structure is proposed to abstract CCTree representation. Through several theorems we show under some conditions the proposed approach fully abstracts the tree representation. The abstract schema of CCTree, named CCTree term, is applied to formalize CCTree parallelism. Through a number of mathematical analysis and experimental results, we show the efficiency and effectiveness of our proposed framework as an automatic tool for spam campaign detection, labeling, ranking, and formalization.
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Xu, Hailu. "Efficient Spam Detection across Online Social Networks." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470416658.

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Wu, Hao. "Detecting spam relays by SMTP traffic characteristics using an autonomous detection system." Thesis, Loughborough University, 2011. https://dspace.lboro.ac.uk/2134/10926.

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Spam emails are flooding the Internet. Research to prevent spam is an ongoing concern. SMTP traffic was collected from different sources in real networks and analyzed to determine the difference regarding SMTP traffic characteristics of legitimate email clients, legitimate email servers and spam relays. It is found that SMTP traffic from legitimate sites and non-legitimate sites are different and could be distinguished from each other. Some methods, which are based on analyzing SMTP traffic characteristics, were purposed to identify spam relays in the network in this thesis. An autonomous combination system, in which machine learning technologies were employed, was developed to identify spam relays in this thesis. This system identifies spam relays in real time before spam emails get to an end user by using SMTP traffic characteristics never involving email real content. A series of tests were conducted to evaluate the performance of this system. And results show that the system can identify spam relays with a high spam relay detection rate and an acceptable ratio of false positive errors.
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Jaroš, Ján. "Detekce nevyžádaných zpráv v mobilní komunikaci a na sociálních sítích." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236082.

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This thesis deals with spam in mobile and social networks. It focuses on spam in SMS messages and web service Twitter. Theoretical part provides brief overview of those two media, informations about what spam is, how to defend against it and where does it comes from. There is also a list of methods for spam detection, many of them have their roots in filtration of email communication. The rest of thesis is about design, implementation of application  for spam detection in SMS and Twitter messages and evaluation of its performance.
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Lam, Ho-Yu. "A learning approach to spam detection based on social networks /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20LAM.

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Nachenahalli, Bhuthegowda Bharath Kumar. "Methods for Analyzing the Evolution of Email Spam." Thesis, University of Oregon, 2019. http://hdl.handle.net/1794/24213.

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Email spam has steadily grown and has become a major problem for users, email service providers, and many other organizations. Many adversarial methods have been proposed to combat spam and various studies have been made on the evolution of email spam, by finding evolution patterns and trends based on historical spam data and by incorporating spam filters. In this thesis, we try to understand the evolution of email spam and how we can build better classifiers that will remain effective against adaptive adversaries like spammers. We compare various methods for analyzing the evolution of spam emails by incorporating spam filters along with a spam dataset. We explore the trends based on the weights of the features learned by the classifiers and the accuracies of the classifiers trained and tested in different settings. We also evaluate the effectiveness of the classifier trained in adversarial settings on synthetic data.
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Vural, Ickin. "Spamming mobile botnet detection using computational intelligence." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/36775.

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This dissertation explores a new challenge to digital systems posed by the adaptation of mobile devices and proposes a countermeasure to secure systems against threats to this new digital ecosystem. The study provides the reader with background on the topics of spam, Botnets and machine learning before tackling the issue of mobile spam. The study presents the reader with a three tier model that uses machine learning techniques to combat spamming mobile Botnets. The three tier model is then developed into a prototype and demonstrated to the reader using test scenarios. Finally, this dissertation critically discusses the advantages of having using the three tier model to combat spamming Botnets.
Dissertation (MSc)--University of Pretoria, 2013.
gm2014
Computer Science
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Neuwirth, David. "Realizace spamového filtru na bázi umělého imunitního systému." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236637.

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Unsolicited e-mails generally present a major problem within the e-mail communication nowadays. There exist several methods that can detect spam and distinguish it from the requested messages. The theoretical part of the masters thesis introduces the ways of detecting unsolicited messages by using artificial immune systems. It presents and subsequently analyses several methods of the artificial immune systems that can assist in the fight against spam. The practical part of the masters thesis deals with the implementation of a spam filter on the basis of the artificial immune systems. The project ends with comparison of effectiveness of the newly designed spam filter and the one which uses common methods for spam detection.
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Hayati, Pedram. "Addressing the new generation of spam (Spam 2.0) through Web usage models." Thesis, Curtin University, 2011. http://hdl.handle.net/20.500.11937/850.

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New Internet collaborative media introduce new ways of communicating that are not immune to abuse. A fake eye-catching profile in social networking websites, a promotional review, a response to a thread in online forums with unsolicited content or a manipulated Wiki page, are examples of new the generation of spam on the web, referred to as Web 2.0 Spam or Spam 2.0. Spam 2.0 is defined as the propagation of unsolicited, anonymous, mass content to infiltrate legitimate Web 2.0 applications.The current literature does not address Spam 2.0 in depth and the outcome of efforts to date are inadequate. The aim of this research is to formalise a definition for Spam 2.0 and provide Spam 2.0 filtering solutions. Early-detection, extendibility, robustness and adaptability are key factors in the design of the proposed method.This dissertation provides a comprehensive survey of the state-of-the-art web spam and Spam 2.0 filtering methods to highlight the unresolved issues and open problems, while at the same time effectively capturing the knowledge in the domain of spam filtering.This dissertation proposes three solutions in the area of Spam 2.0 filtering including: (1) characterising and profiling Spam 2.0, (2) Early-Detection based Spam 2.0 Filtering (EDSF) approach, and (3) On-the-Fly Spam 2.0 Filtering (OFSF) approach. All the proposed solutions are tested against real-world datasets and their performance is compared with that of existing Spam 2.0 filtering methods.This work has coined the term ‘Spam 2.0’, provided insight into the nature of Spam 2.0, and proposed filtering mechanisms to address this new and rapidly evolving problem.
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Books on the topic "Spam detection"

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Rajalingam, Mallikka. Text Segmentation and Recognition for Enhanced Image Spam Detection. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-53047-1.

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Sergeĭ, Litvinov, ed. SPA - chistilishche. Moskva: ĖKSMO, 2007.

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Play it again, Spam: A Pennsylvania Dutch mystery with recipes. New York, New York, U.S.A: Penguin Group, 1999.

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Keene, Carolyn. Secret of the spa. New York: Aladdin Paperbacks, 2005.

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Dhavale, Sunita Vikrant. Advanced Image-Based Spam Detection and Filtering Techniques. IGI Global, 2017.

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sabharwal, munish, and jagmeet kaur. Spam Detection in Online Social Networks Using Feed Forward Neural Network. Independently Published, 2018.

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Rajalingam, Mallikka. Text Segmentation and Recognition for Enhanced Image Spam Detection: An Integrated Approach. Springer International Publishing AG, 2021.

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Rajalingam, Mallikka. Text Segmentation and Recognition for Enhanced Image Spam Detection: An Integrated Approach. Springer International Publishing AG, 2020.

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I Ondskans Spar. Norhaven Paperback, 2003.

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Isaacson, Jeff, and Jeff Isaacson. This Atlanta World: A Sinister Span Mystery. 6134, 2020.

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Book chapters on the topic "Spam detection"

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Martin, Eric, Samuel Kaski, Fei Zheng, Geoffrey I. Webb, Xiaojin Zhu, Ion Muslea, Kai Ming Ting, et al. "Spam Detection." In Encyclopedia of Machine Learning, 906. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_768.

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Najork, Marc. "Web Spam Detection." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_465-2.

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Najork, Marc. "Web Spam Detection." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_465-3.

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Najork, Marc. "Web Spam Detection." In Encyclopedia of Database Systems, 3520–23. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_465.

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Najork, Marc. "Web Spam Detection." In Encyclopedia of Database Systems, 4677–81. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_465.

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Antony, Anu, Anusha Rajendran, and G. Deepa. "YouTube Spam Comment Detection." In Proceedings of the 2nd International Conference on Signal and Data Processing, 387–94. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1410-4_32.

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Gupta, Vashu, Aman Mehta, Akshay Goel, Utkarsh Dixit, and Avinash Chandra Pandey. "Spam Detection Using Ensemble Learning." In Harmony Search and Nature Inspired Optimization Algorithms, 661–68. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0761-4_63.

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Rosso, Paolo, and Leticia C. Cagnina. "Deception Detection and Opinion Spam." In A Practical Guide to Sentiment Analysis, 155–71. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55394-8_8.

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Ibrahim, Asma, and Izzeldin Mohamed Osman. "A Behavioral Spam Detection System." In Advances in Intelligent and Soft Computing, 77–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25538-0_12.

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Liu, Ninghao, and Xia Hu. "Spam Detection on Social Networks." In Encyclopedia of Social Network Analysis and Mining, 1–9. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_110199-1.

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Conference papers on the topic "Spam detection"

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Zhang, Wuxain, and Hung-Min Sun. "Instagram Spam Detection." In 2017 IEEE 22nd Pacific Rim International Symposium on Dependable Computing (PRDC). IEEE, 2017. http://dx.doi.org/10.1109/prdc.2017.43.

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Markines, Benjamin, Ciro Cattuto, and Filippo Menczer. "Social spam detection." In the 5th International Workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1531914.1531924.

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Jindal, Nitin, and Bing Liu. "Review spam detection." In the 16th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1242572.1242759.

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Bensouda, Nissrine, Sanaa El Fkihi, and Rdouan Faizi. "Opinion Spam Detection." In the International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3230905.3230922.

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Rayana, Shebuti, and Leman Akoglu. "Collective Opinion Spam Detection." In KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2783258.2783370.

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Nagaraj, P., K. Muthamil Sudar, P. Thrived, P. Girish Kumar Reddy, Sk Baji Babu, and P. Siva Rama Krishna. "Youtube Comment Spam Detection." In 2023 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2023. http://dx.doi.org/10.1109/iccci56745.2023.10128559.

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Zhou, Dengyong, Christopher J. C. Burges, and Tao Tao. "Transductive link spam detection." In the 3rd international workshop. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1244408.1244413.

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Nelson, Blaine. "Session details: Spam detection." In CCS'12: the ACM Conference on Computer and Communications Security. New York, NY, USA: ACM, 2012. http://dx.doi.org/10.1145/3251569.

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Kishore, Sidharth, Mohammed Awad, and Ahmed Al-Zubidy. "Spam Detection Techniques Recapped." In 2022 International Conference on Electrical and Computing Technologies and Applications (ICECTA). IEEE, 2022. http://dx.doi.org/10.1109/icecta57148.2022.9990450.

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Stanton, Gray, and Athirai A. Irissappane. "GANs for Semi-Supervised Opinion Spam Detection." 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.2019/723.

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Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A key challenge in detecting opinion spam is obtaining ground truth. Though there exists a large set of reviews, only a few of them have been labeled spam or non-spam. We propose spamGAN, a generative adversarial network which relies on limited labeled data as well as unlabeled data for opinion spam detection. spamGAN improves the state-of-the-art GAN based techniques for text classification. Experiments on TripAdvisor data show that spamGAN outperforms existing techniques when labeled data is limited. spamGAN can also generate reviews with reasonable perplexity.
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Reports on the topic "Spam detection"

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Tan, Pang-Ning, and Anil K. Jain. Information Assurance: Detection & Response to Web Spam Attacks. Fort Belvoir, VA: Defense Technical Information Center, August 2010. http://dx.doi.org/10.21236/ada535002.

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Denowh, Chantz, and David Futch. PR-652-213801-R01 Technology Assessment for Detection of Fatigue Cracks on Heavy Wall Gas Risers. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 2022. http://dx.doi.org/10.55274/r0012198.

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The objective of the SPIM-1-2 project was to identify and evaluate the most promising technologies for detecting and sizing fatigue cracks on heavy wall pipelines and risers, preferably without liquid coupling. To complete this evaluation, six full-scale heavy wall pipe samples were fabricated, and a range of fatigue cracks generated in circumferential welds using resonant fatigue. These samples were used in blind test trials on several available inspection technologies in a pull-through testbed. The technologies were evaluated on their ability to detect and size the fatigue cracks generated from resonant fatigue. At the conclusion of the SPIM-1-2 project, the fatigue cracks in the heavy-wall circumferential welds were available to PRCI members to continue testing and development of inspection technologies. At the conclusion of the SPIM-1-2 project, the actual fatigue crack locations and sizes were not included in the final reports to protect the blind nature of the six pipes samples. A follow-up effort under NDE-2-2 was conducted to gather metallurgical truth for several of the fatigue cracks and generate a comprehensive report that evaluated the inspection technology results from SPIM-1-2 against this truth data.
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Rahmani, Mehran, Xintong Ji, and Sovann Reach Kiet. Damage Detection and Damage Localization in Bridges with Low-Density Instrumentations Using the Wave-Method: Application to a Shake-Table Tested Bridge. Mineta Transportation Institute, September 2022. http://dx.doi.org/10.31979/mti.2022.2033.

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This study presents a major development to the wave method, a methodology used for structural identification and monitoring. The research team tested the method for use in structural damage detection and damage localization in bridges, the latter being a challenging task. The main goal was to assess capability of the improved method by applying it to a shake-table-tested prototype bridge with sparse instrumentation. The bridge was a 4-span reinforced concrete structure comprising two columns at each bent (6 columns total) and a flat slab. It was tested to failure using seven biaxial excitations at its base. Availability of a robust and verified method, which can work with sparse recording stations, can be valuable for detecting damage in bridges soon after an earthquake. The proposed method in this study includes estimating the shear (cS) and the longitudinal (cL) wave velocities by fitting an equivalent uniform Timoshenko beam model in impulse response functions of the recorded acceleration response. The identification algorithm is enhanced by adding the model’s damping ratio to the unknown parameters, as well as performing the identification for a range of initial values to avoid early convergence to a local minimum. Finally, the research team detect damage in the bridge columns by monitoring trends in the identified shear wave velocities from one damaging event to another. A comprehensive comparison between the reductions in shear wave velocities and the actual observed damages in the bridge columns is presented. The results revealed that the reduction of cS is generally consistent with the observed distribution and severity of damage during each biaxial motion. At bents 1 and 3, cS is consistently reduced with the progression of damage. The trends correctly detected the onset of damage at bent 1 during biaxial 3, and damage in bent 3 during biaxial 4. The most significant reduction was caused by the last two biaxial motions in bents 1 and 3, also consistent with the surveyed damage. In bent 2 (middle bent), the reduction trend in cS was relatively minor, correctly showing minor damage at this bent. Based on these findings, the team concluded that the enhanced wave method presented in this study was capable of detecting damage in the bridge and identifying the location of the most severe damage. The proposed methodology is a fast and inexpensive tool for real-time or near real-time damage detection and localization in similar bridges, especially those with sparsely deployed accelerometers.
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Haakonsen, Rune. PR-576-163705-R01 Qualification and Guideline of Inspection Technologies for Flexible Pipe Integrity. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), July 2020. http://dx.doi.org/10.55274/r0011741.

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This report summarizes the SPIM-2-1 project with testing of tools and technologies for flexible pipe inspection. The objective of the project is to provide a quantitative assessment of the capabilities of available flexible pipe inspection technologies to detect relevant defects in flexible pipe armour layers. This objective is met by the following project activities: - Identifying existing inspection technologies and vendors/service providers - Mapping inspection technologies - detection capabilities - vendors - Inviting selected vendors and RFI - Define test sample requirements - Procure test samples - Conduct blind testing by participating vendors - Reporting by vendors - Reporting by project team (this report) Eleven inspection tools from eight different vendors have been subject to blind testing on flexible pipes samples developed specifically for the project with representative defects.
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Oliver, Peter, and Gillian Robert. PR-420-183903-R01 Pipeline Right-of-Way River Crossing Monitoring With Satellites. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2022. http://dx.doi.org/10.55274/r0012247.

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The goal of the work described herein is to provide PRCI and the pipeline industry further understanding of the current capabilities and limitations of combined SAR and high resolution optical satellite imagery for the monitoring of pipeline ROWs which span river crossings. Four Areas of Interests (AOIs) with pipeline ROWs that span river crossings were selected for analysis: South Saskatchewan River, Saskatchewan, Canada operated by SaskEnergy Incorporated; Thompson Creek, Louisiana, USA operated by Colonial Pipeline Company; Gila River, Arizona, USA operated by Kinder Morgan Incorporated; and Humber Estuary, UK, operated by National Grid. For each AOI, monitoring requirements were defined by the operators. Amplitude Change Detection (ACD) and Interferometric Synthetic Aperture Radar (InSAR) were performed for all AOIs; results correlated to the defined monitoring requirements are discussed. A high level summary of the role of combined SAR and optical satellite operational monitoring of pipeline river crossings is listed below: - InSAR "Phase" used for (a) Subsidence (b) Slope Movement - SAR "Amplitude" used to both detect and classify (a) large scale Land Cover/Land Use Change (e.g. bridge construction), (b) flooding, (c) river channel changes, (d) river bed exposure, and (e) vessel traffic. - SAR "Amplitude" used to detect changes resulting from (a) small scale Land Cover/ Land Use (e.g. construction of individual buildings), and possibly (b) bank erosion and (c) pipeline exposure. Optical Satellite imagery is required for classification of these changes.
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Alexander, Chris, and Chantz Denowh. PR-652-195104-R01 Development of Heavy Wall ILI Test Samples. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), June 2020. http://dx.doi.org/10.55274/r0011680.

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Guidance is needed for the pipeline industry's in-line inspection (ILI) technologies as current industry practices address mainly thin-wall pipe specifications as well as spool and defect design. Heavy-wall pipe is mostly found in subsea applications where the predominant threats are internal; however, external defects cannot be discounted. Nondestructive evaluation (NDE) sensing technologies for heavy-wall pipe often have very different specs for examining external versus internal features. The end uses for ILI technologies in the heavy-wall subsea applications often put a premium on low limits of detection for purposes of gauging time-dependent growth of internal wall loss rather than for fitness for service assessments. The test samples associated with the current study considers both. The seven categories from the Pipeline Operators Forum (POF), with an emphasis on external integrity condition performance, do not address the reasons pipeline operators may require project-specific, large-scale testing in advance of deployment. Prior PRCI projects have proposed and tested the effects of metal loss fabrication methods on intelligent pig detection and discrimination performance on "standard" pipe wall schedules. Relevance of prior POF style metal loss shapes and severities have gaps with respect to qualifying ILI performance to serve fitness for purpose and corrosion growth purposes. There is a need to provide guidance for the design of ILI test samples to serve both training and blind qualification purposes. For this reason, PRCI commissioned the SPIM-1-6 project, which is the focus of the current document.
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Rahmani, Mehran, and Manan Naik. Structural Identification and Damage Detection in Bridges using Wave Method and Uniform Shear Beam Models: A Feasibility Study. Mineta Transportation Institute, February 2021. http://dx.doi.org/10.31979/mti.2021.1934.

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This report presents a wave method to be used for the structural identification and damage detection of structural components in bridges, e.g., bridge piers. This method has proven to be promising when applied to real structures and large amplitude responses in buildings (e.g., mid-rise and high-rise buildings). This study is the first application of the method to damaged bridge structures. The bridge identification was performed using wave propagation in a simple uniform shear beam model. The method identifies a wave velocity for the structure by fitting an equivalent uniform shear beam model to the impulse response functions of the recorded earthquake response. The structural damage is detected by measuring changes in the identified velocities from one damaging event to another. The method uses the acceleration response recorded in the structure to detect damage. In this study, the acceleration response from a shake-table four-span bridge tested to failure was used. Pairs of sensors were identified to represent a specific wave passage in the bridge. Wave velocities were identified for several sensor pairs and various shaking intensities are reported; further, actual observed damage in the bridge was compared with the detected reductions in the identified velocities. The results show that the identified shear wave velocities presented a decreasing trend as the shaking intensity was increased, and the average percentage reduction in the velocities was consistent with the overall observed damage in the bridge. However, there was no clear correlation between a specific wave passage and the observed reduction in the velocities. This indicates that the uniform shear beam model was too simple to localize the damage in the bridge. Instead, it provides a proxy for the overall extent of change in the response due to damage.
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Hedrick, Ronald, and Herve Bercovier. Characterization and Control of KHV, A New Herpes Viral Pathogen of Koi and Common Carp. United States Department of Agriculture, January 2004. http://dx.doi.org/10.32747/2004.7695871.bard.

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In this project we proposed to characterize the virus genome and the structural virion polypeptides to allow development of improved diagnostic approaches and potential vaccination strategies. These goals have been mostly achieved and the corresponding data were published in three papers (see below) and three more manuscripts are in preparation. The virion polypeptides of KHV strains isolated from USA (KHV-U) and Israel (KHV-I) were found to be identical. Purified viral DNA analyzed with a total of 5 restriction enzymes demonstrated no fragment length polymorphism between KHV-I and KHV-U but both KHV isolates differed significantly from the cyprinid herpesvirus (CHV) and the ictalurid herpesvirus (channel catfish virus or CCV). Using newly obtained viral DNA sequences two different PCR assays were developed that need to be now further tested in the field. We determined by pulse field analysis that the size of KHV genome is around 280 kbp (1-1. Bercovier, unpublished results). Sequencing of the viral genome of KHV has reached the stage where 180 kbp are sequenced (twice and both strands). Four hypothetical genes were detected when DNA sequences were translated into amino acid sequences. The finding of a gene of real importance, the thymidine kinase (TK) led us to extend the study of this specific gene. Four other genes related to DNA synthesis were found. PCR assays based on defined sequences were developed. The PCR assay based on TK gene sequence has shown improved sensitivity in the detection of KHV DNA compared to regular PCR assays. </P> <P><SPAN>With the ability to induce experimental infections in koi with KHV under controlled laboratory conditions we have studied the progress and distribution of virus in host tissues, the development of immunity and the establishment of latent infections. Also, we have investigated the important role of water temperature on severity of infections and mortality of koi following infections with KHV. These initial studies need to be followed by an increased focus on long-term fate of the virus in survivors. This is essential in light of the current &quot;controlled exposure program&quot; used by farmers to produce KHV &quot;naturally resistant fish&quot; that may result in virus or DNA carriers. </SPAN></P> <P><SPAN>The information gained from the research of this project was designed to allow implementation of control measures to prevent the spread of the virus both by improved diagnostic approaches and preventive measures. We have accomplished most of these goals but further studies are needed to establish even more reliable methods of prevention with increased emphases on improved diagnosis and a better understanding of the ecology of KHV. </SPAN>
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Denowh, Chantz, Chris Alexander, and Ahmed Hassanin. PR-652-195104-R02 Development of Heavy Wall ILI Test Samples. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), May 2021. http://dx.doi.org/10.55274/r0012096.

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Guidance is needed for the pipeline industry's in-line inspection (ILI) technologies as current industry practices address mainly thin-wall pipe specifications as well as spool and defect design. Heavy-wall pipe is mostly found in subsea applications where the predominant threats are internal; however, external defects cannot be discounted. Nondestructive evaluation (NDE) sensing technologies for heavy-wall pipe often have very different specifications for examining external versus internal defects. The end uses for ILI technologies in the heavy-wall subsea applications often put a premium on low limits of detection for purposes of gauging time-dependent growth of internal wall loss rather than for fitness for service assessments. The test samples associated with the current study considers both. Post run ILI performance verification via external NDE, as is often employed for onshore applications, is prohibitively expensive for offshore applications. Thus, a high confidence in the ILI tool performance is required prior to run execution. This often drives project specific ILI performance testing and verification via an onshore pull testing as described herein. The seven categories from the Pipeline Operators Forum (POF), with an emphasis on external integrity condition performance, do not address the reasons pipeline operators may require project-specific, large-scale testing in advance of deployment. PRCI commissioned the SPIM-1-6 project to provide guidance on the design of ILI test samples for both training and blind validation purposes. It is envisioned that a standardized design process will lead to resource sharing between operators and ILI providers.
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Brydie, Dr James, Dr Alireza Jafari, and Stephanie Trottier. PR-487-143727-R01 Modelling and Simulation of Subsurface Fluid Migration from Small Pipeline Leaks. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), May 2017. http://dx.doi.org/10.55274/r0011025.

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The dispersion and migration behavior of hydrocarbon products leaking at low rates (i.e. 1bbl/day and 10 bbl/day) from a pipeline have been studied using a combination of experimental leakage tests and numerical simulations. The focus of this study was to determine the influence of subsurface engineered boundaries associated with the trench walls, and the presence of a water table, upon the leakage behavior of a range of hydrocarbon products. The project numerically modelled three products including diesel, diluted bitumen (dilbit) and gasoline; which were chosen to span a range of fluid types and viscosities. Laboratory simulations of leakage were carried out for the most viscous product (i.e. dilbit) in order to capture plume dispersion in semi-real time, and to allow numerical predictions to be assessed against experimental data. Direct comparisons between observed plume dimensions over time and numerically predicted behavior suggested a good match under low moisture conditions, providing confidence that the numerical simulation was sufficiently reliable to model field-scale applications. Following a simulated two year initialization period, the leakage of products, their associated gas phase migration, thermal and geomechanical effects were simulated for a period of 365 days. Comparisons between product leakage rate, product type and soil moisture content were made and the spatial impacts of leakage were summarized. Variably compacted backfill within the trench, surrounded by undisturbed and more compacted natural soils, results porosity and permeability differences which control the migration of liquids, gases, thermal effects and surface heave. Dilbit migration is influenced heavily by the trench, and also its increasing viscosity as it cools and degases after leakage. Diesel and gasoline liquid plumes are also affected by the trench structure, but to a lesser extent, resulting in wider and longer plumes in the subsurface. In all cases, the migration of liquids and gases is facilitated by higher permeability zones at the base of the pipe. Volatile Organic Compounds (VOCs) migrate along the trench and break through at the surface within days of the leak. Temperature changes within the trench may increase due liquid migration, however the change in predicted temperature at the surface above the leak is less than 0.5�C above background. For gasoline, the large amount of degassing and diffusion through the soil results in cooling of the soil by up to 1�C. Induced surface displacement was predicted for dilbit and for one case of diesel, but only in the order of 0.2cm above baseline. Based upon the information gathered, recommendations are provided for the use and placement of generic leak detection sensor types (e.g liquid, gas, thermal, displacement) within the trench and / or above the ground surface. The monitoring locations suggested take into account requirements to detect pipeline leakage as early as possible in order to facilitate notification of the operator and to predict the potential extent of site characterization required during spill response and longer term remediation activities.
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