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Academic literature on the topic 'Popularity detection'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Popularity detection"

Zhang, Xiaoming, Xiaoming Chen, Yan Chen, Senzhang Wang, Zhoujun Li, and Jiali Xia. "Event detection and popularity prediction in microblogging." Neurocomputing 149 (February 2015): 1469–80. http://dx.doi.org/10.1016/j.neucom.2014.08.045.

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NN, Mrs Deepti. "D-SCAN : DEPRESSION DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 23, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem31462.

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Depression is a serious illness that affects millions of people globally. From child to senior citizen are facing depression. Major area is occupied by adults, college going students and teenagers also. In recent years, the task of automation depression detection from speech has gained popularity. We provide a comparative analyses of various features for depression detection by evaluating how a system built on text-based, voice-based, and speech-based system. Detecting texts that express negativity in the data is one of the best ways to detect depression. In this paper, this problem of depression detection on social media and various machine learning algorithms that can be used to detect depression have been discussed. Key Words: Depression, Face detection, Audio detection, Video detection, Healthcare innovation, Result.

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Miao, Zhongchen, Kai Chen, Yi Fang, Jianhua He, Yi Zhou, Wenjun Zhang, and Hongyuan Zha. "Cost-Effective Online Trending Topic Detection and Popularity Prediction in Microblogging." ACM Transactions on Information Systems 35, no. 3 (June 9, 2017): 1–36. http://dx.doi.org/10.1145/3001833.

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Wolcott, M. J. "Advances in nucleic acid-based detection methods." Clinical Microbiology Reviews 5, no. 4 (October 1992): 370–86. http://dx.doi.org/10.1128/cmr.5.4.370.

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Laboratory techniques based on nucleic acid methods have increased in popularity over the last decade with clinical microbiologists and other laboratory scientists who are concerned with the diagnosis of infectious agents. This increase in popularity is a result primarily of advances made in nucleic acid amplification and detection techniques. Polymerase chain reaction, the original nucleic acid amplification technique, changed the way many people viewed and used nucleic acid techniques in clinical settings. After the potential of polymerase chain reaction became apparent, other methods of nucleic acid amplification and detection were developed. These alternative nucleic acid amplification methods may become serious contenders for application to routine laboratory analyses. This review presents some background information on nucleic acid analyses that might be used in clinical and anatomical laboratories and describes some recent advances in the amplification and detection of nucleic acids.

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Hao, Yaojun, Peng Zhang, and Fuzhi Zhang. "Multiview Ensemble Method for Detecting Shilling Attacks in Collaborative Recommender Systems." Security and Communication Networks 2018 (October 11, 2018): 1–33. http://dx.doi.org/10.1155/2018/8174603.

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Faced with the evolving attacks in collaborative recommender systems, the conventional shilling detection methods rely mainly on one kind of user-generated information (i.e., single view) such as rating values, rating time, and item popularity. However, these methods often suffer from poor precision when detecting different attacks due to ignoring other potentially relevant information. To address this limitation, in this paper we propose a multiview ensemble method to detect shilling attacks in collaborative recommender systems. Firstly, we extract 17 user features by considering the temporal effects of item popularity and rating values in different popular item sets. Secondly, we devise a multiview ensemble detection framework by integrating base classifiers from different classification views. Particularly, we use a feature set partition algorithm to divide the features into several subsets to construct multiple optimal classification views. We introduce a repartition strategy to increase the diversity of views and reduce the influence of feature order. Finally, the experimental results on the Netflix and Amazon review datasets indicate that the proposed method has better performance than benchmark methods when detecting various synthetic attacks and real-world attacks.

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Skaperas, Sotiris, Lefteris Mamatas, and Arsenia Chorti. "Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis." IEEE Access 7 (2019): 142246–60. http://dx.doi.org/10.1109/access.2019.2940816.

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Singha, Subroto, and Burchan Aydin. "Automated Drone Detection Using YOLOv4." Drones 5, no. 3 (September 11, 2021): 95. http://dx.doi.org/10.3390/drones5030095.

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Drones are increasing in popularity and are reaching the public faster than ever before. Consequently, the chances of a drone being misused are multiplying. Automated drone detection is necessary to prevent unauthorized and unwanted drone interventions. In this research, we designed an automated drone detection system using YOLOv4. The model was trained using drone and bird datasets. We then evaluated the trained YOLOv4 model on the testing dataset, using mean average precision (mAP), frames per second (FPS), precision, recall, and F1-score as evaluation parameters. We next collected our own two types of drone videos, performed drone detections, and calculated the FPS to identify the speed of detection at three altitudes. Our methodology showed better performance than what has been found in previous similar studies, achieving a mAP of 74.36%, precision of 0.95, recall of 0.68, and F1-score of 0.79. For video detection, we achieved an FPS of 20.5 on the DJI Phantom III and an FPS of 19.0 on the DJI Mavic Pro.

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Madana Mohana, R., Paramjeet Singh, Vishal Kumar, and Sohail Shariff. "Brutality detection and rendering of brutal frames." MATEC Web of Conferences 392 (2024): 01072. http://dx.doi.org/10.1051/matecconf/202439201072.

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The popularity of anime is increasing exponentially in every part of the world due to its unique storyline, nonstop entertainment, fights, and similar type of content that can hold viewers and keeps them at the edge of their seats. However, with the increase of popularity in anime there has also been an exponential increase in violence and brutality in anime videos. Violent scenes have become much more common in anime videos when compared to generic cinema. This survey paper presents a comprehensive view on the detection of violence in movies and different scenarios using various techniques. Most commonly to automate detection of violence, machine learning is used for training the machine to detect violence. Convolution neural networks (CNN) are used very commonly to understand image pattern recognition with high accuracy. Moreover, use of other different methods such as LSTM and Markov models are also used to detect violence. The main goals kept in mind while working is to detect violence with high accuracy and to use less computation or to perform the action at a high-speed rate.

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Satwik, Pallerla. "Hate Speech Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (March 31, 2024): 1646–49. http://dx.doi.org/10.22214/ijraset.2024.59053.

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Abstract: The rise in popularity of microblogging sites such as Facebook, Instagram, and Twitter has resulted in more people from different backgrounds indirectly communicating with one another. Our study aims to design an autonomous Deep Neural Network (DNN) algorithm for social media hate speech detection to tackle this problem. Using cutting-edge Natural Language Processing (NLP) techniques, the objective is to build a strong system that can recognize and categorize hate speech material in text data with accuracy. Our DNN algorithm allows for the real-time detection and moderation of offensive information, providing a proactive strategy against online hate speech. With the deployment of this technology, everyone will be able to access a safer and more welcoming online environment.

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Patil, Vaibhavi, Sakshi Patil, Krishna Ganjegi, and Pallavi Chandratre. "Face and Eye Detection for Interpreting Malpractices in Examination Hall." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1119–23. http://dx.doi.org/10.22214/ijraset.2022.41456.

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Abstract: One of the most difficult problems in computer vision is detecting faces and eyes. The purpose of this work is to give a review of the available literature on face and eye detection, as well as assessment of gaze. With the growing popularity of systems based on face and eye detection in a range of disciplines in recent years, academia and industry have paid close attention to this topic. Face and eye identification has been the subject of numerous investigations. Face and eye detection systems have made significant process despite numerous challenges such as varying illumination conditions, wearing glasses, having facial hair or moustache on the face, and varying orientation poses or occlusion of the face. We categorize face detection models and look at basic face detection methods in this paper. We categorize face detection models and look at basic face detection methos in this paper. Then we’ll go through eye detection and estimation techniques. Keywords: Image Processing, Face Detection, Eye Detection, Gaze Estimation

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Dissertations / Theses on the topic "Popularity detection"

Hsu, Yu-Song, and 許煜松. "A Fast Detection Algorithm on Popularity Modeling." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/03362414636695668535.

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碩士
國立清華大學
資訊工程學系
95
Popularity of publications, such as CDs, books, and movies, is critical to circulations and incomes. However, an erroneous prediction of popularity of publications causes unnecessary costs, or lost due to underproduction. Hence, the analysis of popularity of products has become an important issue. Our purpose in this research was to detect the trend before a publication becomes popular. Generally, the time series of popularity of a product can be divided into three phases – the slow-start phase, the fast-growing phase, and the slow-end phase. We proposed a two-stages detecting algorithm, which monitored the popularity with a CUSUM mechanism, verified the monitoring by comparing the distributions of past and future data to find the outbreak point, predicted the future trend of popularity, and then detected the time that the growth slows down. Thus, the data series was divided into the three mentioned phases. Through some simulation results with real data, the rate of accuracy on detecting outbreak points was over 90%, and over 80% on detecting cool-down points. This exhibits that the proposed algorithm improves efficiency and accuracy while predicting popularity of publications.

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"Social Media Analytics for Crisis Response." Doctoral diss., 2015. http://hdl.handle.net/2286/R.I.29691.

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abstract: Crises or large-scale emergencies such as earthquakes and hurricanes cause massive damage to lives and property. Crisis response is an essential task to mitigate the impact of a crisis. An effective response to a crisis necessitates information gathering and analysis. Traditionally, this process has been restricted to the information collected by first responders on the ground in the affected region or by official agencies such as local governments involved in the response. However, the ubiquity of mobile devices has empowered people to publish information during a crisis through social media, such as the damage reports from a hurricane. Social media has thus emerged as an important channel of information which can be leveraged to improve crisis response. Twitter is a popular medium which has been employed in recent crises. However, it presents new challenges: the data is noisy and uncurated, and it has high volume and high velocity. In this work, I study four key problems in the use of social media for crisis response: effective monitoring and analysis of high volume crisis tweets, detecting crisis events automatically in streaming data, identifying users who can be followed to effectively monitor crisis, and finally understanding user behavior during crisis to detect tweets inside crisis regions. To address these problems I propose two systems which assist disaster responders or analysts to collaboratively collect tweets related to crisis and analyze it using visual analytics to identify interesting regions, topics, and users involved in disaster response. I present a novel approach to detecting crisis events automatically in noisy, high volume Twitter streams. I also investigate and introduce novel methods to tackle information overload through the identification of information leaders in information diffusion who can be followed for efficient crisis monitoring and identification of messages originating from crisis regions using user behavior analysis.
Dissertation/Thesis
Doctoral Dissertation Computer Science 2015

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Books on the topic "Popularity detection"

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Identität ermitteln: Ethnische und postkoloniale Kriminalromane zwischen Popularität und Subversion. Würzburg: Königshausen & Neumann, 2011.

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Revenge of the homecoming queen. New York: Berkley Jam, 2007.

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Gildersleeve, Jessica, and Kate Cantrell. Screening the Gothic in Australia and New Zealand. Nieuwe Prinsengracht 89 1018 VR Amsterdam Nederland: Amsterdam University Press, 2022. http://dx.doi.org/10.5117/9789463721141.

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The persistent popularity of the detective narrative, new obsessions with psychological and supernatural disturbances, as well as the resurgence of older narratives of mystery or the Gothic all constitute a vast proportion of contemporary film and television productions. New ways of watching film and television have also seen a reinvigoration of this ‘most domestic of media’. But what does this ‘domesticity’ of genre and media look like ‘Down Under’ in the twenty.first century? This collection traces representations of the Gothic on both the small and large screens in Australia and New Zealand in the twenty.first century. It attends to the development and mutation of the Gothic in these post. or neo.colonial contexts, concentrating on the generic innovations of this temporal and geographical focus.

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Henderson, Lauren. Nụ hôn thần chết. Hà Nội: NXB Văn hóa thông tin, 2009.

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Scripted. New York, NY: G. P. Putnam's Sons, an imprint of Penguin Group (USA), 2015.

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Henderson, Lauren. Kiss me kill me. New York: Delacorte Press, 2008.

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Henderson, Lauren. Kiss Me Kill Me. New York: Random House Children's Books, 2009.

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Stine, R. L. The dare. London: Pocket Books, 1994.

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Stine, R. L. The dare. New York: Archway Paperbacks, 1994.

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Book chapters on the topic "Popularity detection"

Schedl, Markus, Peter Knees, and Gerhard Widmer. "Improving Prototypical Artist Detection by Penalizing Exorbitant Popularity." In Computer Music Modeling and Retrieval, 196–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11751069_18.

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Sahoo, Somya Ranjan, and B. B. Gupta. "Popularity-Based Detection of Malicious Content in Facebook Using Machine Learning Approach." In First International Conference on Sustainable Technologies for Computational Intelligence, 163–76. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0029-9_13.

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Tolosana, Ruben, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales, and Javier Ortega-Garcia. "An Introduction to Digital Face Manipulation." In Handbook of Digital Face Manipulation and Detection, 3–26. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_1.

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AbstractDigital manipulation has become a thriving topic in the last few years, especially after the popularity of the term DeepFakes. This chapter introduces the prominent digital manipulations with special emphasis on the facial content due to their large number of possible applications. Specifically, we cover the principles of six types of digital face manipulations: (i) entire face synthesis, (ii) identity swap, (iii) face morphing, (iv) attribute manipulation, (v) expression swap (a.k.a. face reenactment or talking faces), and (vi) audio- and text-to-video. These six main types of face manipulation are well established by the research community, having received the most attention in the last few years. In addition, we highlight in this chapter publicly available databases and code for the generation of digital fake content.

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Devaraj, Jayanthi. "A Comparative Analysis of Deep Learning Models for Fake News Detection and Popularity Prediction of Articles." In Intelligent Systems and Sustainable Computational Models, 246–65. Boca Raton: Auerbach Publications, 2024. http://dx.doi.org/10.1201/9781003407959-16.

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Chu, Quanquan, Zhenhao Cao, Xiaofeng Gao, Peng He, Qianni Deng, and Guihai Chen. "Cease with Bass: A Framework for Real-Time Topic Detection and Popularity Prediction Based on Long-Text Contents." In Computational Data and Social Networks, 53–65. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04648-4_5.

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Zhu, Chengang, Guang Cheng, and Kun Wang. "Program Popularity Prediction Approach for Internet TV Based on Trend Detecting." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 142–54. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74176-5_14.

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Narayan, Shaifali, and Brij B. Gupta. "Study of Smartcards Technology." In Handbook of Research on Intrusion Detection Systems, 341–56. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2242-4.ch017.

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Smart cards have gained popularity in many domains by providing different facilities in every domain. Such cards are beneficial for storing credentials and access information. The cards are easy to carry and provides easy and fast computations. The cards have certain limitations due to the possible attacks on them. This chapter gives an overview of the smartcards including its history, physical design, life cycle. It also provides an overview of the possible threats on smartcards and its application area.

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Martinez, Marcos E., Francisco López-Orozco, Karla Olmos-Sánchez, and Julia Patricia Sánchez-Solís. "Mispronunciation Detection and Diagnosis Through a Chatbot." In Handbook of Research on Natural Language Processing and Smart Service Systems, 31–45. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4730-4.ch002.

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The interaction between humans and machines has evolved; thus, the idea of being able to communicate with computers as we usually do with other people is becoming increasingly closer to coming true. Nowadays, it is common to come across intelligent systems named chatbots, which allow people to communicate by using natural language to hold conversations related to a specific domain. Chatbots have gained popularity in different kinds of sectors, such as customer service, marketing, sales, e-commerce, e-learning, travel, and even in education itself. This chapter aims to present a chatbot-based approach to learning English as a second language by using computer-assisted language learning systems.

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Gautam, Shikha, and Anand Singh Jalal. "An Image Forgery Detection Approach Based on Camera's Intrinsic Noise Properties." In Cyber Warfare and Terrorism, 712–22. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2466-4.ch044.

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Digital images are found everywhere from cell phones to the pages of online news sites. With the rapid growth of the Internet and the popularity of digital image capturing devices, images have become major source of information. Now-a-days fudge of images has become easy due to powerful advanced photo-editing software and high-resolution cameras. In this article, the authors present a method for detecting forgery, which is detected by estimating camera's intrinsic noise properties. Differences in noise parameters of the image are used as evidence of Image tampering. The method works in two steps. In the first step, the given image is classified as forge or non-forge. In the second step, the forged region in the image is detected. Results show that the proposed method outperforms the previous methods and shows a detection accuracy of 85.76%.

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Gautam, Shikha, and Anand Singh Jalal. "An Image Forgery Detection Approach Based on Camera's Intrinsic Noise Properties." In Digital Forensics and Forensic Investigations, 92–102. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3025-2.ch008.

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Conference papers on the topic "Popularity detection"

Abbink, Jasper, and Christian Doerr. "Popularity-based Detection of Domain Generation Algorithms." In ARES '17: International Conference on Availability, Reliability and Security. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3098954.3107008.

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zhu, xinyi, and yu zhang. "An auxiliary edge cache strategy based on content popularity in NDN." In Ninth Symposium on Novel Photoelectronic Detection Technology and Applications (NDTA2022), edited by Wenqing Liu, Hongxing Xu, and Junhao Chu. SPIE, 2023. http://dx.doi.org/10.1117/12.2664524.

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Skaperas, Sotiris, Lefteris Mamatas, and Arsenia Chorti. "Early Video Content Popularity Detection with Change Point Analysis." In GLOBECOM 2018 - 2018 IEEE Global Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/glocom.2018.8648121.

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Yang, Tianbao, Prakash Mandaym Comar, and Linli Xu. "Community detection by popularity based models for authored networked data." In ASONAM '13: Advances in Social Networks Analysis and Mining 2013. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2492517.2492520.

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Yang, Tianbao, Yun Chi, Shenghuo Zhu, Yihong Gong, and Rong Jin. "Directed Network Community Detection: A Popularity and Productivity Link Model." In Proceedings of the 2010 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2010. http://dx.doi.org/10.1137/1.9781611972801.65.

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Steuber, Florian, Sinclair Schneider, João A. G. Schneider, and Gabi Dreo Rodosek. "Real-Time Anomaly Detection and Popularity Prediction for Emerging Events on Twitter." In ASONAM '23: International Conference on Advances in Social Networks Analysis and Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3625007.3627517.

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Zhang, Weifeng, Ting Zhong, Ce Li, Kunpeng Zhang, and Fan Zhou. "CausalRD: A Causal View of Rumor Detection via Eliminating Popularity and Conformity Biases." In IEEE INFOCOM 2022 - IEEE Conference on Computer Communications. IEEE, 2022. http://dx.doi.org/10.1109/infocom48880.2022.9796678.

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Sparks, Kevin A., Roger G. Li, Gautam S. Thakur, Robert N. Stewart, and Marie L. Urban. "Facility detection and popularity assessment from text classification of social media and crowdsourced data." In SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/3003464.3003466.

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Ilić, Velibor, and Milovan Medojević. "DETECTION OF ANOMALIES ON THE SURFACE OF WORKPIECES PRODUCED ON CNC MACHINES." In 19th International Scientific Conference on Industrial Systems. Faculty of Technical Sciences, 2023. http://dx.doi.org/10.24867/is-2023-t3.1-2_11141.

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Poor working conditions, insufficient technology, and other factors can all have a substantial impact on the quality of manufactured components throughout the CNC manufacturing process. Poor quality is usually obvious in the form of surface abnormalities among work item flaws. Detecting these flaws guarantees both a defined quality and a high qualifying percentage. A non-manual visual inspection strategy for anomaly identification is necessary to overcome these challenges. Visual inspection automation in industrial products, such as defect inspection and anomaly recognition, is a crucial task in computer vision. As a result, ASAD (Autonomous workpiece surface anomalies detection) based approaches have grown in popularity among manufacturers for surface anomaly detection because they provide an efficient solution to address the disadvantages of human inspection such as low accuracy, poor real-time performance, subjectivity, and labor intensity. This paper describes the application of the Segment Anything Model (SAM) and Convolutional Neural Networks (CNN) to the analysis and image processing of workpieces produced on CNC machines to detect surface scratches and anomalies.

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S. B, Abilash, and Sujitha R. "Instagram Fake and Automated Account Detection: A Review." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/dpaz6258/ngcesi23p29.

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Fake engagement is one of the significant problems in Online Social Networks (OSNs) which is used to increase the popularity of an account in an inorganic manner. The detection of fake engagement is crucial because it leads to loss of money for businesses, wrong audience targeting in advertising, wrong product predictions systems, and unhealthy social network environment. This study is related with the detection of fake and automated accounts which leads to fake engagement on Instagram. Prior to this work, there were no publicly available dataset for fake and automated accounts. For this purpose, two datasets have been published for the detection of fake and automated accounts. For the detection of these accounts, machine learning algorithms like Naive Bayes, Logistic Regression, Support Vector Machines and Neural Networks are applied. Additionally, for the detection of automated accounts, cost sensitive genetic algorithm is proposed to handle the unnatural bias in the dataset. To deal with the unevenness problem in the fake dataset, Smote-nc algorithm is implemented. In this paper investigating various methods used in the existing work for the Instagram fake account detection.

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Reports on the topic "Popularity detection"

Bielinskyi, Andrii, Vladimir Soloviev, Serhiy Semerikov, and Viktoria Solovieva. Detecting Stock Crashes Using Levy Distribution. [б. в.], August 2019. http://dx.doi.org/10.31812/123456789/3210.

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In this paper we study the possibility of construction indicators-precursors relying on one of the most power-law tailed distributions – Levy’s stable distribution. Here, we apply Levy’s parameters for 29 stock indices for the period from 1 March 2000 to 28 March 2019 daily values and show their effectiveness as indicators of crisis states on the example of Dow Jones Industrial Average index for the period from 2 January 1920 to 2019. In spite of popularity of the Gaussian distribution in financial modeling, we demonstrated that Levy’s stable distribution is more suitable due to its theoretical reasons and analysis results. And finally, we conclude that stability α and skewness β parameters of Levy’s stable distribution which demonstrate characteristic behavior for crash and critical states, can serve as an indicator-precursors of unstable states.

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