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Journal articles on the topic 'Videos analytics'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Jain, Puneet, Justin Manweiler, Arup Acharya, and Romit Roy Choudhury. "Scalable Social Analytics for Live Viral Event Prediction." Proceedings of the International AAAI Conference on Web and Social Media 8, no. 1 (2014): 226–35. http://dx.doi.org/10.1609/icwsm.v8i1.14504.

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Large-scale, predictive social analytics have proven effective. Over the last decade, research and industrial efforts have understood the potential value of inferences based on online behavior analysis, sentiment mining, influence analysis, epidemic spread, etc. The majority of these efforts, however, are not yet designed with realtime responsiveness as a first-order requirement. Typical systems perform a post-mortem analysis on volumes of historical data and validate their “predictions” against already-occurred events.We observe that in many applications, real-time predictions are critical an

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Arulraj, Joy. "Accelerating Video Analytics." ACM SIGMOD Record 50, no. 4 (2022): 39–40. http://dx.doi.org/10.1145/3516431.3516442.

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MOTIVATION. The advent of inexpensive, high-quality cameras has led to a rapid increase in the volume of generated video data [19, 16]. It is now feasible to automatically analyze these video datasets at scale due to two developments over the last decade. First, researchers have designed complex, computationally-intensive deep learning (DL) models that capture the contents of a given set of video frames (e.g., objects present in a particular frame [11]) [15]. Second, the computational capabilities of hardware accelerators for evaluating these DL models have increased over the last decade (e.g.

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Wang, Han, Shangyu Xie, and Yuan Hong. "VideoDP: A Flexible Platform for Video Analytics with Differential Privacy." Proceedings on Privacy Enhancing Technologies 2020, no. 4 (2020): 277–96. http://dx.doi.org/10.2478/popets-2020-0073.

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AbstractMassive amounts of videos are ubiquitously generated in personal devices and dedicated video recording facilities. Analyzing such data would be extremely beneficial in real world (e.g., urban traffic analysis). However, videos contain considerable sensitive information, such as human faces, identities and activities. Most of the existing video sanitization techniques simply obfuscate the video by detecting and blurring the region of interests (e.g., faces, vehicle plates, locations and timestamps). Unfortunately, privacy leakage in the blurred video cannot be effectively bounded, espec

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Dolores, Maria, and Jorge Mañana-Rodriguez. "Exploring Engagement in Online Videos for Language Learning through YouTube’s Learning Analytics." EDEN Conference Proceedings, no. 1 (September 21, 2021): 49–58. http://dx.doi.org/10.38069/edenconf-2021-ac0005.

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Until a few years ago, video analytics were not accessible to learning stakeholders, mainly because online video platforms did not share the users’ interactions on the system with stakeholders. However, this scenario has changed, and currently YouTube, the world’s largest media sharing site, offers these data. YouTube is also the main tool for transmitting audio-visual content in Language MOOCs (massive open online courses), and its video engagement data can be monitored through the YouTube Studio channel, which provides free and open access to video analytics. In this paper we present our res

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Kittivorawong, Chanwut, Yongming Ge, Yousef Helal, and Alvin Cheung. "Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations." Proceedings of the VLDB Endowment 17, no. 9 (2024): 2136–48. http://dx.doi.org/10.14778/3665844.3665846.

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Videos that are shot using commodity hardware such as phones and surveillance cameras record various metadata such as time and location. We encounter such geospatial videos on a daily basis and such videos have been growing in volume significantly. Yet, we do not have data management systems that allow users to interact with such data effectively. In this paper, we describe Spatialyze, a new framework for end-to-end querying of geospatial videos. Spatialyze comes with a domain-specific language where users can construct geospatial video analytic workflows using a 3-step, declarative, build-fil

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Zhang, Jingjing, Yicheng Huang, and Ming Gao. "Video Features, Engagement, and Patterns of Collective Attention Allocation." Journal of Learning Analytics 9, no. 1 (2022): 32–52. http://dx.doi.org/10.18608/jla.2022.7421.

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Network analytics has the potential to examine new behaviour patterns that are often hidden by the complexity of online interactions. One of the varied network analytics approaches and methods, the model of collective attention, takes an ecological system perspective to exploring the dynamic process of participation patterns in online and flexible learning environments. This study selected “Fundamentals of C++ programming (Spring 2019)” on XuetangX as an example through which to observe the allocation patterns of attention within MOOC videos, as well as how video features and engagement correl

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Carpenter, Chris. "Computer Vision Analytics Enables Determination of Rig State." Journal of Petroleum Technology 74, no. 01 (2022): 96–98. http://dx.doi.org/10.2118/0122-0096-jpt.

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 204086, “Determining Rig State From Computer Vision Analytics,” by Crispin Chatar, SPE, and Suhas Suresha, Schlumberger, and Laetitia Shao, Stanford University, et al. The paper has not been peer reviewed. While companies cannot agree on a standard deﬁnition of “rig state,” they can agree that, as further use is made of remote operations and automation, rig-state calculation is mandatory in some form. By use of a machine-learning model that relies exclusively on videos collected on the rig ﬂoor to

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Liu, Ying, Dickson K. W. Chiu, and Kevin K. W. Ho. "Short-Form Videos for Public Library Marketing: Performance Analytics of Douyin in China." Applied Sciences 13, no. 6 (2023): 3386. http://dx.doi.org/10.3390/app13063386.

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Short-form video platforms have become an important marketing channel for library resources and services. However, such promotions’ actual performance is not as good as expected. This research examined the performance of library marketing on the dominant short-form video platform in China, Douyin (aka TikTok worldwide), with social media analytics, including topic and correlation analysis. Results indicated that the number of fans of an account is positively correlated with the number of likes (p < 0.001) and independent of the number of videos (p > 0.05). Libraries post videos most ofte

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Pal, Sudeshna, Patsy Moskal, and Anchalee Ngampornchai. "Analyzing Student Success in a Blended Engineering Course Using Video Learning Analytics." International Journal on E-Learning 23, no. 4 (2024): 489–504. https://doi.org/10.70725/117499fkveaa.

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This study investigated the effectiveness of blended instruction in enhancing student success in an advanced undergraduate engineering course. The research used learning analytics captured from pre-recorded lecture videos, course grade data, and student surveys. Results revealed positive correlations between lecture video viewership and course grades, with students demonstrating high satisfaction and improved success rates in the blended format. The findings suggest that video analytics can serve as an effective predictor of student performance in blended engineering courses.

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DAUN, Felipe, and Ana Maria Dianezi GAMBARDELLA. "Educational videos with nutritional approach in YouTube." Revista de Nutrição 31, no. 3 (2018): 339–49. http://dx.doi.org/10.1590/1678-98652018000300007.

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ABSTRACT Objective Produce food and nutrition education videos, post these on YouTube and evaluate their reception over a two-year period. Methods Afterward bibliographic searches, sixteen different themes were developed and explored. An educational objective was defined for each video, took into account food and nutrition aspects in Brazil. The reception of the videos was evaluated using the “YouTube Analytics” tool, which allows analysis of the number of times videos were played, average playing time, and profile of the viewers. Results Sixteen videos were produced from November 2013 to July

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Umm-e-Laila, Khalid Mahboob, Muzammil Ahmed Khan, Muhammad Khurram, and Mustafa Ahmed Khan. "Contextual video analytics and recommendations through natural language processing (NLP) and graph machine learning (GML)." Mehran University Research Journal of Engineering and Technology 44, no. 3 (2025): 65–73. https://doi.org/10.22581/muet1982.0220.

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This research paper explores an innovative approach to video recommender systems by integrating Natural Language Processing (NLP) techniques, graph databases, and advanced analytics. The smart content recommender system goes beyond traditional title or description-based recommendations, aiming to enhance user experience through content-level searching capabilities and spoken-word analysis within videos. Through sentiment analysis (SA), topic analysis (TA), and keyword extraction (KWE), the system tags and timestamps the video transcripts in a detailed and contextual manner and offers recommend

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Luo, Zhiming, Pierre-Marc Jodoin, Song-Zhi Su, Shao-Zi Li, and Hugo Larochelle. "Traffic Analytics With Low-Frame-Rate Videos." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 4 (2018): 878–91. http://dx.doi.org/10.1109/tcsvt.2016.2632439.

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Xiao, Sihan. "More than Data: A Multivocal Inquiry into Video-Based Research on Learning and Teaching." ECNU Review of Education 1, no. 3 (2018): 23–35. http://dx.doi.org/10.30926/ecnuroe2018010302.

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Purpose This commentary aims to echo Wilkinson, Bailey, and Maher's (this volume) arguments about the affordances of videos and video databases in studying learning and teaching. Design/Approach/Methods This article illustrates a multivocal approach to the videos from the Video Mosaic Collaborative (VMC). In particular, three mathematics teachers in Shanghai were invited to watch and discuss a set of VMC videos. Two recurring themes concerning mathematics learning and teaching were identified in this video-cued interview and discussed in relation to the VMC Analytics. Findings The VMC videos p

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Anita, Mukund Pujar, and Arvind Domal Pratibha. "A Real Time Video Streaming Web Portal." Journal of Android and IOS Applications and Testing 4, no. 3 (2019): 16–20. https://doi.org/10.5281/zenodo.3568932.

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<em>Content-based video extracting is very essential now-a-days. The existing data mining algorithms are not directly applied to videos. This proposed work uses different data mining algorithms for indexing, clustering, searching and retrieving content-based videos. A system will be developed in which only admin can upload videos on cloud server. Videos are sorted based on category and videos are automatically uploaded on Cloud Server on the schedule provided by the admin. Users can watch videos online, they can download videos based on video summary and users can rate videos that will be anal

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Geri, Nitza, Amir Winer, and Beni Zaks. "Challenging the six-minute myth of online video lectures: Can interactivity expand the attention span of learners?" Online Journal of Applied Knowledge Management 5, no. 1 (2017): 101–11. http://dx.doi.org/10.36965/ojakm.2017.5(1)101-111.

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Keeping learners engaged in viewing online video lectures is a challenge, which is considered harder as the length of the video is longer. Although it is a known obstacle, in practice, many videos are lengthy and do not contain interactive elements. This study takes an attention economy perspective, and examines if interactivity may enable effective use of longer online video lectures. Google Analytics data was used to measure average online video lecture viewing completion percentage for two ‘long’ and ‘short’ video lecture groups, before and after the addition of interactive components. Prel

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Osztián, Pálma Rozália, Zoltán Kátai, Ágnes Sántha, and Erika Osztián. "Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method." Acta Universitatis Sapientiae, Informatica 14, no. 2 (2022): 273–301. http://dx.doi.org/10.2478/ausi-2022-0016.

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Abstract In this paper we investigate the comments from the AlgoRythmics YouTube channel using the Comment Term Frequency Comparison social media analytics method. Comment Term Frequency Comparison can be a useful tool to understand how a social media platform, such as a Youtube channel is being discussed by users and to identify opportunities to engage with the audience. Understanding viewer opinions and reactions to a video, identifying trends and patterns in the way people are discussing a particular topic, and measuring the effectiveness of a video in achieving its intended goals is one of

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Van Nuland, Sonya E., and Natalie R. Langley. "‘Must See’ Videos? Why Educators Need Better Video Analytics to Measure Learning." FASEB Journal 34, S1 (2020): 1. http://dx.doi.org/10.1096/fasebj.2020.34.s1.02981.

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C, Rahul, and Merin Meleet. "Irregular Events Detection in Videos using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 4268–72. http://dx.doi.org/10.22214/ijraset.2022.45921.

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Abstract: Video analytics for detecting events using machine learning is designed and developed to analyze and detect patterns in the videos. Especially in the field of criminal forensics where a video needs to be analyzed to find out what abnormal events are happening in it and who caused it and how it was caused. This is an easy task for a human as they can recognize criminal events easily but not machines. The objective is to automatically detect the irregular events in videos like burglary, fighting, arson and explosion using a CNN model by preprocessing the videos into frames and extracti

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Dr., N. Muthuvairavan Pillai, Kumar L. Bharath, Ganesh S. B. Harul, and S. R. Jashvanth. "Predictive Modeling of YouTube Using Supervised Machine Learning Algorithm for Identifying Trending Videos and its Impact on Engagement." Predictive Modeling of YouTube Using Supervised Machine Learning Algorithm for Identifying Trending Videos and its Impact on Engagement 8, no. 12 (2023): 6. https://doi.org/10.5281/zenodo.10370614.

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In the era of digital content, predicting the trends and popularity of videos on platforms like YouTube has become paramount. Our project, titled "YouTube Trend Analysis and Prediction," is a data-driven initiative aimed at providing valuable insights and predictive capabilities to content creators and digital marketers. By leveraging machine learning algorithms, including Decision Trees, Random Forest, and Gradient Boosting, our system can analyze key video attributes such as titles, descriptions, likes, dislikes, comments, views, and more. This analysis allows us to identify patterns and cor

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Zhu, Jingjie, and Mingming Cheng. "Automatic videos analytics in tourism: A methodological review." Annals of Tourism Research 108 (September 2024): 103800. http://dx.doi.org/10.1016/j.annals.2024.103800.

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Pan, Yaohua, Zhibin Niu, Jing Wu, and Jiawan Zhang. "InSocialNet: Interactive visual analytics for role—event videos." Computational Visual Media 5, no. 4 (2019): 375–90. http://dx.doi.org/10.1007/s41095-019-0157-9.

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Larson, Benjamin, Jeffrey A Bohler, and Anand Krishnamoorthy. "Innovative Pedagogical Strategies of Streaming, Just-in-Time Teaching, and Scaffolding: A Case Study of Using Videos to Add Business Analytics Instruction Across a Curriculum." Journal of Information Technology Education: Innovations in Practice 20 (2021): 001–19. http://dx.doi.org/10.28945/4694.

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Aim/Purpose: Business analytics is a cross-functional field that is important to implement for a college and has emerged as a critically important core component of the business curriculum. It is a difficult task due to scheduling concerns and limits to faculty and student resources. This paper describes the process of creating a central video repository to serve as a platform for just in time teaching and the impact on student learning outcomes. Background: Industry demand for employees with analytical knowledge, skills, and abilities requires additional analytical content throughout the coll

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Walsh, John N., Michael P. O'Brien, and Darina M. Slattery. "Video Viewing Patterns Using Different Teaching Treatments: A Case Study Using YouTube Analytics." Research in Education and Learning Innovation Archives, no. 22 (June 24, 2019): 78. http://dx.doi.org/10.7203/realia.22.15389.

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This study explores the viewing patterns of 17 instructional videos in both a traditional and flipped classroom environment by 732 business students taking an IT-related module. While previous work has concentrated mainly on outputs(e.g. student satisfaction/results), this study focuses on how the nature of students’ interactions with videos can be determined through a deep analysis of analytics data. The main findings show that there were less interactions with the instructional videos in the flipped classroom environment compared to the traditional environment, and that videos were used more

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Kumar, Lokesh, Pramod Kumar, and Parag Jain. "Object ID Tracking in Videos: A Review." International Transactions in Mathematical Sciences and Computer 15, no. 02 (2022): 157–66. http://dx.doi.org/10.58517/itmsc.2022.15204.

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Assigning and monitoring distinct identifiers to objects or entities in a video stream is known as object ID tracking in videos. For the purpose of tracking and analyzing object movement over time, computer vision, video analytics, and surveillance systems employ this technology extensively. Object detection is the first step in the process, wherein computer vision algorithms locate and identify things within individual video frames. This may entail methods like region-based Convolutional Neural Network (Faster R-CNN) or YOLO (You Only Look Once), which are object detection models based on dee

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Cruz-Oliver, Dulce M., Martha Abshire, Chakra Budhathoki, et al. "Comparison of Traditional Videos With Telenovelas for Hospice Family Caregivers Education." American Journal of Hospice and Palliative Medicine® 38, no. 10 (2021): 1230–37. http://dx.doi.org/10.1177/1049909121991524.

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Background: While research has shown that hospice family caregivers (HFCG) seek additional information related to patient care, pain and symptom management, and self-care, it is unknown how the use of telenovela videos for education in hospice would be received by HFCG. Objective: To explore HFCG perceived benefits and challenges with the use of telenovelas as compared to traditional educational videos during online support group. Methods: A mixed methods study with a concurrent triangulated design that analyzed qualitative interviews and YouTube analytics report to identify how viewers respon

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Manu, Y. M., and G. K. Ravikumar. "Survey on Machine Learning Based Video Analytics Techniques." Journal of Computational and Theoretical Nanoscience 17, no. 11 (2020): 4989–95. http://dx.doi.org/10.1166/jctn.2020.9000.

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Video information has turned into the biggest wellspring of information expended all inclusive. Because of the fast development of applications which are related to video applications and requests of boosting for greater surpassing video administrations, video information volume has expanding violently around the world, which is the serious challenge for media processing, capacity and transmission. Video coding by packing recordings into a lot littler size is also key arrangements; in any case, its advancement has turned out to be soaked somewhat while the pressure proportion consistently deve

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Jędryczkowski, Jacek. "Cybersecurity content in the process of student learning using original teaching videos." Annales Universitatis Mariae Curie-Skłodowska, sectio J – Paedagogia-Psychologia 37, no. 4 (2025): 45–63. https://doi.org/10.17951/j.2024.37.4.45-63.

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The article discusses the course and results of research aimed at analyzing the use of original video content in the learning process of students. Cybersecurity materials were developed for information technology classes conducted at the Faculty of Social Sciences at the University of Zielona Góra. The videos were published on YouTube and the university’s websites. This allowed all forms of interaction with the video material to be recorded by the YouTube Analytics system. Access to the reports obtained in this way enabled the assessment of the viewership of educational materials, as well as t

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Córcoles, César, Germán Cobo, and Ana-Elena Guerrero-Roldán. "The Usefulness of Video Learning Analytics in Small Scale E-Learning Scenarios." Applied Sciences 11, no. 21 (2021): 10366. http://dx.doi.org/10.3390/app112110366.

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A variety of tools are available to collect, process and analyse learning data obtained from the clickstream generated by students watching learning resources in video format. There is also some literature on the uses of such data in order to better understand and improve the teaching-learning process. Most of the literature focuses on large scale learning scenarios, such as MOOCs, where videos are watched hundreds or thousands of times. We have developed a solution to collect clickstream analytics data applicable to smaller scenarios, much more common in primary, secondary and higher educatio

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Chalkias, Ilias, Katerina Tzafilkou, Dimitrios Karapiperis, and Christos Tjortjis. "Learning Analytics on YouTube Educational Videos: Exploring Sentiment Analysis Methods and Topic Clustering." Electronics 12, no. 18 (2023): 3949. http://dx.doi.org/10.3390/electronics12183949.

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The popularity of social media is continuously growing, as it endeavors to bridge the gap in communication between individuals. YouTube, one of the most well-known social media platforms with millions of users, stands out due to its remarkable ability to facilitate communication through the exchange of video content. Despite its primary purpose being entertainment, YouTube also offers individuals the valuable opportunity to learn from its vast array of educational content. The primary objective of this study is to explore the sentiments of YouTube learners by analyzing their comments on educat

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Tackett, Sean, David Green, Michael Dyal, et al. "Use of Commercially Produced Medical Education Videos in a Cardiovascular Curriculum: Multiple Cohort Study." JMIR Medical Education 7, no. 4 (2021): e27441. http://dx.doi.org/10.2196/27441.

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Background Short instructional videos can make learning more efficient through the application of multimedia principles, and video animations can illustrate the complex concepts and dynamic processes that are common in health sciences education. Commercially produced videos are commonly used by medical students but are rarely integrated into curricula. Objective Our goal was to examine student engagement with medical education videos incorporated into a preclinical Cardiovascular Systems course. Methods Students who took the first-year 8-week Cardiovascular Systems course in 2019 and 2020 were

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Mukherjee, Prerana, and Brejesh Lall. "Pedestrian Behavior Analytics on Dashcam Videos in Chaotic Environments." IEEE Sensors Journal 21, no. 14 (2021): 15660–69. http://dx.doi.org/10.1109/jsen.2021.3062762.

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Kadakia, Shevali, Catherine Stratton, Yinfei Wu, Josemari Feliciano, and Yetsa A. Tuakli-Wosornu. "The Accessibility of YouTube Fitness Videos for Individuals Who Are Disabled Before and During the COVID-19 Pandemic: Preliminary Application of a Text Analytics Approach." JMIR Formative Research 6, no. 2 (2022): e34176. http://dx.doi.org/10.2196/34176.

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Background People with disabilities face barriers to in-person physical activity (PA), including a lack of adaptive equipment and knowledgeable instructors. Given this and the increased need for digital resources due to widespread COVID-19 lockdowns, it is necessary to assess the accessibility of digital fitness resources for people with disabilities. To investigate whether YouTube fitness content creators have made videos accessible to people with disabilities would be informative about access to PA during COVID-19 and could also provide insight into the feasibility of individuals who are dis

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Ge, Yongming, Vanessa Lin, Maureen Daum, Brandon Haynes, Alvin Cheung, and Magdalena Balazinska. "Demonstration of apperception." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2767–70. http://dx.doi.org/10.14778/3476311.3476340.

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Many recent video applications---including traffic monitoring, drone analytics, autonomous driving, and virtual reality---require piecing together, combining, and operating over many related video streams. Despite the massive data volumes involved and the need to jointly reason (both spatially and temporally) about these videos, current techniques to store and manipulate such data are often limited to file systems and simple video processing frameworks that reason about a single video in isolation. We present Apperception, a new type of database management system optimized for geospatial video

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Dhyey, Valera, Vishwakarma Amit, Paliwal Ananya, and Kulkarni Veena. "Video analytics for optimizing bank services." i-manager's Journal on Data Science & Big Data Analytics 1, no. 1 (2023): 1. http://dx.doi.org/10.26634/jds.1.1.19480.

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The use of Closed-Circuit Television (CCTV) footage for surveillance, demographic monitoring, and behavior analysis has increased dramatically in the recent years. One of the key challenges in analyzing such recordings is to count people and identify emotions efficiently and accurately. This paper proposed a system for counting people and identifying facial expressions of emotion in CCTV footage. It used Deep Neural Networks (DNN) trained on massive annotation datasets to recognize people in videos, extract facial images, and identify emotions. It evaluated the performance of the method on pub

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Saikrishnan, Venkatesan, and Mani Karthikeyan. "Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification on Surveillance Videos." Engineering, Technology & Applied Science Research 13, no. 5 (2023): 11747–52. http://dx.doi.org/10.48084/etasr.6231.

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Surveillance videos are recordings captured by video recording devices for monitoring and securing an area or property. These videos are frequently used in applications, involving law enforcement, security systems, retail analytics, and traffic monitoring. Surveillance videos can provide valuable visual information for analyzing patterns, identifying individuals or objects of interest, and detecting and investigating incidents. Object detection and classification on video surveillance involves the usage of computer vision techniques to identify and categorize objects within the video footage.

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Dontu, A. I., L. Gaiginschi, and A. Sachelarie. "Automatically collecting data traffic in intersections by using video analytics software for vehicle counting." IOP Conference Series: Materials Science and Engineering 1262, no. 1 (2022): 012063. http://dx.doi.org/10.1088/1757-899x/1262/1/012063.

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Traffic volume and composition data are important for infrastructure planning and increase traffic safety. The actual survey techniques depend on the goals of the survey, the amount of traffic data and the human and financial resources available. The purpose of this research is to improve classical collecting data by creating an automatically collecting data traffic with the help of a video analytics software. Authors present in this paper a new method for automatically collecting data traffic using Camlytics, a video analytics software, which is automatic processing the videos. This method is

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Nguyen, Dien Van, and Jaehyuk Choi. "Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge." Applied Sciences 10, no. 18 (2020): 6391. http://dx.doi.org/10.3390/app10186391.

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Intelligent video analytics systems have come to play an essential role in many fields, including public safety, transportation safety, and many other industrial areas, such as automated tools for data extraction, and analyzing huge datasets, such as multiple live video streams transmitted from a large number of cameras. A key characteristic of such systems is that it is critical to perform real-time analytics so as to provide timely actionable alerts on various tasks, activities, and conditions. Due to the computation-intensive and bandwidth-intensive nature of these operations, however, vide

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Davidson, B., NM Alotaibi, BK Hendricks, and A. Cohen-Gadol. "P.081 Popularity of online multimedia educational resources in neurosurgery: Insights from The Neurosurgical Atlas project." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 45, s2 (2018): S37. http://dx.doi.org/10.1017/cjn.2018.183.

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Background:The Neurosurgical Atlas is a neurosurgical website with informative chapters and videos to promote excellence and safety in neurosurgical techniques. Here, we present our analysis of this website’s viewing data and describe how online neurosurgical resources are being utilized. We hope this will be a useful guide for neurosurgeons interested in online multimedia education. Methods: We analyzed Google Analytics data from The Neurosurgical Atlas between June 2016 and August 2017 which tracked user demographics, geographical location, and the videos watched. Views were also analyzed ca

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Pierce, David, and Geoffre Sherman. "Using Data Analytics to Create a Digital Strategy That Drives Engagement and Views on Social Media." Case Studies in Sport Management 9, S1 (2020): S9—S12. http://dx.doi.org/10.1123/cssm.2019-0028.

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Students are placed into a consulting role with SPT, a sport marketing agency hired to help a sports organization create a new strategy for video content creation on social media. Students are provided a large data set in Tableau with analytics that hold the key to increasing the team’s engagement and views of videos on social media. Can your students find the insights in the data to drive a new video strategy for social media? Can they turn those insights into a creative content plan that will engage and win fans in the future? Students will have the opportunity to demonstrate creativity and

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Gao, Tianhao, Meng Zhang, Yifan Zhu, et al. "Sports Video Classification Method Based on Improved Deep Learning." Applied Sciences 14, no. 2 (2024): 948. http://dx.doi.org/10.3390/app14020948.

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Classifying sports videos is complex due to their dynamic nature. Traditional methods, like optical flow and the Histogram of Oriented Gradient (HOG), are limited by their need for expertise and lack of universality. Deep learning, particularly Convolutional Neural Networks (CNNs), offers more effective feature recognition in sports videos, but standard CNNs struggle with fast-paced or low-resolution sports videos. Our novel neural network model addresses these challenges. It begins by selecting important frames from sports footage and applying a fuzzy noise reduction algorithm to enhance vide

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Moon, Nazmun Nessa, Imrus Salehin, Masuma Parvin, et al. "Natural language processing based advanced method of unnecessary video detection." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5411. http://dx.doi.org/10.11591/ijece.v11i6.pp5411-5419.

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<span>In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze

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Nazmun, Nessa Moon, Salehin Imrus, Parvin Masuma, et al. "Natural language processing based advanced method of unnecessary video detection." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5411–19. https://doi.org/10.11591/ijece.v11i6.pp5411-5419.

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In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare

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Sita, M. Yadav, and M. Chaware Sandeep. "Video Object Detection through Traditional and Deep Learning Methods." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1822–26. https://doi.org/10.35940/ijeat.D6833.049420.

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Object detection in videos is gaining more attention recently as it is related to video analytics and facilitates image understanding and applicable to . The video object detection methods can be divided into traditional and deep learning based methods. Trajectory classification, low rank sparse matrix, background subtraction and object tracking are considered as traditional object detection methods as they primary focus is informative feature collection, region selection and classification. The deep learning methods are more popular now days as they facilitate high-level features and problem

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Sudjit, Boonchom, Somrak Petchartee, and Maneesha Perera. "Video Analytic for Human Management and Security and FPGA Accelerated High Concurrency." Progress in Applied Science and Technology 14, no. 2 (2024): 44–55. http://dx.doi.org/10.60101/past.2024.252733.

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This paper explores the use of video analytics by leveraging accelerated FPGA technology in combination with high-performance computing, specifically utilizing two Xilinx Alveo U50Lv cards and one U55C card. While many applications exist for motion analysis and detection in videos, the use of FPGAs in this context remains relatively scarce. FPGAs offer significant advantages in terms of energy efficiency and throughput. We present results demonstrating the parallelism capabilities in terms of the number of threads within a single Docker container that shares stack memory, as well as across mul

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Zhong, Chengzhang, Amy R. Reibman, Hansel A. Mina, and Amanda J. Deering. "Multi-View Hand-Hygiene Recognition for Food Safety." Journal of Imaging 6, no. 11 (2020): 120. http://dx.doi.org/10.3390/jimaging6110120.

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A majority of foodborne illnesses result from inappropriate food handling practices. One proven practice to reduce pathogens is to perform effective hand-hygiene before all stages of food handling. In this paper, we design a multi-camera system that uses video analytics to recognize hand-hygiene actions, with the goal of improving hand-hygiene effectiveness. Our proposed two-stage system processes untrimmed video from both egocentric and third-person cameras. In the first stage, a low-cost coarse classifier efficiently localizes the hand-hygiene period; in the second stage, more complex refine

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Jagtap, Swati, and Nilkanth B. Chopade. "Object-based image retrieval and detection for surveillance video." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 4 (2024): 4343. http://dx.doi.org/10.11591/ijece.v14i4.pp4343-4351.

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With technological advancement worldwide, the video surveillance market is growing drastically in a versatile field. Monitoring, browsing, and retrieving a specific object in a long video becomes difficult due to the enormous amount of data produced by the surveillance camera. With limitations on human resources and browsing time, there is a need for a new video analytics model to handle more complex tasks, such as object detection and query retrieval. The current approach involves techniques like unsupervised segmentation, multiscale segmentation, and feature-based descriptions. However, thes

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Sumathi, J. k. "Dynamic Image Forensics and Forgery Analytics using Open Computer Vision Framework." Wasit Journal of Computer and Mathematics Science 1, no. 1 (2021): 1–8. http://dx.doi.org/10.31185/wjcm.vol1.iss1.3.

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The key advances in Computer Vision and Optical Image Processing are the emerging technologies nowadays in diverse fields including Facial Recognition, Biometric Verifications, Internet of Things (IoT), Criminal Investigation, Signature Identification in banking and several others. Thus, these applications use image and live video processing for facilitating different applications for analyzing and forecasting." Computer vision is used in tons of activities such as monitoring, face recognition, motion recognition, object detection, among many others. The development of social networking platfo

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Bjarnadottir, Margret V., and Lawrence D. Stone. "Introduction: 2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research." INFORMS Journal on Applied Analytics 53, no. 5 (2023): 333–35. http://dx.doi.org/10.1287/inte.2023.intro.v53.n5.

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The judges for the 2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research selected the four finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics (IJAA). The prestigious Wagner Prize—awarded for achievement in implemented operations research, management science, and advanced analytics—emphasizes the quality and originality of mathematical models along with clarity of written and oral exposition. This year’s winning application describes the design and deployment of a generalized synthetic control, a powerful

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Rajagopal, Thenmozhi, Amutha Balakrishnan, Sreeram Valsalakumar, Thundil Rajagopal, and Senthilarasu Sundaram. "Application of MSVPC- 5G Multicast SDN Network Eminence Video Transmission in Drone Thermal Imaging for Solar Farm Monitoring." Energies 14, no. 24 (2021): 8255. http://dx.doi.org/10.3390/en14248255.

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The impact of multimedia in day-to-day life and its applications will be increased greatly with the proposed model (MSVPC)–5G Multicast SDN network eminence video transmission obtained using PSO and cross layer progress in wireless nodes. The drone inspection and analysis in a solar farm requires a very high number of transmissions of various videos, data, animations, along with all sets of audio, text and visuals. Thus, it is necessary to regulate the transmissions of various videos due to a huge amount of bandwidth requirement for videos. A software-defined network (SDN) enables forwarder se

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Xie, Y., and V. Mariano. "Deep Learning with 3D ResNets for Comprehensive Dual-Lane Speed Climbing Video Analysis." International Journal of Computer Science in Sport 24, no. 1 (2025): 17–34. https://doi.org/10.2478/ijcss-2025-0002.

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Abstract Analyzing dual-lane speed climbing videos provides critical insights into data- driven performance evaluation in sports climbing. This study introduces an enhanced deep learning approach based on 3D ResNets to classify and analyze speed climbing states. Leveraging an annotated dataset of 872 high-resolution videos covering 15 state combinations, the model integrates optimized 3D convolutions and residual connections, achieving significant improvements in classification accuracy and computational efficiency. With a test accuracy of 92.78%, the model significantly outperforms 2D CNNs an

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