Video Quality Research Articles

#PositiveAging: A Content Analysis of Positive Aging TikTok Videos

The communication ecology model of successful aging (CEMSA) proposes that the messages individuals receive from the social environment, and their own communication about aging, shape the communication ecology of aging, which in turn influences the degree to which individuals experience successful aging. This content analysis study provides insight into the communication ecology of aging by examining the messages about aging forwarded by positive aging TikTok videos. Two-hundred and seventy videos from the top five most viewed positive aging hashtags were coded. Messages about appearance and evaluations of aging appear more frequently than messages about ability or advice about aging. Overall, these videos presented aging in a positive light; however, messages about skin, cognitive ability, advice about aging, and the #agingwell hashtag represented aging in a more negative light. The majority of positive aging TikTok videos feature middle-aged and older adults. Implications for the CEMSA are discussed.

AI-Powered Video and Image Processing for Video Meetings

The increasing dependence, on meetings has led to a need for advanced technologies that improve communication and teamwork dynamics swiftly evolving video and image processing technologies backed by AI are reshaping the realm of video conferencing by enabling instant enhancements and enhancing interaction quality and usability This research paper delves into how artificial intelligence is being incorporated into video meetings with a focus on practical uses, like real time background blurring and replacement automatic noise reduction facial expression recognition and participant monitoringIn addition, to that we will explore how AI can enhance video quality, compress images and optimize bandwidth. This showcases its ability to decrease delays and enhance video clarity in areas with bandwidth. We will delve into progress in AI algorithms like using learning for recognizing faces utilizing natural language processing, for instant transcription and detecting emotions.

The Effect of Simulating Virtual Scenes using Artificial Intelligence Techniques in Producing Various Media Materials

Our current era is witnessing continuous change and rapid development in various aspects of life, including technical development and scientific progress. Modern technologies like simulation and artificial intelligence (AI) significantly enhance media content creation and distribution in various ways: Content Creation: AI tools can generate scripts, edit videos, create animations, and produce music, allowing creators to streamline the production process and reduce costs. Personalization: AI algorithms analyze user preferences and behavior to deliver personalized content recommendations, improving user engagement and satisfaction. Simulations: Simulation technologies can create realistic virtual environments for training, gaming, and storytelling, providing immersive experiences for users. Quality Enhancement: AI can automatically enhance audio and video quality, detect errors or inconsistencies, and improve overall production value. Data Analysis: AI helps analyze audience data to inform content strategies, predict trends, and optimize marketing efforts. Simulation and AI are transforming media content by making it more efficient, engaging, and tailored to audiences. The study aims to identify the impact of using these modern technologies in the field of media, which includes the latest technological developments, by utilizing virtual viewer simulations through artificial intelligence techniques, and achieving the highest levels of awareness for producing various media materials. The research also addresses the key stages that a designer goes through and how we can benefit from the study to develop these stages in order to enhance the role of visual communication and interaction between the recipient and the sender through the use of digital media designed within the media content via artificial intelligence to convey the advertising message, and its role in influencing consumer motivations to attract and guide them in making decisions through artificial intelligence techniques that provide a clear vision of the design environment. The research highlights the importance of graphic design as an essential element in improving viewers’ experience of modern digital media and achieving a more realistic simulation, pointing to the development of the concept of simulation used in modern digital media and Computer-Generated Image (CGI) techniques. This research also aims to explore the vital role that graphic design plays at every stage of the production of digital technologies to achieve a more realistic simulation of virtual scenes in digital media. Hence, the research problem the extent to which media content, in terms of form and substance, benefits from these modern technologies (simulation - Artificial intelligence) and the impact of these technologies in making media content more credible through their use. The research hypotheses: Has the media content become more attractive with the use of these technologies (simulation Artificial intelligence)? Has the media content become more shareable across different platforms in form and substance with the use of these technologies (simulation - Artificial intelligence)? Has the media content become more appealing with the use of these technologies (simulation - Artificial intelligence)? Has the media content become more effective in the visual communication process with the use of these technologies (simulation - Artificial intelligence)? Has the use of these technologies (simulation - Artificial intelligence) provided competitive advantages for various media outlets? Has the use of these technologies (simulation - Artificial intelligence) offered marketing and promotional advantages for various media outlets? What are the positives and negatives of these technologies (simulation - Artificial intelligence) in the media? The main aim of the research the extent to which media content, in terms of form and substance, benefits from modern technologies (simulation – Artificial intelligence) and the impact of these technologies on enriching, supporting, and developing media content to make it more competitive and credible through their use. The importance of research in studying the role and impact of modern technologies, such as virtual scene simulation, using artificial intelligence techniques to produce various types of media content. It emphasizes the role of these technologies in influencing different media outlets regarding competitive advantages, marketing, and promotion, as well as highlighting the positives and negatives of these technologies. The research methodology is based on the analytical description approach.

YouTube as a source of education on tunneled catheter insertion: content and quality analysis.

Tunneled catheters can be inserted for many reasons, and in most centers, and in most centers it is not clear who should insert these catheters. Some anesthesiologists may not have seen first-hand the insertion of a tunneled catheter during their residency, depending on the policies of the institution. YouTube is one of the most commonly used online platforms for accessing medical information. The aim of this study was to investigate the reliability of YouTube videos, for tunneled central venous catheter (Hickman and tunneled hemodialysis catheters) insertion for education. The keywords "Tunneled catheter insertion" and "Tunneled central venous catheter insertion" were searched for on YouTube. The first 100 videos ranked by the YouTube algorithm were analysed. Animation and theoretical content videos, as well as videos that included only a part of the catheter insertion, were excluded. The sources of the videos were categorized as medical doctors or professional organizations. medical device advertisement and hospital. Two authors evaluated all videos via the Journal of the American Medical Association (JAMA) benchmark criteria, modified DISCERN scores and the Global Quality Scale (GQS). Twenty-three videos were analysed in the study. The video quality scores were similar across the video sources. The number of views and the number of likes were significantly positively correlated. Furthermore, a significant correlation was found between the JAMA, Modified DISCERN, and GQS scores. Notably, none of the analysed videos achieved full points in all three scoring systems. Relying on a single criterion, such as the video source or number of likes, is not sufficient to determine a video's quality. Therefore, what is learned from videos needs to be double-checked. These platforms should be used as an additional tool, not as the primary source of education.

Video-based peer assessment of collaborative teamwork in a large-scale interprofessional learning activity.

The assessment of team performance within large-scale Interprofessional Learning (IPL) initiatives is an important but underexplored area. It is essential for demonstrating the effectiveness of collaborative learning outcomes in preparing students for professional practice. Using Kane's validity framework, we investigated whether peer assessment of student-produced videos depicting collaborative teamwork in an IPL activity was sufficiently valid for decision-making about team performance, and where the sources of error might lie to optimise future iterations of the assessment. A large cohort of health professional students (n = 1218) of 8 differing professions was divided into teams containing 5-6 students. Each team collaborated on producing a short video to evidence their management of one of 12 complex patient cases. Students from two other teams, who had worked on the same case, individually rated each video using a previously developed assessment scale. A generalisability study quantified sources of error that impacted the reliability of peer assessment of collaborative teamwork. A decision study modeled the impact of differing numbers of raters. A modified Angoff determined the pass/fail mark. Within a large-scale learning activity, peer assessment of collaborative teamwork was reliable (G = 0.71) based on scoring by students from two teams (n = 10-12) for each video. The main sources of variation were the stringency and subjectivity of fellow student assessors. Whilst professions marked with differing stringency, and individual student assessors had different views of the quality of a particular video, none of that individual assessor variance was attributable to the assessors' profession. Teams performed similarly across the 12 cases overall, and no particular professions marked differently on any particular case. A peer assessment of a student-produced video depicting interprofessional collaborative teamwork around the management of complex patient cases can be valid for decision-making about student team performance. Further refining marking rubrics and student assessor training could potentially modify assessor subjectivity. The impact of professions on assessing individual peers and the case-specificity of team performances in IPL settings need further exploration. This innovative approach to assessment offers a promising avenue for enhancing the measurement of collaborative learning outcomes in large-scale Interprofessional learning initiatives.

BS SO13 - Reversal of OAGB- Audit and steps of surgery

Abstract Background OAGB is a well-established procedure that gives good outcomes in terms of benefits from obesity related morbidity and weight loss. The nutritional complications of OAGB are not common. The most common complication for OAGB is bile reflux which can be controlled with medical or surgical management. The need to convert OAGB to normal anatomy or sleeve does not arise frequently. It is needed in a few patients. It is a technically challenging procedure and should be done in expert centers Method We would like to present a video on the steps of reversal surgery. We recorded our procedure and used imovies software to edit it demonstrating each step of surgery with a written commentary for ease of understanding We would also take this opportunity to present an audit of our series of patients. We gathered our operative sheets from December 2016- Jan 2024. There were 12 patients who needed conversion or reversal. 10 needed conversion to RYGB for bile reflux. 2 needed reversal to normal anatomy. Results High quality video presentation with narration talking about steps of surgery and results from our audit Conclusion Reversal surgery is a technically challenging procedure. It should be done in expert center. Also, since this surgery is not needed frequently, it needs expert surgeons.

Optimized evaluation of the quality of sensor video internet of things (VIOT) by the integration of big data and artificial intelligence

AbstractThe application of sensor video internet of things technology to large-scale integrated work can significantly improve the working quality of employees. However, the degree of improvement in working quality is still difficult to measure in a systematic, intelligent, stable, and accurate manner. local optimization and adjustment after evaluation are still relatively challenging, To address these issues, the study proposes a method of optimizing the evaluation of sensor video quality through the integration of big data and AI techniques. A large-scale integrated distance education system in the field of education and training with a certain application basis is adopted as a case. Including big data and AI techniques such as integrated intelligent agent modules, recommendation algorithms, and transaction optimization algorithms, a new agent-oriented system design with fast response speed, strong scalability, convenient local optimization, and greater stability is achieved. According to the network topology structure of the distance education system in colleges and universities, this paper uses queuing theory to analyze the system performance of the system. The focus of this paper is the quantitative relationship between system communication intensity ρ, user arrival rate λ, system channel capacity n and system waiting delay, blocking probability, average queue length, system throughput and other important performance indicators. In teaching evaluation, the key factor that affects the quality of classroom teaching, that is, Developing a comprehensive system for evaluating classroom instruction is crucial. By incorporating student feedback, leveraging data mining techniques, and harnessing computer technology, a holistic framework for gathering, analyzing, and generating actionable insights on teaching performance is established. This approach makes the evaluation process more systematic and evidence-based, identifying 12 key elements that influence classroom education standards. In the experimental section, the student assessment data sets I1 and I2 exhibit experimental values (statistics) that significantly exceed the thresholds, with a minimum support of 0.32 and a confidence level of 0.61. Moreover, the Boolean matrix is divided into 90 points. The rule U1Ua ≥ U2 is identified as a subset of {U1U2Ua} within the large item set, signifying a strong association rule. These findings confirm the robustness of the artificial intelligence model proposed in this paper for video quality prediction. The optimized sensor video quality evaluation method not only meets a satisfactory confidence level and matching value but also demonstrates good reliability and relevance in the evaluation criteria.

STUDENTS’ AND TEACHERS’ PERCEPTION ON USING VIDEO STREAMING PLATFORM TO ENHANCE LISTENING SKILLS

This study investigates the influence of video streaming platforms on effective listening proficiency among English teachers and secondary school students in SMKA Tun Ahmadshah, Kota Kinabalu, Sabah. The research utilized a qualitative approach, interviewing 5 experienced English teachers and five students from Form 3 to Form 5. Among the reasons, the integration of modern technological tools was provided in the school. The theories of cognitivism and content-based learning (CBL) within the book are employed. The research focuses on these theories to explore cognitive and instructional merits of using video streaming platforms. Based on the findings, the content of video streaming platforms significantly sustains students’ engagement and motivation. At the same time, teachers comment on the content being interesting, relevant, and in multimedia format. It combines visual and auditory sense for their engagement. Among the challenges for the students was that they rely on subtitles on the video. The issue of buffering was not mentioned by the students, but it is likely to reduce their engagement. Teachers suggested that the platform content is authentic similar to that on social media, and students can receive other messages beside the expected ones. For effective teaching, the use of some features could be of help considering that buffering poses a major challenge, and some students rely on subtitles. Nevertheless, they could make the process simpler and with motivation by their utilization on the students’ part. The medium is significantly used in distance learning where it is effective since students rely on the authentic content and are engaged. Thus, the platform should be used for effective proficiency in listening with assistance from its video features. They require the employing of the platform features and adequate motivation from the participants. Keyword(s): Listening Skills, Content Based Learning (CBL), Video Streaming Platform, Cognitivism.

Mycetoma: A critical appraisal of educational content on YouTube.

Mycetoma is a fungal neglected tropical disease. Accurate dissemination of information is critical in endemic areas. YouTube, a popular platform for health information, hosts numerous videos on mycetoma, but the quality and reliability of these videos remain largely unassessed. We used modified DISCERN and Global Quality Score (GQS) for reliability and quality respectively. Video duration, views, likes, and comments were recorded. Spearman's rank correlation and Mann-Whitney U tests were used to identify correlations between metrices and quality scores. We included 73 mycetoma-related YouTube videos were analyzed, the median GQS score was 4.00 ((IQR = 3.33-4.00), indicating generally high-quality content, while the median mDISCERN score was 3.00 (IQR = 3.00-3.00) reflecting moderate reliability. Videos produced by professionals had significantly higher scores compared to those from consumer-generated content (p < 0.001). A significantly positive correlation was observed between video duration and both GQS (r = 0.417, p < 0.001) and mDISCERN (r = 0.343, p = 0.003). However, views, likes and comments did not significantly correlate with video quality. Additionally, videos longer in duration (p < 0.001) and older in upload date (p = 0.014) had higher quality scores. The study shows that mycetoma-related videos on YouTube are generally of high quality, with moderate reliability. This emphasizes the need for expert involvement in content creation and efforts to improve health information online.

YouTube as a source of information for parents about silver diamine fluoride.

To depict and evaluate the characteristics, engagement, content, and quality of YouTube videos containing information about silver diamine fluoride (SDF). A total of 200 YouTube™ videos were selected and screened, and the video characteristics and engagement indicators were recorded. They were then reviewed for consistency with current professional guidelines on this topic. Two independent reviewers scored the videos using a customized 8-point scoring and 5-point Global Quality Scale (GQS) to assess the content information and the overall quality of each video. These videos were further classified into good, moderate, and poor videos. Kruskal-Wallis, Chi-squared, and Spearman's correlation tests were used for the statistical analysis. 110 videos met the inclusion criteria. The median total content score was 3 (IQR = 4) and the median GQS score was 2 (IQR = 2). Less than half (n = 49; 45.5%) of the videos were uploaded by healthcare professionals. The video content was classified as good (n = 26; 23.64%), moderate (n = 43; 39.09%), and poor (n = 41; 37.27%). Good-quality videos have a significantly higher information content score than the other groups (P = 0.001). A strong correlation was found between the total content score and GQS score (rho = 0.970, P = 0.001). Longer duration, higher interaction index, and recent upload are associated with higher content and quality scores. A considerable number of videos are available on YouTube about SDF treatment and are attracting public interest. The content and quality of these videos vary widely and are related to several factors.

Beyond videoconferencing: enhancing remote home assessments with 3D modeling technology

Purpose: Occupational therapists in the Veterans Health Administration have transitioned from in-person to videoconferencing for home assessments, benefiting Veterans living in remote and rural areas. However, videoconferencing has limitations, including restricted field of view and poor video quality, affecting hazard identification accuracy. This study aims to introduce and evaluate a three-dimensional (3D) model as an alternative technology for remote home assessments. Materials and methods: We created 3D models using a 360-degree camera and mobile app. Five occupational therapists individually completed virtual training and practice sessions to familiarize themselves with using the 3D model. Each participant then conducted a remote home assessment using the 3D model and completed questionnaires, System Usability Scale (SUS), and semi-structured interviews. Results: Participants spent an average of 10 min training and practicing with the 3D model, and most reported either maintaining or gaining confidence in using it compared to before. All participants successfully completed the assessments, which took an average of 17 min. They rated the 3D model as easy to use, with an average SUS score of 78.5. Participants preferred the 3D model over videoconferencing, noting that it effectively addressed current challenges, and expressed their willingness to integrate it into clinical practice. Conclusion: This study demonstrates that 3D models offer a promising option for remote home assessments. With minimal training, occupational therapists could conduct more effective assessments. It is recommended to use 3D models for an initial understanding of the home environment before videoconferencing-based assessments to enhance the remote assessment experience for occupational therapists and clients.

A compressed video quality enhancement algorithm based on CNN and transformer hybrid network

A compressed video quality enhancement algorithm based on CNN and transformer hybrid network

#TraumaTok-TikTok Videos Relating to Trauma: Content Analysis.

Experiencing a traumatic event can significantly impact mental and emotional well-being. Social media platforms offer spaces for sharing stories, seeking support, and accessing psychoeducation. TikTok (ByteDance), a rapidly growing social media platform, is increasingly used for advice, validation, and information, although the content of this requires further study. Research is particularly needed to better understand TikTok content relating to trauma and the potential implications for young viewers, considering the distressing nature of the subject and the possibility of users experiencing vicarious trauma through exposure to these videos. This study aims to explore the content of trauma-related videos on TikTok, focusing on hashtags related to trauma. Specifically, this study analyzes how TikTok videos present information, advice, stories, and support relating to trauma. A quantitative cross-sectional descriptive content analysis was performed on TikTok in December 2022. A total of 5 hashtags related to trauma were selected: #trauma, #traumatized, #traumatok, #traumatic, and #traumabond, with the top 50 videos from each hashtag analyzed (total N=250 videos). A standardized codebook was developed inductively to analyze the content of the videos, while an existing generic codebook was used to collect the video features (eg, age of people in the video) and metadata (likes, comments, and shares) for each video. A total of 2 major content themes were identified, which were instructional videos (54/250, 21.6%) and videos disclosing personal stories (168/250, 67.3%). The videos garnered significant engagement, with a total of 296.6 million likes, 2.3 million comments, and 4.6 million shares, indicating that users find this content engaging and useful. Alarmingly, only 3.7% (9/250) of videos included a trigger warning, despite many featuring highly distressing stories that young people and those with trauma may be exposed to. The study highlights the potential risks of vicarious trauma due to trauma dumping without trigger warnings on TikTok, and the need for further research to assess the accuracy of advice and information in these videos. However, it also underscores the platform's potential to foster social connections, provide validation, and reduce stigma around mental health issues. Public health professionals should leverage social media to disseminate accurate mental health information, while promoting user education and content moderation to mitigate potential harms. People often use social media, such as TikTok to share advice, stories, and support around mental health, including their experiences with trauma. Out of 250 videos, most were either giving advice (54/250, 21.6%) or sharing personal experiences (168/250, 67.3%). The study found many videos lacked warnings about upsetting content, which could potentially harm young viewers or people suffering from trauma. While TikTok can help people feel connected and reduce the stigma around mental health, it is important to seek support from professionals when needed.

HVASR: Enhancing 360-degree video delivery with viewport-aware super resolution

In recent years, 360-degree videos have gained significant traction due to their capacity to provide immersive experiences. However, the adoption of 360-degree videos substantially escalates bandwidth demands, necessitating approximately four to ten times more bandwidth than traditional video formats do. This presents a considerable challenge in maintaining high-quality videos in environments characterized by limited bandwidth or unstable networks. A trend has emerged where client-side computational power and deep neural networks are employed to enhance video quality while mitigating bandwidth requirements within contemporary video delivery systems. These approaches segment a video into discrete chunks and apply super resolution (SR) models to each segment, streaming low-resolution (LR) chunks alongside their corresponding SR models to the client. Although these methods enhance both video quality and transmission efficiency for conventional videos, they impose greater computational resource demands when applied to 360-degree content, thereby constraining widespread implementation. This paper introduces an innovative method called HVASR for 360-degree videos that leverages viewport information for more precise segmentation and minimizes model training costs as well as bandwidth requirements. Additionally, HVASR incorporates a viewport-aware training strategy that is aimed at further enhancing performance while reducing computational expenses. The experimental results demonstrate that HVASR achieves an average utility increase ranging from 12.46% to 40.89% across various scenes.

Evaluation of the content and quality of schizophrenia on TikTok: a cross-sectional study

Schizophrenia is a common and serious heterogeneous mental disorder that has a significant health and economic impact on families and society. As the prevalence of schizophrenia increases each year, public awareness of the disorder is growing. However, it remains unclear whether the quality of information presented in these videos is satisfactory. Therefore, a comprehensive assessment of the quality and content of schizophrenia-related videos on video-sharing platforms is necessary. We screened 123 schizophrenia-related videos from the video-sharing platform TikTok that met the criteria, and evaluated and analyzed them. First, the basic information provided in the videos was recorded and analyzed. The source and content type of each video was then determined. The educational content and quality of all videos were then evaluated using three rating instruments, Modified DISCERN, Global Quality Scale, and Journal of the American Medical Association (JAMA). Videos from different sources were also compared to see if they were statistically different in terms of educational content and quality. We identified 4 categories of content for the videos. The science introduction category had the largest number of 50, and the least number of videos was etiology and causation at 15. Six categories of sources of videos were also identified: the for-profit organizations category has the lowest number of 10, the health professionals category has the highest number of 45. There was a significant correlation between video source and duration (P = 0.014). The JAMA score was significantly positively correlated with the number of video likes (r = 0.721, P < 0.001). This study evaluated the content and information quality of 123 videos related to schizophrenia on the video-sharing platform TikTok. The findings indicate that while these videos offer some valuable insights into schizophrenia, the overall quality falls short of satisfactory levels, with inadequate reliability and accuracy.

Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models.

Blind video quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users' viewing experience in various real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. Thus, it is crucial to gain a better understanding of existing VQA datasets in order to properly evaluate the current progress in BVQA. Towards this goal, we conduct a first-of-its-kind computational analysis of VQA datasets via designing minimalistic BVQA models. By minimalistic, we restrict our family of BVQA models to build only upon basic blocks: a video preprocessor (for aggressive spatiotemporal downsampling), a spatial quality analyzer, an optional temporal quality analyzer, and a quality regressor, all with the simplest possible instantiations. By comparing the quality prediction performance of different model variants on eight VQA datasets with realistic distortions, we find that nearly all datasets suffer from the easy dataset problem of varying severity, some of which even admit blind image quality assessment (BIQA) solutions. We additionally justify our claims by comparing our model generalization capabilities on these VQA datasets, and by ablating a dizzying set of BVQA design choices related to the basic building blocks. Our results cast doubt on the current progress in BVQA, and meanwhile shed light on good practices of constructing next-generation VQA datasets and models.

Medical Misinformation and Quality of Public Video Content on Cannabis for Chronic Pain Management: A Cross-Sectional Analysis of the YouTube Platform.

As cannabis legalization expands nationally and globally, its use for chronic pain increases, prompting people to seek information on social media platforms like YouTube. This study evaluates the accuracy and quality of information of popular YouTube videos on cannabis for chronic pain. Using search terms related to cannabis for pain, the top 66 videos by view count were selected. Each video was classified as useful, misleading, or neither. The quality and reliability of each video were assessed using the modified DISCERN, mDISCERN, score and the Global Quality Scale, GQS. The video characteristics, usefulness classification, mDISCERN scores, and GQS scores were summarized. Continuous and categorical outcomes were compared using t-test and chi-square, respectively. Of the 66 videos, 22.73% (n=15) were classified as useful, and 77.27% (n=51) were classified as neither. Of useful videos, 40.00% (n=6) were uploaded by physicians, 40.00% (n=6) were uploaded by corporations, and 6.67% (n=1) were uploaded by an independent user. Of videos classified as neither useful nor misleading, news sources uploaded 27.45% (n=14) of these videos (P=0.02). Physicians uploaded 37.50% (n = 18) of videos with a GQS score ≥3 (P=0.04), while independent users uploaded significantly more videos with a mDISCERN score <3 (22.20%, P=0.02). Useful videos had a mean GQS of 4.00 ± 0.65 compared to a mean GQS of 2.76 ± 0.86 for videos deemed neither (P<0.0001). This study suggests a moderate quality of YouTube content on cannabis use for chronic pain. Given cannabis's growing popularity and potential for misinformation on popular social media platforms, healthcare professionals and organizations should consider uploading educational videos on this topic on YouTube.

The Short Video Platform Recommendation Mechanism Based on the Improved Neural Network Algorithm to the Mainstream Media

Currently, the capacity of short videos continues to increase. Video manufacturers hope to enhance user experience and stickiness through recommendation algorithms, while users seek personalized videos to save time and money. Therefore, in order to address the data sparsity and high-dimensional feature extraction, this study proposes a novel short video platform recommendation model. The proposed method utilizes the term frequency inverse document frequency algorithm for text mining, and combines error back propagation neural network for learning to explore the potential connection between users and videos. This method combines natural language processing and image analysis in deep learning to construct accurate user and video models, deeply explore user interests, and improve the accuracy and effectiveness of recommendation systems for user preferences. The research results showed that the recommendation accuracy of this method was 66% and 70% respectively, and the prediction accuracy was 73.50% and 88% respectively. When Num=128, 200 data points were recommended within 0.3678s. The proposed algorithm outperforms the other three methods in terms of recommendation accuracy compared with the ItemCF and UserCF algorithms. This is because the method uses an approach based on image and user vector, combined with relevant features of the video and user. The proposed method can deeply explore the relevant features of videos and users, overcoming the data scarcity in previous collaborative screening, and guiding video recommendation on practical media platforms.

Weakly supervised video anomaly detection based on hyperbolic space

In recent years, there has been a proliferation of weakly supervised methods in the field of video anomaly detection. Despite significant progress in existing research, these efforts have primarily focused on addressing this issue within Euclidean space. Conducting weakly supervised video anomaly detection in Euclidean space imposes a fundamental limitation by constraining the ability to model complex patterns due to the dimensionality constraints of the embedding space and lacking the capacity to model long-term contextual information. This inadequacy can lead to misjudgments of anomalous events due to insufficient video representation. However, hyperbolic space has shown significant potential for modeling complex data, offering new insights. In this paper, we rethink weakly supervised video anomaly detection with a novel perspective: transforming video features from Euclidean space into hyperbolic space may enable the network to learn implicit relationships in normal and anomalous videos, thereby enhancing its ability to effectively distinguish between them. Finally, to validate our approach, we conducted extensive experiments on the UCF-Crime and XD-Violence datasets. Experimental results show that our method not only has the lowest number of parameters but also achieves state-of-the-art performance on the XD-Violence dataset using only RGB information.

Trust and confidence in telehealth-delivered services: a nation-wide cross-sectional study

Sustaining telehealth uptake hinges on people’s desire and ability to effectively engage with it. We explored trust and confidence in telehealth delivered by medical and allied health using cross-sectional survey of 1,116 Australians. Descriptive analysis presented factors that would improve trust and confidence in telehealth cross-tabulated with video consultation experience. Inferential statistics compared levels of trust in medical and allied health telehealth with user-related variables. Trust in medical telehealth was higher than in allied health, but practice with video calls, experience with high-quality telehealth, and good internet were associated with greater levels of trust in both groups. Telehealth with a known health professional and no additional costs were top-ranked factors to improve trust and confidence. Participants confident in troubleshooting trusted telehealth more. This first cross-sectional study on trust and confidence in telehealth suggests that digital upskilling and promoting quality video consultations can potentially enhance telehealth adoption.