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How to prepare a perfect video abstract for your research paper – Pubrica.pdfPubrica
A video abstract is a series of moving pictures taken from a lengthier movie that is significantly shorter than the original yet retains the original's essential meaning.
Learn More : https://bit.ly/3JVyrCW
Reference: https://pubrica.com/services/publication-support/Video-Abstract/
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The advents in this technological era have resulted into enormous pool of information. This information is
stored at multiple places globally, in multiple formats. This article highlights a methodology for extracting
the video lectures delivered by experts in the domain of Computer Science by using Generalized Gamma
Mixture Model. The feature extraction is based on the DCT transformations. In order to propose the model,
the data set is pooled from the YouTube video lectures in the domain of Computer Science. The outputs
generated are evaluated using Precision and Recall.
How to prepare a perfect video abstract for your research paper – Pubrica.pptxPubrica
A video abstract is a series of moving pictures taken from a lengthier movie that is significantly shorter than the original yet retains the original's essential meaning.
Learn More : https://bit.ly/3JVyrCW
Reference: https://pubrica.com/services/publication-support/Video-Abstract/
Why Pubrica:
When you order our services, we promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Content based video retrieval using discrete cosine transformnooriasukmaningtyas
A content based video retrieval (CBVR) framework is built in this paper.
One of the essential features of video retrieval process and CBVR is a color
value. The discrete cosine transform (DCT) is used to extract a query video
features to compare with the video features stored in our database. Average
result of 0.6475 was obtained by using the DCT after implementing it to the
database we created and collected, and on all categories. This technique was
applied on our database of video, check 100 database videos, 5 videos in
Keywords: each category.
Coronary heart disease is a disease with the highest mortality rates in the world. This makes the development of the diagnostic system as a very interesting topic in the field of biomedical informatics, aiming to detect whether a heart is normal or not. In the literature there are diagnostic system models by combining dimension reduction and data mining techniques. Unfortunately, there are no review papers that discuss and analyze the themes to date. This study reviews articles within the period 2009-2016, with a focus on dimension reduction methods and data mining techniques, validated using a dataset of UCI repository. Methods of dimension reduction use feature selection and feature extraction techniques, while data mining techniques include classification, prediction, clustering, and association rules.
Key frame extraction is an essential technique in the computer vision field. The extracted key frames should brief the salient events with an excellent feasibility, great efficiency, and with a high-level of robustness. Thus, it is not an easy problem to solve because it is attributed to many visual features. This paper intends to solve this problem by investigating the relationship between these features detection and the accuracy of key frames extraction techniques using TRIZ. An improved algorithm for key frame extraction was then proposed based on an accumulative optical flow with a self-adaptive threshold (AOF_ST) as recommended in TRIZ inventive principles. Several video shots including original and forgery videos with complex conditions are used to verify the experimental results. The comparison of our results with the-state-of-the-art algorithms results showed that the proposed extraction algorithm can accurately brief the videos and generated a meaningful compact count number of key frames. On top of that, our proposed algorithm achieves 124.4 and 31.4 for best and worst case in KTH dataset extracted key frames in terms of compression rate, while the-state-of-the-art algorithms achieved 8.90 in the best case.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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How to prepare a perfect video abstract for your research paper – Pubrica.pdfPubrica
A video abstract is a series of moving pictures taken from a lengthier movie that is significantly shorter than the original yet retains the original's essential meaning.
Learn More : https://bit.ly/3JVyrCW
Reference: https://pubrica.com/services/publication-support/Video-Abstract/
Why Pubrica:
When you order our services, we promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
The advents in this technological era have resulted into enormous pool of information. This information is
stored at multiple places globally, in multiple formats. This article highlights a methodology for extracting
the video lectures delivered by experts in the domain of Computer Science by using Generalized Gamma
Mixture Model. The feature extraction is based on the DCT transformations. In order to propose the model,
the data set is pooled from the YouTube video lectures in the domain of Computer Science. The outputs
generated are evaluated using Precision and Recall.
How to prepare a perfect video abstract for your research paper – Pubrica.pptxPubrica
A video abstract is a series of moving pictures taken from a lengthier movie that is significantly shorter than the original yet retains the original's essential meaning.
Learn More : https://bit.ly/3JVyrCW
Reference: https://pubrica.com/services/publication-support/Video-Abstract/
Why Pubrica:
When you order our services, we promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Content based video retrieval using discrete cosine transformnooriasukmaningtyas
A content based video retrieval (CBVR) framework is built in this paper.
One of the essential features of video retrieval process and CBVR is a color
value. The discrete cosine transform (DCT) is used to extract a query video
features to compare with the video features stored in our database. Average
result of 0.6475 was obtained by using the DCT after implementing it to the
database we created and collected, and on all categories. This technique was
applied on our database of video, check 100 database videos, 5 videos in
Keywords: each category.
Coronary heart disease is a disease with the highest mortality rates in the world. This makes the development of the diagnostic system as a very interesting topic in the field of biomedical informatics, aiming to detect whether a heart is normal or not. In the literature there are diagnostic system models by combining dimension reduction and data mining techniques. Unfortunately, there are no review papers that discuss and analyze the themes to date. This study reviews articles within the period 2009-2016, with a focus on dimension reduction methods and data mining techniques, validated using a dataset of UCI repository. Methods of dimension reduction use feature selection and feature extraction techniques, while data mining techniques include classification, prediction, clustering, and association rules.
Key frame extraction is an essential technique in the computer vision field. The extracted key frames should brief the salient events with an excellent feasibility, great efficiency, and with a high-level of robustness. Thus, it is not an easy problem to solve because it is attributed to many visual features. This paper intends to solve this problem by investigating the relationship between these features detection and the accuracy of key frames extraction techniques using TRIZ. An improved algorithm for key frame extraction was then proposed based on an accumulative optical flow with a self-adaptive threshold (AOF_ST) as recommended in TRIZ inventive principles. Several video shots including original and forgery videos with complex conditions are used to verify the experimental results. The comparison of our results with the-state-of-the-art algorithms results showed that the proposed extraction algorithm can accurately brief the videos and generated a meaningful compact count number of key frames. On top of that, our proposed algorithm achieves 124.4 and 31.4 for best and worst case in KTH dataset extracted key frames in terms of compression rate, while the-state-of-the-art algorithms achieved 8.90 in the best case.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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Automatic semantic content extraction in videos using a fuzzy ontology and rule based model
1. AUTOMATIC SEMANTIC CONTENT EXTRACTION IN VIDEOS USING
A FUZZY ONTOLOGY AND RULE-BASED MODEL
ABSTRACT:
Recent increase in the use of video-based applications has revealed the need for extracting the
content in videos. Raw data and low-level features alone are not sufficient to fulfill the user 's
needs; that is, a deeper understanding of the content at the semantic level is required. Currently,
manual techniques, which are inefficient, subjective and costly in time and limit the querying
capabilities, are being used to bridge the gap between low-level representative features and high-
level semantic content.
We propose a semantic content extraction system that allows the user to query and retrieve
objects, events, and concepts that are extracted automatically. We introduce an ontology-based
fuzzy video semantic content model that uses spatial/temporal relations in event and concept
definitions. This metaontology definition provides a wide-domain applicable rule construction
standard that allows the user to construct ontology for a given domain.
In addition to domain ontologies, we use additional rule definitions (without using ontology) to
lower spatial relation computation cost and to be able to define some complex situations more
effectively. The proposed framework has been fully implemented and tested on three different
domains. We have obtained satisfactory precision and recall rates for object, event and concept
extraction.
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