Category Archives: Machine Learning

This fall SVG will be presenting a series of White Papers covering the latest advancements and trends in sports-production technology. The full series of SVGs Tech Insight White Papers can be found in the SVG Fall SportsTech Journal HERE.

Following the height of the 2020 global pandemic, live sports are starting to re-emerge worldwide albeit predominantly behind closed doors. For the majority of sports fans, video is the only way they can watch and engage with their favorite teams or players. This means the quality of the viewing experience itself has become even more critical.

With UHD being adopted by both households and broadcasters around the world, there is a marked expectation around visual quality. To realize these expectations in the immediate term, it will be necessary for some years to up-convert from HD to UHD when creating 4K UHD sports channels and content.

This is not so different from the early days of HD, where SD sporting related content had to be up-converted to HD. In the intervening years, however, machine learning as a technology has progressed sufficiently to be a serious contender for performing better up-conversions than with more conventional techniques, specifically designed to work for TV content.

Ideally, we want to process HD content into UHD with a simple black box arrangement.

The problem with conventional up-conversion, though, is that it does not offer an improved resolution, so does not fully meet the expectations of the viewer at home watching on a UHD TV. The question, therefore, becomes: can we do better for the sports fan? If so, how?

UHD is a progressive scan format, with the native TV formats being 38402160, known as 2160p59.64 (usually abbreviated to 2160p60) or 2160p50. The corresponding HD formats, with the frame/field rates set by region, are either progressive 1280720 (720p60 or 720p50) or interlaced 19201080 (1080i30 or 1080i25).

Conversion from HD to UHD for progressive images at the same rate is fairly simple. It can be achieved using spatial processing only. Traditionally, this might typically use a bi-cubic interpolation filter, (a 2-dimensional interpolation commonly used for photographic image scaling.) This uses a grid of 44 source pixels and interpolates intermediate locations in the center of the grid. The conversion from 1280720 to 38402160 requires a 3x scaling factor in each dimension and is almost the ideal case for an upsampling filter.

These types of filters can only interpolate, resulting in an image that is a better result than nearest-neighbor or bi-linear interpolation, but does not have the appearance of being a higher resolution.

Machine Learning (ML) is a technique whereby a neural network learns patterns from a set of training data. Images are large, and it becomes unfeasible to create neural networks that process this data as a complete set. So, a different structure is used for image processing, known as Convolutional Neural Networks (CNNs). CNNs are structured to extract features from the images by successively processing subsets from the source image and then processes the features rather than the raw pixels.

Up-conversion process with neural network processing

The inbuilt non-linearity, in combination with feature-based processing, mean CNNs can invent data not in the original image. In the case of up-conversion, we are interested in the ability to create plausible new content that was not present in the original image, but that doesnt modify the nature of the image too much. The CNN used to create the UHD data from the HD source is known as the Generator CNN.

When input source data needs to be propagated through the whole chain, possibly with scaling involved, then a specific variant of a CNN known as a Residual Network (ResNet) is used. A ResNet has a number of stages, each of which includes a contribution from a bypass path that carries the input data. For this study, a ResNet with scaling stages towards the end of the chain was used as the Generator CNN.

For the Generator CNN to do its job, it must be trained with a set of known data patches of reference images and a comparison is made between the output and the original. For training, the originals are a set of high-resolution UHD images, down-sampled to produce HD source images, then up-converted and finally compared to the originals.

The difference between the original and synthesized UHD images is calculated by the compare function with the error signal fed back to the Generator CNN. Progressively, the Generator CNN learns to create an image with features more similar to original UHD images.

The training process is dependent on the data set used for training, and the neural network tries to fit the characteristics seen during training onto the current image. This is intriguingly illustrated in Googles AI Blog [1], where a neural network presented with a random noise pattern introduces shapes like the ones used during training. It is important that a diverse, representative content set is used for training. Patches from about 800 different images were used for training during the process of MediaKinds research.

The compare function affects the way the Generator CNN learns to process the HD source data. It is easy to calculate a sum of absolute differences between original and synthesized. This causes an issue due to training set imbalance; in this case, the imbalance is that real pictures have large proportions with relatively little fine detail, so the data set is biased towards regenerating a result like that which is very similar to the use of a bicubic interpolation filter.

This doesnt really achieve the objective of creating plausible fine detail.

Generative Adversarial Neural Networks (GANs) are a relatively new concept [2], where a second neural network, known as the Discriminator CNN, is used and is itself trained during the training process of the Generator CNN. The Discriminator CNN learns to detect the difference between features that are characteristic of original UHD images and synthesized UHD images. During training, the Discriminator CNN sees either an original UHD image or a synthesized UHD image, with the detection correctness fed back to the discriminator and, if the image was a synthesized one, also fed back to the Generator CNN.

Each CNN is attempting to beat the other: the Generator by creating images that have characteristics more like originals, while the Discriminator becomes better at detecting synthesized images.

The result is the synthesis of feature details that are characteristic of original UHD images.

With a GAN approach, there is no real constraint to the ability of the Generator CNN to create new detail everywhere. This means the Generator CNN can create images that diverge from the original image in more general ways. A combination of both compare functions can offer a better balance, retaining the detail regeneration, but also limiting divergence. This produces results that are subjectively better than conventional up-conversion.

Conversion from 1080i60 to 2160p60 is necessarily more complex than from 720p60. Starting from 1080i, there are three basic approaches to up-conversion:

Training data is required here, which must come from 2160p video sequences. This enables a set of fields to be created, which are then downsampled, with each field coming from one frame in the original 2160p sequence, so the fields are not temporally co-located.

Surprisingly, results from field-based up-conversion tended to be better than using de-interlaced frame conversion, despite using sophisticated motion-compensated de-interlacing: the frame-based conversion being dominated by the artifacts from the de-interlacing process. However, it is clear that potentially useful data from the opposite fields did not contribute to the result, and the field-based approach missed data that could produce a better result.

A solution to this is to use multiple fields data as the source data directly into a modified Generator CNN, letting the GAN learn how best to perform the deinterlacing function. This approach was adopted and re-trained with a new set of video-based data, where adjacent fields were also provided.

This led to both high visual spatial resolution and good temporal stability. These are, of course, best viewed as a video sequence, however an example of one frame from a test sequence shows the comparison:

Comparison of a sample frame from different up-conversion techniques against original UHD

Up-conversion using a hybrid GAN with multiple fields was effective across a range of content, but is especially relevant for the visual sports experience to the consumer. This offers a realistic means by which content that has more of the appearance of UHD can be created from both progressive and interlaced HD source, which in turn can enable an improved experience for the fan at home when watching a sports UHD channel.

1 A. Mordvintsev, C. Olah and M. Tyka, Inceptionism: Going Deeper into Neural Networks, 2015. [Online]. Available: https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html

2 I. e. a. Goodfellow, Generative Adversarial Nets, Neural Information Processing Systems Proceedings, vol. 27, 2014.

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SVG Tech Insight: Increasing Value of Sports Content Machine Learning for Up-Conversion HD to UHD - Sports Video Group

The total % of ICT Goods Exports around the Globe Increased from 11.20% in 2016 to 11.51% in 2017 UNCTAD

CRIFAX added a new market research report onGlobalMachine Learning as a Service (MLaaS) Market, 2020-2028to its database of market research collaterals consisting of overall market scenario with prevalent and future growth prospects, among other growth strategies used by key players to stay ahead of the game. Additionally, recent trends, mergers and acquisitions, region-wise growth analysis along with challenges that are affecting the growth of the market are also stated in the report.

Be it artificial intelligence (AI), internet of things (IoT) or digital reality, the increased rate of technological advancements around the world is directly proportional to the growth of global Machine Learning as a Service (MLaaS) Market. In the next two years, more than 20 billion devices are predicted to be connected to internet. With hundreds of devices getting connected to internet every second, the worldwide digital transformation in various industries is estimated to provide value-producing prospects in the global Machine Learning as a Service (MLaaS) Market, which is further anticipated to significantly boost the market revenue throughout the forecast period, i.e., 2020-2028.

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From last two decades, the investments by ICT industry has contributed extensively in strengthening the developed, developing and emerging countries economic growth. According to the statistics provided by United Nations Conference on Trade and Development (UNCTAD), the total export (%) of ICT goods such as computers, peripheral, communication and electronic equipment among other IT goods around the world grew from 10.62% in 2011 to 11.51% in 2017. The highest was recorded in Hong Kong, with 51.7% in 2017, followed by Philippines, Singapore and Malaysia. Additionally, growth in global economy coupled with various initiatives proposed by governments of different nations to meet their policy objectives is estimated to hone the growth of theGlobal Machine Learning as a Service (MLaaS) Marketin upcoming years.

Not only the ever growing IT sector brings with it numerous advancements, it also creates fair amount of challenges when it comes to security concerns pertaining to data storage among the users. With increasing availability of internet access leading to rising number of internet users, there is vast amount of user information that is being stored online through cloud services. This has driven many nations to compile laws (such as European Unions GDPR and U.S.s CLOUD Act) in an attempt to protect their citizens data. In addition to that, the growth of the global Machine Learning as a Service (MLaaS) Market might also be obstructed by lack of skilled professionals. To overcome this obstacle, companies should focus on providing skills and required training to their workforce, in order to keep up in this digital era.

Download Sample of This Strategic Report:https://www.crifax.com/sample-request-1000774

Furthermore, to provide better understanding of internal and external marketing factors, the multi-dimensional analytical tools such as SWOT and PESTEL analysis have been implemented in the global Machine Learning as a Service (MLaaS) Market report. Moreover, the report consists of market segmentation, CAGR (Compound Annual Growth Rate), BPS analysis, Y-o-Y growth (%), Porters five force model, absolute $ opportunity and anticipated cost structure of the market.

About CRIFAX

CRIFAX is driven by integrity and commitment to its clients, and provides cutting-edge marketing research and consulting solutions with a step-by-step guide to accomplish their business prospects. With the help of our industry experts having hands on experience in their respective domains, we make sure that our industry enthusiasts understand all the business aspects relating to their projects, which further improves the consumer base and the size of their organization. We offer wide range of unique marketing research solutions ranging from customized and syndicated research reports to consulting services, out of which, we update our syndicated research reports annually to make sure that they are modified according to the latest and ever-changing technology and industry insights. This has helped us to carve a niche in delivering distinctive business services that enhanced our global clients trust in our insights, and helped us to outpace our competitors as well.

For More Update Follow:-LinkedIn|Twitter

Contact Us:

CRIFAX

Email: [emailprotected]

U.K. Phone: +44 161 394 2021

U.S. Phone: +1 917 924 8284

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Europe Next-generation Organic Solar Cell MarketEurope 5G in Healthcare MarketEurope IoT in Elevators MarketEurope Smart Indoor Garden MarketEurope Com

The total % of ICT Goods Exports around the Globe Increased from 11.20% in 2016 to 11.51% in 2017 UNCTAD

CRIFAX added a new market research report onGlobalMachine Learning as a Service (MLaaS) Market, 2020-2028to its database of market research collaterals consisting of overall market scenario with prevalent and future growth prospects, among other growth strategies used by key players to stay ahead of the game. Additionally, recent trends, mergers and acquisitions, region-wise growth analysis along with challenges that are affecting the growth of the market are also stated in the report.

Be it artificial intelligence (AI), internet of things (IoT) or digital reality, the increased rate of technological advancements around the world is directly proportional to the growth of global Machine Learning as a Service (MLaaS) Market. In the next two years, more than 20 billion devices are predicted to be connected to internet. With hundreds of devices getting connected to internet every second, the worldwide digital transformation in various industries is estimated to provide value-producing prospects in the global Machine Learning as a Service (MLaaS) Market, which is further anticipated to significantly boost the market revenue throughout the forecast period, i.e., 2020-2028.

Get Exclusive Sample Report Copy Of This Report @https://www.crifax.com/sample-request-1000774

From last two decades, the investments by ICT industry has contributed extensively in strengthening the developed, developing and emerging countries economic growth. According to the statistics provided by United Nations Conference on Trade and Development (UNCTAD), the total export (%) of ICT goods such as computers, peripheral, communication and electronic equipment among other IT goods around the world grew from 10.62% in 2011 to 11.51% in 2017. The highest was recorded in Hong Kong, with 51.7% in 2017, followed by Philippines, Singapore and Malaysia. Additionally, growth in global economy coupled with various initiatives proposed by governments of different nations to meet their policy objectives is estimated to hone the growth of theGlobal Machine Learning as a Service (MLaaS) Marketin upcoming years.

Not only the ever growing IT sector brings with it numerous advancements, it also creates fair amount of challenges when it comes to security concerns pertaining to data storage among the users. With increasing availability of internet access leading to rising number of internet users, there is vast amount of user information that is being stored online through cloud services. This has driven many nations to compile laws (such as European Unions GDPR and U.S.s CLOUD Act) in an attempt to protect their citizens data. In addition to that, the growth of the global Machine Learning as a Service (MLaaS) Market might also be obstructed by lack of skilled professionals. To overcome this obstacle, companies should focus on providing skills and required training to their workforce, in order to keep up in this digital era.

Download Sample of This Strategic Report:https://www.crifax.com/sample-request-1000774

Furthermore, to provide better understanding of internal and external marketing factors, the multi-dimensional analytical tools such as SWOT and PESTEL analysis have been implemented in the global Machine Learning as a Service (MLaaS) Market report. Moreover, the report consists of market segmentation, CAGR (Compound Annual Growth Rate), BPS analysis, Y-o-Y growth (%), Porters five force model, absolute $ opportunity and anticipated cost structure of the market.

About CRIFAX

CRIFAX is driven by integrity and commitment to its clients, and provides cutting-edge marketing research and consulting solutions with a step-by-step guide to accomplish their business prospects. With the help of our industry experts having hands on experience in their respective domains, we make sure that our industry enthusiasts understand all the business aspects relating to their projects, which further improves the consumer base and the size of their organization. We offer wide range of unique marketing research solutions ranging from customized and syndicated research reports to consulting services, out of which, we update our syndicated research reports annually to make sure that they are modified according to the latest and ever-changing technology and industry insights. This has helped us to carve a niche in delivering distinctive business services that enhanced our global clients trust in our insights, and helped us to outpace our competitors as well.

For More Update Follow:-LinkedIn|Twitter

Contact Us:

CRIFAX

Email: [emailprotected]

U.K. Phone: +44 161 394 2021

U.S. Phone: +1 917 924 8284

More Related Reports:-

Europe Next-generation Organic Solar Cell MarketEurope 5G in Healthcare MarketEurope IoT in Elevators MarketEurope Smart Indoor Garden MarketEurope Compact Industrial Metal AM Printer MarketEurope Counter Drone MarketEurope 5G Applications and Services MarketEurope Smart Manufacturing Platform MarketEurope Emotion Recognition and Sentiment Analysis MarketEurope Construction & Demolition Robots Market

pact Industrial Metal AM Printer MarketEurope Counter Drone MarketEurope 5G Applications and Services MarketEurope Smart Manufacturing Platform MarketEurope Emotion Recognition and Sentiment Analysis MarketEurope Construction & Demolition Robots Market

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Machine Learning as a Service (MLaaS) Market Size Explores Growth Opportunities from 2020 to 2028 - The Think Curiouser

Now, imagine a machine learning system with an understanding of every detail of that persons entire clinical history and the trajectory of their disease. With the clinicians push of a button, such a system would be able to provide patient-specific predictions of expected outcomes if no treatment is provided to support the clinician and patient in making what may be life-or-death decisions[1] This would be a major achievement. The English NHS is currently investing 250 million in Artificial Intelligence (AI). Part of this AI work could help to identify patients most at risk of diseases such as heart disease or dementia, allowing for earlier diagnosis and cheaper, more focused, personalised prevention. [2] Multiple papers have suggested that machine learning outperforms statistical models including cardiovascular disease risk prediction. [3-6] We tested whether it is true with prediction of cardiovascular disease as exemplar.

Risk prediction models have been implemented worldwide into clinical practice to help clinicians make treatment decisions. As an example, guidelines by the UK National Institute for Health and Care Excellence recommend that statins are considered for patients with a predicted 10-year cardiovascular disease risk of 10% or more. [7] This is based on the estimation of QRISK which was derived using a statistical model. [8] Our research evaluated whether the predictions of cardiovascular disease risk for an individual patient would be similar if another model, such as a machine learning models were used, as different predictions could lead to different treatment decisions for a patient.

An electronic health record dataset was used for this study with similar risk factor information used across all models. Nineteen different prediction techniques were applied including 12 families of machine learning models (such as neural networks) and seven statistical models (such as Cox proportional hazards models). It was found that the various models had similar population-level model performance (C-statistics of about 0.87 and similar calibration). However, the predictions for individual CVD risks varied widely between and within different types of machine learning and statistical models, especially in patients with higher CVD risks. Most of the machine learning models, tested in this study, do not take censoring into account by default (i.e., loss to follow-up over the 10 years). This resulted in these models substantially underestimating cardiovascular disease risk.

The level of consistency within and between models should be assessed before they are used for treatment decisions making, as an arbitrary choice of technique and model could lead to a different treatment decision.

So, can a push of a button provide patient-specific risk prediction estimates by machine learning? Yes, it can. But should we use such estimates for patient-specific treatment-decision making if these predictions are model-dependant? Machine learning may be helpful in some areas of healthcare such as image recognition, and could be as useful as statistical models on population level prediction tasks. But in terms of predicting risk for individual decision making we think a lot more work could be done. Perhaps the claim that machine learning will revolutionise healthcare is a little premature.

Yan Li, doctoral student of statistical epidemiology, Health e-Research Centre, Health Data Research UK North, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester.

Matthew Sperrin, senior lecturer in health data science, Health e-Research Centre, Health Data Research UK North, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester.

Darren M Ashcroft, professor of pharmacoepidemiology, Centre for Pharmacoepidemiology and Drug Safety, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester.

Tjeerd Pieter van Staa, professor in health e-research, Health e-Research Centre, Health Data Research UK North, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester.

Competing interests: None declared.

References:

Link:
The consistency of machine learning and statistical models in predicting clinical risks of individual patients - The BMJ - The BMJ

By: Sherril Hayes, Executive Director, Analytics and Data Science Institute and Professor of Conflict Management, Analytics & Data Science Institute, College of Computing and Software Engineering, Kennesaw State UniversityIn the 1950 and 1960s, social and behavioral sciences were at the cutting edge of innovation. Scientific techniques and quantitative analyses were being applied to some of the most pressing social problems. The thinking was If NASA can put men in space, why cant we use these techniques to solve the problems of housing discrimination and school desegregation? Despite the investment, effort, and professionalization of these fields, the consensus was that they were failing. Why? In 1973 Horst Rittel, a mathematician and Professor in the Science of Design at UC Berkeley, and his colleague Melvin Weber introduced the

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Ethical Machine Learning as a Wicked Problem Machine Learning Times - The Predictive Analytics Times

LONDON (PRWEB) November 02, 2020

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VisiRule FastChart is available for immediate release as part of the VisiRule product range at a price of 2500 USD.

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LPA is a small dedicated AI company in London, England which has been providing logic-based software solutions since it was formed in 1981. LPA products have been used in a wide-range of commercial and research applications including legal document assembly, environmental engineering, information modeling, disease diagnosis, fault diagnosis, products sales and recommendations. https://www.lpa.co.uk/

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LPA announce VisiRule FastChart to combine Machine Learning and rule-based expert systems - PR Web

Earlier machine learning techniques have been used extensively for a wide range of tasks including classification, regression and density estimation in a variety of application areas such as bioinformatics, speech recognition, spam detection, computer vision, fraud detection and advertising networks. Machine learning is the main method among those computational application to IoT and there are lots of application both in research and industry including energy, routing, and home automation and so on.

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The report aims to provide an overview of global artificial intelligence and machine learning in IoT market with detailed market segmentation by application, and geography. The global artificial intelligence and machine learning in IoT market is expected to witness exponential growth during the forecast period so as to manage increasingly large amount of unstructured machine data available in almost all industry.

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To evaluate market dynamics effecting the market during the forecast period i.e., drivers, restraints, opportunities, and future trend

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Table Of Content

1 Introduction

2 Key Takeaways

3 Artificial Intelligence and Machine Learning in IoT Market Landscape

4 Artificial Intelligence and Machine Learning in IoT Market Key Industry Dynamics

5 Artificial Intelligence and Machine Learning in IoT Market Analysis- Global

6 Artificial Intelligence and Machine Learning in IoT Market Revenue and Forecasts to 2025 Application

7 Artificial Intelligence and Machine Learning in IoT Market Revenue and Forecasts to 2025 Geographical Analysis

8 Industry Landscape

9 Competitive Landscape

10 Artificial Intelligence and Machine Learning in IoT Market, Key Company Profiles

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Resetting Covid-19 Impact On Artificial Intelligence and Machine Learning in IoT Market Report Explores Complete Research With Top Companies- Google,...