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Scope: Global Machine Learning MarketThe global Machine Learning market report includes the analysis of all the important aspects associated with the Machine Learning market. The detailed study on the CAGR at which the market is anticipated to expand in the future is provided in the study. The detailed information regarding market valuation at different times is included in the report. The market study also covers the study of varying dynamics of the Machine Learning industry.

Vendor Landscape and Profiling:Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP SE (Walldorf, Germany), SAS Institute Inc. (North Carolina, US), Google, Inc. (California, US), Amazon Web Services Inc. (Washington, US), Baidu, Inc. (Beijing, China), BigML, Inc. (Oregon, US), Fair Isaac Corporation (FICO) (California, US), Hewlett Packard Enterprise Development LP (HPE) (California, US), Intel Corporation (California, US), KNIME.com AG (Zurich, Switzerland), RapidMiner, Inc. (Massachusetts, US), Angoss Software Corporation (Toronto, Canada), H2O.ai (California, US), Alpine Data (California, US), Domino Data Lab, Inc. (California, US), Dataiku (Paris, France), Luminoso Technologies, Inc. (Massachusetts, US), TrademarkVision (Pennsylvania, US), Fractal Analytics Inc. (New Jersey, US), TIBCO Software Inc. (California, US), Teradata (Ohio, US), Dell Inc. (Texas, US), and Oracle Corporation (California, US)

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The research report based on the Machine Learning market covers every detail related to the industry. The details on the demands of the global Machine Learning market at different times are offered in the market study. The research report offers detailed information regarding the growth opportunities for the vendors and manufacturers worldwide. The report on the industry provides a detailed analysis on the present market demands along with the data the prediction for future demands of the industry.

Product-based Segmentation:by ServiceProfessional ServicesManaged ServicesMachine learning market by Deployment Model:CloudOn-premises

Application-based Segmentation:by Organization Size:SMEsLarge Enterprises

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The detailed research of all the influencing factors of the industry is included in the Machine Learning industry research report. The market study report offers a thorough analysis of the ups and downs in the Machine Learning industry over the years. The meticulous discussion on the premeditated developments in the sector is included in the Machine Learning market report. The detailed information on the latest trends in the industry is offered in the study. The research report narrowly analyzes all the factors coupled with the industry growth. Along with that the detailed data on the restraining factors is also added in the report. The report provides users with a detailed study on the industry growth strategy. The Machine Learning market research offers the thorough analysis on all the market analysis techniques used to study each and every aspect of the industry in detail.

The following is a complete run-down of geography-based analysis of Machine Learning market:

North America (U.S., Canada, Mexico) Europe (U.K., France, Germany, Spain, Italy, Central & Eastern Europe, CIS) Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific) Latin America (Brazil, Rest of L.A.) Middle East and Africa (Turkey, GCC, Rest of Middle East)

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The research report on the Machine Learning sectors helps the manufacturers across the globe to survive in the intense competition offered by the Machine Learning industry. In addition to that the report also includes the data regarding investment opportunities in the sector. The Machine Learning market research report also covers the in-depth analysis of all technological advancements in the Machine Learning industry. The report on the industry provides a detailed analysis on the present market demands along with the data the prediction for future demands of the industry. The research report based on the market covers every detail related to the Machine Learning industry. The research report is recognized as a comprehensive guide for the in-depth study of the Machine Learning sector.

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Machine Learning Market Predominant Trends and Growth Opportunities by 2028: Microsoft Corporation (Washington, US), IBM Corporation (New York, US),...

This article was originally published here

IEEE Rev Biomed Eng. 2021 Mar 26;PP. doi: 10.1109/RBME.2021.3069213. Online ahead of print.

ABSTRACT

COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.

PMID:33769936 | DOI:10.1109/RBME.2021.3069213

Read more from the original source:
A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis - DocWire News

AI and healthcare professionals are warning of concerns over a machine learning algorithm made for diagnosing COVID-19.

The idea behind these technologies was to help health professionals tell the difference between coronavirus and other similarly-presenting ailments like pneumonia.

But concerned professionals say changes need to be made before the COVID diagnosis machine learning is used in a clinical environment.

More from a recent VentureBeat article:

Of those 62 papers included in the analysis, roughly half made no attempt to perform external validation of training data, did not assess model sensitivity or robustness, and did not report the demographics of people represented in training data.

Frankenstein datasets, the kind made with duplicate images obtained from other datasets, were also found to be a common problem, and only one in five COVID-19 diagnosis or prognosis models shared their code so others can reproduce results claimed in literature.

In their current reported form, none of the machine learning models included in this review are likely candidates for clinical translation for the diagnosis/prognosis of COVID-19, the paper reads. Despite the huge efforts of researchers to develop machine learning models for COVID-19 diagnosis and prognosis, we found methodological flaws and many biases throughout the literature, leading to highly optimistic reported performance.

Publicly available datasets also commonly suffered from lower quality image formats and werent large enough to train reliable AI models.

How does that old expression go? the problem with computers is that they doexactlywhat you tell them to do.

I love that saying because, despite the fact that AI is growing the point of teaching itself without as much human intervention, its still a glorified computer. A model is still only as good as the data being fed to it, and instances like this only underline just how much of a strict science machine learning is.

My TechDecisions Podcast Episode 107: Artificial Intelligence in the Enterprise

Here is the original post:
AI Researchers Say There are Problems with Machine Learning COVID Diagnosis - TechDecisions

Healthcare professionals and AI experts alike are warning about some issues theyve identified in a machine learning model made for diagnosing the coronavirus.

The idea behind these technologies was to help health professionals tell the difference between coronavirus and other similarly-presenting ailments like pneumonia.

But concerned professionals say changes need to be made before the COVID diagnosis machine learning is used in a clinical environment.

More from a recentVentureBeat article:

Of those 62 papers included in the analysis, roughly half made no attempt to perform external validation of training data, did not assess model sensitivity or robustness, and did not report the demographics of people represented in training data.

Frankenstein datasets, the kind made with duplicate images obtained from other datasets, were also found to be a common problem, and only one in five COVID-19 diagnosis or prognosis models shared their code so others can reproduce results claimed in literature.

In their current reported form, none of the machine learning models included in this review are likely candidates for clinical translation for the diagnosis/prognosis of COVID-19, the paper reads. Despite the huge efforts of researchers to develop machine learning models for COVID-19 diagnosis and prognosis, we found methodological flaws and many biases throughout the literature, leading to highly optimistic reported performance.

Publicly available datasets also commonly suffered from lower quality image formats and werent large enough to train reliable AI models.

How does that old expression go? the problem with computers is that they doexactlywhat you tell them to do.

I love that saying because, despite the fact that AI is growing the point of teaching itselfwithout as much human intervention, its still a glorified computer.

After all, a model is still only as good as the data being fed to it, and instances like this only underline just how much of a strict science machine learning is.

Read Next: Artificial Intelligence Speculates on Whether Shakespeare Had Help with Henry VIII

Excerpt from:
There Are Issues with the COVID-19 Diagnosis Machine Learning Algorithms - Commercial Integrator

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Cobus van Heerden is senior product manager for analytics and machine learning software for GE Digital. Cobus has 20 years of experience in developing and implementing industrial software globally. He specializes in helping industrial organizations realize transformational productivity gains through applying digital technology, advanced analytics and machine learning.

GE Digital provides software and IIoT (Industrial Internet of Things) services to industrial manufacturing companies. We operate across four key industries, including Food & Beverage and Consumer Goods, Automotive, Pharmaceuticals, and Water/Wastewater. As part of GE, we are helping industry work better. Driven by people, process and proven technology, we are innovating with our customers to make the complex simple at unparalleled speed and scale.

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Machine Learning and Analytics Made Easy - The Internet of Business