Category Archives: Machine Learning

COVID-19 can affect the global economy in three main ways: by directly affecting production and demand, by creating supply chain and market disruption, and by its financial impact on firms and financial markets. Global Machine Learning in Finance Market size has covered and analysed the potential of Worldwide market Industry and provides statistics and information on market dynamics, market analysis, growth factors, key challenges, major drivers & restraints, opportunities and forecast. This report presents a comprehensive overview, market shares, and growth opportunities of market 2021 by product type, application, key manufacturers and key regions and countries.

Market Research Inc.proclaims a new addition of comprehensive data to its extensive repository titled as, Machine Learning in Financemarket. This informative data has been scrutinized by using effective methodologies such as primary and secondary research techniques. This research report estimates the scale of the global Machine Learning in Finance market over the upcoming year. The recent trends, tools, methodologies have been examined to get a better insight into the businesses.

Request a sample copy of this report @:

https://www.marketresearchinc.com/request-sample.php?id=31104

Top key players::Ignite LtdYodleeTrill A

Additionally, it throws light on different dynamic aspects of the businesses, which help to understand the framework of the businesses. The competitive landscape has been elaborated on the basis of profit margin, which helps to understand the competitors at domestic as well as global level.

The globalMachine Learning in Financemarket has been studied by considering numerous attributes such as type, size, applications, and end-users. It includes investigations on the basis of current trends, historical records, and future prospects. This statistical data helps in making informed business decisions for the progress of the industries. For an effective and stronger business outlook, some significant case studies have been mentioned in this report.

Get a reasonable discount on this premium report @:

https://www.marketresearchinc.com/ask-for-discount.php?id=31104

Key Objectives of Machine Learning in Finance Market Report:

Study of the annual revenues and market developments of the major players that supply Machine Learning in Finance Analysis of the demand for Machine Learning in Finance by component Assessment of future trends and growth of architecture in the Machine Learning in Finance market Assessment of the Machine Learning in Finance market with respect to the type of application Study of the market trends in various regions and countries, by component, of the Machine Learning in Finance market Study of contracts and developments related to the Machine Learning in Finance market by key players across different regions Finalization of overall market sizes by triangulating the supply-side data, which includes product developments, supply chain, and annual revenues of companies supplying Machine Learning in Finance across the globe.

Further information:

https://www.marketresearchinc.com/enquiry-before-buying.php?id=31104

In this study, the years considered to estimate the size ofMachine Learning in Financeare as follows:

History Year: 2016-2019

Base Year: 2020

Forecast Year 2021 to 2028.

About Us

Market Research Inc is farsighted in its view and covers massive ground in global research. Local or global, we keep a close check on both markets. Trends and concurrent assessments sometimes overlap and influence the other. When we say market intelligence, we mean a deep and well-informed insight into your products, market, marketing, competitors, and customers. Market research companies are leading the way in nurturing global thought leadership. We help your product/service become the best they can with our informed approach.

Contact Us

Market Research Inc

Kevin

51 Yerba Buena Lane, Ground Suite,

Inner Sunset San Francisco, CA 94103, USA

Call Us:+1 (628) 225-1818

Write Us@sales@marketresearchinc.com

https://www.marketresearchinc.com

Link:
Machine Learning in Finance Market Benefits, Forthcoming Developments, Business Opportunities & Future Investments to 2028 KSU | The Sentinel...

4Paradigm stays on a leadership position from 2018 to the first half of 2020

BEIJING, Jan. 21, 2021 /PRNewswire/ -- IDC, a premier global provider of market intelligence, has recently published China AI Software and Application (2020 H1) Report (hereinafter referred to as "Report"), where 4Paradigm as an AI innovator recognized for its software standardization level, scope of industrial coverage and solid customer base, has led China's machine learning platform market from 2018 to the first half of 2020 with expanding market share, ahead of leading vendors such as Alibaba, Tencent, Baidu and Huawei.

The report dives into China's AI market in 2020 in retrospect: from 2015 to 2020, every single year has seen new drivers emerging from the AI market and the market landscape continuously evolving from cognition to exploration, to deep application and then to scale-up. An unprecedentedly prosperous AI market has been witnessed since 2020 as both awareness and investment are boosted for AI and data intelligence in the Chinese market driven by pandemic control, new infrastructure initiatives and impact of international trade frictions. Since the second half 2020, a series of policies such as digital transformation of SOE, intelligent computing center launched by governmental authorities are expected to galvanize AI growth to a new height.

Looking into the future, Yanxia Lu, Chief AI Analyst of IDC China says, "Market opportunities generated from continual AI implementation are just around the corner. For further expansion of market shares, it's necessary to leverage technological leadership and product innovation for new market opportunities, to explore replicable and scalable application scenarios and to unite partners with industrial know-how for deployment of technologies on enterprise."

The IDC report recognizes the advantages of 4Paradigm machine learning platform and AutoML products in technological accumulation, enterprise-level product layout, commercial implementation performance, AI industrial ecosystem, etc., hence an important benchmark for enterprises' choice of machine learning platform.

Story continues

4Paradigm has built an AutoML full stack algorithm layout including perceptive, cognitive and decision-making algorithm, enabling enterprises to drive up key decision-making performance and empowering enterprises to scale up AI scenario deployment with low threshold and high efficiency in all-dimensional observation, accurate orientation and optimized decision-making.

4Paradigm released four products this year, respectively are Sage AIOS, an enterprise AI operation system, Sage HyperCycle ML, a fully automatic tool for scaled-up AI development, Sage CESS, a one-stop intelligent operation platform and Sage One, an AI computing power platform for full life cycle, hence building a full stack AI product matrix covering computing power, OS, production platform and business system.

To help enterprises address the booming demand of moving online, 4Paradigm continues to provide online, intelligent and precise operation capabilities to numerous prominent enterprises and organizations in China and abroad, among which are Bank of Communications, Industrial Bank, Huaxia Bank, Guosen Securities, Laiyifen, Feihe, China Academy of Railway Sciences, DHL, Zegna, Budweiser China, KRASTASE, etc., enabling them to embrace digital transformation and seize new opportunities online.

With over 200 partners in 15 sectors, 4Paradigm is experiencing rapid increase in its eco partners and industrial coverage on the basis of existing ecosystem.

Despite the unprecedent boom on AI market, enterprises face mounting challenges in their intelligent transformation in terms of high development threshold of AI, low implementation efficiency and poor business value. In FutureScape China ICT Market Forecast Forum, an annual IDC event recently held, Zhenshan Zhong, Vice President IDC China, offered elaborated insights on the ten predictions of AI market in China from 2021 to 2025, among which AutoML (automated machine learning) ranks the top. IDC holds that AutoML will lower the threshold of AI development to make inclusive AI a reality. It is expected that the number of data analysts and modelling scientists using AutoML technology encapsulation in providing end-to-end machine learning platforms from data preparation to model deployment will double by 2023.

Through product embedding of AutoML technology and rigorous methodology for implementation, 4Paradigm has built a systematic AutoML implementation solutions and pathways, which have enabled successful implementation of over 10,000 AI applications for enterprises in finance, retail, healthcare, manufacturing, internet, media, government, energy, carrier, among other sectors, with positive feedbacks from leaders and innovators in the tide of transformation. In the future, 4Paradigm will continuously commit to promoting the implementation of machine learning platforms and AutoML products in more industries and scenarios, helping more enterprises in their journey of intelligent transformation and upgrade for higher business efficiency while removing obstacles and boosting social productivity.

http://www.4paradigm.com

SOURCE 4Paradigm

Read more here:
4Paradigm Defends its Championship in China's Machine Learning Platform Market in the 1st Half of 2020, According to IDC - Yahoo Finance

ST. PAUL, Minn., Jan. 19, 2021 /PRNewswire/ -- San Diego-based Mission Healthcare, one of the largest home health, hospice, and palliative care providers in California, will adopt Muse Healthcare's machine learning and predictive modeling tool to help deliver a more personalized level of care to their patients.

The Muse technology evaluates and models every clinical assessment, medication, vital sign, and other relevant data to perform a risk stratification of these patients. The tool then highlights the patients with the most critical needs and visually alerts the agency to perform additional care. Muse Healthcare identifies patients as "Critical," which means they have a greater than 90% likelihood of passing in the next 7-10 days. Users are also able to make accurate changes to care plans based on the condition and location of the patient. When agencies use Muse's powerful machine learning tool, they have an advantage and data proven outcomes to demonstrate they are providing more care and better care to patients in transition.

According to Mission Healthcare's Vice President of Clinical and Quality, Gerry Smith, RN, MSN, Muse will serve as an invaluable tool that will assist their clinicians to enhance care for their patients. "Mission Hospice strives to ensure every patient receives the care and comfort they need while on service, and especially in their final days. We are so excited that the Muse technology will provide our clinical team with additional insights to positively optimize care for patients at the end of life. This predictive modeling technology will enable us to intervene earlier; make better decisions for more personalized care; empower staff; and ultimately improve patient outcomes."

Mission Healthcare's CEO, Paul VerHoeve, also believes that the Muse technology will empower their staff to provide better care for patients. "Predictive analytics are a new wave in hospice innovation and Muse's technology will be a valuable asset to augment our clinical efforts at Mission Healthcare. By implementing a revolutionary machine learning tool like Muse, we can ensure our patients are receiving enhanced hands-on care in those critical last 7 10 days of life. Our mission is to take care of people, with Muse we will continue to improve the patient experience and provide better care in the final days and hours of a patient's life."

As the only machine learning tool in the hospice industry, the Muse transitions tool takes advantage of the implemented documentation within the EMR. This allows the agency to quickly implement the tool without disruption. "With guidance from our customers in the hundreds of locations that are now using the tool, we have focused on deploying time saving enhancements to simplify a clinician's role within hospice agencies. These tools allow the user to view a clinical snapshot, complete review of the scheduled frequency, and quickly identify the patients that need immediate attention. Without Muse HC, a full medical review must be conducted to identify these patients," said Tom Maxwell, co-Founder of Muse Healthcare. "We are saving clinicians time in their day, simplifying the identification challenges of hospice, and making it easier to provide better care to our patients. Hospice agencies only get one chance to get this right," said Maxwell.

CEO of Muse Healthcare, Bryan Mosher, is also excited about Mission's adoption of the Muse tool. "We welcome the Mission Healthcare team to the Muse Healthcare family of customers, and are happy to have them adopt our product so quickly. We are sure with the use of our tools,clinicians at Mission Healthcare will provide better care for their hospice patients," said Mosher.

About Mission Healthcare

As one of the largest regional home health, hospice, and palliative care providers in California, San Diego-based Mission Healthcare was founded in 2009 with the creation of its first service line, Mission Home Health. In 2011, Mission added its hospice service line. Today, Mission employs over 600 people and serves both home health and hospice patients through Southern California. In 2018, Mission was selected as a Top Workplace by the San Diego Union-Tribune. For more information visit https://homewithmission.com/.

About Muse Healthcare

Muse Healthcare was founded in 2019 by three leading hospice industry professionals -- Jennifer Maxwell, Tom Maxwell, and Bryan Mosher. Their mission is to equip clinicians with world-class analytics to ensure every hospice patient transitions with unparalleled quality and dignity. Muse's predictive model considers hundreds of thousands of data points from numerous visits to identify which hospice patients are most likely to transition within 7-12 days. The science that powers Muse is considered a true deep learning neural network the only one of its kind in the hospice space. When hospice care providers can more accurately predict when their patients will transition, they can ensure their patients and the patients' families receive the care that matters most in the final days and hours of a patient's life. For more information visit http://www.musehc.com.

View original post here:
Mission Healthcare of San Diego Adopts Muse Healthcare's Machine Learning Tool - Southernminn.com

An illustration of novel coronavirus SARS-CoV-2.

Researchers have published one of the first studies using a Machine Learning (ML) technique called "federated learning" to examine electronic health records to better predict how COVID-19 patients will progress.

The study, published in the Journal of Medical Internet Research - Medical Informatics, indicates that the emerging technique holds promise to create more robust machine learning models that extend beyond a single health system without compromising patient privacy.

These models, in turn, can help triage patients and improve the quality of their care. "Machine Learning models in health care often require diverse and large-scale data to be robust and translatable outside the patient population they were trained on," said co-author Benjamin Glicksberg, Assistant Professor at Mount Sinai.

Federated learning is a technique that trains an algorithm across multiple devices or servers holding local data samples but avoids clinical data aggregation, which is undesirable for reasons including patient privacy issues.

For the study, the researchers implemented and assessed federated learning models using data from electronic health records at five separate hospitals within the Health System to predict mortality in COVID-19 patients.

They compared the performance of a federated model against ones built using data from each hospital separately, referred to as local models.

After training their models on a federated network and testing the data of local models at each hospital, the researchers found the federated models demonstrated enhanced predictive power and outperformed local models at most of the hospitals.

**

The above article has been published from a wire agency with minimal modifications to the headline and text.

Continue reading here:
Researchers Develop New Machine Learning Technique to Predict Progress of COVID-19 Patients | The Weather Channel - Articles from The Weather Channel...

ATLANTACompared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study in Nature Communications led by Georgia State University.

Advanced biomedical technologies such as structural and functional magnetic resonance imaging (MRI and fMRI) or genomic sequencing have produced an enormous volume of data about the human body. By extracting patterns from this information, scientists can glean new insights into health and disease. This is a challenging task, however, given the complexity of the data and the fact that the relationships among types of data are poorly understood.

Deep learning, built on advanced neural networks, can characterize these relationships by combining and analyzing data from many sources. At the Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State researchers are using deep learning to learn more about how mental illness and other disorders affect the brain.

Although deep learning models have been used to solve problems and answer questions in a number of different fields, some experts remain skeptical. Recent critical commentaries have unfavorably compared deep learning with standard machine learning approaches for analyzing brain imaging data.

However, as demonstrated in the study, these conclusions are often based on pre-processed input that deprive deep learning of its main advantagethe ability to learn from the data with little to no preprocessing. Anees Abrol, research scientist at TReNDS and the lead author on the paper, compared representative models from classical machine learning and deep learning, and found that if trained properly, the deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.

We compared these models side-by-side, observing statistical protocols so everything is apples to apples. And we show that deep learning models perform better, as expected, said co-author Sergey Plis, director of machine learning at TReNDS and associate professor of computer science.

Plis said there are some cases where standard machine learning can outperform deep learning. For example, diagnostic algorithms that plug in single-number measurements such as a patients body temperature or whether the patient smokes cigarettes would work better using classical machine learning approaches.

If your application involves analyzing images or if it involves a large array of data that cant really be distilled into a simple measurement without losing information, deep learning can help, Plis said.. These models are made for really complex problems that require bringing in a lot of experience and intuition.

The downside of deep learning models is they are data hungry at the outset and must be trained on lots of information. But once these models are trained, said co-author Vince Calhoun, director of TReNDS and Distinguished University Professor of Psychology, they are just as effective at analyzing reams of complex data as they are at answering simple questions.

Interestingly, in our study we looked at sample sizes from 100 to 10,000 and in all cases the deep learning approaches were doing better, he said.

Another advantage is that scientists can reverse analyze deep-learning models to understand how they are reaching conclusions about the data. As the published study shows, the trained deep learning models learn to identify meaningful brain biomarkers.

These models are learning on their own, so we can uncover the defining characteristics that theyre looking into that allows them to be accurate, Abrol said. We can check the data points a model is analyzing and then compare it to the literature to see what the model has found outside of where we told it to look.

The researchers envision that deep learning models are capable of extracting explanations and representations not already known to the field and act as an aid in growing our knowledge of how the human brain functions. They conclude that although more research is needed to find and address weaknesses of deep-learning models, from a mathematical point of view, its clear these models outperform standard machine learning models in many settings.

Deep learnings promise perhaps still outweighs its current usefulness to neuroimaging, but we are seeing a lot of real potential for these techniques, Plis said.

Read more:
Deep Learning Outperforms Standard Machine Learning in Biomedical Research Applications, Research Shows - Georgia State University News

Metallic alloys for aerospace components are expected to be made faster and more cheaply with the application of machine learning in Project MEDAL.

This is the aim of Project MEDAL: Machine Learning for Additive Manufacturing Experimental Design,which is being led by Intellegens, a Cambridge University spin-out specialising in artificial intelligence, the Sheffield University AMRC North West, and Boeing. It aims to accelerate the product development lifecycle of aerospace components by using a machine learning model to optimise additive manufacturing (AM) for new metal alloys.

How collaboration is driving advances in additive manufacturing

Project MEDALs research will concentrate on metal laser powder bed fusion and will focus on so-called parameter variables required to manufacture high density, high strength parts.

The project is part of the National Aerospace Technology Exploitation Programme (NATEP), a 10m initiative for UK SMEs to develop innovative aerospace technologies funded by the Department for Business, Energy and Industrial Strategy and delivered in partnership with the Aerospace Technology Institute (ATI) and Innovate UK.

In a statement, Ben Pellegrini, CEO of Intellegens, said: The intersection of machine learning, design of experiments and additive manufacturing holds enormous potential to rapidly develop and deploy custom parts not only in aerospace, as proven by the involvement of Boeing, but in medical, transport and consumer product applications.

There are many barriers to the adoption of metallic AM but by providing users, and maybe more importantly new users, with the tools they need to process a required material should not be one of them, added James Hughes, research director for Sheffield University AMRC North West. With the AMRCs knowledge in AM, and Intellegens AI tools, all the required experience and expertise is in place in order to deliver a rapid, data-driven software toolset for developing parameters for metallic AM processes to make them cheaper and faster.

Aerospace components must withstand certain loads and temperature resistances, and some materials are limited in what they can offer. There is also simultaneous push for lower weight and higher temperature resistance for better fuel efficiency, bringing new or previously impractical-to-machine metals into the aerospace sector.

One of the main drawbacks of AM is the limited material selection currently available and the design of new materials, particularly in the aerospace industry, requires expensive and extensive testing and certification cycles which can take longer than a year to complete and cost as much as 1m. Project MEDAL aims to accelerate this process.

The machine learning solution in this project can significantly reduce the need for many experimental cycles by around 80 per cent, Pellegrini said: The software platform will be able to suggest the most important experiments needed to optimise AM processing parameters, in order to manufacture parts that meet specific target properties. The platform will make the development process for AM metal alloys more time and cost-efficient. This will in turn accelerate the production of more lightweight and integrated aerospace components, leading to more efficient aircraft and improved environmental impact.

More:
Project MEDAL to apply machine learning to aero innovation - The Engineer