The Future Of Nano Technology
- Alan Watts
- Anti-Aging Medicine
- David Sinclair
- Gene Medicine
- Gene therapy
- Genetic Medicine
- Genetic Therapy
- Global News Feed
- Hormone Replacement Therapy
- Human Genetic Engineering
- Human Reproduction
- Integrative Medicine
- Life Skills
- Longevity Medicine
- Machine Learning
- Medical School
- Nano Medicine
- Parkinson's disease
- Quantum Computing
- Regenerative Medicine
- Stem Cell Therapy
- Stem Cells
- SPORTS THERAPY – A GREAT WAY TO MAINTAIN A HEALTHY BODY
- How researchers are mapping the future of quantum computing, using the tech of today – GeekWire
- Colorado makes a bid for quantum computing hardware plant that would bring more than 700 jobs – The Denver Post
- The Worldwide Quantum Computing Industry is Expected to Reach $1.7 Billion by 2026 – PRNewswire
- bp Joins the IBM Quantum Network to Advance Use of Quantum Computing in Energy – HPCwire
|Search Immortality Topics:|
Category Archives: Machine Learning
A Nepalese Machine Learning (ML) Researcher Introduces Papers-With-Video Browser Extension Which Allows Users To Access Videos Related To Research…
Amit Chaudhary, a machine learning (ML) researcher from Nepal, has recently introduced a browser extension that allows users to directly access videos related to research papers published on the platform arXiv.
ArXiv has become an essential resource for new machine learning (ML) papers. Initially, in 1991, it was launched as a storage site for physics preprints. In 2001 it was named ArXiv and had since been hosted by Cornell University. ArXiv has received close to 2 million submissions across various scientific research fields.
Amit obtained publicly released videos from 2020 ML conferences. He then indexed the videos and reverse-mapped them to the relevant arXiv links through pyarxiv, a dedicated wrapper for the arXiv API. The Google Chrome extension creates a video icon next to the paper title on the arXiv abstract page, enabling users to identify and access available videos related to the paper directly.
Many research teams are creating videos to accompany their papers. These videos can act as a guide by providing demo and other valuable information on the research document. In several situations, the videos are created as an alternative to traditional in-person presentations at AI conferences. This is useful in current circumstances as almost all panels have moved to virtual forms due to the Covid-19 pandemic.
The Papers-With-Video extension enables direct video links for around 3.7k arXiv ML papers. Amit aims to figure out how to pair documents and videos related effectively but has different titles, and with this, he hopes to expand coverage to 8k videos. He has proposed community feedback and has now tweaked the extensions functionality based on user remarks and suggestions.
The browser extension is not available on the Google Chrome Web Store yet. However, one can find the extension, installation guide, and further information on GitHub.
Comprehensive Report on Cloud Machine Learning Market 2021 | Trends, Growth Demand, Opportunities & Forecast To 2027 |Amazon, Oracle Corporation,…
Cloud Machine Learning Market research report is the new statistical data source added by A2Z Market Research.
Cloud Machine Learning Market is growing at a High CAGR during the forecast period 2021-2027. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market.
Cloud Machine Learning Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis.
Get the PDF Sample Copy (Including FULL TOC, Graphs and Tables) of this report @:
Note In order to provide more accurate market forecast, all our reports will be updated before delivery by considering the impact of COVID-19.
Top Key Players Profiled in this report are:
Amazon, Oracle Corporation, IBM, Microsoft Corporation, Google Inc., Salesforce.Com, .
The key questions answered in this report:
Various factors are responsible for the markets growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Cloud Machine Learning market. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market. The influence of the latest government guidelines is also analyzed in detail in the report. It studies the Cloud Machine Learning markets trajectory between forecast periods.
Regions Covered in the Global Cloud Machine Learning Market Report 2021: The Middle East and Africa (GCC Countries and Egypt) North America (the United States, Mexico, and Canada) South America (Brazil etc.) Europe (Turkey, Germany, Russia UK, Italy, France, etc.) Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)
Get up to 30% Discount on this Premium Report @:
The cost analysis of the Global Cloud Machine Learning Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.
The report provides insights on the following pointers:
Market Penetration: Comprehensive information on the product portfolios of the top players in the Cloud Machine Learning market.
Product Development/Innovation: Detailed insights on the upcoming technologies, R&D activities, and product launches in the market.
Competitive Assessment: In-depth assessment of the market strategies, geographic and business segments of the leading players in the market.
Market Development: Comprehensive information about emerging markets. This report analyzes the market for various segments across geographies.
Market Diversification: Exhaustive information about new products, untapped geographies, recent developments, and investments in the Cloud Machine Learning market.
Table of Contents
Global Cloud Machine Learning Market Research Report 2021 2027
Chapter 1 Cloud Machine Learning Market Overview
Chapter 2 Global Economic Impact on Industry
Chapter 3 Global Market Competition by Manufacturers
Chapter 4 Global Production, Revenue (Value) by Region
Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions
Chapter 6 Global Production, Revenue (Value), Price Trend by Type
Chapter 7 Global Market Analysis by Application
Chapter 8 Manufacturing Cost Analysis
Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10 Marketing Strategy Analysis, Distributors/Traders
Chapter 11 Market Effect Factors Analysis
Chapter 12 Global Cloud Machine Learning Market Forecast
Buy Exclusive Report @:
If you have any special requirements, please let us know and we will offer you the report as you want.
About A2Z Market Research:
The A2Z Market Research library provides syndication reports from market researchers around the world. Ready-to-buy syndication Market research studies will help you find the most relevant business intelligence.
Our Research Analyst Provides business insights and market research reports for large and small businesses.
The company helps clients build business policies and grow in that market area. A2Z Market Research is not only interested in industry reports dealing with telecommunications, healthcare, pharmaceuticals, financial services, energy, technology, real estate, logistics, F & B, media, etc. but also your company data, country profiles, trends, information and analysis on the sector of your interest.
1887 WHITNEY MESA DR HENDERSON, NV 89014
+1 775 237 4147
Machine Learning in Finance Market Benefits, Forthcoming Developments, Business Opportunities & Future Investments to 2028 KSU | The Sentinel…
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 @:
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 @:
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.
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.
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.
Market Research Inc
51 Yerba Buena Lane, Ground Suite,
Inner Sunset San Francisco, CA 94103, USA
Call Us:+1 (628) 225-1818
4Paradigm Defends its Championship in China’s Machine Learning Platform Market in the 1st Half of 2020, According to IDC – Yahoo Finance
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.
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.
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
Researchers Develop New Machine Learning Technique to Predict Progress of COVID-19 Patients | The Weather Channel – Articles from The Weather Channel…
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.