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Machine learning is one of the most used technologies in this generation. It has varied capabilities that can transform businesses across industries for the better. From being considered as a niche technology, machine learning is now seeing an increased adoption within companies in all sectors.

From a global perspective, brands are leveraging machine learning to accelerate innovation and better customer experience. For example, Nike uses machine learning for personalized product recommendations. In the F&B industry, Dominos maintains its 10 minutes or less pizza delivery time using machine learning technologies. Another widely used example is how automobile giant BMW uses machine learning to analyze data from vehicle subsystems and predicts the performance of vehicle components and recommends when they should be serviced.

In 2020, machine learning became a priority for tech companies in order to achieve revenue growth while reducing costs. In 2021, those companies are now exploring many matured applications of this technology. Disruptive tech organizations have been leading this technology across many areas like process automation, customer experience, and security.

Following the continuing growth trend, these five industries are likely to adopt machine learning to change their business processes in 2021.

The coronavirus global pandemic has highlighted the importance of investing on and optimizing the healthcare systems. Machine learning is being considered as the most promising technology that enables healthcare providers to generate large volumes of data for insightful clinical decisions. Machine learning also enables huge processes in drug discovery, cutting down the long discovery and development time and reducing overall costs. It can also improve healthcare delivery systems to better the overall quality of healthcare under low costs. In the future, machine learning is predicted to be a critical part of clinical trials. Including pharmaceuticals and the biotech industry, machine learning will be having a huge impact in all aspects.

The banking sector is already seeing many advanced use cases of machine learning, especially when it comes to fraud detection and automating processes. Machine learning applications will be proactively explored in areas in trading, investment modeling, risk prevention, and customer sentiment analysis. As countries are making digital transactions their primary mode of payment, machine learning is combining predictive analytics to play a pivotal role in helping financial companies to improve transaction efficiencies within the entire transaction lifecycle. Banks and financial institutions will also use machine learning technology to customize their banking products and offerings to stay up to date in the competitive environment.

Media giants like Amazon and Netflix have already popularized the data-based content consumption channels in recent times. When the world got initially struck with the global pandemic, the demand for new consumption models grew and left companies to leverage their artificial intelligence and machine learning capabilities to create value for the customers. In this process, machine learning is going to be crucial for the media and entertainment industry , whether its developing better recommendation engines, delivering hyper-targeted services, or presenting the most relevant content in real-time. Predictive modeling will also be key in communicating with the customers on time, anticipating their future demands, and making good investments.

The retail industry saw a big shift owing to the coronavirus pandemic. The pandemic has disrupted many traditional practices of this industry and machine learning has become a key enabler of change. From the perspective of brick and mortar stores or e-commerce companies, machine learning is helping this sector reinvent their supply chain, inventory management, predicting user behaviour, and analyzing trends. Dynamic pricing is emerging as a key machine learning application to help retailers thrive in the competitive market.

IoT devices have already flooded this industry and it is only going to increase. Machine learning will be critical to bridge the gaps created by huge amounts of data. It will serve as a building block for the industry along with automation, data connectivity, real-time error detection, supply chain visibility, warehousing efficiency, cost reduction, and asset tracking. Keeping traditional processes aside, machine learning will facilitate innovation and efficiency in the coming days.

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In 2021, Machine Learning Is Set To Transform These 5 Industries - Analytics Insight

For Immediate Release: March 19, 2021

Today, the U.S. Food and Drug Administration issued an emergency use authorization (EUA) for the first machine learning-based Coronavirus Disease 2019 (COVID-19) non-diagnostic screening device that identifies certain biomarkers that are indicative of some types of conditions, such as hypercoagulation (a condition causing blood to clot more easily than normal).

The Tiger Tech COVID Plus Monitor is intended for use by trained personnel to help prevent exposure to and spread of SARS-CoV-2, the virus that causes COVID-19. The device identifies certain biomarkers that may be indicative of SARS-CoV-2 infection as well as other hypercoagulable conditions (such as sepsis or cancer) or hyper-inflammatory states (such as severe allergic reactions), in asymptomatic individuals over the age of 5. The Tiger Tech COVID Plus Monitor is designed for use following a temperature reading that does not meet criteria for fever in settings where temperature check is being conducted in accordance with Centers for Disease Control and Prevention (CDC) and local institutional infection prevention and control guidelines. This device is not a substitute for a COVID-19 diagnostic test and is not intended for use in individuals with symptoms of COVID-19.

The FDA is committed to continuing to support innovative methods to fight the COVID-19 pandemic through new screening tools, said Jeff Shuren, M.D., J.D., director of FDAs Center for Devices and Radiological Health. Combining use of this new screening device, that can indicate the presence of certain biomarkers, with temperature checks could help identify individuals who may be infected with the virus, thus helping to reduce the spread of COVID-19 in a wide variety of public settings, including healthcare facilities, schools, workplaces, theme parks, stadiums and airports. The device is an armband with embedded light sensors and a small computer processor. The armband is wrapped around a persons bare left arm above the elbow during use. The sensors first obtain pulsatile signals from blood flow over a period of three to five minutes. Once the measurement is completed, the processor extracts some key features of the pulsatile signals, such as pulse rate, and feeds them into a probabilistic machine learning model that has been trained to make predictions on whether the individual is showing certain signals, such as hypercoagulation in blood. Hypercoagulation is known to be a common abnormality in COVID-19 patients. The result is provided in the form of different colored lights used to indicate if an individual is demonstrating certain biomarkers, or if the result is inconclusive.

The clinical performance of the Tiger Tech COVID Plus Monitor was studied in hospital and school settings. The hospital study, which was considered a validation study, enrolled 467 asymptomatic individuals, including 69 confirmed positive cases, and demonstrated that the Tiger Tech COVID Plus Monitor had a positive percent agreement (proportion of the COVID-19 positive individuals identified correctly by the device to possess certain biomarkers) of 98.6% and a negative percent agreement (proportion of the COVID-19 negative individuals identified correctly by the device to not possess certain biomarkers) of 94.5%. The school study, which was considered a confirmatory study, showed similar performance.

The Tiger Tech COVID Plus Monitor is not a diagnostic device and must not be used to diagnose or exclude SARS-CoV-2 infection. The device is intended for use on individuals without a fever. An individuals underlying condition may interfere with the COVID-19 related performance of the device and could lead to an incorrect screening result.

The FDA issued the EUA to Tiger Tech Solutions, Inc.

The FDA, an agency within the U.S. Department of Health and Human Services, protects the public health by assuring the safety, effectiveness, and security of human and veterinary drugs, vaccines, and other biological products for human use, and medical devices. The agency also is responsible for the safety and security of our nations food supply, cosmetics, dietary supplements, products that give off electronic radiation, and for regulating tobacco products.

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03/19/2021

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Coronavirus (COVID-19) Update: FDA Authorizes First Machine Learning-Based Screening Device to Identify Certain Biomarkers That May Indicate COVID-19...

OAKLAND, Calif.--(BUSINESS WIRE)-- Dascena, Inc., a machine learning diagnostic algorithm company that is targeting early disease intervention to improve patient care outcomes, today announced the publication in Kidney International Reports of results from a study evaluating the companys machine learning algorithm, PreviseTM, for the earlier prediction of acute kidney injury (AKI). Findings showed that Previse was able to predict the onset of AKI sooner than the standard hospital systems, XGBoost AKI prediction model and the Sequential Organ Failure Assessment (SOFA), up to 48 hours in advance of onset. Previse has previously received Breakthrough Device designation from the U.S. Food and Drug Administration (FDA).

AKI is a severe and complex condition that presents in many hospitalized patients, yet it is often diagnosed too late, resulting in significant kidney injury with no effective treatments to reverse damage and restore kidney function, said David Ledbetter, chief clinical officer of Dascena. If we are able to predict AKI onset earlier, physicians may be able to intervene sooner, reducing the damaging effects. These findings with Previse are exciting and further demonstrate the role we believe machine learning algorithms can play in disease prediction. Further, with Breakthrough Device designation from the FDA, we hope to continue to efficiently advance Previse through clinical studies so that we may be able to positively impact as many patients as possible through earlier detection.

The study was conducted to evaluate the ability of Previse to predict for Stage 2 or 3 AKI, as defined by KDIGO guidelines, compared to XGBoost and SOFA. Using convolutional neural networks (CNN) and patient Electronic Health Record (EHR) data, 12,347 patient encounters were analyzed, and measurements included Area Under the Receiver Operating Characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for advanced prediction of AKI onset. Findings from the study demonstrated that on a hold-out test set, the algorithm attained an AUROC of 0.86, compared to 0.65 and 0.70 for XGBoost and SOFA, respectively, and PPV of 0.24, relative to a cohort AKI prevalence of 7.62%, for long-horizon AKI prediction at a 48-hour window prior to onset.

About Previse

Previse is an algorithm that continuously monitors hospitalized patients and can predict acute kidney injury more than a full day before patients meet the clinical criteria for diagnosis, providing clinicians with ample time to intervene and prevent long-term injury.

About Dascena

Dascena is developing machine learning diagnostic algorithms to enable early disease intervention and improve care outcomes for patients. For more information, visit dascena.com

View source version on businesswire.com: https://www.businesswire.com/news/home/20210316005151/en/

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Dascena Announces Publication of Results From Its Machine Learning Algorithm for Prediction of Acute Kidney Injury in Kidney International Reports -...

The GlobalMachine Learning as a Service (MLaaS)Market 2021 by Company, Regions, Type and Application, Forecast to 2025 introduced byIndustryandresearch.compresents an excellent vision on the global market, delivering a information analysis of distinct factors associated with the report. Likewise, this analysis offers broad insights into technological spending across the forecast period, providing a unique view point on the global Machine Learning as a Service (MLaaS) market across each of the categories including in the survey. The global review of the keyword industry assists clients in assessing business challenges and prospects. The research includes the most recent keyword business forecast analysis for the time period in question.Furthermore, The Machine Learning as a Service (MLaaS) market report delivers a wide range of details of various aspects of the Machine Learning as a Service (MLaaS) industry such as the growth strategies, segmentation by product type, applications, regions, and key players. Along with key players, the Machine Learning as a Service (MLaaS) market report includes company perspectives and marketing strategies of the leading companies.

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Other Features of The Report:

The prominent players of the market are:Microsoft, International Business Machine Corporation, Amazon Web Services, Google, Inc., Bigml, Inc., Fico, Hewlett-Packard Enterprise Development Lp..

The study is segmented by the following product type: ,Small And Medium Enterprises, Large Enterprises

Major applications/end-users industry are as follows: ,Education, Banking And Financial Services, Insurance, Automotive And Transportation, Healthcare

Regions covered in the report:Americas(United States, Canada, Mexico, and Brazil),APAC(China, Japan, Korea, Southeast Asia,India, and Australia),Europe(Germany, France, UK, Italy, Russia, and Spain),and Middle East & Africa(Egypt, South Africa, Israel, Turkey, and GCC Countries).

Impact of COVID-19 on Machine Learning as a Service (MLaaS) Market Industry:The coronavirus downturn is a financial downturn occurring across the world economy in 2021 because of the COVID-19 pandemic. The pandemic could influence three primary parts of the worldwide economy: creation, inventory network, and firms and monetary business sectors. The report offers total form of the Machine Learning as a Service (MLaaS) Market will incorporate the effect of the COVID-19 and foreseen change on the future standpoint of the business, by considering the political, monetary, social and innovative boundaries.

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Global Machine Learning as a Service (MLaaS) Market 2021 Future Growth Prospect, Industry Report And Growing Demand Analysis Till 2025 | Microsoft,...

Thelatest survey on Machine Learning Software MarketIndustry managedvarious organizations of the industry from different geographies or regions.The Report study consists of qualitative and quantitative information highlighting key market developments challenges that industry and competition are facing along with gap analysis, new opportunities available and trend also include COVID-19 impact Analysis in Machine Learning Software Market andimpact various factors resulting in boosting Machine Learning Software Market at global as well as regional level. There are huge competitions that take place worldwide and must require the study ofMARKET ANALYSISsuch as Top Competitors /Top Players are:Microsoft, Google, TensorFlow, Kount, Warwick Analytics, Valohai, Torch, Apache SINGA, AWS, BigML, Figure Eight, Floyd Labs.Porters Five Forces Analysis, impact analysis of covid-19, and SWOT Analysisare also mentioned tounderstand the factors impacting consumer and supplier behavior.

Download FREE PDF Sample Copy of Machine Learning Software Market @https://www.syndicatemarketresearch.com/sample/machine-learning-software-market

Dont miss out on business opportunities in Machine Learning Software Market. Speak to our analyst and gain crucial industry insights that will help your business growth while filling Free PDF Sample Reports

We are here to implement a Free PDF Sample Report copy as per your Research Requirement, also including impact analysisofCOVID-19 on Machine Learning Software Market Size

Key Highlights of the TOC provided by Syndicate Market Research:

Major Product Type of Machine Learning Software Covered in Market Research report:On-Premises, Cloud Based

Application Segments Covered in Market Research Report:Large Enterprises, SMEs

Global Machine Learning Software Industry Market: By Region

North America

Europe

Asia Pacific

Latin America

The Middle East and Africa

Competitive Market Share

In terms of Machine Learning Software market, Microsoft, Google, TensorFlow, Kount, Warwick Analytics, Valohai, Torch, Apache SINGA, AWS, BigML, Figure Eight, Floyd Labs are the top players operating in the global market. These behemoths have implemented key business strategies such as product innovation, strategic partnerships & collaborations, new product launches, new service launches, joint ventures, and contracts to reinforce their market position along with gaining a huge chunk of the market share.

In addition, the report also covers key strategic developments of the market including acquisitions & mergers, new type launch, agreements, partnerships, collaborations & joint ventures, research & development, regional expansion of major participants involved in the Machine Learning Software market on a global and regional basis.

TOC include below Mentioned Featured Points:

Chapter 1:: Report Overview

Chapter 2:: Market Snapshot

2.1 Major Companies Overview

2.2 Machine Learning Software Market Concentration

2.3 Six-Year Compound Annual Growth Rate (CAGR)

Chapter 3:: Value Chain of Machine Learning Software Market

3.1 Upstream

3.2 Downstream

3.3 Porters & Five Forces Analysis and SWOT Analysis

Chapter 4:: Players Profiles

4.1 Company Profiles

4.2 Product Introduction

4.3 Production, Revenue

4.4 SWOT Analysis

Chapter 5:: Global Machine Learning Software Market Analysis by Regions

5.1 Machine Learning Software Market Status and Prospect

5.2 Machine Learning Software Market Size and Growth Rate

5.3 Machine Learning Software Market Local Capacity, Import, Export, Local Consumption Analysis

Chapter 6:: North America Machine Learning Software Market Analysis by Countries

Chapter 7:: China Machine Learning Software Market Analysis by Countries

Chapter 8:: Europe Machine Learning Software Market Analysis by Countries

Chapter 9:: Asia-Pacific Machine Learning Software Market Analysis by Countries

Chapter 10:: India Machine Learning Software Market Analysis by Countries

Chapter 11:: the Middle East and Africa Machine Learning Software Market Analysis by Countries

Chapter 12:: South America Machine Learning Software Market Analysis by Countries

Chapter 13:: Global Machine Learning Software Market Segment by Types

Chapter 14:: Global Machine Learning Software Market Segment by Applications

Chapter 15:: Machine Learning Software Market Forecast by Regions

Chapter 16:: Appendix

https://melvinasmarketblogs.blogspot.com/2021/01/leasing-automation-software-market-2020.html

About Syndicate Market Research:

At Syndicate Market Research, we provide reports about a range of industries such as healthcare & pharma, automotive, IT, insurance, security, packaging, electronics & semiconductors, medical devices, food & beverage, software & services, manufacturing & construction, defense aerospace, agriculture, consumer goods & retailing, and so on. Every aspect of the market is covered in the report along with its regional data. Syndicate Market Research committed to the requirements of our clients, offering tailored solutions best suitable for strategy development and execution to get substantial results. Above this, we will be available for our clients 247.

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Global Machine Learning Software Market Detailed Survey and Outlook Report Shows How Top Companies Is Able to Survive in Future FLA News - FLA News