Category Archives: Machine Learning

TORONTO and JERSEY CITY, N.J., March 10, 2020 /PRNewswire/ -- While educational institutions are trying to evolve and become more adapted to the digital age, colleges and universities have still lagged when it comes to improved processes for cross-border tuition payments. Fortunately, PayMyTuition, a leading provider of technology-driven global payment processing solutions for international tuition payments, announced today its solution to this problem. By way of their newly developed artificial intelligence (AI) and machine learning technology, the PayMyTuition platform solution can now enable colleges and universities to settle international tuition payments in real-time.

"Today, we have the ability to make digital payments instantly from our smart-phones, but until now, to make international tuition payments, both students and educational institutions experience a high level of friction within the customer experience, manual reconciliation processes, and delays in the availability of funds to the institution, hindering students from immediate enrollment access," said Arif Harji, Chief Market Strategist at MTFX Group. "PayMyTuition AI and machine learning technology was developed specifically for educational institutions, providing them an alternative solution that can remove all the friction and restrictions that exist within current offerings, while enabling real-time settlement for the first time."

In the always-on digital environment that we live in, customers expect optimal convenience and digital solutions across the entire payment ecosystem, and the element of real-time settlement has, until now, been lacking.

PayMyTuition enables educational institution student information systems to optimize payment processing methods, giving students payment methods and timing flexibility. This technology will help institutions to reduce costs, prevent errors and improve overall speed with the ability of real-time settlement. The utilization of AI and machine learning technology within the platform will also provide institutions with large and complete amounts of rich data, including student information and payment statuses, of which they didn't have visibility on before, making end-to-end payment transactions simple and transparent.

PayMyTuition's real-time cross border tuition payment solution is an industry first and can be seamlessly integrated, by way of their real-time API, into most student information systems including: Banner, Colleague, PeopleSoft, Workday and Jenzabar.

The company is expanding rapidly, with plans to enable 30 educational institutions across North America with real-time tuition settlement in the next 60 days. PayMyTuition will continue working with customers across the globe to be able to provide unparalleled customer experience to all students, while significant efficiencies are delivered to the institution, now, all in real-time.

For more information, visit http://www.paymytuition.com.

About PayMyTuition by MTFX

PayMyTuition is part of the MTFX Group of Companies, a foreign exchange and global payments solution provider with a track record of 23+ years, facilitating payments for over 8,000 corporate and institutional clients across North America.

Media ContactCrystal ReizePayMyTuition[emailprotected]

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paymytuition-mtfx-group.png PayMyTuition - MTFX Group PayMyTuition is part of the MTFX Group of Companies, a foreign exchange and global payments solution provider with a track record of 23+ years, facilitating payments for over 8,000 corporate and institutional clients across North America.

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http://www.paymytuition.com

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PayMyTuition Develops AI and Machine Learning Technology to Settle Real-Time Cross-Border Tuition Payments for Educational Institutions - PRNewswire

Xtalks Life Science Webinars

TORONTO (PRWEB) March 10, 2020

Flow Imaging is a proven method for the characterization of particulates in therapeutic products. It is routinely performed alongside the United States Pharmacopeia (USP) 788/787 Light Obscuration methods to more accurately quantify and characterize the particle subpopulations in drug products (silicone oil, protein aggregate, extrinsic material, etc.). Typical classifications of imaging data use single parameter filters such as aspect ratio to quantify silicone oil compared to protein. However, machine learning provides a sophisticated approach to more accurately classify particles in therapeutic products by leveraging the information present in the raw particle images.

This free webinar will demonstrate how various machine learning algorithms facilitate improved classification compared to the traditional approach, leading to superior sample descriptions. It will also showcase examples of the benefits that machine learning provides for protein products and cell therapy products. Flow Imaging has tremendous potential to monitor particle size distributions, aggregates/agglomerates and extrinsic contaminants from batch to batch. Applying machine learning to flow imaging of pharmaceutical products can assist in defining the criticality of product quality attributes, as well as establishing an integrated control strategy for characterization and control of drug products.

Join Amber Fradkin, Director, Particle Core Facility, KBI Biopharma in a live webinar on Tuesday, March 24, 2020 at 11am EDT (NA) (3pm GMT/UK).

For more information or to register for this event, visit Machine Learning Applications for the Characterization of Particle Profiles of Therapeutic Products.

ABOUT XTALKS

Xtalks, powered by Honeycomb Worldwide Inc., is a leading provider of educational webinars to the global life science, food and medical device community. Every year thousands of industry practitioners (from life science, food and medical device companies, private & academic research institutions, healthcare centers, etc.) turn to Xtalks for access to quality content. Xtalks helps Life Science professionals stay current with industry developments, trends and regulations. Xtalks webinars also provide perspectives on key issues from top industry thought leaders and service providers.

To learn more about Xtalks visit http://xtalks.comFor information about hosting a webinar visit http://xtalks.com/why-host-a-webinar/

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Machine Learning Applications for the Characterization of Particle Profiles of Therapeutic Products, Upcoming Webinar Hosted by Xtalks - PR Web

Researchers have been working to make mind-controlled prosthetics a reality for at least a decade. In theory, an artificial hand that amputees could control with their mind could restore their ability to carry out all sorts of daily tasks, and dramatically improve their standard of living.

However, until now scientists have faced a major barrier: they havent been able to access nerve signals that are strong or stable enough to send to the bionic limb. Although its possible to get this sort of signal using a brain-machine interface, the procedure to implant one is invasive and costly. And the nerve signals carried by the peripheral nerves that fan out from the brain and spinal cord are too small.

A new implant gets around this problem by using machine learning to amplify these signals. A study, published in Science Translational Medicine today, found that it worked for four amputees for almost a year. It gave them fine control of their prosthetic hands and let them pick up miniature play bricks, grasp items like soda cans, and play Rock, Paper, Scissors.

Sign up for The Algorithm artificial intelligence, demystified

Its the first time researchers have recorded millivolt signals from a nervefar stronger than any previous study.

The strength of this signal allowed the researchers to train algorithms to translate them into movements. The first time we switched it on, it worked immediately, says Paul Cederna, a biomechanics professor at the University of Michigan, who co-led the study. There was no gap between thought and movement.

The procedure for the implant requires one of the amputees peripheral nerves to be cut and stitched up to the muscle. The site heals, developing nerves and blood vessels over three months. Electrodes are then implanted into these sites, allowing a nerve signal to be recorded and passed on to a prosthetic hand in real time. The signals are turned into movements using machine-learning algorithms (the same types that are used for brain-machine interfaces).

Amputees wearing the prosthetic hand were able to control each individual finger and swivel their thumbs, regardless of how recently they had lost their limb. Their nerve signals were recorded for a few minutes to calibrate the algorithms to their individual signals, but after that each implant worked straight away, without any need to recalibrate during the 300 days of testing, according to study co-leader Cynthia Chestek, an associate professor in biomedical engineering at the University of Michigan.

Its just a proof-of-concept study, so it requires further testing to validate the results. The researchers are recruiting amputees for an ongoing clinical trial, funded by DARPA and the National Institutes of Health.

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An implant uses machine learning to give amputees control over prosthetic hands - MIT Technology Review

What You Need to Know:

New Chilmark Research report reveals artificial intelligence and machine learning (AI/ML) technologies are capturing the imagination of investors and healthcare organizationsand are poised to expand healthcare frontiers.

The latest report evaluates over 120 commercial AI/ML solutions in healthcare, explores future opportunities, and assesses obstacles to adoption at scale.

Interest and investment in healthcare AI/ML toolsis booming with approximately $4B in capital funding pouring into thishealthcare sector in 2019. Such investment is spurring a vast array of AI/MLtools for providers, patients, and payers accelerating the possibilities fornew solutions to improve diagnostic accuracy, improve feedback mechanisms, andreduce clinical and administrative errors, according to Chilmark Researchs last report.

The Promise of AI & ML in Healthcare ReportBackground

The report,The Promise of AI & ML in Healthcare, is the most comprehensive report published on this rapidly evolving market with nearly 120 vendors profiled. The report explores opportunities, trends, and the rapidly evolving landscape for vendors, tracing the evolution from early AI/ML use in medical imaging to todays rich array of vendor solutions in medical imaging, business operations, clinical decision support, research and drug development, patient-facing applications, and more. The report also reviews types and applications of AI/ML, explores the substantial challenges of health data collection and use, and considers issues of bias in algorithms, ethical and governance considerations, cybersecurity, and broader implications for business.

Health IT vendors, new start-up ventures, providers, payers,and pharma firms now offer (or are developing) a wide range of solutions for anequally wide range of industry challenges. Our extensive research for thisreport found that nearly 120 companies now offer AI-based healthcare solutionsin four main categories: hospital operations, clinical support, research anddrug development, and patient/consumer engagement.

Report Key Themes

This report features an overview of these major areas of AI/ML use in healthcare. Solutions for hospital operations include tools for revenue cycle management, applications to detect fraud detection and ensure payment integrity, administrative and supply chain applications to improve hospital operations, and algorithms to boost patient safety. Population health management is an area ripe in AI/ML innovation, with predictive analytics solutions devoted to risk stratification, care management, and patient engagement.

A significant development is underway in AI/ML solutions for clinical decision support, including NLP- and voice-enabled clinical documentation applications, sophisticated AI-based medical imaging and pathology tools, and electronic health records management tools to mitigate provider burnout. AI/ML-enabled tools are optimizing research and drug development by improving clinical trials and patient monitoring, modeling drug simulations, and enabling precision medicine advancement. A wealth of consumer-facing AI/ML applications, such as chatbots, wearables, and symptom checkers, are available and in development.

Provider organizations will find this report offers deep insight into current and forthcoming solutions that can help support business operations, population health management, and clinical decision support. Current and prospective vendors of AI/ML solutions and their investors will find this reports overview of the current market valuable in mapping their own product strategy. Researchers and drug developers will benefit from the discussion of current AI/ML applications and future possibilities in precision medicine, clinical trials, drug discovery, and basic research. Providers and patient advocates will gain valuable insight into patient-facing tools currently available and in development.

All stakeholders in healthcare technologyproviders, payers, pharmaceutical stakeholders, consultants, investors, patient advocates, and government representativeswill benefit from a thorough overview of current offerings as well as thoughtful discussions of bias in data collection and underlying algorithms, cyber-security, governance, and ethical concerns.

For more information about the report, please visit https://www.chilmarkresearch.com/chilmark_report/the-promise-of-ai-and-ml-in-healthcare-opportunities-challenges-and-vendor-landscape/

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Chilmark Research: The Promise of AI & ML in Healthcare Report - HIT Consultant

Preamble: Intermittently, I will be introducing some columns which introduce some seemingly outlandish concepts. The purpose is a bit of humor, but also to provoke some thought. Enjoy.

Zodiac signs inside of horoscope circle.

Historically, astrology has been a major component of the cultural life in many major civilizations. Significant events such as marriage, moving into a new home, or even travel were planned with astrology in mind. Even in modern times, astrological internet sites enjoy great success and the gurus of the art publish in major newspapers.

Of course, with the advent of scientific methods and formal education, astrology has rapidly lost favor in intellectual society. After all, what could possibly be the causal relationship between the movement of planets and whether someone will get a job promotion? As some have pointed out, even if there was a relationship, the configuration of the stars change, so how could the predictions of the past possibly be valid ?

Pure poppycock. Right? Perhaps. Lets take a deeper look.

Lets consider the central technology at the apex of current intellectual achievement : machine learning. Machine learning is the engine underlying important technologies such as autonomous vehicles including Teslas AutoDrive. What is machine learning at its core? One looks at massive amounts of data and trains a computational engine (ML engine). This ML engine is then used to make future predictions. Sometimes, the training is done in a constrained manner where one looks at particular items, and other times, the training is left unconstrained. Machine learning and the associated field of Artificial Intelligence (AI) is at the forefront of computer science research. Indeed, as we have discussed in past articles, AI is considered to be the next big economic mega-driver in a vast number of markets. After looking at machine learning, an interesting thought comes to mind.

Was astrology really just machine learning done by humans?

Could the thought leaders from great civilizations have looked at large amounts of human behavioral data and used something very reasonable (planetary movements) to train the astrology engine? After all, what really is the difference between machine learning and astrology?

Marketing Chart Comparing Astrology and Machine Learning

Both astrology and machine learning seem to have a concept of training. In astrology, the astrological signs are used as points of interest, and seemingly arbitrary connections are made to individual human circumstances. Even without the understanding of causality, the correlations can be somewhat true. In machine learning, data correlations are discovered, and there is no requirement of causation. This thought process is central to the machine learning paradigm, and gives it much of its power. In fact, as the chart above shows, there are uncomfortable levels of parallels between astrology and machine learning.

What does this mean? Should we take machine learning a little less seriously? Certainly, some caution is warranted, but it appears to be clear that machine learning can provide utility.

So, what about astrology? Perhaps we should take it a bit more seriously .

If you enjoyed this article, you may also enjoy A Better Transportation Option Than A Tesla.

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The Connection Between Astrology And Your Tesla AutoDrive - Forbes

The Machine Learning as a Service market report presents an in-depth assessment of the Machine Learning as a Service together with market drivers, challenges, enabling technologies, applications, key trends, standardization, regulative landscape, case studies, opportunities, future roadmap, worth chain, system player profiles and techniques. The study provides historic data form 2015 to 2019 along with forecast from 2020 to 2025 based on sales (volume and value) and revenue (USD Million). During a recently published report by Reportspedia.com, the global Machine Learning as a Service market is predicted to register a high CAGR during the Forecast period.

The study demonstrates market dynamics that are expected to influence this challenges and future standing of the global Machine Learning as a Service market over the forecast period. This report also offers updates on manufacturers, trends, drivers, restraints, worth forecasts, and opportunities for makers in operation within the global and regional Machine Learning as a Service market.

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Key Players:

GoogleIBM CorporationMicrosoft CorporationAmazon Web ServicesBigMLFICOYottamine AnalyticsErsatz LabsPredictron LabsH2O.aiAT&TSift Science

The key regions and countries covered in this report are:

Assessment of the Machine Learning as a Service Market

The study by Reportspedia.com is a comprehensive analysis of the various factors that are likely to influence the growth of the market. The historical and current market trends are taken into consideration while predicting the future prospects of the market.

The investors, stakeholders, emerging and well-known players can influence the data included in the report to develop impactful growth strategies and improve their position in the current market landscape. The report provides a thorough assessment of the micro and macro-economic factors that are expected to impact the growth of the Machine Learning as a Service Market.

Global Machine Learning as a Service market size by type

Software ToolsCloud and Web-based Application Programming Interface (APIs)Other

The 2020 series of global Machine Learning as a Service market size, share, and outlook and growth prospects is a comprehensive analysis on global market conditions.

Global Machine Learning as a Service market share by applications

ManufacturingRetailHealthcare & Life SciencesTelecomBFSIOther (Energy & Utilities, Education, Government)

Amidst increasing emphasis on new applications and stagnant growth of conventional large applications, the report presents in-depth insights into each of the leading Machine Learning as a Service end user verticals along with annual forecasts to 2025

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Table of Contents for market shares by application, research objectives, market sections by type and forecast years considered.

The report addresses the following queries related to the Machine Learning as a Service Market

Table of Content:

1 Machine Learning as a Service Market Survey

2 Executive Synopsis

3 Global Machine Learning as a Service Market Race by Manufacturers

4 Global Machine Learning as a Service Production Market Share by Regions

5 Global Machine Learning as a Service Consumption by Regions

6 Global Machine Learning as a Service Production, Revenue, Price Trend by Type

7 Global Machine Learning as a Service Market Analysis by Applications

8 Machine Learning as a Service Manufacturing Cost Examination

9 Advertising Channel, Suppliers and Clienteles

10 Market Dynamics

11 Global Machine Learning as a Service Market Estimate

12 Investigations and Conclusion

13 Important Findings in the Global Machine Learning as a Service Study

14 Appendixes

15 company Profile

Continued.

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Global Machine Learning as a Service Market: Industrial Output, Import & Export, Consumer Consumption And Forecast 2025 - News Times