Category Archives: Machine Learning
Leveraging digital innovation to work smarter and streamline business – Crain’s Cleveland Business
When the pandemic thrust the world into a work-from-home reality, many businesses were unprepared to take their operations virtual. From paper-based payroll to manual collections, automation and digitization of operations could have alleviated many pain points, streamlined operations and increased efficiencies to keep business moving.The financial services industry is among those harnessing the power of artificial intelligence and machine learning to help customers reduce fraud exposure, execute faster payments and provide data-driven business insights.
Though artificial intelligence and machine learning sound futuristic, the capabilities are widely available now and can enable significant improvement in business operations, said Pat Pastore, PNC regional president of Cleveland.Chris Ward, PNCs executive vice president and head of data, digital and innovations for PNC Treasury Management, said artificial intelligence and machine learning drive the three important Is of the economy immediacy; interconnectivity; and interruption.
To that end, digital innovation, he said, helps companies make decisions faster, create integrated experiences and interrupt their own business or that of their competitors.
CASH FORECASTING
Until now, the cash forecasting process has been a manual, labor intensive, time-consuming process.
PNCs Cash Forecasting solution, for example, leverages artificial intelligence, machine learning and a companys historical data to produce a 31-day rolling cash forecast.
The module can help treasurers predict future cash flow, reduce version control issues, plan for a gap or surplus, and ultimately, provide better insights on current and future cash positions. Ward said the solution can be tailored, integrating with a companys existing systems as a standalone offering, or combined with PNCs PINACLE corporate online and mobile banking platform.
REAL-TIME PAYMENTS
In the business-to-business payments space, Ward said more than half of payments are still made by check. That can be a problem under crisis conditions.
The RTP network from The Clearing House, the first new payment system in 40 years, is a real-time payments platform for account-to-account transactions. What sets RTP apart from other payment systems, Ward said, is that it delivers payments 24/7/365, providing immediacy and interconnectedness.
AI and machine learning-enabled technologies are crucial for monitoring and analyzing transactions in the fraction of a second necessary to facilitate real-time payments, Ward said.
RECEIVABLES AUTOMATION
Receivables automation enables companies to streamline the receipt of payments from customers, reduce costs and maximize cash flow. Ward noted that as the pace of business increases and companies continue to embrace efficiencies, automating the often complex receivables process allows companies to consolidate traditional checks and all electronic payment formats into a single remittance stream.
With access to virtual batching, integrated remote deposit capture and workflow management, companies can minimize exceptions and operate with greater efficiency.
THE FUTURE
Pastore said competition is increasing every day and businesses are searching for ways to improve operations. This has only been compounded by the pandemic.
PNC has advanced technology to help companies become more efficient and, in turn, competitive.
From an industry perspective, Pastore and Ward said to expect increased adoption in the coming years as more businesses leverage advanced technology to make decisions and resolve problems.
This technology is so important, because even without AI and machine learning, people would still be trying to find ways to move money faster, Ward said.
PNC and PINACLE are registered marks of The PNC Financial Services Group, Inc. (PNC). RTP is a registered trademark of The Clearing House Payments Company, LLC.
2021 The PNC Financial Services Group, Inc. All rights reserved.
To view the print PDF, Click HERE.
Read more:
Leveraging digital innovation to work smarter and streamline business - Crain's Cleveland Business
Leveraging Machine Learning to Optimize CO2 Adsorption – Techiexpert.com – TechiExpert.com
Scientists employ artificial intelligence to guide the design of biomass waste-derived novel materials for CO2capture
Biomass waste can be used to produce porous carbons capable of sequestering CO2gas emitted from large point sources (e.g., power plants, cement industries). However, there are no general guidelines on how such high-quality porous carbons should be synthesized or their optimal operational conditions. In a recent study, scientists employed machine learning-based method to determine which core factors should be prioritized in biomass waste-derived porous carbons to achieve the best CO2adsorption performance, paving the way to a circular economy.
If we are to mitigate climate change, we must find cost-effective and sustainable ways to reduce industrial carbon dioxide (CO2) emissions. Unfortunately, most well-established methods for carbon capture and storage (CCS) in industrial post-combustion sources bear significant downsides, such as a high cost, environmental toxicity, or durability issues. Against this backdrop, many researchers have focused on what may be our best bet for next-generation CCS systems: CO2adsorption using solid porous carbon materials.
One notorious advantage of using porous carbons for CO2sequestration is that they can be produced from biomass waste, such as agricultural waste, food waste, animal waste, and forest debris. This makes biomass waste-derived porous carbons (BWDPCs) attractive not only due to their low cost, but also because they provide an alternative way to put biomass waste to good use. Although BWDPCs could definitely bring us closer to a circular economy, this field of study is relatively young, and no clear guidelines or consensus exist between scientists as to how BWDPCs should be synthesized or what material properties and compositions they should strive for.
Could artificial intelligence (AI) help us out in this conundrum? In a recent studypublished inEnvironmental Science and Technology, a collaborative research team from Korea University and the National University of Singapore employed a machine learning-based approach that may guide the development of future porous carbon synthesis strategies. The scientists noted that there are three core factors influencing the CO2adsorption properties in BWDPCs: the elemental composition of the porous solid, its textural properties, and the adsorption parameters at which it operates, such as temperature and pressure. However, how these core factors should be prioritized when developing BWDPCs has remained unclear, until now.
To help settle this matter, the team first conducted a literature review and selected 76 publications describing both the synthesis and performance of various BWDPCs. After curation, these papers provided over 500 datapoints that were used to train and test three tree-based models. The main purpose of our work was to elucidate how machine learning tools can be leveraged for predictive analytics and used to draw valuable insights into the process of CO2adsorption using BWDPCs, explains Professor Yong Sik Ok from Korea University, who led the study.
The input features of the models were the three core factors, whereas the output was the level of CO2adsorption. Although the models themselves become essentially black boxes after the training process, they can be used to make accurate predictions on the performance of BWDPCs based solely on the core factors considered. Most importantly, through feature analyses, the research team determined the relative importance of each of the input features for making accurate predictions. In other words, they established which of the core factors is the most important to achieve high CO2adsorption. The results indicate that the adsorption parameters contributed much more than the other two core factors for the models to make correct predictions, underlining the importance of optimizing operational conditions first. The textural properties of the BWDPCs, such as their pore size and surface area, came in second place, and their elemental composition came last.
Worth noting, the predictions of the models and the results of the feature importance analyses were backed by existing literature and our current understanding of the mechanisms behind the CO2capture process. This cemented the real-world applicability of this data-driven strategy not only for BWDPCs, but for other types of materials, as Prof. Ok explains, Our modeling approach is cross-deployable and can be used to investigate other types of porous carbons for CO2adsorption, such as zeolites and metalorganic frameworks, and not just those derived from biomass waste.
The team now plans to devise a synthesis strategy for BWDPCs by focusing on optimizing the two most important core factors. Moreover, they will keep adding experimental data points to the database used in this study and make it open source so that the research community may also benefit from it.
Let us hope that all these efforts lead us to truly sustainable societies that can stop climate change and achieve the UN Sustainable Development Goals, such asGoal 13: Climate Action.
Read the original:
Leveraging Machine Learning to Optimize CO2 Adsorption - Techiexpert.com - TechiExpert.com
Cogitativo Releases Visin, A First-Of-Its-Kind Machine Learning Tool Built to Tackle the Growing Deferred Care Crisis – PRNewswire
Machine Learning to assist in addressing deferred care crisis
"Millions of Americans have gone without critical screenings and treatment for 18 months, creating a deferred care crisis that requires immediate and proven solutions to support those in need," said Gary Velasquez, CEO of Cogitativo. "We believe Visin will play a vital role in preventing acute medical events for vulnerable individuals and enabling health care organizations to mitigate many of the challenges that are on the horizon."
Cogitativo's new solution comes as health care payors and providers are reporting a rise in medical needs among individuals who were unable to receive care during the pandemic, including those with chronic conditions like cardiovascular disease, chronic kidney disease, diabetes, HIV, and mental health challenges. In addition, many providers are also struggling to manage a surge in patient visits, with the virus continuing to spread at the same time that people are returning to medical facilities for appointments, screenings, and treatment.
Visin analyzes patient health records through the lens of peer-reviewed literature on disease progression, social determinants of health, climate change, and other relevant data sources to predict elevated risk for an acute clinical event. These temporal-based predictions will enable healthcare payors and providers to identify members and patients most likely to require greater medical attention in the months ahead. This information will, in turn, facilitate health care payors and providers to proactively conduct outreach and render prophylactic care to at-risk beneficiaries and offer individualized recommendations on preventive care.
A version of Cogitativo's new machine learning platform was used by a host of health care leaders and public health officials during the pandemic. For example, Blue Shield of California used it to deliver personalized care and support to vulnerable beneficiaries; it helped guide mobile vaccination efforts in the City of Compton, California; and it provided insights to the U.S. Department of Health and Human Services.
"Visin is the field-tested machine learning tool that so many health care payors have been waiting for, and it cannot come soon enough for those managing the fallout from the deferred care crisis," said Dr. Terry Gilliland, Chief Science Officer at Cogitativo and former Executive Vice President of Health Care Quality and Affordability at Blue Shield of California.
"Cogitativo's new machine learning tool can help physicians throughout the country identify their highest-risk patients and conduct proactive outreach, providing those patients the critical care and attention they need while also preventing unpredictable waves of patient visits that create capacity problems," said Dr. Hector Flores, Director of the Family Care Specialists Medical Group. Dr. Flores used a version of Visin during the pandemic to support his most vulnerable patients.
About Cogitativo Inc.Cogitativo is a Berkeley-based data science company founded in 2015 with a mission to create and implement innovative, scalable solutions to the most complex challenges facing the healthcare system. Leveraging machine learning, proprietary data sets, and expertise from leaders with decades of experience working with public health agencies, Cogitativo can deliver actionable insights and save lives. To date, Cogitativo has successfully applied data science solutions to more than 200 unique operational challenges to significantly improve the efficiency of our healthcare systems and protect vulnerable patients and communities. Visitwww.cogitativo.comfor more information.
Media Contact:Joshua Rosen[emailprotected]Phone: (610) 2473482
Company Contact:Amy Domangue[emailprotected] Phone: (225) 337 -6402
SOURCE Cogitativo, Inc.
Dive Deep Into Machine Learning With Over 75 Hours Of Expert Led Training – IGN SOUTH EAST ASIA
As we push forward into the future, it seems more and more certain that artificial intelligence and machine learning are going to be massive pieces of our collective future. Continuously producing and conceiving countless breakthroughs,new technologies, and industry-changing developments the world of AI and machine learning is rife with potential for new minds to help build and shape tomorrow. If you'd like to join the party, there's a lot to learn.
One way to dive deep into these pivotal technologies is to take advantage of this deal onThe Premium Machine Learning Artificial Intelligence Super Bundle, which is on sale for $36.99 (reg. $2,388). This nearly 80-hour collection of courses and lessons breaks down fundamental lessons on deep learning, machine learning, Python, and other development tools used to help grow these sections of the tech industry.
Upon subscribing and taking advantage of this incredible, you'll begin with Machine Learning with Python, which is a course that teaches you the fundamentals of machine learning with Python. In this practical, hands-on course you'll get the foundational lessons and examples on approaching data processing, linear regression, logistic regression, decision trees, and more. This course is taught by Juan E. Galvan, who is a top instructor, digital entrepreneur, and recipient of a 4.4/5 star instructor rating.
These are some of the other courses included in the attractive Premium Machine Learning Artificial Intelligence Super Bundle: The Machine Learning and Data Science Developer Certification Program, The Complete Machine Learning & Data Science with Python A-Z, and Deep Learning with Python. Each of these well-reviewed and well-curated courses will help you on your path to becoming an informed player in the growing world of AI and machine learning.
Don't miss your chance to grabThe Premium Machine Learning Artificial Intelligence Super Bundlefor only $36.99 (reg. $2,388).
Go here to see the original:
Dive Deep Into Machine Learning With Over 75 Hours Of Expert Led Training - IGN SOUTH EAST ASIA
Golden Gate University and MetricStream Bring Together Machine Learning and Edge Computing to Assess and Mitigate Risk in Enterprise Business…
SAN FRANCISCO, Sept. 16, 2021 /PRNewswire/ -- MetricStream, the industry leader in supporting the Governance, Risk, and Compliance (GRC) space, and Golden Gate University, announced the successful completion of the first phase of their "DeepEdge" project, using emerging technologies to bring innovation to business solutions.
The project started in 2019 with the goal of letting GGU faculty and graduate students in the MS in Business Analytics and MS in Information Technologies programs partner with MetricStream employees to develop new risk management solutions. The teams set out to use emerging model-based AI augmented with Machine Learning, Elastic Edge Computing, Agile methodologies maturing to DevOps, and Zero-touch Self-Managing service orchestration. The teams have successfully implemented the first application to assess and mitigate risk in enterprise contract management process, resulting in MetricStream adopting it as a part of their product suite.
"Contracts are legally binding agreements," said Vidya Phalke, Chief Technology Evangelist at MetricStream. "Knowing the obligations for every contract, monitoring and assuring compliance is labor-intensive process and error-prone. The DeepEdge project uses model-based AI, machine learning and automation of extracting the knowledge of the obligations for every contract. It integrates with processes already in place and improves monitoring and contract obligation fulfillment at scale. This type of industry-academia collaboration is what is needed to power what is next in the post-pandemic world."
Judith Lee, Business Innovation & Technology department chair, said the project "allowed graduate students and GGU faculty to work jointly with MetricStream to push the boundaries of machine learning and edge computing technologies."
"We chose edge computing for security and data privacy reasons, and the deployment was facilitated by a zero-touch operations environment supported by Platina Systems," said Ross Millerick, program director, MS/IT Management. "It allowed us to remotely access the infrastructure at MetricStream during the Covid pandemic, when our laboratory on campus was not available."
"Bringing together thought leadership in AI that goes beyond deep learning and edge computing allows us to teach our students how to push the boundaries with federated AI and edge computing" said Rao Mikkilineni, distinguished adjunct professor.
The project spanned five terms and a succession of students. The students completed their capstone obligation with the project output with support from MetricStream. The project will continue to drive innovation in various enterprise business processes. Its vision is to build a long-term mutually beneficial partnership between the GGU business school and MetricStream, to inform the surrounding business community about the importance of GRC, and to provide an ongoing local forum for dialogue and education.
Leveraging the power of AI, MetricStream is the global market leader in Governance, Risk, and Compliance and Integrated Risk Management solutions, providing the most comprehensive solutions for Enterprise and Operational Risk, Regulatory Compliance, Internal Audit, IT and Cyber Risk and Third-Party Risk Management on one single integrated platform.
Golden Gate University, a private nonprofit, has been helping adults achieve their professional goals by providing undergraduate and graduate education in accounting, law, taxation, business and related areas since 1901. Programs offer maximum flexibility with evening, weekend and online options. GGU is accredited by the American Bar Association (ABA) and the WASC Senior College and University Commission.
Media Contacts: For MetricStream Amy Rhodes, [emailprotected]; For GGU: Judith Lee [emailprotected],edu,Michael Bazeley, [emailprotected]
SOURCE Golden Gate University
Go here to see the original:
Golden Gate University and MetricStream Bring Together Machine Learning and Edge Computing to Assess and Mitigate Risk in Enterprise Business...
AI, machine learning will change the way we live – The Hindu
Artificial Intelligence (AI) and Machine Learning (ML) will change the way we live, and virtual cloud-enabled seamless data connectivity would usher in a digital revolution by 2040, said D. Narayana Rao, Pro-Vice Chancellor of SRM University.
A senior scientist in the field of Atmospheric Science Research and Radar Technology and former Director, National Atmospheric Research Laboratory, Dr. Narayana Rao said that just as the world has seen revolutionary changes between 2000 and 2020, current technologies would become obsolete by the year 2040.
AI and MI are some of the future technologies that are going to shape our lives in the next two decades, Dr. Narayana Rao said during the celebrations of Engineers Day on September 15, held to mark the birth anniversary of Bharat Ratna Mokshagundam Visvesvaraya.
Data and information will be available virtually as air. Everything on the go will take on a literal meaning and the word connect will be meaningless for most of our gadgets. Data will just move seamlessly whether you are in an elevator, car or an aeroplane, he said.
AI and ML will make us believe that the world revolves around us. As we talk, discuss, act, AI will surround us with actions and suggestions and actionable inputs at a wink. AI will resemble Real Intelligence (RI). Driverless and automated intelligent cars will move around by themselves and self-park. Peoples job profiles will change. They will need to work less and most routine and hazardous work will be carried out by robots. Typing on gadgets will be redundant and will be replaced by voice commands, gestures and even thought controls. Natural Language Processing (NLP) will remove the language barriers in trade and travel. NLP will do the translation of spoken language and will ensure a global world, Dr. Narayana Rao said.
Space tourism will turn from fantasy into a reality. Holiday tours to Switzerland, Bali, and Seychelles will be replaced by tours to Venus, Mars and the moon, he said, adding that 3D printing technology will be used to construct buildings, structures and several products within a few hours/days which presently takes months and years to do.
When our country became independent, India was the poorest of the poor countries with a literacy rate of just around 12% and a life expectancy of 32 years. Today, in 70 years, India has become one of the top five economies in the world. What made this remarkable transformation possible was the application of science and technology in building the nation, Dr. Narayana Rao said.
Originally posted here:
AI, machine learning will change the way we live - The Hindu
UI professor uses machine learning to indicate a body shape-income relationship – UI The Daily Iowan
University of Iowa professor Suyong Song and University of Virginia professor Steven Baek used old Air Force data to analyze the correlation between physical attractiveness and family income.
New research conducted by a professor in the University of Iowa Tippie College of Business shows evidence of a beauty premium, where someone who is more physically attractive based on modern beauty standards is paid more.
Professor of Economics Suyong Songs study used data from 20-year-old research conducted by the U.S. Air Force, which included a socioeconomic survey and 3D full-body scans to gather precise measurements from participants.
Song said stature, or height, was positively correlated with family income for males. There was a negative correlation between obesity and family income for females, he said. The study maintained factors such as occupation, education level, and location of participants to ensure accurate results.
The data indicates a beauty premium, Song said, where someone who is more physically attractive based on modern beauty standards is paid more. When two individuals share the same abilities or level of education, he said the taller male makes an estimated $998 more on average annually than the shorter male.
For females, Song said a female with a smaller Body Mass Index would end up with a higher family income compared to a more overweight coworker by more than $930 annually. Both correlations were discovered in families that earn $70,000 per year.
Song said these characteristics height and obesity level were shared between both male and female data, but female hip-to-waist ratio emerged as a third important feature correlated with family income.
The Air Force data was collected during the Air Forces Civilian American and European Surface Anthropometry Resource project from participants in North America, Italy, and the Netherlands.
Song said traditional studies of the beauty premium rely on self-reported measurements, which could create reporting errors.
Instead of relying on surveyed, individual appearances, we can utilize this measured, three-dimensional scan data, Song said. We kind of resolve the issue of the reporting error in the existing literature.
Steven Baek, an associate professor of data science at the University of Virginia and founder of the UI Visual Intelligence Laboratory, partnered with Song on the research study.
Baek said the research was unique because the 3D scans provided more than 100,000 data points across each participants body. To analyze this unique amount of data, Baek and Song used machine-learning technology.
The role of technology was essentially to enable more accurate quantification, more accurate representation of human body shape using the tools of machine-learning, Baek said.
The algorithm, he said, was responsible for creating its own ways of describing the body shapes of the participants, to remove researcher subjectivity from the equation.
Song said in more traditional studies of the beauty premium, researchers delivered very subjective evaluations of someones beauty based on survey questions about attractive facial or physical beauty.
The machine-learning technology helped Song and Baek compress and analyze a massive amount of extremely accurate data, Baek said.
Song said he believes more workplace awareness and education about the beauty premium could help to alleviate the correlation between physical attractiveness and family income that the study details. He said interviews conducted over the phone or virtually could help reduce the correlation, but wouldnt entirely solve the problem.
You cant tell Im six feet tall through this virtual interview, Song said in a phone interview with The Daily Iowan. [Through] this kind of virtual setting or phone call setting, we can have partially reduced implicit bias in the hiring process. Its not perfect either, because the hiring process is not the only reason why we have this beauty premium.
Dana Dominguez, the associate director of operations and communication at the UIs Pomerantz Career Center, wrote they hired a new staff member in August whose role is to work with employers on increasing awareness of disparities such as the beauty premium.
She said the center has also been involved in helping their employer partners recognize and learn from statistics and research about these biases.
Dominguez wrote in an email to the DIthat the center is working to educate organizations on bias in recruitment, hiring, and other important decisions in the workplace, and ultimately reduce the gaps and disparities that may be present within their own organizations.
Baek said given the complexity of this issue, there is no single solution that exists. The first step is to recognize and understand that there is this income disparity between more physically attractive and less physically attractive individuals, he said.
I dont believe that my mission is to provide the solutions, Baek said. But rather, its more on raising the issue and throwing out questions to policy-makers and corporate leadership, so that they can start thinking about what changes to make.
Excerpt from:
UI professor uses machine learning to indicate a body shape-income relationship - UI The Daily Iowan
Machine Learning as a Service (MLaaS) Market Big Things Are Happening In Technology and Media Industry up to 2031 – Digital Journal
Pune, Maharashtra, India, September 15 2021 (Wiredrelease) Prudour Pvt. Ltd :Machine Learning as a Service (MLaaS) Market (News 2021) Entry, Expansion, and Business Strategies Forecasts By 2031, issued by Market.us is a globally trusted and knowledge base firm in the market. As it presents an encyclopedic market size outline and alternative in-depth market description features like market growth-supporting factors, controlling factors, trends, opportunities, market risk factors, forward-looking Machine Learning as a Service (MLaaS) market view competition, product and services advancements, and launches, product/services connected rules review, and up to date developments for the mentioned forecast amount.
Additionally, the report provides a key examination of market players operative within the specific Machine Learning as a Service (MLaaS) market 2021 and analysis and outcomes connected with the target marketplace. The report covers a brief on these trends that can benefit the manufacturers working in the industry to understand the Machine Learning as a Service (MLaaS) market and strategist for their business expansion accordingly. The Machine Learning as a Service (MLaaS) research report analyzes the market size, business share, growth, essential segments, CAGR, and key drivers.
The industry experts have identified the major factors impacting the development rate of the Machine Learning as a Service (MLaaS) industry including various opportunities and gaps. A thorough analysis of the Machine Learning as a Service (MLaaS) markets with regards to the growth trends in each category makes the overall study interesting. When studying the Machine Learning as a Service (MLaaS) market the researchers also dig deep into their future prospect and contribution to the Machine Learning as a Service (MLaaS) industry. Moreover, the research report assessed market key players and features such as capacity utilization rate, consisting of revenue.
Know more about the global trends impacting the future, download a PDF sample:https://market.us/report/machine-learning-as-a-service-mlaas-market/request-sample/
The analysis objectives of the report are:
1. To research and forecast the size of Machine Learning as a Service (MLaaS) industry in the global sector.
2. To evaluate the major global players, PESTEL analysis, dignity, and global Machine Learning as a Service (MLaaS) market share for major players.
3. To determine, illuminate and predict the industry by type, end-use, and also geography.
4. Investigate and analyze global Machine Learning as a Service (MLaaS) industrial status and forecast including key regions.
5. To find out which major global regions have further benefits and potential, challenges and opportunities, obstacles and dangers.
6. To determine important trends and Machine Learning as a Service (MLaaS) factors driving the development of the sector.
7. Review the market opportunities for stakeholders by identifying the higher growth sections.
8. To analyze each Machine Learning as a Service (MLaaS) market segment related to the individual expansion trend and their participation in the market.
9. To analyze competitive developments, for example, extensions, arrangements, new product launches, and market acquisitions.
10. Profile the Machine Learning as a Service (MLaaS) key players and analyze their growth plans.
Planning to lay down future strategy? speak with a market.us analyst to learn [emailprotected]https://market.us/report/machine-learning-as-a-service-mlaas-market/#inquiry
Scale and share of Machine Learning as a Service (MLaaS) Market Analysis:
Evaluation of Dominant Market 2021 Players:
Google, IBM Corporation, Microsoft Corporation, Amazon Web Services, BigML, FICO, Yottamine Analytics, Ersatz Labs, Predictron Labs, H2O.ai, AT and T, Sift Science
Machine Learning as a Service (MLaaS) Market Segment By Types, Estimates and Forecast by 2031
Software Tools, Cloud and Web-based Application Programming Interface (APIs), Other
Machine Learning as a Service (MLaaS) Market Segment By Applications, Estimates and Forecastby 2031
Manufacturing, Retail, Healthcare and Life Sciences, Telecom, BFSI, Other (Energy and Utilities, Education, Government)
In this study, the years considered to estimate the market size of Machine Learning as a Service (MLaaS) 2021 are as follows:
Historic Year: 2015-2020
Base Year: 2021
Forecast Year: 2022-2031
Geographically, the market has been bifurcated into four major regions, which covers
Europe (Germany, France, UK, Italy, Russia, Spain)
Americas (United States, Canada, Mexico, Brazil)
Middle East & Africa (Egypt, South Africa, Israel, Turkey, GCC Countries)
APAC (China, Japan, Korea, Southeast Asia, India, Australia)
Recommended reading, new updates ofMachine Learning as a Service (MLaaS)[emailprotected]https://market.us/report/machine-learning-as-a-service-mlaas-market/
Key Reasons toInvest in Machine Learning as a Service (MLaaS) Market Report:
1. To provide the complete structure and fundamental overview of Machine Learning as a Service (MLaaS) Industry Market.
2. To offer insights into vital Machine Learning as a Service (MLaaS) aspects like growth trajectory, CAGR value, market share, and revenue analysis.
3. To evaluate the growth opportunities, threats, market drivers, and risks involved.
4. To understand the global Machine Learning as a Service (MLaaS) market competition by analyzing the top business people, with their market profile, import-export details, revenue, profits, and market share.
5. To represent the pricing structure, import-export details, supply chain analysis, SWOT analysis to facilitate the key decision-making process.
6. To promote the ultimate growth, investment analysis, and upcoming growth opportunities with the analysis of emerging Machine Learning as a Service (MLaaS) market segments and sub-segments.
7. To understand the knowledge sources, intended research methodology, and important conclusions.
(FAQ) Questions answered in this research report:
1. What are the country revenue and forecast breakdowns? Which are the major country revenue pockets for growth in the Machine Learning as a Service (MLaaS) market?
2. At what pace is the Machine Learning as a Service (MLaaS) market growing, globally? What will be a growing trend in the future?
3. What are the various application areas and how they are poised to grow?
4. Who are the top 5 market key players?
5. What are the key drivers and inhibitors in the current market? What will be the impact of drivers and inhibitors in the future?
6. How is the market predicted to develop in the future?
To boost your business, read tech market reports news [emailprotected]https://techmarketreports.com/news/
Note: If you have any special requirements, please let us know and we will offer you the report as you want.
Media Contact
Company Name:Market.us(Powered By Prudour Pvt. Ltd.)
Contact Person: Lawrence John
Email:[emailprotected]
Phone: +1 718 618 4351
Address: 420 Lexington Avenue, Suite 300 New York City, NY 10170, United States
Connect with us atLinkedIn|Facebook|Twitter
Explore ourtop report from trusted publications
Automotive Exhaust Gas Recirculation Systems Market Size Advanced Technologies & Growth Opportunities In Global Industry By 2031
Vehicle Motors Market to Witness Regular Growth Throughout 2022-2031
Vacuum Filtration Equipment Market 2021 Business Challenges, Enterprise Overview and Forecast Analysis Examine 2031
Water Fire Extinguishers Market Efficiency Evaluation by Covid-19 Impression Perspective with Element Forecast by 2031
Portable Dissolved Oxygen Meter Market Pattern Evaluation and Main Elements Forecast Report until 2031
Plating Lines Market Report By Growth Enablers, Geography, Restraints And Trends Global Forecast To 2031
Flat Slings Market To Showcase An Annual Healthy Growth Rate Over 2021-2031
Portable Generator Set Market By Latest Trend, Growing Demand And Technology Advancement 2021-2031
Underground Mining Machinery Market With (COVID-19) Affect Evaluation, Trade Demand, Developments, Forecast To 2031
This content has been published by Prudour Pvt. Ltd company. The WiredRelease News Department was not involved in the creation of this content. For press release service enquiry, please reach us at [emailprotected].
Read more here:
Machine Learning as a Service (MLaaS) Market Big Things Are Happening In Technology and Media Industry up to 2031 - Digital Journal
What Are Raymond James Financial Stocks Prospects Over The Next One Month? – Forbes
UKRAINE - 2021/05/19: In this photo illustration the Raymond James logo of an US investment banking ... [+] company is seen on a smartphone and a pc screen. (Photo Illustration by Pavlo Gonchar/SOPA Images/LightRocket via Getty Images)
Raymond James Financial stock (NYSE: RJF) lost 1.9% in the last five trading days and currently trades close to $136 per share. Raymond James Financial is a financial holding company that provides services such as investment management, sales & trading, corporate and retail banking, etc. Its stock has gained almost 43% YTD, as compared to the 19% rise in S&P500.
The company recently approved a three-for-two stock split in the form of a 50% stock dividend. It means that its shareholders will receive one additional share of RJF for every two shares of RJF owned by them. Further, shareholders on record as of 9th September will be eligible for the process, which is to be completed on 21st September. Additionally, the firm has decreased the dividend amount per share from $0.39 to $0.26 per share to compensate for the increased number of shares.
But will RJF stock continue its downward trajectory over the coming weeks, or is a rise in the stock more likely? According to the Trefis Machine Learning Engine, which identifies trends in a companys stock price data for the last ten years, returns for RJF stock average close to 3.1% in the next one-month (21 trading days) period after experiencing a 1.9% drop over the last one week (five trading days) period. Also, there is a 69% chance that the stock will give positive returns over the next one-month period.
But how would these numbers change if you are interested in holding RJF stock for a shorter or a longer time period? You can test the answer and many other combinations on the Trefis Machine Learning to test Raymond James Financial stock chances of a rise after a fall and vice versa. You can test the chance of recovery over different time intervals of a quarter, month, or even just one day!
MACHINE LEARNING ENGINE try it yourself:
IF RJF stock moved by -5% over five trading days, THEN over the next 21 trading days, RJF stock moves an average of 2.7 percent with a 68.3% probability of positive returns.
Average Return
Some Fun Scenarios, FAQs & Making Sense of Raymond James Financial Stock Movements:
Question 1: Is the average return for Raymond James Financial Stock higher after a drop?
Answer:
Consider two situations,
Case 1: Raymond James Financial stock drops by -5% or more in a week
Case 2: Raymond James Financial stock rises by 5% or more in a week
Is the average return for Raymond James Financial stock higher over the subsequent month after Case 1 or Case 2?
RJF stock fares better after Case 1, with an average return of 2.7% over the next month (21 trading days) under Case 1 (where the stock has just suffered a 5% loss over the previous week), versus, an average return of 1.3% for Case 2.
In comparison, the S&P 500 has an average return of 3.1% over the next 21 trading days under Case 1, and an average return of just 0.5% for Case 2 as detailed in our dashboard that details the average return for the S&P 500 after a fall or rise.
Try the Trefis machine learning engine above to see for yourself how Raymond James Financial stock is likely to behave after any specific gain or loss over a period.
Question 2: Does patience pay?
Answer:
If you buy and hold Raymond James Financial stock, the expectation is over time the near-term fluctuations will cancel out, and the long-term positive trend will favor you - at least if the company is otherwise strong.
Overall, according to data and Trefis machine learning engines calculations, patience absolutely pays for most stocks!
For RJF stock, the returns over the next N days after a -5% change over the last five trading days is detailed in the table below, along with the returns for the S&P500:
Average Return
Question 3: What about the average return after a rise if you wait for a while?
Answer:
The average return after a rise is understandably lower than after a fall as detailed in the previous question. Interestingly, though, if a stock has gained over the last few days, you would do better to avoid short-term bets for most stocks.
RJFs returns over the next N days after a 5% change over the last five trading days is detailed in the table below, along with the returns for the S&P500:
Average Return
Its pretty powerful to test the trend for yourself for Raymond James Financial stock by changing the inputs in the charts above.
Invest with Trefis Market Beating Portfolios
See allTrefis Featured AnalysesandDownloadTrefis Datahere
Excerpt from:
What Are Raymond James Financial Stocks Prospects Over The Next One Month? - Forbes
Applications of AI And Machine learning In Computer Science and Electrical Engineering – Analytics Insight
Applications of AI And Machine Learning In Computer Science and Electrical Engineering
Technologically, we are evolving with every passing day. Progress in the field of Artificial intelligence and machine learning has transformed our lives for the better. Today, these magnificent technologies are used to optimize systems and meet the desired organizations goals. AI and machine learning not only boost the performance of the system but also address the problems of the business like never before. Additionally, problems are addressed efficiently and faster than before. All in all, implementing the latest applications of AI and machine learning might end up being a path for achieving greater heights. Computer engineering systems and electrical engineering systems generate huge volumes of data. Thus, we can apply data mining to discover new relationships in these systems. With the advent of deep neural networks thanks to the advancement in technology, we can learn new mappings between inputs and output of these systems. On that note, have a look at some of the greatest applications of AI and machine learning in the field of Computer engineering and electrical engineering that have simplified our lives.
Power systems
One of the best applications of AI when it comes to computer engineering has been on power systems. Right from identifying malfunctions to forecasting, AI has covered it all. Artificial intelligence has done a magnificent job in reducing the workload of human operators by taking up tasks such as data processing, routine maintenance, training, etc.
Application of Artificial intelligence in Electrical Equipment
First things first, we all know how complex the electrical equipment structure is. In reality, it not only needs knowledge pertaining to electronics, circuits, electromagnetic fields, motors, automation, etc. but also the necessity to understand the generators, sensors and other components of the role and mechanism. It is here that AI turns out to be no less than a saviour. Through programming and operation by computer technology, AI can realize the automatic operation of electrical equipment and replace human labour as well, thereby reducing the labour cost to a large extent. Additionally, Artificial intelligence technology greatly improves the speed and precision of the work.
Fault diagnosis
Artificial intelligence can be used in the logic of fuzzy neural network expert systems timely. With this, it is not only possible to accurately detect the faults, but also used to determine the cause of the failure, type and location of thefailure, and timely control of fault repair.
More secure systems
With the help of advanced search algorithms, Artificial intelligence and machine learning, identifying potential threats and data breaches in real-time has become easier than ever. Well, this is not it there is more to this. These advanced technologies also provide the necessary solutions to avoid those issues in the future. Well, there is no denying that when it comes to computer science, data security becomes way more relevant, right?
Server optimization
We all know that hosting servers have millions of inbound requests on a day-to-day basis. However, a point of concern is that due to the continuous flow of queries, some of these servers may end up slowing down and become unresponsive. Well, Artificial intelligence to the rescue it is! AI holds the potential of optimizing the host server and enhancing the operations, thereby boosting customer service.
What everything boils down to is the fact that AI and machine learning are changing many sectors, particularly IT/computer and electrical engineering because of the amount of data sets it can process at greater speeds and ability to learn faster than the human brain.
Share This ArticleDo the sharing thingy
About AuthorMore info about author
Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.
See original here:
Applications of AI And Machine learning In Computer Science and Electrical Engineering - Analytics Insight