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New York, June 29, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Machine learning as a Service Market Size, Share & Industry Trends Analysis Report By End User, By Offering, By Organization Size, By Application, By Regional Outlook and Forecast, 2022 2028" - https://www.reportlinker.com/p06289268/?utm_source=GNW It is designed to include artificial intelligence (AI) and cognitive computing functionalities. Machine learning as a service (MLaaS) refers to a group of cloud computing services that provide machine learning technologies.

Increased demand for cloud computing, as well as growth connected with artificial intelligence and cognitive computing, are major machine learning as service industry growth drivers. Growth in demand for cloud-based solutions, such as cloud computing, rise in adoption of analytical solutions, growth of the artificial intelligence & cognitive computing market, increased application areas, and a scarcity of trained professionals are all influencing the machine learning as a service market.

As more businesses migrate their data from on-premise storage to cloud storage, the necessity for efficient data organization grows. Since MLaaS platforms are essentially cloud providers, they enable solutions to appropriately manage data for machine learning experiments and data pipelines, making it easier for data engineers to access and process the data.

For organizations, MLaaS providers offer capabilities like data visualization and predictive analytics. They also provide APIs for sentiment analysis, facial recognition, creditworthiness evaluations, corporate intelligence, and healthcare, among other things. The actual computations of these processes are abstracted by MLaaS providers, so data scientists dont have to worry about them. For machine learning experimentation and model construction, some MLaaS providers even feature a drag-and-drop interface.

COVID-19 Impact

The COVID-19 pandemic has had a substantial impact on numerous countries health, economic, and social systems. It has resulted in millions of fatalities across the globe and has left the economic and financial systems in tatters. Individuals can benefit from knowledge about individual-level susceptibility variables in order to better understand and cope with their psychological, emotional, and social well-being.

Artificial intelligence technology is likely to aid in the fight against the COVID-19 pandemic. COVID-19 cases are being tracked and traced in several countries utilizing population monitoring approaches enabled by machine learning and artificial intelligence. Researchers in South Korea, for example, track coronavirus cases using surveillance camera footage and geo-location data.

Market Growth Factors

Increased Demand for Cloud Computing and a Boom in Big Data

The industry is growing due to the increased acceptance of cloud computing technologies and the use of social media platforms. Cloud computing is now widely used by all companies that supply enterprise storage solutions. Data analysis is performed online using cloud storage, giving the advantage of evaluating real-time data collected on the cloud. Cloud computing enables data analysis from any location and at any time. Moreover, using the cloud to deploy machine learning allows businesses to get useful data, such as consumer behavior and purchasing trends, virtually from linked data warehouses, lowering infrastructure and storage costs. As a result, the machine learning as a service business is growing as cloud computing technology becomes more widely adopted.

Use of Machine Learning to Fuel Artificial Intelligence Systems

Machine learning is used to fuel reasoning, learning, and self-correction in artificial intelligence (AI) systems. Expert systems, speech recognition, and machine vision are examples of AI applications. The rise in the popularity of AI is due to current efforts such as big data infrastructure and cloud computing. Top companies across industries, including Google, Microsoft, and Amazon (Software & IT); Bloomberg, American Express (Financial Services); and Tesla and Ford (Automotive), have identified AI and cognitive computing as a key strategic driver and have begun investing in machine learning to develop more advanced systems. These top firms have also provided financial support to young start-ups in order to produce new creative technology.

Market Restraining Factors

Technical Restraints and Inaccuracies of ML

The ML platform provides a plethora of advantages that aid in market expansion. However, several parameters on the platform are projected to impede market expansion. The presence of inaccuracy in these algorithms, which are sometimes immature and underdeveloped, is one of the markets primary constraining factors. In the big data and machine learning manufacturing industries, precision is crucial. A minor flaw in the algorithm could result in incorrect items being produced. This would exorbitantly increase the operational costs for the owner of the manufacturing unit than decrease it.

End User Outlook

Based on End User, the market is segmented into IT & Telecom, BFSI, Manufacturing, Retail, Healthcare, Energy & Utilities, Public Sector, Aerospace & Defense, and Others. The retail segment garnered a substantial revenue share in the machine learning as a service market in 2021. E-commerce has proven to be a key force in the retail trade industry. Machine intelligence is used by retailers to collect data, evaluate it, and use it to provide customers with individualized shopping experiences. These are some of the factors that influence the retail industries demand for this technology.

Offering Outlook

Based on Offering, the market is segmented into Services Only and Solution (Software Tools). The services only segment acquired the largest revenue share in the machine learning as a service market in 2021. The market for machine learning services is expected to grow due to factors such as an increase in application areas and growth connected with end-use industries in developing economies. To enhance the usage of machine learning services, industry participants are focusing on implementing technologically advanced solutions. The use of machine learning services in the healthcare business for cancer detection, as well as checking ECG and MRI, is expanding the market. Machine learning services benefits, such as cost reduction, demand forecasting, real-time data analysis, and increased cloud use, are projected to open up considerable prospects for the market.

Organization Size Outlook

Based on Organization Size, the market is segmented into Large Enterprises and Small & Medium Enterprises. The small and medium enterprises segment procured a substantial revenue share in the machine learning as a service market in 2021. This is because implementation of machine learning lets SMEs optimize its processes on a tight budget. AI and machine learning are projected to be the major technologies that allow SMEs to save money on ICT and gain access to digital resources in the near future.

Application Outlook

Based on Application, the market is segmented into Marketing & Advertising, Fraud Detection & Risk Management, Computer vision, Security & Surveillance, Predictive analytics, Natural Language Processing, Augmented & Virtual Reality, and Others. The marketing and advertising segment acquired the largest revenue share in the machine learning as a service market in 2021. The goal of a recommendation system is to provide customers with products that they are currently interested in. The following is the marketing work algorithm: Hypotheses are developed, tested, evaluated, and analyzed by marketers. Because information changes every second, this effort is time-consuming and labor-intensive, and the findings are occasionally wrong. Machine learning allows marketers to make quick decisions based on large amounts of data. Machine learning allows businesses to respond more quickly to changes in the quality of traffic generated by advertising efforts. As a result, the business can spend more time developing hypotheses rather than doing mundane tasks.

Regional Outlook

Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa. The Asia Pacific region garnered a significant revenue share in the machine learning as a service market in 2021. Leading companies are concentrating their efforts in Asia-Pacific to expand their operations, as the region is likely to see rapid development in the deployment of security services, particularly in the banking, financial services, and insurance (BFSI) sector. To provide better customer service, industry participants are realizing the significance of providing multi-modal platforms. The rise in AI application adoption is likely to be the primary trend driving market growth in this area. Furthermore, government organizations have taken important steps to accelerate the adoption of machine learning and related technologies in this region.

The major strategies followed by the market participants are Product Launches and Partnerships. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Machine learning as a Service Market. Companies such Amazon Web Services, Inc., SAS Institute, Inc., IBM Corporation are some of the key innovators in the Market.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Hewlett-Packard Enterprise Company, Oracle Corporation, Google LLC, Amazon Web Services, Inc. (Amazon.com, Inc.), IBM Corporation, Microsoft Corporation, Fair Isaac Corporation (FICO), SAS Institute, Inc., Yottamine Analytics, LLC, and BigML.

Recent Strategies deployed in Machine learning as a Service Market

Partnerships, Collaborations and Agreements:

Mar-2022: Google entered into a partnership with BT, a British telecommunications company. Under the partnership, BT utilized a suite of Google Cloud products and servicesincluding cloud infrastructure, machine learning (ML) and artificial intelligence (AI), data analytics, security, and API managementto offer excellent customer experiences, decrease costs, and risks, and create more revenue streams. Google aimed to enable BT to get access to hundreds of new business use-cases to solidify its goals around digital offerings and developing hyper-personalized customer engagement.

Feb-2022: SAS entered into a partnership with TecCentric, a company providing customized IT solutions. SAS aimed to fasten TecCentrics journey towards discovery with artificial intelligence (AI), machine learning (ML), and advanced analytics. Under the partnership, TecCentric aimed to work with SAS to customize services and solutions for a broad range of verticals from the public sector, to banking, education, healthcare, and more, granting them access to the complete analytics cycle with SASs enhanced AI solution offering as well as its leading fraud and financial crimes analytics and reporting.

Feb-2022: Microsoft entered into a partnership with Tata Consultancy Services, an Indian company focusing on providing information technology services and consulting. Under the partnership, Tata Consultancy Services leveraged its software, TCS Intelligent Urban Exchange (IUX) and TCS Customer Intelligence & Insights (CI&I), to enable businesses in providing hyper-personalized customer experiences. CI&I and IUX are supported by artificial intelligence (AI), and machine learning, and assist in real-time data analytics. The CI&I software empowered retailers, banks, insurers, and other businesses to gather insights, predictions, and recommended actions in real-time to enhance the satisfaction of customers.

Jun-2021: Amazon Web Services entered into a partnership with Salesforce, a cloud-based software company. The partnership enabled to utilize complete set of Salesforce and AWS capabilities simultaneously to rapidly develop and deploy new business applications that facilitate digital transformation. Salesforce also embedded AWS services for voice, video, artificial intelligence (AI), and machine learning (ML) directly in new applications for sales, service, and industry vertical use cases.

Apr-2021: Amazon formed a partnership with Basler, a company known for its product line of area scan, line scan, and network cameras. The partnership began as Amazon launched a succession of services for industrial machine learning, including its latest Lookout for Vision cloud AI service for factory inspection. Customers can integrate AWS Panorama SDK within its platform, and thus utilize a common architecture to perform multiple tasks and accommodate a broad range of performance and cost. The integration of AWS Panorama empowered customers to adopt and run machine learning applications on edge devices with additional support for device management and accuracy tracking.

Dec-2020: IBM teamed up with Mila, a Quebec Artificial Intelligence Institute. Under the collaboration, both organizations aimed to quicken machine learning using Oron, an open-source technology. After the integration of Milas open-source Oron software and IBMs Watson Machine Learning Accelerator, IBM also enhanced the deployment of state-of-the-art algorithms, along with improved machine learning and deep learning capabilities for AI researchers and data scientists. IBMs Spectrum Computing team based out of Canada Lab contributes substantially to Orons code base.

Oct-2020: SAS entered into a partnership with TMA Solutions, a software outsourcing company based in Vietnam. Under the partnership, SAS and TMA Solutions aimed to fasten the growth of businesses in Vietnam through Artificial Intelligence (AI) and Data Analytics. SAS and TMA helped clients in Vietnam quicken the deployment and growth of advanced analytics and look for new methods to propel innovation in AI, especially in the fields of Machine Learning, Computer Vision, Natural Language Processing (NLP), and other technologies.

Product Launches and Product Expansions:

May-2022: Hewlett Packard launched HPE Swarm Learning and the new Machine Learning (ML) Development System, two AI and ML-based solutions. These new solutions increase the accuracy of models, solve AI infrastructure burdens, and improve data privacy standards. The company declared the new tool a breakthrough AI solution that focuses on fast-tracking insights at the edge, with attributes ranging from identifying card fraud to diagnosing diseases.

Apr-2022: Hewlett Packard released Machine Learning Development System (MLDS) and Swarm Learning, its new machine learning solutions. The two solutions are focused on simplifying the burdens of AI development in a development environment that progressively consists of large amounts of protected data and specialized hardware. The MLDS provides a full software and services stack, including a training platform (the HPE Machine Learning Development Environment), container management (Docker), cluster management (HPE Cluster Manager), and Red Hat Enterprise Linux.

May-2021: Google released Vertex AI, a novel managed machine learning platform that enables developers to more easily deploy and maintain their AI models. Engineers can use Vertex AI to manage video, image, text, and tabular datasets, and develop machine learning pipelines to train and analyze models utilizing Google Cloud algorithms or custom training code. After that the engineers can install models for online or batch use cases all on scalable managed infrastructure.

Mar-2021: Microsoft released updates to Azure Arc, its service that brought Azure products and management to multiple clouds, edge devices, and data centers with auditing, compliance, and role-based access. Microsoft also made Azure Arc-enabled Kubernetes available. Azure Arc-enabled Machine Learning and Azure Arc-enabled Kubernetes are developed to aid companies to find a balance between enjoying the advantages of the cloud and maintaining apps and maintaining apps and workloads on-premises for regulatory and operational reasons. The new services enable companies to implement Kubernetes clusters and create machine learning models where data lives, as well as handle applications and models from a single dashboard.

Jul-2020: Hewlett Packard released HPE Ezmeral, a new brand and software portfolio developed to assist enterprises to quicken digital transformation across their organization, from edge to cloud. The HPE Ezmeral goes from a portfolio consisting of container orchestration and management, AI/ML, and data analytics to cost control, IT automation and AI-driven operations, and security.

Acquisitions and Mergers:

Jun-2021: Hewlett Packard completed the acquisition of Determined AI, a San Francisco-based startup that offers a strong and solid software stack to train AI models faster, at any scale, utilizing its open-source machine learning (ML) platform. Hewlett Packard integrated Determined AIs unique software solution with its world-leading AI and high-performance computing (HPC) products to empower ML engineers to conveniently deploy and train machine learning models to offer faster and more precise analysis from their data in almost every industry.

Scope of the Study

Market Segments covered in the Report:

By End User

IT & Telecom

BFSI

Manufacturing

Retail

Healthcare

Energy & Utilities

Public Sector

Aerospace & Defense

Others

By Offering

Services Only

Solution (Software Tools)

By Organization Size

Large Enterprises

Small & Medium Enterprises

By Application

Marketing & Advertising

Fraud Detection & Risk Management

Computer vision

Security & Surveillance

Predictive analytics

Natural Language Processing

Augmented & Virtual Reality

Others

By Geography

North America

o US

o Canada

o Mexico

o Rest of North America

Europe

o Germany

o UK

o France

o Russia

o Spain

o Italy

o Rest of Europe

Asia Pacific

o China

o Japan

o India

o South Korea

o Singapore

o Malaysia

o Rest of Asia Pacific

LAMEA

o Brazil

o Argentina

o UAE

o Saudi Arabia

o South Africa

o Nigeria

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The Global Machine learning as a Service Market size is expected to reach $36.2 billion by 2028, rising at a market growth of 31.6% CAGR during the...

Deep Learning Market Is Expected To Reach USD 68.71 Billion By 2027 At A CAGR Of 41.5 percent.

Maximize Market Research has published a report on theDeep Learning Marketthat provides a detailed analysis for the forecast period of 2021 to 2027.

Deep Learning Market Scope:

The report provides comprehensive market insights for industry stakeholders, including an explanation of complicated market data in simple language, the industrys history and present situation, as well as expected market size and trends. The research investigates all industry categories, with an emphasis on key companies such as market leaders, followers, and new entrants. The paper includes a full PESTLE analysis for each country. A thorough picture of the competitive landscape of major competitors in the Deep Learning market by goods and services, revenue, financial situation, portfolio, growth plans, and geographical presence makes the study an investors guide.

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Deep Learning Market Overview:

Deep learning, also known as deep structured learning, is a subclass of machine learning that uses layered computer models to analyze data. It is an essential component of data science, which uses statistics and prescriptive analytics to gather, evaluate, and understand massive volumes of data. It also involves the application of artificial intelligence (AI) to mimic how the human brain processes data, generate trends and makes decisions. This technology is widely utilized in facial recognition software, natural language processing (NLP) and voice synthesis software, self-driving vehicles, and language translation services, and it performs several roles in commerce, healthcare, automobile, farming, military, and industrial settings.

Deep Learning MarketDynamics:

The rising usage of cloud-based services, as well as the large-scale generation of unstructured data, has raised the demand for deep learning solutions. Besides that, the growing number of robotic devices, such as Sophia, produced by Hanson Robotics, as well as the growing implementations of deep learning in recent years for image/speech recognition, data processing, and language explanations, are some of the key drivers of the deep learning industry. The increased efforts of key market participants in developing machine learning and deep learning techniques in the field are expected to drive market growth. Likewise, the rapid increase in the volume of data created in numerous end-use sectors is estimated to driveindustry growth. Also, the increased need for human-machine interaction is creating new possibilities for software vendors to supply enhanced services and skills.

Furthermore, the predominance of deep learning incorporation with big data analytics, as well as the rapidly increasing need to boost processing capacity and reducehardware costs due to deep learning algorithms capacity to perform or execute faster on a GPU as compared to a CPU, is culminating in public adoption of deep learning technologies across industries, which is estimated to drive theglobal growth.

Various setbacksare anticipated to hinder theoverall market growth. The lack of standards and protocols, as well as a lack of technical expertise in deep learning, are limiting industry growth. Additionally, complex integrated systems, as well as the integration of deep learning solutions and software into legacy systems, are time-consuming processes that impede growth.

Deep Learning MarketRegional Insights:

North America is anticipated todominate the global Deep Learning market at the end of the forecastperiod. By 2027, North America is expected to have the greatest market share of nearly 40 percent. This is due to increased investment in artificial intelligence and neural networks. The regions significant use of imaging and monitoring applications is expected to provide new growth opportunities over the forecast period. Likewise, the region is a modern technology pioneer, allowing enterprises to expedite the implementation of deep learning capability.

Deep Learning MarketSegmentation:

By Component:

By Application:

By Architecture Industry:

By End-Use Industry:

Deep Learning Market Key Competitors:

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Here is the original post:
Global Deep Learning Market Is Expected To Reach USD 68.71 Billion At A CAGR Of 41.5% And Forecast To 2027 - Digital Journal

Quadintel published a new report on theEurope Machine LearningMarket. The research report consists of thorough information about demand, growth, opportunities, challenges, and restraints. In addition, it delivers an in-depth analysis of the structure and possibility of global and regional industries.

The value of the machine learning market in Europe is expected to reach USD 3.96 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 33.5% during 2018-2023.

Machine learning the ability of computers to learn through experiences to improve their performance. Separate algorithms and human intervention are not required to train the computer. It merely learns from its past experiences and examples. In recent times, this market has gained utmost importance due to the increased availability of data and the need to process the data to obtain meaningful insights.Europe stands in the second position after North America in the machine learning market.

Request To Download Sample of This Strategic Report: https://www.quadintel.com/request-sample/europe-machine-learning-market/QI042

The market can be classified into four primary segments based on components, service, organization size and application.

Based on region, the market is segmented into the European Union five (EU5), rest of Europe.

Based on componentsthe market can be segmented into software tools, cloud and web-based application programming interfaces (APIs) and others.

Based on service, the sub-segments are composed of professional services and managed services.

Based on organization size, the sub-segments include small and medium enterprises (SMEs) and large enterprises.

Based on application, the market is divided into the sub-segments, banking, financial services and insurance (BFSI), automotive, healthcare, government and others.

The trend of supporting, educating, enforcing and steering the economy towards a machine learning-friendly environment is seen to be followed throughout Europe.

European countries are successfully bridging the gap between additional renewable energy and excess power into the grid by making ultra-accurate forecasts of the demand and supply in real time by making use of the machine learning technologies, thereby saving energy and cost.

Key growth factors

The world-class research facilities, the emerging start-up culture, the innovation and commercialisation of machine intelligence technologies is giving thrust to the machine intelligence market in Europe.Amongst all regions, Europe has the largest share of intraregional data flow. This, together with the machine learning technologies, is boosting the market in Europe.The excessive usage of the machine learning technology across economy in all facets of businesses is proving to be a big thrust to the machine learning market. Profound usage has been found in sectors such as agriculture, healthcare and media for optimisation of prices and carrying out predictive maintenance in manufacturing.

Threats and key players

Investors in Europe are more concerned about the ROI from investing in the machine learning market. The adoption of machine learning by the start-ups is a farce in Europe since research suggests that only 5% of the start-ups investing in machine learning end up with a revenue of more than $50 Mn in revenue. Also, opportunities for external investments are bleak.

The machine learning market is in a stage of infancy; there is a lacuna between the skills required and that which is inherent in the workers. It requires a considerable amount of time to pick up the skills. Also, the Europeans are concerned about the penetration of machine learning into their lives, and how it is going to impact employment in the country. Concerns environing these factors are hindering the further developments in the machine learning market.

Given that machine intelligence depends on the easy availability of data, the practice of data minimisation and data privacy standards act as a barrier to the further development of the machine learning market in Europe.

The key players are Microsoft, Google Inc., IBM Watson, Amazon, Intel, Facebook and Apple.

DOWNLOAD FREE SAMPLE REPORThttps://www.quadintel.com/request-sample/europe-machine-learning-market/QI042

What is covered in the report?

1. Overview of themachine learning in Europe.2. Market drivers and challenges in the machine learning in Europe.3. Market trends in the machine learning in Europe.4. Historical, current and forecasted market size data for the machine learning market in Europe.5. Historical, current and forecasted market size data for the components segment (software tools, cloud and web-based APIs and others).6. Historical, current and forecasted market size data for the service segment (professional services and managed services).7. Historical, current and forecasted market size data for the organisation size segment (SMEs and large enterprises).8. Historical, current and forecasted market size data for the application segment (BFSI, automotive, healthcare, government and others).9. Historical, current and forecasted regional (the European Union five (EU5), rest of Europe) market size data for machine learning market.10. Analysis of machine learning market in Europe by value chain.11. Analysis of the competitive landscape and profiles of major competitors operating in the market.

Why buy?

1. Understand the demand for machine learning to determine the viability of the market.2. Determine the developed and emerging markets for machine learning.3. Identify the challenge areas and address them.4. Develop strategies based on the drivers, trends and highlights for each of the segments.5. Evaluate the value chain to determine the workflow.6. Recognize the key competitors of this market and respond accordingly.7. Knowledge of the initiatives and growth strategies taken by the major companies and decide on the direction of further growth.

The report further discusses the market opportunity, compound annual growth rate (CAGR) growth rate, competition, new technology innovations, market players analysis, government guidelines, export and import (EXIM) analysis, historical revenues, future forecasts etc. in the following regions and/or countries:

North America (U.S. & Canada) Market Size, Y-O-Y Growth, Market Players Analysis & Opportunity OutlookLatin America (Brazil, Mexico, Argentina, Rest of Latin America) Market Size, Y-O-Y Growth & Market Players Analysis & Opportunity OutlookEurope (U.K., Germany, France, Italy, Spain, Hungary, Belgium, Netherlands & Luxembourg, NORDIC(Finland, Sweden, Norway, Denmark), Ireland, Switzerland, Austria, Poland, Turkey, Russia, Rest of Europe), Poland, Turkey, Russia, Rest of Europe) Market Size, Y-O-Y Growth Market Players Analys & Opportunity OutlookAsia-Pacific (China, India, Japan, South Korea, Singapore, Indonesia, Malaysia, Australia, New Zealand, Rest of Asia-Pacific) Market Size, Y-O-Y Growth & Market Players Analysis & Opportunity OutlookMiddle East and Africa (Israel, GCC (Saudi Arabia, UAE, Bahrain, Kuwait, Qatar, Oman), North Africa, South Africa, Rest of Middle East and Africa) Market Size, Y-O-Y Growth Market Players Analysis & Opportunity Outlook

Request full Report Description, TOC, Table of Figure, Chart, etc. @ https://www.quadintel.com/request-sample/europe-machine-learning-market/QI042

Table of Contents:

About Quadintel:

We are the best market research reports provider in the industry. Quadintel believes in providing quality reports to clients to meet the top line and bottom line goals which will boost your market share in todays competitive environment. Quadintel is a one-stop solution for individuals, organizations, and industries that are looking for innovative market research reports.

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Europe Machine Learning Market Is Likely to Experience a Tremendous Growth in Near Future | Microsoft, Google Inc., IBM Watson, Amazon, Intel,...

HONG KONG, CHINA / ACCESSWIRE / July 2, 2022 /TickerWin, one of the leading market research companies, has released a report on 'How Blockchain Improving the Efficiency of AI and Machine Learning'. AI, Machine Learning, and Blockchain technologies have boosted the all sectors.

The main aim of the financial sector has been to provide customer-centric solutions. User experience is a critical parameter, and for the new generation of customers, speed and ease of access without compromising security are essential. This generation loathes going to the bank, filling out documents, printing, and signing them. The main aim will be entirely automating the financial processes and getting rid of manual processes completely. They have enabled companies to process a huge amount of data set and reach conclusions due to their ability to analyze real-time patterns, helping with quick decision-making. They are improving the effectiveness and at the same time working efficiently. This has made different processes in banking time saving and also cost-effective. New technologies increase employee productivity by 40~50% in many industries.

Blockchain is frequently used in connection to cryptocurrencies. However, the banking industry is also implementing it for the improvement of workflow dynamics. Blockchain technology will provide a highly secure transaction on both ends. This will be greatly helpful to prevent fraud and help in easy compliance of audits and regulatory requirements. With the help of blockchain & defi transfers, payments and investments can become faster and error-free. It is said that blockchain will impact the packaging sector with the highest intensity in the year 2022. Needless to say, blockchain and the security it provides are here to stay.

According to TickerWin's view, new technologies have reduced human defaults and made transactions safer, all for a better customer experience. By 2030, financial agencies will be able to reduce costs by 20~30% saving trillions. Many Fin-Tech firms are continuously researching the areas of AI that will be helpful for banks and their fraud detection processes, customer service, credit service and loan decisions.

In addition, the e-shopping market has substantially increased in the last two years; there is a high demand for hassle-free digital payment options. Therefore, a majority of the e-shopping players have collaborated with Fin-Tech firms to create custom gateways and portals to ensure that the customers do not leave the site due to payment options. The smooth check-out process has become a crucial part of e-shopping sales as methods for a swift and effective payment process are essential to enhance conversion rates. According to a recent study, there is an increase of 5% in the global cross-border payment flow. Because of e-shopping, international transactions offer enormous growth potential for even small businesses as most people expect easy and simple payment solutions.

About TickerWin

TickerWin offers marketing research reports on industry trends, especially in AI, Cloud Computing, AR/VR, Big Data, NFT, Cryptocurrency, and DeFi fields. It offers customers with real-time visibility, transparency, and traceable through the tracking of the project's database throughout the complete lifecycle of a researching project all on an immutable ledger with continuous insights.

Media Contact

Company: TickerWin Marketing Research LtdContact: Ronald LuoAddress: Room 12C, 22/G, Sheung Wan Building, 345 Queen's Road Central, HKSAREmail: support@tickerwin.comWebsite: https://www.TickerWin.com

SOURCE: TickerWin Marketing Research Ltd

View source version on accesswire.com: https://www.accesswire.com/707438/TickerWin-Releases-Report-on-How-Blockchain-is-Improving-the-Efficiency-of-AI-and-Machine-Learning

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TickerWin Releases Report on 'How Blockchain is Improving the Efficiency of AI and Machine Learning' - Yahoo Finance

About the position

This PhD project is in line with the research activities performed at the Department of Information Security and Communication Technology (IIK) and is closely linked to the Innovation Project for the Industrial Sector named SALT - Secure privacy preserving Authentication using faciaL biometrics to proTect your identity sponsored from Norwegian Research Council, Norway.

The objective of the project is to create the next generation face authentication services with strong presentation attack detection and privacy-preserving techniques.

The PhD candidates will have the opportunity to collaborate with researchers in this project consortia and can benefit from the research and collaborative training activities together with leading biometrics start-up Mobai AS and leading financial companies such as Vipps, BankID and SpareBank 1.

The position reports Head of Department.

Duties of the position

Required selection criteria

The qualification requirement is that you have completed a masters degree or second degree (equivalent to 120 credits) with a strong academic background in Computer Science or equivalent education with a grade of B or better in terms ofNTNUs grading scale. If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you are unable to meet these criteria you may be considered only if you can document that you are particularly suitable for education leading to a PhD degree.

In addition, the candidate must have:

The appointment is to be made in accordance with Regulations concerning the degrees ofPhilosophiaeDoctor (PhD)andPhilosodophiaeDoctor (PhD) in artistic researchnational guidelines for appointment as PhD, post doctor and research assistant

Preferred selection criteria

Personal characteristics

We offer

Salary and conditions

PhD candidates are remunerated in code 1017, and are normally remunerated at gross from NOK 491 200 per annum before tax, depending on qualifications and seniority. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.

The period of employment is 3 years.

Appointment to a PhD position requires that you are admitted to thePhD programme inInformation Security and Communication Technologywithin three months of employment, and that you participate in an organized PhD programme during the employment period.

The engagement is to be made in accordance with the regulations in force concerningState Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criteria in the latter law will be prohibited from recruitment to NTNU. After the appointment you must assume that there may be changes in the area of work.

It is a prerequisite you can be present at and accessible to the institution daily.

About the application

Applicants must upload the following documents within the closing date:

Please submit your application electronically via Jobbnorge website. The application and supporting documentation to be used as the basis for the assessment must be in English. Applications submitted elsewhere/incomplete applications will not be considered.

NTNU is committed to following evaluation criteria for research quality according toThe San Francisco Declaration on Research Assessment - DORA.

General information

Working at NTNU

A good work environment is characterized by diversity. We encourage qualified candidates to apply, regardless of their gender, functional capacity or cultural background.

The city of Gjvikhas a population of 30 000 and is a town known for its rich music and cultural life. The beautiful nature surrounding the city is ideal for an active outdoor life! The Norwegian welfare state, including healthcare, schools, kindergartens and overall equality, is probably the best of its kind in the world.

As an employeeatNTNU, you must at all times adhere to the changes that the development in the subject entails and the organizational changes that are adopted.

Information Act (Offentleglova), your name, age, position and municipality may be made public even if you have requested not to have your name entered on the list of applicants.

If you have any questions about the position, please contact email:raghavendra.ramachandra@ntnu.no. If you have any questions about the recruitment process, please contact Katrine Rennan, e-mail:Katrine.rennan@ntnu.no.

Please submit your application electronically via jobbnorge.no with your CV, diplomas and certificates. Applications submitted elsewhere will not be considered. Diploma Supplement is required to attach for European Master Diplomas outside Norway. Chinese applicants are required to provide confirmation of Master Diploma fromChina Credentials Verification (CHSI).

If you are invited for interview you must include certified copies of transcripts and reference letters. Please refer to the application number 2022/22061 when applying.

Application deadline: 15.08.2022

NTNU - knowledge for a better world

The Norwegian University of Science and Technology (NTNU) creates knowledge for a better world and solutions that can change everyday life.

Department of Information Security and Communication Technology

Research is vital to the security of our society. We teach and conduct research in cyber security, information security, communications networks and networked services. Our areas of expertise include biometrics, cyber defence, cryptography, digital forensics, security in e-health and welfare technology, intelligent transportation systems and malware. The Department of Information Security and Communication Technology is one of seven departments in theFaculty of Information Technology and Electrical Engineering.

Deadline15th August 2022EmployerNTNU - Norwegian University of Science and TechnologyMunicipalityGjvikScopeFulltimeDuration TemporaryPlace of service Campus Gjvik

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PhD Candidate in Advance Machine Learning towards Generalized Face Presentation Attack Detection job with NORWEGIAN UNIVERSITY OF SCIENCE &...