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Machine Learning as a Service (MLaaS) Market 2022-2028

A New Market Study, Titled Machine Learning as a Service (MLaaS) Market Upcoming Trends, Growth Drivers and Challenges has been featured on fusionmarketresearch.

Description

This global study of theMachine Learning as a Service (MLaaS)Marketoffers an overview of the existing market trends, drivers, restrictions, and metrics and also offers a viewpoint for important segments. The report also tracks product and services demand growth forecasts for the market. There is also to the study approach a detailed segmental review. A regional study of the globalMachine Learning as a Service (MLaaS)industryis also carried out in North America, Latin America, Asia-Pacific, Europe, and the Near East & Africa.The report mentions growth parameters in the regional markets along with major players dominating the regional growth.

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Machine Learning is a multidisciplinary interdisciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines.This report contains market size and forecasts of Machine Learning as a Service (MLaaS) in Global, including the following market information:Global Machine Learning as a Service (MLaaS) Market Revenue, 2017-2022, 2023-2028, ($ millions)Global top five companies in 2021 (%)

The global Machine Learning as a Service (MLaaS) market was valued at 2103.3 million in 2021 and is projected to reach US$ 7923.8 million by 2028, at a CAGR of 20.9% during the forecast period.The U.S. Market is Estimated at $ Million in 2021, While China is Forecast to Reach $ Million by 2028.Private Clouds Segment to Reach $ Million by 2028, with a % CAGR in next six years.

The global key manufacturers of Machine Learning as a Service (MLaaS) include Amazon, Oracle, IBM, Microsoftn, Google, Salesforce, Tencent, Alibaba and UCloud, etc. In 2021, the global top five players have a share approximately % in terms of revenue.

Fusion Market Research (FMR) has surveyed the Machine Learning as a Service (MLaaS) manufacturers, suppliers, distributors and industry experts on this industry, involving the sales, revenue, demand, price change, product type, recent development and plan, industry trends, drivers, challenges, obstacles, and potential risks.

Competitor AnalysisThe report also provides analysis of leading market participants including:Key companies Machine Learning as a Service (MLaaS) revenues in global market, 2017-2022 (Estimated), ($ millions)Key companies Machine Learning as a Service (MLaaS) revenues share in global market, 2021 (%)Key companies Machine Learning as a Service (MLaaS) sales in global market, 2017-2022 (Estimated), (K Units)Key companies Machine Learning as a Service (MLaaS) sales share in global market, 2021 (%)Further, the report presents profiles of competitors in the market, key players include:

Total Market by Segment:Global Machine Learning as a Service (MLaaS) Market, by Type, 2017-2022, 2023-2028 ($ Millions) & (K Units)

Global Machine Learning as a Service (MLaaS) Market, by Application, 2017-2022, 2023-2028 ($ Millions) & (K Units)

Market segment by Region, regional analysis covers

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Table of Contents

1 Introduction to Research & Analysis Reports

2 Global Machine Learning as a Service (MLaaS) Overall Market Size

3 Company Landscape

4 Market Sights by Product

5 Sights by Application

6 Sights by Region

7 Players Profiles

8 Conclusion

9 Appendix

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The post Machine Learning as a Service (MLaaS) Market 2022, Industry Size, Trends, Share, Growth, Analysis and Forecast to 2028 appeared first on 360PRWire.

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Machine Learning as a Service (MLaaS) Market 2022, Industry Size, Trends, Share, Growth, Analysis and Forecast to 2028 Business - Inter Press Service

Summary: Researchers have developed a novel hybrid machine learning approach to muscle gesture recognition in prosthetic arms.

Source: Beijing Institute of Technology Press

Engineering researchers have developed a hybrid machine-learning approach to muscle gesture recognition in prosthetic hands that combines an AI technique normally used for image recognition with another approach specialized for handwriting and speech recognition. The technique is achieving far superior performance than traditional machine learning efforts.

A paper describing the hybrid approach was published in the journalCyborg and Bionic Systemson November 8th, 2021.

Motor neurons are those parts of the central nervous system that directly control our muscles. They transmit electrical signals that cause muscles to contract. Electromyography (EMG) is a method of measuring muscle response by recording this electrical activity through the insertion of electrode needles through the skin and into the muscle. Surface EMG (sEMG) performs this same recording process in a non-invasive fashion with the electrodes placed on the skin above the muscle, and is used for non-medical procedures such as sports and physiotherapy research.

Over the last decade, researchers have begun investigating the potential use of surface EMG signals to control prostheses for amputees, especially with respect to the complexity of movements and gestures required by prosthetic hands in order to deliver smoother, more responsive, and more intuitive activity of the devices than is currently possible.

Unfortunately, unexpected environmental interference such as a shift of the electrodes introduces a great deal of noise to the process of any device attempting to recognize the surface EMG signals. Such shifts regularly occur in daily wear and use of such systems. To try to overcome this problem, users must engage in a lengthy and tiring sEMG signal training period prior to use of their prostheses. Users are required to laboriously collect and classify their own surface EMG signals in order to be able to control the prosthetic hand.

In order to reduce or eliminate the challenges of such training, researchers have explored various machine learning approachesin particular deep learning pattern recognitionto be able to distinguish between different, complex hand gestures and movements despite the presence of environmental signal interference.

A reduction in the training is in turn obtained by optimizing the network structure model of that deep learning. One possible improvement that has been trialed is the use of a convolutional neural network (CNN), which is analogous to the connection structure of the human visual cortex. This type of neural network offers improved performance with images and speech and as such is at the heart of computer vision.

Researchers up to now have achieved some success with CNN, significantly improving upon the recognition (extraction) of the spatial dimensions of sEMG signals related to hand gestures. But while good dealing with space, they struggle with time. Gestures are not static phenomena, but take place over time, and CNN ignores time information in the continuous contraction of muscles.

Recently, some researchers have begun to apply a long short-term memory (LSTM) artificial neural network architecture to the problem. LSTM involves a structure that involves feedback connections, giving it superior performance in processing classifying, and making predictions based on sequences of data over time, especially where there are lulls, gaps or interferences of unexpected duration between the events that are important. LSTM is a form of deep learning that has been best applied to tasks that involve unsegmented, connected activity such as handwriting and speech recognition.

The challenge here is that while researchers have achieved better gesture classification of sEMG signals, the size of the computational model required is a serious problem. The microprocessor needed to be used is limited. Using something more powerful would be too costly. And finally, while such deep learning training models work with the computers in the lab, they are difficult to apply via the sort of embedded hardware found in a prosthetic device

Convolutional neural networks were after all conceived with image recognition in mind, not control of prostheses, said Dianchun Bai, one of the authors of the paper and professor of electrical engineering at Shenyang University of Technology. We needed to couple CNN with a technique that could deal with the dimension of time, while also ensuring feasibility in the physical device that the user must wear.

So the researchers developed a hybrid CNN and LSTM model that combined the spacial and temporal advantages of the two approaches. This reduced the size of the deep learning model while achieving high accuracy, with more robust resistance to interference.

After developing their system, they tested the hybrid approach on ten non-amputee subjects engaging in a series of 16 different gestures such as gripping a phone, holding a pen, pointing, pinching and grasping a cup of water. The results demonstrated far superior performance compared to CNN alone or other traditional machine learning methods, achieving a recognition accuracy of over 80 percent.

The hybrid approach did however struggle to accurately recognize two pinching gestures: a pinch using the middle finger and one using the index finger. In future efforts, the researchers want to optimize the algorithm and improve its accuracy still further, while keeping the training model small so it can be used in prosthesis hardware. They also want to figure out what is prompting the difficulty in recognizing pinching gestures and expand their experiments to a much larger number of subjects.

Ultimately, the researchers want to develop a prosthetic hand that is as flexible and reliable as a users original limb.

Author: Ning XuSource: Beijing Institute of Technology PressContact: Ning Xu Beijing Institute of Technology PressImage: The image is credited to Dr. Tie Liu, School of Electrical Engineering, Shenyang University of Technology

Original Research: Open access.Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model by Tie Liu et al. Cyborg and Bionic Systems

Abstract

Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model

The deep learning gesture recognition based on surface electromyography plays an increasingly important role in human-computer interaction. In order to ensure the high accuracy of deep learning in multistate muscle action recognition and ensure that the training model can be applied in the embedded chip with small storage space, this paper presents a feature model construction and optimization method based on multichannel sEMG amplification unit.

The feature model is established by using multidimensional sequential sEMG images by combining convolutional neural network and long-term memory network to solve the problem of multistate sEMG signal recognition.

The experimental results show that under the same network structure, the sEMG signal with fast Fourier transform and root mean square as feature data processing has a good recognition rate, and the recognition accuracy of complex gestures is 91.40%, with the size of 1MB.

The model can still control the artificial hand accurately when the model is small and the precision is high.

Read more here:
Hybrid Machine-Learning Approach Gives a Hand to Prosthetic-Limb Gesture Accuracy - Neuroscience News

Global Big Data and Machine Learning in Telecom Market research report is the latest evaluation of market growth. The report highlights future opportunities, analyzes market risks, and focuses on upcoming innovations. The report provides information about current market trends and development, drivers, consumption, technologies, and top grooming companies. The current trends that are expected to influence the future prospects of the Big Data and Machine Learning in Telecom market are analyzed in the report. The report further investigates and assesses the current landscape of the ever-evolving business sector and the present and future effects of COVID-19 on the market.

The global Big Data and Machine Learning in Telecom market is anticipated to rise at a considerable rate during the forecast period, 2022 to 2028.

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The Big Data and Machine Learning in Telecom market report provides a thorough analysis of the key strategies with a focus on the corporate structure, RandD methods, localization strategies, production capabilities, sales, and performance of various companies. The study conducts a SWOT analysis to evaluate the strengths and weaknesses of the key players in the Big Data and Machine Learning in Telecom market. The researcher provides an extensive analysis of the Big Data and Machine Learning in Telecom market size, share, trends, overall earnings, gross revenue, and profit margin to accurately draw a forecast and provide expert insights to investors to keep them updated with the trends in the market.

Global Big Data and Machine Learning in Telecom market competition by TOP MANUFACTURERS, with production, price, revenue (value), and each manufacturer including

Top Key players of Big Data and Machine Learning in Telecom Market are:AllotArgyle dataEricssonGuavusHUAWEIIntelNOKIAOpenwave mobilityProcera networksQualcommZTEGoogleAT&TAppleAmazonMicrosoft

The leading players are focusing mainly on technological advancements in order to improve efficiency. The long-term development patterns for this market can be captured by continuing the ongoing process improvements and financial stability to invest in the best strategies.

Types covered in this report are:Descriptive analyticsPredictive analyticsFeature engineering

Applications covered in this report are:ProcessingStorageAnalyzing

Regional Analysis For Big Data and Machine Learning in Telecom MarketNorth America (the United States, Canada, and Mexico)Europe (Germany, France, UK, Russia, and Italy)Asia-Pacific (China, Japan, Korea, India, and Southeast Asia)South America (Brazil, Argentina, Colombia, etc.)The Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, and South Africa)

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The global Big Data and Machine Learning in Telecom market size is expected to gain market growth in the forecast period of 2022 to 2028, with a Growing CAGR in the forecast period of 2022 to 2028 and will expected to reach USD million by 2028, from USD million in 2021.

The Big Data and Machine Learning in Telecom market report provides a detailed analysis of global market size, regional and country-level market size, segmentation market growth, market share, competitive Landscape, sales analysis, impact of domestic and global market players, value chain optimization, trade regulations, recent developments, opportunities analysis, strategic market growth analysis, product launches, area marketplace expanding, and technological innovations.

The content of the study subjects includes a total of 15 chapters:

Chapter 1, to describe Big Data and Machine Learning in Telecom product scope, market overview, market opportunities, market driving force and market risks. Chapter 2, to profile the top manufacturers of Big Data and Machine Learning in Telecom, with price, sales, revenue and global market share of Big Data and Machine Learning in Telecom in 2017 2021. Chapter 3, the Big Data and Machine Learning in Telecom competitive situation, sales, revenue and global market share of top manufacturers are analyzed emphatically by landscape contrast. Chapter 4, the Big Data and Machine Learning in Telecom breakdown data are shown at the regional level, to show the sales, revenue and growth by regions, from 2017 to 2021. Chapter 5, 6, 7, 8 and 9, to break the sales data at the country level, with sales, revenue and market share for key countries in the world, from 2016 to 2020. Chapter 10 and 11, to segment the sales by type and application, with sales market share and growth rate by type, application, from 2016 to 2020. Chapter 12, Big Data and Machine Learning in Telecom market forecast, by regions, type and application, with sales and revenue, from 2022 to 2028. Chapter 13, 14 and 15, to describe Big Data and Machine Learning in Telecom sales channel, distributors, customers, research findings and conclusion, appendix and data source.

Some of the key questions answered in this report:

What will the market growth rate, growth momentum or acceleration market carries during the forecast period? Which are the key factors driving the Big Data and Machine Learning in Telecom market? What was the size of the emerging Big Data and Machine Learning in Telecom market by value in 2021? What will be the size of the emerging Big Data and Machine Learning in Telecom market in 2028? Which region is expected to hold the highest market share in the Big Data and Machine Learning in Telecom market? What trends, challenges and barriers will impact the development and sizing of the Global Big Data and Machine Learning in Telecom market? What are sales volume, revenue, and price analysis of top manufacturers of Big Data and Machine Learning in Telecom market? What are the Big Data and Machine Learning in Telecom market opportunities and threats faced by the vendors in the global Big Data and Machine Learning in Telecom Industry?

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At last, the Big Data and Machine Learning in Telecom Market report includes investment come analysis and development trend analysis. The present and future opportunities of the fastest growing international industry segments are coated throughout this report. This report additionally presents product specification, manufacturing method, and product cost structure, and price structure.

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Big Data and Machine Learning in Telecom Market 2022 Seeking Growth from Emerging Markets, Study Drivers, Restraints and Forecast Talking Democrat -...