Global Healthcare Cognitive Computing Market Report 2019 7ᵗʰ edition Top Companies, Sales, Revenue, Forecast and Detailed Analysis - Market Trends

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Healthcare Cognitive Computing market report is based on present industry situations, market demands, business strategies utilized by prominent players involved in this market along with their growth synopsis. This report has been segmented into types, applications and regions. The report also comprises major drivers boosting this market. Healthcare Cognitive Computing market worth about XX million USD in 2018 and it is expected to reach YY million USD in 2026 with a CAGR of AA% during the forecast period. Cognitive computing (CC) describes technology platforms that are based on the scientific disciplines of artificial intelligence and signal processing.


Cognitive Computing Market Is Projected to Grow at a Healthy CAGR During 2016 - 2024 - Press Release - Digital Journal

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Persistence Market Research delivers pertinent insights on the growth of the Cognitive Computing Market and identifies key market dynamics impacting this growth. New York City, NY -- (SBWIRE) -- 01/27/2017 -- In the ever changing world of information technology, business organizations are left with humongous amount of data with them. This data includes very critical information for business use, but business organizations are only able to utilize 20% of whole data available with them with the use of traditional data analytics technology. To process and interpret the reaming 80% of the data that is in the form of videos, images, and human voice (also called as dark data), there is a need of cognitive computing systems. Cognitive computing systems are typical combination of hardware and software that constitute natural language processing (NLP) and machine language, and have capability to collect, process, and interpret the dark data available with business organizations.



Cognitive Computing Market – Verified Market Research Demand, Development Analysis Share, Industry Growth, Size, Analysis and Forecast 2026 - The Market Research News

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Qualitative and quantitative analysis of the market based on segmentation involving both economic as well as non-economic factors Provision of market value (USD Billion) data for each segment and sub-segment • Indicates the region and segment that is expected to witness the fastest growth as well as to dominate the market • Analysis by geography highlighting the consumption of the product/service in the region as well as indicating the factors that are affecting the market within each region • Competitive landscape which incorporates the market ranking of the major players, along with new service/product launches, partnerships, business expansions and acquisitions in the past five years of companies profiled • Extensive company profiles comprising of company overview, company insights, product benchmarking and SWOT analysis for the major market players • The current as well as future market outlook of the industry with respect to recent developments (which involve growth opportunities and drivers as well as challenges and restraints of both emerging as well as developed regions • Includes an in-depth analysis of the market of various perspectives through Porter's five forces analysis • Provides insight into the market through Value Chain • Market dynamics scenario, along with growth opportunities of the market in the years to come • 6-month post sales analyst support


Machine Learning Market Statistics - Ironpaper

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The global cognitive computing market is expected to reach 12.5 billion in 2019, up from 2.5 billion in 2014, at a CAGR of 38%. Cognitive computing is another way of describing machine learning, an area of computer science which sees machines recognizing patterns and learning new material -- without being explicitly programmed by a human. As more businesses identify benefits of cognitive computing, the machine learning market is growing. Possible applications of machine learning are vast and varied. A survey from 451 Research outlines future initiatives that will impact the growth, use of, and quality of data under management, which is a critical component of machine learning and AI.