Category Archives: Artificial Intelligence

DENVER With wildfires becoming bigger and more destructive as the West dries out and heats up, agencies and officials tasked with preventing and battling the blazes could soon have a new tool to add to their arsenal of prescribed burns, pick axes, chain saws and aircraft.

The high-tech help could come by way of an area not normally associated with fighting wildfires: artificial intelligence. And space.

Lockheed Martin Space, based in Jefferson County, is tapping decades of experience of managing satellites, exploring space and providing information for the U.S. military to offer more accurate data quicker to ground crews. They are talking to the U.S. Forest Service, university researchers and a Colorado state agency about how their their technology could help.

By generating more timely information about on-the-ground conditions and running computer programs to process massive amounts of data, Lockheed Martin representatives say they can map fire perimeters in minutes rather than the hours it can take now. They say the artificial intelligence, or AI, and machine learning the company has applied to military use can enhance predictions about a fires direction and speed.

The scenario that wildland fire operators and commanders work in is very similar to that of the organizations and folks who defend our homeland and allies. Its a dynamic environment across multiple activities and responsibilities, said Dan Lordan, senior manager for AI integration at Lockheed Martins Artificial Intelligence Center.

Lockheed Martin aims to use its technology developed over years in other areas to reduce the time it takes to gather information and make decisions about wildfires, said Rich Carter, business development director for Lockheed Martin Spaces Mission Solutions.

The quicker you can react, hopefully then you can contain the fire faster and protect peoples properties and lives, Carter said.

The concept of a regular fire season has all but vanished as drought and warmer temperatures make Western lands ripe for ignition. At the end of December, the Marshall fire burned 991 homes and killed two people in Boulder County. The Denver area just experienced its third driest-ever April with only 0.06 of an inch of moisture, according to the National Weather Service.

Colorado had the highest number of fire-weather alerts in April than any other April in the past 15 years. Crews have quickly contained wind-driven fires that forced evacuations along the Front Range and on the Eastern Plains. But six families in Monte Vista lost their homes in April when a fire burned part of the southern Colorado town.

Since 2014, the Colorado Division of Fire Prevention and Control has flown planes equipped with infrared and color sensors to detect wildfires and provide the most up-to-date information possible to crews on the ground. The onboard equipment is integrated with the Colorado Wildfire Information System, a database that provides images and details to local fire managers.

Last year we found almost 200 new fires that nobody knew anything about, said Bruce Dikken, unit chief for the agencys multi-mission aircraft program. I dont know if any of those 200 fires would have become big fires. I know they didnt become big fires because we found them.

When the two Pilatus PC-12 airplanes began flying in 2014, Colorado was the only state with such a program conveying the information in near real time, Dikken said. Lockheed Martin representatives have spent time in the air on the planes recently to see if its AI can speed up the process.

We dont find every single fire that we fly over and it can certainly be faster if we could employ some kind of technology that might, for instance, automatically draw the fire perimeter, Dikken said. Right now, its very much a manual process.

Something like the 2020 Cameron Peak fire, which at 208,663 acres is Colorados largest wildfire, could take hours to map, Dikken said.

And often the people on the planes are tracking several fires at the same time. Dikken said the faster they can collect and process the data on a fires perimeter, the faster they can move to the next fire. If it takes a couple of hours to map a fire, what I drew at the beginning may be a little bit different now, he said.

Lordan said Lockheed Martin engineers who have flown with the state crews, using the video and images gathered on the flights, have been able to produce fire maps in as little as 15 minutes.

The company has talked to the state about possibly carrying an additional computer that could help crunch all that information and transmit the map of the fire while still in flight to crews on the ground, Dikken said. The agency is waiting to hear the results of Lockheed Martins experiences aboard the aircraft and how the AI might help the state, he added.

Actionable intelligence

The company is also talking to researchers at the U.S. Forest Service Missoula Fire Sciences Laboratory in Montana. Mark Finney, a research forester, said its early in discussions with Lockheed Martin.

They have a strong interest in applying their skills and capabilities to the wildland fire problem, and I think that would be welcome, Finney said.

The lab in Missoula has been involved in fire research since 1960 and developed most of the fire-management tools used for operations and planning, Finney said. Were pretty well situated to understand where new things and capabilities might be of use in the future and some of these things certainly might be.

However, Lockheed Martin is focused on technology and thats not really been where the most effective use of our efforts would be, Finney said.

Prevention and mitigation and preemptive kind of management activities are where the great opportunities are to change the trajectory were on, Finney said. Improving reactive management is unlikely to yield huge benefits because the underlying source of the problem is the fuel structure across large landscapes as well as climate change.

Logging and prescribed burns, or fires started under controlled conditions, are some of the management practices used to get rid of fuel sources or create a more diverse landscape. But those methods have sometimes met resistance, Finney said.

As bad as the Cameron Peak fire was, Finney said the prescribed burns the Arapaho and Roosevelt National Forests did through the years blunted the blazes intensity and changed the flames movement in spots.

Unfortunately, they hadnt had time to finish their planned work, Finney said.

Lordan said the value of artificial intelligence, whether in preventing fires or responding to a fire, is producing accurate and timely information for fire managers, what he called actionable intelligence.

One example, Lordan said, is information gathered and managed by federal agencies on the types and conditions of vegetation across the country. He said updates are done every two to three two years. Lockheed Martin uses data from satellites managed by the European Space Agency that updates the information about every five days.

Lockheed is working with Nvidia, a California software company, to produce a digital simulation of a wildfire based on an areas topography, condition of the vegetation, wind and weather to help forecast where and how it will burn. After the fact, the companies used the information about the Cameron Peak fire, plugging in the more timely satellite data on fuel conditions, and generated a video simulation that Lordan said was similar to the actual fires behavior and movement.

While appreciating the help technology provides, both Dikken with the state of Colorado and Finney with the Forest Service said there will always be a need for ground-truthing by people.

Applying AI to fighting wildfires isnt about taking people out of the loop, Lockheed Martin spokesman Chip Eschenfelder said. Somebody will always be in the loop, but people currently in the loop are besieged by so much data they cant sort through it fast enough. Thats where this is coming from.

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Artificial intelligence tapped to fight Western wildfires - Portland Press Herald - Press Herald

During the first few months of a babys life, it is usually difficult to tell the exact sex or age by sight alone.

A machine learning model showed that it is possible to identify both indicators, the sex and the age of a baby, based on its temperament, thanks to artificial intelligence.

A babys propensity to show fear, smile or laugh can tell a lot about their age or gender, in the eyes of artificial intelligence.

This was demonstrated by a research team led by professionals from Washington State University and the University of Idaho, United States, through a study in which a machine learning model was used to analyze temperament data in 4,438 babies, in an attempt to classify babies by gender and age.

Working with this many babies is an almost intractable task for just one research laboratory. However, obtaining this sample was possible thanks to the collection of reliable information from different scientific sources, to collect child behavior data collected between 2006 and 2019.

The data with which we worked in this research come from a questionnaire that applies a temperament measurement scale, in which parents recorded the frequency of 191 different behaviors that their children from 3 to 12 months of age showed during one week. These behaviors can be categorized into 14 different dimensions of temperament, such as smiling, activity level, anger or frustration, and fear. Overall, the sample collects data from 2,298 boys and 2,093 girls.

Working with this background, an AI model capable of using these indicators to identify the age and sex of a baby was developed. Previously, research has already been carried out that, in an isolated way, investigates the relationship between temperament and the age or sex of the baby. This research, on the other hand, is the first to combine these elements during the analysis.

During the first 48 weeks of a babys life, it was easier for algorithms to decipher a babys age than its gender. However, after that period, the system was able to specify their classification, which suggests that gender differences are accentuated from that age.

It is at least suggestive of a picture in which temperament begins to differentiate by gender in a more powerful way around the age of one year, commented Maria Gartstein, lead author of the study and professor of psychology at Washington State University.

For purposes of this study, research co-author Erich Seamon of the University of Idaho Institute for Modeling, Collaboration and Innovation used machine learning algorithms to classify babies as male or female under three age ranges: 0- 24 weeks of age, 24-48 weeks of age, and older than 48 weeks, based on their scores for the 14 dimensions of temperament.

Accuracy rates increased with age, from a low of 38% for age group one to 57% for age group three.

It was a good opportunity to do a kind of demonstration study using these machine learning techniques that require really large data sets and are not very common in socio-emotional development researchGartstein commented. It gave us the opportunity for the first time to really consider the extent to which gender differences are informed by childhood age.

Although the results of this research are subject to the multiple influences that may affect its study sample, the work presented is consistent with previous research that already indicated that the effects of socialization begin to become visible around one year of age.

Read more here:

They present artificial intelligence capable of predicting the age and sex of a baby based on its temperament - InTallaght

Artificial Intelligence in Medical Imaging Market gives a detailed study of the industry, replete with a spec sheet, market segmentation, and other information, in Data Bridge Market Research. The study analyses the potential and present market scenario for the projected period of 2022-2028, offering data and updates on the worldwide Artificial Intelligence in Medical Imaging markets major segments. An influential Artificial Intelligence in Medical Imaging market report is a window to the industry which explains what market definition, classifications, applications, engagements and market trends are. By keeping end users at the centre point, a team of researchers, forecasters, analysts and industry experts work exhaustively to formulate this market research report. The market is supposed to grow during the forecast period due to growing demand at the end user level. According to this market report, new highs will take place in the Artificial Intelligence in Medical Imaging market. Artificial Intelligence in Medical Imaging market survey report offers the best professional in-depth study on the current state for the industry.

Artificial intelligence in medical imaging market is expected to gain market growth in the forecast period of 2021 to 2028. Data Bridge Market Research analyses the market to reach at an estimated value of USD 1,579.33 million and grow at a CAGR of 4.11% in the above-mentioned forecast period. Increased numbers of diagnostic procedures drives the artificial intelligence in medical imaging market.

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Top Key Players Included in This Report:

BenevolentAI, OrCam, Babylon, Freenome Inc., Clarify Health Solutions, BioXcel Therapeutics, Ada Health GmbH, GNS Healthcare, Zebra Medical Vision Inc., Qventus Inc, IDx Technologies Inc., K Health, Prognos, Medopad Ltd., Viz.ai Inc., Voxel Technology, Renalytix AI plc, Beijing Pushing Technology Co. Ltd., PAIGE, mPulse Mobile, Suki AI Inc., BERG LLC, Zealth Inc., OWKIN INC., and Your.MD Ltd. UK

The Study Is Segmented By Following:

By Technology (Deep Learning, Computer Vision, NLP, Others), Offering (Hardware, Software, Services), Deployment Type (On-Premise, Cloud), Application (X-Ray, CT, MRI, Ultrasound, Molecular Imaging), Clinical Applications (Breast, Lung, Neurology, Cardiovascular, Liver, Prostate, Colon, Musculoskeletal, Others), End-User (Hospitals, Clinics, Research Laboratories, Others)

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Geographic Segment Covered in the Report:

The Artificial Intelligence in Medical Imaging report provides information on the market area, which is divided into sub-regions and countries / regions. In addition to the market share in each country and sub-region, this chapter in this report also contains information on profit opportunities. This chapter of the report mentions the market share and growth rate for each region, country and sub-region during the estimated period.

The study has covered and anatomized the eventuality of Worldwide Artificial Intelligence in Medical Imaging Industry and provides statistics and information on market dynamics, growth factors, crucial challenges, major motorists & conditions, openings and Forecast 2028. Businesses can confidently rely on the information mentioned in the reliable Artificial Intelligence in Medical Imaging Market analysis report as it is derived only from the valuable and genuine resources. This market research report delivers comprehensive analysis of the market structure along with the estimations of the various segments and sub-segments of the market. The transformation in market landscape is analyzed in the finest Artificial Intelligence in Medical Imaging marketing report which is mainly observed due to the moves of key players or brands which include developments, product launches, joint ventures, mergers and acquisitions that in turn change the view of the global face of the industry.

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The report has been prepared using the latest methods and tools for primary and secondary research. Our analysts rely on government documents, white papers, press releases, reliable investor information, financial and quarterly reports, and public and private interviews to gather data and information about the market in which they operate.

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Artificial Intelligence in Medical Imaging Market In Depth Analysis Along with Statistics and Forecast 2022-2029 Queen Anne and Mangolia News - Queen...

Grand View Research, Inc. Market Research And Consulting

According to new report available with Grand View Research, the global artificial intelligence in healthcare industry is anticipated to grow owing to increasing demand for personalized medicine and reducing care expenses, from 2022 to 2030.

Artificial Intelligence In Healthcare Industry Overview

The global artificial intelligence in healthcare market size was valued at USD 10.4 billion in 2021 and is expected to reach USD 208.2 billion by 2030, registering a CAGR of 38.4% over a forecast period.

The growing datasets of patient health-related digital information, increasing demand for personalized medicine, and the rising demand for reducing care expenses are some of the major driving forces of the market growth. The growing global geriatric population, changing lifestyles, rising prevalence of chronic diseases has contributed to the surge in demand for diagnosing and improved understanding of diseases in their initial stages. Artificial Intelligence (AI) and machine learning (ML) algorithms are being widely adopted and integrated into healthcare systems to accurately predict diseases in their early stage based on historical health datasets.

Gather more insights about the market drivers, restrains and growth of the Global Artificial Intelligence In Healthcare Market

Furthermore, deep learning technologies,predictive analytics, content analytics, and Natural Language Processing (NLP) tools are enabling care professionals to diagnose patients underlying health conditions at an earlier stage. The Covid-19 pandemic positively influenced the demand for AI technologies and unearthed the potential held by these advanced technologies. Healthcare systems widely adopted these technologies in the rapid diagnosis and detection of different virus strains and utilized personalized information in improving the management of the outbreak. AI/ML algorithms were utilized in the diagnosis sector wherein these technologically driven modules were trained with datasets of chest CT images, symptoms, pathological findings, and exposure history to diagnose Covid-19 positive patients rapidly and accurately.

Moreover, the growing shortage of healthcare workforce drove the adoption of AI/ML technologies. Therefore, AI algorithms can be trained to analyze patient health information which further supports care providers in quickly diagnosing the condition and devising an accurate treatment regime. Supportive government initiatives, the rising number of mergers and acquisitions and technological collaborations, and the ongoing Covid-19 pandemic had a significant role in boosting the growth of the market and accelerating the adoption rate of AI in healthcare. AI/ML algorithms are being widely implemented in rapid and accurate diagnosis of medical conditions after the initial implementation in the detection of Covid-19 positive patients using personalized patient information and data consolidation.

As per an NCBI study in 2020, AI-based algorithms accurately detected 68% of COVID-19 positive cases in a dataset of twenty-five patients which were diagnosed as negative cases by care professionals. The implementation of AI/ML technologies in enhancing patient care, reducing machine downtime, and minimizing care expenses are some of the driving forces of artificial intelligence (AI) in healthcare market growth. Owing to the Covid-19 pandemic, AI-based technologies witnessed a significant boost in adoption and are set to experience a dramatic growth trajectory. Healthcare functions such as diagnostics, patient management, medication management, claims management, workflow management, integration of machines, and cybersecurity saw a remarkable surge in the integration of AI/ML technologies.

The growing penetration of Artificial Intelligence technologies in healthcare applications led to key market participants focusing on product innovation and technological collaborations to expand their product portfolio and meet the growing demands during the pandemic. For instance, in May 2020, MIT-IBM Watson AI Lab promoted the implementation of AI technologies in research projects studying the health and economic consequences of the pandemic. Similarly, Qventus introduced AI-based patient flow automation systems covering critical resource control, hospitalization duration optimization, Covid-19 scenario planner, and ICU capacity creation in its healthcare facilities.

Artificial Intelligence In Healthcare Market Segmentation

Based on the Component Insights, the market is segmented into Software Solutions, Hardware, and Services.

Based on the Application Insights, the market is segmented into Robot-Assisted Surgery, Virtual Assistants, Administrative Workflow Assistants, Connected Machines, Diagnosis, Clinical Trials, Fraud Detection, Cybersecurity, and Dosage Error Reduction.

Based on the Regional Insights, the market is segmented into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa.

Market Share Insights

Key Companies Profile:

Increasing investments in R&D, innovative product developments and launches, the rising number of technological collaborations, and service differentiation are the key strategies players are focusing on to gain a competitive edge in the market.

Some of the prominent players in the artificial intelligence in healthcare market include,

Order a free sample PDF of the Artificial Intelligence In Healthcare Market Intelligence Study, published by Grand View Research.

About Grand View Research

Grand View Research is a full-time market research and consulting company registered in San Francisco, California. The company fully offers market reports, both customized and syndicates, based on intense data analysis. It also offers consulting services to business communities and academic institutions and helps them understand the global and business scenario to a significant extent. The company operates across multitude of domains such as Chemicals, Materials, Food and Beverages, Consumer Goods, Healthcare, and Information Technology to offer consulting services.

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Artificial Intelligence in Healthcare Market To Witness An Explosive CAGR of 38.4% Till 2030, Driven By R - Benzinga

Artificial Intelligence in Healthcare market analysis report highlights the idea of high level analysis of major market segments and recognition of opportunities. Market analysis and market segmentation has been reviewed here in terms of markets, geographic scope, years considered for the study, currency and pricing, research methodology, primary interviews with key opinion leaders, DBMR market position grid, DBMR market challenge matrix, secondary sources, and assumptions. This market report carries out comprehensive analysis of company profiles of key market players that offers a competitive landscape. Artificial Intelligence in Healthcare market document exhibits important product developments and tracks recent acquisitions, mergers and research in the healthcare industry by the top market players.

The market study and analysis of the large scale Artificial Intelligence in Healthcare report lends a hand to figure out types of consumers, their views about the product, their buying intentions and their ideas for advancement of a product. To attain knowledge of all the above factors, this transparent, extensive and supreme market report is generated. And for the same, the report also describes all the major topics of the market research analysis that includes market definition, market segmentation, competitive analysis, major developments in the market, and excellent research methodology. This Artificial Intelligence in Healthcare market research report has been formed with the vigilant efforts of innovative, enthusiastic, knowledgeable and experienced team of analysts, researchers, industry experts, and forecasters.

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Leading Key Players Operating in the Artificial Intelligence in Healthcare Market Includes:NVIDIA Corporation (US), Intel Corporation (US), IBM (US), Google LLC (US), Microsoft (US), General Vision Inc. (US), Johnson & Johnson Services, Inc. (US), Siemens Healthcare GmbH (Germany), Medtronic (Ireland), CloudMedx Inc. (US)

Market Analysis and Insights: Global Artificial Intelligence in Healthcare Market:

This Artificial Intelligence in Healthcare market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on Data Bridge Market Research Artificial Intelligence in Healthcare market contact us for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Browse Full Report Along With Facts and Figures @ https://www.databridgemarketresearch.com/reports/global-artificial-intelligence-in-healthcare-market?AZ

Market Analysis and Insights: Global Artificial Intelligence in Healthcare Market:

This Artificial Intelligence in Healthcare market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on Data Bridge Market Research Artificial Intelligence in Healthcare market contact us for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Artificial Intelligence in Healthcare Market, By Region:

Global Artificial Intelligence in Healthcare market is analyzed and market size insights and trends are provided by country, product as referenced above.

The countries covered in the Artificial Intelligence in Healthcare market report are the U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America.

North America dominates the Artificial Intelligence in Healthcare market because of the rise in the cases of arrhythmic diseases, favorable reimbursement policies for patients, high demand for advanced treatment methods and developed healthcare infrastructure in the region. Asia-Pacific is estimated to grow in the forecast period due to the high prevalence of cardiovascular diseases, increase in adoption of advanced digital devices, large population and launch of new innovative products.

Table of Contents

Global Artificial Intelligence in Healthcare Market Size, status and Forecast to 2029

1 Market summary2 Manufacturers Profile3 Global Artificial Intelligence in Healthcare Sales, Overall Revenue, Market Share and Competition by Manufacturer4 Global Artificial Intelligence in Healthcare market analysis by numerous Regions5 North America Artificial Intelligence in Healthcare by Countries6 Europe Artificial Intelligence in Healthcare by Countries7 Asia-Pacific Artificial Intelligence in Healthcare by Countries8 South America Artificial Intelligence in Healthcare by Countries9 Middle east and Africas Artificial Intelligence in Healthcare by Countries10 Global Artificial Intelligence in Healthcare Market phase by varieties11 Global Artificial Intelligence in Healthcare Market phase by Applications12 Artificial Intelligence in Healthcare Market Forecast13 Sales Channel, Distributors, Traders and Dealers14 Analysis Findings and Conclusion15 Appendix

Check Complete Table of Contents with List of Table and Figures @ https://www.databridgemarketresearch.com/toc/?dbmr=global-artificial-intelligence-in-healthcare-market&AZ

What are the market opportunities, market risks, and market overviews of the Artificial Intelligence in Healthcare Market?

Who are the distributors, traders, and merchants in the Artificial Intelligence in Healthcare Market?

What is the analysis of sales, income, and prices of the leading manufacturers in the Artificial Intelligence in Healthcare Market?

What are the Artificial Intelligence in Healthcare market opportunities and threats faced by the global Artificial Intelligence in Healthcare Market vendors?

What are the main factors driving the worldwide Artificial Intelligence in Healthcare Industry?

What are the Top Players in Artificial Intelligence in Healthcare industry?

What is the analysis of sales, income, and prices by type, application of the Artificial Intelligence in Healthcare market?

What is regional sales, income, and price analysis for Artificial Intelligence in Healthcare Market?

Research Methodology: GlobalLiquid Chromatography Devices Market

Data collection and base year analysis is done using data collection modules with large sample sizes. The market data is analyzed and estimated using market statistical and coherent models. Also market share analysis and key trend analysis are the major success factors in the market report. To know more please request an analyst call or can drop down your enquiry.

The key research methodology used by DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Global versus Regional and Vendor Share Analysis.

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Artificial Intelligence in Healthcare Market Global Analysis, Opportunities and Forecast to 2029 Queen Anne and Mangolia News - Queen Anne and...

This market report contains market insights and analysis for this industry which are backed up by SWOT analysis. This market report examines market drivers, market restraints, challenges, opportunities, and key developments in this industry. This report provides with complete knowledge and information of rapidly changing market landscape, what is already there in the market, future trends or market expectations, the competitive environment, and competitor strategies that aids in planning your own strategies with which you can outshine the competitors. This market report has been designed by keeping in mind the customer requirements which assist them in increasing their return on investment (ROI).

Moreover, this research report highlights numerous industry verticals such as company profile, contact details of manufacturer, product specifications, geographical scope, production value, market structures, recent developments, revenue analysis, market shares and possible sales volume of the company. To gain actionable market insights to build sustainable and money-spinning business strategies with ease, This market research report is a great option. This report provides top to bottom analysis and estimation of various market related factors that plays key role in better decision making. This report describes CAGR (compound annual growth rate) values and its fluctuations for the specific forecast period.

Data Bridge Market Research analyses theartificial intelligence in aviation marketwill exhibit a CAGR of 46.3% for the forecast period of 2022-2029 and is likely to reach the USD 9,995.84 million by 2029.

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Top Players Analysed in the Report are:

Some of the major players operating in the artificial intelligence in aviation market are IBM, Microsoft, Amazon Web Services, Inc., Airbus S.A.S., Xilinx, NVIDIA Corporation, Intel Corporation, General Electric, Micron Technology, Inc., Garmin Ltd., Lockheed Martin Corporation, SAMSUNG, Thales Group, MINDTITAN, Mitsubishi Electric Corporation, OMRON Corporation, TAV Technologies and IRIS Automation, among others.

Porters five forces model in the report provides insights into the competitive rivalry, supplier and buyer positions in the market and opportunities for the new entrants in the global Artificial Intelligence in Aviation market over the period. Further, Growth Matrix gave in the report brings an insight into the investment areas that existing or new market players can consider.

Research Methodology

A) Primary Research

Our primary research involves extensive interviews and analysis of the opinions provided by the primary respondents. The primary research starts with identifying and approaching the primary respondents, the primary respondents are approached include

Key Opinion Leaders Internal and External subject matter experts Professionals and participants from the industry

Our primary research respondents typically include

Executives working with leading companies in the market under review Product/brand/marketing managers CXO level executives Regional/zonal/ country managers Vice President level executives.B) Secondary Research

Secondary research involves extensive exploring through the secondary sources of information available in both the public domain and paid sources. Each research study is based on over 500 hours of secondary research accompanied by primary research. The information obtained through the secondary sources is validated through the crosscheck on various data sources.

Read Detailed Index of full Research Study @https://www.databridgemarketresearch.com/reports/global-artificial-intelligence-in-aviation-market

Who Will Get Advantage of This Report?

The prime aim of theGlobal Artificial Intelligence in Aviation Marketis to provide industry investors, private equity companies, company leaders and stakeholders with complete information to help them make well-versed strategic decisions associated to the chances in the Concealed Door Closer market throughout the world.

The secondary sources of the data typically include

Company reports and publications Government/institutional publications Trade and associations journals Databases such as WTO, OECD, World Bank, and among others Websites and publications by research agencies

Key Market Segmentation:

On the basis of technology, the artificial intelligence in aviation has been segmented into computer vision, machine learning, context awareness computing and natural language processing. Machine learning is further segmented into deep learning, supervised learning, unsupervised learning, reinforcement learning and semi-supervised learning.

Based on offering, the artificial intelligence in aviation market has been segmented into hardware, software and services. Hardware is further segmented into processors, memory and networks. Software is further segmented into AI solutions and AI platforms. Services are further segmented into deployment and integration and support and maintenance.

On the basis of application, the artificial intelligence in aviation market has been segmented into dynamic pricing, virtual assistants, flight operations, smart maintenance, manufacturing, surveillance, training and other applications. Manufacturing is further segmented into material movement, predictive maintenance and machinery inspection, production planning, quality control and reclamation.

Key Elements that the report acknowledges:

Market size and growth rate during the forecast period. Key factors driving this market Key market trends cracking up the growth of this market Challenges to market growth Key vendors of this market Detailed SWOT analysis Opportunities and threats face by the existing vendors in this global market Trending factors influencing the market in the geographical regions Strategic initiatives focusing on the leading vendors PEST analysis of the market in the five major regions

To check the complete Table of Content click here: @https://www.databridgemarketresearch.com/toc/?dbmr=global-artificial-intelligence-in-aviation-market

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Global Artificial Intelligence in Aviation Market Size, Share And Trends Analysis Report By Product Types, And Applications Forecast Queen Anne and...

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Drug Discovery Market Research Report by Offering (Services and Software), Application, End User, Region (Americas, Asia-Pacific, and Europe, Middle East & Africa) - Global Forecast to 2026 - Cumulative Impact of COVID-19" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence in Drug Discovery Market size was estimated at USD 566.22 million in 2020, USD 701.05 million in 2021, and is projected to grow at a Compound Annual Growth Rate (CAGR) of 25.06% to reach USD 2,166.65 million by 2026.

Competitive Strategic Window:

The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period.

FPNV Positioning Matrix:

The FPNV Positioning Matrix evaluates and categorizes the vendors in the Artificial Intelligence in Drug Discovery Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Market Share Analysis:

The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market. It provides the idea of its revenue generation into the overall market compared to other vendors in the space. It provides insights into how vendors are performing in terms of revenue generation and customer base compared to others. Knowing market share offers an idea of the size and competitiveness of the vendors for the base year. It reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.

The report provides insights on the following pointers:

1. Market Penetration: Provides comprehensive information on the market offered by the key players

2. Market Development: Provides in-depth information about lucrative emerging markets and analyze penetration across mature segments of the markets

3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments

4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, certification, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players

5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and breakthrough product developments

The report answers questions such as:

1. What is the market size and forecast of the Global Artificial Intelligence in Drug Discovery Market?

2. What are the inhibiting factors and impact of COVID-19 shaping the Global Artificial Intelligence in Drug Discovery Market during the forecast period?

3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Artificial Intelligence in Drug Discovery Market?

4. What is the competitive strategic window for opportunities in the Global Artificial Intelligence in Drug Discovery Market?

5. What are the technology trends and regulatory frameworks in the Global Artificial Intelligence in Drug Discovery Market?

6. What is the market share of the leading vendors in the Global Artificial Intelligence in Drug Discovery Market?

7. What modes and strategic moves are considered suitable for entering the Global Artificial Intelligence in Drug Discovery Market?

Market Dynamics

Drivers

Restraints

Opportunities

Challenges

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/mgxxoa

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Global Artificial Intelligence in Drug Discovery Market Research Report to 2026 - AI Cloud to Create a Streamlined and Automated Approach in Drug...

With the advancement of technology, artificial intelligence and machine learning (AI/ML) is on the verge of becoming an integral part of every industry, and education is no exception. With AI being enabled, learning can be customised for students. In the last few years, due to the emergence of machine learning, data has been treated as a prime knowledge resource and it is valued. Simultaneously, tech-based industry has upped the demand for AI/ML rapidly, therefore more students are taking up the course due to good career opportunities, Rajesh Khanna, professor, president, NIIT University, said.

Besides courses, it is has been observed that the such technology is being leveraged by ed-tech platforms as an business strategy enhancing tool. Starting from career counselling to exam proctoring, ed-techs have utilised AI/ML to accelerate the accuracy and productivity. There are multiple options when it comes to career counselling and opportunities. AI/ML can provide a good fit option for students when the right algorithm is taken into consideration, Rohan Pasari, CEO, Cialfo, said.

Further, AI/ML is used to conduct various tests online, so much so that it being now believed that it puts a seal to the authenticity of the process, as it gives the ability to remotely invigilate the test. On photographs taken at an interval, an AI algorithm is run to analyse the accuracy and authenticity of the examination. In CY21, Mettle conducted 20 million assessments across the globe on the online platform, out of which 16 million were remotely invigilated, Siddhartha Gupta, CEO, Mercer Mettle, a tech-based exam assessment platform, said.

According to Rackspaces AI/ML Annual Research Report 2022, AI/ML has been considered as the top two most important strategic technologies, along with cybersecurity. The report shows that up to 72% of respondents have noted AI/ML as part of their business strategy, IT strategy or both. Initially, the kind of industries which would have benefited from AI/ML were the financial market based companies. But the time has come that heavy machinery is now opening up to AI and ML to figure out and address the problems in a more distinct and accurate manner, Khanna added.

Although penetration of tech-based education can upskill the students, it is believed that enabling technologies is associated with various challenges and risk factors. The ministry report on school education 2020-21 revealed that post-pandemic, the dropout rate of students increased to 8.9% from 2.6% as the main reason being closure of schools and irregular online classes. In places like rural India, accessibility has always been a point of contention, which has also resulted in a digital divide. Whenever a new technology is enabled, the magnification of inequality also takes place. Places where devices and connectivity are not strongly available, they will definitely suffer. But the gap has to be filled by non-government orgnisation (NGOs) and government intervention by providing ways to resolve the issues, Khanna said.

Read Also: Union Education Minister, Shri Dharmendra Pradhan chairs meeting on formulation of HECI

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Adoption of AI/ML: How artificial intelligence is scaling up the education industry - The Financial Express

With enabled instant location of Bluetooth Low Energy (BLE) tags connected to individuals and critical equipment, to supply real-time and historic contact tracing, with increased precision accuracy and additional new features.

PARIS, FRANCE News Direct 10 May 2022 Alcatel-Lucent Enterprise, a leading provider of network, communications and cloud solutions tailored to customers industries is placing AI (Artificial Intelligence) and ML (Machine Learning) at the heart of its technology development. ALEs enhanced OmniAccess Stellar Asset Tracking solution now offers new customisable push-button alerts and an AI/ML powered real-time location algorithm for environments that require improved accuracy compared with standard tools.

Designed to quickly locate assets or individuals, use analytics to optimise workflows, and simplify the ability to provide contact tracing, Alcatel-Lucent Enterprise Asset Tracking is set to deliver an enriched user experience with finer location precision thanks to its AI and Machine Learning capabilities.

Further enhancements of the solution include equipping BLE tags with a new alert button, to notify users of activity at the touch of a button, or by sending automated notifications from an indoor geofenced area and immediately share vital information in real-time.

This solution holds powerful potential for the healthcare industry, for use cases such as calling medical staff for assistance, locating and assessing the availability of critical equipment, and improving safety of patients and staff.

The alert button function is also fully programmable for use case flexibility and enables configuration for button press request action, with real-time location, extending its value beyond the healthcare sector to be used to enhance campus security for staff and students in schools or enable security personnel to call for assistance in a variety of indoor environments.

Asset tracking users can also receive alerts via a range of media, making sure information is delivered to the right person, or group, at the right time, through the most convenient channel.

Notifications are sent instantly to the Alcatel-Lucent OmniVista Cirrus Asset Manager and distributed via Android push notification to the OmniAccess Stellar Asset Tracking app, web push to desktop or mobile device, email, SMS, Rainbow and other third-party systems, such as IQ Messenger. This message server includes additional notification media such as Alcatel-Lucent desktop, DECT and WLAN phones, nurse call systems, etc.

Daniel Faurlin, Business Line Manager, Network Business Division at Alcatel-Lucent Enterprise, comments:

Our OmniAccess Stellar Asset Tracking solution has proved an essential tool for our customers and they can now track, locate and monitor the usage patterns of their assets with even greater accuracy and efficiency. Although contact tracing and asset tracking came to light most prominently during the health crisis, its ability to improve performance across numerous industries extends beyond the turbulence of the pandemic and can be harnessed to bring operations into the digital age.

As ALE continues to enrich its offer under the traditional CAPEX model, it has also expanded to a new hybrid Network as a Service offering, combining both CAPEX & OPEX options.

In line with customer requirements, ALE plans to add asset tracking and contact tracing capabilities to its Network-as-a-Service offer. The company also provides a pay-as-you-grow model for businesses looking to ramp up their digital transformation with a manageable predictable monthly fee and the opportunity to benefit from the latest technology updates with a reduced initial investment.

Our aim is always to make accessing high-performance and data-rich solutions as easy as possible for our customers. As we continue to innovate and enhance our solutions, so too will we develop new models to make digital transformation universally accessible with options for every business and industry, adds Nolwenn Simon, Product Line Manager Network Value added solutions, Alcatel-Lucent Enterprise.

Alcatel-Lucent Enterprise delivers the customised technology experiences enterprises need to make everything connect.

ALE provides digital-age networking, communications and cloud solutions with services tailored to ensure customers success, with flexible business models in the cloud, on premises, and hybrid. All solutions have built-in security and limited environmental impact.

Over 100 years of innovation have made Alcatel-Lucent Enterprise a trusted advisor to more than a million customers all over the world.

With headquarters in France and 3,400 business partners worldwide, Alcatel-Lucent Enterprise achieves an effective global reach with a local focus.

al-enterprise.com | LinkedIn | Twitter | Facebook | Instagram

#AlcatelLucentEnterprise

The issuer is solely responsible for the content of this announcement.

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Alcatel-Lucent Enterprise enhances its Asset Tracking solution with Artificial Intelligence capabilities and push-button alerts - Macau Business

The European approach to artificial intelligence (AI) will help build a resilient Europe for the Digital Decade where people and businesses can enjoy the benefits of AI. It focuses on 2 areas: excellence in AI and trustworthy AI. The European approach to AI will ensure that any AI improvements are based on rules that safeguard the functioning of markets and the public sector, and peoples safety and fundamental rights.

To help further define its vision for AI, the European Commission developed an AI strategy to go hand in hand with the European approach to AI. The AI strategy proposed measures to streamline research, as well as policy options for AI regulation, which fed into work on the AI package.

The Commission published its AI package in April 2021, proposing new rules and actions to turn Europe into the global hub for trustworthy AI. This package consisted of:

Fostering excellence in AI will strengthen Europes potential to compete globally.

The EU will achieve this by:

The Commission and Member States agreed boost excellence in AI by joiningforces on AI policy and investment. The revised Coordinated Plan on AI outlines a vision to accelerate, act, and align priorities with the current European and global AI landscape and bring AI strategy into action.

Maximising resources and coordinating investments is a critical component of the Commissions AI strategy. Through the Digital Europe and Horizon Europe programmes, the Commission plans to invest 1 billion per year in AI. It will mobilise additional investments from the private sector and the Member States in order to reach an annual investment volume of 20 billion over the course of the digital decade.

The newly adopted Recovery and Resilience Facility makes 134 billion available for digital. This will be a game-changer, allowing Europe to amplify its ambitions and become a global leader in developing cutting-edge, trustworthy AI.

Access to high quality data is an essential factor in building high performance, robust AI systems. Initiatives such as the EU Cybersecurity Strategy, the Digital Services Act and the Digital Markets Act, and the Data Governance Act provide the right infrastructure for building such systems.

Building trustworthy AI will create a safe and innovation-friendly environment for users, developers and deployers.

The Commission has proposed 3 inter-related legal initiatives that will contribute to building trustworthy AI:

The Commission aims to address the risks generated by specific uses of AI through a set of complementary, proportionate and flexible rules. These rules will also provide Europe with a leading role in setting the global gold standard.

This framework gives AI developers, deployers and users the clarity they need by intervening only in those cases that existing national and EU legislations do not cover. The legal framework for AI proposes a clear, easy to understand approach, based on four different levels of risk: unacceptable risk, high risk, limited risk, and minimal risk.

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A European approach to artificial intelligence | Shaping ...