Mediaboss Marketing

Note: This special issue is a Thematic Track at IP&MC2022. For more information about IP&MC2022, please visit https://www.elsevier.com/events/conferences/information-processing-and-management-conference.

IP&MC2022 INTERDISCIPLINARY-TOPICS (VSI: IPMC2022 INTERDISCIPLINE)

University: Huazhong University of Science and Technology

Email: yangyanwu@hust.edu.cn

University: Beijing Jiaotong University

Email: cjzhang@bjtu.edu.cn

University: Sun Yat-sen University

Email: mnswq@mail.sysu.edu.cn

University: Naval Academy Research Institute

Email: christophe.claramunt@gmail.com

This track at IP&MC2022 and a special issue in Information Processing and Management will publish cutting-edge original research at the intersection of information science, computing, artificial intelligence, economics, and social science concerning theory, methods, or applications in a range of domains, including but not limited to information science, information technology, management, advertising and marketing, energy, health, economics and social computing, and metaverse.

The track aims to serve the interests of primary researchers but also practitioners in furthering knowledge at the intersection of information science, computing, artificial intelligence, and social science by providing an effective forum for the timely dissemination of advanced and topical issues. The track is especially interested in original research articles, research survey articles, research method articles, and articles addressing critical applications of research.

Specifically, the track is interested in four types of manuscripts, which are:

We invite authors to submit their research work (including full-length, original, and unpublished research papers based on theoretical or experimental contributions and review studies), especially in areas of information science, information technology, management, advertising and marketing, energy, health, economics and social computing, and metaverse.

Topics of interest include, but are not limited to:

Submit your manuscript to the Special Issue category (VSI: IPMC2022 INTERDISCIPLINE) through the online submission system of Information Processing & Management. https://www.editorialmanager.com/ipm/

Authors will prepare the submission following the Guide for Authors on IP&M journal at (https://www.elsevier.com/journals/information-processing-and-management/0306-4573/guide-for-authors). All papers will be peer-reviewed following the IP&MC2022 reviewing procedures.

The authors of accepted papers will be obligated to participate in IP&MC2022 and present the paper to the community to receive feedback. The accepted papers will be invited for revision after receiving feedback on the IP&MC 2022 conference. The submissions will be given premium handling at IP&M following its peer-review procedure and, (if accepted), published in IP&M as full journal articles, with also an option for a short conference version at IP&MC2022.

Please see this infographic for the manuscript flow:https://www.elsevier.com/__data/assets/pdf_file/0003/1211934/IPMC2022Timeline10Oct2022.pdf

For more information about IP&MC2022, please visit https://www.elsevier.com/events/conferences/information-processing-and-management-conference.

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IP&MC2022 Interdisciplinary Topics - Information Processing and Management Conference - Knovel

Artificial Intelligence (AI) in Beauty and Cosmetics Market report focused on the comprehensive analysis of current and future prospects of the Artificial Intelligence (AI) in Beauty and Cosmetics industry. This report is a consolidation of primary and secondary research, which provides market size, share, dynamics, and forecast for various segments and sub-segments considering the macro and micro environmental factors. An in-depth analysis of past trends, future trends, demographics, technological advancements, and regulatory requirements for the Artificial Intelligence (AI) in Beauty and Cosmetics market has been done in order to calculate the growth rates for each segment and sub-segments.

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Some of the Top Key Vendors of this Market are:

LOrals (Modiface, Hair Coach), Beiersdorf (NIVEA SKiN GUiDE), Olay (Skin Care App), Shiseido (Optune System), CRIXlabs (DBA Quantified Skin), Procter & Gamble (Opte Wand), Yours Skincare, My Beauty Matches, EpigenCare Inc., mySkin, Haut.AI, Luna Fofo, Revieve, ANOKAI. CA., Youth Laboratories, Pure & Mine, Glory Skincare, Nioxin, New Kinpo Group, Perfect Corp, Symrise (Philyra), Function of Beauty LLC, Coty Inc. (Rimmel), Este Lauder, Sephora USA, Inc. (Virtual Artist), Spruce Beauty , Givaudan, Beautystack and Polyfins Technology Inc.

The report on Artificial Intelligence (AI) in Beauty and Cosmetics Market provides a unique tool to assess the market, highlight the opportunities and support strategic decision making and tactical. This report recognizes that in this rapidly changing competitive environment, up-to-date marketing information is essential for monitoring performance and making critical decisions for growth and profitability. It provides information on trends and developments and focuses on market capabilities and the changing structure of Artificial Intelligence (AI) in Beauty and Cosmetics.

Various factors are responsible for the markets growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Artificial Intelligence (AI) in Beauty and Cosmetics market. This report is a consolidation of primary and secondary research, which provides market size, share, dynamics, and forecast for various segments and sub-segments considering the macro and micro environmental factors. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market.

Regional Analysis Artificial Intelligence (AI) in Beauty and Cosmetics Market:

North America, Latin America, Asia-Pacific, Africa and Europe and others have been extensively studied according to various parameters of companies such as type, size, application and end users. The global market for Artificial Intelligence (AI) in Beauty and Cosmetics is presented for niche reading. The report presents portfolios of different strategies and best practices governing companies. Certain applicable sales methodologies have been included in this research report and may influence commercial production.

Global Artificial Intelligence (AI) in Beauty and Cosmetics Market report highlights:

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The influence of the latest government guidelines is also analysed in detail in the report. It studies the Artificial Intelligence (AI) in Beauty and Cosmetics markets trajectory between forecast periods. The cost analysis of the Global Artificial Intelligence (AI) in Beauty and Cosmetics Market has been performed while keeping in view manufacturing expenses, labour cost, and raw materials and their market concentration rate, suppliers, and price trend.

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Table of Contents

Global Artificial Intelligence (AI) in Beauty and Cosmetics Market Research Report 2022 2029

Chapter 1 Artificial Intelligence (AI) in Beauty and Cosmetics Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Artificial Intelligence (AI) in Beauty and Cosmetics Market Forecast

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Original post:
Artificial Intelligence (AI) in Beauty and Cosmetics Market to Witness Huge Growth by 2029 | L'Oral's (Modiface, Hair Coach), Beiersdorf (NIVEA SKiN...

AI

Theres nothing magical about artificial intelligence, its simply code designed by fallible humans using fallible data. The magic comes from the humans working with or seeing the benefits of AI. So the questions are: are we expecting too much from AI? Too what extent should companies and their executives rely on the output delivered by AI?

This was the subject of debate at a panel hosted at AI Summit in New York, held in early December, focusing on risks in the emerging role of AI in the financial services sector, but the discussion had wide-ranging implications across all industries. (I had the opportunity to co-chair the conference, and moderate the panel.)

We think AI is telling us something, but its not, cautioned Rod Butters, chief technology officer for Aible. Its just a bunch of code. It doesnt know. This is the fantasy we all fall into. Somehow we think that model has embodies something. The reality is that an AI is just a statistical engine, and in a lot of cases, its a bad statistical engine.

With AI these days, the biggest systemic risk in the notion that artificial intelligence is artificial, said Rik Willard, founder and managing director of Agentic Group, and member of the advisory board of the World Ethical Data Foundation. Its all done by humans; its all manifested by humans. When we look at risk versus returns, its only as good as the financial institutions, and the regulatory frameworks around those institutions. Are we supporting the same human and economic algorithms that we set up before technology, or are we working to make those better and more inclusive?

In addition, AI is still a relatively immature technology, said Drew Scarano, vice president of global financial services at AntWorks. Ten years ago we werent even talking about AI, but today, its a multi-billion dollar industry, he said. said Scarano. We might be too reliant on this technology, forgetting about the humans in the loop and how they play an integral part in complementing artificial intelligence in order to get desired results.

Another challenge is AI systems tend to get built in relative isolation. AI is just code, and the people building these systems may have limited perspectives on its value to the business, Butters cautioned. When we tell data scientists go out and create a model, were asking them to be a mind reader and a fortune teller, he said. Those are two bad job sets, it doesnt work. The data scientist is trying to do the right thing, creating a responsible and solid model, but based on what? Ultimately when they build a model, unless theyve got this combination to create transparency, create expandability, actually communicate that across to the business constituency both at a strategic and tactical, who is in charge? Just creating a great model does not necessarily solve all problems.

In the process of building data models, data scientists need to understand the objectives of the enterprise, taking into account the human implications, Scarano said. You can have engineer build a great bridge. So if its not going over what its intended to do, its just a great bridge, right? Im afraid that people in business, especially financial services. will just keep relying too much on technology. We need a holistic approach, in coexistence with humans.

Look beyond the technology and statistics of AI, and focus on what ultimately serves the customer, Scarano urged. Its about how we complement humans with artificial intelligence to drive business, and also drive customer reality, customer success and customer satisfaction at the end of the day.

The path to AI in service of business objectives relies on the establishment of consistent frameworks that guide its development, panelists agreed. I was raised in a fail-fast environment, said Willard. You build code, you test, and fix what's broken. You fix it on the fly. You build it, it kind of works, you let it loose, then you refine it over time based on input to the feedback loop. However, with AI, the issue is that we put it in a position of judgment. Like in the criminal justice system, where it does a lot of harm before you get it right. In the banking system its loan, no loan; score, no score; or credit, no credit. How do we build testing frameworks and sandboxes that have the accuracy thats necessary to be launched at scale, while doing less harm along the way?

AI is being used for many purposes across the financial services industry, but the risk is in de-humanizing the interpersonal qualities that helped build the industry. Today we can use AI for anything from approving a credit card to approving a mortgage to approving any kind of lending vehicle, said Scarano. But without human intervention to be able to understand there's more to a human than a credit score, there's more to a person than getting approved or denied for a mortgage.

Customer experience is the foundation of financial services, and this needs to be front and center of all AI initiatives. There needs to feedback loops in AI-driven systems that incorporate human input. As we implement AI-based solutions, we need to ensure that the end users, the customers, who are consuming the product are also happy with that investment and solution as well, said Robert Magno, solutions architect with Run:AI. It makes a lot of sense to have robots moving packages around, automated in a warehouse. But from a customer service standpoint, if a person interacting with a chatbot is getting frustrated, there needs to be a feedback loop to ensure solutions you're implementing are resonating with your customers, and they're enjoying the experience as much as you're enjoying creating the experience.

More:
Artificial Intelligence May Be Just Code, But Its Our Code - Forbes

DUBLIN, Dec. 22, 2021 /PRNewswire/ -- The "Artificial Intelligence In Remote Patient Monitoring Market Research Report by Product, by Solution, by Technology, by Application, by Region - Global Forecast to 2026 - Cumulative Impact of COVID-19" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence In Remote Patient Monitoring Market size was estimated at USD 712.67 million in 2020 and expected to reach USD 892.99 million in 2021, at a CAGR 25.63% to reach USD 2,803.19 million by 2026.

Market Statistics:

The report provides market sizing and forecast across five major currencies - USD, EUR GBP, JPY, and AUD. It helps organization leaders make better decisions when currency exchange data is readily available. In this report, the years 2018 and 2019 are considered historical years, 2020 as the base year, 2021 as the estimated year, and years from 2022 to 2026 are considered the forecast period.

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 Remote Patient Monitoring 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.

Company Usability Profiles:

The report profoundly explores the recent significant developments by the leading vendors and innovation profiles in the Global Artificial Intelligence In Remote Patient Monitoring Market, including 100 Plus, AiCure, Binah.ai, Biofourmis, Cardiomo, ChroniSense Medical, ContinUse Biometrics (Cu-Bx), Current Health, Ejenta, Eko, Engagely.ai, Feebris, GYANT, iHealth, Medical Device + Diagnostic Industry (MD+DI), Medopad, Myia, Neoteryx, LLC, Neteera, Tech Vedika, ten3T Healthcare, and Vitls.

The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on the market offered by the key players2. Market Development: Provides in-depth information about lucrative emerging markets and analyze penetration across mature segments of the markets3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, certification, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players5. 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 Remote Patient Monitoring Market?2. What are the inhibiting factors and impact of COVID-19 shaping the Global Artificial Intelligence In Remote Patient Monitoring 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 Remote Patient Monitoring Market?4. What is the competitive strategic window for opportunities in the Global Artificial Intelligence In Remote Patient Monitoring Market?5. What are the technology trends and regulatory frameworks in the Global Artificial Intelligence In Remote Patient Monitoring Market?6. What is the market share of the leading vendors in the Global Artificial Intelligence In Remote Patient Monitoring Market?7. What modes and strategic moves are considered suitable for entering the Global Artificial Intelligence In Remote Patient Monitoring Market?

Key Topics Covered:

1. Preface

2. Research Methodology

3. Executive Summary

4. Market Overview4.1. Introduction4.2. Cumulative Impact of COVID-19

5. Market Dynamics5.1. Introduction5.2. Drivers5.2.1. ICT infrastructure development in developing countries5.2.2. Rise in adoption of AI in remote patient monitoring due to real time monitoring and improved patient engagement5.2.3. Growth in demand due to optimizing management and lower human errors5.3. Restraints5.3.1. Lack of awareness in remote areas5.3.2. Expensive as compared to traditional facilities5.4. Opportunities5.4.1. Rapid digitalization and extensive use of social media of consumer5.4.2. Shift in trend towards wearable technology5.5. Challenges5.5.1. Increasing concern related to cybersecurity and privacy

6. Artificial Intelligence In Remote Patient Monitoring Market, by Product6.1. Introduction6.2. Special Monitors6.2.1. Anaesthesia Monitors6.2.2. Blood Glucose Monitor6.2.3. Cardiac Rhythm Monitor6.2.4. Fetal Heart Rate Monitor6.2.5. Multi-Parameter Monitors6.2.6. Prothrombin Monitors6.2.7. Respiratory Monitor6.3. Vital Monitors6.3.1. Blood Pressure Monitor6.3.2. Brain Monitor6.3.3. Heart Rate Monitor6.3.4. Pulse Oximeter6.3.5. Respiratory Monitor6.3.6. Temperature Monitor

7. Artificial Intelligence In Remote Patient Monitoring Market, by Solution7.1. Introduction7.2. Hardware7.3. Services7.4. Software

8. Artificial Intelligence In Remote Patient Monitoring Market, by Technology8.1. Introduction8.2. Machine Learning (ML)8.3. Natural Language Processing (NLP)8.4. Querying Method (QM)8.5. Speech Recognition (SR)

9. Artificial Intelligence In Remote Patient Monitoring Market, by Application9.1. Introduction9.2. Cancer9.3. Cardiovascular Diseases9.4. Dehydration9.5. Diabetes9.6. Infections9.7. Respiratory Issues9.8. Sleep Disorder9.9. Viral Infection9.10. Weight Management & Fitness Monitoring

10. Americas Artificial Intelligence In Remote Patient Monitoring Market10.1. Introduction10.2. Argentina10.3. Brazil10.4. Canada10.5. Mexico10.6. United States

11. Asia-Pacific Artificial Intelligence In Remote Patient Monitoring Market11.1. Introduction11.2. Australia11.3. China11.4. India11.5. Indonesia11.6. Japan11.7. Malaysia11.8. Philippines11.9. Singapore11.10. South Korea11.11. Taiwan11.12. Thailand

12. Europe, Middle East & Africa Artificial Intelligence In Remote Patient Monitoring Market12.1. Introduction12.2. France12.3. Germany12.4. Italy12.5. Netherlands12.6. Qatar12.7. Russia12.8. Saudi Arabia12.9. South Africa12.10. Spain12.11. United Arab Emirates12.12. United Kingdom

13. Competitive Landscape13.1. FPNV Positioning Matrix13.1.1. Quadrants13.1.2. Business Strategy13.1.3. Product Satisfaction13.2. Market Ranking Analysis13.3. Market Share Analysis, By Key Player13.4. Competitive Scenario13.4.1. Merger & Acquisition13.4.2. Agreement, Collaboration, & Partnership13.4.3. New Product Launch & Enhancement13.4.4. Investment & Funding13.4.5. Award, Recognition, & Expansion

14. Company Usability Profiles14.1. 100 Plus14.2. AiCure14.3. Binah.ai14.4. Biofourmis14.5. Cardiomo14.6. ChroniSense Medical14.7. ContinUse Biometrics (Cu-Bx)14.8. Current Health14.9. Ejenta14.10. Eko14.11. Engagely.ai14.12. Feebris14.13. GYANT14.14. iHealth14.15. Medical Device + Diagnostic Industry (MD+DI)14.16. Medopad14.17. Myia14.18. Neoteryx, LLC14.19. Neteera14.20. Tech Vedika14.21. ten3T Healthcare14.22. Vitls

15. Appendix

For more information about this report visit https://www.researchandmarkets.com/r/7x6wlp

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Insights on the Artificial Intelligence in Remote Patient Monitoring Global Market to 2026 - Featuring 100 Plus, AiCure and Cardiomo Among Others -...

Xiaomi has unveiled MIUI 13 which it plans to unleash on the world in the first quarter of the new year. The firm said that the operating system will be expanded beyond smartphones and tablets to Artificial Intelligence of Things (AIoT) devices such as smart watches, speakers, and TVs. The firm has also improved its software so that it operates better under heavy usage.

According to the company, MIUI 13 improves core functions, increasing the systems fluidity by a whopping 52%. The core apps have also been optimised so they run better while the system is getting bogged down by third-party apps. Xiaomi has also developed technologies called Atomized Memory and Liquid Storage which reduce deterioration by over 5% over a 36-month period; this should help you hold onto devices for longer.

To make MIUI more interoperable with smart devices, the new update will introduce the beta of Mi Smart Hub. Commenting on the new tool, Xiaomi said:

As of Q3 2021, the number of connected devices on Xiaomis IoT platform exceeds 400 million. While leading the industry with its smart hardware portfolio, MIUI 13 will introduce the beta of Mi Smart Hub, which will help realize a more connected experience between smart devices. With Mi Smart Hub, users can find nearby devices and with a simple gesture to seamlessly share and access content such as music, display, even apps across multiple devices.

Finally, MIUI 13 brings new personalisation options through new widgets, dynamic wallpapers, and more. The global version of MIUI 13 will be delivered over-the-air beginning in Q1 2022. The first devices to get the update will be the Mi 11, Mi 11 Ultra, Mi 11i, Mi 11X Pro, Mi 11X, Xiaomi Pad 5, Redmi 10, Redmi 10 Prime, Xiaomi 11 Lite 5G NE, Xiaomi 11 Lite NE, Redmi Note 8 (2021), Xiaomi 11T Pro, Xiaomi 11T, Redmi Note 10 Pro, Redmi Note 10 Pro Max, Redmi Note 10, Mi 11 Lite 5G, Mi 11 Lite, and Redmi Note 10 JE.

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Xiaomi debuts MIUI 13 with support for the Artificial Intelligence of Things - Neowin