IBM Watson Assistant and Node-RED-Based Movie Ticketing Bot Design

2021 ◽  
pp. 307-316
Author(s):  
Arpita Ghosh
Keyword(s):  
Ibm Watson

IBM Watson Engagement Advisor

2014 ◽  
Author(s):  
Mitch Kramer
Keyword(s):  
Ibm Watson

IBM Watson Engagement Advisor Update

2015 ◽  
Author(s):  
Mitchell Kramer
Keyword(s):  
Ibm Watson

scholarly journals Characteristics and Classification of Technology Sector Companies in Digital Health for Diabetes

2021 ◽  
Vol 13 (9) ◽  
pp. 4839
Author(s):  
Satoru Kikuchi ◽  
Kota Kadama ◽  
Shintaro Sengoku
Keyword(s):  
Customer Value ◽  
Business Models ◽  
Digital Health ◽  
Information And Communications Technology ◽  
Smart Devices ◽  
Innovation Ecosystem ◽  
Alliance Networks ◽  
Technology Sector ◽  
Ibm Watson ◽  
Start Ups

In recent years, technological progress in smart devices and artificial intelligence has also led to advancements in digital health. Digital health tools are especially prevalent in diabetes treatment and improving lifestyle. In digital health’s innovation ecosystem, new alliance networks are formed not only by medical device companies and pharmaceutical companies but also by information and communications technology (ICT) companies and start-ups. Therefore, while focusing on digital health for diabetes, this study explored the characteristics of companies with high network centralities. Our analysis of the changes in degree, betweenness, and eigenvector centralities of the sample companies from 2011 to 2020 found drastic changes in the company rankings of those with high network centrality during this period. Accordingly, the following eight companies were identified and investigated as the top-ranking technology sector companies: IBM Watson Health, Glooko, DarioHealth, Welldoc, OneDrop, Fitbit, Voluntis, and Noom. Lastly, we characterized these cases into three business models: (i) intermediary model, (ii) substitute model, and (iii) direct-to-consumer model, and we analyzed their customer value.

Computer Vision Tagging the Metropolitan Museum of Art's Collection

2021 ◽  
Vol 14 (3) ◽  
pp. 1-17
Author(s):  
Elena Villaespesa ◽  
Seth Crider
Keyword(s):  
Computer Vision ◽  
Selection Criteria ◽  
Object Image ◽  
Metropolitan Museum ◽  
Museum Collections ◽  
Vision Systems ◽  
Computer Vision Systems ◽  
Similarities And Differences ◽  
Ibm Watson

Computer vision algorithms are increasingly being applied to museum collections to identify patterns, colors, and subjects by generating tags for each object image. There are multiple off-the-shelf systems that offer an accessible and rapid way to undertake this process. Based on the highlights of the Metropolitan Museum of Art's collection, this article examines the similarities and differences between the tags generated by three well-known computer vision systems (Google Cloud Vision, Amazon Rekognition, and IBM Watson). The results provide insights into the characteristics of these taxonomies in terms of the volume of tags generated for each object, their diversity, typology, and accuracy. In consequence, this article discusses the need for museums to define their own subject tagging strategy and selection criteria of computer vision tools based on their type of collection and tags needed to complement their metadata.

scholarly journals Comparative study of cloud platforms to develop a Chatbot

2017 ◽  
Vol 6 (3) ◽  
pp. 57 ◽  
Author(s):  
Amit Patil ◽  
Marimuthu K ◽  
Nagaraja Rao A ◽  
Niranchana R
Keyword(s):  
Artificial Intelligence ◽  
Comparative Study ◽  
Time Complexity ◽  
Completion Time ◽  
Setup Time ◽  
Human Being ◽  
Cloud Platform ◽  
New Era ◽  
Ibm Watson ◽  
Aws Lambda

Before chatbots there were simply bots: The invention of a chatbot brought us to the new era of technology, the era of conversation service. A chatbot is a virtual person that can effectively talk to any human being with the help of interactive conversion textual skill. Now a days there are many cloud-based platforms available for developing and deploying the chatbot such as Microsoft bot framework, IBM Watson, Kore, AWS lambda, Microsoft Azure bot service, Chatfuel, Heroku and many more but all those techniques has some drawbacks such as built-in Artificial Intelligence, NLP, conversion service, programming etc. This paper represents the comparison between all cloud-based chatbot technologies with some constraint such as built-in AI, setup time, completion time, complexity etc. Finally, by the comparison, we will get to know that which cloud platform is efficient and suitable for developing chatbot.

scholarly journals A Cross-Sectional Analysis of Oral Health Spending Over the Lifespan in Commercial and Medicaid Insured Populations

2021 ◽  
Author(s):  
Eric Tranby ◽  
Julie Frantsve-Hawley ◽  
Myechia Minter-Jordan ◽  
James Thommes ◽  
Matt Jacob ◽  
...  
Keyword(s):  
Oral Health ◽  
Oral Cancer ◽  
Dental Care ◽  
Care Delivery ◽  
Health Spending ◽  
Dental Service ◽  
Fee For Service ◽  
Cross Sectional ◽  
Medical Spending ◽  
Ibm Watson

Background: Life course theory creates a better framework to understand how oral health needs and challenges align with specific phases of the lifespan, care models, social programs, and changes in policy. Methods): Data are from the 2018 IBM Watson Multi-State Medicaid Marketscan Database (31 million claims) and the 2018 IBM Watson Dental Commercial and Medicare Supplemental Claims Database (45 million claims). Analysis compares per enrollee spending fee-for-service dental claims and medical spending on dental care from ages 0 to 89. Results: Dental utilization and spending are lower during the first 4 years of life and in young adulthood than in other periods of life. Stark differences in the timing, impact, and severity of caries, periodontal disease, and oral cancer are seen between those enrolled in Medicaid and commercial dental plans. Early childhood caries and oral cancer occur more frequently and at younger ages in Medicaid populations. Conclusions: This unique lifespan analysis of the U.S. multi-payer dental care system demonstrates the complexities of the current dental service environment and a lack of equitable access to oral healthcare. Practical Implications: Health policies should be focused on optimizing care delivery to provide effective preventive care at specific stages of the lifespan.

DearDiary—An IBM Watson Powered Mental Healthcare Tracker

2017 ◽  
pp. 65-71 ◽  
Author(s):  
Abhilash Kishore ◽  
Amisha Agarwal ◽  
Anisha Mascarenhas ◽  
Arjun Rao
Keyword(s):  
Mental Healthcare ◽  
Ibm Watson
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