A Modelling of Context-Aware Elderly Healthcare Eco-System-(CA-EHS) Using Signal Analysis and Machine Learning Approach

Sujaya, B. L.; Bhaskar, R

doi:10.1007/s11277-021-08341-2

Cited by 12 publications

(7 citation statements)

References 26 publications

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“…Machines will very certainly eventually be smarter than humans in practically every way. As a consequence, solving the difficulties highlighted below and finding out how to collaborate with robots will be a constant problem for the human species (Sujaya and Bhaskar, 2021).…”

Section: Discussionmentioning

confidence: 99%

Role of machine learning in changing social and business eco-system – a qualitative study to explore the factors contributing to competitive advantage during COVID pandemic

Sanil¹,

Singh²,

Raj³

et al. 2021

WJE

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Purpose “Machine learning (ML)” in business aids in increasing company scalability and boosting company operations for businesses all over the world. “Artificial intelligence (AI)” technologies and several “ML” algorithms have grown in prominence in the business analytics sector. In the era of a huge quantum of data being generated by the virtue of the integration of the various software with the business operations, the relevance of “ML” is continuously increasing. As a result, companies may now profit from knowing how companies may use “ML” and incorporating it into their own operations. “ML” derives useful results from the data to address very dynamic and difficult social and business problems. ML helps in establishing a system that learns automatically and produces results in less time and effort, allowing machines to discover. ML is developing at a breakneck pace, fuelled mostly by new computer technology to competitive advantages during the COVID pandemic. Design/methodology/approach For firms all around the world, “ML” in business aids in expanding scalability and boosting operations. In the field of business analytics, artificial intelligence (AI) and machine learning (ML) algorithms have become increasingly popular. The importance of “ML” is growing in an era when a massive amount of data is generated as a result of the integration of various applications with company activities. As a result, businesses can now benefit from understanding how other businesses are using “ML” and adopting it into their own operations. In order to handle very dynamic and demanding societal and business challenges, machine learning (ML) extracts valuable results from data. Machine learning (ML) aids in the development of a system that learns automatically and generates outcomes with less time and effort, allowing machines to discover. ML is progressing at a dizzying pace, fueled primarily by new computer technology and used to gain competitive advantages during the COVID pandemic. Findings According to a new study published by the Accenture Institute for High Performance, “AI” might double yearly economic growth rates in several wealthy nations by 2035. With broad AI deployment, the yearly growth rate in the USA increased from 2.6% to 4.6%, resulting in an extra $8.3tn. In the UK, AI may contribute $814bn to the economy, raising the yearly growth rate from 2.5% to 3.9%. The authors are already in a business period when huge technological development is assisting us in addressing a variety of difficulties to achieve maximum development. AI technology has enormous developmental consequences. In addition, big data analytics is helping to make AI more enterprise ready. Future developments in “ML” cannot be understated. Machines will very certainly eventually be smarter than humans in practically every way. Originality/value The introduction of AI into the market has enabled small businesses to use tried-and-true strategies for achieving greater business objectives. AI is continually offering a competitive advantage to start-ups, whilst large corporations provide a platform for building novel solutions. AI has become an integral component of reality, from functioning as a robot in a production unit to self-driving automobiles and voice activated resources in complex medical procedures. As a consequence, solving the difficulties highlighted below and finding out how to collaborate with robots will be a constant problem for the human species (Sujaya and Bhaskar, 2021).

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Section: Discussionmentioning

confidence: 99%

Role of machine learning in changing social and business eco-system – a qualitative study to explore the factors contributing to competitive advantage during COVID pandemic

Sanil¹,

Singh²,

Raj³

et al. 2021

WJE

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show abstract

“…Married elderly people may have better mental status [ 9 ]. Studies have further pointed out that divorce and remarriage provide health protection for the elderly, especially for elderly females [ 10 ]. Good lifestyle habits also improve the health status of the elderly, such as regular exercise and reducing smoking and alcohol habits [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

The effect of smartphones on the self-rated health levels of the elderly

2022

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Background Due to the penetration of Internet use and the popularity of “Internet + elderly care” among seniors in recent years, the elderly are gradually integrating into the information society. This study examined the impact of smartphones on the self-rated health levels of the elderly. Methods We studied 3042 elderly people over 55 years of age in Jiangxi, China in 2018. The effect of smartphones was measured from three aspects: smartphone usage, smartphone usage ability, and smartphone usage purpose, and the multivariate ordered logistic model was applied. Furthermore, considering the potential endogeneity of the smartphone usage of elderly people, the propensity score matching (PSM) method was used to analyze the net effect of smartphones on the health levels of the elderly. Results (1) The use of smartphones had a significant positive impact on the self-rated health levels of the elderly, with its significance being at the level of 1%. Smartphone usage ability, and using smartphone to learn or search for health information, had significant positive impacts (at the level of 5%) on the self-rated health levels of the elderly. (2) The k-nearest neighbor matching, kernel matching and radius matching methods were used to calculate the net effect of smartphone usage on the self-rated health levels of the elderly. The results were 13.26, 15.33 and 14.80%, respectively. (3) The age of the participants significantly (at the level of 1%) negatively affected their self-rated health levels. Other characteristics of the elderly, including income, education level, living with children or spouse and children’s living conditions, significantly (all at the level of 1%) positively affected their self-rated health levels. Conclusions Smartphone usage, smartphone usage ability, and smartphone usage purposes all improved the self-rated health of the elderly. The Internet factor should be focused on in the process of active aging. We should improve the Internet use ability of the elderly through voluntary training or public lectures.

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“…Among the variety of application types, mobile applications can introduce live monitoring tools, which have many advantages, such as coping with dynamic changes and promoting mobility [34]. Many context-aware recommendation systems target older populations and patients and provide monitoring and other services based on the guidance of health experts [36][37][38]. However, context-aware recommendation systems for healthcare applications have not yet widely been developed for young people, which is the main objective of this study.…”

Section: Context-aware Recommendations In Healthcarementioning

confidence: 99%

Context-aware Based Personalized Recommendation on Mobile for Monitoring Excessive Sugar Consumption of Thai Adolescents

Suwan

Temdee

Prasad

2022

JMM

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Given the harmful effects of excessive sugar consumption, everyone should be aware of the amount of sugar they consume in daily life. Generally, managing nutrition and preventing negative effects from sugar intake requires a nutritional specialist or specialized knowledge, which may not always be readily accessible or understandable. Therefore, there is a need for a mobile application that can track excessive sugar consumption. This study proposes a context-aware personalized recommendation mobile application for monitoring excessive sugar consumption and providing individual recommendations (based on a predefined set of 144 rules) to Thai adolescents. The application is user friendly and can be used to provide recommendations to users for sugar consumption and proper exercise each day. Personal, health, and lifestyle data are collected and analyzed to provide individualized recommendations to each user. Experiments were conducted with 140 Thai adolescents aged 15 to 25 years old. Users’ preferences regarding degree of awareness in presentation style were also investigated. Users rated their satisfaction with the proposed mobile application as very high in terms of both function and personalization. In addition, fully automated recommendations were found to be the preferred degree of awareness among the test group.

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A Modelling of Context-Aware Elderly Healthcare Eco-System-(CA-EHS) Using Signal Analysis and Machine Learning Approach

Cited by 12 publications

References 26 publications

Role of machine learning in changing social and business eco-system – a qualitative study to explore the factors contributing to competitive advantage during COVID pandemic

Role of machine learning in changing social and business eco-system – a qualitative study to explore the factors contributing to competitive advantage during COVID pandemic

The effect of smartphones on the self-rated health levels of the elderly

Context-aware Based Personalized Recommendation on Mobile for Monitoring Excessive Sugar Consumption of Thai Adolescents

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