Artificial Intelligence and Internet of Things Based Healthcare 4.0 Monitoring System

Kishor, Amit; Chakraborty, Chinmay

doi:10.1007/s11277-021-08708-5

Cited by 135 publications

(45 citation statements)

References 32 publications

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“…However, very few researchers have been examining how to assess the scalability performances of integrating several health applications into a single device to address the complex care needs of patients with multi-morbidity (Dinsmore et al, 2018 ; Waschkau et al, 2019 ). Further, few authors have proposed several data platforms leveraging big data technology that enable the discovery of emergent multi-morbid patterns and emanating longitudinal risk (Malecki et al, 2020 ; Kishor and Chakraborty, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The authors used features from patient vital signal measurements and activity tracking features. The training dataset was based on publicly available datasets from Irish elderly people, named TILDA (Deparis et al, 2018 ; Kishor and Chakraborty, 2021 ). Monitoring multi-morbidity conditions in real-time for thousands of patients simultaneously would generate many data streams.…”

Section: Literature Reviewmentioning

confidence: 99%

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Autonomous Tool for Monitoring Multi-Morbidity Health Conditions in UAE and India

Atalla

Amin

Kumar³

et al. 2022

Front. Artif. Intell.

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Multi-morbidity is the presence of two or more long-term health conditions, including defined physical or mental health conditions, such as diabetes or schizophrenia. One of the regular and critical health cases is an elderly person with a multi-morbid health condition and special complications who lives alone. These patients are typically not familiar with advanced Information and Communications Technology (ICT), but they are comfortable using smart devices such as wearable watches and mobile phones. The use of ICT improves medical quality, promotes patient security and data security, lowers operational and administrative costs, and gives the people in charge to make informed decisions. Additionally, the use of ICT in healthcare practices greatly reduces human errors, enhances clinical outcomes, ramps up care coordination, boosts practice efficiencies, and helps in collecting data over time. The proposed research concept provides a natural technique to implement preventive health care innovative solutions since several health sensors are embedded in devices that autonomously monitor the patients' health conditions in real-time. This enhances the elder's limited ability to predict and respond to critical health situations. Autonomous monitoring can alert doctors and patients themselves of unexpected health conditions. Real-time monitoring, modeling, and predicting health conditions can trigger swift responses by doctors and health officials in case of emergencies. This study will use data science to stimulate discoveries and breakthroughs in the United Arab Emirates (UAE) and India, which will then be reproduced in other world areas to create major gains in health for people, communities, and populations.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Autonomous Tool for Monitoring Multi-Morbidity Health Conditions in UAE and India

Atalla

Amin

Kumar³

et al. 2022

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…The IoT implementation in healthcare will make life easier for everyone involved, including doctors and patients, while also enhancing the quality of care [12,13,14]. IoT sensors collect patient data, which is then analyzed using machine learning techniques [15]. For patients, it is simple to access and use sensors that are built into their health monitoring devices [16,17].…”

Section: Iot Healthcare Technologymentioning

confidence: 99%

Fitness Dependent Optimizer for IoT Healthcare using Adapted Parameters: A Case Study Implementation

Aladdin¹,

Abdullah

Salih

et al. 2022

Preprint

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This discusses a case study on Fitness Dependent Optimizer or so-called FDO and adapting its parameters to the Internet of Things (IoT) healthcare. The reproductive way is sparked by the bee swarm and the collaborative decision-making of FDO. As opposed to the honey bee or artificial bee colony algorithms, this algorithm has no connection to them. In FDO, the search agent's position is updated using speed or velocity, but it's done differently. It creates weights based on the fitness function value of the problem, which assists lead the agents through the exploration and exploitation processes. Other algorithms are evaluated and compared to FDO as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the original work. The key current algorithms—The Salp-Swarm Algorithms (SSA), Dragonfly Algorithm (DA), and Whale Optimization Algorithm (WOA) have been evaluated against FDO in terms of their results. Using these FDO experimental findings, we may conclude that FDO outperforms the other techniques stated. There are two primary goals for this chapter: first, the implementation of FDO will be shown step-by-step so that readers can better comprehend the algorithm method and apply FDO to solve real-world applications quickly. The second issue deals with how to tweak the FDO settings to make the meta-heuristic evolutionary algorithm better in the IoT health service system at evaluating big quantities of information. Ultimately, The target of this chapter's enhancement is to adapt the IoT healthcare framework based on FDO to spawn effective IoT healthcare applications for reasoning out real-world optimization, aggregation, prediction, segmentation, and other technological problems.

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“…They ultimately give us results and possibly return modified logic to the same devices. Interconnected devices in order to enhance learning and collective intelligence, and to take advantage of objects' artificial intelligence capabilities, they must understand the value of the information provided to them and used in informal and automated networks [8]. Artificial intelligence of things (AIoT) provides a unique opportunity to enhance learning and personalization at the same time.…”

Section: Literature Reviewmentioning

confidence: 99%

Green supply chain based on artificial intelligence of things (AIoT)

Ghahremani-Nahr

Nozari

Sadeghi

2021

IJIMES

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The dramatic growth of the Internet of Things in various fields necessitates the use of artificial intelligence capabilities in the optimal use of data. By combining these technologies, it reduces cost, automation and productivity more dynamically. This hybrid technology is called artificial intelligence of things (AIoT). Methodology: Intelligent solutions in the supply chain, i.e. the use of the Internet of Things with the capability of artificial intelligence, has been able to make various industries great. Findings: Due to the colorful role of IoT technology in the sustainability of industrial systems, this paper provides a framework for the implementation of an AIoT-based green supply chain. This framework shows a clear path to understanding the impact of this hybrid supply chain technology. Originality/Value: In his paper, a framework for the implementation of an AIoT-based green supply chain is provided.

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Artificial Intelligence and Internet of Things Based Healthcare 4.0 Monitoring System

Cited by 135 publications

References 32 publications

Autonomous Tool for Monitoring Multi-Morbidity Health Conditions in UAE and India

Autonomous Tool for Monitoring Multi-Morbidity Health Conditions in UAE and India

Fitness Dependent Optimizer for IoT Healthcare using Adapted Parameters: A Case Study Implementation

Green supply chain based on artificial intelligence of things (AIoT)

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