Smart skin health monitoring using AI-enabled cloud-based IoT

Juyal, Shuchi; Sharma, Sachin; Shukla, Amal Shankar

doi:10.1016/j.matpr.2021.01.074

Cited by 32 publications

(8 citation statements)

References 18 publications

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“…The responses may be subject to further interpretation, such as respondents' knowledge of the most advanced platform, network and manager functionalities that are currently available. As some of the most recent literatures have indicated, some of the criteria such as AI and MIS (and even the IoT/IoMT, which is familiar to most) are still under rapid technological development [30,46,47,55]. The maturity and wider implementation of individual functionalities over connective network or platform aspects may have an impact on survey responses.…”

Section: Discussionmentioning

confidence: 99%

“…In the context of smart hospitals, AI has been increasingly used for analyzing medical data, especially big medical data [46,47]. AI can now assist medical professionals in most types of clinical decision-making, as non-human intelligence can now acquire knowledge about normal versus abnormal decisions by using the data generated by the health professionals and the patient through the feedbacks from machine and deep learning [48].…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

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BIM for Smart Hospital Management during COVID-19 Using MCDM

Lin

Chen

2021

Sustainability

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In context of the recent COVID-19 pandemic, smart hospitals’ contributions to pre-medical, remote diagnosis, and social distancing has been further vetted. Smart hospital management evolves with new technology and knowledge management, which needs an evaluation system to prioritize its associated criteria and sub-criteria. The global effect of the COVID-19 pandemic further necessitates a comprehensive research of smart hospital management. This paper will utilize Analytical Hierarchy Process (AHP) within Multiple Criteria Decision Making (MCDM) to establish a smart hospital evaluation system with evaluation criteria and sub-criteria, which were then further prioritized and mapped to BIM-related alternatives to inform asset information management (AIM) practices. This context of this study included the expert opinions of six professionals in the smart hospital field and collected 113 responses from hospital-related personnel. The results indicated that functionalities connected to end users are critical, in particular IoT’s Network Core Functionalities, AI’s Deep Learning and CPS’s Special Network Technologies. Furthermore, BIM’s capability to contribute to the lifecycle management of assets can relate and contribute to the asset-intensive physical criteria of smart hospitals, in particular IoT, service technology innovations and their sub-criteria.

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Section: Discussionmentioning

confidence: 99%

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

BIM for Smart Hospital Management during COVID-19 Using MCDM

Lin

Chen

2021

Sustainability

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“…The raw data is processed and computed using the FPGA scheme, and pathological condition is shown on the patients' wearable IoT device. Juyal et al [13] present a novel 'Intelligent' Skin Monitoring Device concept, which enables patients in rural regions for monitoring skin disease remotely. The presented method has AI and cloud based IoT, in which CNN is utilized for analyzing the disease predictions and medical images.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

Madhu¹,

Bharadwaj²,

Boddeda³

et al. 2022

Computers, Materials &Amp; Continua

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In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems, called IDDM-EAC technique. The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition, clustering, and disease diagnosis. In addition, the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering (CSOEAC) technique to group the IoT devices into clusters and select cluster heads (CHs). Moreover, a new coyote optimization algorithm (COA) with deep belief network (DBN), called COA-DBN technique is employed for the disease diagnostic process. The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model. In order to inspect the betterment of the IDDM-EAC technique, a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository. The experimental results demonstrate the promising performance with the minimal total energy consumption of 63% whereas the EEPSOC, ABC, GWO, and ACO algorithms have showcased a higher total energy consumption of 69%, 78%, 83%, and 84% correspondingly.

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“…The skin surface is an important parameter into the preparation of cosmetic products, image, and face recognition in security applications, and diagnosis of dermatological diseases like dryness, skin fungus, or allergic symptoms (Juyal et al, 2021). Nowadays, skin diseases are increasing, therefore, their diagnosis using proper analysis of skin texture is significant.…”

Section: Skin Hydrationmentioning

confidence: 99%

A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration

et al. 2021

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Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, with a prevalence of 1–2% in the community, increasing the risk of stroke and myocardial infarction. Early detection of AF, typically causing an irregular and abnormally fast heart rate, can help reduce the risk of strokes that are more common among older people. Intelligent models capable of automatic detection of AF in its earliest possible stages can improve the early diagnosis and treatment. Luckily, this can be made possible with the information about the heart's rhythm and electrical activity provided through electrocardiogram (ECG) and the decision-making machine learning-based autonomous models. In addition, AF has a direct impact on the skin hydration level and, hence, can be used as a measure for detection. In this paper, we present an independent review along with a comparative analysis of the state-of-the-art techniques proposed for AF detection using ECG and skin hydration levels. This paper also highlights the effects of AF on skin hydration level that is missing in most of the previous studies.

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Smart skin health monitoring using AI-enabled cloud-based IoT

Cited by 32 publications

References 18 publications

BIM for Smart Hospital Management during COVID-19 Using MCDM

BIM for Smart Hospital Management during COVID-19 Using MCDM

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration

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