An overview of AI enabled M-IoT wearable technology and its effects on the conduct of medical professionals in Public Healthcare in Pakistan

Dahri, Abdul Samad; Massan, Shafiq-ur-Rehman; Thebo, Liaquat Ali

doi:10.17993/3ctecno/2020.v9n2e34.87-111

Cited by 13 publications

(10 citation statements)

References 15 publications

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“…For this meta-analysis, the relation between the SI and BI constructs relied on data from 66 other estimations, reaching a sample of 19,454 respondents (Table 1). Some studies in the literature indicated the non-significance of the relation between SI > BI (Dahri et al, 2020;Reyes-Mercado, 2018;Rubin and Ophoff, 2018). On the other hand, other estimations suggest an adverse effect for this relation (Wu et al, 2016a(Wu et al, , 2016b.…”

Section: Discussionmentioning

confidence: 99%

A meta-analysis of a comprehensive m-health technology acceptance

Calegari

R.D.

Fettermann

2023

IJLSS

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Purpose The evolution of e-health technologies presents promising alternatives for health-care excellence. Despite the benefits arising from mobile e-health (m-health) and wearables technologies, the literature stands many contradictories signs regarding how users accept and engage in using these technologies. This study aims to synthesize the estimations about m-health user acceptance technologies. Design/methodology/approach A meta-analytic structural equation modeling was carried out using the 778 relationships estimated by 100 previous research. The estimations follow the relations and constructs proposed in the UTAUT2 technological acceptance model. Findings The results indicate the performance expectancy, effort expectancy, social influence and habit constructs are most important for predicting the behavioral intention of use of m-health technologies. The Latin American users of e-health technologies are still underestimated in the literature. Originality/value The study presents a guide to understanding the acceptance process of m-health technologies and delivers a general orientation for developing new m-health devices considering their acceptance by users.

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Section: Discussionmentioning

confidence: 99%

A meta-analysis of a comprehensive m-health technology acceptance

Calegari

R.D.

Fettermann

2023

IJLSS

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“…(d24), and 36-in. (d36) offset from the center of the loaded plate (0, 30.5, 61.0 and 91.4 cm, respectively) (Alderete & Brizuela, 2014).…”

Section: Methodsmentioning

confidence: 99%

“…However, through new technologies or state-of-the-art instruments that apply high-performance testing techniques, known as Non-Destructive Testing (NDT), roadway monitoring has improved (Alderete & Brizuela, 2014). Among these techniques, the use of the Falling Weight Deflectometer (FWD), whose function is to analyze the structural condition of pavements by interpreting the deflections produced under dynamic loads that simulate the effect of traffic, stands out.…”

Section: Referencias Bibliográficasmentioning

confidence: 99%

Untitled

2021

3C Tecnología

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Since the origin of cell phones, the development of mobile applications has grown exponentially, opening a new field of research in the field of software engineering. The need to produce an application with high quality indices has led to the study and formulation of new methodologies that cover all the corresponding aspects related to quality and production time.The objective of this research is to carry out an exhaustive study to extract relevant data from each framework through bibliographic and field research for the construction of a state of the art that verifies through an analysis the methodology indicated for the development of mobile applications. The result obtained from the analysis shows that, despite finding a certain similarity between the methodologies with agile principles, Scrum is characterized as the indicated methodology for the development of mobile applications. This fact gave rise to the emergence of new software development methodologies with approaches to the so-called agile practices whose objective is the production of quality software.

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“…Their proposed SSI detection system for the Internet is said to be precise, efficient, and effective. [ 23 – 26 ]. Flow-based network intrusion detection systems (NIDSs) [ 27 – 29 ] use flow records as an input to determine if a given flow is benign or malicious.…”

Section: Related Workmentioning

confidence: 99%

Detection of Middlebox-Based Attacks in Healthcare Internet of Things Using Multiple Machine Learning Models

Abdulwahid

2022

Computational Intelligence and Neuroscience

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The huge number of network traffic data, the abundance of available network features, and the diversity of cyber-attack patterns mean that intrusion detection remains difficult even though many earlier efforts have succeeded in building the Internet of Healthcare Things (IoHT). The implementation of an effective algorithm to filter out most of the probable outliers of Round Trip Time (RTT) of packets recorded in the Internet environment is urgently required. Congestion and interference in networks can arise when numerous biosensors in an IoHT system all attempt to communicate at once. Internet of Health Things networks are susceptible to both intra- and internetwork interference. In this research, the Server-Side Includes (SSI) attack is a key issue because it allows for network compromise as part of Internal Attacks. Despite recent advancements, SSI detection remains difficult due to the vast amounts of network traffic data, the abundance of network features, and the diversity of cyber-attack patterns (DDoS, DoS, Satan, spoofing, etc.). With the help of sensors, physiological data may be collected and sent to distant servers, where they can be analyzed in real time by doctors to help them catch diseases in their earliest stages. This is made possible by the Internet of medical things (IoMT). Wireless data transfer, however, leaves it vulnerable to hackers, especially if the data being transferred are particularly private or sensitive. Security measures designed for devices with more storage space and processing power will not work on those with less. However, machine learning for intrusion detection can give a tailored security response to the needs of IoMT systems. For SSI detection, current methods are either inefficient because of the large number of packets that need to be caught and analyzed or unsuccessful because of outlier values in the RTTs obtained from the captured TCP packets. To the same end, “downstream detection” refers to the process of calculating the total length of all connections made after a certain point. As a means of improving the SSI detection algorithm’s throughput in a network environment, packet RTT outliers will be eliminated. Flow records are used as inputs by flow-based NIDS to determine whether or not a given flow is malicious. In order to detect middlebox-based attacks from two Medical Health IoT datasets, this paper proposes a unique architecture of explainable neural networks (XNN). The model’s accuracy in classifying attacks in dataset 1 of the IoHT is 99.7%t, besides achieving 99.4% accuracy in categorising attacks on IoHT dataset 2.

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An overview of AI enabled M-IoT wearable technology and its effects on the conduct of medical professionals in Public Healthcare in Pakistan

Cited by 13 publications

References 15 publications

A meta-analysis of a comprehensive m-health technology acceptance

A meta-analysis of a comprehensive m-health technology acceptance

Untitled

Detection of Middlebox-Based Attacks in Healthcare Internet of Things Using Multiple Machine Learning Models

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