Study of Block Chain and its Impact on Internet of Health Things (IoHT):Challenges and Opportunities

Naqvi, Muhammad Raza; Aslam, Muhammad; Iqbal, Muhammad Waseem; Shahzad, Syed Khuram; Malik, Marium; Tahir, Muhammad Usman

doi:10.1109/hora49412.2020.9152846

Cited by 15 publications

(6 citation statements)

References 11 publications

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“…The advancement in software applications is leading to the exploration of various areas including health care, manufacturing industry, and so on [33]- [42]. The speedy advancement of the strong branches of artificial intelligence in the field of medical imaging and the improving performance of deep learning approaches in the detection of COVID-19 has opened a gate for many more fruitful pieces of research to sprout.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Towards Covid-19 Detection in X-ray images using Convolutional Neural Network

2021

IJATCSE

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Over the past 18 months, COVID-19 is continuously causing deadly consequences and devastating economic situations around the world. The vaccinations are still not adequately mature and vaccinating all the people around the world is a long process. Moreover, there is a rapid generation of variants of COVID-19 that are difficult to handle promptly. The skyrocketing pandemic of COVID-19 has knocked down the regular lifestyle of everyone, from individuals to massive corporations. The health industry is overburdened by the increasing number of patients that they are receiving. Efficient and stable screening methods of this disease will help in speeding up the detection process and take appropriate measures to flatten the curve on this disease. There is a high demand for COVID-19 discovery frameworks where the existing approaches still have some drawbacks. The most commonly used methods such as RTPCR have drawbacks of high false-positive rates, costly special kits, providing delayed and less accurate test results. Therefore, other diagnostic methods are actively explored. In this scenario, Artificial Intelligence comes to utilization with its branch of deep learning. Convolutional neural networks (CNNs) are a class of deep learning that are utilized in analyzing images, including medical imaging like Chest X-rays. This paper aims to analyze the power brought by CNNs to medical imaging in solving the global problem of early identification of COVID-19 in potential patients by analyzing their X-ray scans. Moreover, the entire process of image acquisition and analysis, its positive impact by minimizing asymptomatic patient contact with others and increasing the efficiency of the work environment at the frontline hospitals is discussed. The test results in this study showed that an accuracy of 95.11% was obtained on a dataset of 2905 images and an accuracy of 96.07% was obtained on an augmented dataset of 9337 images to detect Covid-19 in X-ray images, thus proving that deep learning models work better if the size of the training dataset is increased.

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Section: Discussion and Future Workmentioning

confidence: 99%

Towards Covid-19 Detection in X-ray images using Convolutional Neural Network

2021

IJATCSE

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“…Reverse engineering: in reverse engineering attacks, intruders damaged the embedded IoT devices and generated serious issues by analyzing IoT software applications such as firmwares. Attackers look for input parsing errors in the program's code, and then, the attacker advertised his skills to resolve the issue and get access to the device's sensitive data [148,149].…”

Section: Journal Of Sensorsmentioning

confidence: 99%

A Review of Security and Privacy Concerns in the Internet of Things (IoT)

et al. 2022

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The recent two decades have witnessed tremendous growth in Internet of things (IoT) applications. There are more than 50 billion devices connected globally. IoT applications’ connectivity with the Internet persistently victimized them with a divergent range of traditional threats, including viruses, worms, malware, spyware, Trojans, malicious code injections, and backdoor attacks. Traditional threats provide essential services such as authentication, authorization, and accountability. Authentication and authorization are the process of verifying that a subject is bound to an object. Traditional authentication and authorization mechanisms use three different factors to identity a subject to verify if the subject has the right capability to access the object. Further, it is defined that a computer virus is a type of malware. Malware includes computer viruses, worms, Trojan horses, spyware, and ransomware. There is a high probability that IoT systems can get infected with a more sophisticated form of malware and high-frequency electromagnetic waves. Purpose oriented with distinct nature IoT devices is developed to work in a constrained environment. So there is a dire need to address these security issues because relying on existing traditional techniques is not good. Manufacturers and researchers must think about resolving these security and privacy issues. Most importantly, this study identifies the knowledge and research gap in this area. The primary objective of this systematic literature review is to discuss the divergent types of threats that target IoT systems. Most importantly, the goal is to understand the mode of action of these threats and develop the recovery mechanism to cover the damage. In this study, more than 170 research articles are systematically studied to understand security and privacy issues. Further, security threats and attacks are categorized on a single platform and provide an analysis to explain how and to what extent they damage the targeted IoT systems. This review paper encapsulates IoT security threats and categorizes and analyses them by implementing a comparative study. Moreover, the research work concludes to expand advanced technologies, e.g., blockchain, machine learning, and artificial intelligence, to guarantee security, privacy, and IoT systems.

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“…Daytime sleepiness may result from sleep disturbances, and students often fall asleep Students' cognitive abilities and academic success are closely linked to the consistency and quantity of their sleep. [24,25].…”

Section: Smartphone Addictionmentioning

confidence: 99%

Impact of Excessive Usage of Smartphone in Kids

2021

IJATCSE

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Excess of anything leave negative impact so is the excessive use of smartphone effected health of kids. This research presents usage and impact of smartphone in kids. To analyze addiction and impact of smartphone on kids’ health, this study focused on three different age groups in particular. From literature we choose sleep deprivation, anxiety/depression and social interaction as primary health issue to study under this research. We conducted an experiment on 1500 school going kids and asked them to fill a survey having several questions on usage of smartphones. Results shown addiction of smartphone in most of the kids while dividing addiction in three levels; low, average and high. Results revealed 737 kids use smartphone at low level addiction, 454 kids use smartphone at average level and 352 kids found in the addiction of smartphone usage. Kids with high level of smartphone addiction have found to sleep depreciated, had anxiety and social issues. On major issue discovered was social anxiety which kids shown having social gatherings and even uncomfortable in communication with family and parents.

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Study of Block Chain and its Impact on Internet of Health Things (IoHT):Challenges and Opportunities

Cited by 15 publications

References 11 publications

Towards Covid-19 Detection in X-ray images using Convolutional Neural Network

Towards Covid-19 Detection in X-ray images using Convolutional Neural Network

A Review of Security and Privacy Concerns in the Internet of Things (IoT)

Impact of Excessive Usage of Smartphone in Kids

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