Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

Hussain, Muzammil; Javed, Waheed; Hakeem, Owais; Yousafzai, Abdullah; Younas, Alisha; Awan, Mazhar Javed; Nobanee, Haitham; Zain, Azlan Mohd

doi:10.3390/su132413646

Cited by 63 publications

(20 citation statements)

References 131 publications

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“…Mapping research summarizes the present knowledge and identifies essential topics through a thorough literature review. The qualities and values of the technical systemic literature review for particular research contexts are discussed in this section [41]. This is not the objective of systematic mapping research because the papers are not thoroughly examined.…”

Section: Slr Research Methodologymentioning

confidence: 99%

“…Multiple search engines and digital libraries were used to gather information during Internet researches. The acquired results were carefully assembled to obtain the best information sources to answer the given research topics [41]. It was decided to use specific search engines and digital libraries based on their scientific content and relevance to the paper's goals.…”

Section: Search Stringmentioning

confidence: 99%

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Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

et al. 2021

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The COVID-19 pandemic has frightened people worldwide, and coronavirus has become the most commonly used phrase in recent years. Therefore, there is a need for a systematic literature review (SLR) related to Big Data applications in the COVID-19 pandemic crisis. The objective is to highlight recent technological advancements. Many studies emphasize the area of the COVID-19 pandemic crisis. Our study categorizes the many applications used to manage and control the pandemic. There is a very limited SLR prospective of COVID-19 with Big Data. Our SLR study picked five databases: Science direct, IEEE Xplore, Springer, ACM, and MDPI. Before the screening, following the recommendation, Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) were reported for 893 studies from 2019, 2020 and until September 2021. After screening, 60 studies met the inclusion criteria through COVID-19 data statistics, and Big Data analysis was used as the search string. Our research’s findings successfully dealt with COVID-19 healthcare with risk diagnosis, estimation or prevention, decision making, and drug Big Data applications problems. We believe that this review study will motivate the research community to perform expandable and transparent research against the pandemic crisis of COVID-19.

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Section: Slr Research Methodologymentioning

confidence: 99%

Section: Search Stringmentioning

confidence: 99%

Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

et al. 2021

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“…From the denser decomposition process, the sparse convolution operation is performed using Equation (6).…”

Section: Intrusion Detection Using Optimized Sparse Convolution Neura...mentioning

confidence: 99%

“…The SLR Blockchain paper suggested the world population utilizes around 10 billion IoT devices [6] to improve their lifestyle. The high utilization of IoT devices faces IoT security issues in the fast expansion of smart appliances because of connecting to the network.…”

Section: Introductionmentioning

confidence: 99%

Threat Analysis and Distributed Denial of Service (DDoS) Attack Recognition in the Internet of Things (IoT)

et al. 2022

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The Internet of Things (IoT) plays a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although this IoT paradigm has suffered from intrusion threats and attacks that cause security and privacy issues, existing intrusion detection techniques fail to maintain reliability against the attacks. Therefore, the IoT intrusion threat has been analyzed using the sparse convolute network to contest the threats and attacks. The web is trained using sets of intrusion data, characteristics, and suspicious activities, which helps identify and track the attacks, mainly, Distributed Denial of Service (DDoS) attacks. Along with this, the network is optimized using evolutionary techniques that identify and detect the regular, error, and intrusion attempts under different conditions. The sparse network forms the complex hypotheses evaluated using neurons, and the obtained event stream outputs are propagated to further hidden layer processes. This process minimizes the intrusion involvement in IoT data transmission. Effective utilization of training patterns in the network successfully classifies the standard and threat patterns. Then, the effectiveness of the system is evaluated using experimental results and discussion. Network intrusion detection systems are superior to other types of traditional network defense in providing network security. The research applied an IGA-BP network to combat the growing challenge of Internet security in the big data era, using an autoencoder network model and an improved genetic algorithm to detect intrusions. MATLAB built it, which ensures a 98.98% detection rate and 99.29% accuracy with minimal processing complexity, and the performance ratio is 90.26%. A meta-heuristic optimizer was used in the future to increase the system’s ability to forecast attacks.

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“…Several approaches for predicting and detecting COVID19 were explored. Most ML-based tactics have been used to forecast the presence, trends, or expansion of COVID19, whereas most Deep Learning approaches have been used to detect COVID19, showing that this problem has a diverse set of solutions and methodologies [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48]. The suggested approach is innovative because, in previous analogous techniques, just a few algorithms are employed and results are compared, or the dataset utilized is confined to one nation or fewer, or the dataset is made up of a smaller number of observations [43][44][45][46][47][48].…”

Section: Related Workmentioning

confidence: 99%

FLOSSC: A Computer-Aided Tool for Detecting Corona Virus Infections through Chest X-Ray Images

Ahmad

Javed

Aftab

et al. 2021

2021 International Conference on Innovative Computing (ICIC)

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Artificial intelligence has radically altered the world, and it continues to progress at an alarming rate as time passes. AI applications include healthcare and medical solutions, illness diagnostics, agriculture, constructing security infrastructures, autonomous cars, intelligent systems, industrial production, robotics, and much more. COVID19 is a deadly virus that first appeared in China in 2019 and soon spread over the world. By 2020, the globe had witnessed a tremendous epidemic, with countless lives lost as a result of this dreadful virus, which has now become a severe health danger. Furthermore, in 2021, several nations will be infected with new Covid19 forms that are more deadly and spread quicker. The research describes the proposed methodology for diagnosing covid-19 and pneumonia from human chest X-ray images using transfer learning with Resnet-18 and VGG-16 neural networks. The focal loss function was also used to homogenize the imbalanced dataset, which included X-ray images of normal, pneumonia, and Covid-19 patients. The purpose is to assess the performance and accuracy of fine-tuned neural networks after including Binary Cross-Entropy (BCE) and Focal Loss (FL) functions. However, when we used our Resnet-18 and VGG-16 neural networks with BCE and FL functions, the VGG-16 with FL function outperformed all other models, with training and validation accuracy of 98.37 percent and 97.37 percent, correspondingly.

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Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

Cited by 63 publications

References 131 publications

Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

Threat Analysis and Distributed Denial of Service (DDoS) Attack Recognition in the Internet of Things (IoT)

FLOSSC: A Computer-Aided Tool for Detecting Corona Virus Infections through Chest X-Ray Images

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