Novel Multi Security and Privacy Benchmarking Framework for Blockchain-Based IoT Healthcare Industry 4.0 Systems

Qahtan, Sarah; Yatim, Khaironi; Zaidan, A. A.; Alsattar, Hassan A.; Albahri, O. S.; Zaidan, B. B.; Alamoodi, A. H.; Zulzalil, Hazura; Osman, Mohd Hafeez; Mohammed, R. T.

doi:10.1109/tii.2022.3143619

Cited by 81 publications

(28 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, this sector has passed its 60th years anniversary. It is slowly established as a result of realizations that these two areas had a lot of overlapping bases, and could greatly benefit from a focus on their combination [15]. These two must now be integrated with today's modern society [7].…”

Section: ) Overview Of Iortmentioning

confidence: 99%

Toward IoRT Collaborative Digital Twin Technology Enabled Future Surgical Sector: Technical Innovations, Opportunities and Challenges

et al. 2022

View full text Add to dashboard Cite

In today's world, it is anticipated that DTw and IoRT will transform the idea of digital healthcare and advancement of it what was previously unknown. DTw is (physical or virtual) machines or computer-based models capable of mimicking or "twinning" the lives of actual entities such as objects, humans, or human-related features. Since the advent of digital and smart healthcare, the world has swiftly integrated several technologies in healthcare to support improved patient service, extend life expectancy, and lower healthcare expenses.DTw and IoRT are promising technologies and great improvements in this field. In recent years, the concepts of IoRT and DTw have captured the interest of the surgical sector. The flawless integration of data between a physical and virtual machine in either direction is the best way to conduct smart surgery. Artificial intelligence, nanotechnology, various kinds of robotic equipment, and the Internet of Things (IoT) are our pillars of completing the task. Using the DTw technological framework and IoRT infrastructure, this review paper proposes a comparative overview of traditional and IoRT collaborated DTwT and executes an adaptive perspective, opportunities, and challenges in the healthcare sector, namely surgical advances. This is an important addition to digital surgeries and to improve healthcare operations. Our literature review has sorted them according to research areas: the technological innovation, the prospective and opportunities we have, problems facing the surgical industry today and in the future, and offers an evaluation of the enabling technologies for the betterment of employing DTw and IoRT for the developing and secure surgical sector for both patients and doctors.INDEX TERMS Digital twin technology (DTwT), Internet of Robotic things (IoRT), surgery, Future surgical sector, Healthcare,

show abstract

Section: ) Overview Of Iortmentioning

confidence: 99%

Toward IoRT Collaborative Digital Twin Technology Enabled Future Surgical Sector: Technical Innovations, Opportunities and Challenges

et al. 2022

View full text Add to dashboard Cite

show abstract

“…• Qahtan et al [53] presented the formulation of fuzzy weighted with zero inconsistency (FWZIC) scheme under the spherical fuzzy environment. Their scheme combined "GRATOPSIS and the BES optimization."…”

Section: A Existing Security Schemes In Healthcare 40mentioning

confidence: 99%

Healthcare 5.0 Security Framework: Applications, Issues and Future Research Directions

et al. 2022

View full text Add to dashboard Cite

Healthcare 5.0 is a system that can be deployed to provide various healthcare services. It does these services by utilising a new generation of information technologies, such as Internet of Things (IoT), Artificial Intelligence (AI), Big data analytics, blockchain and cloud computing. Due to the introduction of healthcare 5.0, the paradigm has been now changed. It is disease-centered to patientcentered care where it provides healthcare services and supports to the people. However, there are several security issues and challenges in healthcare 5.0 which may cause the leakage or alteration of sensitive healthcare data. This demands that we need a robust framework in order to secure the data of healthcare 5.0, which can facilitate different security related procedures like authentication, access control, key management and intrusion detection. Therefore, in this review article, we propose the design of a secure generalized healthcare 5.0 framework. The details of various applications of healthcare 5.0 along with the security requirements and threat model of healthcare 5.0 are provided. Next, we discuss about the existing security mechanisms in healthcare 5.0 along with their performance comparison. Some future research directions are finally discussed for the researchers working in healthcare 5.0 domain.INDEX TERMS Healthcare 5.0, Internet of Things (IoT), artificial intelligence, cyber security, blockchain.

show abstract

“…Considering that FS theory is a very effective tool for modelling uncertainty, it has several applications in the modelling and solving of issues in various domains, including medical science, data mining, and clustering. Therefore, FWZIC is extended under different fuzzy environments such as trapezoidal fuzzy numbers, 38 Pythagorean fuzzy set (PyFS), 64 neutrosophic fuzzy set, 65 spherical fuzzy set (SFS), 66 T‐spherical fuzzy set (T‐SFS), 67 and q‐rung orthopair fuzzy sets 68 to address issues of uncertainty and vagueness caused by expert subjectivity. Although prior versions of FWZIC addressed the uncertainty and vagueness issues, they remain an outstanding issue.…”

Section: Introductionmentioning

confidence: 99%

Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex T‐spherical fuzzy‐weighted zero‐inconsistency method

Albahri

Zaidan

Alsattar³

et al. 2022

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

Autism spectrum disorders (ASD) are a diverse group of conditions characterized by difficulty with social interaction and communication. ASD is expected to be a high‐risk disease. Recent studies have focused on the diagnosis based on sociodemographic and family characteristics factors. The development of a diagnosis model, which is primarily based on machine learning methods, has been carried out to alleviate the detection of autism. However, they neglected the importance of ASD features in a training dataset, especially because some features have different levels of contributions to the processing data and possess more relevancies to the classification information than others. Such limitations use preprocessing techniques for the construction of the machine learning model, but the role of the physician's experience towards feature contributions remains limited. However, for certain autism datasets, the relevancies of sociodemographic and family characteristic feature concerning the given class labels should be considered. Accordingly, this study developed a new machine learning model for the diagnosis of ASD based on multi‐criteria decision‐making (MCDM). By using three methodology phases, the model combines two representative theories, namely, MCDM and machine learning. The identification phase for imbalance ASD dataset and application of pre‐possessing stages by imputing missing values, feature selection of sociodemographic and family characteristics, and data imbalanced approach resulted in balanced ASD dataset, including 107,573 cases. The development phase for the new model was achieved by the proposed complex T‐spherical fuzzy‐weighted zero‐inconsistency (CT‐SFWZIC) method. CT‐SFWZIC was developed based on a new fuzzy set (i.e., complex T‐spherical fuzzy) for weighting affected features, and then applied for training and testing the machine learning model considering various complex T‐spherical fuzzy membership functions (i.e., T = 1, 2, 3, 5, 7, and 10). The results obtained from a 10‐fold cross‐validation test for all T values by using nine machine learning classifiers were measured under seven evaluation metrics, namely AUC, accuracy, F1, precision, recall, training time (s), and test time (s). Performance evaluation results reveal that AdaBoost can be used to boost the ASD diagnosis as the best machine learning algorithm for all T values based on all metrics to improve the diagnosis based on physician's assessment. Under the most extreme evaluation metric, which is accuracy, the results of the AdaBoost classifiers for T = 1, 2, 3, 5, 7, 10 have obtained 0.99948, 0.99934, 0.99930, 0.99939, 0.99910, and 0.99930, respectively.

show abstract

Novel Multi Security and Privacy Benchmarking Framework for Blockchain-Based IoT Healthcare Industry 4.0 Systems

Cited by 81 publications

References 24 publications

Toward IoRT Collaborative Digital Twin Technology Enabled Future Surgical Sector: Technical Innovations, Opportunities and Challenges

Toward IoRT Collaborative Digital Twin Technology Enabled Future Surgical Sector: Technical Innovations, Opportunities and Challenges

Healthcare 5.0 Security Framework: Applications, Issues and Future Research Directions

Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex T‐spherical fuzzy‐weighted zero‐inconsistency method

Contact Info

Product

Resources

About