The collaborative role of blockchain, artificial intelligence, and industrial internet of things in digitalization of small and medium-size enterprises

Khan, Abdullah Ayub; Laghari, Asif Ali; Dootio, Mazhar Ali; Karim, Shahid

doi:10.1038/s41598-023-28707-9

Cited by 55 publications

(24 citation statements)

References 31 publications

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“…Here, the Quantum Delegated Proof of Stake (QDPoS) model enables fast decentralization with increased efficiency. Khan et al 41 investigated the major effects and opportunities of using blockchain and AI in IIoT systems. Zhang et al 42 designed an Elastic Batched Byzantine Fault Tolerance Consensus Protocol (EB-BFT) to improve the security of business applications.…”

Section: Related Workmentioning

confidence: 99%

A quantum trust and consultative transaction-based blockchain cybersecurity model for healthcare systems

Shitharth

Mouratidis

2023

Sci Rep

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Many researchers have been interested in healthcare cybersecurity for a long time since it can improve the security of patient and health record data. As a result, a lot of research is done in the field of cybersecurity that focuses on the safe exchange of health data between patients and the medical setting. It still has issues with high computational complexity, increased time consumption, and cost complexity, all of which have an impact on the effectiveness and performance of the complete security system. Hence this work proposes a technique called Consultative Transaction Key Generation and Management (CTKGM) to enable secure data sharing in healthcare systems. It generates a unique key pair based on random values with multiplicative operations and time stamps. The patient data is then safely stored in discrete blocks of hash values using the blockchain methodology. The Quantum Trust Reconciliation Agreement Model (QTRAM), which calculates the trust score based on the feedback data, ensures reliable and secure data transfer. By allowing safe communication between patients and the healthcare system based on feedback analysis and trust value, the proposed framework makes a novel contribution to the field. Additionally, during communication, the Tuna Swarm Optimization (TSO) method is employed to validate nonce verification messages. Nonce message verification is a part of QTRAM that helps verify the users during transmission. The effectiveness of the suggested scheme has been demonstrated by comparing the obtained findings with other current state-of-the-art models after a variety of evaluation metrics have been analyzed to test the performance of this security model.

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Section: Related Workmentioning

confidence: 99%

A quantum trust and consultative transaction-based blockchain cybersecurity model for healthcare systems

Shitharth

Mouratidis

2023

Sci Rep

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“…Assessment of CNN models trained on synthetic data is an open research problem that needs further investigation. Future studies can investigate the possibility of using GAN-ST generated data in combination with IOT devices and blockchain technology to build a safe system for gathering and processing sensitive data [ 28 ]…”

Section: Discussionmentioning

confidence: 99%

Generative adversarial network based synthetic data training model for lightweight convolutional neural networks

Rather

Kumar

2023

Multimed Tools Appl

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Inadequate training data is a significant challenge for deep learning techniques, particularly in applications where data is difficult to get, and publicly available datasets are uncommon owing to ethical and privacy concerns. Various approaches, such as data augmentation and transfer learning, are employed to address this problem, which help to some extent in removing this limitation. However, after a certain amount of data augmentation, the quality of the generated data stalls, and transfer learning suffers from the issue of negative transfer. This paper proposes a novel generative adversarial network-based synthetic data training (GAN-ST) model to generate synthetic data for training a lightweight convolutional neural network (CNN). An enhanced generator is proposed to quickly saturate and cover the colour space of the training distribution. The GAN-ST model is based on Deep Convolutional Generative Adversarial Network(s) (DCGAN) and Conditional Generative Adversarial Network(s) (CGAN) models, which consist of an enhanced generator. The study evaluates the accuracy of a CNN model on the MNIST and CIFAR 10 datasets using both original and synthetic data. The results revealed an impressive classifier accuracy on the MNIST dataset, achieving an accuracy of 99.38% on GAN-ST-generated synthetic training data, which is only 0.05% lower than the performance on original data-based training. The classifier performance on the CIFAR dataset is also remarkable, achieving an accuracy of 90.23%. The performance of CNN trained using GAN-ST-based synthetic data is notable, with the most considerable improvement of 0.66% and 7.06%, over a single GAN-based synthetic data training for the MNIST and CIFAR datasets, respectively. By training two GANs independently, the GAN-ST model covers different parts of the original data distribution, resulting in a more diverse and realistic training data set for the classifier. This diverse set of synthetic data, when used to train a CNN, shows better generalization to new data, leading to improved classification accuracy.

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“…One of the biggest challenges in the current design and development of the metaverse is privacy protection and security, especially in the gaming industrial environment (Chiariotti et al, 2023;Fernandes & Werner, 2023;Kalhoro et al, 2023;Khan, Laghari, Li, Dootio, & Karim, 2023;D. Wu et al, 2023;T.…”

Section: Virtual Device Connectivity and Content Privacy Protection L...mentioning

confidence: 99%

Metaverse in Multimedia Gaming: Quality of Experience (QoE) Challenges and Standards Requirements for Immersive Media Consumption

Khan,

Yang,

Laghari

et al. 2023

Preprint

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The immersive development in computing and communication systems, especially the evaluation of multimedia technology, has created various new paradigms. One of the concepts that has popped up recently is the metaverse. The technology opens different environments to the users for rapid connection on any of the socio-cyber platforms. With the emergence of 3D virtual gaming and its association, this technology has become one of the hot research areas, whether the work on headsets or the quality of experience (QoE) while using recent headset models by users and assessment. In multimedia systems, gaming is the only domain that is successfully involved among users, which generates millions of dollars in revenue in a single day. However, the collaboration of the metaverse in the gaming industry takes the user experience to the next level in terms of being completely disconnected from the real-world scenario, which means a fully immersive virtual environment. It includes the concept of lightweight visualization, a place with free headset activities, and a moving, immersive 3D multimedia experience. Although these are the advantages of the collaborative approach, various QoE challenges, standard requirements, and media consumption problems have emerged. By analyzing these prospects, this paper contributes to two subdomains. First, a novel framework for securely connecting metaverse technology in multimedia gaming is proposed. While using conventional metaverse technology, the role of QoE is addressed in the general-purpose gaming environment deployment. Second, this paper presents a gamer-oriented experience and assessments for the betterment of technological maturity and future developments. In addition, this paper mentioned a possible solution to the list of comprehensive standard requirements and related challenges.

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The collaborative role of blockchain, artificial intelligence, and industrial internet of things in digitalization of small and medium-size enterprises

Cited by 55 publications

References 31 publications

A quantum trust and consultative transaction-based blockchain cybersecurity model for healthcare systems

A quantum trust and consultative transaction-based blockchain cybersecurity model for healthcare systems

Generative adversarial network based synthetic data training model for lightweight convolutional neural networks

Metaverse in Multimedia Gaming: Quality of Experience (QoE) Challenges and Standards Requirements for Immersive Media Consumption

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