Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Tanwar, Sudeep; Bhatia, Qasim; Patel, Pruthvi P.; Kumari, Aparna; Singh, Pradeep Kumar; Hong, Wei‐Chiang

doi:10.1109/access.2019.2961372

Cited by 286 publications

(106 citation statements)

References 64 publications

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“…Nuno Lopes et al [78] in 2018 given a classification model for FER in the elderly and also present the differences of FER in the elderly and other age people. They used the Support Vector Machine with a multi-class classification for classifying the emotions [159]. They proposed two architectures, the first approach removes the wrinkles, nasolabial fold, and other facial features, using edge-preserving smoothing techniques.…”

Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

et al. 2020

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With the advancements in machine and deep learning algorithms, the envision of various critical real-life applications in computer vision becomes possible. One of the applications is facial sentiment analysis. Deep learning has made facial expression recognition the most trending research fields in computer vision area. Recently, deep learning-based FER models have suffered from various technological issues like under-fitting or over-fitting. It is due to either insufficient training and expression data. Motivated from the above facts, this paper presents a systematic and comprehensive survey on current state-of-art Artificial Intelligence techniques (datasets and algorithms) that provide a solution to the aforementioned issues. It also presents a taxonomy of existing facial sentiment analysis strategies in brief. Then, this paper reviews the existing novel machine and deep learning networks proposed by researchers that are specifically designed for facial expression recognition based on static images and present their merits and demerits and summarized their approach. Finally, this paper also presents the open issues and research challenges for the design of a robust facial expression recognition system. INDEX TERMS Facial sentiment analysis, machine learning, deep learning, convolutional neural network, deep belief network, artificial intelligence.

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Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

et al. 2020

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“…Another important function in the energy sector is the smart grid; different kinds of threats are possible in the smart grid during data transmission. 22 So it is essential to enhance security to the smart grid. 21 Another important function in the energy sector is the smart grid; different kinds of threats are possible in the smart grid during data transmission.…”

Section: Energymentioning

confidence: 99%

Adoption of blockchain technology in various realms: Opportunities and challenges

Akram

Malik

Singh

et al. 2020

Security and Privacy

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Our society is becoming “smarter” and more interconnected, as evidenced by technological trends such as smart farming, smart healthcare, supply chain and logistics, and smart city and nation. Security and privacy are two major key considerations in these smart applications, such as blockchain‐based security solutions for Industry 4.0‐based applications (the focus of this study). In this study, we present a systematic review of existing blockchain‐based solutions, particularly for Industry 4.0‐based applications. We present the merits and demerits of various state‐of‐the‐art approaches. We also highlight the challenges of the blockchain with the scope of interoperability and governance so that this gives a clear understanding to researchers and professionals.

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“…Industry 4.0 revolutionized engineering and manufacturing solutions has provided system automation at a large scale in smart technology applications. Healthcare 4.0 is one of such smart technology applications that uses various industry 4.0 concepts in healthcare [9]- [11]. Healthcare 4.0 includes IoT, IIoT, healthcare, cognitive computing, artificial intelligence, machine learning, virtual reality, augmented reality and cloud computing.…”

Section: Introductionmentioning

confidence: 99%

A Novel Smart Healthcare Design, Simulation, and Implementation Using Healthcare 4.0 Processes

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Blockchain technology is found to have its applicability in almost every domain because of its advantages such as crypto-security, transparency, immutability, decentralized data network. In present times, a smart healthcare system with a blockchain data network and healthcare 4.0 processes provides transparency, easy and faster accessibility, security, efficiency, etc. Healthcare 4.0 trends include industry 4.0 processes such as the internet of things (IoT), industrial IoT (IIoT), cognitive computing, artificial intelligence, cloud computing, fog computing, edge computing, etc. The goal of this work is to design a smart healthcare system and it is found to be possible through integration and interoperability of Blockchain 3.0 and Healthcare 4.0 in consideration with healthcare ground-realities. Here, healthcare 4.0 processes used for data accessibility are targeted to be validated through statistical simulation-optimization methods and algorithms. The blockchain is implemented in the Ethereum network, and with associated programming languages, tools, and techniques such as solidity, web3.js, Athena, etc. Further, this work prepares a comparative and comprehensive survey of state-of-the-art blockchain-based smart healthcare systems. The comprehensive survey includes methodology, applications, requirements, outcomes, future directions, etc. A list of groups, organizations, and enterprises are prepared that are working in electronic health records (EHR), electronic medical records (EMR) or electronic personal records (EPR) mainly, and a comparative analysis is drawn concerning adopting the blockchain technology in their processes. This work has explored optimization algorithms applicable to Healthcare 4.0 trends and improves the performance of blockchain-based decentralized applications for the smart healthcare system. Further, smart contracts and their designs are prepared for the proposed system to expedite the trust-building and payment systems. This work has considered simulation and implementation to validate the proposed approach. Simulation results show that the Gas value required (indicating block size and expenditure) lies within current Etherum network Gas limits. The proposed system is active because block utilization lies above 80%. Automated smart contract execution is below 20 seconds. A good number (average 3 per simulation time) is generated in the network that indicates a health competition. Although there is error observed in simulation and implementation that lies between 0.55% and 4.24%, these errors are not affecting overall system performance because simulated and actual (taken in state-of-the-art) data variations are negligible.

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Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Cited by 286 publications

References 64 publications

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

Adoption of blockchain technology in various realms: Opportunities and challenges

A Novel Smart Healthcare Design, Simulation, and Implementation Using Healthcare 4.0 Processes

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