BinDaaS: Blockchain-Based Deep-Learning as-a-Service in Healthcare 4.0 Applications

Bhattacharya, Pronaya; Tanwar, Sudeep; Bodkhe, Umesh; Tyagi, Sudhanshu; Kumar, Neeraj

doi:10.1109/tnse.2019.2961932

Cited by 185 publications

(88 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These surveys have mainly focused on traditional methods like support vector machine (SVM), decision tree classifiers, and artificial neural network (ANN). The DL methods [28], [29] have rarely been explored by the researchers working in the same field. So, in this paper, we analyzed the surveys on facial sentiment analysis and presented a comparative analysis.…”

Section: B Scope Of the Surveymentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: B Scope Of the Surveymentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The blockchain applications use the APIs for the post and get services of the resources like databases. A variety of applications such as healthcare, financial sector, insurance, energy domain, supply-chain, and IoT can be enabled with the blockchain to minimize the cost and time [51]- [56].…”

Section: A: Blockchain Applicationsmentioning

confidence: 99%

Blockchain for Industry 4.0: A Comprehensive Review

et al. 2020

Self Cite

View full text Add to dashboard Cite

Due to the proliferation of ICT during the last few decades, there is an exponential increase in the usage of various smart applications such as smart farming, smart healthcare, supply-chain & logistics, business, tourism and hospitality, energy management etc. However, for all the aforementioned applications, security and privacy are major concerns keeping in view of the usage of the open channel, i.e., Internet for data transfer. Although many security solutions and standards have been proposed over the years to enhance the security levels of aforementioned smart applications, but the existing solutions are either based upon the centralized architecture (having single point of failure) or having high computation and communication costs. Moreover, most of the existing security solutions have focussed only on few aspects and fail to address scalability, robustness, data storage, network latency, auditability, immutability, and traceability. To handle the aforementioned issues, blockchain technology can be one of the solutions. Motivated from these facts, in this paper, we present a systematic review of various blockchain-based solutions and their applicability in various Industry 4.0-based applications. Our contributions in this paper are in four fold. Firstly, we explored the current state-of-the-art solutions in the blockchain technology for the smart applications. Then, we illustrated the reference architecture used for the blockchain applicability in various Industry 4.0 applications. Then, merits and demerits of the traditional security solutions are also discussed in comparison to their countermeasures. Finally, we provided a comparison of existing blockchain-based security solutions using various parameters to provide deep insights to the readers about its applicability in various applications.

show abstract

“…The studies conducted till now illustrate quite an importance of the blockchain in the health sector, and these studies suggest that interoperability between health sector organizations is necessary for longitudinal health information, health data management, patient identification, and data access authorization. 41,42…”

Section: Healthmentioning

confidence: 99%

Adoption of blockchain technology in various realms: Opportunities and challenges

Akram

Malik

Singh

et al. 2020

Security and Privacy

Self Cite

126

View full text Add to dashboard Cite

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.

show abstract

BinDaaS: Blockchain-Based Deep-Learning as-a-Service in Healthcare 4.0 Applications

Cited by 185 publications

References 40 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

Blockchain for Industry 4.0: A Comprehensive Review

Adoption of blockchain technology in various realms: Opportunities and challenges

Contact Info

Product

Resources

About