A Framework for Automatic Categorization of Social Data Into Medical Domains

Sharma, Gaurav; Srivastava, Gautam; Mago, Vijay

doi:10.1109/tcss.2019.2950153

Cited by 10 publications

(2 citation statements)

References 35 publications

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“…Every participant on Channel1 must use our newly developed Dapp to participate in the network and send a transaction to the peers where the Chaincode1 is installed. Dapp commonly consists of a UI front end, REST API [36] backend, and Fabric Software Development Kit [37] (SDK, based on node.js). utilizers can compare health examination data hashes and read health examination data, respectively.…”

Section: Dynamichain Network Based On Hyperledger Fabricmentioning

confidence: 99%

DynamiChain: Development of Medical Blockchain Ecosystem Based on Dynamic Consent System

et al. 2021

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Although blockchain is acknowledged as one of the most important technologies to lead the fourth industrial revolution, major technical challenges regarding security breach and privacy issues remain. This issue is particularly sensitive in applied medical fields where personal health information is handled within the network. In addition, contemporary blockchain-converged solutions do not consider restricted medical data regulations that are still obstacles in many countries worldwide. This implies a crucial need for a system or solution that is suitable for the healthcare sector. Therefore, this article proposes the development of a dynamic consent medical blockchain system called DynamiChain, based on a ruleset management algorithm for handling health examination data. Moreover, medical blockchain-related studies were systematically reviewed to prove the novelty of DynamiChain. The proposed system was implemented in a scenario where the exercise management healthcare company provided health management services based on data obtained from the data provider’s hospital. The proposed research is envisioned to provide a widely compatible blockchain medical system that could be applied in future healthcare fields.

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Section: Dynamichain Network Based On Hyperledger Fabricmentioning

confidence: 99%

DynamiChain: Development of Medical Blockchain Ecosystem Based on Dynamic Consent System

et al. 2021

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“…Many researchers have stated that CNN models as hierarchical representation learning models and RNN models as sequence learning models [30]- [32]. Here a substantial question of how we will choose a model for language processing?…”

Section: B Deep Learning Techniquesmentioning

confidence: 99%

SeFACED: Semantic-Based Forensic Analysis and Classification of E-Mail Data Using Deep Learning

et al. 2021

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Artificial Intelligence (AI), in combination with the Internet of Things (IoT), called (AIoT), an emerging trend in industrial applications, is capable of intelligent decision-making with self-driven analytic. With its extensive usage in diverse scenarios, IoT devices generate bulk data that gets contrived by attackers to disrupt normal operations and services. Hence, there is a daring need for proactive data analyses that must prevent cyber-attacks and crimes. To investigate crimes involving Electronic Mail (email), analysis of both the header and the email body is required since the semantics of communication helps to identify the source of potential evidence. With the continued growth of data shared via emails, investigators now face the daunting challenge of extracting the required semantic information from the bulks of emails, thereby causing a delay in the investigation process. This gives an edge to the criminal in erasing their footprints of malicious acts. The existing keyword-based search techniques and filtration often result in extraneous, short sequence emails, which skips meaningful information. To overcome the above limitation, we successfully designed a novel efficient approach called SeFACED that uses Long short-term memory (LSTM) based Gated Recurrent Neural Network (GRU) for multiclass email classification. SeFACED not only caters to short sequences but long dependencies of 1000+ characters as well. SeFACED focuses on tuning LSTM based GRU parameters to attain the best performance, which has its assessment by comparing it with traditional Machine Learning (ML) and Deep Learning (DL) models and state-of-the-art studies on the subject. Experimental results on self-extended benchmark datasets exhibit that SeFACED effectively outperforms existing methods while keeping the classification process robust and reliable.

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