Blockchain Enabled Smart Healthcare System Using Jellyfish Search Optimization With Dual-Pathway Deep Convolutional Neural Network
Blockchain (BC) and Artificial intelligence (AI) based technologies have earned a better reputation amongst the research community, especially in the medical field. BC technology has emerged as a promising solution to revolutionize the medical field by addressing challenges related to efficiency, da...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10214265/ |
| _version_ | 1797739022135590912 |
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| author | Fahad F. Alruwaili Bayan Alabduallah Hamed Alqahtani Ahmed S. Salama Gouse Pasha Mohammed Amani A. Alneil |
| author_facet | Fahad F. Alruwaili Bayan Alabduallah Hamed Alqahtani Ahmed S. Salama Gouse Pasha Mohammed Amani A. Alneil |
| author_sort | Fahad F. Alruwaili |
| collection | DOAJ |
| description | Blockchain (BC) and Artificial intelligence (AI) based technologies have earned a better reputation amongst the research community, especially in the medical field. BC technology has emerged as a promising solution to revolutionize the medical field by addressing challenges related to efficiency, data security, and interoperability. A BC-aided smart healthcare system leverages the immutable and decentralized nature of BC to construct a secured and transparent ecosystem to manage processes and healthcare data. It leverages the secure and decentralized nature of BC to optimize the processes, security, interoperability, and efficiency of medical data. The existing system is exposed to security attacks on healthcare data. It can be necessary to construct a real-time detection device utilizing a cyber-physical system (CPS) with BC technology in a significant way. This article designs a novel Blockchain-Enabled Smart Healthcare System using Jellyfish Search Optimization with Dual-Pathway Deep Convolutional Neural Network (JSO-DPCNN) technique. The presented JSO-DPDCNN technique exploits the concept of BC-enabled secure data transmission and DL-based diagnosis model for moneypox disease on smart healthcare monitoring. To accomplish this, the JSO-DPCNN technique uses Ethereum-based public BC to secure the privacy of healthcare images. In addition, the JSO-DPCNN technique applies a feature extraction module using DPCNN, which extracts the suitable set of features in the input images. Moreover, the multiplicative long short-term memory (MLSTM) approach was used for the disease detection process. Lastly, the JSO system can be employed for the parameter tuning of the MLSTM model. The simulation result of the JSO-DPCNN system was executed on a benchmark medical dataset. The comprehensive outcomes highlighted the significant outcome of the JSO-DPCNN approach in terms of different measures. |
| first_indexed | 2024-03-12T13:52:15Z |
| format | Article |
| id | doaj.art-c2998e2c4f214516abbf709b5c61fe16 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T13:52:15Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c2998e2c4f214516abbf709b5c61fe162023-08-22T23:00:19ZengIEEEIEEE Access2169-35362023-01-0111875838759110.1109/ACCESS.2023.330426910214265Blockchain Enabled Smart Healthcare System Using Jellyfish Search Optimization With Dual-Pathway Deep Convolutional Neural NetworkFahad F. Alruwaili0Bayan Alabduallah1https://orcid.org/0000-0002-6252-1800Hamed Alqahtani2Ahmed S. Salama3Gouse Pasha Mohammed4https://orcid.org/0000-0003-1583-9872Amani A. Alneil5Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer Science, Center of Artificial Intelligence, Unit of Cybersecurity, King Khalid University, Abha, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, EgyptDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaBlockchain (BC) and Artificial intelligence (AI) based technologies have earned a better reputation amongst the research community, especially in the medical field. BC technology has emerged as a promising solution to revolutionize the medical field by addressing challenges related to efficiency, data security, and interoperability. A BC-aided smart healthcare system leverages the immutable and decentralized nature of BC to construct a secured and transparent ecosystem to manage processes and healthcare data. It leverages the secure and decentralized nature of BC to optimize the processes, security, interoperability, and efficiency of medical data. The existing system is exposed to security attacks on healthcare data. It can be necessary to construct a real-time detection device utilizing a cyber-physical system (CPS) with BC technology in a significant way. This article designs a novel Blockchain-Enabled Smart Healthcare System using Jellyfish Search Optimization with Dual-Pathway Deep Convolutional Neural Network (JSO-DPCNN) technique. The presented JSO-DPDCNN technique exploits the concept of BC-enabled secure data transmission and DL-based diagnosis model for moneypox disease on smart healthcare monitoring. To accomplish this, the JSO-DPCNN technique uses Ethereum-based public BC to secure the privacy of healthcare images. In addition, the JSO-DPCNN technique applies a feature extraction module using DPCNN, which extracts the suitable set of features in the input images. Moreover, the multiplicative long short-term memory (MLSTM) approach was used for the disease detection process. Lastly, the JSO system can be employed for the parameter tuning of the MLSTM model. The simulation result of the JSO-DPCNN system was executed on a benchmark medical dataset. The comprehensive outcomes highlighted the significant outcome of the JSO-DPCNN approach in terms of different measures.https://ieeexplore.ieee.org/document/10214265/Blockchainsustainabilitysmart healthcaredeep learningjellyfish search optimizer |
| spellingShingle | Fahad F. Alruwaili Bayan Alabduallah Hamed Alqahtani Ahmed S. Salama Gouse Pasha Mohammed Amani A. Alneil Blockchain Enabled Smart Healthcare System Using Jellyfish Search Optimization With Dual-Pathway Deep Convolutional Neural Network IEEE Access Blockchain sustainability smart healthcare deep learning jellyfish search optimizer |
| title | Blockchain Enabled Smart Healthcare System Using Jellyfish Search Optimization With Dual-Pathway Deep Convolutional Neural Network |
| title_full | Blockchain Enabled Smart Healthcare System Using Jellyfish Search Optimization With Dual-Pathway Deep Convolutional Neural Network |
| title_fullStr | Blockchain Enabled Smart Healthcare System Using Jellyfish Search Optimization With Dual-Pathway Deep Convolutional Neural Network |
| title_full_unstemmed | Blockchain Enabled Smart Healthcare System Using Jellyfish Search Optimization With Dual-Pathway Deep Convolutional Neural Network |
| title_short | Blockchain Enabled Smart Healthcare System Using Jellyfish Search Optimization With Dual-Pathway Deep Convolutional Neural Network |
| title_sort | blockchain enabled smart healthcare system using jellyfish search optimization with dual pathway deep convolutional neural network |
| topic | Blockchain sustainability smart healthcare deep learning jellyfish search optimizer |
| url | https://ieeexplore.ieee.org/document/10214265/ |
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