Mixed Game-Based AoI Optimization for Combating COVID-19 With AI Bots

Yang, Yaoqi; Wang, Weizheng; Yin, Zhimeng; Xu, Renhui; Zhou, Xiaokang; Kumar, Neeraj; Alazab, Mamoun; Gadekallu, Thippa Reddy

doi:10.1109/jsac.2022.3215508

Cited by 39 publications

(12 citation statements)

References 46 publications

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“…Some recent cohorts have focused on the information freshness notion. Yang et al 55 offset the COVID-19 effects by controlling the diffusion of the epidemic by introducing Age of Information (AoI), a measure for the quantification of information freshness, an optimization scheme using artificial intelligence-based diagnostic bots. Initially, they formed a health state monitoring system where the diagnostic biosensing data was transmitted through bots using edge servers.…”

Section: Importance In the Clinical Management And The Survival Of Gb...mentioning

confidence: 99%

Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans

Qureshi

Hussain

Ibrar

et al. 2023

Sci Rep

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Accurate radiogenomic classification of brain tumors is important to improve the standard of diagnosis, prognosis, and treatment planning for patients with glioblastoma. In this study, we propose a novel two-stage MGMT Promoter Methylation Prediction (MGMT-PMP) system that extracts latent features fused with radiomic features predicting the genetic subtype of glioblastoma. A novel fine-tuned deep learning architecture, namely Deep Learning Radiomic Feature Extraction (DLRFE) module, is proposed for latent feature extraction that fuses the quantitative knowledge to the spatial distribution and the size of tumorous structure through radiomic features: (GLCM, HOG, and LBP). The application of the novice rejection algorithm has been found significantly effective in selecting and isolating the negative training instances out of the original dataset. The fused feature vectors are then used for training and testing by k-NN and SVM classifiers. The 2021 RSNA Brain Tumor challenge dataset (BraTS-2021) consists of four structural mpMRIs, viz. fluid-attenuated inversion-recovery, T1-weighted, T1-weighted contrast enhancement, and T2-weighted. We evaluated the classification performance, for the very first time in published form, in terms of measures like accuracy, F1-score, and Matthews correlation coefficient. The Jackknife tenfold cross-validation was used for training and testing BraTS-2021 dataset validation. The highest classification performance is (96.84 ± 0.09)%, (96.08 ± 0.10)%, and (97.44 ± 0.14)% as accuracy, sensitivity, and specificity respectively to detect MGMT methylation status for patients suffering from glioblastoma. Deep learning feature extraction with radiogenomic features, fusing imaging phenotypes and molecular structure, using rejection algorithm has been found to perform outclass capable of detecting MGMT methylation status of glioblastoma patients. The approach relates the genomic variation with radiomic features forming a bridge between two areas of research that may prove useful for clinical treatment planning leading to better outcomes.

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Section: Importance In the Clinical Management And The Survival Of Gb...mentioning

confidence: 99%

Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans

Qureshi

Hussain

Ibrar

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“… [17] proposes a blockchain-based platform to combat COVID-19, and discusses some blockchain-based applications in solving the problem arising from the pandemic. By applying edge-based wireless and AI technologies, [18] develops a mixed game-based Age of Information (AoI) optimization scheme for monitoring the spread of COVID-19. In [19] , Internet of Things and edge computing technologies are used to build a COVID-19 tackling system, which can detect diversified types of health-related COVID-19 symptoms and generate reports and alerts for medical decision support.…”

Section: Related Workmentioning

confidence: 99%

A coalitional game-based joint monitoring mechanism for combating COVID-19

Huang

Liu

et al. 2023

Computer Communications

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“…[21] proposed a user feature extraction method based on trust and preference similarity. By calculating and ranking the trust and interest similarity between all user pairs, and then reselecting the candidate core user set according to the frequency and location weight of the user’s travel in the nearest neighbors in the two policy lists, the core users are divided according to the user’s ability.Yaoqi Yang [22] and Wenwen Gong et al. [23] have also done a lot of work in relevant fields.…”

Section: Research Statusmentioning

confidence: 99%