Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis

Alzubi, Jafar A.; Alzubi, Omar A.; Beseiso, Majdi; Budati, Anil Kumar; Shankar, K.

doi:10.1111/exsy.12879

Cited by 28 publications

(17 citation statements)

References 31 publications

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“…We tested six of the most commonly used classes of machine learning algorithms which were chosen based on their clinical utility in predicting patient deterioration in critical care settings [11][12][13]25]. We acknowledge that there are a multitude of high performing machine learning algorithms with clinical and medical informatics utility and cannot exclude that these additional classes would have superior predictive ability [30][31][32]. During the capture period Australia experienced two distinct 'waves'; an initial wave from 27 February to 30 June 2020 and a second wave from 1 July to the 7 th of March 2021.…”

Section: Plos Onementioning

confidence: 99%

Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients

Karri

Chen²,

Burrell³

et al. 2022

PLoS ONE

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Objective(s) To use machine learning (ML) to predict short-term requirements for invasive ventilation in patients with COVID-19 admitted to Australian intensive care units (ICUs). Design A machine learning study within a national ICU COVID-19 registry in Australia. Participants Adult patients who were spontaneously breathing and admitted to participating ICUs with laboratory-confirmed COVID-19 from 20 February 2020 to 7 March 2021. Patients intubated on day one of their ICU admission were excluded. Main outcome measures Six machine learning models predicted the requirement for invasive ventilation by day three of ICU admission from variables recorded on the first calendar day of ICU admission; (1) random forest classifier (RF), (2) decision tree classifier (DT), (3) logistic regression (LR), (4) K neighbours classifier (KNN), (5) support vector machine (SVM), and (6) gradient boosted machine (GBM). Cross-validation was used to assess the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of machine learning models. Results 300 ICU admissions collected from 53 ICUs across Australia were included. The median [IQR] age of patients was 59 [50–69] years, 109 (36%) were female and 60 (20%) required invasive ventilation on day two or three. Random forest and Gradient boosted machine were the best performing algorithms, achieving mean (SD) AUCs of 0.69 (0.06) and 0.68 (0.07), and mean sensitivities of 77 (19%) and 81 (17%), respectively. Conclusion Machine learning can be used to predict subsequent ventilation in patients with COVID-19 who were spontaneously breathing and admitted to Australian ICUs.

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Section: Plos Onementioning

confidence: 99%

Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients

Karri

Chen²,

Burrell³

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

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“…The standard RNNs are inclined to gradient vanishing and explosion in the training because of the repeating multiplication of weighted matrix. IRNN is a novel recurrent network which efficiently resolves the gradient problem by altering the time-based gradient back propagation [16][17][18][19][20]. Also, IRNN is employ unsaturated function (i.e., rectified linear unit (ReLU)) as an activation function and retains higher robustness and then training.…”

Section: Irae Based Classificationmentioning

confidence: 99%

Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

Duhayyim¹,

Mohamed²,

Alotaibi³

et al. 2022

Computers, Materials &Amp; Continua

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Cyberbullying (CB) is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB. The recently developed deep learning (DL) models pave the way to design CB classifier models with maximum performance. At the same time, optimal hyperparameter tuning process plays a vital role to enhance overall results. This study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification (TLGODL-CBC) model in Social Media. The proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media context. Initially, the input data is cleaned and pre-processed to make it compatible for further processing. Followed by, independent recurrent autoencoder (IRAE) model is utilized for the recognition and classification of CBs. Finally, the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the work. To assuring the improved outcomes of the TLGODL-CBC approach, a wide range of simulations are executed and the outcomes are investigated under several aspects. The simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches.

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“…Finally, the hyperparameter of HCNN-LSTM model are optimized with the help of HSO algorithm which in turn results in improved outcomes [21][22][23][24]. HSO algorithm is stimulated based on foraging and hunger behaviour of animals [25].…”

Section: Hyperparameter Optimizationmentioning

confidence: 99%

Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

Shaiba¹,

Eltahir²,

Marzouk³

et al. 2022

Computers, Materials &Amp; Continua

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Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords, account identifiers, bank details, etc. In general, these kinds of cyberattacks are made at users through phone calls, emails, or instant messages. The anti-phishing techniques, currently under use, are mainly based on source code features that need to scrape the webpage content. In third party services, these techniques check the classification procedure of phishing Uniform Resource Locators (URLs). Even though Machine Learning (ML) techniques have been lately utilized in the identification of phishing, they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing offenses. The tremendous growth and evolution of Deep Learning (DL) techniques paved the way for increasing the accuracy of classification process. In this background, the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification (HSOHDL-PDC) model. The presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website URLs. In addition, SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no importance. Moreover, a hybrid Convolutional Neural Network-Long Short Term Memory (HCNN-LSTM) technique is also applied for identification and classification of phishing. The hyperparameters involved in HCNN-LSTM model are optimized with the help of HSO algorithm which in turn produced improved outcomes. The performance of the proposed HSOHDL-PDC model was validated using 6426 CMC, 2022, vol.73, no.3 different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.

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Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis

Cited by 28 publications

References 31 publications

Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients

Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients

Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

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