Applying deep learning-based multi-modal for detection of coronavirus

Rani, Geeta; Oza, Meet Ganpatlal; Dhaka, Vijaypal Singh; Pradhan, Nitesh; Verma, Sahil; Rodrigues, Joel J. P. C.

doi:10.1007/s00530-021-00824-3

Cited by 21 publications

(7 citation statements)

References 23 publications

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“…Coronavirus is an animal-origin pathogen that can cause disease in humans [ 31 , 32 ]. A model for phenotype identification of notorious disease is urgently needed to be developed [ 33 , 34 ]. In the paper, we present a deep learning model of cross-species coronavirus infection that combines a one-dimensional convolutional neural network with a bidirectional gated recurrent unit network.…”

Section: Discussionmentioning

confidence: 99%

Prediction of pandemic risk for animal-origin coronavirus using a deep learning method

et al. 2021

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Background Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes. Methods A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models. Results The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5–25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor. Conclusions Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

Prediction of pandemic risk for animal-origin coronavirus using a deep learning method

et al. 2021

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“…Columns like IP addresses, dates, and ids that aren't strictly necessary are taken out of the dataset. All NaN and null values are replaced with the median of the related columns [23]. Columns containing strings can have their values converted to numbers using label encoding.…”

Section: Preparing Datasetsmentioning

confidence: 99%

Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

Vatambeti¹,

Mamidisetti²

2023

IDA

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Smart cities, ITS, supply chains, and smart industries may all be developed with minimal human interaction thanks to the increasing prevalence of automation enabled by machine-type communication (MTC). Yet, MTC has substantial security difficulties because of diverse data, public network access, and an insufficient security mechanism. In this study, we develop a novel IIOT attack detection basis by joining the following four main steps: (a) data collection, (b) pre-processing, (c) attack detection, and (d) optimisation for high classification accuracy. At the initial stage of processing, known as "pre-processing," the collected raw data (input) is normalised. Attack detection requires the creation of an intelligent security architecture for IIoT networks. In this work, we present a learning model that can recognise previously unrecognised attacks on an IIoT network without the use of a labelled training set. An IoT network intrusion detection system-generated labelled dataset. The study also introduces a hybrid optimisation algorithm for pinpointing the optimal LSTM weight when it comes to intrusion detection. When trained on the labelled dataset provided by the proposed method, the improved LSTM outperforms the other models with a finding accuracy of 95%, as exposed in the research.

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“…It has been shown both theoretically [14] and empirically [15,16] that models aggregating data from multiple modalities outperform their uni-modal counterparts due to the enriched features and patterns to be learned from the multi-modal data. The usage of multi-modal learning has seen success in a wide range of learning tasks such as object detection [17], semantic segmentation [18], video action recognition [19], and detection of disease [20,21].…”

Section: Detection Of Mgmt Methylation Status Based On Mri Datamentioning

confidence: 99%

An Attentive Multi-Modal CNN for Brain Tumor Radiogenomic Classification

Xiao

2022

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Medical images of brain tumors are critical for characterizing the pathology of tumors and early diagnosis. There are multiple modalities for medical images of brain tumors. Fusing the unique features of each modality of the magnetic resonance imaging (MRI) scans can accurately determine the nature of brain tumors. The current genetic analysis approach is time-consuming and requires surgical extraction of brain tissue samples. Accurate classification of multi-modal brain tumor images can speed up the detection process and alleviate patient suffering. Medical image fusion refers to effectively merging the significant information of multiple source images of the same tissue into one image, which will carry abundant information for diagnosis. This paper proposes a novel attentive deep-learning-based classification model that integrates multi-modal feature aggregation, lite attention mechanism, separable embedding, and modal-wise shortcuts for performance improvement. We evaluate our model on the RSNA-MICCAI dataset, a scenario-specific medical image dataset, and demonstrate that the proposed method outperforms the state-of-the-art (SOTA) by around 3%.

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Applying deep learning-based multi-modal for detection of coronavirus

Cited by 21 publications

References 23 publications

Prediction of pandemic risk for animal-origin coronavirus using a deep learning method

Prediction of pandemic risk for animal-origin coronavirus using a deep learning method

Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

An Attentive Multi-Modal CNN for Brain Tumor Radiogenomic Classification

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