RETRACTED ARTICLE: India perspective: CNN-LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability

Ketu, Shwet; Mishra, Pramod Kumar

doi:10.1007/s00500-021-06490-x

Cited by 62 publications

(28 citation statements)

References 37 publications

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“…Except for recurrent layers, convolutional layers are also applied in various studies [ 22 ]. Ketu et al [ 23 ] proposed a hybrid CNN-LSTM deep learning model for correctly forecasting the COVID-19 epidemic across India (29 states). This model uses several convolutional layers (CNN), for extracting meaningful information and learning from a time series dataset, while also using LSTM layers to identify long-term and short-term dependencies.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level

Kavouras

Kaselimi

Protopapadakis

et al. 2022

Sensors

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COVID-19 evolution imposes significant challenges for the European healthcare system. The heterogeneous spread of the pandemic within EU regions elicited a wide range of policies, such as school closure, transport restrictions, etc. However, the implementation of these interventions is not accompanied by the implementation of quantitative methods, which would indicate their effectiveness. As a result, the efficacy of such policies on reducing the spread of the virus varies significantly. This paper investigates the effectiveness of using deep learning paradigms to accurately model the spread of COVID-19. The deep learning approaches proposed in this paper are able to effectively map the temporal evolution of a COVID-19 outbreak, while simultaneously taking into account policy interventions directly into the modelling process. Thus, our approach facilitates data-driven decision making by utilizing previous knowledge to train models that predict not only the spread of COVID-19, but also the effect of specific policy measures on minimizing this spread. Global models at the EU level are proposed, which can be successfully applied at the national level. These models use various inputs in order to successfully model the spatio-temporal variability of the phenomenon and obtain generalization abilities. The proposed models are compared against the traditional epidemiological and Autoregressive Integrated Moving Average (ARIMA) models.

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Section: Related Workmentioning

confidence: 99%

COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level

Kavouras

Kaselimi

Protopapadakis

et al. 2022

Sensors

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“…Windowing is to control the amount of data processing, and only the data in the window are processed at a time. e frequency range in the fast Fourier transform spectrum is very wide, which leads to the speech signal not following the linear scale [24][25][26]. erefore, it is necessary to pass the Mel scale filter bank as shown in Figure 2.…”

Section: Extraction and Processing Of Audio Featuresmentioning

confidence: 99%

Research on Audio Recognition Based on the Deep Neural Network in Music Teaching

Cui

2022

Computational Intelligence and Neuroscience

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Solfeggio is an important basic course for music majors, and audio recognition training is one of the important links. With the improvement of computer performance, audio recognition has been widely used in smart wearable devices. In recent years, the development of deep learning has accelerated the research process of audio recognition. However, there is a lot of sound interference in music teaching environment, which leads to the performance of the audio classifier that cannot meet the actual demand. In order to solve this problem, an improved audio recognition system based on YOLO-v4 is proposed, which mainly improves the network structure. First, Mel frequency cepstrum number is used to process the original audio and extract the corresponding features. Then, try to apply the YOLO-v4 model in the field of deep learning to the field of audio recognition and improve it by combining with the spatial pyramid pool module to strengthen the generalization ability of data in different audio formats. Second, the stacking method in ensemble learning is used to fuse the independent submodels of two different channels. Experimental results show that compared with other deep learning technologies, the improved YOLO-v4 model can improve the performance of audio recognition, and it has better performance in processing data of different audio formats, which shows better generalization ability.

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“…Convolutional LSTM (CNN-LSTM) is another extension of the standard LSTM network that can interpret 2D spatio-temporal data ( Shastri et al., 2020 ). A CNN-LSTM network-based forecasting model was proposed by Ketu and Mishra (2021) to forecast the total number of COVID-19 cases across 29 states in India. Zain and Alturki (2021) demonstrated that the CNN-LSTM forecasting model for global COVID-19 patients outperformed other forecasting models such as CNN and LSTM network-based models as well as statistical models such as ARIMA.…”

Section: Literature Reviewmentioning

confidence: 99%