Deep Learning for Fading Channel Prediction

Jiang, Wei; Schotten, Hans D.

doi:10.1109/ojcoms.2020.2982513

Cited by 134 publications

(87 citation statements)

References 80 publications

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“…Deep learning methods like recurrent neural networks (RNN) proved to be effective for prediction (Jiang and Schotten (2020)) due to automatically extracting relevant features from the training samples, feeding the activation from the previous time step as input for the current time step and networks self-connections. RNN is good at processing data and exhibiting great potential in time-series prediction (Connor et al (1994)) through storing large historical information in its internal state.…”

Section: Lstm Based Technique For Prediction Of Covid-19mentioning

confidence: 99%

Prediction for the spread of COVID-19 in India and effectiveness of preventive measures

Tomar

Gupta

2020

Science of The Total Environment

398

330

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The spread of COVID-19 in the whole world has put the humanity at risk. The resources of some of the largest economies are stressed out due to the large infectivity and transmissibility of this disease. Due to the growing magnitude of number of cases and its subsequent stress on the administration and health professionals, some prediction methods would be required to predict the number of cases in future. In this paper, we have used data-driven estimation methods like long short-term memory (LSTM) and curve fitting for prediction of the number of COVID-19 cases in India 30 days ahead and effect of preventive measures like social isolation and lockdown on the spread of COVID-19. The prediction of various parameters (number of positive cases, number of recovered cases, etc.) obtained by the proposed method is accurate within a certain range and will be a beneficial tool for administrators and health officials.

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Section: Lstm Based Technique For Prediction Of Covid-19mentioning

confidence: 99%

Prediction for the spread of COVID-19 in India and effectiveness of preventive measures

Tomar

Gupta

2020

Science of The Total Environment

398

330

View full text Add to dashboard Cite

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“…FFNN was applied for the mapping of number of total cases (NTC), number of active cases (NAC), number of recovered cases (NRC), number of deceases persons (NDP) as a function of number of days. FFNN based relationship was used for the prediction and forecasting of COVID-19 spreading rate ( Apostolopoulos & Mpesiana, 2020 ; Desai et al, 2008 ; Jiang & Schotten, 2020 ; Tomar & Gupta, 2020 ). Total data of 99 days starting from 1 st Feb, 2020 were used for the mapping of FFNN modeling.…”

Section: Methodsmentioning

confidence: 99%

Reaction order and neural network approaches for the simulation of COVID-19 spreading kinetic in India

Chakraborty¹,

Choudhary²,

Sarma³

et al. 2020

Infectious Disease Modelling

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COVID-19 has created a pandemic situation in the whole world. Controlling of COVID-19 spreading rate in the social environment is a challenge for all individuals. In the present study, simulation of the lockdown effect on the COVID-19 spreading rate in India and mapping of its recovery percentage (until May, 2020) were investigated. Investigation of the lockdown impact dependent on first order reaction kinetics demonstrated higher effect of lockdown 1 on controlling the COVID-19 spreading rate when contrasted with lockdown 2 and 3. Although decreasing trend was followed for the reaction rate constant of different lockdown stages, the distinction between the lockdown 2 and 3 was minimal. Mathematical and feed forward neural network (FFNN) approaches were applied for the simulation of COVID-19 spreading rate. In case of mathematical approach, exponential model indicated adequate performance for the prediction of the spreading rate behavior. For the FFNN based modeling, 1-5-1 was selected as the best architecture so as to predict adequate spreading rate for all the cases. The architecture also showed effective performance in order to forecast number of cases for next 14 days. The recovery percentage was modeled as a function of number of days with the assistance of polynomial fitting. Therefore, the investigation recommends proper social distancing and efficient management of corona virus in order to achieve higher decreasing trend of reaction rate constant and required recovery percentage for the stabilization of India.

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“…From the various deep learning methods, we can say recurrent Neural Network(RNN) has convinced to be the most robust for prediction as it can automatically excerpt the necessary features from the training samples, delivering the activation from the previous time step as the load for the present time step and the network self-connections [14]. As Connor et al mentioned that RNN is satisfying at processing data and manifests promising results in the time-series prediction through hoarding enormous historical information in its internal state [15].…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

Chowdhury

Hasan

Hoque

2020

Preprint

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Objectives: The dangerously contagious virus named \newline SARS-CoV-2 has hit the world hard that has locked downed billion people in their homes for stopping further spread. All the researchers and scientists in various fields are working around the clock to come up with a vaccine and prevention methods to save the world from this invisible pathogen. However, reliable prediction of the epidemic may help contain the contagion until cure becomes available. The machine learning techniques is one of the frontier in predicting the future trend and behavior of this outbreak. Our research is focused on finding a suitable machine learning model that can predict on small dataset with higher accuracy.Methods: In this research, we have used the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the long short-term memory[LSTM] to foresee the newly infected cases in Bangladesh. We have compared both the results of the experiments and it can be forenamed that LSTM has shown more satisfactory results.Results: Upon study and testing on several models, we have showed that LSTM works better on scenario based model for Bangladesh with MAPE 4.51, RMSE 6.55 and Correlation Coefficient 0.75. Conclusion: This study is expected to shade light on Covid-19 prediction models for researchers working with machine learning techniques and help avoid proven failures specially for small imprecise dataset.

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Deep Learning for Fading Channel Prediction

Cited by 134 publications

References 80 publications

Prediction for the spread of COVID-19 in India and effectiveness of preventive measures

Prediction for the spread of COVID-19 in India and effectiveness of preventive measures

Reaction order and neural network approaches for the simulation of COVID-19 spreading kinetic in India

Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

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