MPS realization selection with an innovative LSTM tool

Azamifard, Arash; Ahmadi, Mehdi; Rashidi, Fariborz; Pourfard, Mohammadreza; Dabir, Bahram

doi:10.1016/j.jappgeo.2020.104107

Cited by 5 publications

(3 citation statements)

References 53 publications

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“…It consists of 900 records and weighs 4.8 MB. The following is how the data from this period were used: 79% is used for training, and 20% is used to evaluate model parameters for suitability [ 38 , 39 ]. Once training was completed, the next step was testing.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…The suggested approach involves using Reinforcement Learning (RL), specifically Deep Q-network (DQN), to train the agent with input data and conditions. The training tests provide scenarios for agents to acquire knowledge and adjust their strategies [ 37 , 38 ]. Reward (R e ): ' Immediate Reward’ refers to the reward received when an action is taken in a specific stage, leading to transitioning to the next state.…”

Section: Deep Learning Approachmentioning

confidence: 99%

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A deep learning-based approach for predicting COVID-19 diagnosis

Munshi,

Khayyat,

Ben Slama

et al. 2024

Heliyon

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Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Deep Learning Approachmentioning

confidence: 99%

A deep learning-based approach for predicting COVID-19 diagnosis

Munshi,

Khayyat,

Ben Slama

et al. 2024

Heliyon

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“…The dataset was split into training and evaluation sets, with around 79% allocated for training and the remaining 20% used for assessing the model parameters. This method was employed in previous studies by Box ( 2018 ), Azamifard et al ( 2020 ), Kutlu and Camgözlü ( 2021 ), Shrestha et al ( 2021 ), Sulthana et al ( 2021 ), Vadyala et al ( 2021 ), Valente and Laurini ( 2021 ), Aslan et al ( 2022 ), Garg et al ( 2022 ), Ghimire et al ( 2022 ), Ma et al ( 2022 ), and Shahidzadeh et al ( 2022 ).…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases

Alhhazmi,

Alferidi,

Almutawif

et al. 2024

Front. Artif. Intell.

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Healthcare is a topic of significant concern within the academic and business sectors. The COVID-19 pandemic has had a considerable effect on the health of people worldwide. The rapid increase in cases adversely affects a nation's economy, public health, and residents' social and personal well-being. Improving the precision of COVID-19 infection forecasts can aid in making informed decisions regarding interventions, given the pandemic's harmful impact on numerous aspects of human life, such as health and the economy. This study aims to predict the number of confirmed COVID-19 cases in Saudi Arabia using Bayesian optimization (BOA) and deep learning (DL) methods. Two methods were assessed for their efficacy in predicting the occurrence of positive cases of COVID-19. The research employed data from confirmed COVID-19 cases in Saudi Arabia (SA), the United Kingdom (UK), and Tunisia (TU) from 2020 to 2021. The findings from the BOA model indicate that accurately predicting the number of COVID-19 positive cases is difficult due to the BOA projections needing to align with the assumptions. Thus, a DL approach was utilized to enhance the precision of COVID-19 positive case prediction in South Africa. The DQN model performed better than the BOA model when assessing RMSE and MAPE values. The model operates on a local server infrastructure, where the trained policy is transmitted solely to DQN. DQN formulated a reward function to amplify the efficiency of the DQN algorithm. By examining the rate of change and duration of sleep in the test data, this function can enhance the DQN model's training. Based on simulation findings, it can decrease the DQN work cycle by roughly 28% and diminish data overhead by more than 50% on average.

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