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

Research Square (Research Square), Journal Year: 2020, Volume and Issue: unknown

Published: Aug. 10, 2020

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

Language: Английский

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Data science in economics: comprehensive review of advanced machine learning and deep learning methods

Published: Oct. 20, 2020

This paper provides a state-of-the-art investigation of advances in data science emerging economic applications. The analysis was performed on novel methods four individual classes deep learning models, hybrid machine learning, and ensemble models. Application domains include wide diverse range economics research from the stock market, marketing, e-commerce to corporate banking cryptocurrency. Prisma method, systematic literature review methodology, used ensure quality survey. findings reveal that trends follow advancement which, based accuracy metric, outperform other algorithms. It is further expected will converge toward advancements sophisticated

Language: Английский

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Revolutionizing COVID-19 Diagnosis: Advancements in Chest X-ray Analysis through Customized Convolutional Neural Networks and Image Fusion Data Augmentation

BIO Web of Conferences, Journal Year: 2024, Volume and Issue: 97, P. 00014 - 00014

Published: Jan. 1, 2024

COVID-19 is produced by a new coronavirus called SARS-CoV-2, has wrought extensive damage. Globally, Patients present wide range of challenges, which forced medical professionals to actively seek out cutting-edge therapeutic approaches and technology advancements. Machine learning technologies have significantly enhanced the comprehension control issue. enables computers emulate human-like behavior efficiently recognizing patterns extracting valuable insights. Cognitive capacity aptitude for handling substantial quantities data. Amidst battle against COVID-19, firms promptly employed machine-learning expertise in several ways, such as improving consumer communication, enhance transmission mechanism expedite research treatment. This work centered around utilization deep techniques predictive modeling. individuals impacted with COVID-19. A data augmentation phase included, utilizing multiexposure picture fusion techniques. Chest X-ray images healthy patients make up our dataset.

Language: Английский

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

Research Square (Research Square), Journal Year: 2020, Volume and Issue: unknown

Published: Oct. 23, 2020

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

Language: Английский

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Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer

Published: Oct. 22, 2020

An accurate outbreak prediction of COVID-19 can successfully help to get insight into the spread and consequences infectious diseases. Recently, machine learning (ML) based models have been employed for disease outbreak. The present study aimed engage an artificial neural network-integrated by grey wolf optimizer predictions employing Global dataset. Training testing processes performed time-series data related January 22 September 15, 2020 validation has 16 October 2020. Results evaluated mean absolute percentage error (MAPE) correlation coefficient (r) values. ANN-GWO provided a MAPE 6.23, 13.15 11.4% training, validating phases, respectively. According results, developed model could cope with task.

Language: Английский

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