Introduction to machine learning

Advances in computers, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

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Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy

The European Journal of Health Economics, Journal Year: 2021, Volume and Issue: 23(6), P. 917 - 940

Published: Aug. 4, 2021

The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 deaths. This article analyzed several time series forecasting methods to predict spread COVID-19 during pandemic's second wave Italy (the period after October 13, 2020). autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), neural network autoregression (NNAR) trigonometric model with Box-Cox transformation, ARMA errors, and trend seasonal components (TBATS), all their feasible hybrid combinations were employed forecast number patients hospitalized mild symptoms intensive care units (ICU). data February 2020-October 2020 extracted from website Italian Ministry Health ( www.salute.gov.it ). results showed (i) better at capturing linear, nonlinear, patterns, significantly outperforming respective single both series, (ii) numbers COVID-19-related hospitalizations ICU projected increase rapidly mid-November 2020. According estimations, necessary ordinary beds expected double 10 days triple 20 days. These predictions consistent observed trend, demonstrating may facilitate public health authorities' decision-making, especially short-term.

Language: Английский

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Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook

The Science of The Total Environment, Journal Year: 2021, Volume and Issue: 803, P. 149834 - 149834

Published: Aug. 21, 2021

Language: Английский

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A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Feb. 14, 2022

This study aims to develop an assumption-free data-driven model accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization tune the Gaussian process regression (GPR) hyperparameters efficient GPR-based for forecasting recovered and confirmed cases in two highly impacted countries, India Brazil. However, machine learning models do not consider time dependency data series. Here, dynamic information has been taken into account alleviate limitation by introducing lagged measurements constructing investigated models. Additionally, assessed contribution of incorporated features prediction using Random Forest algorithm. Results reveal that significant improvement can be obtained proposed In addition, results highlighted superior performance GPR compared other (i.e., Support vector regression, Boosted trees, Bagged Decision tree, Forest, XGBoost) achieving averaged mean absolute percentage error around 0.1%. Finally, provided confidence level predicted based on showed predictions are within 95% interval. presents a promising shallow simple approach predicting

Language: Английский

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A Genetic Algorithm for the Waitable Time-Varying Multi-Depot Green Vehicle Routing Problem

Symmetry, Journal Year: 2023, Volume and Issue: 15(1), P. 124 - 124

Published: Jan. 1, 2023

In an era where people in the world are concerned about environmental issues, companies must reduce distribution costs while minimizing pollution generated during process. For today’s multi-depot problem, a mixed-integer programming model is proposed this paper to minimize all incurred entire transportation process, considering impact of time-varying speed, loading, and waiting time on costs. Time directional; hence, problems considered study modeled based asymmetry, making problem-solving more complex. This proposes genetic algorithm combined with simulated annealing solve issue, inner outer layers solving for optimal path planning respectively. The mutation operator replaced layer by neighbor search approach using solution acceptance mechanism similar avoid local optimum solution. extends problem (vehicle-routing problem) provides alternative networks.

Language: Английский

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A predictive analytics model for COVID-19 pandemic using artificial neural networks

Decision Analytics Journal, Journal Year: 2021, Volume and Issue: 1, P. 100007 - 100007

Published: Oct. 30, 2021

The COVID-19 pandemic spread rapidly around the world and is currently one of most leading causes death heath disaster in world. Turkey, like countries, has been negatively affected by COVID-19. aim this study to design a predictive model based on artificial neural network (ANN) predict future number daily cases deaths caused generalized way fit different countries' spreads. In study, we used dataset between 11 March 2020 23 January 2021 for countries. This provides an ANN assist government take preventive action hospitals medical facilities. results show that there 86% overall accuracy predicting mortality rate 87% cases.

Language: Английский

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Deep learning for Covid-19 forecasting: State-of-the-art review.

Neurocomputing, Journal Year: 2022, Volume and Issue: 511, P. 142 - 154

Published: Sept. 8, 2022

Language: Английский

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A Comprehensive Review on RSM-Coupled Optimization Techniques and Its Applications

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(8), P. 4831 - 4853

Published: June 23, 2023

Language: Английский

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A quantization assisted U-Net study with ICA and deep features fusion for breast cancer identification using ultrasonic data

PeerJ Computer Science, Journal Year: 2021, Volume and Issue: 7, P. e805 - e805

Published: Dec. 16, 2021

Breast cancer is one of the leading causes death in women worldwide-the rapid increase breast has brought about more accessible diagnosis resources. The ultrasonic modality for relatively cost-effective and valuable. Lesion isolation images a challenging task due to its robustness intensity similarity. Accurate detection lesions using can reduce rates. In this research, quantization-assisted U-Net approach segmentation proposed. It contains two step segmentation: (1) (2) quantization. quantization assists U-Net-based order isolate exact lesion areas from sonography images. Independent Component Analysis (ICA) method then uses isolated extract features are fused with deep automatic features. Public ultrasonic-modality-based datasets such as Ultrasound Images Dataset (BUSI) Open Access Database Raw Ultrasonic Signals (OASBUD) used evaluation comparison. OASBUD data extracted same However, classification was done after feature regularization lasso method. obtained results allow us propose computer-aided design (CAD) system identification modalities.

Language: Английский

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A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions

Sensors, Journal Year: 2022, Volume and Issue: 22(22), P. 8615 - 8615

Published: Nov. 8, 2022

Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain’s requirements analytics-based risk predictions. This manuscript proposes mechanism experimented diverse healthcare scenarios, towards constructing catalogue most ML algorithms be used depending on scenario’s datasets, efficiently predicting onset disease. context, seven (7) different (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed top scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based variety performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has identified that sub-set are more predictions under specific why envisioned prioritizes used, scenarios’ nature needed metrics. Further evaluation must performed considering additional involving state-of-the-art techniques (e.g., cloud deployment, federated ML) improving mechanism’s efficiency.

Language: Английский

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