Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India DOI Creative Commons
Ankit Kumar Srivastava, Saurabh Tripathi, Sachin Kumar

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 95106 - 95124

Published: Jan. 1, 2022

The novel coronavirus (nCOV) is a new strain that needs to be hindered from spreading by taking effective preventive measures as swiftly possible. Timely forecasting of COVID-19 cases can ultimately support in making significant decisions and planning for implementing measures. In this study, three common machine learning (ML) approaches via linear regression (LR), sequential minimal optimization (SMO) regression, M5P techniques have been discussed implemented disease-2019 (COVID-19) pandemic scenarios. To demonstrate the forecast accuracy aforementioned ML approaches, preliminary sample-study has conducted on first wave scenario different countries including United States America (USA), Italy, Australia. Furthermore, contributions study are extended conducting an in-depth scenarios first, second, third waves India. An accurate model proposed, which constructed basis results models findings research highlight LR potential approach outperforms all other tested herein present scenario. Finally, used likely onset fourth

Language: Английский

Using artificial intelligence to improve public health: a narrative review DOI Creative Commons
David B. Olawade,

Ojima J. Wada,

Aanuoluwapo Clement David-Olawade

et al.

Frontiers in Public Health, Journal Year: 2023, Volume and Issue: 11

Published: Oct. 26, 2023

Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, public health, the widespread employment only began recently, with advent COVID-19. This review examines advances health potential challenges that lie ahead. Some ways aided delivery are via spatial modeling, risk prediction, misinformation control, surveillance, disease forecasting, pandemic/epidemic diagnosis. implementation not universal due to factors including limited infrastructure, lack technical understanding, data paucity, ethical/privacy issues.

Language: Английский

Citations

103
Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study DOI Creative Commons

Abdulelah Alkesaiberi,

Fouzi Harrou, Ying Sun

et al.

Energies, Journal Year: 2022, Volume and Issue: 15(7), P. 2327 - 2327

Published: March 23, 2022

Wind power represents a promising source of renewable energies. Precise forecasting wind generation is crucial to mitigate the challenges balancing supply and demand in smart grid. Nevertheless, major difficulty its high fluctuation intermittent nature, making it challenging forecast. This study aims develop efficient data-driven models accurately forecast generation. Crucially, main contributions this work are listed following elements. Firstly, we investigate performance enhanced machine learning univariate time-series data. Specifically, employed Bayesian optimization (BO) optimally tune hyperparameters Gaussian process regression (GPR), Support Vector Regression (SVR) with different kernels, ensemble (ES) (i.e., Boosted trees Bagged trees) investigated their performance. Secondly, dynamic information has been incorporated construction further enhance models. introduce lagged measurements enable capturing time evolution into design considered Furthermore, more input variables (e.g., speed direction) used improve prediction Actual from three turbines France, Turkey, Kaggle verify efficiency The results reveal benefit considering data better power. also showed that optimized GPR outperformed other

Language: Английский

Citations

87
Using sequences of life-events to predict human lives DOI
Germans Savcisens, Tina Eliassi‐Rad, Lars Kai Hansen

et al.

Nature Computational Science, Journal Year: 2023, Volume and Issue: 4(1), P. 43 - 56

Published: Dec. 18, 2023

Language: Английский

Citations

35
Estimate the incubation period of coronavirus 2019 (COVID-19) DOI Open Access
Ke Men, Yihao Li, Xia Wang

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 158, P. 106794 - 106794

Published: March 30, 2023

Language: Английский

Citations

30
Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models DOI Creative Commons
Lanouar Charfeddine, Esmat Zaidan, Ahmad Qadeib Alban

et al.

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 98, P. 104860 - 104860

Published: Aug. 15, 2023

Accurately modelling and forecasting electricity consumption remains a challenging task due to the large number of statistical properties that characterize this time series such as seasonality, trend, sudden changes, slow decay autocrrelation function, among many others. This study contributes literature by using comparing four advanced econometrics models, machine learning deep models1 analyze forecast during COVID-19 pre-lockdown, lockdown, releasing-lockdown, post-lockdown phases. Monthly data on Qatar's total has been used from January 2010 December 2021. The empirical findings demonstrate both econometric models are able capture most important features characterizing consumption. In particular, it is found climate change based factors, e.g temperature, rainfall, mean sea-level pressure wind speed, key determinants terms forecasting, results indicate autoregressive fractionally integrated moving average three state markov switching with exogenous variables outperform all other models. Policy implications energy-environmental recommendations proposed discussed.

Language: Английский

Citations

28
The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals DOI Creative Commons
George Lăzăroiu, Tom Gedeon, Elżbieta Rogalska

et al.

Oeconomia Copernicana, Journal Year: 2024, Volume and Issue: 15(1), P. 27 - 58

Published: March 30, 2024

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis symptom tracing, optimize intensive care unit admission, use clinical data to determine patient prioritization mortality risk, being pivotal qualitative provision, reducing errors, increasing survival rates, thus diminishing the massive healthcare system burden relation severe inpatient stay duration, while operational costs throughout organizational management of hospitals. Data-driven financial scenario-based contingency planning, predictive modelling tools, risk pooling mechanisms should be deployed for additional equipment unforeseen demand expenses. Purpose article: We show that deep decision making systems likelihood treatment outcomes with regard susceptible, infected, recovered individuals, performing accurate analyses by modeling based on vital signs, surveillance data, infection-related biomarkers, furthering hospital facility optimization terms bed allocation. Methods: The review software employed article screening quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, SRDR. Findings & value added: support tools forecast spread, confirmed cases, infection rates data-driven appropriate resource allocations effective therapeutic protocol development, determining suitable measures regulations using symptoms comorbidities, laboratory records across units, impacting financing infrastructure. As a result heightened personal protective equipment, pharmacy medication, outpatient treatment, supplies, revenue loss vulnerability occur, also due expenses related hiring staff critical expenditures. Hospital care, screening, capacity expansion, lead further losses affecting frontline workers patients.

Language: Английский

Citations

13
Enhancing Predictive Capabilities: Machine Learning Approaches for Predicting Mechanical Behavior in Friction Stir Welded Aluminum Alloys DOI
Abdelhakim Dorbane, Fouzi Harrou, Bekir Dursun

et al.

Journal of Materials Engineering and Performance, Journal Year: 2024, Volume and Issue: unknown

Published: April 8, 2024

Language: Английский

Citations

7
The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review DOI Creative Commons
Joseph Okeibunor, Anelisa Jaca, Chinwe Juliana Iwu

et al.

Frontiers in Public Health, Journal Year: 2023, Volume and Issue: 11

Published: July 4, 2023

Background Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable simulating and performing tasks usually done by human beings. The aim this scoping review to map existing evidence on the use AI in delivery medical care. Methods We searched PubMed Scopus March 2022, screened identified records for eligibility, assessed full texts potentially eligible publications, extracted data from included studies duplicate, resolving differences through discussion, arbitration, consensus. then conducted narrative synthesis data. Results Several methods have been used detect, diagnose, classify, manage, treat, monitor prognosis various health issues. These models conditions, including communicable diseases, non-communicable mental health. Conclusions Presently available shows that models, predominantly deep learning, machine can significantly advance care regarding detection, diagnosis, management, monitoring different illnesses.

Language: Английский

Citations

16
A novel framework for modeling quarantinable disease transmission DOI Creative Commons

Wenchen Liu,

Chang Liu, Dehui Wang

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0317553 - e0317553

Published: Feb. 12, 2025

The COVID-19 pandemic has significantly challenged traditional epidemiological models due to factors such as delayed diagnosis, asymptomatic transmission, isolation-induced contact changes, and underreported mortality. In response these complexities, this paper introduces a novel CURNDS model prioritizing compartments transmissions based on levels, rather than merely symptomatic severity or hospitalization status. framework surpasses conventional uniform mixing static rate assumptions by incorporating adaptive power laws, dynamic transmission rates, spline-based smoothing techniques. provides accurate estimates of undetected infections undocumented deaths from data, uncovering the pandemic’s true impact. Our analysis challenges assumption homogeneous between infected non-infected individuals in models. By capturing nuanced dynamics infection confirmation, our offers new insights into spread different strains. Overall, robust for understanding complex patterns highly contagious, quarantinable diseases.

Language: Английский

Citations

0
Bayesian optimisation algorithm based optimised deep bidirectional long short term memory for global horizontal irradiance prediction in long-term horizon DOI Creative Commons
Manoharan Madhiarasan

Frontiers in Energy Research, Journal Year: 2025, Volume and Issue: 13

Published: Feb. 19, 2025

With the continued development and progress of industrialisation, modernisation, smart cities, global energy demand continues to increase. Photovoltaic systems are used control CO 2 emissions manage demand. (PV) system public utility, effective planning, control, operation compels accurate Global Horizontal Irradiance (GHI) prediction. This paper is ardent about designing a novel hybrid GHI prediction method: Bayesian Optimisation algorithm-based Optimized Deep Bidirectional Long Short Term Memory (BOA-D-BiLSTM). work attempts fine-tune hyperparameters employing optimisation. Globally ranked fifth in solar photovoltaic deployment, INDIA Two Region Solar Dataset from NOAA-National Oceanic Atmospheric Administration was assess proposed BOA-D-BiLSTM approach for long-term horizon. The superior performance highlighted with help experimental results comparative analysis grid search random search. Furthermore, forecasting effectiveness compared other models, namely, Persistence Model, ARIMA, BPN, RNN, SVR, Boosted Tree, LSTM, BiLSTM. Compared models according resulting evaluation error metrics, suggested model has minor Root Mean Squared Error: 0.0026 0.0030, Absolute Error:0.0016 0.0018, Mean-Squared 6.6852 × 10 −06 8.8628 R-squared: 0.9994 0.9988 on both dataset 1 respectively. outperforms baseline models. Thus, viable potential distributed generation planning control.

Language: Английский

Citations

0