Simulating daily PM2.5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data

Chemosphere, Journal Year: 2023, Volume and Issue: 340, P. 139886 - 139886

Published: Aug. 21, 2023

Language: Английский

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Monthly climate prediction using deep convolutional neural network and long short-term memory

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 31, 2024

Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include neural network (ANN), recurrent NN (RNN), long short-term memory (LSTM), deep convolutional (CNN), CNN-LSTM. These used forecast six climatic factors on a monthly ahead. data for 72 years (1 January 1951–31 December 2022) this study average atmospheric temperature, extreme minimum maximum precipitation, relative humidity, sunlight hours. time series 12 month delayed as input signals the models. efficiency examined utilizing diverse evaluation criteria namely mean absolute error, root square error (RMSE), correlation coefficient (R). modeling result inherits that hybrid CNN-LSTM model achieves greater accuracy than other compared significantly reduces forecasting one step For instance, RMSE values ANN, RNN, LSTM, CNN, temperature stage 2.0669, 1.4416, 1.3482, 0.8015 0.6292 °C, respectively. findings simulations shows potential improve forecasting. prediction will contribute meteorological disaster prevention reduction, well flood control drought resistance.

Language: Английский

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Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 25, 2025

Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization urbanization, Liaocheng has experienced increasing ozone concentration over several years. Therefore, become a major environmental problem in City. Long short-term memory (LSTM) artificial neural network (ANN) models are established predict concentrations City from 2014 2023. The results show general improvement accuracy LSTM model compared ANN model. Compared ANN, an increase determination coefficient (R2), value 0.6779 0.6939, decrease root mean square error (RMSE) 27.9895 μg/m3 27.2140 absolute (MAE) 21.6919 20.8825 μg/m3. prediction is superior terms R, RMSE, MAE. In summary, promising technique for predicting concentrations. Moreover, by leveraging historical data enables accurate predictions future on global scale. This will open up new avenues controlling mitigating pollution.

Language: Английский

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Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(6), P. 3267 - 3301

Published: Feb. 19, 2024

Language: Английский

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Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysis

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(8)

Published: July 29, 2024

Abstract In healthcare, medical practitioners employ various imaging techniques such as CT, X-ray, PET, and MRI to diagnose patients, emphasizing the crucial need for early disease detection enhance survival rates. Medical Image Analysis (MIA) has undergone a transformative shift with integration of Artificial Intelligence (AI) Machine Learning (ML) Deep (DL), promising advanced diagnostics improved healthcare outcomes. Despite these advancements, comprehensive understanding efficiency metrics, computational complexities, interpretability, scalability AI based approaches in MIA is essential practical feasibility real-world environments. Existing studies exploring applications lack consolidated review covering major stages specifically focused on evaluating approaches. The absence structured framework limits decision-making researchers, practitioners, policymakers selecting implementing optimal healthcare. Furthermore, standardized evaluation metrics complicates methodology comparison, hindering development efficient This article addresses challenges through review, taxonomy, analysis existing AI-based taxonomy covers image processing stages, classifying each stage method further analyzing them origin, objective, method, dataset, reveal their strengths weaknesses. Additionally, comparative conducted evaluate over five publically available datasets: ISIC 2018, CVC-Clinic, 2018 DSB, DRIVE, EM terms accuracy, precision, Recall, F-measure, mIoU, specificity. popular public datasets are briefly described analyzed. resulting provides landscape facilitating evidence-based guiding future research efforts toward scalable meet current needs.

Language: Английский

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A machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 25, 2025

Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order comprehend the effects toughness on athlete engagement, this study utilizes relevant machine prediction model, so find intrinsic connection between them. The construction comparison algorithms investigated evaluate level in determine optimal model. results show that PSO-SVR model performs best predicting with accuracy 0.9262, along low RMSE (0.1227), MSE (0.0146) MAE (0.0656). exhibits an obvious advantage. This advantage mainly attributed its strong generalization ability, nonlinear processing ability optimize adapt feature space. Particularly noteworthy reduces (7.54%), (17.05%), (3.53%) significantly, while improves R2 (1.69%), when compared advanced such SWO. These indicate not only prediction, but also enhances reliability making it powerful tool for engagement. summary, provides new perspective understanding important practical guidance improving overall performance. By adopting we can more accurately identify optimise key factors affecting thus bringing far-reaching implications research practice sport science related fields.

Language: Английский

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Artificial Intelligence in Healthcare: Fall Risk Assessment in Older Adults Using Machine Learning Techniques

Journal of Polytechnic, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 1

Published: March 7, 2025

There are many attempts to provide the elderly with a life more independently. One of main problems facing people in this age group is fall events. Falls one most common accidents among and may result extended hospitalization increased medical costs. The requirement for care services, such as detection, increasing because growing population people. In study, machine learning techniques- Logistic Regression, Random Forest, Decision Tree used predict risk Fall assessment methods obtain inputs outputs addition physical clinical features dataset. This study aimed facilitate process health professionals determine factors individuals make predictions. Based on results prediction, individualized prevention interventions can be developed reduce rates individuals.

Language: Английский

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Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach

Journal of Functional Morphology and Kinesiology, Journal Year: 2024, Volume and Issue: 9(4), P. 176 - 176

Published: Sept. 26, 2024

Over one billion people worldwide suffer from neurological conditions that cause mobility impairments, often persisting despite rehabilitation. Chronic disease (CND) patients who lack access to continuous rehabilitation face gradual functional decline. The International Classification of Functioning, Disability, and Health (ICF) provides a comprehensive framework for assessing these patients.

Language: Английский

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Identifying mining-induced chromium contamination in soil through visible-near infrared spectroscopy and machine learning

Deleted Journal, Journal Year: 2024, Volume and Issue: 1(2), P. 132 - 139

Published: June 1, 2024

The extraction, purification, and utilization of mineral resources have been among the largest anthropogenic sources chromium (Cr) in soil. Determining Cr contamination soil is a key issue prior to its appropriate remediation. Nevertheless, efficient identification large-scale requires continuous research. present study proposes continental-scale method rapidly identify using visible-near infrared spectroscopy (vis-NIR) machine learning (ML). A large dataset containing 18,675 topsoil samples from Land Use/Land Cover Area Frame Survey 2009 projects across Europe was compiled. Five advanced ML algorithms were compared, hyperparameter optimization conducted grid search method. Permutation importance employed calculate rank each spectral wavelength, shedding light on most sensitive wavelength for contamination. Results indicate that had significant performance improvement support vector (SVM), exhibiting an increase training 0.795 0.868. achieved optimal SVM accuracy, area under receiver operating feature curve, sensitivity, specificity 0.78, 0.85, 0.66, respectively, indicating excellent predictive classification. model revealed important band classifying 1430–1433 nm. This finding implies adsorption molecular water closely related classification current introduces first vis-NIR, which has guiding significance remediation other heavy metals vis-NIR.

Language: Английский

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Combining process-based model and machine learning to predict hydrological regimes in floodplain wetlands under climate change

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 626, P. 130193 - 130193

Published: Sept. 20, 2023

Language: Английский

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