AIMC Topic: Seasons

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Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran.

Scientific reports Aug 20, 2021
This study was aimed to investigate the air pollutants impact on heart patient's hospital admission rates in Yazd for the first time. Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5 year...
Time Factors Child, Preschool Child Air Pollutants Middle Aged Infant Adolescent Linear Models Multivariate Analysis Patient Admission Geography Air Pollution Seasons Iran Infant, Newborn Hospitalization Aged Male Cardiovascular Diseases Young Adult Adult Humans Neural Networks, Computer Female
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A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.

Nature communications Jul 9, 2021
Deep learning algorithms trained on instances that violate the assumption of being independent and identically distributed (i.i.d.) are known to experience destructive interference, a phenomenon characterized by a degradation in performance. Such a v...
Humans ROC Curve Decision Support Systems, Clinical Electrocardiography Seasons Clinical Decision-Making Deep Learning Time Factors Arrhythmias, Cardiac Datasets as Topic Models, Cardiovascular
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Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach.

The Science of the total environment Jul 8, 2021
Allergic diseases have been the epidemic of the century among chronic diseases. Particularly for pollen allergies, and in the context of climate change, as airborne pollen seasons have been shifting earlier and abundances have been becoming higher, p...
Pollen Rhinitis, Allergic, Seasonal Environmental Monitoring Seasons Allergens Humans Deep Learning
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Data-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso.

Parasites & vectors Jun 29, 2021
BACKGROUND: Improving the knowledge and understanding of the environmental determinants of malaria vector abundance at fine spatiotemporal scales is essential to design locally tailored vector control intervention. This work is aimed at exploring the...
Malaria Burkina Faso Seasons Environment Insect Bites and Stings Mosquito Control Machine Learning Mosquito Vectors Animals Humans Rural Population
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Predictive analysis of the number of human brucellosis cases in Xinjiang, China.

Scientific reports Jun 1, 2021
Brucellosis is one of the major public health problems in China, and human brucellosis represents a serious public health concern in Xinjiang and requires a prediction analysis to help making early planning and putting forward science preventive and ...
China Incidence Brucellosis Models, Biological Seasons Retrospective Studies Female Male Humans Neural Networks, Computer Forecasting
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Neural networks for increased accuracy of allergenic pollen monitoring.

Scientific reports May 31, 2021
Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in imag...
Seasons Allergens Neural Networks, Computer Environmental Monitoring Pollen Parietaria
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Determining suitable machine learning classifier technique for prediction of malaria incidents attributed to climate of Odisha.

International journal of environmental health research Mar 26, 2021
This study investigated the influence of climate factors on malaria incidence in the Sundargarh district, Odisha, India. The WEKA machine learning tool was used with two classifier techniques, Multi-Layer Perceptron (MLP) and J48, with three test opt...
Climate Neural Networks, Computer Malaria Machine Learning Humans Seasons
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Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.

PloS one Mar 25, 2021
Crop yield monitoring demonstrated the potential to improve agricultural productivity through improved crop breeding, farm management and commodity planning. Remote and proximal sensing offer the possibility to cut crop monitoring costs traditionally...
Sorghum Remote Sensing Technology Chlorophyll Machine Learning Seasons Crops, Agricultural Biomass Bayes Theorem
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Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis.

BMC infectious diseases Mar 19, 2021
BACKGROUND: Brucellosis is a major public health problem that seriously affects developing countries and could cause significant economic losses to the livestock industry and great harm to human health. Reasonable prediction of the incidence is of gr...
Models, Statistical Predictive Value of Tests Seasons China Humans Neural Networks, Computer Incidence Brucellosis
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Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach.

PloS one Mar 16, 2021
OBJECTIVE: Hemorrhagic fever with renal syndrome (HFRS), one of the main public health concerns in mainland China, is a group of clinically similar diseases caused by hantaviruses. Statistical approaches have always been leveraged to forecast the fut...
Seasons Datasets as Topic Epidemiological Monitoring China Incidence Humans Models, Statistical Forecasting Hemorrhagic Fever with Renal Syndrome Neural Networks, Computer Disease Outbreaks Orthohantavirus Machine Learning
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