AIMC Topic: Seasons

Clear Filters Showing 121 to 130 of 228 articles

Simulation and Prediction of Fungal Community Evolution Based on RBF Neural Network.

Computational and mathematical methods in medicine
Simulation and prediction of the scale change of fungal community. First, using the experimental data of a variety of fungal decomposition activities, a mathematical model of the decomposition rate and the relationship between the bacterial species w...

A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors.

Sensors (Basel, Switzerland)
Falling represents one of the most serious health risks for elderly people; it may cause irreversible injuries if the individual cannot obtain timely treatment after the fall happens. Therefore, timely and accurate fall detection algorithm research i...

Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning.

Sensors (Basel, Switzerland)
The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensor...

Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms.

PloS one
Climate change and global warming have serious adverse impacts on tropical forests. In particular, climate change may induce changes in leaf phenology. However, in tropical dry forests where tree diversity is high, species responses to climate change...

Capabilities of deep learning models on learning physical relationships: Case of rainfall-runoff modeling with LSTM.

The Science of the total environment
This study investigates the relationships which deep learning methods can identify between the input and output data. As a case study, rainfall-runoff modeling in a snow-dominated watershed by means of a long short-term memory (LSTM) network is selec...

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

Scientific reports
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...

A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.

Nature communications
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...

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
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...

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
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...

Predictive analysis of the number of human brucellosis cases in Xinjiang, China.

Scientific reports
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 ...