AIMC Topic: Neural Networks, Computer

Clear Filters Showing 15041 to 15050 of 31376 articles

iELMNet: Integrating Novel Improved Extreme Learning Machine and Convolutional Neural Network Model for Traffic Sign Detection.

Big data
Traffic sign detection (TSD) in real-time environment holds great importance for applications such as automated-driven vehicles. Large variety of traffic signs, different appearances, and spatial representations causes a huge intraclass variation. In...

Interpretable generative deep learning: an illustration with single cell gene expression data.

Human genetics
Deep generative models can learn the underlying structure, such as pathways or gene programs, from omics data. We provide an introduction as well as an overview of such techniques, specifically illustrating their use with single-cell gene expression ...

Application of NIRs coupled with PLS and ANN modelling to predict average droplet size in oil-in-water emulsions prepared with different microfluidic devices.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
In this study, the potential of microfluidic systems with different microchannel geometries (microchannel with teardrop micromixers and microchannel with swirl micromixers) for the preparation of oil-in-water (O/W) emulsions using two different emuls...

Coal identification based on a deep network and reflectance spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The rapid identification of coal types in the field is an important task. This research combines spectroscopy with deep learning algorithms and proposes a method for quickly identifying coal types in the field. First, we collect field spectral data o...

Forecasts of cardiac and respiratory mortality in Tehran, Iran, using ARIMAX and CNN-LSTM models.

Environmental science and pollution research international
Cardiovascular diseases belong to the leading causes of disability and premature death worldwide, including in Iran. It is predicted that the burden of the disease in Iran in 2025 will be more than doubled compared to 2005. Therefore, many forecastin...

Simulation of time-series groundwater parameters using a hybrid metaheuristic neuro-fuzzy model.

Environmental science and pollution research international
The estimation of qualitative and quantitative groundwater parameters is an essential task. In this regard, artificial intelligence (AI) techniques are extensively utilized as accurate, trustworthy, and cost-effective tools. In the present paper, two...

Facial expression recognition based on deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Facial expression recognition technology will play an increasingly important role in our daily life. Autonomous driving, virtual reality and all kinds of robots integrated into our life depend on the development of facial ex...

Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach.

Sensors (Basel, Switzerland)
A pivotal topic in agriculture and food monitoring is the assessment of the quality and ripeness of agricultural products by using non-destructive testing techniques. Acoustic testing offers a rapid in situ analysis of the state of the agricultural g...

Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features.

International journal of environmental research and public health
This paper puts forward a new method of landscape recognition and evaluation by using aerial video and EEG technology. In this study, seven typical landscape types (forest, wetland, grassland, desert, water, farmland, and city) were selected. Differe...

DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning.

BMC bioinformatics
BACKGROUND: A limitation of traditional differential expression analysis on small datasets involves the possibility of false positives and false negatives due to sample variation. Considering the recent advances in deep learning (DL) based models, we...