scholarly journals Hyperspectral imaging of human skin aided by artificial neural networks

2019 ◽  
Vol 10 (7) ◽  
pp. 3545 ◽  
Author(s):  
Evgeny Zherebtsov ◽  
Viktor Dremin ◽  
Alexey Popov ◽  
Alexander Doronin ◽  
Daria Kurakina ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hyperspectral Imaging ◽  
Human Skin ◽  
Artificial Neural

scholarly journals Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1944 ◽  
Author(s):  
Lei Feng ◽  
Susu Zhu ◽  
Fucheng Lin ◽  
Zhenzhu Su ◽  
Kangpei Yuan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Blue Mold ◽  
Artificial Neural

Neural Networks Application on Human Skin Biophysical Impedance Characterizations

2021 ◽  
Vol 16 (01) ◽  
pp. 9-19
Author(s):  
Srdjan Ribar ◽  
Vojislav V. Mitic ◽  
Goran Lazovic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Skin ◽  
Basic Element ◽  
Input Signals ◽  
Biological Neural Networks ◽  
Massively Parallel Processing ◽  
Artificial Neural ◽  
Living Organisms ◽  
Parallel Connections

Artificial neural networks (ANNs) are basically the structures that perform input–output mapping. This mapping mimics the signal processing in biological neural networks. The basic element of biological neural network is a neuron. Neurons receive input signals from other neurons or the environment, process them, and generate their output which represents the input to another neuron of the network. Neurons can change their sensitivity to input signals. Each neuron has a simple rule to process an input signal. Biological neural networks have the property that signals are processed through many parallel connections (massively parallel processing). The activity of all neurons in these parallel connections is summed and represents the output of the whole network. The main feature of biological neural networks is that changes in the sensitivity of the neurons lead to changes in the operation of the entire network. This is called adaptation and is correlated with the learning process of living organisms. In this paper, a set of artificial neural networks are used for classifying the human skin biophysical impedance data.

scholarly journals Early Detection of Excess Nitrogen Consumption in Cucumber Plants Using Hyperspectral Imaging Based on Hybrid Neural Networks and the Imperialist Competitive Algorithm

Agronomy ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 575
Author(s):  
Sajad Sabzi ◽  
Razieh Pourdarbani ◽  
Mohammad Hossein Rohban ◽  
Ginés García-Mateos ◽  
Jitendra Paliwal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Early Detection ◽  
Hyperspectral Imaging ◽  
Imaging Technique ◽  
Imperialist Competitive Algorithm ◽  
Nutritional Deficiencies ◽  
Excess Nitrogen ◽  
Competitive Algorithm ◽  
Artificial Neural

To achieve healthy and optimal yields of agricultural products, the principles of nutrition must be observed and appropriate fertilizers must be applied. Nutritional deficiencies or overabundance reduce the quality and yield of the products. Thus, their early detection prevents physiological disorders and associated diseases. Most research efforts have focused on spectroscopy, which extracts only spectral data from a single point of the product. The present study aims to detect early excess nitrogen in cucumber plants by using a new hyperspectral imaging technique based on a hybrid of artificial neural networks and the imperialist competitive algorithm (ANN-ICA), which can provide spectral and spatial information on the leaves at the same time. First, cucumber seeds were planted in 18 pots. The same inputs were applied to all the pots until the plants grew; after that, 30% excess nitrogen was applied to nine pots with irrigation water, while it remained constant in the other nine pots. Each day, six leaves were collected from each pot, and their images were captured using a hyperspectral camera (in the range of 400–1100 nm). The wavelengths of 715, 783 and 821 nm were determined as the most effective for early detection of excess nitrogen using a hybrid of artificial neural networks and the artificial bee colony algorithm (ANN-ABC). The parameter of days of treatment was classified using ANN-ICA. The performance of the classifier was evaluated using different criteria, namely recall, accuracy, specificity, precision and the F-measure. The results indicate that the differences between different days were statistically significant. This means that the hyperspectral imaging technique was able to detect plants with excess nitrogen in the near-infrared range (NIR), with a correct classification rate of 96.11%.

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