Using ATR-FTIR spectra and convolutional neural networks for characterizing mixed plastic waste

2021 ◽  
Vol 155 ◽  
pp. 107547
Author(s):  
Shengli Jiang ◽  
Zhuo Xu ◽  
Medhavi Kamran ◽  
Stas Zinchik ◽  
Sidike Paheding ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Plastic Waste ◽  
Ftir Spectra

scholarly journals Automatic identification of hallucinogenic amphetamines based on their ATR-FTIR spectra processed with Convolutional Neural Networks

2021 ◽  
Vol 342 ◽  
pp. 05003
Author(s):  
Catalin Negoita ◽  
Mirela Praisler ◽  
Iulia-Florentina Darie
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Drugs Of Abuse ◽  
Synthetic Cannabinoids ◽  
Attenuated Total Reflection ◽  
Ftir Spectra ◽  
Computer Applications ◽  
Automatic Identification ◽  
Detection Accuracy ◽  
Great Success

New psychoactive drugs that are leading to severe intoxications are constantly seized on the European black market. Recent studies indicate that most of these new substances are synthetic cannabinoids and hallucinogenic amphetamines. In this study, we are presenting the results obtained with an expert system that was built to identify automatically the class identity of these types of drugs of abuse, based on their Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectra processed with Convolutional Neural Networks (CNNs). CNNs have been applied with great success in recent years in various computer applications, such as image classification, but little work has been done in using this kind of deep learning models for spectral data classification. The aim of this study was to improve the detection accuracy (classification performance) that we have already obtained with other statistical mathematics and artificial intelligence techniques. The performances of the CNN system are discussed in comparison with those of the later models.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Convolutional Neural Networks for Underwater Pipeline Segmentation using Imperfect Datasets

2021 ◽  
Author(s):  
Edgar Medina ◽  
Roberto Campos ◽  
Jose Gabriel R. C. Gomes ◽  
Mariane R. Petraglia ◽  
Antonio Petraglia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Underwater Pipeline
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