scholarly journals Recognition Method of Petroleum Fluorescence Spectra Based on Convolutional Neural Network

2021 ◽  
Vol 2138 (1) ◽  
pp. 012021
Author(s):  
Zhongdong Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fluorescence Spectrum ◽  
Relative Error ◽  
Main Tool ◽  
Raw Material ◽  
Ecological Environment ◽  
Mixed Solution ◽  
Recognition Experiment ◽  
Neural Network Algorithm

Abstract As an important raw material and petrochemical tool, petroleum not only brings convenience to mankind, but also brings huge socio-economic and cultural value to our social development, but at the same time it also causes a lot of serious damage to our ecological environment. The identification and measurement of petroleum pollutants has become the main tool to identify pollution sources, control their pollutants and protect their ecological environment. This paper explores the petroleum fluorescence spectrum identification method based on convolutional neural network. Based on extensive research on this method, a simple analysis and understanding of petroleum fluorescence spectrum identification technology and petroleum-related principles are carried out, and then summarized according to relevant data find out the main factors that affect fluorescence spectrum recognition, and prepare for the experiment. The feasibility of the method is verified through the petroleum fluorescence spectrum recognition experiment of the convolutional neural network. The experimental results show that the relative error of the fluorescence spectrum recognition under different concentrations of petroleum both are within the range of 9%. Through the analysis of the relative error, it can be seen that the relative error of resolution shows a downward trend with the increase of the concentration. According to the above data, it can be seen that when the convolutional neural network algorithm is used to identify the components of the petroleum mixed solution, the qualitative analysis can be completed well. When the components in the mixed solution are quantitatively analyzed, there is a certain relative error.

Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method

2021 ◽  
Author(s):  
Farrel Athaillah Putra ◽  
Dwi Anggun Cahyati Jamil ◽  
Briliantino Abhista Prabandanu ◽  
Suhaili Faruq ◽  
Firsta Adi Pradana ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Method ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals An Improved Convolutional Neural Network Algorithm and Its Application in Multilabel Image Labeling

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Jianfang Cao ◽  
Chenyan Wu ◽  
Lichao Chen ◽  
Hongyan Cui ◽  
Guoqing Feng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Annotation ◽  
Low Frequency ◽  
Dual Channel ◽  
Extended Training ◽  
Pascal Voc ◽  
Proposed Model ◽  
Neural Network Algorithm ◽  
Similar Accuracy

In today’s society, image resources are everywhere, and the number of available images can be overwhelming. Determining how to rapidly and effectively query, retrieve, and organize image information has become a popular research topic, and automatic image annotation is the key to text-based image retrieval. If the semantic images with annotations are not balanced among the training samples, the low-frequency labeling accuracy can be poor. In this study, a dual-channel convolution neural network (DCCNN) was designed to improve the accuracy of automatic labeling. The model integrates two convolutional neural network (CNN) channels with different structures. One channel is used for training based on the low-frequency samples and increases the proportion of low-frequency samples in the model, and the other is used for training based on all training sets. In the labeling process, the outputs of the two channels are fused to obtain a labeling decision. We verified the proposed model on the Caltech-256, Pascal VOC 2007, and Pascal VOC 2012 standard datasets. On the Pascal VOC 2012 dataset, the proposed DCCNN model achieves an overall labeling accuracy of up to 93.4% after 100 training iterations: 8.9% higher than the CNN and 15% higher than the traditional method. A similar accuracy can be achieved by the CNN only after 2,500 training iterations. On the 50,000-image dataset from Caltech-256 and Pascal VOC 2012, the performance of the DCCNN is relatively stable; it achieves an average labeling accuracy above 93%. In contrast, the CNN reaches an accuracy of only 91% even after extended training. Furthermore, the proposed DCCNN achieves a labeling accuracy for low-frequency words approximately 10% higher than that of the CNN, which further verifies the reliability of the proposed model in this study.

An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans

2020 ◽  
Vol 134 (4) ◽  
pp. 328-331 ◽  
Author(s):  
P Parmar ◽  
A-R Habib ◽  
D Mendis ◽  
A Daniel ◽  
M Duvnjak ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computed Tomography ◽  
Diagnostic Accuracy ◽  
Convolutional Neural Network ◽  
Middle Turbinate ◽  
Concha Bullosa ◽  
Computed Tomography Images ◽  
Computed Tomography Scans ◽  
Neural Network Algorithm

AbstractObjectiveConvolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.MethodConsecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.ResultsThe trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.ConclusionA trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.

scholarly journals proABC-2: PRediction Of AntiBody Contacts v2 and its application to information-driven docking

2020 ◽  
Author(s):  
F. Ambrosetti ◽  
T. H. Olsen ◽  
P. P. Olimpieri ◽  
B. Jiménez-García ◽  
E. Milanetti ◽  
...  
Keyword(s):  
Neural Network ◽  
Monoclonal Antibodies ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Rational Design ◽  
Computational Approaches ◽  
Fundamental Step ◽  
Link Type ◽  
Network Algorithm ◽  
Neural Network Algorithm

AbstractMonoclonal antibodies (mAbs) are essential tools in the contemporary therapeutic armoury. Understanding how these recognize their antigen is a fundamental step in their rational design and engineering. The rising amount of publicly available data is catalysing the development of computational approaches able to offer valuable, faster and cheaper alternatives to classical experimental methodologies used for the study of antibody-antigen complexes.Here we present proABC-2, an update of the original random-forest antibody paratope predictor, based on a convolutional neural network algorithm. We also demonstrate how the predictions can be fruitfully used to drive the docking in HADDOCK.The proABC-2 server is freely available at: https://bianca.science.uu.nl/proabc2/.

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