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Intelligent geochemical interpretation of mass chromatograms:Based on convolution neural network

Petroleum Science 2024年第2期21卷 752-764页

作者： Kai-Ming Su Jun-Gang Lu Jian Yu Zi-Xing Lu Shi-Jia Chen Hubei Key Laboratory of Petroleum Geochemistry and Environment Wuhan430100HubeiChina College of Resources and Environment Yangtze UniversityWuhan430100HubeiChina School of Geoscience and Technology Southwest Petroleum UniversityChengdu610500SichuanChina Changqing Oilfield Company PetroChinaXi©an710021ShaanxiChina

Gas chromatography-mass spectrometry(GC-MS)is an extremely important analytical technique that is widely used in organic geochemistry.It is the only approach to capture biomarker features of organic matter and provides the key evidence for oil-source correlation and thermal maturity determination.However,the conventional way of processing and interpreting the mass chromatogram is both timeconsuming and labor-intensive,which increases the research cost and restrains extensive applications of this method.To overcome this limitation,a correlation model is developed based on the convolution neural network(CNN)to link the mass chromatogram and biomarker features of samples from the Triassic Yanchang Formation,Ordos Basin,China.In this way,the mass chromatogram can be automatically interpreted.This research first performs dimensionality reduction for 15 biomarker parameters via the factor analysis and then quantifies the biomarker features using two indexes(i.e.MI and PMI)that represent the organic matter thermal maturity and parent material type,respectively.Subsequently,training,interpretation,and validation are performed multiple times using different CNN models to optimize the model structure and hyper-parameter setting,with the mass chromatogram used as the input and the obtained MI and PMI values for supervision(label).The optimized model presents high accuracy in automatically interpreting the mass chromatogram,with R2values typically above 0.85 and0.80 for the thermal maturity and parent material interpretation results,respectively.The significance of this research is twofold:(i)developing an efficient technique for geochemical research;(ii)more importantly,demonstrating the potential of artificial intelligence in organic geochemistry and providing vital references for future related studies.

关键词： Organic geochemistry Biomarker Mass chromatographic analysis Automated interpretation convolution neural network Machine learning

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Breast Mammogram Analysis and Classification Using Deep convolution neural network

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Computer Systems Science & Engineering 2022年第10期43卷 275-289页

作者： V.Ulagamuthalvi G.Kulanthaivel A.Balasundaram Arun Kumar Sivaraman Department of Computer Science and Engineering Sathyabama UniversityChennai600119India Department of Electrical Electronics and Communication EngineeringNITTTRChennai600113India Centre for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India

One of the fast-growing disease affecting women’s health seriously is breast cancer.It is highly essential to identify and detect breast cancer in the earlier stage.This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately.Deep learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical images.Existing methods focused on using various conventional and machine learning methods for processing natural and medical images.It is inadequate for the image where the coarse structure matters most.Most of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in classification.Whereas deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate prediction.Hence this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram images.The performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

关键词： Medical image processing deep learning convolution neural network breast cancer feature extraction classification

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A robust discretization method of factor screening for landslide susceptibility mapping using convolution neural network,random forest,and logistic regression models

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International Journal of Digital Earth 2023年第1期16卷 408-429页

作者： Zheng Zhao Jianhua Chen State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China College of Geophysics Chengdu University of TechnologyChengduPeople’s Republic of China

The selection of discretization criteria and interval numbers of landslide-related environmental factors generally fails to quantitatively determine orfilter,resulting in uncertainties and limitations in the performance of machine learning(ML)methods for landslide susceptibility mapping(LSM).The aim of this study is to propose a robust discretization criterion(RDC)to quantify and explore the uncertainty and subjectivity of different discretization methods.The RDC consists of two steps:raw classification dataset generation and optimal dataset extraction.To evaluate the robustness of the proposed RDC method,Lushan County of Sichuan Province in China was chosen as the study area to generate the LSM based on three datasets(optimal dataset,original dataset with continuous values,and statistical dataset)using three popular ML methods,namely,convolution neural network,random forest,and logistic regression.The results show that the areas under the receiver operating characteristic curve(AUCs)of the optimal dataset for the abovementioned ML models are 0.963,0.961,and 0.930 which are higher than those of the original dataset(0.938,0.947,and 0.900)and statistical dataset(0.948,0.954,and 0.897).In conclusion,the RDC method can extract the more representative features from environmental factors and outperform the other conventional discretization methods.

关键词： Discretization machine learning landslide susceptibility mapping spatial statistics convolution neural network

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Fine-grained classification of grape leaves via a pyramid residual convolution neural network

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International Journal of Agricultural and Biological Engineering 2022年第2期15卷 197-203页

作者： Hanghao Li Yana Wei Hongming Zhang Huan Chen Jiangfei Meng College of Information Engineering Northwest A&F UniversityYangling 712100ShaanxiChina College of Enology Northwest A&F UniversityYangling 712100ShaanxiChina

The value of grape cultivars varies.The use of a mixture of cultivars can negate the benefits of improved cultivars and hamper the protection of genetic resources and the identification of new hybrid cultivars.Classifying cultivars based on their leaves is therefore highly practical.Transplanted grape seedlings take years to bear fruit,but leaves mature in months.Foliar morphology differs among cultivars,so identifying cultivars based on leaves is feasible.Different cultivars,however,can be bred from the same parents,so the leaves of some cultivars can have similar morphologies.In this work,a pyramid residual convolution neural network was developed to classify images of eleven grape cultivars.The model extracts multi-scale feature maps of the leaf images through the convolution layer and enters them into three residual convolution neural networks.Features are fused by adding the value of the convolution kernel feature matrix to enhance the attention on the edge and center regions of the leaves and classify the images.The results indicated that the average accuracy of the model was 92.26%for the proposed leaf dataset.The proposed model is superior to previous models and provides a reliable method for the fine-grained classification and identification of plant cultivars.

关键词： fine-grained classification grape cultivars identification pyramid residual network convolution neural network

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convolution neural network and 77 GHz millimeter wave radar based intelligent liquid classification system

Journal of Information and Intelligence

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Journal of Information and Intelligence 2023年第4期1卷 352-363页

作者： Jiayu Chen Xinhuai Wang Yin Xu Ye Peng Wen Wang Junyan Xiang Qihang Xu Xidian University and National Key Laboratory of Antennas and Microwave Technology Xidian UniversityXi'an 710071China

An intelligent liquid classification system based on 77 GHz millimeter wave radar and convolution neural network are proposed in this paper.The data are collected by the AWR1843 radar platform and processed by the neural network on the host PC in real-time.The doppler heatmap generated by radar signal processing is tried for the first time as the input of the system.The information carried by the heatmap in 2 dimensions is analyzed and the reason why the doppler heatmap could be used for classification is explained.The feasible experiment proved that the proposed method can successfully classify 8 kinds of common liquid with high accuracy.The result of the experiment is explained and the limitations of the experiment are discussed.It can be drawn that the combination of FMCW millimeter wave radar and convolution neural network is a method with great potential for liquid classification.The advantages of real time,non-invasive and unilateral measurement can also be used for the detection of dangerous liquids.

关键词： convolution neural network Doppler heatmap Frequency modulated continuous wave Liquid classification 77 GHz millimeter wave radar

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Recognition of cotton growth period for precise spraying based on convolution neural network

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Information Processing in Agriculture 2021年第2期8卷 219-231页

作者： Shanping Wang Yang Li Jin Yuan Laiqi Song Xinghua Liu Xuemei Liu College of Mechanical&Electronic Engineering Shandong Agricultural UniversityTai’an 271018China Shandong Provincial Key Laboratory of Horticultural Machinery and Equipment Tai’an 271018China

Dynamic acquisition of crop morphology is beneficial to real-time variable decision of precise spraying operations in fields.However,the existing spraying quantity regulation has high tolerance on the statistical characteristics of regional morphology,so expensive LiDAR and ultrasonic radar can’t make full use of their high accuracy,and can reduce decision speed because of too much detail of branches and leaves.Therefore,designing a novel recognition system embedded machine learning with low-cost monocular vision is more feasible,especially in China,where the agricultural implements are medium sizes and cost-sensitive.In addition,we found that the growth period of crops is an important reference index for guiding spraying.So,taking cotton as a case study,a cotton morphology acquisition by a single camera is established,and a cotton growth period recognition algorithm based on convolution neural network(CNN)is proposed in this paper.Through the optimization process based on confusion matrix and recognition efficiency,an optimized CNN model structure is determined from 9 different model structures,and its reliability was verified by changing training sets and test sets many times based on the idea of kfold test.The accuracy,precision,recall,F1-score and recognition speed of this CNN model are 93.27%,95.39%,94.31%,94.76%and 71.46 ms per image,respectively.In addition,compared with the performance of VGG16 and AlexNet,the convolution neural network model proposed in this paper has better performance.Finally,in order to verify the reliability of the designed recognition system and the feasibility of the spray decision-making algorithm based on CNN,spraying deposition experiments were carried out with 3 different growthperiods of cotton.The experiments’results validate that after the optimal spray parameters were applied at different growth periods respectively,the average optimum index in 3 growth periods was 42.29%,which was increased up to 62.24%than the operations without distinguishing growth periods.

关键词： Precision spraying Growth period of cotton Target perception convolution neural network Image classification

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An Emotion Analysis Method Using Multi-Channel convolution neural network in Social networks

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Computer Modeling in Engineering & Sciences 2020年第10期125卷 281-297页

作者： Xinxin Lu Hong Zhang School of Computer Science and Technology Zhoukou Normal UniversityZhoukou466001China School of network Engineering Zhoukou Normal UniversityZhoukou466001China

As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practical application value.Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research.The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period,so as to understand their normal state,abnormal state and the reason of state change from the information they wrote.In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences,and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model,an emotional analysismodel using the emotional dictionary andmultichannel convolutional neural network is proposed in this paper.Firstly,the input matrix of emotion dictionary is constructed according to the emotion information,and the different feature information of sentences is combined to form different network input channels,so that the model can learn the emotion information of input sentences from various feature representations in the training process.Then,the loss function is reconstructed to realize the semi supervised learning of the network.Finally,experiments are carried on COAE 2014 and self-built data sets.The proposed model can not only extract more semantic information in emotional text,but also learn the hidden emotional information in emotional text.The experimental results show that the proposed emotion analysis model can achieve a better classification performance.Compared with the best benchmark model gram-CNN,the F1 value can be increased by 0.026 in the self-built data set,and it can be increased by 0.032 in the COAE 2014 data set.

关键词： Emotion analysis model emotion dictionary convolution neural network semi supervised learning deep learning pooling feature feature mapping

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Credit Card Fraud Detection Using Improved Deep Learning Models

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Computers, Materials & Continua 2024年第1期78卷 1049-1069页

作者： Sumaya S.Sulaiman Ibraheem Nadher Sarab M.Hameed Computer Science Department Al-Mustansiriya UniversityBaghdad10001Iraq Computer Science Department University of BaghdadBaghdad10001Iraq Faculty of Basic Education Al-Mustansiriya UniversityBaghdad10001Iraq

Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),convolution neural network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

关键词： Card fraud detection hyperparameter tuning deep learning autoencoder convolution neural network long short-term memory resampling

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Multi-Level Parallel network for Brain Tumor Segmentation

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 741-757页

作者： Juhong Tie Hui Peng School of Software Engineering Chengdu University of Information TechnologyChengdu610225China

Accurate automatic segmentation of gliomas in various sub-regions,including peritumoral edema,necrotic core,and enhancing and non-enhancing tumor core from 3D multimodal MRI images,is challenging because of its highly heterogeneous appearance and shape.Deep convolution neural networks(CNNs)have recently improved glioma segmentation performance.However,extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution,resulting in the loss of accurate spatial and object parts information,especially information on the small sub-region tumors,affecting segmentation performance.Hence,this paper proposes a novel multi-level parallel network comprising three different level parallel subnetworks to fully use low-level,mid-level,and high-level information and improve the performance of brain tumor segmentation.We also introduce the Combo loss function to address input class imbalance and false positives and negatives imbalance in deep learning.The proposed method is trained and validated on the BraTS 2020 training and validation dataset.On the validation dataset,ourmethod achieved a mean Dice score of 0.907,0.830,and 0.787 for the whole tumor,tumor core,and enhancing tumor core,respectively.Compared with state-of-the-art methods,the multi-level parallel network has achieved competitive results on the validation dataset.

关键词： convolution neural network brain tumor segmentation parallel network

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Improved Integrated Deep Model for Pap-Smear Cell Analysis

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Journal of Systems Science and Information 2024年第1期12卷 113-124页

作者： Somasundaram DEVARAJ Nirmala MADIAN Gnanasaravanan SUBRAMANIAM Rithaniya CHELLAMUTHU Muralitharan KRISHANAN School of Electronics Engineering Vellore Institute of TechnologyVellore 632014India Department of Biomedical Engineering Dr.N.G.P Institute of TechnologyCoimbatore 641045India Karunya Institute of Technology and Sciences Coimbatore 641114India Department of Computer Science Institute of Mathematical ScienceSungkyunkwan UniversitySuwon 16419South Korea

Cervical cancer is the fourth most common malignancy to strike a woman globally.If discovered early enough,it can be effectively treated.Although there is a chance of error owing to human error,the Pap smear is a good tool for first screening for cervical cancer.It also takes a lot of time and effort to complete.The aim of this study was to reduce the possibility of error by automating the process of classifying cervical cancer using Pap smear images.For the purpose of this study,dual convolution neural networks with LSTM were employed to classify images due to deep learning approaches inspire distinct features and powerful classifiers for many computer vision applications.The proposed deep learning model based on convolution neural networks(CNN)with the long short-term memory(LSTM)network is to learn features which give better recognition accuracy.The overall model is known as Smear-net.In which‘smear’indicates‘pap-smear cancer cells’and‘net’refers to neural network.The parameters such as,Accuracy,Precision,Recall,Accuracy,Sensitivity,and Specificity are used to validate the models.The proposed method provides the improved accuracy of 99.57 percentage for classification of the pap-smear cells.The proposed approaches demonstrate the effectiveness of our contributions by testing and comparing with the state-of-the-art techniques.

关键词： pap-smear deep learning convolution neural network smear-net

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