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Deep Learning-Assisted Visualized Fluorometric Sensor Array for Biogenic Amines Detection

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Chinese Journal of Chemistry 2022年第5期40卷 609-616页

作者： Xiaoqing Tan Yingying Ye Hong Liu Jianxin Meng Lin-Lin Yang Fengyu Li College of Chemistry and Materials Science Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and ApplicationsJinan UniversityGuangzhouGuangdong510632 China Department of Pediatrics Guangdong Women and Children HospitalGuangzhouGuangdong510000 China

Biogenic amines(BAs)are important biomarkers for monitoring food quality and assisting in the diagnosis of ***,portable,accurate and high-throughput BAs detection is still challenging by the specific sensor compounds development or the complicated instrument *** learning(DL)algorithms are blooming for their superiority on the nonlinear and multidimensional data analysis,which endow the great advantage for the artificial intelligence assisted large sample analysis of the environmental or daily health *** this work,we developed a deep learning-assisted visualized fluorometric array-based sensing *** commercial fluorescent dyes were selected and combined into sensor *** in the alkalinity of BAs causes significant and distinct fluorescence changes of the *** conjunction with pattern recognition by the pretrained CNN models,the sensor array clearly differentiates seven BAs with 99.29%prediction accuracy and allows rapid single and multi-component quantification with a volume fraction range from 200 cm^(3)/m^(3)to 2500 cm^(3)/m^(3).This method also provides a new way for meat freshness *** envision that this novel analytical method for BAs can be used as an alternative and promising tool for the detection of a wider variety of analytes.

关键词： deeplearning Convolutional neural network Sensors Fluorescencel Amines

Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

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Computer Systems Science & Engineering 2022年第2期40卷 735-747页

作者： K.Sangeetha D.Prabha Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning *** this,most research uses sentiment analysis to track students’*** sentence-level sentiment analysis focuses on the whole sentence *** studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a *** is a need to extract the targets in a given sentence which helps to find the sentiment towards those *** extraction is the subtask of targeted sentiment *** this paper,we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields(CRF).A Parallel fusion neural network model is designed to perform this *** evaluate the model using the Michigan dataset and we build a dataset for target extraction from student *** experimental results show that our proposed fusion model achieves better results compared to baseline models.

关键词： Feedback sentimental analysis deeplearning integrated CRF

A Novel Deep Learning Method for Application Identification in Wireless Network

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China Communications 2018年第10期15卷 73-83页

作者： Jie Ren Zulin Wang School of Electronic and Information Engineering Beihang University Beijing 100191 China Collaborative Innovation Center of Geospatial Technology Wuhan 430079 China

In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the Quality of Experience(Qo E) has received much attention and has become a key performance measurement for the application and service. In order to meet the users' expectations, the management of the resource is crucial in wireless network, especially the Qo E based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing Qo E for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic data, and the experiments verify the effectiveness of our method.

关键词： quality of experience application identification protocol identification deeplearning feature extraction

Prediction Model Using Reinforcement Deep Learning Technique for Osteoarthritis Disease Diagnosis

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Computer Systems Science & Engineering 2022年第7期42卷 257-269页

作者： R.Kanthavel R.Dhaya Department of Computer Engineering King Khalid UniversityAbhaSaudi Arabia Department of Computer Science King Khalid University-Sarat Abidha CampusAbhaSaudi Arabia

Osteoarthritis is the most common class of arthritis that involves tears down the soft cartilage between the joints of the *** regeneration of this cartilage tissue is not possible,and thus physicians typically suggest therapeutic measures to prevent further deterioration over ***,bringing about joint replacement is a remedial course of *** itself in joint pain recog-nized with a normal *** learning plays a vital role in predicting the early stages of osteoarthritis by using the MRI pictures of muscles of the knee *** can be used to accurately measure the shape and texture of biological structures can be measured consistently from X-ray ***,deep learning-based computation can be used to design framework to predict whether a given patient will develop *** a framework can identify clear biochemical changes in the focal point of ligaments of the knees of patients who have exhibit pre-indications in standard *** study proposes framework to identify cases of osteoarthritis by using deep learning and reinforcement *** can be used as a clinical mechanism to predict the occurrence of osteoarthritis so that patients can beneﬁt from early intervention.

关键词： Osteoarthritis deeplearning reinforcementlearning arthritis early detection trainingandframework

A Novel Deep Learning Representation for Industrial Control System Data

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Intelligent Automation & Soft Computing 2023年第6期36卷 2703-2717页

作者： Bowen Zhang Yanbo Shi Jianming Zhao Tianyu Wang Kaidi Wang Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang110016China Shenyang Institute of Automation Chinese Academy of SciencesShenyang110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110169China Shenyang Aircraft Corporation Shenyang110850China Molarray Research TorontoL4B3K1Canada

Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensional features,data heterogeneity,and data noise due to the diversity of data dimensions,formats and noise of sensors,controllers and ***,a novel unsupervised learn-ing autoencoder model is proposed for ICS data in this *** traditional methods only capture the linear correlations of ICS features,our deep industrial representation learning model(DIRL)based on a convolutional neural network can mine high-order features,thus solving the problem of high-dimensional and heterogeneous ICS *** addition,an unsupervised denoising autoencoder is introduced for noisy ICS data in *** the denoising autoencoder allows the model to better mitigate the sensor noise *** this way,the represen-tative features learned by DIRL could help to evaluate the safety state of ICSs more *** tested our model with absolute and relative accuracy experi-ments on two large-scale ICS *** with other popular methods,DIRL showed advantages in four common indicators of AI algorithms:accuracy,precision,recall,and *** study contributes to the effective analysis of large-scale ICS data,which promotes the stable operation of ICSs.

关键词： Industrialcontrolsystem machinelearning deeplearning autoencoder

A review of deep learningg methods for ligand based drug virtual screening

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Fundamental Research 2024年第4期4卷 715-737页

作者： Hongjie Wu Junkai Liu Runhua Zhang Yaoyao Lu Guozeng Cui Zhiming Cui Yijie Ding School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhou 32400China

Drug discovery is costly and time consuming,and modern drug discovery endeavors are progressively reliant on computational methodologies,aiming to mitigate temporal and financial expenditures associated with the *** particular,the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 ***,the performance of deep learning methods in drug virtual screening has been particularly *** has become a concern for researchers how to summarize the existing deep learning in drug virtual screening,select different models for different drug screening problems,exploit the advantages of deep learning models,and further improve the capability of deep learning in drug virtual *** review first introduces the basic concepts of drug virtual screening,common datasets,and data representation ***,large numbers of common deep learning methods for drug virtual screening are compared and *** addition,a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual ***,the existing challenges and future directions in the field of virtual screening are presented.

关键词： Virtual screening deeplearning Drug discovery Drug-targetinteraction Drug-target affinity

Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer

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Cancer Innovation 2023年第4期2卷 283-289页

作者： Xuantong Gong Xuefeng Liu Xiaozheng Xie Yong Wang Department of Ultrasound National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBejjing Advanced Innovation Center for Big Data and Brain Computing(BDBC)Beihang UniversityBeijingChina School of Computer and Communication Engi neering.University of Science and Technology Beijing BeijingChina

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women *** means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients'*** extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's ***,radiomics provides a new approach to noninvasive assessment of breast cancer *** is one of the commonest clinical means of examining breast *** recent years,some results of research into ultrasound radiomics for diagnosing breast cancer,predicting lymph node status,treatment response,recurrence and survival times,and other aspects,have been *** this article,we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer *** aim to provide a reference for radiomics researchers,promote the development of ultrasound radiomics,and advance its clinical application.

关键词： breast cancer deeplearning prognosisprediction radiomics ultrasound

Deeper Attention-Based Network for Structured Data

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国际计算机前沿大会会议论文集 2020年第1期 259-267页

作者： Xiaohua Wu Youping Fan Wanwan Peng Hong Pang Yu Luo School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China

Deep learning methods are applied into structured data and in typical methods,low-order features are discarded after combining with high-order featuresfor prediction ***,in structured data,ignorance of low-order features may cause the low prediction *** address this issue,in this paper,deeper attention-based network(DAN)is *** DAN method,to keep both low-and high-order features,attention average pooling layer was utilized to aggregate features of each ***,by shortcut connections from each layer to attention average pooling layer,DAN can be built extremely deep to obtain enough *** results show DAN has good performance and works effectively.

关键词： Structured data deeplearning Feature aggregation

A System of Image Recognition-Based Railway Foreign Object Intrusion Monitoring Design

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Mechanical Engineering Science 2023年第2期5卷 30-36页

作者： Beiyuan WANG Lingqi WANG Chuanya GU Shanghai Bearing Technology Research Institute Co. Ltd.Shanghai201801China

The monitoring system designed in this paper is on account of YOLOv5(You Only Look Once)to monitor foreign objects on railway tracks and can broadcast the monitoring information to the locomotive in real ***,the general structure of the system is determined through demand analysis and feasibility analysis,the foreign object intrusion recognition algorithm is designed,and the data set required for foreign object intrusion recognition is ***,according to the functional demands,the system selects a suitable neural web,and the programming is *** last,the system is simulated to validate its functionality(identification and classification of track intrusion and determination of a safe operating zone).

关键词： Railway deeplearning YOLOv5 Image intelligent recognition Obstacle detection