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6th International Conference on Computing and Artificial Intelligence, ICCAI 2020

作者： Deng, Jianzhi Zhou, Yuehan Cheng, Xiaohui Li, Tianyu Qin, Chuling Guilin University of Technology Guilin541004 China Guilin University of Technology Guangxi Key Laboratory Embedded Technology and Intelligent System Guilin541004 China

ISBN: (纸本)9781450377089

In our research, we try to find out the Competing Endogenous RNA Network (ceRNA) and the biomarker of Liver cancer (LC). 490 differentially expressed mRNAs, 248 differentially expressed lncRNAs and 66 differentially expressed miRNAs were screened from the TCGA liver data. Among then, the differentially expressed mRNAs were enriched in 88 biological process, 16 cellular component and 27 molecular function of the gene ontology. And they were mostly enriched in extracellular region, extracellular space, integral component of plasma membrane, regulation of transcription and DNAlated sequence-specific DNA binding. 14 DElncRNAs, 11 DEmiRNAs and 4 DEmRNAs were built the ceRNA network based on their inter-regulatory. The up-regulated mRNA in liver tumor samples, CCNE1, was regard as the biomarker of liver cancer by the proof of survival analysis and receiver operating characteristic analysis. © 2020 ACM.

关键词： RNA

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Deep Learning-based Mortality Prediction of COVID-19 Patients with Cardiovascular Disease 9

Deep Learning-based Mortality Prediction of COVID-19 Patient...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Zheng, Huilin Liang, Yingyi Zhang, Daming Shi, Kai Dong, Luxi Xie, Xiaolan Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology College of Computer Science and Engineering Guilin China

ISBN: (纸本)9798350391367

Coronavirus disease 2019 (COVID-19) is one of the most dangerous diseases in recorded human history due to its high contagiousness and rapid dissemination. Moreover, people who already have medical issues like cardiovascular disease (CVD) have a higher risk of getting seriously ill. Therefore, this study proposes a deep learning-based prediction model for the mortality of COVID-19 patients with CVD. The research substances are introduced as follows. First, we obtain the original dataset from the Kaggle website provided by the Mexican government, and extract our experimental data. After that, we develop a deep learning-based prediction model for the mortality of COVID-19 patients with CVD while applying a 5-fold cross-validation approach for the model training and validation. Finally, we evaluate the performance of the proposed model and also compare it with many common machine learning models. In our experimental results, the proposed model achieved higher performance than all of the other models. It facilitates decision-making and provides an accurate mortality prediction for COVID-19 patients with CVD. © 2024 IEEE.

关键词： COVID-19

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Bioinformatics analysis of differentially expressed genes and identifycation of AGER as a key gene in lung cancer 3

Bioinformatics analysis of differentially expressed genes an...

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3rd International Conference on Biological Information and Biomedical Engineering, BIBE 2019

作者： Deng, Jianzhi Zhou, Yuehan Cheng, Xiaohui Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin541004 China College of Pharmacy Guilin Medical University Guilin Guangxi541004 China

ISBN: (纸本)9783800750269

Lung cancer (LC) is one of the most fatal diseases. Over a quarter of cancer-related death was caused by LC. It would cause some up- or down-regulated genes accompany with the lung cancer that could provide some clues for treatment and prevention. In the present research, the gene data of 227 samples were downloaded from gene expression omnibus (GEO) datasets GSE10072 and GSE19804. By using R software and package, the common differential expression genes(DEGs) were screened out from the two profiles. Then, we did the gene ontology(GO) analysis and protein protein interaction(PPI) network analysis according to the DEGs. The selected key gene was studied by online tools. There were 11 genes up-regulated and 34 genes down-regulated simultaneously in the samples. The 45 common DEGs were enriched in 36 different GO terms, 26 biological process, 11 cellular component and 9 molecular function. Protein expression of AGER in LC specimens was lower than the normal lung tissues, and the survival curves also proved this feature. By using bioinformatics analysis, LC would cause some DEGs enriched in different GO terms. Among then, AGER expressed higher in lung than other organisms and significantly associated with LC. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Gene expression

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Big data analysis of differentially expressed mrna in acute lymphoblastic leukemia patients with different genders 10

Big data analysis of differentially expressed mrna in acute ...

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10th International Conference on Information technology in Medicine and Education, ITME 2019

作者： Deng, Jianzhi Zhou, Yuehan Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin China Pharmacy School Guilin Medical University Guilin China

ISBN: (纸本)9781728139173

Acute lymphoblastic leukemia is a typical fatal cancer of immature system. Early study found that the incidence and survival time are different in gender. Male patients are in significantly higher risk with poor prognosis. In this paper, we try to find out the different expression in mRNAs between male and female acute lymphoblastic leukemia patients. The mRNA data was downloaded from Therapeutically Applicable Research to Generate Effective Treatment acute lymphoblastic leukemia phase II datasets. Differentially expressed mRNAs were screened by limma R package and R software. Gene ontology of the differentially expressed mRNAs were analyzed by online tool DAVID. And Kyoto Encyclopedia of Genes and Genomes pathway analysis of the dysregulated mRNAs were enriched by clusterProfiler and pathview R package. The relationship between survival rate and dysregulated mRNAs were analyzed by kaplan-meier method and shown in kmplot survival curves. We found that the dysregulated mRNAs in male are related to the high risk of acute lymphoblastic leukemia. © 2019 IEEE.

关键词： Gene Ontology

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Financial Fraud Detection of Listed Companies Based on DNSAE Fusion and Improved Random Forest 7

Financial Fraud Detection of Listed Companies Based on DNSAE...

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7th International Conference on intelligent Information Processing, ICIIP 2022

作者： Huang, Haibin Liu, Yarong Xie, Xiaolan School of Information Science and Engineering Guilin University of Technology China College of Information Science and Engineering Guangxi Key Laboratory of Embedded Technology and Intelligent System China

ISBN: (纸本)9781450396714

The falsification of financial data of listed companies is easy to mislead the market, causing investors to lose their investment and the personal economy is severely damaged. In view of the above problems, this paper proposes a financial fraud detection model of listed companies based on DNSAE fusion and improved random forest to detect the financial fraud of listed companies. The experimental results show that the model has a good detection effect in the financial fraud of listed companies. © 2022 Owner/Author.

关键词： Investments

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Towards Semi-Supervised Segmentation of Retinal Fundus Images via Self-Training 3

Towards Semi-Supervised Segmentation of Retinal Fundus Image...

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3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Deng, Jianzhi Zhang, Fengming Li, Shuiwang Bao, Jindi Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin China Guilin University of Technology School of Information Science and Engineering Guilin China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Glaucoma is an eye disease that may cause blindness by damaging the optic nerve. The optic cup-to-disc ratio is one of the most important criteria in the diagnosis of glaucoma. However, accurately partitioning a retinal fundus image into optic cup and optic disc regions is crucial to precisely estimating the cup-to-disc ratio automatically. With the emergence of Deep Neural Networks (DNN) and available large-scale manually labeled training data, generic image segmentation has made great progress in recent years. However, large-scale well-labeled medical images are usually expensive and difficult to obtain. To address this problem, in this paper we propose a semi-supervised learning method for retinal fundus image segmentation via self-training based on the MR-Net. The proposed approach uses a self-training semi-supervised learning framework to generate pseudo-labels for unlabeled images. To improve the accuracy of the pseudo-labels, a dense conditional random field is introduced to refine the generated pseudo-labels during the self-training process. Experimental results show that the proposed method remarkably achieves state-of-the-art performance on the RIGA benchmark using only 50% of the annotated data for training, well alleviating the shortage of annotated training data in retinal fundus image segmentation. © 2022 IEEE.

关键词： Image segmentation

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Design of Landslide Warning system 5th

Design of Landslide Warning System

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5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Yang, Xiaoping Du, Jixuan Su, Zhaoyu Nong, Pubin Qin, Zhirong Chen, Bailin College of Information Science and Engineering Guilin University of Technology Guilin541004 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541004 China

ISBN: (纸本)9789811501203

Aiming at geological disaster monitoring and prevention work, a real-time monitoring and early warning system is proposed for low-power consumption of landslides to meet the needs of landslide monitoring in remote mountainous areas. The inclination angle of the mountain body was detected by a mechanical inclination sensor, and a plurality of inclination sensors were placed on each landslide body to form an array distribution. The landslide body was stereoscopically monitored. Each mesh node had a different node address, and different inclination thresholds were set in advance. When the sensor detection value reached the threshold, an alarm message was sent to the system main control end, and the main control end generated an audible and visual alarm, and an alarm message was sent at the same time. Compared with the current landslide warning system on the market, the system achieves expected results and its power consumption is extremely low. The sensor terminal of the mountain monitoring is powered by dry battery and can work for 6 years in the field without external power supply. It avoids the damages made by weather, livestock and human, and has broad application prospects. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Alarm systems

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privacy protection method for deep learning based on local random nodes 7

privacy protection method for deep learning based on local r...

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7th International Conference on intelligent Information Processing, ICIIP 2022

作者： Zhang, Panfeng Zhang, Wenyong College of Information Science and Engineering Guilin University of Technology Guilin541006 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006 China

ISBN: (纸本)9781450396714

Deep learning algorithm needs a lot of data for training to achieve better training results, in practical applications often need to multi-partner data for training. But the centralized training does not meet the standards of privacy protection. Therefore, a cooperative training scheme is proposed. This scheme can achieve privacy protection at a small computational cost. The main idea is to transfer the training process between the first layer and the second layer of the traditional neural network to local, and make the process between them irreversible so as to guarantee the privacy of the training data. The experiment shows that the accuracy of the model can achieve good results. © 2022 ACM.

关键词： Deep learning

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Efficiency and Precision Trade-offs in UAV Tracking with Filter Pruning and Dynamic Channel Weighting 8

Efficiency and Precision Trade-offs in UAV Tracking with Fil...

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8th International Conference on Fuzzy systems and Data Mining, FSDM 2022

作者： Zhong, Pengzhi Zeng, Dan Wang, Xucheng Li, Shuiwang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology China Research Institue of Trustworthy Autonomous Systems Southern University of Science and Technology China

ISBN: (纸本)9781643683461

Due to the limitations of computing resources, battery capacity, and maximum load of unmanned aerial vehicle (UAV), efficiency is a critical issue in UAV tracking. Deep learning (DL)-based trackers are known for their high precision but hardly achieve real-time tracking on a single CPU. The traditional framework of discriminative correlation filters (DCF) is famous for its high efficiency but its precision is barely satisfactory. Despite the inferior precision, DCF-based trackers rather than DL-based ones are widely adopted in UAV tracking to trade precision for efficiency. This paper aims to trade off efficiency and precision for UAV tracking using model compression techniques (i.e., filter pruning), which has not been well explored before. To combat the possible precision drop caused by pruning, we propose a dynamic channel weighting strategy to improve the rank-based pruning method which uses the average rank of each filter response as the pruning criterion. The dynamic channel weighting seeks to adjust the contribution to the loss of each channel through optimization. As a result, we achieved better precisions when compressing the original model SiamFC++ with a global pruning ratio instead of tedious layer-wise ones. Extensive experiments on four UAV benchmarks, i.e., UAV123@10fps, DTB70, UAVDT, and Vistrone2018, show that the proposed tracker demonstrates a remarkable balance between precision and efficiency. © 2022 The authors and IOS Press.

关键词： Efficiency

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Prescribed Performance Path-Following Control for Rotor-Assisted Vehicles via an Improved Reinforcement Learning Mechanism

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IEEE Transactions on Neural Networks and Learning systems 2025年 PP卷 PP页

作者： Zhang, Guoqing Li, Zhihao Li, Jiqiang Zhang, Weidong Qiu, Bin Navigation College Liaoning Dalian116026 China State Key Laboratory of Maritime Technology and Safety Liaoning Dalian116026 China Hainan University School of Information and Communication Engineering Hainan Haikou570228 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Guilin University of Technology School of Computer Science and Engineering Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541004 China

This article investigates an adaptive prescribed performance path-following control algorithm for rotor-assisted vehicles, incorporating reinforcement learning (RL) to execute energy-saving cruising missions. For obtaining a high-performance path-following controller, a concise prescribed performance control (PPC) algorithm is designed to tightly constrain the output errors within the defined boundaries, while a shifting function is introduced to solve the problem of initial condition restrictions. Furthermore, through integrating the Backstepping method and the optimal control technique, an improved RL with the form of actor–critic neural networks (AC-NNs) is proposed to offer an innovative approach to the challenges of the model uncertainties and external disturbances. In this approach, the actor NN is employed to create an appropriate control policy, while the critic NN is aimed at evaluating the cost-to-go function to modify the system action. Semi-global uniform ultimate bounded (SGUUB) stable properties of the proposed algorithm are guaranteed via the Lyapunov theory. Finally, the superiority and feasibility of the proposed algorithm are verified by two numerical experiments. © 2012 IEEE.

关键词： Cost functions

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