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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinlong Wen Yifei Guo Shuoying Wei Wenhan Long Lida Zhu Rongbo Zhu Huazhong Agricultural University Wuhan China College of Informatics Huazhong Agricultural University Wuhan China Shenzhen Institute of Nutrition and Health Huazhong Agricultural University Shenzhen China Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China Huazhong University of Science and Technology Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Molecular representation learning is widely used in the field of drug discovery, due to its ability to accurately capture the complex features of compounds in high-dimensional space. However, existing molecular representation learning models are prone to be influenced by spurious parts during distribution shifts (also known as out-of-distribution, or OOD), which results in models mistakenly treating these spurious parts as crucial features of molecules, thereby limiting the generalization capability of the models. To tackle this issue, a novel invariant molecular representation learning model, called Causal Invariant Hierarchical Molecular Representation Graph Neural Networks (CHiMoGNN), is proposed for OOD molecular property prediction. In CHiMoGNN, a Feature Enhancement (FE) module is designed to leverage the multi-level molecular parts to enhance the expression of invariant features, thereby enhancing the model’s capability to capture key molecular information. In addition, a Cartesian Product based Environmental Impact (EI) module is adopted to generate counterfactual samples with environmental diversity. Consequently, these samples are utilized to train a classifier that maintains consistent performance across various environments. Extensive experiments on seven real-world datasets demonstrate that CHiMoGNN outperforms 9 state-of-the-art models, achieving a 5.73% increase in average ROC-AUC, and the results also show that CHiMoGNN can effectively maintain generalization in various distribution shifts. Code and datasets are available at https://***/Chertuion/CHiMoGNN.

关键词： Representation learning Limiting Codes Predictive models Feature extraction Iron Graph neural networks Drug discovery Data mining Compounds

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Simulation of realistic infrared texture of grassland

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Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 2011年第12 SUPPL.期32卷 103-108页

作者： Liu, Qian Zhu, Feng Hao, Yingming Shenyang Institute of Automation Chinese Acad. of Sci. Shenyang 110016 China Graduate School of the Chinese Acad. of Sci. Beijing 110080 China Key Laboratory of Opto-Electronics Information Processing Chinese Acad. of Sci. Shenyang 110016 China Key Lab. of Image Understanding and Computer Vision Liaoning Shenyang 110016 China

A new method is introduced to simulate realistic infrared texture of grassland. First, the radiative temperature of grassland is solved by integrating material composition, environmental and meteorological factors. As the spectroscopic properties of grass and soil vary greatly, the infrared radiation characteristics are computed respectively. Next, the texture structure of grassland is modeled based on the analysis of spatial distribution of natural grassland. Finally, the infrared radiance of grass and soil are mapped to image space and a realistic grassland texture is generated. Experiment results show that similar visual features exhibit between simulated textures and actual grassland textures, which justifies the effectiveness of the proposed method.

关键词： Infrared radiation

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Quality Inspection of Phalaenopsis Hybrids Using Hyperspectral Band Selection Techniques

Quality Inspection of Phalaenopsis Hybrids Using Hyperspectr...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yen-Chieh Ouyang Bo-Han Chen Meng-Chueh Lee Tsang-Sen Liu Mang Ou-Yang Hsian-Min Chen Chao-Cheng Wu Chia-Hsien Wen Min-Shao Shih Chein-I Chang Yung-Jhe Yan Department of Electrical Engineering National Chung Hsing University Taiwan Institute of communication Engineering National Chung Hsing University Taiwan Taiwan Agriculture Research Institute Nantou Taiwan Department of Electrical and Computer Engineering National Chiao-Tung University Hsinchu City Taiwan Center for Quantitative Imaging in Medicine Taichung Veterans General Hospital Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Management Providence University Taiwan Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore MD USA

Fusarium wilt on Phalaenopsis is a disease that makes farmers suffer seriously. Although Phalaenopsis does not die immediately with Fusarium wilt, it seriously decreases the quality that buyers cannot accept. In this paper, we introduce an emerging method to detect Fusarium wilt at the base of Phalaenopsis stems. The detection model divides Phalaenopsis samples into two categories, healthy and infection. The band selection (BS) processing technique based on band prioritization (BP) is applied to extract significant bands and eliminate redundant bands. Subsequently, some algorithms which are constrained energy minimization (CEM), spectral information divergence(SID) and SeQuential N-FINDER to detect the Fusarium wilt, and we hope the research would help farmers decrease their losses.

关键词： Hyperspectral imaging Support vector machines Diseases Signal to noise ratio Hybrid fiber coaxial cables Correlation

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SU-E-J-109: Evaluation of Deformable Accumulated Parotid Doses Using Different Registration Algorithms in Adaptive Head and Neck Radiotherapy

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Medical Physics 2015年第6PART8期42卷 3289-3289页

作者： S Xu B Liu Key Laboratory of Particle & Radiation Imaging (Tsinghua University) Ministry of Education Beijing 100084 China Chinese PLA General Hospital Beijing 100853 China Image processing center Beihang University Beijing 100191 China

Purpose: Three deformable image registration (DIR) algorithms are utilized to perform deformable dose accumulation for head and neck tomotherapy treatment, and the differences of the accumulated doses are evaluated. Methods: Daily MVCT data for 10 patients with pathologically proven nasopharyngeal cancers were analyzed. The data were acquired using tomotherapy (TomoTherapy, Accuray) at the PLA General Hospital. The prescription dose to the primary target was 70Gy in 33 *** DIR methods (B-spline, Diffeomorphic Demons and MIMvista) were used to propagate parotid structures from planning CTs to the daily CTs and accumulate fractionated dose on the planning CTs. The mean accumulated doses of parotids were quantitatively compared and the uncertainties of the propagated parotid contours were evaluated using Dice similarity index (DSI). Results: The planned mean dose of the ipsilateral parotids (32.42±3.13Gy) was slightly higher than those of the contralateral parotids (31.38±3.19Gy)in 10 patients. The difference between the accumulated mean doses of the ipsilateral parotids in the B-spline, Demons and MIMvista deformation algorithms (36.40±5.78Gy, 34.08±6.72Gy and 33.72±2.63Gy ) were statistically significant (B-spline vs Demons, P<0.0001, B-spline vs MIMvista, p =0.002). And The difference between those of the contralateral parotids in the B-spline, Demons and MIMvista deformation algorithms (34.08±4.82Gy, 32.42±4.80Gy and 33.92±4.65Gy ) were also significant (B-spline vs Demons, p =0.009, B-spline vs MIMvista, p =0.074). For the DSI analysis, the scores of B-spline, Demons and MIMvista DIRs were 0.90, 0.89 and 0.76. Conclusion: Shrinkage of parotid volumes results in the dose increase to the parotid glands in adaptive head and neck radiotherapy. The accumulated doses of parotids show significant difference using the different DIR algorithms between kVCT and MVCT. Therefore, the volume-based criterion (i.e. DSI) as a quantitative evaluation of registration ac

关键词： Dosimetry Data analysis image analysis image registration Cancer

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Alternate INRAE-Bordeaux Soil Moisture and L-Band Vegetation Optical Depth Products from SMOS and SMAP: Current Status and Overview

Alternate INRAE-Bordeaux Soil Moisture and L-Band Vegetation...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xiaojun Li Roberto Fernandez-Moran F. Frappart Lei Fan Gabrielle De Lannoy Xiangzhuo Liu Huan Wang Zanping Xing Mengjia Wang Yao Xiao C. Moisy Jean-Pierre Wigneron INRAE UMR1391 ISPA Villenave d'Ornon France Image Processing Lab (IPL) University of Valencia Valencia Spain Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical Sciences Southwest University Chongqing China Department of Earth and Environmental Sciences KU Leuven Heverlee Belgium College of Urban and Environmental Sciences Key Laboratory for Earth Surface Processes of the Ministry of Education Peking University Beijing China Cryosphere Research Station on the Qinghai-Tibet Plateau State Key Laboratory of Cryospheric Science Northwest Institute of Eco-Environment and Resource Chinese Academy of Sciences Lanzhou China School of Geoscience and Technology Zhengzhou University China

Between 2018 and 2022, INRAE Bordeaux (IB) has developed a series of soil moisture (SM) and L-band Vegetation Optical depth (L-VOD) retrieval products from SMOS and SMAP, which are currently the only two operational L-band passive microwave satellite missions. These IB products rely on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which requires little ancillary information. The products are found to be accurate, and very well-suited for application in hydrology, agriculture, climate and vegetation monitoring. In this communication, we present an overview of the development, evaluation and new applications of these IB SM or L-VOD products.

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Dynamic Ca2+ sensitivity of BK channels

Dynamic Ca2+ sensitivity of BK channels

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The 4th International Ion Channel Conference(第四届国际离子通道会议)

作者： Panpan Hou Jianming Cui Ding Jiuping Feng Xiao Ming Yuchi Guohui Zhang Ying Wu Wei Wang Wenping Zeng Mingyue Ding Zhengxing Wu Department of pharmacology Soochow University college of pharmaceutical Sciences SuzhouChina Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Department of Biomedical Engineering College of Life Science and Technology Wuhan China Department of Biomedical Engineering Center for the Investigation of Membrane Excitability Disorders Cardiac Bioelectricity and Arrhythmia Center Washington University St Louis MOUSA Key Laboratory of Molecular Biophysics Huazhong University of Science and Technology Ministry of Education College of Life Science and Technology Wuhan China Department of Biomedical Engineering Center for the Investigation of Membrane Excitability Disorders Cardiac Bioelectricity and Arrhythmia Center Washington University St Louis MOUSA

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Fs-Woa-Stacking: A Novel Ensemble Model for Early Diagnosis of Breast Cancer

SSRN

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SSRN 2024年

作者： Xiao, Tianyun Kong, Shanshan Zhang, Zichen Liu, Fengchun Yang, Aimin Hua, Dianbo Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing101149 China

In this paper, a new integrated scheme is proposed to accurately predict breast cancer, help doctors make early diagnosis and treatment plans, and improve the prognosis of patients. We selects five mainstream machine learning models: support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost). The Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) are used as datasets to investigate the predictive performance of these single and ensemble models. Then, we use multiple linear regression method for feature selection (FS), the experimental results show that the change of feature subset will significantly affect the performance of the model. After that, we apply whale optimization algorithm (WOA) to optimize the hyperparameters of the model to improve their prediction performance. In addition, we ensemble these models by stacking, investigate the performance changes of the ensemble model when different models are used as meta learners. Finally, the FS-WOA-Stacking model achieves 99.56% accuracy on WBCD and 99.65% accuracy on WDBC. Compared with the existing breast cancer prediction models, the performance of the proposed model is at an excellent level. © 2024, The Authors. All rights reserved.

关键词： Adaptive boosting

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Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review

arXiv

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arXiv 2021年

作者： Wu, Dongrui Xu, Jiaxin Fang, Weili Zhang, Yi Yang, Liuqing Xu, Xiaodong Luo, Hanbin Yu, Xiang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China The College of Public Administration Huazhong University of Science and Technology Wuhan430074 China The University of Michigan Ann ArborMI48109 United States The School of Management and Sino European Institute for Intellectual Property Huazhong University of Science and Technology Wuhan430074 China

Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based biometrics. Physiological computing increases the communication bandwidth from the user to the computer, but is also subject to various types of adversarial attacks, in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output, leading to possible user confusion, frustration, injury, or even death. However, the vulnerability of physiological computing systems has not been paid enough attention to, and there does not exist a comprehensive review on adversarial attacks to them. This paper fills this gap, by providing a systematic review on the main research areas of physiological computing, different types of adversarial attacks and their applications to physiological computing, and the corresponding defense strategies. We hope this review will attract more research interests on the vulnerability of physiological computing systems, and more importantly, defense strategies to make them more secure. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan images

arXiv

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arXiv 2020年

作者： Rajinikanth, V. Dey, Nilanjan Raj, Alex Noel Joseph Hassanien, Aboul Ella Santosh, K.C. Raja, N. Sri Madhava Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Chennai600119 India Department of Information Technology Techno India College of Technology KolkataWest Bengal700156 India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University Shantou Guangdong China Faculty of Computers and Artificial Intelligence Cairo University Egypt Department of Computer Science University of South Dakota 414 E Clark St VermillionSD57069 United States

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts;(ii) image enhancement using Harmony-Search-Optimization and Otsu thresholding;(iii) image segmentation to extract infected region(s);and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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A high-order disturbance observer based sliding mode velocity control of mobile wheeled inverted pendulum systems

A high-order disturbance observer based sliding mode velocit...

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World Congress on Intelligent Control and Automation (WCICA)

作者： Songhyok Ri Jian Huang Chunjing Tao Myonghyok Ri Yongsok Ri Daeho Han School of Automation Huazhong University of Science and Technology Wuhan CHINA Department of Control Science University of Sciences Pyongyang D.P.R. of Korea Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan CHINA R&D department National Research Center for Rehabilitation Technical Aids Beijing Automation school Kim Chaek University of Technology Pyongyang D.P.R. of Korea Department of Phisics University of Sciences Pyongyang D.P.R. of Korea

ISBN: (纸本)9781467384155

In this paper, a novel disturbance observer (DO) for the Mobile Wheeled Inverted Pendulum (MWIP) system is proposed. A choice method of optimal gain matrices is also proposed for a given robust gain, which can improve the estimation precision of the DO. Combining the proposed DO and Sliding Mode Control (SMC), a new sliding mode velocity control method is designed for the MWIP system. The convergency of the DO is proved by Lyapunov theorem. And the stability of the closed-loop system is achieved through the appropriate selection of sliding surface coefficients. The effectiveness of all proposed methods is verified by simulation results for the MWIP system.

关键词： Wheels Automation Velocity control Uncertainty Observers Robustness

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