检索结果-内蒙古大学图书馆

Nonlinear Cross-Domain Feature Representation Learning Method Based on Dual Constraints

学校读者我要写书评

暂无评论

Nonlinear Cross-Domain Feature Representation Learning Metho...

IEEE International Conference on big Knowledge (ICBK)

作者： Han Ding Yuhong Zhang Shuai Yang Yaojin Lin Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Data Science and Intelligence Application Fujian Province University Zhangzhou China

Feature representation learning is a research focus in domain adaptation. Recently, due to the fast training speed, the marginalized Denoising Autoencoder (mDA) as a standing deep learning model has been widely utilized for feature representation learning. However, the training of mDA suffers from the lack of nonlinear relationship and does not explicitly consider the distribution discrepancy between domains. To address these problems, this paper proposes a novel method for feature representation learning, namely Nonlinear cross-domain Feature learning based Dual Constraints (NFDC), which consists of kernelization and dual constraints. Firstly, we introduce kernelization to effectively extract nonlinear relationship in feature representation learning. Secondly, we design dual constraints including Maximum Mean Discrepancy (MMD) and Manifold Regularization (MR) in order to minimize distribution discrepancy during the training process. Experimental results show that our approach is superior to several state-of-the-art methods in domain adaptation tasks.

关键词： Noise reduction Manifolds Training Adaptation models Kernel Deep learning Feature extraction

Fairness and accuracy in federated learning

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Huang, Wei Li, Tianrui Wang, Dexian Du, Shengdong Zhang, Junbo School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China Institute of Artificial Intelligence Southwest Jiaotong University Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China JD Intelligent Cities Business Unit JD Digits Beijing China JD Intelligent Cities Research Beijing China

In the federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure privacy. Normally, inconsistent distribution of data across different devices in a federated network and limited communication bandwidth between end devices impose both statistical heterogeneity and expensive communication as major challenges for federated learning. This paper proposes an algorithm to achieve more fairness and accuracy in federated learning (FedFa). It introduces an optimization scheme that employs a double momentum gradient, thereby accelerating the convergence rate of the model. An appropriate weight selection algorithm that combines the information quantity of training accuracy and training frequency to measure the weights is proposed. This procedure assists in addressing the issue of unfairness in federated learning due to preferences for certain clients. Our results show that the proposed FedFa algorithm outperforms the baseline algorithm in terms of accuracy and fairness. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

One for Multiple: Physics-informed Synthetic data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Wang, Zi Yu, Xiaotong Wang, Chengyan Chen, Weibo Wang, Jiazheng Chu, Ying-Hua Sun, Hongwei Li, Rushuai Li, Peiyong Yang, Fan Han, Haiwei Kang, Taishan Lin, Jianzhong Yang, Chen Chang, Shufu Shi, Zhang Hua, Sha Li, Yan Hu, Juan Zhu, Liuhong Zhou, Jianjun Lin, Meijing Guo, Jiefeng Cai, Congbo Chen, Zhong Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Human Phenome Institute Fudan University China Philips Healthcare China Siemens Healthineers Ltd. China United Imaging Research Institute of Intelligent Imaging China Department of Nuclear Medicine Nanjing First Hospital China Shandong Aoxin Medical Technology Company China Department of Radiology The First Affiliated Hospital of Xiamen University China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University China China Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Department of Radiology Ruijin Hospital Shanghai Jiaotong University School of Medicine China Medical Imaging Department The First Affiliated Hospital of Kunming Medical University China Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China Department of Applied Marine Physics and Engineering Xiamen University China Department of Microelectronics and Integrated Circuit Xiamen University China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering Imperial College London United Kingdom

Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal during image reconstruction. Although Deep Learning (DL) has proven effective for fast MRI image reconstruction, its broader applicability across various imaging scenarios has been constrained. Challenges include the high cost and privacy restrictions associated with acquiring large-scale, diverse training data, coupled with the inherent difficulty of addressing mismatches between training and target data in existing DL methodologies. Here, we present a novel Physics-Informed Synthetic data learning framework for Fast MRI, called PISF. PISF marks a breakthrough by enabling generalized DL for multi-scenario MRI reconstruction through a single trained model. Our approach separates the reconstruction of a 2D image into many 1D basic problems, commencing with 1D data synthesis to facilitate generalization. We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%. Additionally, PISF exhibits remarkable generalizability across multiple vendors and imaging centers. Its adaptability to diverse patient populations has been validated through evaluations by ten experienced medical professionals. PISF presents a feasible and cost-effective way to significantly boost the widespread adoption of DL in various fast MRI applications. Importantly, it alleviates the ethical and practical challenges associated with in vivo human data acquisitions, opening new horizons for advancing fast MRI technologies. Copyright © 20

关键词： Magnetic resonance imaging

-Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial

学校读者我要写书评

暂无评论

Fuel 2023年第Part1期332卷

作者： Hai, Tao Hikmat Hama Aziz, Kosar Zhou, Jincheng Dhahad, Hayder A. Sharma, Kamal Fahad Almojil, Sattam Ibrahim Almohana, Abdulaziz Fahmi Alali, Abdulrhman Ismail Kh, Teeba Mehrez, Sadok Abdelrahman, Anas School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun 558000 China nstitute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Uttar Pradesh Mathura 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belvédère Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China

In this research, we try to investigate a solar-geothermal energy system. This system includes three turbines for power production, a PEM electrolyzer for hydrogen production, and a thermoelectric for generating electricity from excess heat. In addition, the seawater will be passed through the osmotic cycle to gain fresh water. The required power for this osmotic cycle will be obtained through the energy produced by the main turbines. The generated load, hydrogen production flow rate, purified water flow rate, and heating consumption are assessed in this study. The results showed that this system can produce 3.8 megawatts of electricity as well as 8 g per second of hydrogen fuel at the operating point. Also, the energy efficiency of this system is estimated to be 19%. Afterward, machine learning methods are used to optimize designing parameters, and the optimum operating point in terms of useful power and stored fuel flow rate is obtained by a genetic algorithm. The optimum operating point of this energy system has a useful power output of 4.099 megawatts and a hydrogen flow rate of 29 g per second. In the end, the distribution of the design parameters is displayed for points of the beam curve. © 2022

关键词： Biofuel Electrolyzer Fuel production Hydrogen fuel Optimization Solar energy

METHODS of EXTRACTING ELECTROCARDIOGRAMS from ELECTRONIC SIGNALS and IMAGES in the PYTHON ENVIRONMENT

学校读者我要写书评

暂无评论

Diagnostyka 2020年第3期21卷 95-101页

作者： Zholmagambetova, Bakhytgul Mazakov, Talgat Jomartova, Sholpan Izat, Adilzhan Bibalayev, Olzhas Department of Information Technology and Security Karaganda State Technical University Shakhtarov Avenue 70-191 Karaganda100000 Kazakhstan Department of Computer Science Al-Farabi Kazakh National University Zhanar st. 37a1 Almaty500045 Kazakhstan Department of Artificial Intelligence and Big Data Al-Farabi Kazakh National University Zhanar st. 37a1 Almaty500045 Kazakhstan Department of Applied Mathematics and Computer Science Karaganda State University named after Academician E. A. Buketov Shakhtarov Avenue 70-191 Karaganda100000 Kazakhstan Department of Robotics and Intelligent Machine Scientific Research Laboratory Karaganda State University named after Academician E. A. Buketov Satybaldina str. 16-53 Karaganda100000 Kazakhstan

High-quality signal processing of an electrocardiogram (ECG) is an urgent problem in present day diagnostics for revealing dangerous signs of cardiovascular diseases and arrhythmias in patients. The used methods and programs of signal analysis and classification work with the arrays of points for mathematical modeling that must be extracted from an image or recording of an electrocardiogram. The aim of this work is developing a method of extracting images of ECG signals into a one-dimensional array. An algorithm is proposed based on sequential color processing operations and improving the image quality, masking and building a one-dimensional array of points using Python tools and libraries with open access. The results of testing samples from the ECG database and comparing images before and after processing show that the signal extraction accuracy is approximately 95 %. In addition, the presented application design is simple and easy to use. The proposed program for analyzing and processing the ECG data has a great potential in the future for the development of more complex software applications for automatic analyzing the data and determining arrhythmias or other pathologies. © 2020 Polish Society of Technical Diagnostics. All rights reserved.

关键词： Electrocardiograms

Generalized quantum teleportation of shared quantum secret with quantum walks

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Li, Hengji Li, Jian Chen, Xiubo Information Security Center State Key Laboratory Networking and Switching Technology Beijing University of Posts Telecommunications Beijing100876 China School of Artificial Intelligence Beijing University of Posts Telecommunications Beijing100876 China Center for Quantum Information Research ZaoZhuang University ZaoZhuang Shandong277160 China GuiZhou University Guizhou Provincial Key Laboratory of Public Big Data GuiyangGuizhou550025 China

Very recently, Lee et al. proposed the first secure quantum teleporation protocol, where quantum information shared by an arbitrary number of senders can be transferred to another arbitrary number of receivers. Here, by introducing quantum walks, a novel secure (n, m) quantum teleportation of shared quantum secret between n senders and m receivers is presented. Firstly, two kinds of (n, 2) teleportation schemes are proposed by n-walker quantum walks on the line, the first walker of which is driven by three coins, respectively, based on two kinds of coin operators: the homogeneous coins and the position-dependent coins. Secondly, by increasing the amount of the coins of the first walker to m + 1, the previous (n, 2) scheme can be generalized to (n, m) teleportation scheme. Then, we give the proof of the information security of our proposed scheme, in which neither any single nor subparties of senders and receivers can fully access the secret quantum information. Moreover, the projective measurements are needed, instead of the joint Bell measurements that are necessary in Lee et al.’s protocol. Our work can also be extended further to QWs on the cycle. This work provides an additional relevant instance of the richness of quantum walks for quantum information processing tasks and thus opens the wider application purpose of quantum walks. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum optics

Research on Video Recommendation Algorithm Based on Knowledge Reasoning of Knowledge Graph

学校读者我要写书评

暂无评论

Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and big data, APWeb-WAIM 2018

作者： Xu, Zhihong Zhao, Xing Dong, Yongfeng Yan, Wenjie Yu, Ziqi School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Computing Tianjin300401 China

ISBN: (纸本)9783030012977

Since collaborative filtering algorithm cannot make full use of video attribute information, mining implicit information of video, this paper proposes a video recommendation algorithm based on knowledge reasoning of knowledge graph. The algorithm uses a knowledge graph with powerful semantic processing capabilities and open interconnection capabilities. The ontology model is used to formalize the implicit semantics in the data. By using the knowledge inference method of the knowledge graph, the existing triplet information is used to establish new relationships between entities and assign corresponding weights to the paths. All the semantic information is embedded in the low-dimensional vector space, and the potential semantic similarity of the video is calculated by combining the path weights. And then, integrate semantic similarity into collaborative filtering for recommendation. Experiments show that this algorithm can make up for the deficiency that collaborative filtering algorithms cannot fully utilize the hidden information of video, and enhance the effectiveness of recommendation at the semantic level, what is more, the recommendation results are interpretable. To a certain extent, it can solve the problem of data sparseness. © 2018, Springer Nature Switzerland AG.

关键词： Collaborative filtering

AI-GENERATED CONTENT ENHANCED COMPUTER-AIDED DIAGNOSIS MODEL FOR THYROID NODULES: A CHATGPT-STYLE ASSISTANT

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Yao, Jincao Wang, Yunpeng Lei, Zhikai Wang, Kai Li, Xiaoxian Zhou, Jianhua Hao, Xiang Shen, Jiafei Wang, Zhenping Ru, Rongrong Chen, Yaqing Zhou, Yahan Chen, Chen Zhang, Yanming Liang, Ping Xu, Dong Department of Radiology Zhejiang Cancer Hospital Hangzhou310022 China Chinese Academy of Sciences Hangzhou310000 China Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province Hangzhou310022 China Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology Hangzhou310000 China Wenling Medical Big Data and Artificial Intelligence Research Institute 24th Floor Machang Road Taizhou310061 China Taizhou317502 China College of Optical Science and Engineering Zhejiang University No.38 of Zheda Road Zhejiang Province Hangzhou China Zhejiang Provincial Hospital of Chinese Medicine 54 Youdian Road Hangzhou310003 China Department of Ultrasound The Affiliated Dongyang Hospital of Wenzhou Medical University Dongyang322100 China Department of Ultrasound Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou510060 China Affiliated Xiaoshan Hospital Hangzhou Normal University No.728 North Yucai Road Hangzhou311202 China Zhejiang Provincial People's Hospital Affiliated People's Hospital Hangzhou Medical College Hangzhou314408 China Key Laboratory of Endocrine Gland Diseases of Zhejiang Province Hangzhou314408 China Department of Ultrasound Chinese PLA General Hospital Chinese PLA Medical School Beijing100853 China

An artificial intelligence-generated content-enhanced computer-aided diagnosis (AIGC-CAD) model, designated as ThyGPT, has been developed. This model, inspired by the architecture of ChatGPT, could assist radiologists in assessing the risk of thyroid nodules through semantic-level human-machine interaction. A dataset comprising 19,165 thyroid nodule ultrasound cases from Zhejiang Cancer Hospital was assembled to facilitate the training and validation of the model. After training, ThyGPT could automatically evaluate thyroid nodule and engage in effective communication with physicians through human-computer interaction. The performance of ThyGPT was rigorously quantified using established metrics such as the receiver operating characteristic (ROC) curve, area under the curve (AUC), sensitivity, and specificity. The empirical findings revealed that radiologists, when supplemented with ThyGPT, markedly surpassed the diagnostic acumen of their peers utilizing traditional methods as well as the performance of the model in isolation. These findings suggest that AIGC-CAD systems, exemplified by ThyGPT, hold the promise to fundamentally transform the diagnostic workflows of radiologists in forthcoming years. Copyright © 2024, The Authors. All rights reserved.

关键词： Computer aided diagnosis

Multiview feature selection combining latent space and similarity structure learning

学校读者我要写书评

暂无评论

Pattern Recognition 2026年 169卷

作者： Zhuowen Li Hongmei Chen Tengyu Yin Zhong Yuan Chuan Luo Shi-Jinn Horng Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 PR China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 PR China College of Computer Science Sichuan University Chengdu 610065 China Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung 404327 Taiwan

Unsupervised multiview feature selection dependent on similar or clustering structures has dramatically progressed, but both ignore the mutually reinforcing relationship between structure learning. Firstly, the two methods of appeal ignore the possibility of shared joint learning, although they can learn coherent information. Secondly, they are inadequate for data structure learning and lack awareness of co-learning global and local structures. This paper proposes an unsupervised feature selection method (SCSF_FS) that integrates similarity and clustering structures learning to capture intrinsic information in data. Specifically, we use latent space learning to partition multiple data matrices into view-specific base matrices and clustering metrics. Structural learning is used to transform specific clustering metrics into coherent clustering structures. In addition, adaptive weights are used on each view’s similarity matrix to learn a consistent similarity structure, while laplacian graph learning is introduced to unify the similarity and clustering structures. Consequently, a unified framework is designed to unify multiview consistency learning, global structure, and local structure preservation. Moreover, an optimization iteration algorithm is designed to solve it. Comparison with eight algorithms shows the effectiveness of the proposed method.

关键词：