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A review of federated learning technology and its research progress in healthcare applications

Applied Intelligence 2025年第10期55卷 1-30页

作者： Ma, Zezhong Ruhaiyem, Nur Intan Raihana Zhang, Meng Musa, Kamarul Imran Hanis, Tengku Muhammad Xiao, Tianyun Hua, Dianbo Li, Hao School of Computer Sciences Universiti Sains Malaysia Gelugor Malaysia Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing China School of Cyberspace Security (School of Cryptology) Hainan University Haikou China School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Department of Community Medicine Faculty of Medicine Universiti Sultan Zainal Abidin Medical Campus Kuala Terengganu Malaysia Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Tangshan China College of Science North China University of Science and Technology Tangshan China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Tangshan China

Data-driven medical applications, powered by big data and artificial intelligence, generate scalable models from extensive datasets. This innovation attracts both academia and industry, significantly enhancing healthcare quality while posing integration challenges. Federated learning emerges as a transformative approach in healthcare, facilitating collaborative machine learning while preserving data privacy. This article reviews research on federated learning in healthcare, utilizing the "Web of science" database to examine development trends and application progress. Initially, the federated learning system is dissected, and its application methods are analyzed, summarizing 18 federated learning frameworks and 9 privacy-preserving methods suitable for federated learning, and exploring its limitations in healthcare applications. Subsequently, recent literature on federated learning in four major areas—disease diagnosis and risk assessment, medical image analysis, drug discovery and development, and disease management—is reviewed, summarizing the general process of applying Federated Learning in healthcare. Finally, the challenges faced by federated learning in the medical field and the solutions currently being explored by scholars are summarized. This article aims to comprehensively summarize the research progress and application trends of federated learning technology in the medical field, analyze its limitations and challenges, and anticipate its future development to further promote sustainable advancement in the healthcare industry.

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AI-Enhanced Cybersecurity for Intelligent Systems (IS) Towards Criminals

AI-Enhanced Cybersecurity for Intelligent Systems (IS) Towar...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Kumaran U S.Padmanaban Rakesh Kumar Ramy Riad Al-Fatlawy Manpreet Kaur Sukanya Konatam Department of CSE Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Science and Humanities Karpagam Institute of Technology Coimbatore Department of Computer Engineering and Applications GLA University Mathura India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India The University of Texas Austin Texas USA

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

One of the strongest examples for this scenario is artificial intelligence (AI) in which AI interacts between individuals and their environment. Artificial intelligence powering smart homes is also still young, but it has huge promise in the eyes of experts. In this article, we will be looking at a very straightforward yet an elegant AI-powered home automation solution. The focus of this research is to develop the AI-based home lighting system that appears to operate just like normal; when you're still out in your cars at night. We used Arduino hardware for the physical building of AI based smart house and coded its software to make it real-world compatible. Over 5,500 over result vectors and nine inputs were subseqentially used to establish a unique dataset that mimic actual evening light patterns in the real world as well as presented an appearance of presence within home. Ta. In plain English: AI can, yes indeed understand and control your home night light. Comparison of Performance: The proposed system is evaluated against common machine learning classification techniques like multi perceptually layered neurons, the Gaussian naive Bayes method, linear discriminant analysis methods and vector machines. This is an encouraging realization that AI can indeed make the home safer and bolster defenses against break-ins..

关键词： Prevention and mitigation Neurons Lighting Smart homes Machine learning Vectors Software Telecommunication computing Naive Bayes methods Artificial intelligence

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Adaptive Convolutional Neural Network for Image Super-resolution

arXiv

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arXiv 2024年

作者： Tian, Chunwei Zhang, Xuanyu Wang, Tao Zhang, Yongjun Zhu, Qi Lin, Chia-Wen The School of Software Northwestern Polytechnical University Xi’an710129 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China The Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang215400 China The School of Computer Science Northwestern Polytechnical University Xi’an710129 China The College of Computer Science and Technology Guizhou University Guiyang550025 China The School of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing210016 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Taiwan

Convolutional neural networks can automatically learn features via deep network architectures and given input samples. However, the robustness of obtained models may face challenges in varying scenes. Bigger differences in network architecture are beneficial to extract more diversified structural information to strengthen the robustness of an obtained superresolution model. In this paper, we proposed a adaptive convolutional neural network for image super-resolution (ADSRNet). To capture more information, ADSRNet is implemented by a heterogeneous parallel network. The upper network can enhance relation of context information, salient information relation of a kernel mapping and relations of shallow and deep layers to improve performance of image super-resolution. That can strengthen adaptability of an obtained super-resolution model for different scenes. The lower network utilizes a symmetric architecture to enhance relations of different layers to mine more structural information, which is complementary with a upper network for image super-resolution. The relevant experimental results show that the proposed ADSRNet is effective to deal with image resolving. Codes are obtained at https://***/hellloxiaotian/ADSRNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Dual-Level Decoupled Transformer for Video Captioning

arXiv

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arXiv 2022年

作者： Gao, Yiqi Hou, Xinglin Suo, Wei Sun, Mengyang Ge, Tiezheng Jiang, Yuning Wang, Peng School of Computer Science Northwestern Polytechnical University China National Engineering Lab for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Alibaba Group China

Video captioning aims to understand the spatio-temporal semantic concept of the video and generate descriptive sentences. The defacto approach to this task dictates a text generator to learn from offline-extracted motion or appearance features from pre-trained vision models. However, these methods may suffer from the so-called "couple" drawbacks on both video spatio-temporal representation and sentence generation. For the former, "couple" means learning spatio-temporal representation in a single model(3DCNN), resulting the problems named disconnection in task/pre-train domain and hard for end-to-end training. As for the latter, "couple" means treating the generation of visual semantic and syntax-related words equally. To this end, we present D2 - a dual-level decoupled transformer pipeline to solve the above drawbacks: (i) for video spatio-temporal representation, we decouple the process of it into "first-spatial-then-temporal" paradigm, releasing the potential of using dedicated model(e.g. image-text pre-training) to connect the pre-training and downstream tasks, and makes the entire model end-to-end trainable. (ii) for sentence generation, we propose Syntax-Aware Decoder to dynamically measure the contribution of visual semantic and syntax-related words. Extensive experiments on three widely-used benchmarks (MSVD, MSR-VTT and VATEX) have shown great potential of the proposed D2 and surpassed the previous methods by a large margin in the task of video captioning. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Lexicon-matched Word Injection for Chinese NER 22

Lexicon-matched Word Injection for Chinese NER

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Proceedings of the 2022 5th International Conference on Machine Learning and Machine Intelligence

作者： Qingyu Wang Guokai Sun Jianguo Sun Yuan Zhuang Lu Li Tianyi Gao School of Computer Science and Technology Harbin Engineering University China and Wuhan Sanjiang Aerospace Network Communication Co. LTD China School of Computer Science and Technology Harbin Engineering University China Cyberspace Security Lab Xidian University China Network Security Department Defence Industry Secrecy Examination and Certification Center China Network Security Technology Application Center Wuhan Sanjiang Aerospace Network Communication Co. LTD China

ISBN: (纸本)9781450397551

Recently, Chinese named entity recognition has attracted a lot of attention. Most of the work utilizes words matching with lexicon which integrates potential word information with lattice structure or graph structure. Although existing approaches have been proved to be effective for exploiting abundant word boundary information, it is difficult to model global semantic interactions due to the inherent one-way sequential nature of the DAG structure. Meanwhile, more interfering lexicon words have been introduced leading to word boundary *** address the above issues, this paper proposes a knowledge fusion method based on lexicon matching word injection, which captures sentence context features through a pre-trained learning model and then injects lexicon knowledge into each character. With the power of Transformer and well-designed encoding, it becomes easier to obtain accurate word information using the character encoding vector of transformer encoder model. Besides, the self-attention module integrating characters with different words is fully leveraged to improve recognition accuracy. Experiments on three Chinese public datasets show that the proposed method outperformed other lexicon-based methods in performance and efficiency.

关键词： Chinese named entity recognition Word injection Multi-headed self-attention

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Incomplete Multi-view Clustering Based on Joint Concept Decomposition and Anchor Graph Learning 11th

Incomplete Multi-view Clustering Based on Joint Concept Dec...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Li, Zhuowen Chen, Hongmei Xiang, Biao Yuan, Zhong Luo, Chuan Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China College of Computer Science Sichuan University Chengdu610065 China

ISBN: (纸本)9783031927430

The main objective of incomplete multi-view clustering is to effectively utilize the existing view information to fill in the missing data and to mine the complementary information and potential associations between multiple views to effectively group samples. The existing primary means for recovering missing information are classified into two parts: one uses matrix decomposition or low-rank constraints to fill in the missing views, and the other constructs multiple graph structures and uses graph regularization to recover the corresponding missing parts. However, the two methods are relatively independent and are not used jointly to improve the model performance. This paper proposes an incomplete multi-view clustering method with complete clustering metrics and complete anchor graphs (CFAG). Specifically, each view’s missing samples are recovered by a matrix of consistent clustering metrics and represented by an anchor graph. The recovered complete data performs anchor graph construction and anchor learning and utilizes the orthogonal variation principle to learn the consistency structure of multiple anchor graphs to mine the complementary information among multiple views. The clustering metrics are obtained based on the consistency structure, and the consistency of the clustering metrics is also considered to obtain the final clustering results. The effectiveness of the proposed method is verified on multiple datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graphic methods

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Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding

arXiv

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arXiv 2023年

作者： Xu, Lixiang Cui, Qingzhe Hong, Richang Xu, Wei Chen, Enhong Yuan, Xin Li, Chenglong Tang, Yuanyan The College of Artificial Intelligence and Big Data Hefei University Hefei230027 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China The School of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia The School of Artificial Intelligence Anhui University Hefei230601 China The Zhuhai UM Science and Technology Research Institute FST University of Macau China

In recent years, the results of view-based 3D shape recognition methods have saturated, and models with excellent performance cannot be deployed on memory-limited devices due to their huge size of parameters. To address this problem, we introduce a compression method based on knowledge distillation for this field, which largely reduces the number of parameters while preserving model performance as much as possible. Specifically, to enhance the capabilities of smaller models, we design a high-performing large model called Group Multi-view Vision Transformer (GMViT). In GMViT, the view-level ViT first establishes relationships between view-level features. Additionally, to capture deeper features, we employ the grouping module to enhance view-level features into group-level features. Finally, the group-level ViT aggregates group-level features into complete, well-formed 3D shape descriptors. Notably, in both ViTs, we introduce spatial encoding of camera coordinates as innovative position embeddings. Furthermore, we propose two compressed versions based on GMViT, namely GMViT-simple and GMViT-mini. To enhance the training effectiveness of the small models, we introduce a knowledge distillation method throughout the GMViT process, where the key outputs of each GMViT component serve as distillation targets. Extensive experiments demonstrate the efficacy of the proposed method. The large model GMViT achieves excellent 3D classification and retrieval results on the benchmark datasets ModelNet, ShapeNetCore55, and MCB. The smaller models, GMViT-simple and GMViT-mini, reduce the parameter size by 8 and 17.6 times, respectively, and improve shape recognition speed by 1.5 times on average, while preserving at least 90% of the classification and retrieval performance. The code is available at https://***/bigdatagraph/*** Codes 68 Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Effects of Optimized Operating Parameters on Combustion Characteristics and NO_(x)Emissions of a Burner based on Orthogonal Analysis

引用

Journal of Thermal science 2021年第4期30卷 1212-1223页

作者： XU Qian AKKURT Nevzat YANG Gang ZHU Lidong SHI Kejian WANG Kang ZOU Zhenwei LIU Zhihui WANG Jiulong DU Zhiwei School of Energy and Environmental Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory for Energy Conservation and Emission Reduction for Metallurgical Industry Beijing 100083China Department of Mechanical Engineering Munzur UniversityTunceli 62000Turkey Department of Big Data Technology and Application Development Computer Network Information CenterChinese Academy of SciencesBeijing 100190China AVIC Xinxiang Aviation Industry(group)CO. LTDHenan 453002China

To optimize the structure of the burner,improve the combustion performance,and reduce the emission of NO_(x),a self-circulating low NO_(x)combustion technology was used to design a new type of flue gas self-circulating low NO_(x)*** on previous research on the numerical model of combustion and the composition of mixed gas on combustion and NO_(x)emissions,the effect of various factors on the ejection coefficient of the flue gas self-circulating structure was analyzed using the orthogonal test method,and the burner operating parameters,such as preheating temperature and excess air coefficient,were deeply studied through the three-dimensional finite element numerical model in this *** results show that the diameter ratio of the nozzle and the length of the cylindrical section of the flue gas self-circulating structure have great influence on its ejection and mixing *** optimal ejection coefficient was ***,the amount of NO_(x)emissions greatly increased from 6.23×10^(-6)(volume fraction)at the preheating temperature 973 K to 3.5×10^(-3)at preheating temperature 1573 *** the excess air coefficient decreased from 1.2 to 1,the maximum combustion temperature decreased from 2036.3 K to 1954.22 K,and the NO_(x)emissions decreased from 352.29×10^(-6)to 159.73×10^(-6).

关键词： preheating temperature excess air coefficient self-circulating flue gas low oxygen combustion low-NO_(x)emissions

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Study of fuzzy expert systems towards prediction and detection of fraud case in health care insurance

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Materials Today: Proceedings 2022年 56卷 477-480页

作者： Kalra, Gunjan Rajoria, Yogendra Kumar Boadh, Rahul Rajendra, P. Pandey, Pratibha Khatak, Naveen Kumar, Ajay Department of the Mathematics School of Basic and Applied Science K.R. Mangalam University Sohna Road Gurugram Haryana 122103 India Department of Mathematics CMR Institute of Technology Bengaluru India Department of Computer Application Uttaranchal Institute of Management Uttaranchal University Arcadia Grant P.O. Chandanwari Premnagar Uttarakhand Dehradun248007 India Department of Mechanical Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122103 India Institute of Engineering and Technology Maharshi Dayanand University Rohtak Haryana Rohtak 124001 India

The size of this issue is sufficiently enormous to focus on it issue for wellbeing structures. Unseemly installments by protection associations or outsider payers happen due to mistakes, fraud and misrepresentation. Determined to pinpoint has been some property/loss protection inclusion that incorporates a medical consideration part. Fraud and misuse happen at many focuses in the medical services framework. Specialists, clinics, nursing homes, symptomatic offices, clinical gear providers, and lawyers have been referred to in tricks to dupe the framework. Medical care assumes a vital part in human existence and it should be reasonable. The rate of health care coverage extortion continues to expand each year, particularly during coronavirus. Extortion and maltreatment on clinical cases turned into a basic issue for the health care coverage industry. We have tracked down that large numbers of the distortions planned to bring about an unapproved advantage were made in the Medical health care coverage. Manual identification of fraud in medical coverage is an exceptionally intense undertaking. In this paper, Fuzzy expert system has been carried out to recognize medical coverage frauds. The point is to recognize extortion asserts precisely inside a brief timeframe. Extortion in medical coverage is truly exceptional and exorbitant for the two policyholders and insurance agencies in all areas of the protection area. © 2022

关键词： Health care

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CrossDiff: Exploring Self-Supervised Representation of Pansharpening via Cross-Predictive Diffusion Model

arXiv

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arXiv 2024年

作者： Xing, Yinghui Qu, Litao Zhang, Shizhou Zhang, Kai Zhang, Yanning Ran, Lingyan Zhang, Xiuwei Yin, Hanlin The Shaanxi Provincial Key Laboratory of Speech and Image Information Processing The National Engineering Laboratory for Integrated Aerospace-GroundOcean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710072 China The Research Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Fusion of a panchromatic (PAN) image and corresponding multispectral (MS) image is also known as pansharpening, which aims to combine abundant spatial details of PAN and spectral information of MS. Due to the absence of high-resolution MS images, available deep-learning-based methods usually follow the paradigm of training at reduced resolution and testing at both reduced and full resolution. When taking original MS and PAN images as inputs, they always obtain sub-optimal results due to the scale variation. In this paper, we propose to explore the self-supervised representation of pansharpening by designing a cross-predictive diffusion model, named CrossDiff. It has two-stage training. In the first stage, we introduce a cross-predictive pretext task to pre-train the UNet structure based on conditional DDPM, while in the second stage, the encoders of the UNets are frozen to directly extract spatial and spectral features from PAN and MS, and only the fusion head is trained to adapt for pansharpening task. Extensive experiments show the effectiveness and superiority of the proposed model compared with state-of-the-art supervised and unsupervised methods. Besides, the cross-sensor experiments also verify the generalization ability of proposed self-supervised representation learners for other satellite’s datasets. We will release our code for reproducibility. © 2024, CC BY.

关键词： Deep learning

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