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A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique

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arXiv 2022年

作者： Rehman, Abdur Abbas, Sagheer Khan, M.A. Ghazal, Taher M. Adnan, Khan Muhammad Mosavi, Amir School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Riphah School of Computing and Innovation Faculty of Computing Riphah International university Pakistan School of Information Technology Skyline University College University City Sharjah Sharjah1797 United Arab Emirates Selangor Bangi43600 Malaysia Department of Software Gachon University Seongnam13120 Korea Republic of Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava Bratislava81107 Slovakia John von Neumann Faculty of Informatics Obuda University Budapest1034 Hungary Faculty of Civil Engineering TU-Dresden Dresden01062 Germany

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the e

关键词： Blockchain

How to Exploit Optimization Experience? Revisiting Evolutionary Sequential Transfer Optimization: Part A - Benchmark Problems

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TechRxiv

TechRxiv 2022年

作者： Xue, Xiaoming Yang, Cuie Feng, Lianghuan Zhang, Kai Song, Linqi Tan, Kay Chen The Department of Computer Science City University of Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The College of Computer Science Chongqing University Chongqing400044 China The Civil Engineering School Qingdao University of Technology Qingdao266520 China The Department of Computing Hong Kong Polytechnic University Hong Kong

Evolutionary sequential transfer optimization (ESTO), which attempts to enhance the evolutionary search of a target task using the knowledge captured from several previously-solved source tasks, has been receiving increasing research attention in recent years. Despite the tremendous approaches developed, it is worth noting that existing benchmark problems for ESTO are not well designed, as they are often simply extended from other benchmarks in which the relationships between the source and target tasks are not well analyzed. Consequently, the comparisons conducted on these problems are not systematic and can only provide numerical results without a deeper analysis of how an ESTO algorithm performs on problems with different properties. Taking this clue, this two-part paper revisits a large body of solution-based ESTO algorithms on a group of newly developed test problems, to help researchers and practitioners gain a deeper understanding of how to better exploit optimization experience towards enhanced optimization performance. Part A of the series designs a problem generator based on several newly defined concepts to generate benchmark problems with diverse properties, which are competitive in resembling real-world problems. Part B of the series empirically revisits various algorithms by answering five key research questions related to knowledge transfer. The results demonstrated that the performance of many ESTO algorithms is highly problem-dependent, which suggest the necessity of more research efforts on transferability measurement and enhancement in ESTO algorithm design. The source code of the benchmark suite developed in part A is available at https://***/XmingHsueh/Revisiting-S-ESTOs-PartA. © 2022-12-08, CC BY.

关键词： Benchmarking

How to Exploit Optimization Experience? Revisiting Evolutionary Sequential Transfer Optimization: Part B - Algorithm Analysis

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TechRxiv

TechRxiv 2022年

This paper is the second part of a two-part series on evolutionary sequential transfer optimization (ESTO). The first part designs a problem generator to generate benchmark problems with diverse properties that can better resemble real-world problems. This part proposes five research questions (RQs) to empirically revisit a large body of knowledge transfer techniques in the context of solution-based ESTO (S-ESTO). Particularly, knowledge transfer techniques are organized from a perspective of transfer strategy, including solution selection, solution adaptation, and the integration of them. The results reveal that the performance of many algorithms is highly sensitive to similarity distribution of STOPs, which suggest the necessity of more flexible algorithm design in future studies. Specifically, when an STOP is full of source tasks that are highly similar to the target task in terms of optimum, one can randomly select one source task and reuse its elite solution(s) to accelerate the target search significantly. When an STOP contains source tasks with mixed similarities to the target task, an effective similarity metric that can represent the transferability is able to identify highly transferable solution(s). When an STOP is full of source tasks with low similarities to the target task, the transferability is expected to be improved by solution adaptation. In solution adaptation, a similarity metric in the form of a maximization (or minimization) objective function that is responsible for representing the transferability is optimized to obtain a mapping function for adapting source solution(s). The source code for reproducing our experiments is available at https://***/XmingHsueh/Revisiting-S-ESTOs-PartB. © 2022-12-08, CC BY.

关键词： Evolutionary algorithms

Initial Study on Implementation of Smoothing Filters for the Purpose of Bioimpedance Spectroscopy Parameters Classification

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IFAC-PapersOnLine 2022年第4期55卷 393-398页

作者： Aleksandra Kawala-Sterniuk Amir F. Al-Bakri Mariusz Pelc Katarzyna Cichoń Wojciech Chlewicki Stepan Ozana Radek Martinek Jakub Możaryn Volodymyr Khoma Halyna Kenyo Edward Jacek Gorzelańczyk Michał Podpora Jarosław Zygarlicki Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics 45-758 Opole Poland Department of Biomedical Engineering College of Engineering University of Babylon 51001 Babylon Iraq University of Greenwich School of Computing and Mathematical Sciences SE10 9LS London UK West Pomeranian University of Technology in Szczecin Faculty of Electrical Engineering 70-313 Szczecin Poland Faculty of Mechatronics Warsaw University of Technology 02-525 Warsaw Poland Lviv Polytechnic National University Institute of Computer Technologies Automation and Metrology Lviv Ukraine Institute of Philosophy Kazimierz Wielki University 85-092 Bydgoszcz Poland Faculty of Mathematics and Computer Science Adam Mickiewicz University in Poznań 61-614 Poznań Poland

Bioimpedance is a commonly used method for various conditions monitoring. In this paper, the authors carried out some research where they implemented various smoothing filters to enable identification of the bioimpedance spectroscopy parameters. The proposed filtering methods may also be used for application on embedded systems, which have smaller computing power but have become recently very popular. The obtained results with the implementation of smoothing filters were promising, however, some of the obtained results were unsatisfactory. This work also contains a brief introduction to bioimpedance spectroscopy and smoothing filters.

关键词： Bioimpedance Spectroscopy smoothing-filtering Savitzky-Golay Filter Median Filtering Moving-Average Filters

A deep learning-based approach to extracting periosteal and endosteal contours of proximal femur in quantitative CT images

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arXiv 2021年

作者： Deng, Yu Wang, Ling Zhao, Chen Tang, Shaojie Cheng, Xiaoguang Deng, Hong-Wen Zhou, Weihua School of Automation Xi'an University of Posts and Telecommunications Xi'an Shaanxi710121 China Department of Radiology Beijing Jishuitan Hospital Beijing100035 China College of computing Michigan Technological university HoughtonMI49931 United States Tulane University Department of Biomedical Engineering New OrleansLA70118 United States

Objective: Automatic CT segmentation of proximal femur is crucial for the diagnosis and risk stratification of orthopedic diseases. In this study, we proposed an approach based on deep learning for the automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments. Methods: A three-dimensional (3D) end-to-end fully convolutional neural network, which can better combine the information between neighbor slices and get more accurate segmentation results, was developed for our segmentation task. 100 subjects aged from 50 to 87 years with 24,399 slices of proximal femur CT images were enrolled in this study. The separation of cortical and trabecular bone derived from the QCT software MIAF-Femur was used as the segmentation reference. We randomly divided the whole dataset into a training set with 85 subjects for 10-fold cross-validation and a test set with 15 subjects for evaluating the performance of models. Results: Two models with the same network structures were trained and they achieved a dice similarity coefficient (DSC) of 97.87% and 96.49% for the periosteal and endosteal contours, respectively. To verify the excellent performance of our model for femoral segmentation, we measured the volume of different parts of the femur and compared it with the ground truth and the relative errors between predicted result and ground truth are all less than 5%. It demonstrated a strong potential for clinical use, including the hip fracture risk prediction and finite element analysis. Significance: This approach is helpful to measure the BMD of cortical bone and trabecular bone respectively, and to evaluate the bone strength based on finite element analysis. Copyright © 2021, The Authors. All rights reserved.

关键词： Image segmentation

Prompt engineering for Healthcare: Methodologies and Applications

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arXiv 2023年

作者： Wang, Jiaqi Shi, Enze Yu, Sigang Wu, Zihao Ma, Chong Dai, Haixing Yang, Qiushi Kang, Yanqing Wu, Jinru Hu, Huawen Yue, Chenxi Zhang, Haiyang Liu, Yiheng Pan, Yi Liu, Zhengliang Sun, Lichao Li, Xiang Ge, Bao Jiang, Xi Zhu, Dajiang Yuan, Yixuan Shen, Dinggang Liu, Tianming Zhang, Shu The School of Computer Science Northwestern Polytechnical University Xi’an710072 China The School of Automation Northwestern Polytechnical University Xi’an710072 China The School of Computing The University of Georgia Athens30602 United States The Department of Computer Science and Engineering Lehigh University PA18015 United States The Department of Radiology Massachusetts General Hospital Harvard Medical School Boston02115 United States The Department of Computer Science and Engineering The University of Texas at Arlington Arlington76019 United States The School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China The Department of Electronic Engineering City University of Hong Kong 999077 Hong Kong The Department of Electronic Engineering Chinese University of Hong Kong 999077 Hong Kong The School of Physics and Information Technology Shaanxi Normal University Xi’an710119 China The School of Glasgow College University of Electronic Science and Technology of China Chengdu611731 China The School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China

Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks. With the recent advancements in large language models, prompt engineering has shown significant superiority across various domains and has become increasingly important in the healthcare domain. However, there is a lack of comprehensive reviews specifically focusing on prompt engineering in the medical field. This review will introduce the latest advances in prompt engineering in the field of natural language processing for the medical field. First, we will provide the development of prompt engineering and emphasize its significant contributions to healthcare natural language processing applications such as question-answering systems, text summarization, and machine translation. With the continuous improvement of general large language models, the importance of prompt engineering in the healthcare domain is becoming increasingly prominent. The aim of this article is to provide useful resources and bridges for healthcare natural language processing researchers to better explore the application of prompt engineering in this field. We hope that this review can provide new ideas and inspire for research and application in medical natural language processing. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical applications

Review of Large Vision Models and Visual Prompt engineering

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arXiv 2023年

作者： Wang, Jiaqi Liu, Zhengliang Zhao, Lin Wu, Zihao Ma, Chong Yu, Sigang Dai, Haixing Yang, Qiushi Liu, Yiheng Zhang, Songyao Shi, Enze Pan, Yi Zhang, Tuo Zhu, Dajiang Li, Xiang Jiang, Xi Ge, Bao Yuan, Yixuan Shen, Dinggang Liu, Tianming Zhang, Shu School of Computer Science Northwestern Polytechnical University Xi’an710072 China School of Computing The University of Georgia Athens30602 United States School of Automation Northwestern Polytechnical University Xi’an710072 China School of Physics and Information Technology Shaanxi Normal University Xi’an710119 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China Department of Electronic Engineering Chinese University of Hong Kong 999077 Hong Kong Department of Electronic Engineering City University of Hong Kong 999077 Hong Kong School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China Glasgow College University of Electronic Science and Technology of China Chengdu611731 China Department of Computer Science and Engineering The University of Texas at Arlington Arlington76019 United States Department of Radiology Massachusetts General Hospital Harvard Medical School Boston02115 United States

Visual prompt engineering is a fundamental methodology in the field of visual and image Artificial General Intelligence (AGI). As the development of large vision models progresses, the importance of prompt engineering becomes increasingly evident. Designing suitable prompts for specific visual tasks has emerged as a meaningful research direction. This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering. We present influential large models in the visual domain and a range of prompt engineering methods employed on these models. It is our hope that this review provides a comprehensive and systematic description of prompt engineering methods based on large visual models, offering valuable insights for future researchers in their exploration of this field. Copyright © 2023, The Authors. All rights reserved.

关键词： Vision

MPTA-Net: Multi-Scale Perception Network with Triple-View Attention for Polyp Segmentation

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MPTA-Net: Multi-Scale Perception Network with Triple-View At...

Neural Networks, Information and Communication engineering (NNICE), International Conference on

作者： Zhihong Chen Lisha Yao Yue Liu Bingjiang Qiu Chu Han Haoyang Tang Yujia Du Zaiyi Liu Gang Fang Institute of Computing Science and Technology Guangzhou University Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China School of Medicine South China University of Technology Guangzhou China Department of Radiology Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou China School of Information Engineering Jiangxi College of Applied Technology Ganzhou China Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital Guangdong Academy of Sciences Guangzhou China Medical Research Institute Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

Accurate segmentation of polyps in endoscopic images is pivotal for the early detection and diagnosis of colorectal cancer (CRC). Nonetheless, the varied appearance of polyp foregrounds and the intricate background interference significantly diminish the efficacy of pixel-level predictions. To address this issue, we propose a multi-scale perception network with triple-view attention (MPTA-Net) for polyp segmentation. The model primarily revolves around two core principles. The first integrates multiscale spatial information into the network's hierarchical encoding process to better preserve detailed polyp semantic information. The second introduces a triple-view attention mechanism that analyzes feature maps from three distinct perspectives to focus on and localize semantically relevant areas, thereby enhancing segmentation accuracy. Experimental results reveal that the proposed model outperforms existing mainstream segmentation models, effectively addressing under-segmentation, over-segmentation, and edge blurriness issues in polyp segmentation, thereby achieving better identification of challenging areas.

关键词：

LINEARIZATION IN MAGNETOELASTICITY

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arXiv 2024年

作者： Almi, Stefano Kružík, Martin Molchanova, Anastasia University of Naples Federico II Department of Mathematics and Applications Renato Caccioppoli via Cintia Monte S. Angelo Naples80126 Italy Czech Academy of Sciences Institute of Information Theory and Automation Pod vodárenskou věží 4 Prague 8182 00 Czech Republic Faculty of Civil Engineering Czech Technical University Thákurova 7 Prague 6166 29 Czech Republic University of Vienna Faculty of Mathematics Oskar-Morgenstern-Platz 1 Vienna1090 Austria TU Wien Institute of Analysis and Scientific Computing Wiedner Hauptstraße 8–10 Wien1040 Austria

Starting from a model of nonlinear magnetoelasticity where magnetization is defined in the Eulerian configuration while elastic deformation is in the Lagrangian one, we rigorously derive a linearized model that coincides with the standard one that already appeared in the literature, see, e.g., [19] and where the zero-stress strain is quadratic in the magnetization. The relation of the nonlinear and linear model is stated in terms of the Γ-convergence and convergence of minimizers. © 2024, CC BY.

关键词： Elastic deformation