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检索条件"任意字段=2022 International Conference on Algorithms, Network and Computer Technology, ICANCT 2022"

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Machine Learning Applications For Protecting The Information Of Health Care Department Using Smart Internet Of Things Appliances -A REVIEW 2

Machine Learning Applications For Protecting The Information...

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2nd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Surve, Jyoti Umrao, Dhananjay Madhavi, M. Rajeswari, T.S. Bangare, Sunil L. Chakravarthi, M. Kalyan International Institute of Information Technology Department of Information Technology Hinjewadi Pune India Engineering & Management Institute Department Computer Science and Engineering Institute/ Organization: Naraina Vidya Peet Panki Kanpur Ganga Gunj India Vr Siddhartha Engineering College Department of Computer Science and Enginnering Chalasani Nagar Kanuru Vijayawada India Koneru Lakshmaiah Education Foundation Department of English AP Vaddeswaram India Sinhgad Academy of Engineering Savitribai Phule Pune University Department of Information Technology Pune India VIT-AP University School of Electronics Engineering Amaravathi India

ISBN: (纸本)9781665437899

So, let us begin with Machine learning (ML), which is a type of neural network (AI) that empowers software programmers to start increasing prediction without being done with full to do so. Because data is so valuable, improving strategies for intelligently having to manage the now-ubiquitous content infrastructures is a necessary part of the process toward completely autonomous agents. computer vision and computer vision have improved a wide range of industries, including medical diagnoses, data display and procedures, science and research, and so on A number of tiny IoT devices on the market for heart rate sensor, allowing patients to roam about freely while their hearts are continually monitored. It is still difficult to guarantee ultra-accurate findings, however most current instruments can achieve accuracy rates of over 90 percentage... Monitoring cardiac rhythms, as well as glucose levels, may be challenging, and even those who are represented at medical institutions. Intermittent heart rate assessments cannot protect against sudden changes in vital signs, and standard techniques of heart rhythm surveillance used in hospitals require patients to be permanently attached to wired apparatus, limiting their mobility. © 2022 IEEE.

关键词： Health care

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A Stochastic Approach for Blockchain Internet of Things Integration 14

A Stochastic Approach for Blockchain Internet of Things Inte...

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14th international conference on Advanced computer Science and Information Systems, ICACSIS 2022

作者： Khalifa, Othman O. Ahmed, Muhammed Z. Saeed, Rashid A Kida, Aliyu Musa Albagoul, Abdul Gani Esgiar, Abdelrahim N International Islamic University Malaysia Elect. & Computer Engineering Dept. Kuala Lumpur Malaysia College of Computers and Information Technology Taif University Taif Saudi Arabia University of Maiduguri Computer Engineering Maiduguri Nigeria Libyan Centre for Engineering Research and Information Technology Baniwaleed Libya Sirt University Department of Electrical and Electronic Engineering Sirt Libya

ISBN: (数字)9781665489362

ISBN: (纸本)9781665489362

The ease of adaptability of Internet of Things (IoT) enables multiple devices to communicate with the aim of shearing data and information resources. The IoT architecture of operation is by default centralized. However, the distributed and decentralized architecture of Blockchain enables efficient and scalable connectivity between conventional devices and IoT devices. A Blockchain is considered as a technology with a distributed ledger that enables data transactions with integrity. In this paper, an analysis of the benefits and drawbacks of integrating IoT and Blockchain technology is efficiently conducted. A problem is formulated to analyze the system model by considering both internal and external attacks on a network. Also, a security analysis is conducted by formulating equations. Use-case analysis is proposed to seamlessly locate an IoT device and ensure access control for inbound and outbound link throughout the IoT system. In our results, MATLAB2019b is used in presenting graphical results for compromised node, cooperative nodes, and total number of node count. This analysis is conducted using stochastic approach to determine successful defense for node attach in percentage counts. © 2022 IEEE.

关键词： Internet of things

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A Survey on Automated CAD System of Liver Tumor using US Images 7

A Survey on Automated CAD System of Liver Tumor using US Ima...

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7th international conference on Communication and Electronics Systems, ICCES 2022

作者： Uplaonkar, Deepak S. Virupakshappa Rangayya Patil, Nagabhushan International Institute of Information Technology Department of Information Technology Pune India Sharnbasva University Department of Computer Science and Engineering Kalburagi India Sharnbasva University Department of Elctronics and Telecommunication Engineering Kalburagi India Poojya Doddappa Appa College of Engineering Department of Electrical and Elctronics Engineering Kalburagi India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Visual analysis of human organs is currently one of the most active subjects under study in the field of computer vision. The ability of a machine to analyse human organs is a critical aspect in its development. Medical imaging is a procedure that allows clinicians to examine a part of the human body that is not naturally visible. In Medical Image Processing, analysis and visualization application allows for quantitative analysis and visualization of a variety of medical imaging modalities. The objective of this survey is to present an overview of current computer-assisted diagnosis approaches for detecting tumor lesions using ultrasound images. Research papers published between 2019 and 2021 from various standard databases were considered in preparation for the survey. The paper initially discusses the various speckle reduction approaches on ultrasound images, then various segmentation approaches based on level set are discussed. Finally various approaches for discrete wavelet transformation-based feature extraction and neural network-based classification are also discussed. The review gives an insight on the work carried out so far for reduction of speckle noise in ultrasound image, level set-based segmentation methods, discrete wavelet transformation-based feature extraction methods and artificial neural network based classification methods. © 2022 IEEE.

关键词： Surveys

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An Unsupervised GAN-based Quality-enhanced Medical Image Fusion network

An Unsupervised GAN-based Quality-enhanced Medical Image Fus...

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2022 IEEE conference on Telecommunications, Optics and computer Science, TOCS 2022

作者： Yanli, Liu Zimu, Li Junce, Feng Gu, Yuanjie Fuzhou University Maynooth International Engineering College No.2 Xueyuan Road New University District Fuzhou City Fujian Province 350116 China College of Science Donghua University N0.2999 North Renmin Road Songjiang District Shanghai City 201600 China Yantai Institute China Agricultural University No.2006 Binhai Zhong Road Lai Shan District Yantai City Shandong Province 264000 China Jiangnan University HorizonFlow Lab No. 1800 Lihu Avenue Wuxi City Jiangsu Province 214000 China

ISBN: (纸本)9781665470537

Medical image fusion technology can improve the precision of clinical diagnosis by fusing medical information from different modalities. However, the quality of fusion is restricted due to the particular imaging mechanism. This paper proposes a quality-enhanced medical image fusion algorithm based on a generative adversarial network for the lossless fusion of MRI and PET images. It consists of a lightweight image enhancement depth network to make the quality of the fused image suit human vision perceptual system better and a generative adversarial network to enhance texture details and edge information further. Our model is unsupervised and does not require paired fused images for training. The test results show that our algorithm performs better in both subjective visual effects and objective evaluation metrics. © 2022 IEEE.

关键词： Generative adversarial networks

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Building Disease Prediction Model Using Machine Learning algorithms on Electronic Health Records' Logs 7

Building Disease Prediction Model Using Machine Learning Alg...

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7th international conference on Digital Technologies in Education, Science and Industry, DTESI 2022

作者： Rakhmetulayeva, Sabina Kulbayeva, Aliya International Information Technology University Manas St. 34/1 Almaty Kazakhstan

The number of tasks devoted to predicting the incidence of infectious diseases is growing rapidly due to the availability of statistical data supporting the analysis. This article describes the main solutions currently available for creating short and long-term disease forecasts. Their limitations and practical applications are shown. Much attention is given to the Naïve Bayes classification, logistic regression, artificial neural network algorithm and k-means artificial neural networks as methods of model analysis based on machine learning. This article provides an overview of two popular machine learning algorithms used to predict diseases. The standard datasetis used for a wide range of diseases including fungal infection, allergy, GERD, chronic cholestasis, peptic ulcer disease, diabetes, bronchial asthma, migraine, paralysis (brain hemorrhage) and more. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Neural networks

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Comparison of LSCP and Cluster Coverage Model for Emergency Train's Assets Management: Use Case;North Eastern Railway

Comparison of LSCP and Cluster Coverage Model for Emergency ...

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2022 international conference on Maintenance and Intelligent Asset Management, ICMIAM 2022

作者： Mishra, Kshitij Mishra, Swastika Abhyuday International Institute of Information Technology Computer Engineering Hyderabad India Netaji Subhas University of Technology Mechanical Engineering New Delhi India Indian Institute of Technology Indian Railways Service of Mechanical Engineers Kharagpur India

ISBN: (纸本)9781665461795

Deployment of Accident Relief Medical Vans (ARMV) used for accident relief and rescue operations is generally done heuristically. ARMV is an ambulance-train equipped with various tools like jacks, heavy cutting tools, generators, operation theatre, artificial breathing equipment etc. to ease the rescue process. Taking North Eastern Railway (NER) as a case study, Location Set Covering Problem (LSCP) model will be used to find the minimum number of ARMVs required for the given network and their locations so that all demand points can be "covered"within a maximum of one hour @ 100 kmph for each of the nearest facility. This paper proposes a new mathematical model based on the Cluster Coverage Model (CCM) to provide a better coverage of ARMV's demands. We have used Girvan-Newman (GN) algorithm, a weight-based graph for clustering of NER network where nodes are taken as depots with ARMV parking facilities & crew availability and distance between these nodes is taken as edge weight of the graph. Finally, we present a comparison of results between the proposed cluster coverage models with LSCP and suggest relocations of the existing ARMVs and show that our proposed model is economical and provides a better coverage of ARMVs demands. © 2022 IEEE.

关键词： Location

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An End-to-end Foreground Category Attention Boosting network 12

An End-to-end Foreground Category Attention Boosting Network

引用

12th international conference on Information technology in Medicine and Education, ITME 2022

作者： Zhao, Guangwei Zhang, Yu Sun, Li Gao, Jinfeng Liu, Fang Zhang, Renzhi Huanghuai University College of Computer and Artificial Intelligence Zhumadian China Henan Provincial Key Laboratory of Smart Lighting Zhumadian China Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots Zhumadian China

ISBN: (纸本)9798350310153

In this paper, we introduce a foreground category attention boosting (CAB) framework for coarse-to-fine image segmentation, which makes full use of the coarse category information in low resolution layers. Our contributions can be summarized as: Firstly, we introduce the category attention mechanism to build the CAB module, which realizes a new stronger learner by aggregating the coarse category information and the feature maps of the current layer. Secondly, we propose an end-to-end foreground category attention boosting (FAB) neural network, which fully utilizes the complementary effects among different resolution feature maps and enhances the contribution from low resolution layers to fine segmentation with low computational cost. Thirdly, Extensive experiments on public dataset show that our approach can achieve superior performance over the state-of-the-art methods. © 2022 IEEE.

关键词： Image segmentation

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Dynamic Wind Turbine Blade 3D Model Reconstruction with Event Camera

Dynamic Wind Turbine Blade 3D Model Reconstruction with Even...

引用

UNIfied conference of international Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, international conference on Maintenance Engineering, IncoME-V 2023, international conference on the Efficiency and Performance Engineering network, TEPEN 2023

作者： Li, Qiuxian Wang, Zheng Jie, Leiping Hu, Yiyang Deng, Rongfeng Zhang, Hui Department of Computer Science and Technology BNU-HKBU United International College Zhuhai China School of Industrial Automation Beijing Institute of Technology Zhuhai519088 China

ISBN: (纸本)9783031494208

In this paper, we proposed E2DSNeRF, a new image-based method that is highly suitable for 3D reconstruction of wind turbines. Specifically, considering the smooth and textureless characteristics of wind turbine blades that can lead to blurring and ghosting, we use event cameras with high dynamic range and low latency instead of regular RGB-D cameras to capture images of wind turbines. Then, compared to the dozens of images required by the vanilla neural radiation field method NeRF, we used very sparse input views, which greatly reduced the burden of image acquisition and preprocessing. In addition, the use of deep supervision not only improves the rendering quality of sparse input views, but also eliminates blurring and ghosting caused by low textures on wind turbine blades. At the same time, in order to obtain a more accurate camera pose, we added necessary feature points to the background when collecting the wind turbine dataset to ensure that the camera pose obtained by COLMAP is sufficiently accurate. Due to sparse input views, our method has significantly improved training speed compared to NeRF. The experimental results demonstrate the feasibility and effectiveness of this method, which has lower cost and clearer reconstruction results compared to traditional methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cameras

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Research on the initial scale of The Belt and Road international Earthquake Cooperation Fund Based on VaR method of BP neural network model of big data 3

Research on the initial scale of The Belt and Road Internati...

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3rd international conference on Electronics and Communication;network and computer technology (ECNCT)

作者： Deng, Guo-qu Han, Qian Li, Can Chen, Hu Henan Univ Sci & Technol Sch Management Luoyang 471023 Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

The area along The Belt and Road is located in the Mediterranean Sea-the Himalaya Seismic Belt and the Pacific Rim Seismic Belt, which is one of the regions with the most frequent earthquake disasters and the most severe losses in the world. The establishment of The Belt and Road earthquake international cooperation fund can help maintain regional society peace, stability and prosperity. Based on The Belt and Road earthquake data of EM-DAT,this paper uses the neural network model VaR method to calculate the fund size of The Belt and Road earthquake international cooperation fund under different risk tolerances. According to the research results, when the risk tolerance is 90%, 95%, 99%, and 99.5%, the initial fund sizes are predicted to be 250.8 million U.S. dollars, 444.8 million U.S. dollars, 633.2 million U.S. dollars, and 910.8 million U.S. dollars, respectively.

关键词： The Belt and Road international Earthquake Cooperation Fund BP Neural network Model

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Swarm Intelligence for Multi-objective Portfolio Optimization 1

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4th international conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Chen, Li Wang, Yongjin Liu, Jia Tan, Lijing College of Management Shenzhen University Shenzhen518060 China Greater Bay Area International Institute for Innovation Shenzhen University Shenzhen518060 China School of Management Shenzhen Institute of Information Technology Shenzhen518172 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

This paper employs five classical multi-objective swarm intelligence algorithms to solve portfolio optimization (PO) problem with background returns. The potential investment ratio is considered as an individual. In the experiments, we consider five different PO cases. The simulation results show that multi-objective evolutionary algorithm based on decomposition (MOEA/D) and weighted optimization framework (WOF) perform significantly better than the other four in solving the high-dimensional objective problem, and WOF obtains a more uniform solution to the high-dimensional problem. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Swarm intelligence

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