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检索条件"任意字段=Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition"

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Leveraging Deep Learning Models and Ethereum Smart Contracts to Secure EHR in HL7 Environment 12

Leveraging Deep Learning Models and Ethereum Smart Contracts...

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12th international conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Ginavanee, A. Prasanna, S. School of Computing Sciences Vistas Chennai India Agurchand Manmull Jain College Department of Computer Applications Chennai India Vistas Department of Computer Applications Chennai India

ISBN: (纸本)9798350369861

the secure and efficient sharing of healthcare data is crucial in the rapidly evolving healthcare landscape. this paper addresses the critical need for secure and efficient healthcare data sharing in today's rapidly evolving healthcare landscape. While sharing health information can significantly improve healthcare quality and treatment coordination, challenges like security, privacy concerns, data consistency, and timely access across healthcare facilities remain. To overcome these obstacles, our proposed solution harnesses Ethereum Blockchain technology, Artificial Intelligence (AI), and the Health Level 7 standard (HL7). this integrated approach redefines data modeling, creating a flexible and scalable system that prioritizes medical data and service privacy and security. the fusion of AI and Ethereum Blockchain technology effectively tackles Health Information Exchange challenges. Additionally, our BERT-CNN classifier, achieving an impressive 94.2% accuracy, excels in classifying medical data, outperformed MLP, SVM, and Random Forest classifiers. We employ modern cryptographic methods like Fully Homomorphic Encryption over the Torus (TFHE) to ensure secure computation and protect sensitive medical data. these strategies promise more effective and secure medical data sharing, leading to improved patient care, advances in medical research, and increased confidence across the healthcare ecosystem. © 2023 IEEE.

关键词： Blockchain

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COVID-19 Trend Prediction Using CLS-Net Hybrid Model 12

COVID-19 Trend Prediction Using CLS-Net Hybrid Model

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12th international conference of Information and Communication Technology, ICTech 2023

作者： Chen, YaShu Liu, Jun Yu, Bo College of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350332919

the break out of Corona Virus Disease 2019 has caused great danger and effect for people's health and life. therefore, predicting the trend of the epidemic is crucial for politicians and medical conductors go for make correct policy ahead of time. the SEIR model is now a more mature epidemic prediction model, but the dynamic prediction of the outbreak cannot be achieved because in this model, the infection rate of virus can be calculated mathematically. Based on this problem, this thesis proposes a CLS-Net model to predict the trend of global epidemic of Corona Virus Disease 2019 under the influence of human mobility. the model extracts features from a large amount of population migration data by CNN model, and then predicts the transmission rate of New Coronavirus in real time with the excellent time-series learning ability of LSTM. Finally, the live updated virus infection rate apply into the SEIR model to realize the dynamic prediction of epidemic development trend. To validate the approach proposed in paper, the statistical data of neocrown pneumonia from various countries around the world were collected for experiments. Take Mean Absolute Error (MAE), R2(R-Squared) and Root Mean Square Error (RMSE) as the evaluation indexes of the model in this paper. the experimental results show that the model proposed in this paper has better performance compared with the classical infectious disease prediction model. © 2023 IEEE.

关键词： Forecasting

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Research on Industrial Non-Destructive Testing Technology Based on Improved YOLOv5s 12

Research on Industrial Non-Destructive Testing Technology Ba...

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12th international conference of Information and Communication Technology, ICTech 2023

作者： Yu, Bo Chen, Wenhao Wang, Wei Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School of Computer Sciences and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350332919

Non-destructive testing technology plays a pivotal role in the manufacturing process of modern industrial products, and is of incredible importance for moving forward the item quality of workpieces. Currently, the commonly used non-destructive testing technology has disadvantages such as complicated operation, high testing cost, and limited scope of application. this paper suggests an upgraded multi-scale target detection algorithm based on YOLOv5s in light of the aforementioned issues. Firstly, an improved channel attention mechanism module ECA-Net is added into the backbone network. Second, the maximum pooling operation in the SPP module is replaced with atrous convolution in this paper. Finally, the channel weight information is added to the concat operation of the PAN structure, which improves the sensitivity of the network to the channel information of the feature map. this paper uses the NEU-DET steel surface defect data set to do a series of experiments to verify the detection effect of the model. According to the experimental results, the mAP of the algorithm proposed in this paper is 73.47%, which is 7.18% higher than that of YOLOv5s, and the FPS is 68.5 frames/s, which satisfies the needs of industrial production environments. © 2023 IEEE.

关键词： Convolution

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Classification of Alphabetic Handwriting of Pre-toddlers Using Support Vector Machine Method 12

Classification of Alphabetic Handwriting of Pre-toddlers Usi...

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12th IEEE international conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Riyadi, Sabila Amanda Putri Gunawan, Putu Harry Ihsan, Aditya Firman Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350341102

Writing is an important aspect of literacy and an important learning foundation in addition to reading for children. the main challenge is to handle the unique characteristics and writing styles that are not yet fully formed in children's handwriting. therefore, technology is needed that can help children learn to write, especially alphabetic characters. this research aims to build a model based on machine learning for recognizing the alphabetic handwriting of pre-toddler children. this research uses the support vector machine (SVM) method and the directional element feature (DEF) feature extraction in grayscale images to effectively classify children's alphabet handwriting. In this study, four kernels in the SVM model were tested. the best results of classification using the linear kernel obtained an average accuracy of 86.33% with a cost optimization parameter of 0. In the use of the RBF kernel, the best average accuracy is 84%, with a cost optimization parameter of 50. With the polynomial kernel, the best average accuracy is 68%, with the cost optimization parameter being 10. In the sigmoid kernel, the best average accuracy is 85.67% with a cost optimization parameter of 50. As a conclusion, the SVM model has proven effective for optimizing handwriting recognition, especially in children's handwriting recognition. © 2023 IEEE.

关键词： Support vector machines

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Adaptive Fusion Attention Network for Facial Expression recognition 23

Adaptive Fusion Attention Network for Facial Expression Reco...

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proceedings of the 2023 12th international conference on computing and pattern recognition

作者： Guodong Li Cheng Peng Kun Zou Bowen Zhang School of Computer Science and Engineering(School of Cybersecurity) University of Electronic Science and Technology of China China School of Computing University of Electronic Science and Technology of China Zhongshan Institute China College of Big Data and Internet Shenzhen Technology University China

ISBN: (纸本)9798400707988

In this paper, we propose a novel facial expression recognition network, called Adaptive Fusion Attention Network (AFAN). Facial expression recognition is different from traditional image classification tasks. there are potentially similar features between different categories, and the differences between them may be subtle and difficult to distinguish. To solve this problem, we propose AFAN, which not only can get multi-dimensional features, but also can generate an attention map with stronger representation ability. the comparative experiments on AffectNet-7 dataset verify the excellent performance of the proposed method on facial expression recognition tasks.

关键词： Adaptive convolutional blocks Facial expression recognition Fine-grained classification Multi-head attention networks

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27th international conference on Advanced computing and Communications, ADCOM 2022

27th International Conference on Advanced Computing and Comm...

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27th international conference on Advanced computing and Communications, ADCOM 2022

ISBN: (纸本)9781839539107

the proceedings contain 12 papers. the topics discussed include: circular polarized multi band dual sense square slot antenna fed by coplanar waveguide;spectral features based speech emotion recognition using artificial neural network;design and analysis of infrastructure to vehicle visible light communication channel modeling;asymmetric CPW - FED ground plane with circularly polarized printed monopole antenna with SRR;Classifying Internet of things (IoT) devices and non-IoT devices in data center edge network using statistical and network flow features by one dimensional convolutional neural network;on-chain off-chain blockchain model for IoT using IPFS;an efficient hybrid MPI-OpenMP based parallel approach for longest common subsequence;and design of false data injection attacks and their detection and mitigation in cyber-physical systems.

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Unsupervised Pre-Training of Deep Neural Classifiers 12

Unsupervised Pre-Training of Deep Neural Classifiers

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12th IEEE international conference on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2023

作者： Kroshchanka, Aliaksandr Golovko, Vladimir Peiwen, Shi Lubanska, Zofia Quintary Ai Paphos8011 Cyprus John Paul Ii University in Biala-Podlaska Biala-Podlaska21-500 Poland Jilin International Studies University Changchun City130117 China

ISBN: (纸本)9798350358056

the paper is devoted to studying the issues of pre-training of deep neural network models. A generalized approach for pre-training deep models is proposed, which allows achieving better performance in final accuracy and can be used when training on small datasets. the effectiveness of the proposed approach is demonstrated on examples of training deep neural classifiers models using the MNIST and CIFAR-10 datasets. the obtained results can be used to train fully connected and convolutional types of layers. © 2023 IEEE.

关键词： Convolutional Restricted Boltzmann Machine deep neural network pre-training Restricted Boltzmann Machine

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Justification of the Project Initiation Process Using the Markov Model 12

Justification of the Project Initiation Process Using the Ma...

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12th IEEE international conference on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2023

作者： Kolesnikova, Kateryna Alpysbay, Nursultan Olekh, Tetiana Chinibayeva, Tolganay International It University Almaty Kazakhstan Odessa National Polytechnic University Odesa Ukraine

ISBN: (纸本)9798350358056

the paper presents a Markov model of the basic scheme of interaction between project participants. A Markov chain has been identified to describe a specific project, based on the determination of a set of transition probabilities unique for each project. this allows you to customize the Markov chain to reflect the properties of a particular project. Using the developed Markov model, the sensitivity of the system to a change in one of the main parameters, the level of competence of the project manager and the project team as a whole, was assessed. the mathematical description of the project model by Markov chains allows modeling the quantitative parameters of projects, namely, changes in the probabilities of system states depending on the number of project steps. © 2023 IEEE.

关键词： directed graph Markov's chain matrix of transition project project team project-oriented systems

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A Survey on Ensemble Multi-label Classifiers 1

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14th international conference on Soft computing and pattern recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2022

作者： Guehria, Sonia Belleili, Habiba Azizi, Nabiha LRI Laboratory Badji Mokhtar University BP 12 Annaba23000 Algeria LABGED Laboratory Badji Mokhtar University BP 12 Annaba23000 Algeria

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

Ensemble approach for multi-label classification has received great attention from the Machine Learning community over the last decade. It has been developed on top of the problem transformation or algorithm adaptation methods to overcome some scientific challenges introduced by multi-label learning. the success of ensemble models in this classification paradigm arises by offering an appealing solution to different problems, such as label dependencies, class imbalance, and high dimensionality. In this study, we present a structured synthesis of different ensemble multi-label classifiers including their different taxonomies cited in the literature. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Investigation of Machine Learning Algorithms for pattern recognition in Image Processing 5

Investigation of Machine Learning Algorithms for Pattern Rec...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Kate, Chennaiah Kalpana, C. Sharma, Arvind Yadav, Ajay Singh Kumar, Ashok Kumar, S. Sandeep St. Peter's Engineering College Department of Information Technology Telangana Hyderabad500100 India Npr College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Dindigul624401 India Government Women Engineering College Department of Electronics and Communication Engineering Rajasthan Ajmer305002 India Srm Institute of Science and Technology Department of Mathematics Uttar Pradesh Ghaziabad201204 India BanasthaliVidyapith Department of Computer Science Rajasthan 304022 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh 522502 India

ISBN: (纸本)9798350321425

In order to recognize patterns in images, this study tests the performance of many 'machine learning algorithms' and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods ('deep learning, support vector machines, decision trees, and random forests') and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). the efficacy of each algorithm is measured in terms of its 'accuracy, precision, recall, and F1 score', among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. the results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms. © 2023 IEEE.

关键词： Feature extraction

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