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Graph-based adaptive node enhancement for Alzheimer’s disease prediction 16

Graph-based adaptive node enhancement for Alzheimer’s disea...

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16th international conference on Digital Image Processing, ICDIP 2024

作者： Sun, Guangming Wang, Huabin Yuan, Wei Shang, Dongxu Wang, Zilin Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei Anhui230601 China Stony Brook Institute Anhui University Anhui230601 China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

Currently, there are issues of sample imbalance and insufficient sample quantity in graph-based Alzheimer's disease prediction methods. This can lead to classifiers being biased towards the majority class samples and result in overfitting. To address this problem, a graph-based data augmentation node expansion algorithm is proposed. Firstly, graph representation learning is used to reduce the original feature vectors to a low-dimensional space. This message aggregation method ensures that the low-dimensional vectors contain the potential structural information of the data, preventing structural damage that may arise from direct expansion on the original data. Secondly, in the low-dimensional space, an adaptive-weight node expansion algorithm is employed to generate new nodes, overcoming the boundary fuzziness issue of traditional oversampling algorithms. This weight expansion algorithm adjusts the priority of each expansion node to control the generation position and quantity of new nodes. Finally, the expanded graph is fed into a Graph Neural network classifier for prediction. Quantitative experiments on the Tadpole dataset and NACC dataset demonstrate that the proposed graph-based data augmentation model achieves the highest accuracy. The average accuracy was 93.84% vs 92.8% on the Tadpole dataset and 90.11% vs.88.29% on the NACC dataset. In addition, additional ablation experiments have demonstrated the effectiveness of node expansion in graph structures. © 2024 SPIE.

关键词： Graph neural networks

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Target Detection method for Based on Deep Learning 14

Target Detection method for Based on Deep Learning

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14th international conference on Measuring technology and Mechatronics Automation, ICMTMA 2022

作者： Chai, Yuan Liu, Jing Li, Yang Department of Network Engineering TJ E-Sports School of Network Science Haikou University of Economics Hainan Haikou571132 China Research Center for Healthcare Data Science Zhejiang Lab Zhejiang Hangzhou310000 China Foreign Languages Department Oriential School of International Trade and Foreign Languages Haikou University of Economics Hainan Haikou571132 China

ISBN: (纸本)9781665499781

Target detection is an important method in the field of deep learning. In view of the low efficiency and low accuracy of traditional target detection methods, this paper mainly focuses on the analysis of two-stage detection algorithm Faster R-CNN and single-stage detection algorithm Yolo. Based on the Paddle Detection model library, the above algorithms are replicated. Detecting the same image, the advantages and disadvantages of these algorithms will be compared in accuracy and efficiency. Based on the analysis of anchor frame and real frame, it is found that network mismatch and overfitting lead to low detection accuracy and low efficiency. In order to solve the above problems, this paper proposes the method of connecting multiple mixed detectors in the existing detection network, which can not only improve the quality of samples, but also keep the diversity of samples. Experimental results show that compared with traditional target detection methods, the accuracy of the proposed method is about 10% higher. The proposed algorithm can solve the problem of network mismatch as well as reduce the overfitting problem, effectively improve the efficiency and accuracy of network detection, and better meet the requirements of high precision and fast identification. © 2022 IEEE

关键词： Efficiency

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6th international conference on Digital Transformation and Global Society, DTGS 2021

6th International Conference on Digital Transformation and G...

引用

6th international conference on Digital Transformation and Global Society, DTGS 2021

ISBN: (纸本)9783030937140

The proceedings contain 38 papers. The special focus in this conference is on Digital Transformation and Global Society. The topics include: Institutional Factors for Building Trust in Information Technologies: Case-Study of Saint Petersburg;main Regulatory Plans in European Union’s New Digital Regulation Package;lex Informatica: Information technology as a Legal Tool;detection the Relevance of Urban Functions for Value-Based Smart City Management;Identifying Troubles and Expectations of the Citizens Towards Their Habitat Based on PPGIS Approach;smart Technologies and Their Role in the Modernization of Non-motorized Urban Transport in Russia;support for RoboCops: Measuring Effects of Attitudes Towards Police and Policing Technologies;learning Hard or Hardly Learning: Smartphones in the University’s Classrooms;designing Educational Trajectories for Generation Z: Identifying Cognitive Factors;a Semi-automated Pipeline for Mapping the Shifts and Continuities in Media Discourse;attitudes Towards Digital Educational Technologies, Academic Motivation and Academic Achievements Among Russian University Students;the Role of Values in Academic Cheating at University Online;designing Workflow for Improving Literature Review Process Based on Co-citation networks;interpretable Machine Learning in Social Sciences: Use Cases and Limitations;normalization Issues in Digital Literary Studies: Spelling, Literary Themes and Biographical Description of Writers;prototyping of a Client for Board Games Automated Testing and Analysis;automated Classification of Potentially Insulting Speech Acts on Social network Sites;following the Lead When Nothing is Certain? Exploring the Image of Russia in Kazakhstani and Ukrainian Digital News Media;participation of Transnational Migrants in the Formation of the Host Country Image Through Mass Self-communication.

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Research on the Influence of Inter-Industry Cascade Relationships Based on Reinforcement Learning 24

Research on the Influence of Inter-Industry Cascade Relation...

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4th international conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2024

作者： Li, Wenhao Zhang, Hongtu Hu, Jiahao Zuo, Fang School of Software Henan University Kaifeng Henan China Henan International Joint Laboratory of Intelligent Network Theory and Key Technology Henan University Kaifeng Henan China

ISBN: (纸本)9798400710247

In recent years, with the rapid advancement of technology and information technology, reinforcement learning has demonstrated tremendous potential in complex networks. The interdependence among urban infrastructures is becoming increasingly significant, underscoring the need for rapid and accurate assessment of interdependencies among foundational urban industries. This paper aims to establish a network model based on input-output tables across multiple urban industries, and to develop a cascading propagation model using the RAS algorithm. By integrating the DDPG algorithm from reinforcement learning, we minimize costs to achieve loss targets in urban foundational industries. Additionally, we introduce an adaptive algorithm to optimize the execution steps and reward modules of the DDPG algorithm. Experimental results demonstrate that our proposed enhanced DDPG-SAM algorithm not only achieves faster convergence in single executions but also improves model accuracy. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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One-Shot Classification Is Enough for Automatic Label Mapping 27th

One-Shot Classification Is Enough for Automatic Label Mappi...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Lin, Xiaowen Aysa, Alimjan Ubul, Kurban School of Computer Science and Technology Xinjiang University Ürümqi China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Ürümqi China Xinjiang Key Laboratory of Multilingual Information Technology Ürümqi China

ISBN: (纸本)9783031781681

In recent years, prompt-based learning has achieved some success in various natural language tasks. In text classification tasks, the construction of prompt templates and label mapping have a significant impact on the results. Therefore, many methods for constructing label mappings have emerged, such as manual and automatic construction. However, manual label construction is time-consuming, expensive, and has low scalability, while automatic label construction requires a large number of samples and other limitations. Therefore, we have explored a automatic label mapping method that only requires one sample. On the basis of only requiring one sample, we achieved good experimental results. In this paper, we propose an effective one-shot method for automatically constructing label mapping, ALaM. Our method is based on statistical algorithms and label semantic information for one to many label mapping. Our experiments demonstrate that ALaM achieves competitive performance on the GLUE benchmark without human effort or external resources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Performance Evaluation of Termite Hill Routing Algorithm on Mobile and Static Sources of RPMS

Performance Evaluation of Termite Hill Routing Algorithm on ...

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2022 international conference on Smart Applications, Communications and networking, SmartNets 2022

作者： Boikanyo, Kegomoditswe Zungeru, Adamu Murtala Yahya, Abid Lebekwe, Casper K. Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana

ISBN: (纸本)9781665487580

Remote Patient Monitoring System (RPMS) contains sensor nodes with limited power supplies, memory capacity, processing capability, and bandwidth. These sensor networks require multiple hops for event forwarding due to their high density and limited communication range. In RPMS research, metrics such as network lifetime, reliability, and quality of service are needed to be considered due to the requirements of the system. It is observed that nodes that are close to sink nodes run out of energy more quickly than those far away from sink nodes in static RPMS sensors. Nodes near sinks tend to consume more energy due to the need to forward all traffic generated by the nodes farther away to the sink thus affecting the network. There are several solutions that researchers have developed for improving the performance of static nodes, such as mobile sinks. As a result, most algorithms perform better when routing mobile sinks. It is crucial to identify how some algorithms will function when RPMS sources are mobile since they are becoming more portable. Accordingly, this paper evaluates the Termite Hill routing algorithm, which simulates the behavior of real termites on hill buildings. This algorithm is tested on static and mobile RPMS sources. Our research using the Routing Modeling Application Simulation Environment (RMASE) showed that our proposed routing algorithm is able to balance network traffic loads and prolong network lifetimes when tested in static RPMS sources. In mobile sources, this algorithm performed poorly, which presents a huge opportunity for improvement. © 2022 IEEE.

关键词： Quality of service

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Synthetic data generation for medical by generative adversarial network

Synthetic data generation for medical by generative adversar...

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2022 international conference on Electronics and Devices, Computational Science, ICEDCS 2022

作者： Liu, Yaoxuan Xu, Hao Tang, Ziling Beijing Jiaotong University School of Computer and Information Technology Beijing China Internet of Things Engineering University College Dublin Beijing Dublin International Collage Dublin Ireland Monash University Faculty of Engineering Department of Mechanical and Aerospace Engineering Melbourne Australia

ISBN: (纸本)9781665455411

As the world develops, medical data privacy is becoming an important issue. Differential privacy can better ensure accuracy and reduce the reflection of personal privacy, while generative adversarial networks can generate data sets. Obviously, the combination of differential privacy and generative adversarial network (GAN) can synthesize sensitive medical data to form new data sets, which not only protects personal privacy but also does not affect medical research. We summarize the current difficulties in medical data, compare the common medical data types with typical DP methods of different GAN, analyze the advantages and disadvantages of each method, and discuss future development. At the same time, we also introduce GAN applied in the image data field. Because this approach is very mature, we only introduce some commonly used GANs, compare their improvements, and introduce their applications in data generation. This may help in the future development of medical data privacy algorithms. © 2022 IEEE.

关键词： Generative adversarial networks

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A Review of Electronic Tongue for Liquid Classification 2

A Review of Electronic Tongue for Liquid Classification

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2nd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2022

作者： Mohamed, Salma Khaled, Shadwa Dahshan, Maha Mohamed, Bassant Badr, Mostafa Hussein, Khaled Misr International University Faculty of Computer Science Cairo Egypt

ISBN: (纸本)9781665466776

The electronic tongue (E-Tongue) is a novel technology that is used for the taste evaluation of foods. A human taste is determined by the interactions of five basic tastes. Electronic tongues aim to discriminate, analyze, and beverages food. They are well known as sensing technologies that greatly contribute to quality management. Electronic tongues are a new, promising technology that has already proven to be a useful tool for assessing and predicting the taste of beverages and associated items. The main idea of the electronic tongue system is to study the tastes of the tongue and classify the liquids according to their sourness, acidity, and other tastes. Liquids will be tested in different concentrations to be classified with high accuracy. Some algorithms would be used to measure the accuracy such as Support Vector Machine (SVM), and Random Forest (RF). Also, deep learning algorithms would be used as Multi-layer Perceptron (MLPs). © 2022 IEEE.

关键词： Support vector machines Radio frequency Tongue Liquids Ubiquitous computing Prediction algorithms Classification algorithms

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A Community Detection Approach for Academic Social network: The Case of the Cross-Strait Academic conference network 3

A Community Detection Approach for Academic Social Network: ...

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3rd international conference on Electronics, Communications and Information technology, CECIT 2022

作者： Zhang, Xinxin Xu, Li Gao, Min Fan, Jianxi Sun, Hung-Min Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China Fudan University The School of Computer Science Shanghai China Soochow University The School of Computer Science and Technology Suzhou China National Tsing Hua University The Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350331974

With the fast development of information networks, academic social network analysis has attracted significant attention, we regard these researchers sharing common interests as a research community. The community has been widely used as the underlying structure of social networks. To utilize network information as much as possible, we capture the structural characteristics and attribute features to detect communities in the cross-strait academic conference network. In this paper, we first consider 2-hop neighbors with the Pareto principle to filtrate the central seed nodes in networks. Then we propose a new seed shrinkage strategy to get the initial communities and define the similarity according to the structure and attributes of social networks to merge the communities. Finally, we define a new indicator named inner average distance (IAD) to measure the aggregation density for each community. A case study of the cross-strait academic social network was developed to test the applicability of the proposed model, the experiment analysis suggests that our method can be more accurate to identify communities in most situations, which contributes to controlling the information spreading in networks. © 2022 IEEE.

关键词： Pareto principle

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MRI and PET/SPECT Image Fusion Based on Residual network 15

MRI and PET/SPECT Image Fusion Based on Residual Network

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15th international conference on Graphics and Image Processing, ICGIP 2023

作者： Chen, Miantao Wang, Huabin Zuo, Zhengxiang 1.Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging Anhui University Hefei 230601 China 2. School of Computer Science and Technology Anhui University Hefei 230601 China

ISBN: (纸本)9781510675018

Aiming at the problems of blurred edge structure, loss of texture details, distortion and slow operation speed in medical image fusion, this paper proposes a medical image fusion model based on residual network. The network mainly consists of an encoder, fuser and decoder. A feature extraction module MSDN consisting of residual attention mechanism and dense blocks is designed in the encoder for extracting multi-scale deep features of the source image. A learnable fusion network is used in the fuser to replace the manually designed fusion rules, eliminating the adverse effects of manually designed fusion strategies on the fusion effect. The decoder obtains the fused image by layer-by-layer decoding and up-sampling. We use a two-stage training strategy to train the fusion model;in the first stage, the image reconstruction task is used to drive the training of the encoder-decoder;in the second stage, the trained encoder-decoder is fixed, and the residual fusion network is trained using an appropriate loss function. The experimental results show that the subjective visual effect of the fused image contains rich texture details and color information, and the comprehensive performance of the objective evaluation index is better than that of the comparison algorithm. © 2024 SPIE. All rights reserved.

关键词： Textures

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