检索结果-内蒙古大学图书馆

An Analysis of Kidney Disease Prediction and Diagnosis using Machine Learning Strategies

An Analysis of Kidney Disease Prediction and Diagnosis using...

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Gurla, Vedavyasa Latha, S. Bhargavi School of Computer Science and Engineering Reva University Karnataka Bengaluru India Geethanjali College of Engineering & Technology Telangana Hyderabad India

ISBN: (纸本)9798331543617

With the advent of AI and machine learning (ML), clinical medicine has undergone a sea change, opening up new possibilities for advancements in both practice and research. The present state and future possibilities of using ML and Deep Learning (DL) to chronic kidney disease (CKD) are examined in this narrative review. Some examples that highlight the substantial importance of ML-diagnostic strategies are the rapid discovery of renal illness and the following prompt treatment of the condition. Predicting the onset of chronic kidney disease (CKD) using DL is an emerging area of research that seeks to equip doctors with AI-powered tools. A number of studies have attempted to compare and contrast various ML approaches in terms of their practicality, efficacy, and feasibility. One obvious limitation, though, has always been the absence of a comprehensive review of this material. So, this research familiarizes two contexts - one for ML-strategies, which classifies distinct essentials of kidney-disease-diagnosis(KDD), and another for medical sub-fields connected to DL - and provides a thorough literature evaluation of ML and DL utilizations in KDD. Also mentioned are areas where research is lacking and potential avenues for further investigation. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Spiking High-Dimensional Information Coding Based on Wavelet Decomposition Technique 21

Spiking High-Dimensional Information Coding Based on Wavelet...

引用

21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： He, Liu Ao, Ren Jianping, Li College of Computer Science Chongqing University Chongqing401331 China Chongqing Academy of Educational Sciences Chongqing400015 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

This paper studies the Spiking high-dimensional information coding method based on wavelet decomposition technology. Firstly, the wavelet decomposition technique is used to extract the multi-scale features of the input signal to capture the time-frequency characteristics of the signal. Subsequently, the extracted features are mapped to the pulse sequence of the Spiking neural network to achieve efficient coding of highdimensional information. In this paper, the influence of wavelet decomposition parameters on coding performance is analyzed, and the advantages of the proposed method in processing highdimensional data are verified by experiments. The experimental results show that the proposed method can significantly reduce the data dimension and computational complexity while maintaining the accuracy of information, which provides a new effective way for high-dimensional information processing. © 2024 IEEE.

关键词： Network coding

来源：评论

学校读者我要写书评

暂无评论

Multi-view Fusion Enhanced Social Text Representation for Depression Detection 11th

Multi-view Fusion Enhanced Social Text Representation for D...

引用

11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Chen, Jie You, Xuetong Zhao, Shu Xiao, Jingxuan Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Province Hefei 230601 China School of Computer Science and Technology Anhui University Anhui Province Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province Hefei 230601 China Faculty of Innovation Engineering Department of Computer Science Macau University of Science and Technology Macau China

ISBN: (纸本)9783031927409

Depression has become a major global public health challenge. Social media texts reflect users’ emotional and psychological states, providing a valuable data source for depression detection through natural language analysis. However, current research still faces limitations in modeling the collaborative representation of user texts and dynamic behaviors. To address this, we propose MVDep, a model that integrates multi-view features to enhance social text representation for depression detection. Specifically, we enhance text representation by investigating depression from multi-view, including social media texts, user profiles, and social behaviors, enabling more effective depression detection. First, we conduct initial filtering of social media texts using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and then screen out the uncertain domain of data samples based on the three-way decision (3WD). Next, we extract quantified user profiles and social behavioral features to enhance the representation of depression tendencies for uncertain domain samples. Finally, we fuse the multi-view features derived from social media texts, user profiles and social behaviors, improving the model’s ability to detect depression. We evaluate our model on the SWDD dataset, demonstrating its superiority over state-of-the-art baseline models in depression detection tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3WD Depression Detection Multi-View Feature Fusion Social Media Texts

来源：评论

学校读者我要写书评

暂无评论

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics 40

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

引用

40th IEEE International Conference on Data engineering, ICDE 2024

作者： Chen, Yu Zhang, Sheng Zhou, Ziying Wang, Xiaokun Liang, Yu Chen, Ning Yan, Yuting Xiao, Mingjun Wu, Jie Qian, Zhuzhong Xu, Harry Nanjing University State Key Lab. for Novel Software Technology China School of Systems Science Beijing Normal University China School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China China Temple University Center for Networked Computing United States Samueli School of Engineering University of California Computer Science Department Los Angeles United States

ISBN: (纸本)9798350317152

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy;meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches. © 2024 IEEE.

关键词： Profitability

来源：评论

学校读者我要写书评

暂无评论

A Privacy-preserving Surface Defect Detection Scheme for Strip Steel 38

A Privacy-preserving Surface Defect Detection Scheme for Str...

引用

38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Tang, Xuan Zheng, Xiao Wang, Xiujun Huang, Jun Anhui University of Technology School of Computer Science and Technology Maanshan China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Maanshan China

ISBN: (纸本)9798350303636

As the Industrial Internet becomes an integral part of the steel industry, the next question is naturally how to combine powerful computing power as well as data sharing capabilities for effective detection of surface defects in strip steel. However, existing classification algorithms focus on the accuracy of the classification without considering how to protect the privacy of the manufacturer or the company, as the data is uploaded to the cloud and easily discovered by attackers. To address this problem, in this paper, we propose a privacy-preserving classification scheme that provides both privacy protection and improved classification accuracy in the industrial Internet. Specifically, our approach first adaptively encrypts the original steel image using a homomorphic encryption scheme. The homomorphic encryption scheme is followed by designing a convolutional neural network which can handle the encrypted images well and provide better classification accuracy. In addition, the execution efficiency of our method can be further improved under the edge computing architecture by using distributed computing with cloud-edge architecture. Experiments conducted on real image datasets of strip steel surface show that the encryption scheme not only protects the privacy of manufacturers or companies contained in the steel images and guarantees classification accuracy, but also outperforms existing methods in terms of efficiency. © 2023 IEEE.

关键词： Convolution neural network Defect category detection Edge computing Homomorphic encryption Privacy-preserving Strip steel

来源：评论

学校读者我要写书评

暂无评论

VGWO: Variant Grey Wolf Optimizer with High Accuracy and Low Time Complexity

引用

computers, Materials & Continua 2023年第11期77卷 1617-1644页

作者： Junqiang Jiang Zhifang Sun Xiong Jiang Shengjie Jin Yinli Jiang Bo Fan School of Information Science and Engineering Hunan Institute of Science and TechnologyYueyang414006China Department of Computer and Information Science Linköping UniversityLinköping58183Sweden Department of Safety and Quality Hunan Construction Investment GroupChangsha410004China

The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf *** with its advantages of simple principle and few parameters setting,GWO bears drawbacks such as low solution accuracy and slow convergence speed.A few recent advanced GWOs are proposed to try to overcome these ***,they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early *** solve the abovementioned issues,a high-accuracy variable grey wolf optimizer(VGWO)with low time complexity is proposed in this *** first uses the symmetrical wolf strategy to generate an initial population of individuals to lay the foundation for the global seek of the algorithm,and then inspired by the simulated annealing algorithm and the differential evolution algorithm,a mutation operation for generating a new mutant individual is performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing global search and fast convergence capability of VGWO.A series of experiments are conducted on 19 benchmark functions from CEC2014 and CEC2020 and three real-world engineering *** 19 benchmark functions,VGWO’s optimization results place first in 80%of comparisons to the state-of-art GWOs and the CEC2020 competition winner.A further evaluation based on the Friedman test,VGWO also outperforms all other algorithms statistically in terms of robustness with a better average ranking value.

关键词： Intelligence optimization algorithm grey wolf optimizer(GWO) manhattan distance symmetric coordinates

来源：评论

学校读者我要写书评

暂无评论

Tensor Canonical Correlation Analysis Networks for Multi-view Remote Sensing Scene Recognition (Extended Abstract) 39

Tensor Canonical Correlation Analysis Networks for Multi-vie...

引用

39th IEEE International Conference on Data engineering, ICDE 2023

作者： Yang, Xinghao Liu, Weifeng Liu, Wei College of Control Science and Engineering Qingdao China University of Technology Sydney School of Computer Science Sydney Australia

ISBN: (纸本)9798350322279

Remote sensing (RS) images are frequently observed from multiviews. In this paper, we propose the tensor canonical correlation analysis network (TCCANet) to tackle the multiview RS recognition problem. Particularly, TCCANet learns filter banks by simultaneously maximizing arbitrary number of views with high-order-correlation and solves the optimization problem by decomposing a covariance tensor. After the convolutional stage, we utilize binarization and block-wise histogram strategies to generate the final feature. Furthermore, we also develop a Multiple Scale version of TCCANet, i.e., MS-TCCANet, to extract enriched representation of the RS data by incorporating all previous convolutional layers. Numerical experiment results on RSSCN7 and SAT-6 datasets demonstrate the advantages of TCCANet and MS-TCCANet for RS scene recognition. © 2023 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

CodGRU: Covariate gated attention GRU for short term runoff forecasting 24

CodGRU: Covariate gated attention GRU for short term runoff ...

引用

4th International Conference on Signal Processing and Communication technology, SPCT 2024

作者： Cao, Jiahao Xiao, Tong Cao, Zhe School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China Shenzhen Branch of China Academy of Urban Planning and Design Guangdong Shenzhen China

ISBN: (纸本)9798400710636

Runoff simulation and forecasting are critical for effective watershed management, yet their complexity and nonlinear characteristics pose significant challenges. The generation of runoff is the result of the interaction between multiple factors such as precipitation, soil moisture, topography, and human activities, making this dynamic process extremely complex and difficult to model accurately. Traditional models are often based on an understanding of these natural processes but may fall short when dealing with complex real-world scenarios. To address these issues, this study has developed a dynamic gated feature selection model with attention mechanisms (CodGRU), specifically designed for multi-watershed hourly runoff forecasting. By dynamically adjusting the weights of input variables and selecting the most important features, CodGRU can better capture the key factors influencing runoff, thereby improving the accuracy of forecasting. The test results of five watersheds on the real dataset show that the CodGRU model performs excellently in multi-watershed hourly-scale forecasting, outperforming the existing mainstream models and providing reliable support for water resource management and disaster forecasting and mitigation. © 2024 Copyright held by the owner/author(s).

关键词： Soil moisture

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Image Classification Abiotic Plant Disease Detection Classification Using MobileNet 4

Deep Learning Image Classification Abiotic Plant Disease Det...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Aggarwal, Ritu Aggarwal, Eshaan Jain, Anurag Aluvalu, Rajanikanth Maheswari, Uma Choudhury, Tanupriya Department of Computer Science and Engineering India School of Computer Science and Engineering Iilm University Haryana Gurugram India Symbiosis Institute of Technology Hyderabad Campus Hyderabad India Symbiosis International University Pune India Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India School of Computer Sciences University of Petroleum and Energy Studies Upes Uttarakhand Dehradun248007 India

ISBN: (纸本)9798350387490

In the field of agriculture sector the key role of feature food which has a major role in the emergent population with their economy. In the food production for plant disease, it may cause significant loss for the era of diversity in species and the crop production rate in the plant disease detection and diagnosis the accurate detecting techniques could increase the superiority in food productivity and reduce the economic loss for the image categorization and object recognition. As per computed results and analysis of the various techniques, deep learning is as best approach to get the accuracy means and classify the images. In this proposed work to tune the parameters required to train load balancing overload forecasting load balancing overload forecasting the models using CNN. The model performance is evaluated by confusion matrix parameters such as accolade balancing overload forecasting accuracy, precision, Recall, sensitivity, specificity, f-measure, etc. In the plants, the stress will destroy the crop's growth, and yields are affected negatively due to the water supply's suboptimal quantity, soil erection, and abnormal temperature due to other physiological conditions and changes in biochemicals. In this paper, the current model Mobile, which is used for image detection and classification in disease, achieves an accuracy of 99.8%. © 2024 IEEE.

关键词： Climate change Crop disease production Drought Heat stress Plant responses Stress management

来源：评论

学校读者我要写书评

暂无评论

Multi-cost fusion stereo matching algorithm based on guided filter aggregation 3

Multi-cost fusion stereo matching algorithm based on guided ...

引用

3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Liu, Jingwen Zhang, Xuedong School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9781510663473

Aiming at the low matching accuracy of existing local stereo matching algorithms in weak texture areas, a local stereo matching algorithm based on multi-matching cost fusion and guided filtering cost aggregation with adaptive parameters is proposed. First, use the gradient direction to improve the gradient cost, and calculate the matching cost by combining the gradient cost with the Census transform and color cost. Secondly, the cost is aggregated by the guided filtering of adaptive parameters;Finally, the final disparity map is obtained through disparity calculation and multi-step disparity refinement. The improved algorithm is tested on 15 training sets on the Middlebury3 platform, and the average false matching rates of bad4.0 in all areas and non-occluded areas are 19.9% and 13.2%, respectively, which is improved compared with AD-Census and other algorithms. © 2023 SPIE.

关键词： Textures

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：