检索结果-内蒙古大学图书馆

12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Zhang, Jun Yue, Kun School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9798350340181

Node classification is to predict the labels of the unlabeled nodes in a graph, which is useful for various applications of social network and biological information analysis. To measure the uncertainty of structural information, we propose the structural entropy theory based method for graph node classification. First, we calculate the structural entropy of different graph structures, since the smaller the structural entropy, the less the uncertainty of the structural relationship in the graph. Then, we use the minimal structural entropy to determine the uncertainty of graphical structures. Finally, the nodes with similar structural relationships in the graph are classified. Experimental results show that our method outperforms some state-of-the-art competitors. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

An Input Space Modeling Method for Software Testing Considering Timing 23

An Input Space Modeling Method for Software Testing Consider...

引用

23rd IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Zhang, Bo J., Li Z., Fan Y., Wang School of Reliability and Systems Engineering Beihang University Beijing China Tianjin University Computer Science and Technology Division of Intelligence and Computing Tianjin China

ISBN: (纸本)9798350359398

Specification based testing is a common method in software testing. Specification based testing is divided into behavior-based testing, such as finite state machine method;and input-based testing, such as random testing, combination testing, decision table testing, equivalence class testing, boundary value testing method and so on. At present, there are many modeling methods for behavior-based testing and test cases can be generated automatically through these models. However, for these input-based testing methods, test cases are generated manually by searching for parameters and parameter values, which is too inefficient. To solve this problem, we want to improve the efficiency of input-based testing methods by automatically generating test cases. Therefore, this paper presents a software input space model that can represent parameter values and constraint relationships as the basis of automatic generation of test cases. Some safety-critical software tests need to consider the input timing of parameters, so we also express the input timing of parameters in the input space model. Then we propose the process of modeling the input space, which is finally expressed in the form of a directed graph. The values of parameters and the input timing of parameters are expressed on the edges of the directed graph, and the constraints between parameters are described by the way of node splitting, and all valid test sequences are represented by the path traversing the graph. Finally, the proposed input space modeling method is applied to a real test project to demonstrate the feasibility of the method. © 2023 IEEE.

关键词： Software testing

来源：评论

学校读者我要写书评

暂无评论

A Method for Measuring Node Similarity in Heterogeneous Information Networks 9

A Method for Measuring Node Similarity in Heterogeneous Info...

引用

9th International Conference on Fuzzy systems and Data Mining, FSDM 2023

作者： Zhan, Liudi Chen, Hongmei Zhou, Lihua Xiao, Qing School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9781643684703

Meta-path-based methods for measuring the similarities between nodes in Heterogeneous Information Networks (HINs) have attracted attention from researchers due to excellent performance. However, these methods suffer from some issues: (1) it is difficult for users to provide effective meta-paths of complex HINs;(2) it is inefficient to enumerate all instances of meta-paths. In order to solve the above issues, this paper proposes a novel method, K-order Neighbors based Heterogeneous Graph Neural Network (KN-HGNN), for measuring node similarity. Firstly, KN-HGNN generates meta-paths based on network schema. Then, KN-HGNN obtains the features of nodes by aggregating itself type feature and its k-order neighbors' type features under the constraints of meta-paths. Finally, KN-HGNN calculates the similarities between nodes based on the features of nodes. The experimental results on real datasets show that KN-HGNN outperforms the baselines. © 2023 The authors and IOS Press.

关键词： Information services

来源：评论

学校读者我要写书评

暂无评论

Detection of Brain Tumor from MRI Images Using VGG-16 CNN Model with Transfer Learning 3

Detection of Brain Tumor from MRI Images Using VGG-16 CNN Mo...

引用

3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Singh Rathore, Bhanu Pratap Dave, Rutul Dhanasekaran, K. Priyadarsini, K. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur603203 India

ISBN: (纸本)9798350322842

Brain tumors are a serious health concern caused by the abnormal growth of cells in or around the brain. These tumors can either be benign or malignant. Few of the causes for Brain tumors are genetic mutations, exposure to radiation, or environmental factors. Early detection of brain tumors is essential for effective treatment and improved patient outcomes. To address this, a novel approach has been proposed for brain tumor detection using transfer learning with the VGG-16 convolutional neural network (CNN) architecture. By leveraging the pre-trained VGG-16 model on the ImageNet dataset, which provides a broad range of data for learning and representing complex features of various objects, meaningful features can be extracted from MRI scans and differentiate between tumor and normal tissue accurately. This refined VGG-16 model can provide a more precise and reliable diagnosis, which is crucial for successful treatment. This research study demonstrates that the utilization of VGG-16 model for brain tumor diagnosis can provide an efficient and accurate method for identifying brain tumors. Moreover, this approach can be applied to develop automated systems to assist medical professionals in diagnosing and treating brain tumors more effectively. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED CRIMINAL DETECTION USING A DEEP LEARNING APPROACH

ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Jagadeesan, S. Sujigarasharma, K. Manjula, R. Vetriselvi, T. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350305708

This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases. © 2023 IEEE.

关键词： CNN Criminal detection Face detection Machine learning

来源：评论

学校读者我要写书评

暂无评论

Deep Video Demoireing via Compact Invertible Dyadic Decomposition

Deep Video Demoireing via Compact Invertible Dyadic Decompos...

引用

IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Quan, Yuhui Huang, Haoran He, Shengfeng Xu, Ruotao South China University of Technology School of Computer Science and Engineering Guangzhou 510006 China Pazhou Lab Guangzhou 510335 China Singapore Management University School of Computing and Information Systems 188065 Singapore

ISBN: (纸本)9798350307184

Removing moire patterns from videos recorded on screens or complex textures is known as video demoireing. It is a challenging task as both structures and textures of an image usually exhibit strong periodic patterns, which thus are easily confused with moire patterns and can be significantly erased in the removal process. By interpreting video demoireing as a multi-frame decomposition problem, we propose a compact invertible dyadic network called CIDNet that progressively decouples latent frames and the moire patterns from an input video sequence. Using a dyadic cross-scale coupling structure with coupling layers tailored for multi-scale processing, CIDNet aims at disentangling the features of image patterns from that of moire patterns at different scales, while retaining all latent image features to facilitate reconstruction. In addition, a compressed form for the networks output is introduced to reduce computational complexity and alleviate overfitting. The experiments show that CIDNet outperforms existing methods and enjoys the advantages in model size and computational efficiency.

关键词： Deep Video Computational Efficiency Image Features Model Size Video Sequences Image Pattern Latent Features Compressed Format Complex Texture Multiscale Process Latent Image Deep Neural Network Attention Mechanism Video Frames Peak Signal-to-noise Ratio Multiscale Analysis Feature Alignment Adjacent Frames Supplement For Details Discrete Cosine Transform Frame Alignment U-shaped Structure Alignment Blocks Multi-scale Structure Reversible Loss Color Shift Information Fidelity Latent Code Spatial Pyramid Pooling Spatial Pyramid

来源：评论

学校读者我要写书评

暂无评论

Improving Fundus Detection Precision in Diabetic Retinopathy Using Derivative-Based Deep Neural Networks

引用

Computer Modeling in engineering & Sciences 2025年第3期142卷 2487-2511页

作者： Asma Aldrees Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Mohd Anjum Department of Informatics and Computer Systems College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India

Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves,blood vessels,retinal health,and the impact of diabetes on the optic *** disorders are a major global health concern,affecting millions of people worldwide due to their widespread *** photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic *** a result,accurate fundus detection is essential for early diagnosis and effective treatment,helping to prevent severe complications and improve patient *** address this need,this article introduces a Derivative Model for Fundus Detection using Deep NeuralNetworks(DMFD-DNN)to enhance diagnostic *** selects key features for fundus detection using the least derivative,which identifies features correlating with stored fundus *** filtering relies on the minimum derivative,determined by extracting both similar and varying *** this research,the DNN model was integrated with the derivative *** images were segmented,features were extracted,and the DNN was iteratively trained to identify fundus regions *** goal was to improve the precision of fundoscopic diagnosis by training the DNN incrementally,taking into account the least possible derivative across iterations,and using outputs from previous *** hidden layer of the neural network operates on the most significant derivative,which may reduce precision across *** derivatives are treated as inaccurate,and the model is subsequently trained using selective features and their corresponding *** proposed model outperforms previous techniques in detecting fundus regions,achieving 94.98%accuracy and 91.57%sensitivity,with a minimal error rate of 5.43%.It significantly reduces feature extraction time to 1.462 s and minimizes computational overhead,thereby improving operational efficiency and ***,the propo

关键词： Deep neural network feature extraction fundus detection medical image processing

来源：评论

学校读者我要写书评

暂无评论

Integrating random regret minimization-based discrete choice models with mixed integer linear programming for revenue optimization

引用

Iran Journal of Computer Science 2024年第1期8卷 21-35页

作者： Talebi, Amirreza Haeri Boroujeni, Sayed Pedram Razi, Abolfazl Department of Integrated Systems Engineering The Ohio State University Columbus OH United States School of Computing Clemson University Clemson 29632 SC United States ENSIMAG Grenoble Alpes University Grenoble France

This paper explores the critical domain of revenue management (RM) within operations research (OR), focusing on intricate pricing dynamics. Utilizing mixed integer linear programming (MILP) models, the study enhances revenue optimization by considering product prices as decision variables and emphasizing the interplay between demand and supply. Recent advancements in discrete choice models (DCMs), particularly those rooted in random regret minimization (RRM) theory, are investigated as potent alternatives to established random utility maximization (RUM)-based DCMs. Despite the widespread use of DCMs in RM, a significant gap exists between cutting-edge RRM-based models and their practical integration into RM strategies. The study addresses this gap by incorporating an advanced RRM-based DCM into MILP models, addressing pricing challenges in both capacitated and uncapacitated supply scenarios. The developed models demonstrate the feasibility and offer diverse interpretations of consumer choice behavior, drawing inspiration from established RUM-based frameworks. This research contributes to bridging the existing gap in the application of advanced DCMs within practical RM implementations. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2024.

关键词： Choice-based optimization Decision-making behavior Demand prediction Mixed integer linear programming Operations research Pricing

来源：评论

学校读者我要写书评

暂无评论

Automation 5.0: The Key to systems Intelligence and Industry 5.0

引用

IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

来源：评论

学校读者我要写书评

暂无评论

Chat with ChatGPT on Industry 5.0:Learning and Decision-Making for Intelligent Industries

引用

IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 831-834页

作者： Fei-Yue Wang Jing Yang Xingxia Wang Juanjuan Li Qing-Long Han State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

THE current ChatGPT phenomenon has signaled a new era of Artificial Intelligence moving from Algorithmic Intelligence to Linguistic Intelligence where interactive activities between actual and artificial,real and virtual,human and machine play an active and important role online and in *** IEEE/CAA JAS,we are interested in investigating the impact and significance of this new era on industrial development,especially control and automation for manufacturing and production.

关键词： GPT artificial automation

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：