检索结果-内蒙古大学图书馆

3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Lingappa, Ediga Sriramya, P. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai600056 India Malla Reddy College of Engineering TS Dhulapally Secunderabad500100 India Department of Artificial Intelligence and Data Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai600056 India

ISBN: (纸本)9789819783540

Bone cancer kills most people worldwide. X-rays, MRIs, and CT scans aid bone cancer diagnosis. Manual processes take too much time and knowledge. Thus, a machine learning system must distinguish malignant bone from healthy bone. Cancer-damaged bones feel different. Cancer and healthy bone scans were similar. Classifying them is hard. Despite continuous innovation in the area of cancer research, it still remains one of the most fatal diseases in the world. It is paramount to harness all resources to bring breakthroughs in cancer treatment. Detection at the initial stage would be a significant stride in prolonging the life of the patient and decreasing the mortality rate. Hence, it is imperative to develop techniques that are innovative, efficient, and with lesser undesirable effects. This paper involves the study of all elements of bone cancer and features to evaluate/estimate its type. ACM is one of the most well-known techniques for segmenting a bone image into individual parts and the random forest method employs a clustering strategy with a maximum of five nodes. Insight into the organ's volumes and contours from an image is seen as highly challenging. It's a collection of operations including filtering out background noise, dividing up the data, mining for features, and making a final pick. It detaches the pixels from the edges of the object. They are treated further to bring about the intended effect. The achieved results are satisfactory, and this method is among the best ones for segmenting greyscale images currently available. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc networks

引用

computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

来源：评论

学校读者我要写书评

暂无评论

UNVEILING THE SAMPLING DENSITY IN NON-UNIFORM GEOMETRIC GRAPHS 11

UNVEILING THE SAMPLING DENSITY IN NON-UNIFORM GEOMETRIC GRAP...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Paolino, Raffaele Bojchevski, Aleksandar Günnemann, Stephan Kutyniok, Gitta Levie, Ron Ludwig-Maximilians-Universität München Germany CISPA Helmholtz Center for Information Security Germany Department of Computer Science Munich Data Science Institute Technical University of Munich Germany Faculty of Mathematics Technion - Israel Institute of Technology Israel

A powerful framework for studying graphs is to consider them as geometric graphs: nodes are randomly sampled from an underlying metric space, and any pair of nodes is connected if their distance is less than a specified neighborhood ***, the literature mostly focuses on uniform sampling and constant neighborhood ***, real-world graphs are likely to be better represented by a model in which the sampling density and the neighborhood radius can both vary over the latent *** instance, in a social network communities can be modeled as densely sampled areas, and hubs as nodes with larger neighborhood *** this work, we first perform a rigorous mathematical analysis of this (more general) class of models, including derivations of the resulting graph shift *** key insight is that graph shift operators should be corrected in order to avoid potential distortions introduced by the non-uniform ***, we develop methods to estimate the unknown sampling density in a self-supervised ***, we present exemplary applications in which the learned density is used to 1) correct the graph shift operator and improve performance on a variety of tasks, 2) improve pooling, and 3) extract knowledge from *** experimental findings support our theory and provide strong evidence for our model. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Approaches for Road Damage Detection Using YOLOv8 and Custom Convolutional Neural Network 6

Deep Learning Approaches for Road Damage Detection Using YOL...

引用

6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Khan, Muhammad Waseem Mahmood, Khalid Hussain, Israr Talha Qureshi, Muhammad Muhammad Sanaullah Badar, Hafiz Gao, Wu School of Computer Science Northwestern Poly technical University Center for Bio-inspired System-on-Chip Xi'an China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Taiwan Institute of Computing Mns University of Agriculture Multan Pakistan Henan University Kaifeng450046 China

ISBN: (纸本)9798331541750

The identification of road damage is deemed very important to the preservation of infrastructure. Since recent development in deep learning provide great potential approaches, in this paper, three deep learning models, namely EfficientNet, CNN and YOLOv8 are considered and their performance is evaluated on a dataset with 8,586 images with labelled road damages consisting of seven subclasses including longitudinal cracks and potholes. Besides, other issues like class imbalance and variabilities in the environment were studied and considered in its assessment based on the Models accuracy, precision, recall, and F1-score. With an accuracy of 64%, YOLOv8 is the most accurate model for identifying various types of damages, particularly in identifying multiple damages present in one or more types of damage, as was seen in the high accuracy rate of a split sample of 44 images of multiple damages. Our findings showcase YOLOv8 as a strong contender for real-time road damage detection with potential of enhancement with better augmentation and fine-tuning. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

An Optimal Scheme for WSN Based on Compressed Sensing

引用

computers, Materials & Continua 2022年第7期72卷 1053-1069页

作者： Firas Ibrahim AlZobi Ahmad Ali AlZubi Kulakov Yurii Abdullah Alharbi Jazem Mutared Alanazi Sami Smadi Department of Information Systems and Networks Faculty of Information TechnologyThe World Islamic Sciences&Education UniversityJordan Computer Science Department Community CollegeKing Saud UniversitySaudi Arabia Department of Computer Engineering National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic InstituteUkraine

Wireless sensor networks(WSNs)is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks,bio-medical engineering,agriculture,industry and many *** has been used in the internet-of-things(IoTs)applications.A method for data collecting utilizing hybrid compressive sensing(CS)is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network *** cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes,and then the cluster heads are selected in order based on the distance between the determined cluster head node and the undetermined candidate cluster head ***,each ordinary node joins the cluster that is nearest to *** greedy CS is used to compress data transmission for nodes whose data transmission volume is greater than the threshold in a data transmission tree with the Sink node as the root node and linking all cluster head *** simulation results demonstrate that when the compression ratio is set to ten,the data transfer volume is reduced by a factor of *** compared to clustering and SPT without CS,it is reduced by 75%and 65%,*** compared to SPT with Hybrid CS and Clustering with hybrid CS,it is reduced by 35%and 20%,*** and SPT without CS are compared in terms of node data transfer volume standard *** with Hybrid CS and clustering with Hybrid CS were both reduced by 62%and 80%,*** compared to SPT with hybrid CS and clustering with hybrid CS,the latter two were reduced by 41%and 19%,respectively.

关键词： Compressed sensing computer networks sensor networks ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Framework for Loss Range Prediction of Hollow-core Anti-resonant Fibers

Machine Learning Framework for Loss Range Prediction of Holl...

引用

2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Jewani, Yordanos Petry, Michael Sanchez-Arias, Rei Habib, Md Selim Department of Data Science and Business Analytics Florida Polytechnic University LakelandFL33805 United States Department of Aerospace and Geodesy Technical University of Munich Munich80333 Germany Department of Electrical Engineering and Computer Science Florida Institute of Technology FL32901 United States

A supervised machine learning framework is implemented to predict the propagation loss of randomly structured nested hollow-core anti-resonant fiber for the first time. The random forest classifier outperforms other m... 详细信息

ISBN: (纸本)9781957171395

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Knowledge Graph based Mutual Attention for Machine Reading Comprehension over Anti-Terrorism Corpus

引用

data Intelligence 2023年第3期5卷 685-706页

作者： Feng Gao Jin Hou Jinguang Gu Lihua Zhang School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065Hubei The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content Insitute of Scientic and Technical Information of ChinaBeijing 100038China Wuhan University of Science and Technology Big Data Science and Engineering Research Institute Wuhan 430065Hubei Eastchina Jiaotong University Nanchang 330013Jiangxi

Machine reading comprehension has been a research focus in natural language processing and intelligence ***,there is a lack of models and datasets for the MRC tasks in the anti-terrorism ***,current research lacks the ability to embed accurate background knowledge and provide precise *** address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic ***,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the *** eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the *** results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.

关键词： Machine reading comprehension Anti-terrorism domain Knowledge embedding Knowledge attention Mutual attention

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Framework for Loss Range Prediction of Hollow-core Anti-resonant Fibers

Machine Learning Framework for Loss Range Prediction of Holl...

引用

CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

引用

Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Unified mechanism-specific amplification by subsampling and group privacy amplification 24

Unified mechanism-specific amplification by subsampling and ...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jan Schuchardt Mihail Stoian Arthur Kosmala Stephan Günnemann Dept. of Computer Science & Munich Data Science Institute Technical University of Munich Dept. of Engineering University of Technology Nuremberg

ISBN: (纸本)9798331314385

Amplification by subsampling is one of the main primitives in machine learning with differential privacy (DP): Training a model on random batches instead of complete datasets results in stronger privacy. This is traditionally formalized via mechanism-agnostic subsampling guarantees that express the privacy parameters of a subsampled mechanism as a function of the original mechanism's privacy parameters. We propose the first general framework for deriving mechanism-specific guarantees, which leverage additional information beyond these parameters to more tightly characterize the subsampled mechanism's privacy. Such guarantees are of particular importance for privacy accounting, i.e., tracking privacy over multiple iterations. Overall, our framework based on conditional optimal transport lets us derive existing and novel guarantees for approximate DP, accounting with Rényi DP, and accounting with dominating pairs in a unified, principled manner. As an application, we analyze how subsampling affects the privacy of groups of multiple users. Our tight mechanism-specific bounds outperform tight mechanism-agnostic bounds and classic group privacy results.

关键词：

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：