检索结果-内蒙古大学图书馆

2nd International Conference on Advancement in Computation and Computer Technologies, InCACCT 2024

作者： Pratap, Harshita Guru Prasad, M.S. Agarwal, Poorvi Bhardwaj, Ayushi Mehra, Ayushi Mathpati, Shilpa Dept. of Computer Science and Engg Dehradun India Reva University School of computing & Information Technology Bengaluru India

ISBN: (纸本)9798350371314

In the realm of agriculture, soybeans have played a vital role in diets for centuries, providing a consistent source of plant protein. Beyond protein, soybeans offer essential components like fibres, vitamins (including B vitamins), minerals (such as iron, calcium, and magnesium), and antioxidants. This necessitates precise quality assessment for factors like size, shape, colour, and damage. Leveraging advanced machine learning techniques such as VGG16, AlexNet and CNN, the model aims to automate the identification and categorization of soybean quality. To implement the proposed work the standard soybean dataset is considered. It consists of five soybean seed types complete, spotted, immature, broken, and skin-damaged. The proposed work effectively categories the quality of soybean seeds and it has the potential to improve the agricultural process as compared to traditional work. In this work different pretrained models are experimented in that VGG16 provides the highest of all showing accuracy 90%, AlexNet accuracy 78.49% and CNN accuracy 74.55%. © 2024 IEEE.

关键词： Damage detection

来源：评论

学校读者我要写书评

暂无评论

Poisoning attacks and countermeasures in intelligent networks:Status quo and prospects

引用

Digital Communications and Networks 2022年第2期8卷 225-234页

作者： Chen Wang Jian Chen Yang Yang Xiaoqiang Ma Jiangchuan Liu School of Computer Science and Information Engineering Hubei UniversityWuhanChina School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhan430074China School of Computing Science at Simon Fraser University British ColumbiaCanada

Over the past years,the emergence of intelligent networks empowered by machine learning techniques has brought great facilitates to different aspects of human ***,using machine learning in intelligent networks also presents potential security and privacy threats.A common practice is the so-called poisoning attacks where malicious users inject fake training data with the aim of corrupting the learned *** this survey,we comprehensively review existing poisoning attacks as well as the countermeasures in intelligent networks for the first *** emphasize and compare the principles of the formal poisoning attacks employed in different categories of learning algorithms,and analyze the strengths and limitations of corresponding defense methods in a compact *** also highlight some remaining challenges and future directions in the attack-defense confrontation to promote further research in this emerging yet promising area.

关键词： Machine learning Poisoning attack Intelligent networks Security threat

来源：评论

学校读者我要写书评

暂无评论

UAV Networks Geometry Configuration Aided Cooperative Positioning Algorithm 14

UAV Networks Geometry Configuration Aided Cooperative Positi...

引用

14th IEEE International Conference on Signal Processing, Communications and computing, ICSPCC 2024

作者： Song, Zhe Zhang, Yi Zhu, Xingxing Yu, Yang Cheng, Zeyu Tang, Chengkai School of Electronics and Information Northwestern Polytechnical University Xi'an China Aeronautics Computing Technology Research Institute Xi'an China

ISBN: (纸本)9798350366556

Due to their flexible and lightweight nature, unmanned aerial vehicles (UAVs) find extensive application in cooperative navigation and positioning technology. Additionally, wireless sensor networks (WSNs) serve as a conduit for exchanging and merging information among cooperative UAV nodes. Addressing the need for lightweight design and real-time adaptability in UAV networks, this study introduces a cooperative positioning algorithm employing manifold gradient filtering assisted by Geometric Dilution of Precision (GDOP). The algorithm leverages the measurement model among cooperative sensor nodes within the UAV network to construct a Riemannian manifold. By computing the gradient on this manifold, the algorithm identifies the steepest descent direction during iteration. Furthermore, it derives the GDOP corresponding to the geometric configuration of cooperative UAV nodes, thereby adjusting the iterative descent rate to expedite convergence. Simulation results demonstrate the algorithm's rapid convergence and high accuracy. © 2024 IEEE.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Data Acquisition with Privacy Protection in UAV Cluster Network

引用

Computer Modeling in Engineering & Sciences 2023年第10期137卷 879-902页

作者： Lemei Da Hai Liang Yong Ding Yujue Wang Changsong Yang Huiyong Wang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information SecurityGuilin University of Electronic TechnologyGuilin541004China Cyberspace Security Research Center Pengcheng LaboratoryShenzhen518055China School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin541004China

The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information *** to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security *** paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition *** UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative *** successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for *** are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,*** security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution *** also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)*** performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.

关键词： Unmanned aerial vehicle cluster network certificateless signcryption certificateless signature batch verification source authentication data privacy blockchain

来源：评论

学校读者我要写书评

暂无评论

Radar Spectrum Allocation for Vehicular Networks with QMIX-LSTM Network 24

Radar Spectrum Allocation for Vehicular Networks with QMIX-L...

引用

24th IEEE International Conference on Communication technology, ICCT 2024

作者： Fan, Yuxin Wang, Xinyi Huang, Jingxuan Fei, Zesong Zhou, Yiqing School of Information and Electronics Beijing100081 China Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China

ISBN: (纸本)9798350363760

To ensure driving safety, radar detection is an essential function of autonomous vehicle. However, with the increasing number of automotive radars and limited spectrum resources, the co-channel interference among radars seriously affects the detection performance. Spectrum allocation is a representative method for interference elimination. However, the centralized scheme suffers from long latency and is not applicable to delay-sensitive scenarios. In this paper, we construct a noval signal mutual interference model, and build the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) framework. In particular, the QMIX-LSTM algorithm based on Centralized Training with Decentralized Execution (CTDE) architecture is used for spectrum allocation to mitigate the mutual interference and improve the detection probability. Simulation results show that the proposed scheme achieves a higher radar detection probability compared with myopic scheme. © 2024 IEEE.

关键词： Radar interference

来源：评论

学校读者我要写书评

暂无评论

Real-Time Helmet Detection System with Vehicle Number Extraction for Two-Wheeler Vehicles using YOLOv8 2

Real-Time Helmet Detection System with Vehicle Number Extrac...

引用

2nd International Conference on Advancement in Computation and Computer Technologies, InCACCT 2024

作者： Kumar, Abhash Guru Prasad, M.S. Soni, Dheeraj Rani, Vibha Arora, Kanika Ganesh, D.R. Dept. of Computer Science & Engineering Dehradun India REVA University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9798350371314

In the face of increasing two-wheeler usage and the persistent issue of riders neglecting helmet compliance, this paper proposes a groundbreaking Real-Time Helmet Detection System with Vehicle Number Extraction. The existing literature on helmet detection systems is reviewed, revealing a range of methodologies, accuracies, and limitations in current approaches. Building upon this foundation, the three-stage detector combines the efficiency of YOLO (You Only Look Once) for object detection with a specialized number plate extraction system. The system aims to provide real-time detection of two-wheeler riders, classification of helmet usage, and extraction of vehicle numbers. The proposed methodology, utilizing a pre-trained YOLO model for object detection and EasyOCR library, demonstrates an accuracy rate of 64% for vehicle detection, 78% for helmet detection, and 92% for number plate extraction. The practical implementation of this system showcases its potential for enhancing road safety, automating traffic management, and promoting regulatory enforcement. While showing potential, the ongoing research and development face challenges such as limitation of computational resources and privacy considerations. The Real-Time Helmet Detection System stands as a promising solution for improving road safety, with the potential for broader use in traffic management and safety enforcement. Continuous improvement and adaptation will guarantee its effectiveness in contributing to a safer and more controlled traffic environment. © 2024 IEEE.

关键词： Accident prevention

来源：评论

学校读者我要写书评

暂无评论

Prediction of Genetic Diseases using Genomic Data and XGBoost Algorithm: A High-Accuracy Approach

Prediction of Genetic Diseases using Genomic Data and XGBoos...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Priya, S. Baghavathi Mahendira Kumar, S. Madeswaran, TamilSelvi Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India University of Technology and Applied Sciences Dept. of Information Technology Nizwa Oman

ISBN: (纸本)9798350379525

Genomic data analysis is of utmost importance in the healthcare sector as it seeks to solve the main problem of detection and genetic disease forecasting. This thesis surveys the utilization of sophisticated machine learning algorithms, specifically XGBoost, to forecast genetic diseases from genomic information. We use a database from Kaggle that focuses on genetic diseases using genomic data, providing multiple genetic markers and associated disease outcomes. In our research we focus on genetics disease prediction models using XGBoost with an efficient gradient enhancement algorithm. Through meticulous preprocessing and feature engineering, we transform raw genomic data into meaningful input features, ensuring the dataset's quality and relevance. Using the XGBoost algorithm, we build a much more efficient model that can easily detect all types of genetic disorders Our study shows an impressive accuracy of about 98%, a dataset that shows how well XGBoost performs on discovery of genetic disease patterns in the genomic. This high accuracy validation underlines the proficiency of machine learning in genomics, thus it embeds the possibility of early disease detection, personalized medicine, and targeted interventions. This research highlights the efficacy of XGBoost in genetic disease prediction and emphasizes the importance of curated datasets in advancing genomic research, contributing valuable insights to precision medicine. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

An information Fusion Emergency Positioning Algorithm for UAVs Based on LEO Constellation 14

An Information Fusion Emergency Positioning Algorithm for UA...

引用

14th IEEE International Conference on Signal Processing, Communications and computing, ICSPCC 2024

作者： Liu, Jiaqi Zhang, Yi Tang, Chengkai Yu, Yang Song, Zhe Zhu, Xingxing School of Electronics And Information Northwestern Polytechnical University Xi'an China Aeronautics Computing Technology Research Institute Xi'an China

ISBN: (纸本)9798350366556

Low-earth-orbit(LEO) satellite can ensure the acquisition of external timing and positioning for UAVs in Global Navigation Satellite System(GNSS) -denied environments. This paper proposes a multi-source information fusion positioning algorithm within the framework of Riemannian information Geometry, which based on LEO Instantaneous Doppler positioning and the inertial navigation sensors(INS) carried by drones. The algorithm derives the covariance propagation process of LEO Doppler positioning based on the Newton-LS method, completes the modeling of probability distribution functions, and maps the probability distribution functions of heterogeneous sensors to Riemannian space to unify the navigation source information format. Utilizing the more precise information granularity of Riemannian space, the proposed algorithm solved the multi-sensor information fusion positioning problem through convex optimization. The accuracy of LEO positioning in GNSS-denied environments has been effectively improved. Compared with single LEO Instantaneous Doppler positioning and INS, the positioning accuracy has been improved by 41.9% and 32.34%, respectively. © 2024 IEEE.

关键词： Distribution functions

来源：评论

学校读者我要写书评

暂无评论

A Quest?!: The Secret Life of Gameworld Punctuation

引用

Proceedings of the ACM on Human-Computer Interaction 2023年第CHIPLAY期7卷 526–557页

作者： Lalone, Nicolas Toups Dugas, Phoebe O. Cormier, Michelle V. School of Interactive Games and Media Rochester Institute of Technology OmahaNE United States Department of Human-Centred Computing Faculty of Information Technology Monash University MelbourneVIC Australia

At times, the interfaces of videogames - gameworlds - contain tiny details that go unnoticed. One such detail is how designers employ ! and ? to communicate to players. These punctuation marks have existed in videogames since their creation, yet remain undiscussed by designers. They are used as ways to promote curiosity, as objects, as ways to symbolize excitement, and as a prompt to react. Their varied history is deserving of attention, so we present a chronicle of two pieces of gameworld punctuation: ! and ?. We discuss current and past uses and identify more ways that these could be used in the future. These symbols may present a useful space of inquiry not only for games and games research, but more generally, in terms of the rapid communication of complex information. © 2023 ACM.

关键词： Interactive computer graphics

来源：评论

学校读者我要写书评

暂无评论

FEDINVERSE: EVALUATING PRIVACY LEAKAGE IN FEDERATED LEARNING 12

FEDINVERSE: EVALUATING PRIVACY LEAKAGE IN FEDERATED LEARNING

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Wu, Di Bai, Jun Song, Yiliao Chen, Junjun Zhou, Wei Xiang, Yong Sajjanhar, Atul School of Mathematics Physics and Computing University of Southern Queensland Australia School of Information Technology Deakin University Australia The University of Adelaide Australia Computer Center Peking University China School of Science Computing and Engineering Technologies Swinburne University of Technology Australia

Federated Learning (FL) is a distributed machine learning technique where multiple devices (such as smartphones or IoT devices) train a shared global model by using their local data. FL promises better data privacy as the individual data isn't shared with servers or other participants. However, this research uncovers a groundbreaking insight: a model inversion (MI) attacker, who acts as a benign participant, can invert the shared global model and obtain the data belonging to other participants. In such scenarios, distinguishing between attackers and benign participants becomes challenging, leading to severe data-leakage risk in FL. In addition, we found even the most advanced defense approaches could not effectively address this issue. Therefore, it is important to evaluate such data-leakage risks of an FL system before using it. Motivated by that, we propose FedInverse to evaluate whether the FL global model can be inverted by MI attackers. In particular, FedInverse can be optimized by leveraging the Hilbert-Schmidt independence criterion (HSIC) as a regularizer to adjust the diversity of the MI attack generator. We test FedInverse with three typical MI attackers, GMI, KED-MI, and VMI. The experiments show that FedInverse can effectively evaluate the data leakage risk that attackers successfully obtain the data belonging to other participants. The code of this work is available at https://***/Jun-B0518/FedInverse. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Data privacy

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：