检索结果-内蒙古大学图书馆

12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Hao Yu, Yongsheng Luo, Tiejian Fan, Heng Zhang, Libo School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Software Chinese Academy of Sciences China Department of Computer Science University of Rochester United States Department of Computer Science and Engineering University of North Texas United States

Vanilla image completion approaches exhibit sensitivity to large missing regions, attributed to the limited availability of reference information for plausible generation. To mitigate this, existing methods incorporate the extra cue as a guidance for image completion. Despite improvements, these approaches are often restricted to employing a single modality (e.g., segmentation or sketch maps), which lacks scalability in leveraging multi-modality for more plausible completion. In this paper, we propose a novel, simple yet effective method for Multi-modal Guided Image Completion, dubbed MaGIC, which not only supports a wide range of single modality as the guidance (e.g., text, canny edge, sketch, segmentation, depth, and pose), but also adapts to arbitrarily customized combination of these modalities (i.e., arbitrary multi-modality) for image completion. For building MaGIC, we first introduce a modality-specific conditional U-Net (MCU-Net) that injects single-modal signals into a U-Net denoiser for single-modal guided image completion. Then, we devise a consistent modality blending (CMB) method to leverage modality signals encoded in multiple learned MCU-Nets through gradient guidance in latent space. Our CMB is training-free, thereby avoiding the cumbersome joint re-training of different modalities, which is the secret of MaGIC to achieve exceptional flexibility in accommodating new modalities for completion. Experiments show the superiority of MaGIC over state-of-the-art methods and its generalization to various completion tasks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Blockchain: Secured Solution for Signature Transfer in Distributed Intrusion Detection System

引用

computer Systems science & engineering 2022年第1期40卷 37-51页

作者： Shraddha R.Khonde Venugopal Ulagamuthalvi Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Engineering M.E.S.College of EngineeringS.P.Pune UniversityPune411001India

Exchange of data in networks necessitates provision of security and *** networks compromised by intruders are those where the exchange of data is at high *** main objective of this paper is to present a solution for secure exchange of attack signatures between the nodes of a distributed *** activities are monitored and detected by the Intrusion Detection System(IDS)that operates with nodes connected to a distributed *** IDS operates in two phases,where the first phase consists of detection of anomaly attacks using an ensemble of classifiers such as Random forest,Convolutional neural network,and XGBoost along with genetic algorithm to improve the performance of *** novel attacks detected in this phase are converted into signatures and exchanged further through the network using the blockchain framework in the second *** phase uses the cryptosystem as part of the blockchain to store data and secure it at a higher *** blockchain is implemented using the Hyperledger Fabric v1.0 and v2.0,to create a prototype for secure signature *** exchanges signatures in a much more secured manner using the blockchain architecture when implemented with version 2.0 of Hyperl-edger *** performance of the proposed blockchain system is evaluated on UNSW NB15 *** performance has been evaluated in terms of execution time,average latency,throughput and transaction processing *** evidence of the proposed IDS system demonstrates improved performance with accuracy,detection rate and false alarm rate(FAR)as key parameters *** and detection rate increase by 2%and 3%respectively whereas FAR reduces by 1.7%.

关键词： Blockchain intrusion detection system machine learning algorithms secured communication ensemble approach

来源：评论

学校读者我要写书评

暂无评论

Performance Improvement: Modulation Recognition Scheme Using CVCNN-LSTM Method 1

Performance Improvement: Modulation Recognition Scheme Using...

引用

1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Krishnamoorthy, Ramkumar Agarwal, Trapty Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

The process for automatic modulation recognition (AMR) is important to industrial cognitive radio Models. Although they have made significant strides in modulation detection, state-of-the-art real-valued CNNs compromise model interpretability and signal phase integrity by treating complicated signal components as distinct actual inputs. The CVCNN-LSTM is a novel AMR technique that is presented in this research. In order to capitalize on the advantages associated with both networks while minimizing their drawbacks, this study intends to use CNNs along with a LSTM. This method improves recognition performance by strengthening the learning for the initial in-phase/quadrature information (I/Q) signal characteristics. The suggested network consists of one LSTM layer and many local features learning unit (LFLUs). Convolutional and average pooling layers, which are the main components of an LFLU, are essential for allowing hierarchical correlation extraction and local correlation learning. Researchers recommend employing end-to-end complex-valued CNN & LSTM for modulation recognition rather than dual-channel real-valued networks as in-phase/quadrature information naturally including complex-valued system. Researchers analyse complex-valued networks via experiments and show that this methodology outperforms the state-of-the-art techniques in AMR area. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Empowering NGOs with Remote Sensing and CNN-LSTM Models for Social and Environmental Transformation

引用

Remote Sensing in Earth Systems sciences 2024年第4期7卷 554-568页

作者： Ramachandran, Jayakarthik Gupta, Ashwani Kumar Syamala, Maganti Banerjee, Sudipta Baburao, D. Ranadive, Amol Deivasigamani, Shanmugavel Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India School of Business and Commerce Chandigarh University Unnao India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune India Campus Symbiosis International (Deemed University) (SIU) Maharashtra Pune India Department of Computer Science and Engineering Vidya Jyothi Institute of Technology (VJIT) Telangana Hyderabad India BIMHRD Sri Balaji University Pune India Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Anna University Tamilnadu Chennai India

Non-governmental organizations (NGOs) play a pivotal role in addressing some of the most pressing environmental and social challenges across the globe. With the rise of advanced technologies, NGOs have an unprecedented opportunity to enhance their impact through the adoption of innovative tools. Among these, remote sensing technology has emerged as a cornerstone in environmental monitoring, offering real-time data on land, water, and atmospheric conditions. The integration of remote sensing with CNN-LSTM models offers a transformative approach for NGOs to tackle environmental and social challenges. This study explores the efficacy of these technologies in monitoring deforestation and habitat loss. By leveraging high-resolution satellite imagery and ground truth data, our proposed CNN-LSTM model successfully identifies and tracks changes in land cover. Throughout various tests, the model demonstrated a high accuracy of 0.97, showcasing its potential in providing reliable, real-time data for environmental conservation efforts. The findings suggest that combining CNNs and LSTMs enhances the ability to interpret complex spatiotemporal data, thus supporting NGOs in their decision-making processes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： CNN-LSTM models Deforestation Environmental monitoring Habitat loss Non-governmental organizations (NGOs) Remote sensing Satellite imagery Spatial pattern recognition Temporal dynamics

来源：评论

学校读者我要写书评

暂无评论

Advancing PCB Quality Control:Harnessing YOLOv8 Deep Learning for Real-Time Fault Detection

引用

computers, Materials & Continua 2024年第10期81卷 345-367页

作者： Rehman Ullah Khan Fazal Shah Ahmad Ali Khan Hamza Tahir Faculty of Cognitive Sciences and Human Development Universiti Malaysia SarawakKota Samarahan94300Malaysia School of Computer Science and Engineering Anhui University of Science and TechnologyHuainan232001China Department Electronic Quaid-i-Azam UniversityIslamabad45320Pakistan

Printed Circuit Boards(PCBs)are materials used to connect components to one another to form a working *** play a crucial role in modern electronics by connecting various *** trend of integrating more components onto PCBs is becoming increasingly common,which presents significant challenges for quality control *** the potential impact that even minute defects can have on signal traces,the surface inspection of PCB remains pivotal in ensuring the overall system *** address the limitations associated with manual inspection,this research endeavors to automate the inspection process using the YOLOv8 deep learning algorithm for real-time fault detection in ***,we explore the effectiveness of two variants of the YOLOv8 architecture:YOLOv8 Small and YOLOv8 *** rigorous experimentation and evaluation of our dataset which was acquired from Peking university’s Human-Robot Interaction Lab,we aim to assess the suitability of these models for improving fault detection accuracy within the PCB manufacturing *** results reveal the remarkable capabilities of YOLOv8 Small models in accurately identifying and classifying PCB *** model achieved a precision of 98.7%,a recall of 99%,an accuracy of 98.6%,and an F1 score of *** findings highlight the potential of the YOLOv8 Small model to significantly improve the quality control processes in PCB manufacturing by providing a reliable and efficient solution for fault detection.

关键词： Printed circuit boards(PCB) YOLOv8 YOLOv8 Nano YOLOv8 Small deep learning object detection

来源：评论

学校读者我要写书评

暂无评论

Data Distribution Valuation 38

Data Distribution Valuation

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Xu, Xinyi Wang, Shuaiqi Foo, Chuan-Sheng Low, Bryan Kian Hsiang Fanti, Giulia Department of Computer Science National University of Singapore Singapore Department of Electrical and Computer Engineering Carnegie Mellon University United States Institute for Infocomm Research Agency for Science Technology and Research Singapore

Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces. Existing data valuation methods define a value for a discrete dataset. However, in many use cases, users are interested in not only the value of the dataset, but that of the distribution from which the dataset was sampled. For example, consider a buyer trying to evaluate whether to purchase data from different vendors. The buyer may observe (and compare) only a small preview sample from each vendor, to decide which vendor's data distribution is most useful to the buyer and purchase. The core question is how should we compare the values of data distributions from their samples? Under a Huber characterization of the data heterogeneity across vendors, we propose a maximum mean discrepancy (MMD)-based valuation method which enables theoretically principled and actionable policies for comparing data distributions from samples. We empirically demonstrate that our method is sample-efficient and effective in identifying valuable data distributions against several existing baselines, on multiple real-world datasets (e.g., network intrusion detection, credit card fraud detection) and downstream applications (classification, regression). © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MRI techniques using image processing Brain Tumor Detection

MRI techniques using image processing Brain Tumor Detection

引用

2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Mahesh, T.R. Geetha, G. Deemed-to-be University Faculty of Engineering and Technology Jain Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350322309

A brain tumor develops when cells in the brain divide in an abnormal way that is not controlled by the body. This results in the growth of the tumor. Manual dissection of brain tumors identified by MRI is a process that is both necessary and time-consuming. However, it is also an extremely important step. Identifying the precise location of a brain tumor as well as its size is of the utmost importance when it comes to the efficient screening of patients and the treatment of tissue. Because of this, a proper diagnosis of a brain tumor needs to be carried out with as much promptness and accuracy as is humanly *** detection of a brain tumor using imaging technology for medical purposes is thought to be a challenging task because of the nature of the disease being investigated. The goal of this study is to provide a method that will assist in the treatment of brain tumors. This strategy will make use of the covert operations of VGG19 as well as the process of transfer learning. The approach that has been suggested will be improved throughout the course of this inquiry, with the ultimate objective being the development of a hybrid methodology. Utilizing the VGG19 and Transfer learning approaches, the purpose of this study is to develop an algorithm for the identification of tumors using magnetic resonance imaging (MRI). These methods have been applied to a wide variety of data collections that pertain to patients. They have the ability to organize their cooling effect on the final portion of the body tissues that are associated with the tissues, while at the same time keeping better the borders between the various components of the ti © 2023 IEEE.

关键词： Brain

来源：评论

学校读者我要写书评

暂无评论

Crime Type and Occurrence Prediction Using Machine Learning 2

Crime Type and Occurrence Prediction Using Machine Learning

引用

2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Kavya, T.M. Lavanya, T.N. Ithihas, Maddi Shetty, Namitha R. Amrutha, H.N. Hebbar, Sushruth Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India Department of P.G Studies and Research in Computer Science Kuvempu University Karnataka Shivamogga India

ISBN: (纸本)9798350383867

Nowadays, crime has become a way to get people and people into trouble. Rising crime has fueled unrest at polling stations across the country. Understanding crime patterns is necessary to identify and respond to these criminal activities. This study conducts this crime analysis using crime data obtained from the Kaggle open source which has been used to predict most crimes recently. The main aim of the project is to predict what type of crime is most likely to occur and when and where the crime occurs. This project introduces some machine learning like KNN,SVM to detect various crimes and the accuracy achieved is higher than previous ones. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

ETL Data Pipeline to Analyze Scraped Data 3rd

ETL Data Pipeline to Analyze Scraped Data

引用

3rd International Conference on Information technology, InCITe 2023

作者： Gandhi, Rashmi Khurana, Sparsh Manchanda, Harsh Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida India

ISBN: (纸本)9789819959969

In this day and age, corporations are moving towards data driven decision making. They are utilizing data to make applications that can assist them with business intelligence. Data assists them with working on their efficiency and income by chipping away at the business challenges. By utilizing data, corporations can generate market patterns, improve client experience and enable improved business decisions. The key problem is the abundance of data and its form. Data needs to be in a form that can be effectively be used to generate tangible insights. The complete cycle of taking data and then generating insights from it is a long one but there are modern solutions to this problem which simplify and improve this process. The scope of this literary research is to examine the use of Data Pipelining to enable better data insights. Data pipelines can be utilized to move data starting with one spot then onto the next using ETL (Extract Transform Load), ELT (Extract Load Transform), data improvement, and continuous data examination. An automated data pipeline is a relatively new solution which can provide impressive results without the need for manual intervention every time. The result of automated pipelines is automatic processing of bigdata from source to destination without any user input. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Dynamic Data Placement Strategy in Distributed Cloud Environment 1st

Comparative Analysis of Dynamic Data Placement Strategy in D...

引用

1st International Conference on Data engineering and Machine Intelligence, ICDEMI 2023

作者： Shankar, B. Prabhu Singh, Uday Kumar Vijayalakshmi Viji, C. Rajkumar, N. Stalin, M. Nachiappan, Balusamy Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bangalore India Prologis DenverCO80202 United States Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819776153

In general, the dynamic data optimization technique is used to place the user’s data in a cloud system to reduce the latency, intercommunication traffic, data transfer cost, execution cost, load balancing, and bandwidth utilization. The user data is dispersed and stored in a distributed cloud environment, so it must ensure data availability and reliability in cloud data centers. These data placement techniques involve two steps: first user data identification and second identifying the node to place the data in a geographically distributed cloud. The primary goal of the data placement approach is to find the best or optimal node to store user data in terms of latency and storage costs. This comparative analysis provides various survey data placement algorithms and techniques used to analyze latency delay, execution time, access cost, load balance, server over, bandwidth, and server capacity with different parameters. This study also analyzes the merits and demerits of data placement strategies. This survey helps to concentrate on the dynamic data optimization technique in the Distributed or multiple cloud environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distributed cloud

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：