检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

6,705 篇 会议
116 册 图书
70 篇 期刊文献

馆藏范围

6,891 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,560 篇 工学
- 3,366 篇 计算机科学与技术...
- 1,736 篇 软件工程
- 1,054 篇 信息与通信工程
- 673 篇 电气工程
- 228 篇 控制科学与工程
- 179 篇 电子科学与技术（可...
- 143 篇 机械工程
- 97 篇 动力工程及工程热...
- 97 篇 生物工程
- 94 篇 测绘科学与技术
- 42 篇 生物医学工程（可授...
- 40 篇 仪器科学与技术
- 39 篇 光学工程
- 34 篇 建筑学
- 29 篇 土木工程
- 28 篇 网络空间安全
- 27 篇 安全科学与工程
771 篇 理学
- 568 篇 数学
- 112 篇 生物学
- 102 篇 统计学（可授理学、...
- 100 篇 物理学
- 75 篇 系统科学
- 37 篇 化学
511 篇 管理学
- 383 篇 管理科学与工程(可...
- 197 篇 工商管理
- 155 篇 图书情报与档案管...
82 篇 医学
- 80 篇 临床医学
- 25 篇 基础医学(可授医学...
42 篇 经济学
- 42 篇 应用经济学
35 篇 法学
- 25 篇 社会学
12 篇 农学
10 篇 文学
8 篇 教育学
8 篇 军事学
1 篇 艺术学

主题

898 篇 distributed comp...
802 篇 computer network...
513 篇 concurrent compu...
450 篇 wireless sensor ...
377 篇 parallel process...
354 篇 computer science
352 篇 routing
344 篇 neural networks
341 篇 computational mo...
304 篇 computer archite...
260 篇 application soft...
255 篇 network topology
253 篇 peer to peer com...
253 篇 protocols
244 篇 bandwidth
235 篇 grid computing
232 篇 software enginee...
189 篇 costs
177 篇 intelligent netw...
176 篇 artificial intel...

机构

28 篇 institute of com...
24 篇 shandong provinc...
20 篇 key laboratory o...
15 篇 shandong fundame...
14 篇 department of co...
13 篇 university of ch...
12 篇 department of co...
11 篇 department of co...
11 篇 department of co...
11 篇 institute of com...
11 篇 national laborat...
11 篇 beijing universi...
10 篇 college of compu...
10 篇 institute of inf...
10 篇 school of cyber ...
10 篇 department of co...
10 篇 school of comput...
9 篇 shandong enginee...
9 篇 department of co...
9 篇 school of comput...

作者

16 篇 jiannong cao
15 篇 m. takizawa
15 篇 li dongsheng
15 篇 shen hong
15 篇 hong shen
14 篇 shietung peng
14 篇 eyas el-qawasmeh
13 篇 lemin li
13 篇 rothermel kurt
12 篇 hai jin
11 篇 dongsheng li
11 篇 wei zhang
10 篇 koibuchi michihi...
10 篇 jack dongarra
10 篇 t. enokido
10 篇 wang yang
10 篇 stephen l. scott
10 篇 j. duato
10 篇 m. ould-khaoua
9 篇 duerr frank

语言

6,711 篇 英文
168 篇 其他
17 篇 中文

检索条件"任意字段=IASTED International Conference on Parallel and Distributed Computing and Networks"

共 6891 条记录，以下是541-550 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Design and implementation of big data processing system based on Hadoop 6

Design and implementation of big data processing system base...

引用

6th international conference on Futuristic Trends in networks and computing Technologies, FTNCT 2024

作者： Huang, Siqi Xie, Zhenqiang Wang, Jiaxiang Lv, Penghui Wang, Wenrong Guizhou Vocational Technology College of Electronics and Information Kaili556000 China CETC Big Data Research Institute Co. Ltd. Guiyang550022 China

With the increasing application of big data, efficient big data processing systems can improve the utilization of big data and create greater value. Cloud computing technology is a product of massive data, and this article proposes a big data processing system developed based on the Hadoop cloud computing platform. The system adopts technologies such as distributed file system HDFS, distributed parallel database MapReduce, and distributed computing framework Spark. This system can process massive amounts of data without the need for programming, greatly improving data processing efficiency and having broad application prospects. This article analyzes the design and implementation methods of a big data processing system based on Hadoop, and conducts performance tests on it. The test results show that the system has high performance and meets basic big data processing requirements. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Proceedings - 2021 IEEE 27th international conference on parallel and distributed Systems, ICPADS 2021

Proceedings - 2021 IEEE 27th International Conference on Par...

引用

27th IEEE international conference on parallel and distributed Systems, ICPADS 2021

ISBN: (纸本)9781665408783

The proceedings contain 117 papers. The topics discussed include: a novel iBeacon deployment scheme for indoor pedestrian positioning;near-optimal resource allocation and virtual network function placement at network edges;predicting downside in stock market using knowledge and news data;two-layer traffic signal optimization: a EDGE-assisted pressure balance approach based on cooperative game;passenger payment willingness prediction by static and dynamic multi-dimensional ticket attributes fusion;towards network-accelerated ML-based distributed computer vision systems;ATO-EDGE: adaptive task offloading for deep learning in resource-constrained edge computing systems;anti-replay: a fast and lightweight voice replay attack detection system;an electromagnetic covert channel based on neural network architecture;choosing appropriate AI-enabled edge devices, not the costly ones;and BRNN-GAN: generative adversarial networks with bi-directional recurrent neural networks for multivariate time series imputation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Tuberculosis Detection Using ResNet50: A Deep Learning Approach for Medical Image Classification

Tuberculosis Detection Using ResNet50: A Deep Learning Appro...

引用

international conference on parallel distributed and Grid computing (PDGC)

作者： Eshika Jain Sunila Choudhary Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In this study, a robust automated system for TB detection from chest X-ray images using deep learning techniques is developed. The dataset, sourced from Kaggle, comprises 4200 images divided into two classes: Normal and Tuberculosis. Image preprocessing steps included resizing images to 256x256 pixels, the normalization of pixel values, and data augmentation techniques to augment data variability and help to avoid overfitting. The dataset was divided into 80% for training and 20% for validating the model. The final accuracy of this fine-tuned Resnet50 model for this task was 91%. A fully connected classifier was used as part of the architecture and performed well in binary classification. Precision (80%), recall (90.9%), F1-score (85.7%), and a confusion matrix were derived for performance metrics of the model, which demonstrated high recall (90.9 %) for TB cases in timely diagnosis. Yet, with a lower precision (81.2%), it is necessary to reduce false positives. Deep learning, as highlighted in this research, is a scalable and accurate solution for TB detection. Future work will address increasing precision, dataset diversification, and the validation of the model for clinical applicability to a broader population. The results suggest the capabilities of AI in bringing augmentation to healthcare efficiency and hastening the path towards better patient outcomes.

关键词： Deep learning Training Accuracy Tuberculosis Medical diagnostic imaging X-ray imaging Tuning Stress Residual neural networks Image classification

来源：评论

学校读者我要写书评

暂无评论

A Priority-Driven Deep Reinforcement Learning Scheduler for Resource Management

A Priority-Driven Deep Reinforcement Learning Scheduler for ...

引用

Collaborative Advances in Software and computing (CASCON), international conference on

作者： Hajer Ayadi Aijun An Yiming Shao Junjie Deng Jimmy X. Huang Michael Feiman Hao Zhou York university Toronto ON Canada IBM Spectrum Computing Toronto ON Canada

ISBN: (数字)9798331504830

ISBN: (纸本)9798331515621

Deep neural networks (DNNs) have gained popularity in various fields, including computer vision and speech recognition. However, the growing size of data and complexity of models have made training DNNs increasingly time-consuming. To accelerate computation, parallel and distributed training of DNNs using multiple GPUs is widely adopted. This approach, however, introduces significant challenges in managing and scheduling GPU resources. One critical challenge is job prioritization, which ensures that urgent jobs are processed promptly. Current solutions often overlook prioritization settings, leading to suboptimal performance. For example, neglecting prioritization can result in long delays for urgent jobs. Conversely, overly prioritizing urgent jobs without balancing the needs of other jobs can lead to extended completion times (JCT) for less urgent tasks. While existing approaches that incorporate prioritization typically rely on heuristic-based methods, which may not be optimal, deep reinforcement learning (DRL)-based methods offer greater adaptability and can generally outperform traditional heuristic-based approaches. To address these challenges, we introduce Prioritize, a novel GPU job scheduler designed to enhance GPU resource management in distributed DNN training. Prioritize employs a DRL model to efficiently select jobs based on their priority. To minimize the JCT for lower-priority jobs, Prioritize dynamically increases their priority over time, thereby reducing their queueing delays. The DRL model maximizes a priority-based reward to ensure effective job selection. Extensive experiments demonstrate that Prioritize outperforms several baseline methods in terms of queueing time, JCT, and Makespan. The proposed approach not only achieves superior scheduling performance but also effectively addresses prioritization challenges, making Prioritize a promising solution for efficient GPU job scheduling in distributed DNN training scenarios.

关键词： Training Computational modeling Graphics processing units Artificial neural networks Speech recognition Deep reinforcement learning Throughput Software Delays Resource management

来源：评论

学校读者我要写书评

暂无评论

Detecting Network Intrusions with Resilient Approaches Based on Convolutional Neural networks 23rd

Detecting Network Intrusions with Resilient Approaches Based...

引用

23rd international conference on parallel and distributed computing, Applications, and Technologies, PDCAT 2022

作者： Al-Sarray, Fatin Neamah Ridha Zolkepli, Maslina Directorate of Studies and Planning and Follow-Up Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Selangor43400 Malaysia

ISBN: (纸本)9783031299261

An anomaly-dependent network intrusion detection method is proposed by presenting a hybrid of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) approaches where it detects various network attacks by training a CNN on the NSL-KDD dataset. The proposed approach is able to automatically discovers significant traits without human intervention and achieves higher accuracy in detecting attacks which can improve the performance of intrusion detection systems. The CNN-LSTM approach is applied to detect attacks such as DoS, Probe, U2R, and R2L, and identify normal traffic at the same time. The experimental result reached a low false-positive rate, a high accuracy rate, and a low time average. The results show that the proposed approach outperformed the current state-of-the-art by 4.69% in terms of accuracy and conventional CNN achieved a greater accuracy of roughly 3% compared to the current approach, which amounted to 98.95%, 97.81%, and 94.26% respectively. The consistency of the simulated attacks is unequal which leads to issues in the prediction, therefore, additional efforts to collect more consistent traffic on unusual attacks are required. The proposed approach is targeted to be applied as an effective tool for solving complex classification problems such as NIDS. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Partitioning Sparse Deep Neural networks for Scalable Training and Inference 21

Partitioning Sparse Deep Neural Networks for Scalable Traini...

引用

35th ACM international conference on Supercomputing (ICS)

作者： Demirci, Gunduz Vehbi Ferhatosmanoglu, Hakan Univ Warwick Coventry W Midlands England

ISBN: (纸本)9781450383356

The state-of-the-art deep neural networks (DNNs) have significant computational and data management requirements. The size of both training data and models continue to increase. Sparsification and pruning methods are shown to be effective in removing a large fraction of connections in DNNs. The resulting sparse networks present unique challenges to further improve the computational efficiency of training and inference in deep learning. Both the feed-forward (inference) and backpropagation steps in stochastic gradient descent (SGD) algorithm for training sparse DNNs involve consecutive sparse matrix-vector multiplications (SpMVs). We first introduce a distributed-memory parallel SpMV-based solution for the SGD algorithm to improve its scalability. The parallelization approach is based on row-wise partitioning of weight matrices that represent neuron connections between consecutive layers. We then propose a novel hypergraph model for partitioning weight matrices to reduce the total communication volume and ensure computational load-balance among processors. Experiments performed on sparse DNNs demonstrate that the proposed solution is highly efficient and scalable. By utilizing the proposed matrix partitioning scheme, the performance of our solution is further improved significantly.

关键词： Scalable Deep Learning Sparse Deep Neural networks distributed Stochastic Gradient Descent Hypergraph Partitioning Sparse Matrix Vector Multiplication

来源：评论

学校读者我要写书评

暂无评论

Porting SYCL accelerated neural network frameworks to edge devices 23

Porting SYCL accelerated neural network frameworks to edge d...

引用

international conference on OpenCL (IWOCL)

作者： Gonzalez, Hector Arroyo Angus, Dylan Goli, Mehdi Keir, Paul Riordan, James Georgiev, Svetlozar Univ West Scotland Glasgow Lanark Scotland Codeplay Edinburgh Midlothian Scotland

ISBN: (纸本)9798400707452

Portable hardware acceleration has become increasingly necessary with the rise of the popularity of edge computing. Edge computing, referring to the distributed computing paradigm that encourages data to be processed and stored as close to the source of origination as possible, is needed in areas where bandwidth and latency are restricted and network stability, privacy, or security are unreliable or insecure. Examples of such situations are autonomous mobile robotics, such as autonomous tractors, which often have numerous cameras connected to the host, all needing processing in areas where there can be no reliable connection to a cloud-based platform. Additionally, bridge surveying drones, where mapping and path-planning are needed with low latency, can benefit from a lightweight, compact, low-powered device, especially when there are size and energy consumption requirements. Thus, edge devices, which work as small but compact computers, leverage onboard accelerators to tackle various Robotics, Computer Vision and AI tasks directly on the device without needing an external connection. These accelerators often take the popular form of a GPU like Nvidia's Jetson development kit series, which are driven by the same workflows of Nvidia's AI software and cloud-native frameworks while staying lean, compact and less energy-demanding. However, with the increasing popularity of FPGAs, in the future we could see more edge devices like AMD and Xilinx's KR260 robotics development kit, that operate at low power. Hence, with the surge of the usefulness of edge devices and variety in the brand and type of accelerators, the need for hardware portability in edge devices expands as well. Thus, as we will show in this talk, SYCL as an open-standard, high-level parallel programming model which provides portability not only at the API level but also at the compiler level provides this hardware portability by enabling the same software to be run on both CPU, GPU and FPGA-based edge devic

关键词： SYCL Machine Learning Edge Devices Nvidia Jetson Neural networks ONNX

来源：评论

学校读者我要写书评

暂无评论

GPS L1 C/A Spoof Detection Through the Application of Deep Neural networks

GPS L1 C/A Spoof Detection Through the Application of Deep N...

引用

IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Hen-Geul Yeh Geoffrey Daniel Farthing Department of Electrical Engineering California State University Long Beach CA U.S.A

GPS L1 C/A is an unprotected signal crucial to many aspects of everyday life such as maritime and commercial air navigation, commuter navigation, and world’s critical infrastructure. This paper presents a novel approach to GPS L1 C/A spoof detection through the use of deep learning using only the acquisition and tracking stages of a software defined radio (SDR) receiver. This contrasts with other research as this approach does not rely on data extracted from the Navigation Data such as pseudoranges, clock error, and other collected information from the broadcasted ***, the deep learning method applied use a Convolutional Neural networks (CNN), where the number of inputs varies with a variety of receiver data collected. The frequency-domain transformation from the time-domain on the dataset was applied as the input to CNN. Through the application of the frequency-domain CNNs, results indicated an accuracy of 100% is possible by using multiple datasets collected from either the acquisition or tracking stages of a GPS receiver during a Jam and Spoof (JAS) environment.

关键词：

来源：评论

学校读者我要写书评

暂无评论

LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Yuchen Liu Hu Liang Ran Qiu Shengrong Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network’s lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Regression and Classification-Based Model Predictive Maintenance of Aircrafts Using Machine Learning

Regression and Classification-Based Model Predictive Mainten...

引用

international conference on parallel distributed and Grid computing (PDGC)

作者： Aamir Shafi Banday Jasgurpreet Singh Chohan Raman Kumar Department of Mechanical Engineering Chandigarh University Mohali Punjab

ISBN: (纸本)9781665454025

One of the main hurdles of Industry 4.0 is to predict Aircraft failures Since downtime costs money and reduces productivity, the ability to prevent failures is essential. Knowing how many cycles or RULs remain before a breakdown occurs is crucial for Aircraftry. Where possible under the same circumstances, the RUL estimations should be based on prior observations. The development of technologies that track equipment's present state is at the heart of Evaluate the influence research.. Despite the enormous research on this subject, no one universal strategy exists. The lack of a universal method is what drives this notion, which uses recurrent neural networks (RNN) for the monitoring of the proposed approach.

关键词： Productivity Heart Recurrent neural networks Machine learning Predictive models Grid computing Fourth Industrial Revolution

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 51 52 53 54 55 56 57 58 59 60 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：