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

4,137 篇 会议
38 册 图书
6 篇 期刊文献

馆藏范围

4,181 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,738 篇 工学
- 2,465 篇 计算机科学与技术...
- 2,042 篇 软件工程
- 616 篇 生物工程
- 587 篇 信息与通信工程
- 362 篇 生物医学工程（可授...
- 266 篇 控制科学与工程
- 216 篇 电气工程
- 211 篇 光学工程
- 148 篇 安全科学与工程
- 136 篇 机械工程
- 124 篇 电子科学与技术（可...
- 118 篇 化学工程与技术
- 107 篇 交通运输工程
- 91 篇 动力工程及工程热...
1,369 篇 理学
- 675 篇 数学
- 646 篇 生物学
- 332 篇 统计学（可授理学、...
- 307 篇 物理学
- 197 篇 系统科学
- 127 篇 化学
720 篇 管理学
- 393 篇 管理科学与工程(可...
- 388 篇 图书情报与档案管...
- 237 篇 工商管理
340 篇 医学
- 315 篇 临床医学
- 308 篇 基础医学(可授医学...
- 221 篇 药学(可授医学、理...
- 125 篇 公共卫生与预防医...
159 篇 法学
- 138 篇 社会学
157 篇 教育学
- 155 篇 教育学
141 篇 农学
- 140 篇 作物学
89 篇 经济学
9 篇 军事学
5 篇 文学

主题

526 篇 machine learning
243 篇 deep learning
117 篇 support vector m...
99 篇 adversarial mach...
92 篇 accuracy
92 篇 machine learning...
83 篇 forecasting
75 篇 convolutional ne...
70 篇 predictive model...
69 篇 feature extracti...
58 篇 artificial intel...
53 篇 students
51 篇 learning systems
48 篇 decision trees
48 篇 training
47 篇 reinforcement le...
47 篇 computational mo...
47 篇 computer vision
46 篇 internet of thin...
41 篇 neural networks

机构

46 篇 chitkara univers...
15 篇 graphic era hill...
10 篇 college of manag...
9 篇 graphic era deem...
8 篇 computer science...
8 篇 beijing universi...
7 篇 school of comput...
7 篇 school of comput...
7 篇 school of comput...
7 篇 department of in...
6 篇 università di ca...
6 篇 infn sezione di ...
6 篇 north-west unive...
6 篇 computer science...
6 篇 lovely professio...
6 篇 in2p3 lpc campus...
6 篇 kkr and ksr inst...
6 篇 institut univers...
6 篇 national centre ...
6 篇 greater bay area...

作者

18 篇 kukreja vinay
15 篇 mehta shiva
10 篇 william p.
8 篇 sharma vikrant
7 篇 mahamudul hasan
7 篇 shiva mehta
6 篇 chen a.
6 篇 benoit d.m.
6 篇 ferrara g.
6 篇 ardid m.
6 篇 calvo d.
6 篇 gagliardini s.
6 篇 eddymaoui a.
6 篇 kulikovskiy v.
6 篇 labalme m.
6 篇 gal t.
6 篇 bozza c.
6 篇 khatun a.
6 篇 kalekin o.
6 篇 huang f.

语言

3,930 篇 英文
250 篇 其他
34 篇 中文

检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4181 条记录，以下是3391-3400 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：

4th international Workshop on Human Brain and Artificial Intelligence, HBAI 2024

4th International Workshop on Human Brain and Artificial Int...

引用

4th international Workshop on Human Brain and Artificial Intelligence, HBAI 2024

ISBN: (纸本)9789819640003

the proceedings contain 28 papers. the special focus in this conference is on Human Brain and Artificial Intelligence. the topics include: Uncovering Cognitive Taskonomy through Transfer learning in Masked Autoencoder-Based fMRI Reconstruction;Interpersonal Relationship Analysis with Dyadic EEG Signals via learning Spatial-Temporal Patterns;Effect of Music Training in Neural Responses to Emotional Speech Prosody: Insights from EEG and Brain Network Analysis;potential Indicator for Continuous Emotion Arousal by Dynamic Neural Synchrony;Exploring EEG-Based Neural Correlates of Multivariate Ordinal Emotion Representations;the Co-varying Multimodal Pattern in Treatment-Resistant and Non-treatment-Resistant Schizophrenia;investigating the Dynamics of Seizure Neuroactivities Using Hidden Markov Model;suppressing Seizure via Optimal Electrical Stimulation to the Hub of Epileptic Brain Network;SVFormer: A Direct Training Spiking Transformer for Efficient Video Action Recognition;BL-BERT: Extracting Body Language from Behavior Sequences in Freely Moving Mice;Benchmarking Neural Decoding Backbones Towards Enhanced On-Edge iBCI applications;Enhanced Local Attention with Deep Neural Networks for EEG Decoding;Mirror Contrastive Loss Based Sliding Window Transformer for Subject-Independent Motor Imagery Based EEG Signal Recognition;Active Urination Detection Using EEG Based on FBCNet;D2CAN: Domain-Guided Contrastive Adversarial Network for EEG-Based Cross-Subject Cognitive Workload Decoding;group-Specific Fusion Model and Its application in Identifying Multimodal Co-varying Diagnostic Patterns for Psychiatric Disorders;multi-category Brain Tumor Segmentation via Multi-scale and Cross-category Relation Modeling;consistent Brain Age Difference in Childhood Autism Spectrum Disorder and its Subtypes;Brain-Aware Readout Layers in GNNs: Advancing Alzheimer’s Early Detection and Neuroimaging;TSICNet: Importance of Connectome Information for Epilepsy Classification;a Brain-Inspired Distr

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hybrid Quantum-Classical machine learning for Near Real-time Space to Ground Communication of ISS Lightning Imaging Sensor Data

Hybrid Quantum-Classical Machine Learning for Near Real-time...

引用

IEEE Annual Computing and Communication Workshop and conference (CCWC)

作者： Samih Fadli Bharat S Rawal Computer Science Department Capitol Technology University

Near real-time sensor data is growing exponentially faster than our ability to sense and make sense of it as the current classical computing and supercomputing approaches require an enormous volume of computational resources, storage, and training time. Quantum computing is posed to exponentially outperforms today's high-performance computers and accelerate the evolution of information occurring in classical systems and sensor networks. the Lightning Imaging Sensor (LIS) mounted on the international Space Station (ISS) locates, senses, and detects lightning activities from low Earth orbit and measures radiant energy at millisecond timing over a broad regional spectrum. In this paper, we are introducing a space-to-ground hybrid quantum-classical machine learning architecture to demonstrate the application potential and the feasibility of Hybrid Quantum Neural Networks using the ISS LIS lightning dataset and a corresponding background dataset as inputs (1) to train a classical deep neural network model (2) where the feature extraction outputs are encoded as quantum states using multiple calibrated quantum encoding patterns (3) then used as part of a quantum feature mapping process, (4) allowing for enhanced hybrid QNN training, optimization, and auto-differentiation, setting the path towards polynomial advantage. the following is how the rest of the paper is structured: the introduction is covered in section I, and the motivation and our contribution are covered in section II. Section III presents related works. Section IV describes the NRT lightning imaging sensor. Section V describes the space- to-ground hybrid quantum architecture. Section VI. Space-to-ground hybrid quantum-classical machine learning architecture. Section VII describes quantum machine learning. Section VIII presents a performance analysis. Finally, section XI concludes the research paper.

关键词： Training Quantum computing Neural networks Lightning Imaging computer architecture Quantum state

来源：评论

学校读者我要写书评

暂无评论

Deep learning for law enforcement: A survey about three application domains 4

Deep learning for law enforcement: A survey about three appl...

引用

4th international conference on Recent Trends and applications in computer Science and Information Technology, RTA-CSIT 2021

作者： Contardo, Paolo Sernani, Paolo Falcionelli, Nicola Dragoni, Aldo Franco Information Engineering Department Università Politecnica delle Marche Via Brecce Bianche 12 Ancona60131 Italy Gabinetto Interregionale di Polizia Scientifica per le Marche e l’Abruzzo Via Gervasoni 19 Ancona60129 Italy

Deep learning is rapidly growing, obtaining groundbreaking results in speech recognition, image processing, pattern recognition, and many other application domains. Following the success of deep learning, many automatic data analysis techniques are becoming common also in law enforcement agencies. To this end, we present a survey about the potential impact of deep learning on three application domains, peculiar to law enforcement agencies. Specifically, we analyze the findings about deep learning for Face Recognition, Fingerprint Recognition, and Violence Detection. In fact, combining 1) data from the routine procedure of collecting a subject frontal and profile pictures and her/his fingerprints, 2) the pervasiveness of surveillance cameras, and 3) the capability of learning from a huge amount of data, might support the next steps in crime prevention. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Research on Network Pruning based Target Detection Model for CenterFusion

Research on Network Pruning based Target Detection Model for...

引用

computer, Big Data and Artificial Intelligence (ICCBD+AI), international conference on

作者： Junlong Duan Yumei Li Changshun Jiang Hao Lan College of Engineering and Design Hunan Normal University Changsha China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

Deep learning has achieved remarkable success in the field of object detection. However, efficiently deploying these models in embedded systems still poses challenges related to model size and computational complexity. this paper focuses on optimizing the CenterFusion model through the application of the weight-based L1 norm pruning method. We adopt a targeted network pruning approach, specifically addressing the Centernet component within CenterFusion, to reduce the model’s parameter count and complexity, making it more suitable for resource-constrained environments in embedded systems. the innovation of this research lies in the inaugural application of the weight-based L1 norm pruning method to the CenterFusion model. Additionally, it incorporates two iterations of pruning based on weight changes, allowing for further compression of the network model. this provides a practical solution for the efficient deployment of the model on embedded platforms. Testing before and after pruning shows a reduction in the model size by 31.86% and 52.94%, with an overall accuracy loss of only 3.18%. the pruned model significantly reduces volume while maintaining object detection performance. this suggests that the pruned network achieves a lightweight object detection model, demonstrating the feasibility and effectiveness of deploying advanced object detection technology in embedded systems.

关键词： Performance evaluation Deep learning Technological innovation Solid modeling Embedded systems Power demand Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Load prediction optimization based on machine learning in cloud computing environment 22

Load prediction optimization based on machine learning in cl...

引用

proceedings of the 4th international conference on Advanced Information Science and System

作者： Guozheng Feng Jianbo Xu Wei Jian Zhang Liu Hunan University Of Science and Technology China

ISBN: (纸本)9781450397933

the load prediction of host resources is a key issue to enhance the cloud computing aid allocation system. With the change of cloud computing resource load displaying extra and extra complicated characteristics, traditional prediction algorithms can solely predict the linear traits of data, and it is tough to precisely predict useful resource usage. In order to enhance the forecasting accuracy of the model, a blended load forecasting algorithm based totally on machine learning is proposed. the machine learning prediction model can nicely match the nonlinear traits of the data. the linear phase of the algorithm makes use of ARIMA prediction, and the nonlinear section makes use of particle swarm optimization algorithm to optimize LSTM prediction. then, the optimal least squares method is used to redistribute the prediction error weights of the autoregressive differential moving average model (ARIMA) and the long-term and short-term memory network models (LSTM), and finally the prediction results are output. the comparison experiment is carried out with the open actual load data set. the experimental results show that the prediction accuracy of the weight redistribution combination model is significantly higher than that of other traditional prediction models and machine learning prediction models when the prediction time efficiency is similar, and the real-time prediction error of resource load in cloud environment is significantly reduced.

关键词： ARIMA

来源：评论

学校读者我要写书评

暂无评论

Research on Arrhythmia of College Students Based on Convolutional Neural Network 22

Research on Arrhythmia of College Students Based on Convolut...

引用

proceedings of the 2022 5th international conference on machine learning and machine Intelligence

作者： Lei Qi Fuchun Zhang Baiyang Wang Meng Li School of Materials Science and Engineering Linyi University China School of Computer Science and Engineering Linyi University China

ISBN: (纸本)9781450397551

Arrhythmia is a group of common diseases related to irregular heart rate. Accurate classification of electrocardiogram is very important for college students to detect heart disease. Experts will spend a major expenditure of time and effort in the examination and analysis of college students' ECG waveform. In order to solve this problem, this paper proposes a method of college students' arrhythmia detection based on deep learning, which classifies different types of arrhythmias. Firstly, 1-D ECG signals are converted into 2-D ECG images. then the signals of different channels are fused and finally input into the network. In this work, we do experiments on the basis of residual network. For verifying the feasibility of this method, it is trained and verified in the public data set MIT-BIH, and the classification accuracy reaches 99.00%. the experimental results proved that it can effectively and reliably identify ECG signals, and has potential clinical application value. It can assist doctors to screen college students for arrhythmias.

关键词： College student Convolutional neural network Deep learning ECG signal

来源：评论

学校读者我要写书评

暂无评论

A real-time framework for dangerous behavior detection based on deep learning 22

A real-time framework for dangerous behavior detection based...

引用

proceedings of the 2022 4th international conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Heng Yang Pengjie Liu Shiyuan Li Hongyu Liu Haofeng Wang Ark key laboratory Beijing Institute of Computer Technology and Application China

ISBN: (纸本)9781450398343

Identifying dangerous behaviors of workers in complex production operation scenarios is an important research field of intelligent monitoring technology. When implementing practical applications, it often has complex and diverse function requirements and business logic. To this end, this paper proposes a unified and simple framework for monitoring unsafe behavior based on deep learning technology. We first conduct data analysis and demand logic disassembly based on six actual production scenarios and decouple the complex task requirement as a coupled problem of object detection function, adaptive and variable scene recognition function, behavior analysis function, and safety logic reasoning function. then we build a unified detection framework and use four sub-modules for real-time and high-efficiency detection, which are a perceptron-based efficient scene recognition module, a Yolov5s-based real-time object detection module, an area-based behavior judgment module, and a configurable safety rule inference module. While maintaining the characteristics of splitting and combining these modules for different application requirements, their effects on the test set have also reached the best. Among them, the accuracy rate of the scene recognition module has reached 100.00%, and the mAP of the object detection module has reached 99.06%, average FPS of the overall framework reached 72.05.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Interpretation Study of Unknown Oracle Bones Based on Self-training learning 22

Interpretation Study of Unknown Oracle Bones Based on Self-t...

引用

proceedings of the 2022 4th international conference on Video, Signal and Image Processing

作者： Junya Liu Rujia Li Ting Fu Zhijian Yin Zhen Yang Foreign Languages College Jiangxi Science and Technology Normal University China Graduate School Jiangxi Science and Technology Normal University China

ISBN: (纸本)9781450397810

the oracle bone script is the oldest writing system in China, and as the origin of Chinese characters, it is also the earliest cultural heritage in China. With the development of computer technology, deep learning has become an important technology of image classification and played a very significant role in oracle research. In this paper, oracle bone inscription recognition is proposed based on semi-supervised self-training learning, which learns the relationship between oracle bones, clementi, seal script and official script, and uses other existing font features to learn oracle bone characters. In the experiment, the detection accuracy of oracle script fonts has reached 87.9%, and the probability of character recognition is 28.2%, which proves that there is a certain relationship in the development of oracle bone inscriptions.

关键词： Self-training learning Convolutional neural network Oracle bone inscription recognition

来源：评论

学校读者我要写书评

暂无评论

4th international Workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with 24th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2021

4th International Workshop on Predictive Intelligence in Med...

引用

4th international Workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with 24th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2021

ISBN: (纸本)9783030876012

the proceedings contain 25 papers. the special focus in this conference is on Predictive Intelligence in Medicine. the topics include: Self Supervised Contrastive learning on Multiple Breast Modalities Boosts Classification Performance;self-guided Multi-attention Network for Periventricular Leukomalacia Recognition;opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray;multi-task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer;Integrating Multimodal MRIs for Adult ADHD Identification with Heterogeneous Graph Attention Convolutional Network;probabilistic Deep learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution;a Multi-scale Capsule Network for Improving Diagnostic Generalizability in Breast Cancer Diagnosis Using Ultrasonography;prediction of Pathological Complete Response to Neoadjuvant Chemotherapy Using Multi-scale Patch learning with Mammography;the Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification;a Few-Shot learning Graph Multi-trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint;anatomical Structure-Aware Pulmonary Nodule Detection via Parallel Multi-task RoI Head;towards Cancer Patients Classification Using Liquid Biopsy;foreseeing Survival through ‘Fuzzy Intelligence’: A Cognitively-Inspired Incremental learning Based de novo Model for Breast Cancer Prognosis by Multi-Omics Data Fusion;improving Across Dataset Brain Age Predictions Using Transfer learning;uncertainty-Based Dynamic Graph Neighborhoods for Medical Segmentation;FLAT-Net: Longitudinal Brain Graph Evolution Prediction from a Few Training Representative Templates;one Representative-Shot learning Using a Population-Driven Template with application to Brain Connectivity Classification and Evolution Prediction;Mixing-AdaSIN: Constructing a De-biased Dataset Using

关键词：

来源：评论

学校读者我要写书评

暂无评论

STAGRU: Ocean Surface Current Spatio-Temporal Prediction Based on Deep learning 4

STAGRU: Ocean Surface Current Spatio-Temporal Prediction Bas...

引用

4th international conference on computer Information Science and Artificial Intelligence, CISAI 2021

作者： Chen, Ping Chi, Meng-Ya Ocean University of China OUC College of Information Science and Engineering Qingdao China Ocean University of China OUC Department of Educational Technology Qingdao China

ISBN: (纸本)9781665406925

Realizing the accurate prediction of ocean current has very important scientific value, practical application value and research significance. Due to the large scale and complexity of ocean data sets, traditional methods for ocean current prediction have many challenges in terms of efficiency and accuracy. It is proved that the deep learning method can learn the spatiotemporal characteristics of a large amount of data, and it is very effective and accurate to predict data with complex structures. In this paper, we present a new model structure STAGRU, which includes Spatial Module to extract spatial characteristic and the Attention Model to extract the nearest neighbor time correlation information based on the GRU. We conduct a series of experiments on the offshore data set of eastern China, and compare with the previous models. the results show that our model outperforms many baselines in RMSE on the component of U and V has been increased by 5.85% and 3.40% respectively. © 2021 IEEE.

关键词： Forecasting

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419页 << < 336 337 338 339 340 341 342 343 344 345 > >>

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：