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

6,793 篇 会议
630 册 图书
130 篇 期刊文献

馆藏范围

7,552 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

3,899 篇 工学
- 3,090 篇 计算机科学与技术...
- 1,403 篇 软件工程
- 794 篇 电气工程
- 679 篇 信息与通信工程
- 503 篇 控制科学与工程
- 299 篇 生物工程
- 281 篇 生物医学工程（可授...
- 265 篇 机械工程
- 191 篇 光学工程
- 163 篇 电子科学与技术（可...
- 145 篇 仪器科学与技术
- 140 篇 化学工程与技术
- 59 篇 安全科学与工程
- 45 篇 材料科学与工程（可...
- 36 篇 交通运输工程
1,222 篇 理学
- 673 篇 数学
- 356 篇 物理学
- 334 篇 生物学
- 198 篇 统计学（可授理学、...
- 147 篇 化学
- 105 篇 系统科学
553 篇 管理学
- 314 篇 管理科学与工程(可...
- 255 篇 图书情报与档案管...
- 59 篇 工商管理
274 篇 医学
- 230 篇 临床医学
- 109 篇 基础医学(可授医学...
- 75 篇 药学(可授医学、理...
66 篇 法学
- 62 篇 社会学
51 篇 艺术学
- 49 篇 设计学（可授艺术学...
42 篇 农学
- 39 篇 作物学
21 篇 教育学
19 篇 文学
18 篇 经济学
5 篇 军事学

主题

3,150 篇 pattern recognit...
535 篇 feature extracti...
429 篇 artificial intel...
385 篇 neural networks
295 篇 data mining
278 篇 machine learning
234 篇 face recognition
224 篇 pattern matching
215 篇 support vector m...
203 篇 image segmentati...
194 篇 pattern classifi...
193 篇 computer imaging...
183 篇 computer science
182 篇 local binary pat...
168 篇 computational mo...
159 篇 computer vision
147 篇 humans
146 篇 testing
142 篇 accuracy
141 篇 training

机构

40 篇 school of comput...
38 篇 national laborat...
25 篇 institute of ima...
24 篇 center for resea...
24 篇 computer vision ...
22 篇 institute of ima...
22 篇 university of ch...
21 篇 department of co...
20 篇 indian statistic...
19 篇 national laborat...
19 篇 institute of com...
16 篇 tongji universit...
16 篇 chinese academy ...
15 篇 institute for pa...
14 篇 national center ...
14 篇 chinese acad sci...
14 篇 częstochowa univ...
13 篇 agh university o...
13 篇 school of electr...
12 篇 indian stat inst...

作者

35 篇 yang jie
31 篇 de-shuang huang
25 篇 tieniu tan
23 篇 umapada pal
19 篇 ryszard tadeusie...
19 篇 liang wang
18 篇 ajith abraham
17 篇 marcin korytkows...
17 篇 lu hanqing
17 篇 jacek m. zurada
17 篇 leszek rutkowski
16 篇 jie yang
16 篇 zhenan sun
16 篇 shamsuddin siti ...
16 篇 pal umapada
15 篇 wang da-han
15 篇 chew lim tan
15 篇 rafał scherer
14 篇 xu changsheng
14 篇 lotfi a. zadeh

语言

7,403 篇 英文
84 篇 其他
77 篇 中文

检索条件"任意字段=International Conference on Soft Computing and Pattern Recognition"

共 7554 条记录，以下是331-340 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：

Efficient Hybrid Technique for Devanagari Numerals recognition

Efficient Hybrid Technique for Devanagari Numerals Recogniti...

引用

2022 international conference on Machine Learning, Big Data, Cloud and Parallel computing, COM-IT-CON 2022

作者： Bura, Deepa Singh, Meeta Nandal, Poonam Khurana, Pooja Faculty of Engineering & Technology Manav Rachna International Institute of Research and Studies Department of Computer Science & Engineering Faridabad India Manav Rachna International Institute of Research and Studies Department of Applied Sciences Faculty of Engineering & Technology Faridabad India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

As a subset of pattern recognition, Handwritten Devanagari Character recognition (HDCR) is one of the exciting field of research from decades but Devanagari numerals don't receive proper attention from the researchers. In India, Government offices provide preferences for filling the application form in Hindi or English & use the scanner for making the record of those applications. But the drawback of the scanned form is that they cannot be edited without using HDCR. This paper proposes a Handwritten Devanagari Numerals recognition (HDNR) technique which is used to identify Devanagari numerals from given digital images, without human intervention. In this paper a novel technique is proposed which perform pre-processing on the inputted image where features are extracted & based on those features Devanagari numerals are classified. © 2022 IEEE.

关键词： Character recognition

来源：评论

学校读者我要写书评

暂无评论

Intelligent False Data Injection Attack Detection Using soft computing in Cyber-Physical Power Systems 7

Intelligent False Data Injection Attack Detection Using Soft...

引用

7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Nilesh Khairnar, Prerana Bindu, K.V. Ala Walid, Md. Abul Jothimani, S. Subha, B. Srivastava, Aviral Sir Visvesvaraya Institute of Technology Department of ETE Nashik India RMK College of Engineering and Technology Department of EEE Puduvoyal India Department of Computer Science & Engineering Khulna9203 Bangladesh Bannari Amman Institute of Technology Sathyamangalam638401 India Kristu Jayanti College Department of Management Bangalore India Amity University Rajasthan Department of CSE Jaipur300202 India

ISBN: (纸本)9798350340600

The modern power system has developed into a cyber-physical system with the incorporation of smart meters, Phasor Measurement Units (PMUs), extensive area monitoring, control systems and substation automation. False Data Injection Attack (FDIA) has gained popularity in Cyber-Physical Power System (CPPS). In particular, there is an increasing need to examine the possible behavior of adversaries to enhance the cybersecurity nature of CPPS but the FDIA model is frequently built on the effects of attacker or the effect of attack. Therefore, this study presents an Intelligent False Data Injection Attack Detection using soft computing (IFDIAD-SD) technique in the CPPS environment. The aim of the IFDIAD-SD technique is the effectual recognition and classification of FDIAs in the CPPS environment. In the IFDIAD-SD method, fuzzy min-max normalization is used to scale the input data. For FDIA detection and classification, convolutional neural network with autoencoder (CNN-AE) method is applied. At final stage, the multiverse optimization (MVO) technique is used to optimally select the parameter values of the CNN-AE model. A series of experiments were implemented for identifying the superior performance of the IFDIAD-SD technique. The comprehensive comparative result analysis revealed the supremacy of the IFDIAD-SD method when compared to other models. © 2023 IEEE.

关键词： Embedded systems

来源：评论

学校读者我要写书评

暂无评论

Exploiting Knowledge Embedded soft Labels for Image recognition 25

Exploiting Knowledge Embedded Soft Labels for Image Recognit...

引用

25th international conference on pattern recognition (ICPR)

作者： Yuan, Lixian Chen, Riquan Wu, Hefeng Chen, Tianshui Wang, Wentao Chen, Pei Sun Yat Sen Univ Guangzhou Peoples R China DarkMatter AI Res Guangzhou Peoples R China

ISBN: (纸本)9781728188089

Objects from correlated classes usually share highly similar appearance while objects from uncorrelated classes are very different. Most of current image recognition works treat each class independently, which ignores these class correlations and inevitably leads to sub-optimal performance in many cases. Fortunately, object classes inherently form a hierarchy with different levels of abstraction and this hierarchy encodes rich correlations among different classes. In this work, we utilize a soft label vector that encodes the prior knowledge of class correlations as extra regularization to train the image classifiers. Specifically, for each class, instead of simply using a one-hot vector, we assign a high value to its correlated classes and assign small values to those uncorrelated ones, thus generating knowledge embedded soft labels. We conduct experiments on both general and fine-grained image recognition benchmarks and demonstrate its superiority compared with existing methods.

关键词： Image recognition

来源：评论

学校读者我要写书评

暂无评论

Intelligent recognition Algorithm of Specific pattern Content Information based on Mobile Terminal Equipment 4

Intelligent Recognition Algorithm of Specific Pattern Conten...

引用

4th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2022

作者： Sheng, Lei Wu, Lijie Wang, Lei Wang, Chunying Quan, Yizhan Information & Telecommunication Company State Grid Henan Electric Power Company Zhengzhou China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

In order to solve the problem of poor intelligent recognition effect of specific mode content information, an intelligent recognition algorithm of specific mode content information based on mobile terminal equipment is proposed. The mobile terminal equipment is used to collect network information, identify the unidirectional characteristics of specific mode content information, formulate a specific mode content information feature evaluation system based on single features, mine and classify multi-directional information, and optimize the intelligent recognition steps of specific mode content information. Finally, experiments show that the intelligent recognition algorithm of specific mode content information based on mobile terminal equipment has high practicability and accuracy, Fully meet the research requirements. © 2022 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Multimodal Attention Networks for Human Activity recognition From Earable Devices

Multimodal Attention Networks for Human Activity Recognition...

引用

2022 ACM international Joint conference on Pervasive and Ubiquitous computing and the 2022 ACM international Symposium on Wearable Computers, UbiComp/ISWC 2022

作者： Stuchbury-Wass, Jake Ferlini, Andrea Mascolo, Cecilia University of Cambridge Cambridge United Kingdom Nokia Bell Labs Cambridge United Kingdom

ISBN: (纸本)9781450394239

Earables (a.k.a ear-worn wearable devices) are gaining traction in the wearables ecosystem for monitoring user health. Human activity recognition (HAR) is a promising use case of earables due to their placement on the head and the combination of sensors. In this paper, we explore using multimodal attention-based neural networks for HAR from the ear. Attention networks have had a large impact on other disciplines' machine learning tasks and we believe they present opportunities in HAR from earable data. Different methods of utilising attention mechanisms in the literature are discussed as well as the benefits and challenges of using such networks in the context of HAR on real systems. © 2022 Owner/Author.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

2022 1st international conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

2022 1st International Conference on Artificial Intelligence...

引用

1st international conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

ISBN: (纸本)9781665425216

The proceedings contain 49 papers. The topics discussed include: analysis of customer reviews using deep neural network;a comparative investigation on the use of machine learning techniques for currency authentication;a diagnostic survey on sybil attack on cloud and assert possibilities in risk mitigation;e-health security on could computing and its challenges;high speed multiplier using embedded approximate 4-2 compressor for image multiplication;a review on self-stabilizing platform in scope of merchant navy applications;study on information management system that connects students and instructor through chatting;machine learning approaches for microscopic image analysis and microbial object detection(MOD) as a decision support system;and literature survey on video surveillance crime activity recognition.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Character Classification and Actor recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup pattern Analysis 8

Character Classification and Actor Recognition in Yakshagana...

引用

8th IEEE international conference on Distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Murthy, Anantha Prathwini Kulkarni, Sanjeev Savitha, G. Nitte Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India Department of Master of Computer Applications Karkala India Srinivas University Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India

ISBN: (纸本)9798350350593

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form. © 2024 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

patternGPT :A pattern-Driven Framework for Large Language Model Text Generation 12

PatternGPT :A Pattern-Driven Framework for Large Language Mo...

引用

12th international conference on computing and pattern recognition, ICCPR 2023

作者： Xiao, Le Shan, Xin Chen, Xiaolin College of Information Science and Engineering Henan University of Technology Zhengzhou China

ISBN: (纸本)9798400707988

Large language models(LLMS) have shown excellent text generation capabilities, capable of generating fluent human-like responses for many downstream tasks. However, applying large language models to real-world critical tasks remains challenging due to their susceptibility to hallucinations and inability to directly use external knowledge. To cope with the above challenges, this paper proposes patternGPT, a pattern-driven text generation framework for Large Language Models. Firstly, the framework utilizes the extraction capability of Large Language Models to generate rich and diversified structured and formalized patterns, which facilitates the introduction of external knowledge to do the computation, and then draws on the idea of federated learning to use multiple agents to achieve the sharing in order to obtain more diversified patterns, and finally uses judgment criteria and optimization algorithm to search for high-quality patterns to guide the generation of models. Finally, external knowledge such as judgment criteria and optimization algorithms are used to search for high-quality patterns, and the searched patterns are used to guide model generation. This framework has the advantages of generating diversified patterns, protecting data privacy, combining external knowledge, and improving the quality of generation, which provides an effective method to optimize the text generation capability of large language models, and make it better applied to the field of intelligent dialogue and content generation. © 2023 ACM.

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of AE-RBM Based Pedestrian Detectors in Autonomous Vehicles: Substantial Issues and Challenges 1

Performance Evaluation of AE-RBM Based Pedestrian Detectors ...

引用

1st IEEE international conference on Electronics, computing, Communication and Control Technology, ICECCC 2024

作者： Reddy, Abhilash Reddy Pabbath Lal, Madan Boobalan, M. Sapkal, Narayan Pandurang Swetha Reddy, A. Kumar, A.Saran Infobuilders inc Department of IT Georgia University College of Engineering and Technology Department of Mechanical Engineering Bikaner India K S R Institute For Engineering And Technology Department of CSE Tamilnadu Tiruchengode India Dr. D.Y. Patil Institute of Technology Mechanical Engineering Dr. D. Y. Patil Unitech Society's Maharashtra Pune India R.M.K. College of Engineering and Technology Electronics and Communication Engineering Tamilnadu Chennai India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India

ISBN: (纸本)9798350371802

Autonomous vehicles' rising profile is a reflection of the widespread interest in them. Improved dependability, reduced fuel consumption, cost savings, and enhanced passenger convenience are all benefits of these cars. However, autonomous vehicles must first have their functionality problems resolved before they can be extensively used on major routes. Pedestrian detection is a crucial component in autonomous vehicles that helps to avoid accidents. Preprocessing, feature extraction, and model training all rely on proper sequencing. A number of filters, such as the Median, Histogram, Dilation and Erosion, Gaussian, mean, and others, can be used to smooth out images before lane marking recognition. Haar wavelet and LBP are the components that make up feature extraction. In order to train for AE-RBM, one must have precise control over the attributes. This method looks so much more modern than the RBM and AE algorithms of today. The results demonstrated a notable enhancement in accuracy, reaching 95.84%. © 2024 IEEE.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

A Novel Implementation of Esophagus Diagnosis Using Deep Learning 2

A Novel Implementation of Esophagus Diagnosis Using Deep Lea...

引用

2nd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Janaki, R. Lakshmi, D. Sri Sai Ram Institute of Technology Department of ECE Chennai India

ISBN: (纸本)9781665437899

The presented work is a systematic review on recent technologies in deep learning for Barrett's esophagus (BE), a disease which affects the food pipe. Evaluation of this disorder can be made easy with the help of model developed with deep learning and artificial intelligence. The dysplasia and adenocarcinoma exhibits a complex pattern for detecting through endoscopic diagnosis. The diagnosis and automatic detection using computer analysis is beneficial for assisting the endoscopy procedure. The deep learning technique developed through manual and automated segmentation for the evaluation of BE disorders. The review is done by compilation of works published in Springer, Binda Wi, IEEEXplore, and Association for computing Machinery, Science Direct and Pubmed on the category of automatic detection of regions for classification purposes. The problem statement, methodology, objective and result of the selected work have been analyzed. © 2022 IEEE.

关键词： pattern recognition

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 << < 30 31 32 33 34 35 36 37 38 39 > >>

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：