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 of Soft Computing and Pattern Recognition"

共 7554 条记录，以下是1051-1060 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：

Exploiting Cliques for Granular computing-based Graph Classification

Exploiting Cliques for Granular Computing-based Graph Classi...

引用

international Joint conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Baldini, Luca Martino, Alessio Rizzi, Antonello Univ Roma La Sapienza Dept Informat Engn Elect & Telecommun Via Eudossiana 18 I-00184 Rome Italy

ISBN: (纸本)9781728169262

The most fascinating aspect of graphs is their ability to encode the information contained in the inner structural organization between its constituting elements. Learning from graphs belong to the so-called Structural pattern recognition, from which Graph Embedding emerged as a successful method for processing graphs by evaluating their dissimilarity in a suitable geometric space. In this paper, we investigate the possibility to perform the embedding into a geometric space by leveraging to peculiar constituent graph substructures extracted from training set, namely the maximal cliques, and providing the performances obtained under three main aspects concerning classification capabilities, running times and model complexity. Thanks to a Granular computing approach, the employed methodology can be seen as a powerful framework able to synthesize models suitable to be interpreted by field-experts, pushing the boundary towards new frontiers in the field of explainable AI and knowledge discovery also in big data contexts.

关键词： Structural pattern recognition Supervised Learning Embedding Spaces Granular computing Graph Edit Distances

来源：评论

学校读者我要写书评

暂无评论

pattern Classification Can Detect Prodromal Alzheimer’s Neurodegenerations 27th

Pattern Classification Can Detect Prodromal Alzheimer’s Neu...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Przybyszewski, Andrzej W. Nowacki, Jerzy P. Drabik, Aldona Polish-Japanese Academy of Information Technology Warszawa02-008 Poland Department of Neurology UMass Medical School WorcesterMA01655 United States

ISBN: (纸本)9783031876592

Alzheimer’s disease (AD)-related neurodegeneration can begin several decades before the first symptoms appear, and AD biomarkers have only recently been identified. Our research focused on identifying AD-related biomarkers in healthy subjects. We analyzed Biocard data using a group of 150 subjects in different AD stages, with 40 AD patients compared to normal subjects. Using the granular computing method, we identified sets of attributes related to various stages of the disease. We applied this classification to the psychophysical test results of normal subjects to determine if some cases might show granular pattern similarities to groups with AD. Earlier detection can uncover patterns invisible to neuropsychologists in the cognitive like executive or immediate/delayed memory functions. These patterns may help to determine if a patient is getting amnestic or non-amnestic early symptoms that lead to dementia or not. Our analysis of Biocard data identified granules that classify cognitive attributes with disease stage scores (CDRSUM - Clinical Dementia Rating Scale Sum of Boxes). By applying these rules to 21 normal subjects with a CDRSUM of 0, we predicted that one subject might develop mild dementia (CDRSUM > 4.5) with a mixture of executive and cognitive test results. Another patient might get very mild dementia (CDRSUM > 2.25), but with dominating memory problems which might lead to serious amnestic symptoms. Other patients might develop questionable impairment (CDRSUM > 0.75). Our AI-powered method can identify patterns in cognitive attributes in normal subjects that might indicate their pre-dementia amnestic or non-amnestic stages, which may be invisible to neuropsychologists. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Granular computing

来源：评论

学校读者我要写书评

暂无评论

FLIoDT: A Federated Learning Architecture from Privacy by Design to Privacy by Default over IoT

FLIoDT: A Federated Learning Architecture from Privacy by De...

引用

international conference on Fog and Mobile Edge computing (FMEC)

作者： Feras M. Awaysheh Sadi Alawadi Sawsan AlZubi Institute of Computer Science University of Tartu Tartu Estonia School of Information Technology Halmstad University Halmstad Sweden Independent Researcher Tartu Estonia

The Internet of Things (IoT) realized exponential growth of smart devices with decent capabilities, promising an era of Edge Intelligence. This paradigm creates a timely need to shift many computations closer to the data source at the network's edge. Data privacy is paramount, as security breaches can severely impact such an environment with its vast attack surface. The advent of Federated learning (FL), a privacy-by-design with decentralized machine learning (ML), enables participants to collaboratively train a model without sharing their sensitive data. Nevertheless, privacy implications are a glaring concern and perrier for widening the utilization of FL approaches and their mass adoption over IoT applications. This paper introduces the notion of FL over the Internet of Disconnected Things (FLIoDT), a functionality separation of concerns following the air-gapped networks. FLIoDT provides a practical methodology to mitigate Data threats/attacks in the FL domain. FLIoDT proves a practical architectural approach to mitigate several attacks in an Edge environment. Data dredging and adversarial attacks, like data poisoning, to name some. This study investigates human activity recognition of health monitoring patient data over edge computing to validate FLIoDT.

关键词： Privacy Multi-access edge computing Federated learning soft sensors Systems engineering and theory Excavation Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Character recognition using Machine Learning and Deep Learning - A Survey 2

Character Recognition using Machine Learning and Deep Learni...

引用

2nd IEEE international conference on Emerging Smart computing and Informatics (ESCI)

作者： Sharma, Reya Kaushik, Baijnath Gondhi, Naveen Shri Mata Vaishno Devi Univ Comp Sci & Engn Katra India

ISBN: (纸本)9781728152639

Digitization of machine printed or handwritten text documents have become very popular with the advancements in computing and technology. Humans have tried to automatized their work by replacing themselves with machines. The transformation from manual to automatization gave rise to several research areas and text recognition is one among them. Deep learning and machine learning techniques have been proved to be very suitable for optical character recognition. In this work, an up-to-date overview of four machine learning and deep learning architectures, viz., Support vector machine, Artificial neural network, Naive Bayes and Convolutional neural network have been discussed in detail.

关键词： Deep learning machine learning OCR CNN pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Building Crack Detection Using CNN and Class Activation Mapping

Building Crack Detection Using CNN and Class Activation Mapp...

引用

Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), international conference on

作者： Bodduru Keerthana Jathuka Aruna Devi Surisetty Madhuri Marada Srinivasa Rao Koduganti Venkata Rao PSS Geethika Department of Information Technology Anil Neerukonda Institute of Technology and Sciences(A) Visakhapatnam Andhra Prades Department of Computer Science and Engineering(AI&ML DS) Anil Neerukonda Institute of Technology and Sciences(A) Visakhapatnam Andhra Prades Department of Information Technology Vignan Institite of Engineering for women Visakhapatnam Andhra Prades Department of Information Technology MVGR College of Engineering Vizianagaram Andhra Pradesh Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Hyderabad India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

As the population increases, further structures are being erected to meet the demands of the populace. Nonetheless, this surge in development presents the challenge of guaranteeing that each edifice fulfills the aspirations of its inhabitants. Identifying and rectifying damage or defects in structures can be challenging, particularly when conventional techniques typically necessitate comprehensive examinations to pinpoint issues. To address this difficulty, we are utilizing advanced technology such as Deep Learning models, particularly Convolutional Neural Networks (CNNs), along with approaches like Class Activation Mapping (CAM). These models utilize extensive datasets to learn and recognize patterns, enabling them to precisely identify specific forms of damage or defects in structures. We intend to forecast probable damage rates in advance through the application of these Deep Learning approaches. This proactive strategy enables us to avert catastrophic incidents by recognizing and resolving problems prior to their escalation. The great precision of these models ensures that buildings are safe and fulfill tenant expectations, hence enhancing the safety and reliability of structures within our communities. Our model attained an accuracy of 88%.

关键词： Deep learning Quantum computing Computational modeling Buildings Safety pattern recognition Convolutional neural networks Reliability Surges Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Automated Make and Model Identification of Reverse Shoulder Implants Using Deep Learning Methodology 6th

Automated Make and Model Identification of Reverse Shoulder ...

引用

6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

作者： Dubey, Ved Prakash Ramanathan, A. Rajagopalan, Senthilvelan Malathy, C. Gayathri, M. Batta, Vineet Kamineni, Srinath School of Computing SRM Institute of Science and Technology Chennai India Department of Orthopaedics MIOT International Hospital Chennai India Department of Orthopaedics and Trauma Luton and Dunstable University Hospital NHS Trust Luton United Kingdom Department of Orthopaedic Surgery and Sports Medicine Elbow Shoulder Research Center University of Kentucky UK HealthCare LexingtonKY United States

ISBN: (纸本)9783031530845

Identification of an Orthopaedic Implant before a revision surgery is very important. Failure to identify an implant causes surgical planning delays, inability to plan for the correct equipment requirements, and can result in poorer patient outcomes. This paper proposes a framework to identify, make and model of two different reverse shoulder implants from X-ray images using Deep Learning Techniques. Both Anterior Posterior and Lateral views of X-rays were used in the study and a comparison was made to identify which view enables better results in identification. Various pre-trained deep learning models such as VGG16, VGG19 and InceptionV3 were used for classification of implants. The proposed methodology identifies both the make and model of the implant with an accuracy of 95% using both Anterior Posterior and Lateral Views and an accuracy of 86.67% using only the Anterior view. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Anomaly Handwritten Text Detection for Automatic Descriptive Answer Evaluation 22

Anomaly Handwritten Text Detection for Automatic Descriptive...

引用

Proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Nilanjana Chatterjee Palaiahnaakote Shivakumara Umapada Pal Tong Lu Yue Lu Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Faculty of Computer Science and Information Technology University of Malaya Malaysia National Key Lab for Novel Software Technology Nanjing University China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China

ISBN: (纸本)9781450397056

Although there are advanced technologies for character recognition, automatic descriptive answer evaluation is an open challenge for the document image analysis community due to large diversified handwritten text and answers to the question. This paper presents a novel method for detecting anomaly handwritten text in the responses written by the students to the questions. The method is proposed based on the fact that when the students are confident in answering questions, the students usually write answers legibly and neatly while they are not confident, they write sloppy writing which may not be easy for the reader to understand. To detect such anomaly handwritten text, we explore a new combination of Fourier transform and deep learning model for detecting edges. This result preserves the structure of handwritten text. For extracting features for classification of anomaly text and normal text, the proposed method studies the behavior of writing style, especially the variation at ascenders and descenders. Therefore, the proposed work draws principal axis which is invariant to rotation, scaling and some extent to distortion for the edge images. With respect to principal axis, the proposed method draws medial axis using uppermost and lowermost points. The distance between the medial axis and principal axis points are considered as feature vector. Further, the feature vector is passed to Artificial Neural Network for classification of anomaly text. The proposed method is evaluated by testing on our own dataset, standard dataset of gender identification (IAM) and handwritten forgery detection dataset (ACPR 2019). The results on different datasets show that the proposed work outperforms the existing methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Complexity vs. Performance in Granular Embedding Spaces for Graph Classification 12th

Complexity vs. Performance in Granular Embedding Spaces for ...

引用

12th international Joint conference on Computational Intelligence (IJCCI) / 12th international conference on Evolutionary Computation Theory and Applications (ECTA)

作者： Baldini, Luca Martino, Alessio Rizzi, Antonello Univ Roma La Sapienza Dept Informat Engn Elect & Telecommun Via Eudossiana 18 I-00184 Rome Italy

ISBN: (纸本)9789897584756

The most distinctive trait in structural pattern recognition in graph domain is the ability to deal with the organization and relations between the constituent entities of the pattern. Even if this can be convenient and/or necessary in many contexts, most of the state-of the art classification techniques can not be deployed directly in the graph domain without first embedding graph patterns towards a metric space. Granular computing is a powerful information processing paradigm that can be employed in order to drive the synthesis of automatic embedding spaces from structured domains. In this paper we investigate several classification techniques starting from Granular computing-based embedding procedures and provide a thorough overview in terms of model complexity, embedding space complexity and performances on several open-access datasets for graph classification. We witness that certain classification techniques perform poorly both from the point of view of complexity and learning performances as the case of non-linear SVM, suggesting that high dimensionality of the synthesized embedding space can negatively affect the effectiveness of these approaches. On the other hand, linear support vector machines, neuro-fuzzy networks and nearest neighbour classifiers have comparable performances in terms of accuracy, with second being the most competitive in terms of structural complexity and the latter being the most competitive in terms of embedding space dimensionality.

关键词： Structural pattern recognition Supervised Learning Embedding Spaces Granular computing Graph Edit Distances

来源：评论

学校读者我要写书评

暂无评论

Zero-shot Video Emotion recognition via Multimodal Protagonist-aware Transformer Network 21

Zero-shot Video Emotion Recognition via Multimodal Protagoni...

引用

29th ACM international conference on Multimedia (MM)

作者： Qi, Fan Yang, Xiaoshan Xu, Changsheng Hefei Univ Technol Hefei Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

ISBN: (纸本)9781450386517

Recognizing human emotions from videos has attracted significant attention in numerous computer vision and multimedia applications, such as human-computer interaction and health care. It aims to understand the emotional response of humans, where candidate emotion categories are generally defined by specific psychological theories. However, with the development of psychological theories, emotion categories become increasingly diverse and fine-grained, samples are also increasingly difficult to collect. In this paper, we investigate a new task of zero-shot video emotion recognition, which aims to recognize rare unseen emotions. Specifically, we propose a novel multimodal protagonist-aware transformer network, which is composed of two branches: one is equipped with a novel dynamic emotional attention mechanism and a visual transformer to learn better visual representations;the other is an acoustic transformer for learning discriminative acoustic representations. We manage to align the visual and acoustic representations with semantic embeddings of fine-grained emotion labels through jointly mapping them into a common space under a noise contrastive estimation objective. Extensive experimental results on three datasets demonstrate the effectiveness of the proposed method.

关键词： multimodal affective computing zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Abstract Vocabulary as Base for Training with pattern recognition EMG Control

Abstract Vocabulary as Base for Training with Pattern Recogn...

引用

10th international conference on Applied Human Factors and Ergonomics / AHFE international conferences on Usability and User Experience, and Human Factors and Assistive Technology

作者： Haring, Erik Van Akeleyen, Seth Vaes, Kristof Truijen, Steven Verwulgen, Stijn Univ Antwerp Dept Prod Dev Ambtmanstr 1 B-2000 Antwerp Belgium

ISBN: (纸本)9783030191351;9783030191344

The uprising of multi-channel wearable EMG sensors combined with machine learning pattern recognition algorithms offers the possibility to control multiple degree of freedom hand prosthetics. Such human-machine interaction systems require training from the user, mostly to link gestures with underlying EMG patterns. As intended end users have a missing hand, the question arises how to train them to use myo-electric prosthetics without instructing them to perform gestures;A key element to start training with pattern recognition EMG based prosthetic control is creating a shared vocabulary with the participant/patient. The shared vocabulary forms the base for the explanation and communication about the pattern recognition EMG. In this research an abstract form of communication based on animal sounds is used to form a shared vocabulary for a child with missing hands. We found that the abstract communication worked well and motivating when explaining pattern recognition EMG to a child. The communication tool that gives additional interaction makes the explanation much clearer since the participant starts directly with experiencing the pattern recognition EMG. Also, it is concluded that the abstract nature of the tested communication allows the participant to keep an open mind for gestures other than normal healthy hand movements when exploring the possible control contractions. Thus, abstract based communication can offer benefits during the training with pattern recognition EMG.

关键词： EMG pattern recognition EMG Training Children Prosthetic control

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 << < 102 103 104 105 106 107 108 109 110 111 > >>

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：