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711-720 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：

Analysis of Eye Fixations During Emotion recognition in Talking Faces 9

Analysis of Eye Fixations During Emotion Recognition in Talk...

引用

9th international conference on Affective computing and Intelligent Interaction (ACII)

作者： Cao, Houwei Elliott, Forest New York Inst Technol Dept Comp Sci New York NY 10023 USA Oberlin Coll Dept Comp Sci Oberlin OH 44074 USA

ISBN: (纸本)9781665400190

Research on emotion recognition from cues expressed in facial expression has a long-standing tradition. In this study, we investigate human's visual attention and fixation patterns when identifying six basic emotions on expressive talking faces. Stimuli for the current experiments consisted of 92 video clips of facial expression during talking. The whole experiments were divided into two sessions. The video stimuli in the first session were presented in random order across different face identities, while in the second session the video from the same face identity were be played sequentially. The participants' eye movements were recorded by the Tobii X3-120 screen-based eye-tracking system. We defined a set of area-of-interest (AOI) regions, including 4 AOIs of general face areas and 12 AOIs related to specific Action Units (AUs) involved in the coding of the six basic emotions. The gaze pattern analysis was done by looking at the fixation time on this predetermined set of AOIs. Based on the ANOVA analysis, we did not find significant differences in mean fixation time on any AOI for discriminating the six basic emotions, but a subset of significant AOIs was found when we sectioned the six basic emotions into positive, negative, and neutral. Next, we propose to develop a novel emotion perception classifier which can automatically classify an observer's emotion perception based on her gaze patterns and fixation sequence when identifying the basic emotions on expressive talking faces. The fixation time on the 16 predetermined AOIs were used as features to train support vector machine (SVM) models. The proposed models achieved the overall classification accuracy of 84.1% on recognizing 3-way emotions of negative, positive and neutral, suggesting that the proposed eye gaze patterns - the fixation time on 16 predetermined AOIs, are very promising for automatic classification of the perceived emotions. Finally, we divided the data into different gender and race groups, and

关键词： eye fixation emotional face gaze pattern emotion recognition gaze diversity

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Models for the Classification of Skin Cancer

Machine Learning Models for the Classification of Skin Cance...

引用

2022 international conference on Business Analytics for Technology and Security, ICBATS 2022

作者： Arooj, Sahar Khan, Muhammad Farhan Khan, Muhammad Adnan Khan, Muhammad Saleem Taleb, Nasser Riphah International University Riphah School of Computing and Innovation Faculty of Computing Lahore Pakistan University of Health Sciences Department of Forensic Sciences Lahore Pakistan Gachon University Pattern Recognition and Machine Learning Lab Department of Software Seongnam13120 Korea Republic of Ncba School of Computer Science Lahore Pakistan Canadian University Dubai United Arab Emirates

ISBN: (纸本)9781665409209

Skin cancer is a serious illness that requires early identification in order to improve survival rates. Deep learning algorithms for computerized skin cancer detection have now become popular in recent years. These models may increase their performance by having access to additional data, and their prime objective is image categorization. This activity is extremely useful in the realm of health since it may help physicians and experts make the best decisions and accurately assess a patient's condition. Early detection of skin cancer helps patients to receive appropriate treatment and so enhance their survival rate. This proposed methodology is generated to detect and classify skin cancers. In this study, we employed four pre-trained deep learning models (Squeeze net, Alex net, Res net 101, VGG 19) for the classification of four types of skin cancers in more than 6000 skin images including actinic keratoses, intraepithelial carcinoma Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (bkl) and melanocytic nevi (nv). The objective was the identification of the best model in the classification of these breast cancer images with highest accuracy. Experimental results reveal that the Squeeze net model achieved an accuracy of 92.5% which is highest when compared with all other models while Alex net, Res net 101, VGG 19 acquired 91.1%, 83.2%, and 90.4% respectively. © 2022 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Advanced Forecasting of Wind Energy Generation Through Integration of AE-CLSTM

Advanced Forecasting of Wind Energy Generation Through Integ...

引用

Green Energy, computing and Sustainable Technology (GECOST), international conference on

作者： Md Saidur Rashid Mahin Shaharier Kabir Md Ashikul Islam Abu Shufian Dept. of Electrical Engineering Lamar University Texas USA Dept. of Electrical and Electronic Engineering American International University-Bangladesh Dhaka Bangladesh

The advent of sustainable energy solutions marks a pivotal shift in the global energy landscape, with wind energy playing a crucial role due to its minimal environmental impact. However, the inherent unpredictability of wind necessitates advanced forecasting methods for effective grid management. This research introduces the Autoencoder Convolutional Long Short-Term Memory (AE-CLSTM) model, a novel approach that synergizes the dimensionality reduction prowess of Autoencoders with the spatial and temporal pattern recognition capabilities of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. This integrated model aims to enhance the accuracy and reliability of wind energy forecasting, addressing the challenges posed by the stochastic nature of wind behavior. Employing an extensive dataset from a SCADA system of the wind farm, the AE-CLSTM model outperforms conventional CNN Model, LSTM Model, and CNN-LSTM model in terms of accuracy. The key findings of this research are encouraging: the model achieved a Correlation Coefficient of 95.43%, indicating a strong positive relationship between predicted and actual energy outputs, signifying the model's effectiveness in providing reliable forecasts than other models. These outcomes highlight the potential of AE-CLSTM in transforming wind power prediction, contributing significantly to the effective management of smart grids and the expansion of sustainable energy integration.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Quantum Based Deep Learning Models for pattern recognition 6th

Quantum Based Deep Learning Models for Pattern Recognition

引用

6th international conference on Information, Communication and computing Technology (ICICCT)

作者： Shrivastava, Prakhar Soni, Kapil Kumar Rasool, Akhtar MANIT Dept Comp Sci Engn Bhopal Madhya Pradesh India

ISBN: (纸本)9783030883782;9783030883775

The machine learning model influences the pattern recognition based on extraction of relative patterns, but not enough capable of efficiently processing the data set that needs layered interaction. So, deep learning model takes the advantage of artificial neural network for processing the data in layered abstraction by exploring massive parallelism, although the classical implementations of such model may not be competent due to the processing and storage of large neural networks. Current research explores quantum computation potentials and utilizes its significance for supporting inherent parallelism, as the machine perform exponential operations in single step of execution. The possible classical designs of pattern recognition using deep learning are Hopfield network and Boltzmann machine and their equivalent quantum models can remain effective and overcome the processing limitations of classical model by incorporating Grover's search method and quantum Hebbian learning. The aim of writing this article is to introduce the necessity of deep learning model, an emergence of quantum computations, discussion over requisites of data preprocessing techniques, then to propose the classical equivalent quantum deep learning model along with the algorithms, complexity comparison and speedup analysis followed by conclusive aspects that proves effectiveness of quantum deep learning model and future works.

关键词： Quantum pattern recognition Machine learning Deep learning Grover's search Neural networks Superposition Quantum Hebbian learning

来源：评论

学校读者我要写书评

暂无评论

A Ensemble Deep Learning approach on risk management in finance and marketing sector

A Ensemble Deep Learning approach on risk management in fina...

引用

international conference on computing and Networking Technology (ICCNT)

作者： K R Chairma Lakshmi P. Ratnaraju Sumit Kumar Sharma Rambabu Nalagandla Manjula Pattnaik P Kamalarajan Department of Electronics and Instrumentation Engineering R.M.K. Engineering College School of Management and Commerce Malla Reddy University Hyderabad Telangana India Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad Uttar Pradesh India Solution Architect USA Accounting Department College of Business Administration Princess Nourah Bint Abdulrahman University Saudi Arabia Department of Chemistry R.M.D. Engineering College

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The ensemble deep getting to know technique is a powerful and innovative tool for danger control inside the finance and advertising quarter. It combines the strengths of more than one deep getting to know models to provide accurate and comprehensive risk tests. By using harnessing the electricity of artificial intelligence and device learning, this approach can efficiently analyze extensive amounts of facts and perceive capability risks in actual time. This enables monetary and advertising establishments to make informed and well timed selections to mitigate dangers and enhance their normal overall performance. With its potential to conform and learn from new data, the A Ensemble Deep getting to know method is reworking the way threat is controlled in those industries.

关键词： Performance evaluation Accuracy Target recognition Social networking (online) Profitability Finance Market research pattern recognition Risk management Advertising

来源：评论

学校读者我要写书评

暂无评论

Partial Discharge recognition in Real Switchgear Based on soft Scoring Mechanism and Multi-Mode Fusion

Partial Discharge Recognition in Real Switchgear Based on So...

引用

China international Electrical and Energy conference (CIEEC)

作者： Tianyi Zhang Zhenguo Di Simeng Li Zewei Bu Hao Wu Sisi Hui Shengchang Ji Offshore Oil Engineering Co. Ltd. Tianjin China Offshore Oil Engineering Co. Ltd Tianji China State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University Xi'an China

Partial discharge (PD) in switchgear is closely related to its premature insulation degradation, and accurate identification of PD types is an important means to improve its operation condition and to ensure safety of power system. In this paper, four typical PD types are designed in real switchgear: point discharge, suspension potential discharge, strong vertical component along the surface discharge and weak vertical component along the surface discharge. UHF sensor, TEV sensor, AE sensor and HFCT are combined for detection of switchgear PD, and the detection sensitivity of different sensors under the same defect and the spectral characteristics of different defect types are analyzed. Five characteristic parameters of discharge density distribution and correlation coefficient of contour of the extraction spectrum are combined to form a new feature system. A soft scoring method is proposed to identify the PD pattern of switchgear. This method solves the issues associated with traditional recognition methods that rely solely on single spectral characteristics, resulting in low recognition accuracy and inaccurate recognition types. Moreover, it offers a theoretical framework for online monitoring and fault diagnosis of switchgear equipment.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Differential Residual Learning for Facial Expression recognition 21

Differential Residual Learning for Facial Expression Recogni...

引用

5th international conference on Machine Learning and soft computing, ICMLSC 2021

作者： Pu, Lipeng Zhu, Lingyun Chongqing University of Technology Liangjiang International College China

ISBN: (纸本)9781450387613

The facial expression recognition algorithm based on convolution neural network (Convolutional Neural Network) still has the problem of imperfect feature point extraction. Therefore, a facial expression recognition algorithm based on the combination of the detail feature extraction network and pre-training model is proposed. According to the emotional face and neutral face of the residual of the images, learn the residual to extract features, and make the feature information accurate. Then, the face feature markers generated by the feature extraction network are loaded into the pre-training model respectively for classification and recognition. By grouping and crossing experiments on datasets CK+ and FER2013, the average accuracy of face recognition is 95.74% and 73.11%, respectively. Compared with the state-of-the-art recognition model, this method is effective in facial expression recognition to some extent. © 2021 ACM.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Seeing the World in Dots: A Machine Learning Adventure in Colorblind Perception

Seeing the World in Dots: A Machine Learning Adventure in Co...

引用

computing, Applications and Systems (COMPAS), IEEE international conference on

作者： Shah Rafatur Rahman Md Rashedul Islam Aditya Chakrabortty Department of Computer Science and Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh School of Information Technology Deakin University Melbourne Australia

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Color is a vital communication element, conveying the message’s meaning and differentiating the contents. Nevertheless, color vision deficit (CVD), called colorblindness, affects 8% of the global population. This impairment alters color perception, making reading and understanding the message difficult. This issue becomes increasingly significant with the expanding accessibility of multimedia content in digital environments, including web browsers, digital images, entertainment media, and social media. One of the methods of identifying CVD is the Ishihara Color-blind Test. Ishihara images consist of a red-green background and a hidden number. Color-blind persons won’t recognize the number as they cannot recognize the red and green colors. Using color glasses or contact lenses this deficiency can be overcome. The main point is that there is no cure for this deficiency. This research explores machine learning's potential to overcome this challenge. We trained a neural network to detect and classify numbers from Ishihara images, successfully "passing" the test. With an impressive 96.42% accuracy, this technology offers promising hope for bridging the communication gap for colorblind individuals.

关键词： Visualization Image recognition Accuracy Image color analysis Social networking (online) Transfer learning Robustness pattern recognition Noise measurement Vision defects

来源：评论

学校读者我要写书评

暂无评论

Modem Myoelectric Control – Is it Time to Change the Algorithmic Focus?

Modem Myoelectric Control – Is it Time to Change the Algori...

引用

Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Rami N. Khushaba Oluwarotimi W. Samuel Ali H. Al-Timemy Guanglin Li Transport for NSW Alexandria NSW Australia School of Computing and Data Science Research Centre University of Derby Derby United Kigndom Department of Biomedical Eng. Al-Kkwarizmi College of Eng University of Baghdad Baghdad Iraq Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This paper explores the evolving landscape of Electromyogram (EMG) signal analysis, focusing on the growing prominence of deep learning (DL) algorithms for hand, wrist, and finger movement recognition. Such algorithms often come with high computational costs, potentially limiting the clinical translation on resource-limited devices and igniting more research on reduced complexity models. This prompts the question: is it time to shift the algorithmic focus in EMG pattern recognition, given the reported performance of some light-weight traditional or hybrid methods emphasizing synergy between different EMG signals? A comparative study is implemented between state-of-the-art deep learning extension for time series classification, denoted as Random Convolutional Kernel Transform (ROCKET), and simple, yet effective pattern recognition methods tailored to exploit basic forms of EMG signal synergies— Waveform Length Phasors (WLPHASOR), Root-Mean-Squared Phasor (RMSPHASOR), and the proposed novel Multi-Signal Waveform Length (MSWL). Tests are conducted on EMG data from 22 participants performing 11 hand and wrist movements using two EMG armbands (10 and 8 channels, respectively), utilizing the open-source LibEMG toolbox. Preliminary findings suggest that, while DL algorithms exhibit formidable capabilities, the performance gap with traditional EMG feature extraction methods may not be as substantial as anticipated. The observations of this study revealed no significant differences in average accuracies between ROCKET, WLPHASOR, and RMSPHASOR (87% average across participants). Furthermore, MSWL significantly enhances performance to 90%, and the combination of ROCKET+MSWL achieves 91% on average across all subjects. These findings challenge the narrative of DL dominance in EMG pattern recognition, urging a re-evaluation of the algorithmic focus and contributing valuable insights to the debate on information extraction from EMG signals.

关键词： Wrist Deep learning Rockets Convolution Time series analysis Transforms Feature extraction Electromyography pattern recognition Kernel

来源：评论

学校读者我要写书评

暂无评论

Quantum One-class Classification With a Distance-based Classifier

Quantum One-class Classification With a Distance-based Class...

引用

international Joint conference on Neural Networks (IJCNN)

作者： de Oliveira, Nicolas M. de Albuquerque, Lucas P. de Oliveira, Wilson R. Ludermir, Teresa B. da Silva, Adenilton J. Univ Fed Pernambuco Ctr Informat Recife PE Brazil Univ Fed Rural Pernambuco Dept Estat & Informat Recife PE Brazil

ISBN: (纸本)9780738133669

The advancement of technology in Quantum computing has brought possibilities for the execution of algorithms in real quantum devices. However, the existing errors in the current quantum hardware and the low number of available qubits make it necessary to use solutions that use fewer qubits and fewer operations, mitigating such obstacles. Hadamard Classifier (HC) is a distance-based quantum machine learning model for pattern recognition. We present a new classifier based on HC named Quantum One-class Classifier (QOCC) that consists of a minimal quantum machine learning model with fewer operations and qubits, thus being able to mitigate errors from NISQ (Noisy Intermediate-Scale Quantum) computers. Experimental results were obtained by running the proposed classifier on a quantum device and show that QOCC has advantages over HC.

关键词： quantum machine learning quantum computing pattern classification

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 << < 68 69 70 71 72 73 74 75 76 77 > >>

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：